International Journal of Computational and Experimental Science and Engineering
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Comprehensive Evaluation of Treatment Plans in Stereotactic Body Radiation Therapy (SBRT) for Lung Cancer Patients
Non-Small Cell Lung Cancer (NSCLC) is a major public health problem and a leading cause of cancer-related deaths. Stereotactic Body Radiotherapy (SBRT) has emerged as a highly precise treatment modality that provides high biologically effective doses (BED) with superior dose compliance, high conformity and sharp dose falloff, and has an important place in the treatment of NSCLC. This study aims to evaluate the SBRT plans for 22 NSCLC patients by assessing the Conformity Index (CI), Heterogeneity Index (HI) and organs at risk (OAR) doses. Findings demonstrate that SBRT is effective in achieving tumor control in hypoxic and radio resistant tumor regions through precise target coverage and effective intra tumor dose heterogeneity. These features make SBRT a noninvasive and highly effective alternative to surgery for patients with early-stage NSCLC. This study highlights the unique effect of SBRT, a radiotherapy option that reduces doses to organs at risk while improving treatment outcomes, in the treatment of NSCLC and highlights its unique potential in the treatment of NSCLC by evaluating its dosimetric parameters.
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A New Method in Wastewater Treatment: Ecological Ladder Model (EMM)
This article was prepared to introduce a new natural purification system designed by us. The model is theoretic. Ways to use the purified water for agricultural purposes have also been sought. For this purpose, we call the ‘Ecological Ladder Model (EMM)’ was done. The model can be revised in line with the results obtained from the application. It is estimated that it can also be applied to other water sources suffering from drought problems. The general features of the model are as follows: The EMM model; is an ecological treatment system that imitates nature. The treatment period and the area of use for treatment are longer than other systems, and vary according to the flow rate of the water; however, it is an environmentally friendly system. The system; has 2 parts, one large (main unit) and one small (side unit). While the main unit is being cleaned, the side unit can be put into operation and run for a few days. The top of the system is open and swamp mud, filters, etc. can be changed with construction equipment. Since the wastewater with a natural flow from top to bottom comes to the lake by filtering under the effect of the slope, it will be purified and purified like a strainer in peatlands. For this, the topography of the land should have a slope of approximately 15-30%. Wastewater can be purified and released into the lake or used for agricultural purposes. Purification consists of 3 steps: In the first stage, disease-causing microorganisms will be destroyed (with ozone/essential oil plants), In the second stage, the chemical and physical properties of the water such as pH, hardness, O2 will be made suitable (with claystone, marble dust, lime and gypsum rocks, etc.), In the third stage, there is an artificial wetland created from plants determined for the purpose from the aquatic flora of the area and the water will be purified by giving it to the plants, the water filtered from here will be tested with live indicators to see if it is drinkable and the purified water will be delivered to the lake or to the agricultural areas to be used. All of these features have been concretized with a technical drawing and turned into a visual plan. The disposal of the treatment sludge can be made into a valuable fertilizer by feeding it to Red California worms (Eisenia fetida). Burdur is the most important province in worm compost production. It also has very important experiences in agriculture and animal husbandry.
Exploring the Potential of Vacancy-Ordered Cs2PtI6 Perovskite as a Lead-Free Absorber for Next-Generation Solar Cells via Modeling and Simulation
This study explores the potential of the vacancy-ordered perovskite material Cs2PtI6, which is inorganic and lead-free, as an absorber layer in a single-junction solar cell. The research is based on modeling, simulation, and subsequent optimization of the cell structure to achieve optimal conversion efficiency. A baseline structure FTO/TiO2/Cs2PtI6/Spiro-OMeTAD/Au was designed as the starting point for simulations. The effects of various physical factors on key photovoltaic parameters were evaluated, including the thicknesses of the absorber, electron transport layer (ETL), and hole transport layer (HTL), as well as the defect density within the Cs2PtI6 material and at its interfaces with the ETL and HTL. For this purpose, a mathematical model was established to investigate the combined effects of parameters, supported by contour plot analysis. Moreover, alternative materials for the ETL and HTL, featuring different physical properties and band alignments compared to those in the baseline structure, were investigated. The study also examined how the height of the energy barrier at the rear interface between the HTL and the metal layer influences device performance. Based on these findings, an optimized structure FTO/CeO2/Cs2PtI6/Cu2O/Carbon was developed. This configuration achieved a conversion efficiency of 18.54%, demonstrating its superiority over the baseline design and other comparable solar cell architectures. Overall, this work highlights the promising potential of vacancy-ordered Cs2PtI6 perovskite and contributes to the advancement of next-generation solar cell technologies.
Non-Periodic Current Regulation in Digitally Controlled Hysteresis Current Controller
This paper investigates the challenges associated with non-periodic current tracking in digitally controlled Hysteresis Current Controllers (HCC) within grid-connected inverter systems. It examines the dynamic response of the controller when the grid phase undergoes variations, leading to deviations in current tracking. The study explores how phase shifts influence the accuracy of current regulation and their impact on overall system performance. A key aspect of the analysis is the evaluation of total harmonic distortion (THD) in the inverter’s output current, which serves as a critical parameter in assessing grid connection quality and harmonic mitigation.The research employs MATLAB/Simulink simulations environment to analyse the behaviour of the inverter under different grid conditions. The results highlight the difficulties posed by phase variations and their effect on maintaining precise current regulation. Furthermore, the study identifies potential limitations of HCC in ensuring consistent performance under fluctuating grid scenarios. The observed non-periodicity in current tracking suggests the need for controller optimization to enhance synchronization and minimize harmonic distortions. The insights derived from this study contribute to the refinement of digital HCC techniques, aiming to improve inverter efficiency and grid compatibility. By addressing the challenges associated with phase shifts and THD, the findings offer valuable guidance for designing more robust current control strategies in grid-connected applications
GIS Method for Mapping the Individual Education in Rajshahi City of Bangladesh
Education is the nation’s knowledge. The past decades it is increasing debate on how to better prepare school leader for their role. The work sought to examine the education mapping that is the graphical or symbolic representations of education to maps. The paper began with a momentary discussion of education level of Bangladesh. At the beginning overall education trends of Bangladesh has publicized. At last section education level of the head of the households of two case study of Rajshahi City were considered. However, education mapping of the head of households were produced. The tool GIS was used to determine their household location. A structured questionnaire was used to collect their education level data. Spatial software Arc GIS and statistical analytical software (SPSS software) were used to assemble the data. The results demonstrated that 18% of head of households (HH) have no educations and 38% completed primary education. The study identified spatial pattern of education that will contributes to make policy program of education.
Numerical Investigation of Fluid Effects on Heat Transfer of Nanofluids in a Plane Channel: Application to Solar Thermal Panels
In this work, a numerical investigation is conducted to analyze laminar mixed convection of nanofluids in a two-dimensional horizontal channel. The lower wall of the channel dissipates heat at constant hot temperature, whereas the upper wall is adiabatic. The analysis considers five volume concentrations (φ) of copper nanoparticles (Cu), ranging from 0 to 0.2, suspended in common base fluids such as ethylene glycol, soybean oil, and sunflower oil. The effective thermal conductivity and dynamic viscosity of the nanofluid are estimated using the Maxwell-Garnett and Brinkman models, respectively. The set of governing equations is derived and solved numerically through a finite difference discretization. For numerical resolution, a line-by-line sweeping strategy in combination with the Thomas algorithm (TDMA) is implemented. The objective of this study is to investigate how viscosity and thermal conductivity influence both heat transfer and flow behavior, in order to determine the nanofluid that maximizes thermal performance. The outcomes are expected to contribute to enhancing the use of nanofluids in advanced thermal control systems.
A Data Warehouse Optimization Strategy Using Binary Chimp for View Materialization
The Materialized View Selection (MVS) problem is a critical NP-complete challenge in data warehouse design, aimed at optimizing query performance while balancing storage and maintenance costs. This paper proposes BChOMVS, a novel metaheuristic approach that utilizes a Binary Chimp Optimization Algorithm (BChO) to efficiently identify near-optimal view sets. Using the Multiple View Processing Plan (MVPP) for view representation, BChOMVS encodes solutions as binary vectors and evaluates them with a comprehensive cost model. Rigorous experimental evaluation on the TPC-H benchmark up to 50GB demonstrates that BChOMVS significantly outperforms state-of-the-art methods like ACOMVS, PSOMVS, and GTMVS. It achieves superior total cost reduction by consistently selecting the smallest, most impactful view sets, establishing itself as the premier choice for large-scale data warehouse optimization where ultimate cost minimization is paramount.
GenSL-Trans: Direct Visual-to-Visual Arabic-to-English Sign Language Translation via Mobile-Optimized Unet-Transformers in Immersive Environments
We propose a real-time, mobile-interactive pipeline for direct Arabic-to-English Sign Language (ArSL-to-ESL) translation in the metaverse, preserving the visual-spatial nature of sign languages without textual intermediaries. Central to this system is a newly created bilingual mapping dataset between Arabic and English sign language, which enables accurate cross-lingual alignment of gestural patterns and forms the foundation for direct, grammar-preserving translation. The system captures gestures via VR headsets or smartphone cameras at 90 fps (1080p, H.264), with on-device preprocessing (OpenCV) optimized via NNAPI or Core ML. A quantized YOLOv11 (int8) model with Kalman tracking achieves 92% accuracy on the mapping dataset with <11 ms inference on mobile GPUs. Visual features are encoded via 14×14 patch embedding into 256D tokens and processed by GenSL-Trans a lightweight (14M params) vision Transformer (8 heads, FFN=1024) to map sign gestures directly to target ESL representations. The Bi-LSTM, BERT, and GPT-2 decoders generate spatiotemporal sequences with adaptive on-device/cloud execution. A CNN-based renderer with Conv2DT layers and U-Net skips produces 224×224 px video frames, driving a lightweight 3D avatar streamed via glTF and rendered in real time using WebXR, accessible on mobile browsers (iOS/Android) or VR headsets, with end-to-end latency <180 ms. Mobile interactivity allows touch-based control (start/stop, speed, expressions, feedback), ensuring accessibility and personalization. By integrating on-device AI, direct gesture-to-gesture translation, and immersive rendering, our system provides an inclusive communication bridge for Deaf users across Arabic- and English-speaking communities.
A Digital Phase Locked Loop Design from analysis to implementation : application to FSK demodulation
The study of Phase Locked Loops (PLL) has been heavily treated in literature and most of the theoretical and the analytical results of such are verified using simulations. Here we provide guidelines of a digital PLL design and his circuit implementation on Pspice simulator (16.6 version) for both analyzing and verifying the theoretical results. In fact, the digital PLL is a hybrid device where only the phase detector is built from a digital circuit and the remaining blocks are still analog ones.
Spatial Perspective on Location Factors of Telecenter as Determinant for the Sustainability
A spatial perspective is crucial for Telecenter sustainability, as location factors directly influence their sustainability as reflects directly on effectiveness, usage, and long-term viability by determining accessibility for the community and aligning with socio-economy contexts. Key factors for Telecenter sustainability include location related factors, supported by infrastructure and info structure, and the presence of supportive environments, such as commercial complexes, banks, services agencies and so on. This paper aims for develop a conceptual model of spatial location that integrates geographic information systems with survey data that can help identify location of Telecenter users. The visualization of location related factors, with infrastructure and info structure, and supportive environments, at location is important to further analysis, especially the sustainability issues as faced by Telecenter. Suggestion and recommendation will be made for lightening the future prospect of Telecenter.
Bioactive profile and safety of Stipa tenacissima L medicinal plant: polyphenols, antioxidants, toxicity and anti-inflammatory
In addition to evaluating the phytochemical content and antioxidant capacity, this study was the first to examine the acute toxicity profile and anti-inflammatory properties of Stipa tenacissima L., an endemic plant widely distributed in Algeria known as "Halfa". Although its various and important traditional use, little scientific research has been explored into its critical medicinal properties. The results of these studies show that the aqueous extract contains the most phenolic and flavonoid components and has a higher antioxidant capacity. Anti-inflammatory activity was demonstrated in vitro through BSA denaturation inhibition of 89.93% at of 0.5 mg/mL, and in vivo use croton oil to produce ear edema in mice, where showed significant inhibition rate of 71.11% at dose of 100 mg/kg. Furthermore, the toxicological evaluation of the two oral doses of the aqueous extract given to mice groups (2 and 5 g/kg) shows that no mortality was seen and the LD50 is higher than 5000mg/kg. On histological examination, symptoms of mild toxicity comprised elevated liver enzymes ALT and AST, as well as infiltration of moderate hepatic inflammation, but there was no major renal or cardiac damage. These findings suggest that S tenacissima represents an interesting avenue in the development of new natural alternatives to traditional anti-inflammatories.
Enterprise Wi-Fi Infrastructure: Evolution from Supplementary Technology to Mission-Critical Backbone of Digital Transformation
This article examines the evolution of Wi-Fi infrastructure from its emergence as a supplementary technology to its current position as a mission-critical foundation of enterprise digital transformation. The article explores the historical transition from wired to wireless enterprise networks, detailing the economic and operational factors that drove adoption. It provides an in-depth analysis of IEEE 802.11 standards development, highlighting key technological innovations that have dramatically enhanced transmission speeds, bandwidth utilization, and connection reliability. The article examines enterprise scalability considerations, including access point deployment strategies and client management techniques for high-density environments. Additionally, it evaluates the progression of wireless security frameworks from rudimentary protocols to sophisticated multi-layered defense systems. Finally, the article examines Wi-Fi's expanding role as the foundation for converged digital ecosystems that integrate emerging technologies, including IoT, 5G, edge computing, and AI-driven network optimization, to enable intelligent workplace environments.
AI-Powered Dynamic Billing Optimization: Personalized Plan Recommendations Through Generative Intelligence
This article examines the application of Generative AI (GenAI) to transform telecommunications billing systems from static models to dynamic, personalized solutions. The article explores how AI-human collaboration models analyze individual customer usage patterns to generate optimized billing recommendations in natural language, simulate usage scenarios, and deliver actionable insights. The article evaluates cloud-based AI services, data processing frameworks, and natural language generation technologies that enable these capabilities, while addressing implementation challenges related to privacy, bias, and system integration. Findings demonstrate significant improvements in customer retention, satisfaction, operational efficiency, and revenue management. The article concludes with an assessment of environmental and economic sustainability impacts, ethical considerations, research limitations, and practical recommendations for telecommunications providers implementing dynamic billing optimization solutions.
From Legacy to Cloud-Native: How a Global Retailer Achieved 99.99% Uptime
The journey of a global retail enterprise from traditional on-premises infrastructure to cloud-native architecture illustrates the transformative potential of strategic cloud migration in the retail sector. Beginning with a pragmatic lift-and-shift approach that established foundational capabilities while maintaining business continuity, the organization progressively embraced containerization, microservices architecture, and managed services that dramatically enhanced deployment agility and operational resilience. This evolution extended beyond technical architecture to encompass fundamental changes in organizational structure, development practices, and business operations. The migration delivered substantial improvements in deployment frequency, system reliability, and market responsiveness while achieving near-perfect uptime during critical shopping periods. The retailer's experience offers valuable insights into migration strategy selection, technical debt management, and organizational transformation that can guide other enterprises facing similar challenges with legacy infrastructure in an increasingly digital marketplace.
Comparative Assessment of Mohr-Coulomb and Hardening Soil Models for Deep Excavation Design in Stratified Soils: Insights from Numerical and Field Data
This study presents a comprehensive numerical and experimental investigation into the performance of deep excavation support systems in stratified soil conditions. A real-world case study from the OuedSmar metro station in Algiers serves as the basis for evaluating the predictive capabilities of two widely used soil constitutive models: Mohr-Coulomb (MC) and Hardening Soil (HS). The excavation, supported by a diaphragm wall and multi-level anchoring system, was instrumented with inclinometers to capture high-resolution displacement data throughout construction. Finite element simulations were conducted using PLAXIS 3D, and model outputs were compared against field measurements to assess accuracy. Results indicate that the HS model consistently outperformed the MC model, accurately replicating wall deflection magnitudes, deformation profiles, and boundary conditions with errors below 12%, while the MC model significantly overestimated displacements and failed to capture observed behavior. The findings underscore the limitations of simplified constitutive models for serviceability limit state analysis and demonstrate the necessity of using advanced elastoplastic formulations like the HS model in deep urban excavations. This research contributes to improved model selection and calibration practices for safe, reliable, and cost-efficient geotechnical design.
Investigation and Optimization of WEDM Parameters for 15CDV6 Using RSM
The aerospace, defence, automotive, nuclear, and electrical industries all make extensive use of 15CDV6 HSLA steel due to its great strength, durability, and ability to function in extreme conditions. The machining condition of a material have been reduced under the enhancement of the electrode wear rate, machining quality. This paper explains about the machining condition of the particular material by optimizing the parameters using RSM (Response Surface Methodology). It implies the quality, strength and productivity under the machining condition using EDM (Electro Discharge Machining) process for continuous cutting and the development of the specimens exhibits the selecting the proper parameters and improves the surface roughness for various condition. In this methodology the mathematical expression implies the importance of the parameters of a material under various conditions and associated with the optimal results. The significance of the improvement of the cutting process implies the high material removal under low surface condition. It implies the sustainability of the given input parameters and desired responses were checked in RSM analysis. It provokes the better mechanical properties of a 15CDV6 HLSA steel material and develops the machining area. It results 77.5% contribution in cutting of a material under Pulse-On Time, Pulse-Off Time, Gap and Input Current of the machining condition and sustain the out parameters in RSM procedure. This investigation implies and deep insights of a material under surface roughness and have been performed in ANOVA.
Evaluation of RCC building performance with stainless steel hysd reinforcements replacing conventional HYDS steel
The objective of this investigation is to evaluate and contrast the performance of solid concrete (RC) structures that are reinforced with brushed stainless steel HYSD and common HYSD steel reinforcements. The study focuses on how corrosion impacts building durability and maintenance, and it investigates the viability of stainless steel as an option owing to its greater corrosion resistance, mechanical strength, and durability. ETABS software was used to assess RCC building models of varied heights (G+2, G+5, G+8) under various loading conditions, including seismic forces, in accordance with IS:1893:2016 requirements. Tensile and torsion tests on HYSD550 and SS550 reinforcement bars were carried out to confirm the computational results. The results show that stainless steel reinforcements provide comparable structural performance to conventional reinforcements, with the additional benefits of increased ductility, longer service life, and lower maintenance costs. This study shows stainless steel as a sustainable reinforcement material for important infrastructure, particularly in harsh conditions.
Antibiotic Stewardship in Dental Procedures the Role of Family Medicine, Clinical Pharmacy, and Health Administration
The escalating global crisis of antimicrobial resistance (AMR) demands urgent action across all medical disciplines, including dentistry. Dental practitioners are significant contributors to outpatient antibiotic consumption, with current prescribing patterns revealing substantial overuse and misuse for conditions such as irreversible pulpitis and apical abscesses where the primary treatment should be mechanical intervention. This comprehensive research paper argues that optimizing antibiotic use in dental procedures necessitates a fundamental shift from isolated decision-making to a robust, interprofessional collaborative model. The proposed framework integrates the unique and synergistic roles of four key pillars: Dentistry, as the primary diagnostician and proceduralist; Family Medicine, providing holistic patient context and coordinating complex medical needs; Clinical Pharmacy, offering expertise in pharmacotherapy, safety, and patient counseling; and Health Administration, building the essential infrastructure of policy, technology, and feedback mechanisms. By synthesizing current evidence, this paper demonstrates that only through this coordinated, systems-based approach can antimicrobial stewardship (AMS) principles be effectively implemented, sustained, and scaled. The conclusion underscores that such collaboration is not merely beneficial but essential for preserving antibiotic efficacy, enhancing patient safety, and safeguarding public health for future generations.
Management of Chronic Pain in Family Medicine Roles of Nursing, Anesthesia, Medical Coding, Radiology, and Intensive Care
Chronic pain management is a multifaceted challenge that requires a comprehensive approach in family medicine. Nursing plays a pivotal role by providing patient education, emotional support, and coordination of care among various healthcare providers. Nurses assess patients' pain levels, administer prescribed medications, and monitor for side effects, all while fostering an environment that encourages open communication about pain experiences. Furthermore, nurses are instrumental in integrating non-pharmacological therapies, such as physical therapy and mental health support, into pain management plans. Their direct interaction with patients allows them to identify barriers to effective pain management, making them advocates for patient-centered care. Anesthesia professionals contribute significantly to chronic pain management through interventional techniques, such as nerve blocks and epidural injections, which can provide relief for patients when conventional treatments fail. Medical coding specialists ensure that accurate documentation and billing occur for these complex procedures, ensuring healthcare providers receive proper reimbursement and facilitating access to necessary treatments for patients. Radiology plays a crucial diagnostic role by providing imaging studies that identify the underlying causes of pain, guiding treatment decisions and interventions. In more severe cases, where chronic pain overlaps with critical conditions, intensive care units may be involved to manage acute exacerbations of pain and underlying health problems. Together, these disciplines create an integrated network of care that emphasizes a holistic approach to managing chronic pain, ultimately improving patients' quality of life.
Assessment of the adaptability and spectroscopic characterization of cotton plants and their properties in the semi-arid region of El-Meita, Khenchela (Algeria)
Cotton is an important crop for the economy and textile sector in arid and semi-arid areas. This study evaluates the physical and chemical quality of cotton fibers grown in the El Meita region of Khenchela, Algeria, focusing on fiber fineness, length, and strength, as well as chemical analysis of the soil and fibers using Fourier transform infrared spectroscopy (FTIR). Samples taken from several experimental plants showed notable variability in fiber quality, highlighting the impact of local soil and climate conditions. FTIR analysis detected essential organic and inorganic compounds, such as lignin, cellulose, calcium, and silica, revealing positive relationships between various soil elements and the mechanical properties of the fibers. These results provide crucial insights for the selection and improvement of local varieties, enabling increased fiber productivity and quality while promoting sustainable agriculture of cotton in the Khenchela region
Optimization of drilling parameters for Spanish cherry fiber reinforced hybrid composites using Taguchi analysis
Natural fiber-reinforced composites have gained significant attention in engineering applications due to their sustainability, lightweight nature, and mechanical properties. However, machining these materials, particularly drilling, presents challenges such as delamination, fiber pull-out, and surface roughness, which impact component integrity. Limited research exists on optimizing drilling parameters for Spanish cherry fiber-reinforced hybrid composites to minimize machining defects. This study aims to optimize drilling parameters—drill bit diameter, spindle speed, and feed rate—using the Taguchi L27 approach to enhance machining performance. The composite was fabricated using Spanish cherry fiber, vetiver fiber, and coir pith in a polyester resin matrix through compression molding. Experimental trials analyzed the effects of process parameters on thrust force (TF) and surface roughness (SR), supported by ANOVA and regression analysis. Results indicate that spindle speed has a significant influence on TF and SR, followed by drill bit diameter and feed rate. The optimal setting (8 mm drill bit, 1480 rpm spindle speed, 160 mm/min feed rate) achieved minimal TF (3.82 kgf) and SR (8.76 µm). Water absorption increased with exposure time, with a maximum rate of 7.58% at 192 hours. These findings provide insights for improving composite machining and enhancing structural integrity. Future research should explore the effects of tool wear and alternative machining strategies.
Improving Record Linkage through Metaheuristic Optimization
The exponential growth of digital data has amplified the importance of record linkage (RL), a fundamental task in data quality management that identifies and merges records referring to the same real-world entity. A critical step in RL is the blocking process, which reduces computational cost by partitioning records into candidate sets. The effectiveness of blocking depends on the choice of blocking keys (BKs), and poor selection can either increase complexity or degrade linkage quality. Since manual BK selection is costly and supervised approaches require labeled data that are often unavailable, recent research has focused on unsupervised optimization-based methods.In this study, we investigate two bio-inspired metaheuristic algorithms—the Whale Optimization Algorithm (WOA) and the Grey Wolf Optimizer (GWO)—for automatic blocking key selection. Both algorithms reformulate BK selection as a feature selection problem, where candidate subsets of keys are optimized using a wrapper-based evaluation function that balances Pair Completeness (PC), Reduction Ratio (RR), and F-measure. WOA exploits the bubble-net hunting strategy of humpback whales, while GWO models the social hierarchy and cooperative hunting behavior of grey wolves, enabling both to effectively balance exploration and exploitation in high-dimensional search spaces.Experimental evaluations on multiple real-world datasets, including standard RL benchmarks and an Arabic dataset, demonstrate that WOA and GWO outperform traditional blocking strategies and achieve competitive performance compared to recent metaheuristic-based methods. Both approaches yield stable convergence, improved recall, and high reduction ratios, confirming their effectiveness and robustness in enhancing large-scale record linkage.
Zero Trust Segmentation in Critical Infrastructure: Transforming Enterprise Resilience Through Cloud-Native Security Architecture
Contemporary critical infrastructure faces unprecedented cybersecurity challenges requiring fundamental architectural transformation beyond traditional perimeter-based security models. Zero trust segmentation emerges as a pivotal framework for preventing lateral movement attacks while maintaining operational continuity across interconnected enterprise systems, with cloud-native solutions creating opportunities for enhanced availability metrics while introducing complex security considerations that demand systematic implementation strategies. Critical infrastructure sectors demonstrate inherent interdependencies that amplify the potential for cascading disruptions when security breaches occur, necessitating zero trust principles that address vulnerabilities through granular access controls, continuous verification protocols, and isolated system architectures that compartmentalize potential threats. Cloud integration platforms enable scalable disaster recovery capabilities while supporting computational requirements of real-time security monitoring and threat detection systems, though the human dimension of infrastructure security extends beyond traditional IT roles to encompass operational technology specialists, compliance professionals, and cross-functional security teams. Implementation of zero trust architectures requires systematic consideration of organizational change management, training protocols, and competency frameworks that support sustained security posture improvements across diverse critical infrastructure environments, establishing foundational elements for next-generation resilient infrastructure capable of maintaining operational integrity under evolving threat landscapes.
Event-Driven Architecture: The Backbone of Real-Time Enterprise Integration
Event-Driven Architecture (EDA) has emerged as a critical paradigm for modern enterprise integration, enabling organizations to transition from traditional synchronous communication models to more responsive, decoupled systems. This comprehensive exploration begins by establishing the foundational elements of EDA—events, producers, consumers, and brokers—and contrasts these with conventional REST-based and batch processing approaches. The article examines how publish/subscribe messaging patterns serve as the backbone for scalable event distribution, while event sourcing and CQRS patterns provide powerful mechanisms for state management and specialized data access. Through real-world implementations in order processing, inventory management, and contract execution workflows, the article demonstrates the tangible benefits of event-driven systems. Technical considerations, including message broker selection, data integrity mechanisms, and schema evolution strategies, are explored in depth. The discussion culminates with resilience engineering practices for enterprise-scale event streams, covering observability, retry strategies, and scaling considerations for complex business processes like quote-to-cash workflows.
AI-Driven Fulfillment Systems: Social, Ethical, and Workforce Implications
This article examines the evolving landscape of AI-driven fulfillment systems, highlighting the transition from operational efficiency focuses to broader social responsibility considerations. It explores the multifaceted implications of artificial intelligence integration in Order Management Systems (OMS) across operational benefits, ethical challenges, collaborative frameworks, and implementation strategies. The operational promise of AI in fulfillment operations includes enhanced scalability, error reduction, predictive capabilities, and resource optimization, though limitations emerge in purely efficiency-focused implementations. Ethical dimensions encompass workforce displacement concerns, algorithmic bias risks, data privacy considerations, transparency deficits, and geographic equity issues. The article proposes human-AI collaboration frameworks featuring human-in-the-loop architectures, transparent decision models, workforce transition strategies, targeted change management approaches, and balanced oversight mechanisms. Finally, it outlines pathways toward responsible implementation through industry best practices, policy considerations, stakeholder engagement processes, comprehensive impact measurement, and future research directions. Throughout, the article advocates for transparent decision models and human-in-the-loop mechanisms as essential components for ethically sound AI deployment in fulfillment systems.
Decoding Intelligent Contact Routing in Large Enterprises: From Static Rules to Adaptive Assignment Strategies
The evolution of enterprise contact routing from static rule-based systems to smart, adaptive frameworks is a paradigm shift in customer service delivery architecture. This end-to-end investigation explores the technological advancements that allow today's contact centers to handle millions of interactions across various digital channels while ensuring personalized service quality. Convergence of machine learning, natural language processing, and deep reinforcement learning results in self-tuning systems that improve routing decisions in real time through multi-layered feedback loops. Intent-based routing understands customer requirements from unstructured data using a transformer architecture and ensemble classification. It moves past fixed categorization frameworks to probabilistic pattern matching. Metadata-driven architectures maintain semantic consistency across different sources, enabling faster decision-making and offline training operations. Skills-based routing algorithms utilize advanced optimization methods to allocate customer needs and agent abilities, addressing multiple goals at the same time while ensuring working constraints. Adaptive feedback improves routing plans using actor-critic schemes and temporal difference methods. This lets systems learn from experience and discover patterns that go beyond human rules and judgments. These improvements change contact centers from cost centers to value creators. They change how customer needs, agent skills, and business aims work together in today's digital world.
Development of an Experimental Platform for Evaluating and Calibrating Gas Sensors via Reference Sensor Technique
Establishing a test bench for gas sensor characterization is essential to ensure that the developed sensors are efficient, reliable, and suitable for their intended application, while also promoting technological advancements in gas detection. This bench primarily aims to evaluate and analyse sensor performance under controlled conditions. The main objectives include measuring sensitivity, selectivity, response time, and stability when exposed to varying concentrations of target gases, which is crucial for determining their effectiveness in real-world conditions. This work aims to summarize the main achievements in designing and developing an experimental laboratory bench, controlled and managed by an Arduino Uno acquisition module. This gas sensor characterization bench facilitates sensor calibration and the determination of an unknown gas sensor's characteristics based on those of a well-defined sensor.
Machine Learning Framework for Retail Sales Forecasting
Sales forecasting plays a critical role in retail operations, directly impacting inventory management, supply chain planning, and revenue optimization. This paper presents a comprehensive machine learning–based forecasting framework developed for SuperKart, a multi-city retail chain. The proposed approach integrates structured data preprocessing, exploratory data analysis (EDA), feature engineering, and comparative evaluation of regression models including Decision Tree, Random Forest, Gradient Boosting, and XGBoost. Among the tested algorithms, XGBoost outperformed others, achieving an R² score of 0.87 and demonstrating strong generalization on test data. A low-code deployment strategy was implemented using Streamlit and Hugging Face Spaces, enabling business users to generate real-time sales forecasts with minimal technical intervention. This research contributes a scalable, accessible, and data-driven forecasting solution for retail environments, bridging the gap between advanced machine learning techniques and practical business adoption.
Computational screening and qsar study of bastadins as acat1 inhibitors
In the search for new and effective anticancer agents, we performed a QSAR study on a series of sixteen bastadins to evaluate their potential as ACAT1 inhibitors and predict their antiangiogenic activity. Our goal was to establish a clear correlation between their biological responses and a set of molecular descriptors, applying principal component analysis (PCA), multiple linear regression (MLR), multiple nonlinear regression (MNLR), and an artificial neural network (ANN). Among the models generated, the best MLR and MNLR approaches achieved determination coefficients (R²) of 0.71 and 0.91. To further assess their reliability, we performed an external validation on a test set of three compounds, confirmed their predictive accuracy, and yielded R² test values of 0.70 and 0.83, respectively. Furthermore, the ANN model, built with a 4-4-1 architecture, showed excellent performance, achieving a correlation coefficient of 0.96 with leave-one-out cross-validation coefficients (Q²) of 0.79. These results indicate that the selected descriptors and calculated parameters are sufficient to reliably predict the biological activity of bastadins as ACAT1 inhibitors, providing a solid basis for the computer-aided design of novel anticancer agents.
Assessing Public Awareness and Practices Related to Environmental Health and Disease Prevention in Saudi Arabia: A Survey-Based Study
Objective: To assess the level of public awareness, attitudes, and practices (KAP) regarding environmental health and disease prevention among residents of Saudi Arabia, and to identify associated sociodemographic factors. Methods: A cross-sectional, survey-based study was conducted involving 1,169 participants aged 18 years and older from both urban and rural areas. Data were collected using a structured questionnaire and analyzed using SPSS. Results: Most participants (94.2%) exhibited a moderate level of environmental health awareness. Only 1.5% scored in the high category. Positive attitudes were widespread, with over 85% believing in their personal role in improving environmental conditions. However, only 15.8% consistently practiced recycling, and 11.5% never took protective measures against pollution. Education and occupation were significantly associated with awareness (p < 0.001), while age, gender, and residency were not. Conclusion: The study reveals a population that is moderately informed and highly receptive to environmental health initiatives, yet limited by inconsistent practices and knowledge gaps. Tailored educational programs and clinician-led interventions are recommended to enhance public environmental literacy and promote sustainable behaviors.
Assessing Elderly Care in Saudi Arabia: Evaluating Health and Social Support Programs through a Survey-Based Study
Background: As Saudi Arabia’s elderly population grows, understanding the adequacy of health and social support services becomes increasingly critical. This study evaluates the accessibility, quality, and effectiveness of elderly care programs across different regions and sociodemographic groups in Saudi Arabia. Methods: A cross-sectional, survey-based study was conducted with 1,006 participants, including elderly individuals, caregivers, family members, and healthcare providers. A structured, bilingual questionnaire assessed healthcare access, service quality, social support availability, and perceived barriers. Descriptive and inferential statistics were used for analysis. Results: Most participants (64.0%) found healthcare services somewhat accessible; only 6.9% considered them very accessible. Satisfaction with care was moderate, with 50.9% satisfied and 15.0% very satisfied. Key barriers included logistical issues (35.4%), cultural attitudes (30.1%), and lack of awareness (21.8%). Social support services were underutilized, with regional disparities observed—Asir had the highest use, while Riyadh had the lowest awareness. Significant associations were found between healthcare accessibility and age, region, and participant role (P < 0.001). Conclusions: While general satisfaction with elderly care services exists, notable disparities persist based on age, region, and service awareness. To improve outcomes, targeted outreach, better infrastructure in rural areas, and expanded access to social support services are essential.
Management of Head and Neck Cancer a Multidisciplinary Collaboration Involving Oncology, Dentistry, Nursing, Pharmacy, Neurology, and Critical Care
The management of head and neck cancer necessitates a comprehensive and multidisciplinary approach, integrating various specialties to provide the most effective care for patients. Oncology serves as the cornerstone of treatment, with medical and radiation oncologists working collaboratively to devise personalized treatment plans, including chemotherapy and radiotherapy regimens tailored to the tumor's type, stage, and location. Dentistry plays a critical role in this collaboration, as oral health can significantly impact treatment outcomes; dental professionals help manage oral complications, ensure proper hygiene, and prevent infections, which are particularly crucial during extensive cancer treatments. Additionally, nursing teams facilitate care coordination, providing patient education, monitoring for treatment-related side effects, and supporting emotional wellbeing, while pharmacists ensure safe medication management, optimizing drug therapy to mitigate adverse effects and improve quality of life. Furthermore, the involvement of neurology in managing head and neck cancer addresses potential complications such as neuropathies resulting from tumor pressure or treatment-related effects. Neurologists contribute by evaluating neurological function and providing therapeutic interventions to manage pain and other symptoms. Critical care specialists are also integral, particularly for patients facing advanced disease or surgical interventions that may necessitate intensive monitoring and specialized care. This multidisciplinary collaboration enhances the overall patient experience, ensuring that all aspects of care—from diagnosis and treatment to rehabilitation and palliative services—are addressed cohesively. By leveraging the expertise of diverse healthcare professionals, patients with head and neck cancer receive holistic care that aims to improve outcomes and maximize their quality of life throughout the cancer journey.
Interconnection Between Psychological Well-being and Oral Health A Multidisciplinary Nursing and Health Administration Perspective
The interconnection between psychological well-being and oral health is a critical area of study that demonstrates how mental health can significantly influence physical health outcomes. Research suggests that individuals experiencing psychological distress, such as anxiety and depression, are more likely to neglect their oral hygiene routines, which can lead to a cycle of poor oral health. For instance, the stress associated with mental health issues may lead to behaviors such as teeth grinding or neglecting regular dental check-ups, exacerbating conditions such as gum disease or tooth decay. From a multidisciplinary nursing and health administration perspective, understanding this relationship prompts a holistic approach to patient care, where mental health screenings are integrated into routine dental assessments. This collaboration can ultimately lead to the development of tailored interventions that address both psychological and oral health needs, promoting overall well-being. Moreover, addressing the psychological factors influencing oral health can enhance treatment adherence and patient outcomes. Health administrators and nursing professionals are in a unique position to implement preventive measures that promote oral health education and mental health resources within dental practices. This may include training dental staff to recognize signs of anxiety and depression in patients, as well as fostering a supportive environment where individuals feel comfortable discussing their mental health concerns. Additionally, interprofessional collaboration can facilitate the creation of care plans that bridge dental health and psychological support services, ultimately contributing to a more comprehensive healthcare strategy. By advocating for policies that integrate mental health into oral health care settings, health administrators can help reduce the stigma associated with both fields, empowering patients to seek the care they need for both their minds and their mouths.
Proposed Framework for Bioclimatic Design Criteria Using Building Information Modeling (BIM) in Residential Buildings in Egypt
Modern residential buildings in Egypt, especially in hot climates, face increasing challenges related to high energy consumption and low levels of thermal comfort. This study aims to explore the potential for enhancing the sustainability of residential buildings by integrating bioclimatic design criteria with Building Information Modeling (BIM) technologies. The study followed a descriptive-analytical approach, including a comprehensive review and in-depth analysis of published research and studies covering the period between 2015 and 2025, which addressed this integration within the Egyptian context and regions with similar climatic conditions. The results concluded that applying BIM techniques in bioclimatic design can improve energy efficiency by between 22% and 46%, while achieving significant improvements in indoor thermal comfort levels. It also demonstrated the benefits of integrating BIM systems with energy simulation tools during the early stages of design, enabling accurate and in-depth data-driven decisions. However, the study revealed a few technical and economic challenges, including difficulties in software integration and high implementation and training costs, which hinder the widespread adoption of these practices. The study concluded that combining bioclimatic design with BIM technologies represents a strategic opportunity to support sustainable development goals in the Egyptian housing sector. Accordingly, the study recommended the development of supportive policies and local standards that address technical climate issues, as well as investing in professional capacity building and encouraging pilot projects to promote and expand the application of these innovative solutions.
Bridging the Gap: Translating Mobile App Development Principles to the XR Landscape
This article explores the evolving landscape of extended reality (XR) technologies and their relationship to established mobile application development principles. It examines how fundamental concepts from mobile development can be effectively translated to address the unique challenges of immersive XR environments. The theoretical framework establishes core mobile development principles that remain relevant across computing paradigms while acknowledging the substantial adaptations required for spatial computing contexts. A comparative analysis of prominent XR platforms reveals divergent architectural approaches alongside shared objectives in enabling immersive experiences. The article investigates the reimagination of user interface design for three-dimensional environments, performance optimization strategies for resource-intensive XR applications, and specialized approaches to addressing physiological considerations unique to immersive computing. Case studies across multiple sectors demonstrate successful pathways for transitioning from mobile to XR development, highlighting common patterns and critical success factors. The implications extend beyond technical considerations to encompass transformative potential across gaming, healthcare, education, and commercial applications. Emerging trends indicate an evolving landscape characterized by increasing accessibility, social connectivity, and physiological integration. The article concludes by identifying critical areas for future development and unresolved challenges that will shape the continued evolution of XR application development.
Towards a Rewriting Logic-Based Framework for Specifying FIPA Request Protocols
This paper presents a practical and systematic approach for formally specifying interactions in multi-agent systems (MAS) based on the well-established FIPA request protocol. It aims to simplify and streamline the process of translating MAS interaction descriptions expressed with Agent UML diagrams into precise, unambiguous formal specifications using the Real-Time Maude language, which is founded on rewriting logic—a powerful and expressive formalism for specifying and analyzing concurrent and real-time systems. This integration enables enhanced analysis, rigorous verification, and validation of agent behaviors in complex and dynamic scenarios. The effectiveness and usability of the proposed approach are demonstrated through a detailed first aid case study, highlighting its practical applicability and benefits for designing reliable MAS interactions.
Prehospital Care Protocols Employed by Red Crescent Ambulance Services
Prehospital care protocols employed by Red Crescent Ambulance Services focus on providing timely and effective medical assistance in emergencies, particularly in conflict zones or disaster-stricken areas. These protocols are designed to ensure the safety and health of both patients and healthcare providers while adhering to best practices in emergency medical services (EMS). The Red Crescent emphasizes the importance of rapid response times, triage, and the use of advanced life support techniques to stabilize patients en route to medical facilities. The protocols are often tailored to the local context, considering factors such as transportation logistics, available medical supplies, and the types of injuries commonly encountered in different environments. Furthermore, Red Crescent Ambulance Services prioritize training and capacity-building for their volunteer and professional staff, enabling them to effectively implement these prehospital care protocols. Continuous education and simulation training are crucial, as they prepare responders for a variety of scenarios, from natural disasters to armed conflicts. The emphasis on community engagement and collaboration with local healthcare systems enhances the efficacy of these protocols, ensuring a seamless transition from prehospital care to definitive medical treatment. By integrating cultural sensitivity and local health knowledge into their protocols, the Red Crescent can deliver more personalized care, ultimately improving health outcomes in the communities they serve.
Choking First Aid Techniques for Adults and Children by Red Crescent
Choking is a critical emergency that requires immediate action, as it can obstruct airflow and lead to severe consequences within minutes. For adults, the first response is often the Heimlich maneuver, which involves standing behind the individual and wrapping your arms around their waist. The rescuer should make a fist with one hand and place the thumb side against the middle of the person’s abdomen, above the navel. A quick, inward and upward thrust can help expel the object blocking the airway. If the person becomes unconscious, it's essential to call emergency services and initiate CPR, alternating between chest compressions and rescue breaths to maintain circulation and oxygen flow. When dealing with choking in children, techniques vary slightly based on age. For infants under one year, the recommended approach is a series of five back blows, delivered between the shoulder blades using the heel of the hand, followed by five chest thrusts using two fingers on the lower half of the sternum. For children aged one year and older, the Heimlich maneuver can be adapted similarly to adults, though the rescuer should be mindful of using less force appropriate to the child's size. Regardless of age, it is crucial to remain calm and encourage the child to cough forcefully if they can, as this may help dislodge the object on their own. In all cases, seeking professional medical assistance afterward is advisable, even if the object seems to be expelled.
Hemorrhage Control Tourniquets, Pressure, and Wound Firstaid by Red Crescent
Hemorrhage control is a critical component of trauma management, especially in cases of severe bleeding from extremity injuries. One of the most effective tools for controlling life-threatening hemorrhage is the use of tourniquets. When applied properly, a tourniquet can occlude blood flow to a limb, substantially reducing the risk of exsanguination until more advanced medical care can be provided. Various types of tourniquets exist, but those designed for prehospital use have undergone rigorous testing to ensure their effectiveness and safety. The application of a tourniquet requires training, as incorrect usage can lead to complications such as tissue damage; therefore, education on proper technique is paramount for first responders and civilians alike. In conjunction with tourniquets, the use of direct pressure on wounds and hemostatic dressings can also play a vital role in managing bleeding, particularly in cases where a tourniquet may not be suitable. In addition to the immediate management of bleeding, wound care further enhances outcomes for patients with traumatic injuries. After hemorrhage control, ensuring the clean management of wounds helps prevent infection and promotes healing. For larger wounds, the use of pressure dressings can be critical in maintaining stability while waiting for surgical interventions. Medical professionals often employ techniques to assess the depth and extent of the wound, as well as to evaluate for foreign objects that may require removal. Effective wound management incorporates a comprehensive approach, including stabilization, application of appropriate dressings, and, where necessary, the initiation of antibiotic prophylaxis. Ultimately, a well-coordinated strategy involving tourniquet application, pressure management, and thorough wound care significantly contributes to the patient's chances of recovery and long-term outcomes.
Seismic Retrofitting of Non-Compliant RC Structures Using SMA Bracings and Modified DDBD: A Parametric Approach
This paper introduces a novel seismic retrofitting strategy using Shape Memory Alloy (SMA) bracings within a modified Direct Displacement-Based Design (DDBD) framework for non-compliant reinforced concrete (RC) structures. SMA bracings, characterised by superelasticity and energy dissipation, were investigated through parametric analyses considering various configurations, cross-sectional areas, and damping properties. Nonlinear static pushover and nonlinear time history analyses were performed on mid-rise (six-storey) and high-rise (nine-storey) RC buildings. Results indicated that optimal X-bracing designs—1250 mm² cross-sectional area with 0.10 damping for six-storey, and 1600 mm² with 0.15 damping for nine-storey structures—markedly enhanced seismic resilience. Improvements included increased global ductility, reduced inter-storey drifts, and better control of plastic hinge formation. Retrofitted six-storey structures achieved reductions of 94% in peak floor accelerations (PFA) and 95% in residual displacements, while nine-storey structures showed 94% and 63% reductions, respectively. Comparisons with conventional steel bracings confirmed SMA’s superior performance. SMA bracings reduced residual displacements by 70% (six-storey) and 63% (nine-storey), compared to 50% with steel. They also improved global ductility by 35% and 40%, whereas steel achieved only moderate gains. These outcomes highlight SMA bracing’s effectiveness in controlling deformations, enabling recentering, and minimizing permanent damage. Although the study was applied to real Algerian structures, findings can be generalized to RC buildings of varying heights worldwide. SMA bracings emerge as a scalable and cost-efficient solution for seismic-prone regions. Future work should address applications to steel and hybrid structures and assess SMA’s long-term performance under different seismic conditions.
Psychological Counseling and Social Work: Saudi Perspective on Mental Health Care
Psychological counseling and social work in Saudi Arabia have evolved significantly in response to the growing recognition of mental health as a crucial component of overall well-being. Traditionally, mental health issues were often stigmatized, leading many individuals to avoid seeking help. However, increased awareness and education have been pivotal in changing societal attitudes, promoting the importance of psychological counseling as a valuable resource. The Saudi government has made strides in integrating mental health services into the healthcare system, focusing on community-based interventions. This includes the establishment of counseling centers and the incorporation of mental health education within universities and schools, reflecting a commitment to destigmatizing mental health issues and providing accessible support for those in need. Social workers play a vital role in the landscape of mental health care, particularly in navigating the complexities of cultural norms and family dynamics that often influence individual experiences. They are instrumental in providing support not only to individuals with mental health issues but also to their families, helping to bridge the gap between traditional practices and modern therapeutic approaches. By fostering community support networks and advocating for resource allocation, social workers enhance the accessibility of mental health services across diverse populations. As Saudi Arabia continues to modernize its approach to mental health care, integrating psychological counseling and social work into a cohesive framework remains essential for addressing the emerging challenges and improving outcomes for individuals facing mental health issues in the Kingdom.
Enhancing SAP S/4HANA and Salesforce Quality Assurance & Quality Control through AI-Driven Test Automation Frameworks for Regulatory Compliance
Both enterprise resource planning and customer relationship management systems are under increasing pressure to ensure that they remain compliant with their regulations and provide efficient quality assurance processes. The outdated testing models are not capable of keeping up with the dynamics of current enterprise platforms, especially those that are closely regulated, such as the healthcare and medical device manufacturing industries. In this article, an AI-based test automation framework is introduced to support quality assurance and quality control processes during the implementation of SAP S/4HANA and Salesforce by means of machine learning algorithms and natural language processing. It has an intelligent test case generation, predictive risk evaluation, self-healing test scripts, and an automated compliance checking framework. Case studies of implementations show that efficiency of testing, detection of defects, and coverage of regulatory compliance have been greatly enhanced in the financial services and healthcare sectors, as well as within the medical device manufacturing settings. The framework considers major technical issues such as data quality management, the complexity of system integration, and regulatory validation requirements with the help of modular architecture and standard protocols of interfaces.
Annotation Meta-Model for Collaboration among Health Professionals
Collaboration among Health Professionals (HPs) is a cornerstone of high-quality medical care. Its success depends not only on the availability of clinical information but also on the effectiveness of information exchange. Electronic Health Records (EHRs) have become the central medium for storing and sharing patient data, yet current systems rarely capture the dynamics of professional interactions. This limitation reduces the potential of EHRs to fully support collaborative decision making and coordinated care. To address this gap, we propose an annotation-based approach aimed at enhancing collaboration within EHR environments. Our contribution is threefold. First, we define methods to structure interactions by explicitly capturing who communicates what and when, thereby organizing the flow of exchanged information. Second, we introduce semantic enrichment techniques that add contextual and meaningful interpretation to annotations. Third, we design a meta-annotation model, grounded in a Knowledge-Based System (KBS), that formally represents collaborative exchanges and ensures consistency. A software prototype operationalizes these concepts, providing a practical tool to support annotation creation and management. By bridging raw information with structured, semantically rich collaboration, our approach enhances workflows, strengthens decision-making, and ultimately improves healthcare delivery.
Communication Structure Adaptive Control and Collaborative Optimization for Multi-Agent Systems
Multi-agent systems(MAS) rely on adaptive communication architectures to coordinate, scale, and maintain robustness in dynamic settings where fixed topologies fail. By adjusting connections under uncertainty, agents preserve connectivity while reducing resource use, yet combining adaptation with collaborative optimization remains fragmented. Reviewing over fifty key works from 2003 to 2025, we present a unified taxonomy of communication paradigms (static vs. dynamic; directed vs. undirected; hierarchical vs. peer‑to‑peer; sparse vs. dense), adaptive‑control strategies (model‑free protocols, learning‑driven topology updates, fault‑resilient controls), and cooperative optimization methods. Advances in distributed consensus, event-triggered messaging, and quantized communication have dramatically lowered bandwidth requirements without sacrificing performance, and algebraic connectivity is shown critical for convergence rates. Despite progress, challenges persist in scalability, privacy/security in distributed coordination, and trust‑aware human–robot interaction. We propose future directions toward privacy-preserving protocols, enhanced communication security, and interpretable coordination frameworks.
PSO-UFS: A Novel Approach to Univariate Feature Selection Using Particle Swarm Optimization
Univariate Feature Selection (UFS) traditionally involves a labor-intensive process of trial-and error, necessitating the selection of scoring functions and the determination of feature numbers. These choices can inadvertently affect both the performance and interpretability of the model. To address this challenge, we introduce Particle Swarm Optimization for Univariate Feature Selection (PSO-UFS), an innovative method that automates these crucial decisions. PSO-UFS leverages the power of Particle Swarm Optimization (PSO) to autonomously identify the optimal scoring function and feature subset that maximize a machine learning algorithm’s performance metric. Our empirical evaluations across multiple datasets demonstrate that PSO-UFS significantly outperforms traditional UFS in various performance metrics, including accuracy, precision, recall, and F1-score. Importantly, PSO-UFS generates more interpretable feature subsets, thereby enhancing the model’s comprehensibility. This advancement paves the way for broader applications in real-world scenarios where feature reduction and interpretability are paramount.
The Role of Passive Design Strategies in the Optimization of Energy Performance in Algerian Social Housing : A Case Study Application in Bordj Bou Arreridj Using Dynamic Simulation and Uncertainty Analysis
This research presents a comprehensive examination of the various passive design strategies and their impact on energy efficiency in social housing situated in Bordj Bou Arreridj, Algeria. Dynamic thermal simulation studies were carried out, calibrated with measured consumption data, to disaggregate monthly consumption patterns by end-use type (heating, cooling, lighting), thus giving more focused information as to the efficacy of interventions. It was found that energy consumption varies seasonally, with heating demand predominant from November to March at 86.5kWh/m²/year, closely followed by cooling in July and August at 50kWh/m²/year, and lighting consumption is stagnant throughout the year at 16.2 kWh/m²/year. Upon applying climate-responsive passive strategies, overall energy savings of 47.9% were achieved. The reduction in energy consumption was, however, not evenly spread over all categories, with heating showing a reduction of 46%, cooling 54%, and lighting 04%. The statistical analysis has shown that the most effective single measure in this climate characterized by highland is thermal insulation while the combined measures act synergistically to realize a non-linear enhancement of winter thermal performance. This study thus provides empirical evidence in support of Algerian housing policy for climatic-specific architectural interventions and also outlines a methodology for disaggregated energy analysis applicable to similar highland contexts.
Transforming IT Operations with Agentic AI: The Evolution from Reactive to Autonomous Infrastructure Management
Information technology operations transformation by artificial intelligence is a paradigm shift from responsive maintenance paradigms to proactive, self-operating infrastructure management systems. Agentic artificial intelligence brings to the table advanced capabilities to allow organizations to identify and fix system problems prior to user impact, automatically craft contextual resolution plans, and drive multifaceted remediation workflows in distributed computing environments. Advanced monitoring systems utilize machine learning algorithms and pattern detection mechanisms to scrutinize immense volumes of operational telemetry data, detecting faint anomalies before significant events. Self-healing resolution planning integrates historical event information, live environmental factors, and multi-objective optimization methodologies to devise customized remediation plans taking into account system load, resource availability, and business impact drivers. Risk assessment capabilities leverage digital twin technologies and predictive modeling to model possible outcomes prior to the instatement of infrastructure modifications, while automated rollback procedures guarantee service availability through the occurrence of unforeseen complications. Cross-functional workflow optimization dismantles organizational silos through the support of wise coordination between network operations, application development, security, and business functions. Implementation necessitates strong technical architectures that enable massive data processing capacities, enterprise-grade observability platforms, and secure communications between independent agents and managed systems. The transformation requires extensive transformation of the workforce, with a focus on collaboration between people and artificial intelligence, where technology performs routine operational tasks and people address strategic decision-making, ethical implications, and novel business situations demanding creativity and social skills.
Federated Deep Learning for Robust and Scalable Intrusion Detection in the Internet of Medical Things
The Internet of Medical Things (IoMT) connects wearable devices, sensors, and healthcare systems to enable continuous patient monitoring and intelligent diagnostics. While offering significant benefits, this connectivity also exposes IoMT to cyberattacks that threaten data integrity and patient safety. Intrusion detection is therefore essential, but traditional centralized methods raise concerns of privacy leakage, high communication cost, and single points of failure. To address these issues, we propose a federated deep learning framework that employs a hybrid Convolutional Neural Network and Long Short-Term Memory CNN–LSTM architecture for intrusion detection. The federated approach allows collaborative model training across distributed clients without sharing raw medical data, preserving privacy while enhancing scalability. Experiments conducted on the CIC-IoMT2024 dataset under both IID and non-IID data distributions demonstrate that the framework achieves up to 99% accuracy in binary classification and strong robustness in multi-class scenarios. These findings confirm that federated deep learning offers a robust and scalable solution for securing IoMT networks while safeguarding sensitive medical information.
The Influence of Concepts from Number Theory on the Behavior and Security of Hash Functions
This paper explores the influence of number theory concepts on the behavior and security of cryptographic hash functions. Hash functions play a critical role in modern cryptography, ensuring data integrity, authentication, and digital signatures. While they are primarily designed using principles from algebra and complexity theory, number theory significantly contributes to their construction and security analysis. Key number-theoretic conceptssuch as modular arithmetic, prime number distributions, and discrete logarithmsunderpin many hash function designs, especially in schemes that rely on structured algebraic inputs or are constructed from hard mathematical problems. We analyze how these mathematical foundations affect essential properties like collision resistance, pre-image resistance, and avalanche behavior. Additionally, we examine how number-theoretic attacks (e.g., those exploiting modular congruences or integer factorization) pose potential threats to certain classes of hash functions. The paper concludes by highlighting current research trends leveraging advanced number-theoretic techniques to enhance hash function robustness, emphasizing the ongoing interplay between pure mathematics and practical cryptographic design.
Diagnosis and Management of Hypertension Integrating General Medicine, Public Health, Nursing, and Radiology
Hypertension, a prevalent and significant public health concern, necessitates a comprehensive approach to diagnosis and management that incorporates the expertise of general medicine, public health, nursing, and radiology. General practitioners play a pivotal role in identifying and diagnosing hypertension through regular health screenings and patient assessments. Accurate measurement of blood pressure, alongside a thorough evaluation of patient history and risk factors, is essential for effective diagnosis. Public health initiatives also contribute significantly by promoting awareness and preventative measures to mitigate hypertension's risk factors within communities. Campaigns focusing on lifestyle modifications—such as dietary changes, physical activity, and smoking cessation—are integral components of population-level strategies aimed at reducing the incidence of hypertension and its associated complications. The management of hypertension is further enhanced through a multidisciplinary approach that includes nursing care, patient education, and radiological assessments. Nurses are critical for monitoring patients, reinforcing lifestyle modifications, and ensuring adherence to prescribed treatment plans. They frequently serve as the frontline in patient education, providing vital information regarding medication adherence and the importance of regular follow-up. Additionally, radiological tools, such as echocardiograms or imaging tests, can help assess organ damage caused by chronic hypertension, guiding treatment decisions. This integrated approach allows for tailored management strategies that address individual patient needs while addressing broader public health goals, ultimately aiming for improved outcomes and reduced morbidity associated with hypertension.
Patient Handover and Continuity of Care Challenges in Saudi Prehospital Services
In Saudi Arabia, the efficacy of prehospital services is increasingly scrutinized, particularly in the context of patient handover and continuity of care. Effective handover practices are critical to ensuring that patients receive seamless care as they transition from prehospital emergency medical services to hospital facilities. However, challenges such as communication breakdowns, inconsistent information transfer, and variations in protocols can compromise patient safety and outcomes. Many prehospital providers face difficulties in transmitting the patient's medical history, medication lists, and assessment findings, leading to potential delays in treatment and increased risk of adverse events. Ensuring that staff are adequately trained in handover procedures and that standardized protocols are established is vital for addressing these gaps. Continuity of care is another significant challenge within Saudi prehospital services, particularly as the healthcare system becomes more integrated and patient-centric. The lack of an integrated electronic health record system hampers the ability of emergency medical technicians (EMTs) to access and share critical patient information seamlessly with receiving hospitals. This disconnect can result in duplicated tests, medication errors, and miscommunications regarding treatment plans. Additionally, the variability in the level of care provided by different prehospital teams further complicates continuity. To enhance patient care, it is essential to implement systematic training programs, develop unified communication tools, and foster collaboration between prehospital services and healthcare institutions. Such initiatives could significantly improve patient outcomes and ensure a more efficient healthcare continuum.
Role of the Saudi Red Crescent Authority in Out-of-Hospital Cardiac Arrest Survival Rates
The Saudi Red Crescent Authority (SRCA) plays a critical role in improving out-of-hospital cardiac arrest (OHCA) survival rates across Saudi Arabia through its comprehensive emergency medical services (EMS). By implementing extensive training programs for its personnel and collaborating with local hospitals, the SRCA ensures that emergency responders are equipped with the necessary skills to deliver timely and effective cardiopulmonary resuscitation (CPR) and defibrillation. Moreover, the SRCA actively propagates public awareness initiatives to educate citizens on recognizing cardiac arrest symptoms and the importance of calling emergency services immediately. These efforts aim to create a robust community response system, thereby increasing the chances of survival for individuals experiencing cardiac arrest outside hospital settings. In addition to direct emergency care, the SRCA's strategic deployment of automated external defibrillators (AEDs) in public spaces significantly enhances the likelihood of effective resuscitation during OHCA incidents. The organization routinely partners with educational institutions and local businesses to install AEDs and conduct CPR training workshops, empowering citizens to take immediate action in emergencies. Furthermore, the SRCA collects and analyzes data on cardiac arrest incidents, allowing for ongoing evaluation and improvement of their response strategies. These multifaceted approaches not only bolster OHCA survival rates but also contribute to fostering a culture of preparedness and responsiveness regarding cardiac emergencies throughout the Kingdom.
Impact of Rapid Emergency Medical Services Response Times on Patient Outcomes in Saudi Arabia
Rapid Emergency Medical Services (EMS) response times play a crucial role in determining patient outcomes in Saudi Arabia, where the prevalence of acute medical conditions such as cardiac arrests, strokes, and trauma cases is significant. Studies indicate that timely intervention by EMS can drastically improve survival rates and reduce morbidity associated with these emergencies. In urban areas, where traffic congestion can delay response times, the implementation of advanced dispatch systems and dedicated emergency lanes has been shown to enhance the speed of medical assistance. Furthermore, public awareness campaigns about the importance of calling for emergency services promptly can empower citizens to act swiftly in critical situations, ultimately contributing to better health outcomes. In rural regions of Saudi Arabia, the challenges of geographical barriers and limited access to healthcare facilities can exacerbate the impact of delayed EMS responses. The establishment of mobile health units and the integration of telemedicine have emerged as potential solutions to bridge these gaps, ensuring that patients receive timely care regardless of their location. Research underscores the need for continuous training and education for EMS personnel to improve their efficiency and effectiveness in emergency situations. Overall, optimizing EMS response times is essential for enhancing patient survival rates and quality of care across the diverse landscapes of Saudi Arabia.
The Role of the Saudi Red Crescent Authority in Mass-Casualty Incident Preparedness
The Saudi Red Crescent Authority (SRCA) plays a pivotal role in the preparedness and response to mass-casualty incidents in Saudi Arabia. As the primary humanitarian organization in the Kingdom, the SRCA is tasked with providing emergency medical services, disaster response, and health education. The authority collaborates closely with various stakeholders, including government agencies, local communities, and international organizations, to develop comprehensive emergency response plans. Through rigorous training programs, simulations, and community outreach initiatives, the SRCA enhances the capacity of medical personnel and volunteers, ensuring a swift and efficient response during crises. Their focus on preparedness is further exemplified through the establishment of communication networks and protocols that facilitate rapid coordination during emergencies. In addition to training and coordination, the SRCA actively engages in research and development to improve emergency response capabilities in handling mass-casualty incidents. By utilizing innovative technologies and best practices from around the world, the SRCA strives to enhance the resilience of its healthcare systems. The organization also emphasizes public awareness campaigns aimed at educating citizens on emergency preparedness and first aid, which is crucial for minimizing casualties in the event of a disaster. As mass-casualty incidents can occur due to natural disasters, accidents, or terrorist attacks, the SRCA's strategic approach to preparedness is essential in safeguarding public health and ensuring the well-being of the community.
Event-Driven Accounting Transformation: From Batch Processing to Real-Time Intelligence
Modern enterprise accounting infrastructure undergoes a fundamental transformation through event-driven processing models that revolutionize traditional financial management. Apache Kafka's distributed streaming architecture enables real-time transaction processing across massive organizational datasets while maintaining strict ordering guarantees for financial integrity. Contemporary implementations demonstrate sustained message throughput exceeding one million events per second with processing latencies under 1 millisecond for critical operations. Event-driven systems eliminate traditional boundaries between operational processes and financial recording by creating integrated platforms that capture business transactions instantly and generate corresponding accounting entries through sophisticated rule engines. Multi-standard compliance architectures enable concurrent processing across diverse regulatory requirements, including GAAP, IFRS, and region-specific frameworks, through parallel processing streams that preserve comprehensive audit trails. Machine learning algorithms embedded within distributed processing platforms enable real-time anomaly detection and fraud prevention with detection accuracy rates surpassing established benchmarks. Financial services organizations demonstrate significant operational efficiency gains, with month-end closing processes reduced from weeks to days through automated reconciliation workflows. Healthcare institutions achieve substantial administrative cost reductions while maintaining compliance within complex billing infrastructures. Manufacturing enterprises benefit from comprehensive cost allocation visibility across global supply chains, enabling dynamic pricing strategies and real-time operational optimization. Explainable AI architectures provide human-interpretable reasoning capabilities essential for audit requirements and regulatory compliance. Service-oriented design principles facilitate sustainable digital infrastructure evolution with measurable environmental footprint reductions alongside high-performance processing.
Performance of oil well sand concrete reinforced with polypropylene fibers and incorporating dune and alluvial sands
In the context of recycling and valorizing industrial waste for use in sand concrete, this study contributes to finding a solution for sludge that is generally discharged into nature. Its use as an alternative material in sand concrete is being considered.The study aims to evaluate the effect of partially replacing oil well sand with dune sand and alluvial sand at substitution levels of 25 % and 30%, in combination with the addition of polypropylene fibers, on the mechanical properties of oil well sand concrete. The incorporation of dune sand and alluvial sand alongside oil well sand effectively reduces the proportion of oil well sand, resulting in notable improvements in compressive strength, tensile strength, and a decrease in shrinkage in the mixtures. Furthermore, the inclusion of polypropylene fibers produced a more pronounced enhancement in both strength (compressive and tensile) and shrinkage resistance.
Blockchain-Integrated Support Vector Machine Framework for Privacy-Preserving Predictive Analytics
This paper presents a blockchain-integrated Support Vector Machine (SVM) framework that addresses the privacy, trust, and ownership limitations of conventional client–server architectures. In the proposed design, clients train local SVM models and share only encrypted parameters, while blockchain ensures secure aggregation, tamper-proof logging, and transparent auditability.This study proposes a framework that combines blockchain technology with Support Vector Machines (SVM) to improve privacy, transparency, and accountability in predictive analytics. Experiments on MNIST and CIFAR-10 show that the framework achieves accuracy comparable to conventional SVMs (0.92 vs. 0.93), while enabling tamper-resistant predictions and verifiable model operations. Blockchain integration introduces moderate overhead—latency of 18–19 ms and transaction costs around 22,500 gas units—but these are outweighed by gains in data security, decentralized control, and auditability. To the best of our knowledge, this is among the first efforts to merge SVM with blockchain for secure predictive modeling. The framework is scalable, reliable, and well-suited for sensitive domains such as healthcare, finance, and intelligent transportation systems.
Design and Evaluation of an Adaptive Fault Detection and Tolerance Mechanism for Underwater Wireless Sensor Networks
Underwater Wireless Sensor Networks (UWSNs) are a pivotal technology for ocean monitoring, underwater surveillance, and environmental sensing. However, these networks are vulnerable to numerous faults caused by adverse aquatic environments, scarce node resources, and unreliable acoustic communication. This paper presents an adaptive fault detection and tolerance mechanism specially designed for UWSNs. The proposed system integrates a hybrid fault detection approach, combining explicit and implicit approaches with a light-weight tolerance strategy leveraging multi-path rerouting and load-aware decision-making. A dynamic thresholding mechanism is used to enhance fault detection sensitivity and accuracy while ensuring energy efficiency. The mechanism is implemented and evaluated using MATLAB simulations with different fault densities and node behaviors. The findings show significant improvement in detection accuracy, less false alarm, lower latency, and better network lifetime when compared with traditional fault management schemes. This research is a step toward constructing fault-tolerant UWSNs that can maintain operations under the rough and uncertain nature of underwater environments.
Evolution of Ambulance Dispatch Systems in Saudi Arabia: A Saudi Red Crescent Authority Perspective
The evolution of ambulance dispatch systems in Saudi Arabia has significantly transformed in recent years, particularly through the efforts of the Saudi Red Crescent Authority (SRCA). Historically, the dispatch of ambulances relied heavily on manual processes, which often led to inefficiencies and delays in emergency response. The SRCA has recognized the necessity of modernizing these systems to enhance the quality of emergency medical services (EMS) across the nation. The introduction of automated dispatch systems and integration of advanced technologies, such as Geographic Information Systems (GIS) and real-time tracking applications, have streamlined operations, allowing for quicker response times and improved resource allocation. This technological shift not only enhances the efficiency of emergency medical services but also plays a pivotal role in saving lives during critical situations. Furthermore, the SRCA's commitment to continuous improvement and innovation has propelled the ambulance dispatch systems into a new era of sophistication. Training programs for dispatch personnel have been implemented to ensure they are well-versed in utilizing the latest technology and managing emergencies effectively. Collaboration with local hospitals, fire departments, and law enforcement agencies has also been prioritized to establish a more unified approach to emergency management. As the population of Saudi Arabia grows and urban areas expand, the need for an agile and responsive ambulance dispatch system becomes increasingly vital. The SRCA's proactive adaptation to changing challenges demonstrates a forward-thinking approach that not only meets the current demands but also anticipates future healthcare needs within the Kingdom.
Automated Technical Debt Assessment In Legacy Banking Applications
When it comes to banking operations, traditional architecture and obsolete software systems are by no means extraneous. However, the introduction of such systems does carry with itself the burden of technical debt, since it inhibits the ease of change, invites operational risk, and makes the raising of the cost of maintaining such a system inevitable. Making use of technical debt automation is a reasonable approach that is remarkably helpful in identifying, measuring, and prioritizing code and architecture deficiencies in such legacy systems in a structured way. In this article, we explore the methods and techniques aimed at the detection of technical debt in bank-related systems with a focus on software analysis, particularly static analysis, and architectural metrics, and consider regimes of their estimation incorporating machine learning algorithms. In addition, the study addresses certain other characteristics that are application-dependent including issues of compliance, concerns about the availability of services, and compliance with modern digital technologies. The findings turn the attention toward the advantages of introducing automated evaluation tools to simplify the appreciation process, reduce the exposure of the bank to risk and enhance the prospects of updating bank software environments. Monolithic architecture and legacy banking applications, typically based on old technologies, remain significant to financial institutions. However, the operation and improvement of such systems increase the technical debt that damages changes, imposes operational risk, and raises maintenance costs. To combat this, there is an analysis of which is quite systematic – automated technical debt analysis, which is designed to find, measure and prioritize coding and architectural problems in such systems. The article studies and facilities of the automated identification of technical debt in banking software development applies, focusing mainly on how these can be achieved through software static analysis, architectural metrics, and prediction models that rely on artificial intelligence. Furthermore, some defects investigate technology dependence compliance, antifraud, and satisfaction from digital area. The findings further emphasize the significance of automated assessment systems to speed up the making of decisions process, reduce risk, and smoothen the process of transformation.
Vehicle Interface Processors: Revolutionizing Infotainment Systems in Electric and Autonomous Vehicle Ecosystems
Vehicle Interface Processors represent a paradigm shift in automotive infotainment technology, fundamentally transforming the passenger experience in electric and autonomous vehicles. These advanced processing units enable sophisticated multi-screen configurations, augmented reality navigation systems, and personalized user profiles that adapt to individual preferences and driving patterns. VIP technology facilitates seamless integration between entertainment platforms and critical vehicle functions, including real-time range estimation for electric powertrains and dynamic communication with Advanced Driver Assistance Systems. The processors optimize energy consumption through intelligent resource allocation, contributing to enhanced battery performance and reduced environmental impact in electric vehicle applications. VIP-powered systems provide passengers with immersive entertainment options while maintaining essential safety communications and vehicle status updates. The technology supports complex data fusion from multiple vehicle sensors, enabling predictive maintenance alerts and intelligent route optimization based on traffic conditions and charging infrastructure availability. As the automotive industry transitions toward electrification and automation, VIP-enabled infotainment systems emerge as crucial components that redefine mobility experiences, establishing new standards for user interaction, energy efficiency, and technological integration in modern transportation solutions.
Disaster Medical Response Coordinating General Surgery, Family Medicine, Nutrition, Nursing, and Radiography in Mass-Casualty Scenarios
In mass-casualty scenarios, effective disaster medical response coordination is paramount to ensuring the optimal delivery of care. General surgery plays a critical role, as surgical teams must be prepared to manage traumatic injuries that are frequently encountered in such situations. Concurrently, family medicine practitioners provide comprehensive care by addressing the holistic needs of patients, including chronic disease management and mental health support. These healthcare providers work collaboratively to triage patients effectively, ensuring that those with the most critical needs receive immediate attention. The coordinated efforts among these specialties not only improve individual patient outcomes but also enhance the overall efficiency of the healthcare response in the face of overwhelming demand. Nutrition and nursing are also essential components of disaster medical response in mass-casualty settings. Nutritionists assist in planning and delivering appropriate nutritional interventions to sustain both patients and healthcare providers, mitigating the impact of stress and trauma through proper nourishment. Meanwhile, nursing staff function as the backbone of the medical response, providing vital support in patient assessment, monitoring, and coordination of care. Nursing roles have expanded to include triage and leadership responsibilities, as they often serve as the primary point of contact for patients and their families in chaotic environments. Additionally, radiography, with its capacity to quickly assess and diagnose injuries through imaging, supports timely interventions and surgical planning. The interdisciplinary collaboration among these fields ensures a comprehensive approach to disaster management, improving resilience and outcomes in mass-casualty incidents.
Seizure Management in Prehospital Settings Reducing Time to Definitive Care in Epileptic Emergencies
Seizure management in prehospital settings plays a critical role in reducing the time to definitive care for patients experiencing epileptic emergencies. Rapid recognition and appropriate intervention by first responders can significantly influence patient outcomes. In such situations, timely administration of anticonvulsant medications, along with proper airway management and monitoring of vital signs, is essential. Training first responders in the identification of different seizure types and their respective management protocols can enhance the efficiency of care provided on-site. Moreover, the integration of advanced communication systems allows paramedics to relay critical patient information to receiving hospitals, ensuring that neurology teams are prepared for immediate intervention upon arrival. Furthermore, the implementation of standardized protocols and guidelines for seizure management in prehospital settings can streamline the response to epileptic emergencies. These protocols should encompass the use of evidence-based practices, such as the administration of benzodiazepines for prolonged seizures, and the establishment of clear pathways for transporting patients to specialized care facilities. Collaboration between emergency medical services (EMS) and hospitals can facilitate continuous education and training, ensuring that all personnel are equipped with the latest knowledge and skills. By prioritizing swift and effective prehospital care, the healthcare system can significantly mitigate the risks associated with prolonged seizures, ultimately improving patient prognosis and quality of life.
Computer Vision-Enhanced Smart Manufacturing for Continuous Glucose Monitoring Device Production
The production of continuous glucose monitoring devices requires unparalleled accuracy for the safety of the patients and the consistency of the devices used in diabetes management systems. Conventional methods for manual quality inspection add gross variability and do not identify microscopic defects that can jeopardize sensor accuracy and result in life-threatening glucose measurement errors. Recent computer vision technologies and deep learning models offer holistic solutions for automated quality control for CGM manufacturing setups. The deployment integrates high-resolution industrial vision systems with advanced neural network models such as YOLOv4 object detection frameworks and Vision Transformer architectures to enhance defect identification performance in various manufacturing conditions. Real-time process monitoring technology allows instant detection of manufacturing anomalies such as microcracks, contamination particles, membrane misalignment, and adhesive pattern aberrations. Closed-loop control systems dynamically regulate key manufacturing parameters like curing temperatures, adhesive quantities, and assembly rates to avoid defect propagation through downstream manufacturing operations. Digital twin simulation environments produce virtual manufacturing copies that facilitate predictive maintenance scheduling and process optimization via machine learning algorithms. The integration results in drastic improvements in manufacturing efficiency through dramatic scrap rate reductions, higher process yields, and faster defect detection response times. Comprehensive regulatory compliance capabilities ensure adherence to FDA validation standards and ISO medical device quality requirements through automated documentation systems and complete product traceability. Economic benefits include significant cost savings through reduced material waste, eliminated batch failures, and improved overall equipment effectiveness metrics.
The Serverless Paradigm: Abstraction, Elasticity, and Event-Driven Computing in Modern Cloud Architectures
Serverless computing has emerged as a transformative paradigm in cloud computing, fundamentally altering how applications are designed, deployed, and managed. This article explores the core characteristics of serverless architectures—including infrastructure abstraction, event-driven execution models, automatic scaling, and consumption-based billing while distinguishing them from traditional cloud service models. The article demonstrates the significant benefits of serverless adoption, such as reduced operational overhead, accelerated development cycles, and enhanced cost efficiency, particularly for workloads with unpredictable demand patterns. It examines architectural patterns naturally suited to serverless implementation, including microservices integration, event-driven designs, and data processing pipelines. The analysis also addresses critical challenges inherent to the serverless model, including cold start latency, observability complexities, vendor lock-in concerns, and state management in stateless environments. The article concludes with an evaluation of the current serverless landscape across major cloud providers and identifies promising future directions, including convergence with edge computing and artificial intelligence technologies.
Strategic Roadmaps for Sustainable 3D-Printed E-Commerce: Business Models for Personalized Retail
The rapid evolution of e-commerce has heightened consumer expectations for personalization and sustainability, creating the need for innovative business models that go beyond traditional mass-production and supply chain structures. This study investigates the integration of three-dimensional (3D) printing into e-commerce to develop strategic roadmaps for sustainable, personalized retail. Using a mixed-method approach, data were collected from 400 participants, including e-commerce managers, 3D printing experts, and consumers, across India, the United States, and the European Union. Qualitative insights from expert interviews and focus groups identified key enablers and barriers, while quantitative survey data were analyzed using structural equation modeling (SEM), regression analysis, and multi-group comparison. Results reveal that personalization demand is the strongest predictor of business performance, while technological capability and business model innovation significantly influence sustainability outcomes. Regional differences underscore the role of regulatory support and consumer trust, with European respondents reporting the highest readiness for sustainable 3D-printed retail. The study contributes to theory by linking personalization, sustainability, and technology within a business model innovation framework, while offering practical guidance for managers and policymakers in designing scalable, consumer-driven, and eco-conscious strategies. These findings highlight 3D printing not only as a disruptive technology but also as a sustainable pathway for future e-commerce models.
Corporate Strategy for Secure Semiconductor Supply Chains: ML-Driven Risk and Market Intelligence
The semiconductor industry is the foundation of modern digital economies, yet its supply chains remain highly vulnerable to systemic risks, geopolitical tensions, and global market fluctuations. This study examines how corporate strategy, when combined with machine learning (ML) driven risk modeling and market intelligence, can enhance the security and resilience of semiconductor supply chains. Using a mixed-method design, the research analyzed corporate strategy dimensions such as governance, operational resilience, sustainability, and financial adaptability alongside supply chain security parameters covering physical, digital, and systemic risks. Machine learning models, including Gradient Boosting, Random Forest, and LSTM, were applied to multi-source datasets to predict disruptions and evaluate performance metrics, while market intelligence indicators captured emerging demand trends, innovation signals, and trade risks. The findings reveal that operational resilience and financial adaptability exert the greatest impact on supply continuity, while systemic vulnerabilities remain critical due to interdependencies across global networks. Gradient Boosting emerged as the most effective predictive model, offering superior accuracy and reliability. Market intelligence further emphasized the accelerating demand for AI/IoT and automotive semiconductors, as well as the disruptive impact of tariff policies. Overall, the study highlights that secure semiconductor supply chains depend on the strategic integration of corporate decision-making with predictive analytics and intelligence-driven insights, enabling firms to achieve both resilience and competitiveness in a volatile global environment.
Voice-Driven CICD for SAP Supply Chains: Generative Agents Orchestrating Autonomous Ops
The increasing complexity of SAP-based supply chains demands automation frameworks that are both adaptive and human-centric. This study introduces a novel architecture that integrates voice-driven continuous integration and continuous deployment (CICD) with generative AI agents to orchestrate autonomous operations in SAP environments. A mixed-method research design was employed, combining system prototyping, simulation, and statistical analysis. Results demonstrate substantial improvements in CICD performance, with deployment success rates rising from 85.2% to 96.7% and rollback times reduced by over 60%. Generative agents achieved orchestration accuracies above 90%, adapting workflows dynamically and autonomously resolving operational errors. These technical advancements translated into measurable supply chain gains, including a 35% reduction in order fulfillment cycle time, enhanced inventory accuracy, and improved forecasting precision. User evaluations further confirmed strong acceptance, with usability scores averaging 87.4 and adoption intent exceeding 90%. Multivariate analysis revealed significant performance differences across routine, adaptive, and critical tasks, indicating the system’s versatility across varying operational complexities. The findings suggest that voice-driven generative orchestration not only improves efficiency and resilience but also democratizes access to enterprise automation, enabling non-technical stakeholders to participate directly in supply chain optimization. This research contributes to the advancement of intelligent, self-managing enterprise systems, positioning voice interfaces and generative agents as key enablers of the future SAP ecosystem.
Power – Efficient Gaussian Filter Architecture Utilizing Approximate 4:2 Compressors for Edge Detection
Nowadays, approximate computing is an emerging trend widely applied in image processing, signal processing, and computer vision applications. Approximation is suitable for applications which are error tolerant. In this work, approximate computing is used in the Gaussian filtering stage of edge detection. In computer vision applications like medical imaging, transportation etc., Edge detection plays a crucial role. In edge detection algorithms, the Gaussian filtering block is a fundamental module. This block involves power-consuming addition operations, resulting in increased computational complexity, power consumption, and delay. To address these trade-offs, imprecise computation is applied in the Gaussian filter module. The aim of the work is to reduce computational complexity, reduction in usage of power and delay in the Gaussian filtering module using an approximate compressor-based architecture. The proposed Gaussian filter is designed using newly developed approximate compressors. Simulation outcome indicates that the Gaussian filter implemented with proposed newly developed compressors achieves decline in power and delay compared to exact and existing implementations in literature. Error analysis and Edge Detection is performed using different metrics in MATLAB, and the results demonstrate good PSNR values for the proposed compressors, confirming their suitability for edge detection in image processing applications.
Active-Active DNS Architectures: Building Resilient Global-Scale Name Resolution Systems
Active-active DNS architectures are a paradigm shift in terms of the construction of resilient name resolution infrastructure that can satisfy the current needs of the Internet. Classical active/ passive type of failure mode illustrates striking weaknesses when faced with the current demands of uninterrupted availability, extreme latency, and defense against advanced attacks. The architectural development of simultaneous multi-plane operations gets rid of single failure points with systematically redundant heterogeneous technology stacks. These deployments utilize decentralized concepts, spreading zone data across stand-alone resolver planes that preserve self-operation capabilities while synchronizing over high-speed replication pipelines. Defense-in-depth techniques utilize more than one filtering layer, ranging from network-edge volumetric defense to application-layer anomaly detection, building robust protections against dynamic threat environments. Data synchronization technologies find consistency requirements and performance demands in balance through event-driven designs and cryptographic authentication protocols. Operational excellence is realized through ongoing optimization, with chaos engineering techniques confirming resilience hypotheses and remediation automation systems ensuring service continuity. The interaction of these architectural aspects allows DNS infrastructures to meet nearly perfect availability objectives while handling hundreds of billions of queries per day across distributed networks worldwide.
Driveline Control Integrated with Zonal Architectures in Software-Defined Vehicles
Modern automotive drivelines encompass complete all-wheel drive management, precision torque vectoring systems, and integrated regenerative braking functions. Traditional implementations employed domain-specific electronic control units to manage these complex functions through independent operational frameworks. Current software-defined vehicle development initiatives transition toward server-zone architectures incorporating distributed zone control units paired with high-performance central computing platforms. This article presents a service-oriented driveline control framework distributed across multiple zones, where specialized microservices manage torque allocation algorithms, slip detection protocols, and regenerative braking coordination, while centralized arbitration systems provide mode management and operational oversight. Network infrastructure selections prioritize Automotive Ethernet implementations with Time-Sensitive Networking capabilities, ensuring deterministic delivery of time-critical torque commands, complemented by Controller Area Network protocols supporting actuator-local control loops and comprehensive diagnostic communications. Functional safety implementation addresses sensing, estimation, and actuation pathway decomposition through independent monitoring capabilities that enable axle isolation or torque-neutral operational modes during fault conditions. Predictive diagnostic systems integrate inverter thermal characteristics and vibration signature analysis to anticipate performance degradation events. Implementation guidance addresses migration pathways from legacy domain architectures toward zone control unit topologies, demonstrating wiring harness optimization and enhanced feature development capabilities. Industry supplier communications and technical documentation confirm zone control unit and server-zone methodologies achieving commercial deployment across diverse vehicle segments, spanning entry-level through premium market categories.
Hybrid Integration Strategies for SAP S/4HANA: Bridging the Gap between On-Premise and Cloud Applications
This article explores hybrid integration strategies for organizations implementing SAP S/4HANA across mixed IT landscapes. Businesses today face mounting challenges as cloud platforms operate alongside established on-premise systems, making seamless integration a critical priority. The article examines various integration platforms, architectural frameworks, security protocols, and API management techniques enabling fluid connectivity between traditional SAP S/4HANA installations and modern cloud services. Practical implementation examples feature architectural approaches including direct point-to-point connections, centralized hub-and-spoke models, middleware-based Enterprise Service Bus solutions, and layered API connectivity structures. Technology leaders will appreciate how thoughtfully executed integration strategies enhance digital transformation while safeguarding previous infrastructure investments. Security considerations receive thorough attention, covering encryption practices, access management principles, network architecture requirements, and compliance obligations. The API management section reveals how enterprise capabilities evolve into product-focused interfaces, demonstrating the pathway from technical system capabilities toward measurable business outcomes and expanded marketplace opportunities in hybrid environments.
High-Performance AI-Driven Real-Time Risk Analytics for Distributed Financial Systems
Modern economic ecosystems require radical hazard management systems that may take care of big streams of statistics without compromising on regulatory compliance and business transparency. Conventional batch-based risk assessment models exhibit intrinsic shortcomings in addressing millisecond-level market turbulence and intricate network interdependencies that define new trading environments. Sophisticated artificial intelligence platforms embedded in distributed computing environments offer transformational possibilities for real-time risk sensing and mitigation. The suggested architecture develops end-to-end risk analytics capacity via ensemble machine learning algorithms, graph contagion analysis, and explainable AI features to meet strict regulatory demands. Complex data pipelines ingest heterogeneous finance streams from worldwide exchanges, payment networks, and blockchain ledgers in tandem. Tailored graph neural networks examine systemic risk transmission patterns in connected financial institutions while retaining dynamic relationship mapping capabilities. Explainable AI integration presents version interpretability and regulatory adherence through function attribution strategies and robust audit trail retention. Cloud-local infrastructure layout helps elastic scaling throughout multi-cloud environments using fault-tolerant distributed orchestration systems. Performance assessments display large upgrades in detection latency and predictive accuracy relative to standard batch-processing strategies. The design embodies a paradigm shift towards forward-looking, adaptive, and transparent risk management functionality critical to ensuring financial stability in progressively complex market conditions
From SRE to Intelligent Reliability Engineering: Revolutionizing the Discipline with AI
The evolution of Site Reliability Engineering to Intelligent Reliability Engineering is a paradigmatic revolution in managing large-scale distributed systems using artificial intelligence integration. Conventional SRE approaches, although optimal for small-scale environments, are confronted with insurmountable scalability limitations when dealing with the hyper-exponential increases in data volume, transaction rates, and architectural intricacy that define today's hyperscale systems. The cognitive bottlenecks of human-driven monitoring, correlation analysis, and incident remediation procedures introduce systematic barriers to reliability objectives maintenance in complicated microservice structures that operate across multiple cloud regions. This movement towards intelligent reliability frameworks employs advanced machine learning paradigms such as supervised learning for pattern discovery, unsupervised learning for real-time anomaly discovery, and reinforcement learning for adaptive resource optimization. Sophisticated AI solutions provide sub-second anomaly detection abilities, predictive scalability algorithms, and self-healing remediation systems, fixing trivial issues without the need for a human touch. Deployment scenarios in various industry verticals showcase significant business benefits ranging from improved incident detection accuracy, elimination of false positive alerting generation, and overall cost optimization by predictive capacity management. The incorporation includes machine learning-augmented observability pipelines, natural language processing for automated incident analysis, and graph neural networks for intricate dependency mapping in distributed architectures. Still, the areas of data quality assurance, model interpretability needs, ethical governance frameworks, and organizational transformation requirements remain major challenges to AI adoption in reliability engineering applications.
Dynamic Product Categorization with Multi-Modal AI: Leveraging Transformer Architecture for Enhanced Commerce Intelligence
Product categorization using multi-modal artificial intelligence represents a significant advancement in e-commerce infrastructure, transforming how digital commerce platforms organize, classify, and present products to consumers. The integration of transformer-based architectures with comprehensive content analysis enables simultaneous processing of text descriptions, images, and videos to create powerful product understanding systems. Advanced feature extraction techniques leverage natural language processing, computer vision, and temporal analysis to capture meaningful product attributes that manual categorization processes often overlook. Implementation approaches using distributed processing architectures and lambda models demonstrate superior scalability while meeting real-time performance requirements typical of modern commerce platforms. Attention-based fusion of multiple data modalities reveals complex product relationships and consumer preference patterns beyond the capabilities of single-input systems. Enhanced search functionality emerges through semantic understanding capabilities that align user intent with product characteristics across diverse query types and interaction patterns. Personalized recommendation mechanisms benefit from rich categorical data to deliver targeted content that resonates with individual consumer preferences and behavioral patterns. This technological advancement represents a fundamental shift from labor-intensive manual tagging systems toward intelligent automation that adapts to evolving product catalogs and consumer requirements. Commercial implementations demonstrate substantial improvements in search relevance, user engagement, and conversion rates across diverse retail environments. The comprehensive framework establishes new benchmarks for product discovery and recommendation systems in digital commerce platforms.
Journey to Excellence: Strategic Framework for Enterprise BI Migration
Enterprise business intelligence platform migration constitutes a transformative organizational initiative demanding coordination across technical infrastructure, financial considerations, and workforce adaptation dimensions. The framework examines challenges encountered during transitions from established reporting environments such as IBM Cognos, SAP BusinessObjects, and Oracle OBIEE toward contemporary cloud-native analytical platforms including Power BI, Tableau, Looker, and Qlik Sense. Strategic assessment commences with methodical usage pattern examination through metadata extraction utilities and asset classification approaches, facilitating informed choices regarding migration parameters and resource distribution. Platform assessment incorporates total ownership cost analyses, feature compatibility verification through automated assessment instruments, and sustained scalability specifications encompassing elastic computational provisioning alongside real-time streaming analytical capabilities. Technical confirmation secures smooth transitions via systematic data lineage recording using solutions like Alation and Collibra, SQL dialect verification procedures, DAX and MDX translation confirmation, and prototype construction for sophisticated OLAP structures and parameterized reporting components. The execution framework highlights parallel environment functioning throughout migration intervals with automated data comparison scripts, thorough user validation procedures employing regression verification frameworks, and cyclical feedback integration via Agile sprint methodologies. Change facilitation approaches concentrate on stakeholder engagement through organizational meetings and executive monitoring tools, practical training curriculum creation with experimental environments, and incremental retirement procedures minimizing operational interruptions through graduated deployment tactics. Organizations adopting this methodical framework accomplish successful platform conversions while preserving data consistency through ETL confirmation checkpoints, user engagement metric monitoring via telemetry analytical tools, and operational persistence through active-active deployment structures.
Cross-Platform Analytics Harmonization in Multi-Tenant Retail Environments Using Adobe and Tealium
Multi-brand, multi-market, and franchised retailers who have a portfolio of brands and markets under their management find that they are unable to generate reliable and comparable insights across online, application, and store touchpoints as a result of differing taxonomies, inconsistent identity management, and fractured privacy controls. This paper suggests a feasible roadmap of harmonizing the cross-platform analytics with the use of Adobe Analytics and data modeling of the customer experiences between Adobe and Tealium of client- and server-side data gathering and orchestration. The “federal” governance model enforces a standard event and attribute taxonomy while allowing tenant-specific extensions; the identity model unifies device, visitor, and customer identifiers under consent-based rules; and the collection architecture handles enrichment and routing through resilient server-side pipelines. The strategy involves automated conformance validation, KPI alignment, and constant QA to establish parity on metrics between the tenants, as well as readiness of activation through portable audience definitions and experiment metrics. Privacy-by-design (purpose limitation, minimization, and consent propagation), operational safeguards (sandbox isolation, rate-limit observability, and idempotent retries), and change management (via versioned schemas and dual-write migrations) are covered as well. A step-wise roll-out shows how the standard could be adopted in phases by the tenants without disturbing the trading. The outcome is that the analytics platform has been enabled to maintain local control with enterprise-level comparability, with shorter decision-making cycles, and is able to be deployed in an expanding channel surface in a compliant way.
Hybrid Forecasting Systems for Inventory Optimization Using Prophet and Reinforcement Learning
Effective inventory supervision aids modern supply chain operations. However, established methods of forecasting often falter when faced with ever-changing demand, multiple seasons, and promotional peaks, resulting in either stock surplus or shortage, both of which are costly. This study introduces a forecasting methodology that combines Facebook Prophet, a time-series forecasting tool, and Reinforcement Learning (RL) for inventory optimisation. Prophet analyses and forecasts demands by recognising the intricate temporal attributes in the historical sales data, while RL uses these forecasts to continuously refine ordering policies, inventory holding, and shortage costs. The experiments on retail datasets confirm that the new system decreases the forecasting errors by 25% when compared with ARIMA and LSTM and improves inventory service levels by 15–20%, all while cutting the overall inventory expenses. These results underscore the significance of merging statistical forecasting and intelligent decision-making and provide a utilitarian methodology for supply chains to tackle demand and operational variability.
AI Augmented ETL Pipelines for Automated Data Quality Anomaly Detection and Governance
As enterprise businesses rely more and more on real-time, high-volume data pipelines, it has become important to ensure data quality, integrity, and compliance. Traditional Extract, Transform, Load (ETL) processes can be deficient in anomaly detection and enforcement of governance, particularly at scale and speed. This article investigates the new AI-enriched ETL pipelines and how they can become a game-changer in ensuring regulatory compliance and automation of data quality assurance. Incorporating machine learning models in data workflows, organizations will enable their processes to dynamically identify anomalies, implement data governance verification, categorize sensitive data, and establish audit traces. Case studies across various industries show an increase in the accurate detection of targets, fewer cases of false positives, and better rates of compliance. The paper also addresses such topics as architectural considerations, major issues related to federated learning (including model drift and explainability), and future research in federated learning and explainable AI. These smart pipelines focus on a transition from reactive observing to highly autonomous, data management structures.
A Benchmark-Driven Approach to Detecting and Fixing Performance Regressions
Performance regressions can be significant obstacles in software systems, resulting in reduced performance, affect elements of the user experience, affect quality of service or reliability levels for subsequent versions of software applications. A benchmark-based approach is a systematic and objective way to identify and fix these regressions, with a specific benchmarking process that is based on standard tests, results, and metrics to measure and report software performance. This article reviews benchmark-based models, applications, and effectiveness in the contexts of software optimization, machine learning, industrial automation systems, and financial risk management. The article describes how benchmarks can help discover hidden performance regressions, enable more accurate predictive modeling, and facilitate targeted performance optimization opportunities. The discussion presents qualitative and quantitative frameworks for implementation and adaption of benchmark-based approaches to regression in multiple software domains, illustrating the flexibility and scalability of benchmark-based approaches to early identification and mitigation of performance regressions. The article concludes with reflections on the challenges of benchmark processes including design, evaluating with multiple metrics, and variability of implementation, to summarize meta-discoveries about how software systems can prevent negative regressions to create and maintain performance and reliability in software that is complex and evolving.
Compliance-Aware Monetization Workflows in Multi-Tenant SaaS Platforms: A Review
With a growing complexity and geographic dispersion of Software-as-a-Service (SaaS) platforms, they have to perform workflows of monetizing, and the issue that has recently become of primary concern is the implementation of monetization processes governed by regional legislation and tenant-specific policies. Monetization that is conscious of compliance in multi-tenant SaaS systems is a meeting of the functions of financial operations, legal boundaries, and changing cloud design. This review explores the essential elements that make such workflows possible, such as policy-as-code compliance, jurisdiction data controls, a la carte pricing, contract-based billing, observability, and revenue attestation. The article explains, by the criterion of analysis of peer-reviewed articles by the main academic journals, how compliance-sensitive monetization pipelines need to evolve to embrace data residency requirements, industry compliance, cross-tenant diversity, and audits. It focuses on the architectural and operational trends that assist in the support of scalable, transparent, and lawfully supported monetization trends of SaaS environments. Future-focused areas of discussion, including what may be introduced in terms of AI-driven entitlement optimization, explainable billing policies, and standardized governance models, are also discussed, providing a roadmap that organizations can utilize in order to modernize and de-risk the SaaS billing operations, especially in environments that are compliance-centric.
Response Time Optimization and Its Effect on Patient Outcomes in Prehospital Emergency Services
Response time optimization in prehospital emergency services is a critical factor influencing patient outcomes. The initial moments following a medical emergency are crucial, as timely intervention can significantly reduce morbidity and mortality rates. Research indicates that every minute counts in emergencies such as cardiac arrests, strokes, and severe trauma. By implementing strategies such as enhanced dispatch protocols, advanced training for first responders, and the use of technology like GPS tracking, emergency medical services (EMS) can minimize response times. These improvements not only ensure that patients receive care faster but also enhance the overall efficiency of emergency services, leading to better resource allocation and improved outcomes. Furthermore, the impact of optimized response times extends beyond immediate clinical results; it also affects patient satisfaction and community trust in emergency services. When patients receive timely care, it fosters a sense of security and confidence in the healthcare system. Additionally, studies have shown that quicker response times correlate with higher survival rates and better long-term health outcomes, particularly in critical conditions. As EMS agencies continue to prioritize response time optimization through data-driven approaches and community engagement, the potential for improved patient outcomes remains significant, underscoring the importance of efficient prehospital care in the broader healthcare landscape.
Serverless 2.0: Unlocking Performance and Portability with WebAssembly
Serverless computing has transformed the development of applications in the cloud since it abstracts the infrastructure operation, enabling it to scale itself automatically. Legacy Function-as-a-Service offerings have been characterized by severe constraints such as high cold start delay, runtime constraints, language bias, and performance flooding, making them inapplicable in latency-sensitive and distributed applications. WebAssembly has become a groundbreaking platform to overcome these issues by creating a lightweight binary instruction, close-to-native speed execution, and a computer-independent compilation target platform. This article presents the idea of Serverless 2.0, which is an enacted paradigm of incorporating WebAssembly modules in serverless computing to achieve enhanced performance aspects without compromising operational simplicity. The architecture makes polyglot development and deployment easy, as it replaces language-specific execution engines with single-execution engines of WebAssembly. Patterns like edge-first deployment use the WebAssembly small footprint when local distributed computing would be effective, making computation closer to the data sources and end users. The extensive benchmarking can be shown to have made significant gains in various key measures, and cold start latency gains, a higher runtime performance, as well as better resource usage are dwelt upon in the above expounded. The combination is supporting new architectural patterns that were once impractical with traditional serverless frameworks, and should not only be limited to straightforward event-driven processing, but can be intensely real-time, application-latency-compromised, and globally distributed applications. Regardless of the remaining room to improve in the areas of tooling maturity and debugging support, WebAssembly-based serverless computing is an important advancement in cloud-native architecture, with promising performance benefits when it comes to cloud-critical and multi-cloud applications.
Leveraging Artificial Intelligence for Advanced Threat Detection and Response in Modern Cybersecurity Frameworks
The study investigates the role of artificial intelligence technologies including machine learning deep learning and natural language processing in transforming the threat detection and response procedures within the current cybersecurity model. The fast-changing nature of cyberattacks, traditional security systems are being found incapable of identifying and countering advanced attacks. Artificial intelligence has led to the emergence of disruptive technologies in the cybersecurity sector. It is now possible to use proactive, adaptive and intelligent defense strategies. The research is the study of mixed research, using a literature review and empirical study. The literature review relates to the present state of AI techniques and tools that have already been introduced to cybersecurity systems and include anomaly detection, behavior analysis and threat intelligence. The qualitative data will be achieved through expert online interview questionnaires about cybersecurity specialists. The ability of AI-enhanced systems to perform according to specific measurement parameters is measured by using such performance parameters as detection accuracy, false positive rate and response time. It is evident that AI proves to be very effective in detecting and containing the advanced threats owing to its capabilities of detecting the complicated patterns as well as its recent real-time impact response action. AI into the cybersecurity systems, not only is the resilience of the system boosted, but also the response time and human error. There are some issues with model interpretability, data privacy. It is adversarial AI that are to be resolved to achieve AI potential. The research has arrived at the conclusion that humans and AI must work in their cybersecurity roles and establish sturdy and future-proof cybersecurity infrastructures.
Basic Investigation on the Electrical Structure Characteristics of 72V Battery Charging Connector
This paper presents a numerical study of a 72-V battery-charging connector, covering electric-field distribution, abnormal 8/20, and transient current during internal short circuits. The connector geometry conforms to KS R 6100-2:2024. While the broader project combines testing and simulation to assess structural and electrical performance, this paper focuses on the simulation methodology and results. Two excitation models are employed: (i) a double-exponential function that reproduces the 8/20 surge waveform specified in KS R 6100-2 and (ii) an exponential rise-decay model that approximates the transient current during an internal short circuit. Electric-field calculations use Maxwell’s equations under a quasi-static assumption, reduced to Poisson’s equation for the scalar potential. Based on the analysis, we propose targeted modifications aD design improvements to the standardized terminal and present an optimized connector model.
Electrical Resistivity and Minority Carrier Lifetime Distribution in Directional Solidification of Silicon Ingot
In this work, the main electrical wafer qualities were studied through resistivity and minority carrier lifetime variation, as a function of wafer position. The multi-crystalline silicon ingot was grown by directional solidification using the so-called -Heat Exchanger Method (HEM). The solid-liquid interface moves upwards from the bottom to top of the crucible. The growth rate is controlled so as to favour the growth of a high quality crystal structure by finely adjusting the position of the heat exchanger -HEX -block and the heater temperature value. The goal is to obtain vertically aligned grains which confirm a good directional growth. The achieved analyses were conducted by using the quasi-stationary photo conductance (QSSPC) and the four probe techniques. The ingot produced exhibits uniform large grain morphology with vertically-oriented grain boundaries. The resistivity for corner and center brick’s present a maximum value of 2.3 Ω.cm and 1.69 Ω.cm respectively, after half (50%) of growth, and a minimal value in the first and final stages of solidification. In the beginning of growth, the initial minority carrier lifetime value is 3.23µs and 3.96µs for corner and center bricks respectively. Both lifetimes values grow to the maximum with 8.55µs and 14.05µs for corner and center bricks respectively, at the middle solidification position. The variation of electrical resistivity and minority carrier lifetime is probably affected by the impurity concentration, structural defects and grain structure.
Performance Enhancement of Adaptive Neuro-Fuzzy Inference System through Population-Based Algorithms
The Adaptive Neuro-Fuzzy Inference System (ANFIS) is a powerful hybrid artificial intelligence model that combines the learning capabilities of neural networks with the reasoning of fuzzy logic. It is widely used to model complex relationships between input and output parameters across various domains. However, the performance of ANFIS is highly dependent on the optimal setting of its internal parameters, making their optimization a significant research focus. This work aims to enhance ANFIS performance by optimizing its parameters using seven well-recognized metaheuristic algorithms, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Salp Swarm Algorithm (SSA), and Grey Wolf Optimizer (GWO). The proposed hybrid models were evaluated on three different datasets. Experimental results demonstrate that the hybrid models, which integrate these optimization algorithms with ANFIS, achieve a significant performance improvement compared to the standard ANFIS model.
Designing Resilient Insights Platforms: Data Architecture Principles for Scalable Decision Intelligence in Financial Services
Banks are under unprecedented pressure to provide real-time, explainable, and scalable decision-making power in credit origination, fraud prevention, and regulatory compliance. Historically, many banks and fintechs have made huge investments and are still hindered by legacy architectures of isolated data warehouses, brittle ETL workflows, and centralized data lakes that cannot adapt to changing business needs. This article introduces the resilient insights platform as a metadata-driven, modular architecture for ongoing intelligence in regulated financial services environments. A historical path is followed that shows how successive generations evolved from inflexible data warehouses via big data lakes and cloud-first analytics to contemporary distributed platforms, emphasizing how each generation built upon existing capabilities but also added new complexity. Important architectural patterns such as data mesh for domain-focused decentralization, data fabric for a single-source governance, lakehouse storage that balances flexibility and transactional assurance, feature stores for machine learning reuse, and ModelOps for production control are discussed in the context of financial services regulation. Use cases illustrate how robust platforms power real-time underwriting engines, streaming fraud detection, end-to-end customer intelligence, and automated regulatory compliance reporting. In addition to technical potential, these platforms provide economic advantages through cost savings on infrastructure and faster innovation, social benefits through greater financial inclusion and mitigation of bias, and environmental stewardship through cloud-native efficiency. The shift of data architecture from back-office infrastructure to competitive strategy is a fundamental change in the operation, innovation, and service of financial institutions to diverse customer bases in the more digital economy.
AI-Powered Clinical-Trial Recruitment: Protocol-to-Claims NLP, Geospatial Density Modeling, and Feasibility Outcomes
This technical article examines the transformative role of artificial intelligence in revolutionizing clinical trial recruitment and site selection processes. The traditional approach to trial site selection, historically dependent on enrollment reports and feasibility surveys, is being supplanted by a sophisticated data-driven methodology that integrates multiple dimensions of healthcare information. The article details a multi-layered AI solution that leverages longitudinal claims data, clinical pattern recognition, influence mapping, and geographic intelligence to identify optimal research sites and healthcare professionals. Through a systematic workflow that includes market basket creation, claims analysis, network mapping, and composite ranking, the system creates targeted outreach strategies that significantly improve enrollment efficiency. A case study of a chronic obstructive pulmonary disease trial across numerous sites demonstrates the practical effectiveness of this approach, with substantial enrollment increases at participating research centers. The article argues that this paradigm shift from intuition-led to evidence-optimized strategies represents a fundamental advancement in clinical operations with profound implications for accelerating therapeutic development and expanding patient access to clinical trials.
Securing the Digital Public Sector: Cloud Transformation of Government Infrastructure
This article examines the critical intersection of cloud transformation and cybersecurity in government infrastructure, analyzing how public sector entities can leverage cloud technologies while maintaining robust security postures that protect citizen data and ensure operational resilience. The article explores the compelling drivers for government cloud adoption, including the need to modernize aging infrastructure, enable scalable service delivery, and leverage advanced technologies such as artificial intelligence and machine learning for enhanced security capabilities. Through comprehensive analysis of policy-centric security frameworks, the article demonstrates how government agencies must transition from traditional perimeter-based security models to zero-trust architectures that address the unique challenges of cloud environments, including multi-tenancy risks, data sovereignty concerns, and complex regulatory requirements. The article examines the evolving threat landscape facing government entities, highlighting how sophisticated adversaries employ advanced persistent threats, AI-powered attacks, and multi-vector strategies that require adaptive defensive measures leveraging cloud-native security features, automated response capabilities, and continuous monitoring. Furthermore, the article emphasizes the paramount importance of preserving citizen trust through robust data protection measures, including emerging technologies such as homomorphic encryption, blockchain integration, and privacy-preserving analytics, while addressing the practical challenges of implementing privacy-by-design principles in government contexts. The article reveals that successful government cloud transformation requires a holistic approach integrating technical controls, policy frameworks, workforce development, and cultural transformation to create resilient digital infrastructures that deliver efficient citizen services while maintaining the highest standards of security and privacy protection.
Retarding Effect of the Mediating Sulfane (-S-) Group on Chelation Efficacy of Thiolic-Sulfur Toward Mercury in 2-(2-Mercaptothiazol-5-yl) acetic acid Derivative. DFT-Theoretical Study.
Mercapto-containing chelates: 2-(2-mercapto-4-methyl-2,3-dihydrothiazol-5-yl) acetic acid (AS2NHM), 2-(2-mercapto-4-methylthiazol-5-yl) acetic acid (AS2NM), 2-(2-mercapto-5-methyl-3H-pyrrol-4-yl) acetic acid (ACSNM), 2-(5-mercapto-3-methylthiophen-2-yl) acetic acid (AS2CM), 2-(2-mercaptothiazol-5-yl) acetic acid (AS2N) and 4-ethylthiazole-2-thiol (S2NM) were studied using DFT method of calculation employing B3LYP/LanL2DZ level of theory.Computational results have showed that mediating groups (Sulfane -S- and imine =N-) along with the attached substituents (methyl -CH3 and carboxylic -COOH) surprisingly have a dramatic effect on charge density localization/delocalization on thiolic-sulfur as a donor atom and thus on its capability of binding the mercuric divalent ion.Responses for modifications brought by in all simulates were tracked by calculating the charge density on thiolic-sulfur and atoms in proximity. Related changes in geometrical parameters, namely, bond lengths and bond angles in the neighborhood of thiolic-sulfur were also monitored to hopefully provide us with more insights about the effect of the performed modifications. Images of HOMO-LUMO orbitals and charge density distribution surfaces are also presented.Effects of Sulfane (-S-) mediating group on electron density enrichment of thiolic-sulfur, thus its chelation effectiveness for mercuric ion have been investigated. This is in order to deep understand the chelation weakness of 2-(2-Mercaptothiazol-5-yl) acetic acid (AS2NM) toward mercury(II) ions in particular, which has been encountered in earlier experimental research work.Findings of the study, have clarified the drawbacks of AS2NM that lay behind its failure in stabilizing the divalent mercuric ion through effective coordination. Nevertheless, it was able of developing stronger binding with Hg metal ions compared to the other chelates, this was attributed to softness close matching with Hg ion.
Enhanced Algorithms for Solving Nonlinear Systems: Beyond NEWTON-RAPHSON with Example of Advanced Nonlinear Solver
Solving nonlinear systems of equations is a central challenge in scientific computing, impacting a wide range of fields such as engineering, physics, and applied mathematics.Although the Newton-Raphson method is popular for its quadratic convergence near solutions, it faces notable difficulties, including reliance on the initial guess, potential failure with ill-conditioned Jacobians, and complications when multiple or closely situated roots are present. In this study, we investigate the creation of new iterative algorithms aimed at overcoming these obstacles by promoting better global convergence and improving numerical stability. The proposed approaches utilize adaptive step-size management, quasi-Newton techniques, and hybrid strategies that integrate trust-region and homotopy concepts. Results from numerical tests on standard benchmark problems show that these algorithms provide enhanced robustness for a wide array of nonlinear systems. Compared to Newton-Raphson, the new methods expand the convergence domain and frequently deliver equal or better accuracy and computational speed. This work paves the way for developing more trustworthy solvers for complex nonlinear systems in contemporary computational practice.
Optimization of Sediment Management in Dam Reservoirs: A Comprehensive Study of Dohuk Dam, Iraq
The long-term viability of water infrastructure throughout the world is seriously threatened by reservoir sedimentation; the Dohuk Dam in northern Iraq is already losing a significant amount of storage capacity as a result of deposited sediments. Sedimentation compromises downstream ecosystem integrity, water supply dependability, and hydropower production in addition to decreasing reservoir volume. Traditional mitigating techniques are still dispersed and inadequate, especially in semi-arid areas where land-use demands and climate change are intensifying. The study presents a thorough methodology for assessing and improving sediment management choices under the unique hydrological and environmental conditions of Dohuk Dam.The study evaluates sediment dynamics and management options by combining government information, satellite images, and published hydrological records from 1988 to 2024. Critical erosion zones that provide more than 60% of the total sediment load from less than 20% of the catchment area were identified by a calibrated sediment transport model based on Yang's unit stream power method and backed by SWAT watershed simulations. Continuous turbidity monitoring with high predicted accuracy was made possible by remote sensing analysis of multi-temporal Sentinel-2 data, and reliable predictions of sediment load variations were produced using machine learning models, such as Long Short-Term Memory networks. Economic analysis conducted over a 100-year period showed that integrated solutions, which combined seasonal hydraulic flushing, turbidity current venting, and targeted watershed interventions, may achieve favorable benefit-cost ratios and lower sedimentation rates by up to 80%. Monte Carlo simulations confirmed the validity of the suggested framework by further quantifying uncertainty under anticipated climate change scenarios. The results demonstrate how an optimization framework integrating hydrological modeling, remote sensing, and machine learning may promote sustainable reservoir management in semi-arid areas.
Effect of temperature on Microstructure and Corrosion Behavior of Carbon Steel: Experimental study and modeling
Oil field water source wells are vulnerable to corrosion issues because of their intricate operating environments. Because corrosion-related failures result in significant downtime, managing corrosion is a crucial and complicated task in the petroleum and gas industry. This study used X-ray diffraction (XRD), an electrochemical measuring technique, to examine the impact of tempering temperatures (200, 400, and 500 °C) on the corrosion behavior of American Petroleum Institute (API) N80 steel in albian water at various gradient temperatures. The API N80 steel samples crystallize in a ferrite-type structure, according to XRD patterns. All of the API N80 samples, we noted, have a nanometric grain size . As the tempering temperature rises, there is a correlation between the corrosion rates of samples and structural changes . Excellent corrosion resistance and a decreased corrosion current density were demonstrated by steel N80 at a higher tempering temperature. Increased grain sizes and decreased residual stress and hardness are the reasons for the corrosion resistance difference. The Quadratic Corrosion Model was used to determine the optimal operating conditions for minimizing the corrosion rate. The statistical model predicted the optimal conditions for the selected t variables as a tempering temperature of approximately 450 °C , and a thermal gradient (ΔT) of around 1 °C, Under these conditions, the predicted corrosion rate is in the range of 37 μm/year.
Multi objective optimization of existing buildings: “A Study of a Higher Educational Laboratory in Cairo, Egypt”
This research presents a comprehensive methodology for optimizing building performance in the context of visual and thermal comfort of a computer laboratory in higher educational buildings in hot dry climates, focuses on minimizing energy use intensity (EUI) and maximizing annual thermal comfort ratio and daylighting through maximizing useful daylighting illumination (UDI) and spatial daylighting Autonomy (sDA). This study conducts a parametric optimization approach of building envelope openings and materials to integrate multi-objective optimization (MOO), aiming to explore and find optimal solutions for improving the laboratory's overall performance. Throughout Rhino Grasshopper platform for simulation purpose. The methodology begins with the development of a parametric model of the computer laboratory, which allows for the manipulation of key design variables, including window size, Window wall ratio (WWR) orientation, shading devices, wall materials and properties, glazing types. These variables are used as design parameters linked to performance metrics that capture the visual comfort (via daylighting analysis), thermal comfort (evaluating indoor temperature variations and HVAC loads), and EUI (calculated through energy simulation). The design space is explored using multi-objective optimization by Genetic algorithms NSGAII with Wallacie solver, which balance trade-offs between the 124 key design parameters to enhance five objective functions performance criteria. The results show that significant improvements can be achieved in the computer laboratory’s visual and thermal comfort, while simultaneously reducing energy use intensity by around 2.35% maximizing (sDA) and (UDI) to 1.3%, maximizing annual thermal comfort ratio (ATCR) to 1.9%. The optimized solutions exhibit a balance between natural and artificial lighting, effective thermal insulation, and strategic shading. In some cases, up to a 26% reduction in energy consumption (EUI) is observed, with notable improvements in both daylight quality and occupant thermal satisfaction.
Students' misunderstanding of the concepts of energy and thermal processes in thermodynamics
This study investigates university students’ misconceptions about energy and thermal processes in the thermodynamics course. It aimed to diagnose conceptual difficulties related to the fundamental principles of thermodynamics, especially the concept of energy and the related notions such as heat, work, and internal energy. The sample consisted of 88 students enrolled in exact sciences at the Universities of Djelfa and Laghouat in Algeria during the 2024/2025 academic year. Data were collected through semi-structured interviews with students and instructors, in addition to a two-tier diagnostic test composed of four alternatives with one correct answer. Students’ responses were classified into different categories reflecting varying levels of understanding of these concepts. The results revealed a number of common alternative conceptions and conceptual errors among students, as most of them were unable to provide a correct justification for their answers. These findings implicitly reflect the teaching methods adopted in university and pre-university education, which may contribute to reinforcing these misconceptions. This highlights the need for targeted pedagogical strategies to improve students’ understanding of thermodynamics topics. Moreover, these results can be used to develop effective instructional strategies focusing on addressing and correcting such conceptions, especially in introductory thermal physics courses.
Density Functional Theory Analysis of Structural and Electronic Properties of IV–VI Semiconductors: PbSe, PbS, and PbSe₁₋ₓSₓ Alloys
This study presents first-principles calculations performed within the framework of density functional theory (DFT) using the full-potential linearized augmented plane wave (FP-LAPW) method as implemented in WIEN2k. Exchange–correlation interactions are described through the Wu–Cohen generalized gradient approximation (WC-GGA), while the modified Becke–Johnson (mBJ) potential is applied to improve the accuracy of the electronic band gap. The analysis initially focuses on the structural and electronic properties of PbS and PbSe in their NaCl-type phase. The results confirm that both compounds exhibit a direct band gap located at the L point. The investigation then extends to the ternary PbSe₁₋ₓSₓ alloys, where the influence of composition on lattice parameters and bulk modulus is systematically examined. The findings reveal clear deviations from Vegard’s law, interpreted within the theoretical framework proposed by Zunger and co-workers. Furthermore, the dependence of the band gap on alloy composition is analyzed under different pressure conditions. In addition to electronic properties, the optical response is also explored, including the dielectric function, refractive index, absorption coefficient, and reflectivity, highlighting the potential of these materials for optoelectronic and photovoltaic applications.
LCOE Optimization based on Reliability and corrective Maintenance for Wind Energy Systems
This work presents an approach to minimizing the Levelized Cost of Energy (LCOE) for wind power systems integrated reliability and a corrective maintenance. League Championship Algorithm (LCA) is a metaheuristic Used for optimization. It considers decision variables such as capacity factor (FC),system availability (A), corrective maintenance (Ccm). Based on the results obtained; assist operation and maintenance managers know the types of maintenance contracts in order to avoid wasting money the optimal availability obtained from the LCOE model. This study is the subject of a real case, which is the Kabertanne wind farm, located in the W. Adrar who counts 12 wind power turbines, type G.52- 850 kW. The results obtained show a final optimized LCOE of 0.02324 $/kWh, with a corrective maintenance cost of $64645 per year and an availability of 0.87.
Enhancing the Quality and Stability of Solar Photovoltaic (PV) Energy Integrated into Electrical Networks through an Advanced Active Filtering Technique
This study proposes a dynamic filtering based framework aimed at improving the performance and reliability of power grid control systems. To achieve this, several distortion identification techniques are first examined to determine the most efficient method for real-time operation. The selected dynamic filter is subsequently incorporated into a photovoltaic generator (PVG) to support the delivery of high quality electrical energy and to optimize power flow supervision. The obtained results confirm that integrating renewable generation with intelligent energy management significantly enhances grid stability, control accuracy, and overall system efficiency.
Building the Digital Core of Insurance: Combining Product Strategy, Core Suite Platforms, and AI Solutions
The insurance industry is undergoing unprecedented digital transformation, driven by evolving customer expectations, regulatory pressures, and competitive dynamics. This research examines the strategic integration of product development methodologies, core suite platforms, and artificial intelligence solutions to construct a comprehensive digital infrastructure for modern insurance operations. Through analysis of market data, vendor performance metrics, and implementation case studies from 2024-2025, this study reveals that successful digital transformation initiatives can achieve operational cost reductions of up to 48%, processing time improvements exceeding 80%, and revenue enhancements of 25% within five years of implementation. The convergence of cloud-native core platforms, AI-driven automation, and customer-centric product strategies represents a fundamental shift from legacy operational models toward agile, data-driven insurance ecosystems. Current market analysis indicates that while 87% of insurers plan AI adoption by 2024, only 7% have successfully implemented these technologies at scale, highlighting significant opportunities for competitive differentiation through comprehensive digital core architectures.
Enhancing DevOps with AIOps: Leveraging Artificial Intelligence for Efficient Incident Management
DevOps teams struggle with incident management in distributed systems where traditional monitoring creates more problems than solutions. Alert storms overwhelm operations centers while genuine issues hide among thousands of false positives. Engineers waste time correlating data from dozens of different tools instead of fixing actual problems that impact users. Most organizations handle incidents the hard way. Systems break, alerts fire, and teams scramble to understand what happened while customers complain. This reactive cycle burns through engineering talent and damages business relationships during extended outages. Manual correlation across microservice architectures becomes impossible as systems grow more complex. Intelligent operations platforms address this operational chaos by processing massive data volumes that overwhelm individual engineers during crises. Algorithmic models identify subtle system behaviors that signal developing problems, catching potential failures before they impact end users or cascade across service dependencies. These platforms adapt their detection capabilities based on observed incident histories and changing infrastructure patterns. Organizations deploying intelligent operations report substantial improvements in incident response metrics. Automated correlation eliminates hours of manual investigation, while predictive analytics enable proactive maintenance during scheduled windows rather than emergencies. Teams finally escape the constant firefighting that prevents strategic infrastructure improvements and architectural optimization.
Building Scalable Cloud UI Applications: 5 Key Architectural Principles
In this article, the strategic significance of user interface (UI) engineering in professional software development is discussed, and a systematic framework for developing efficient interfaces to improve user satisfaction and technical sustainability is provided. The article explores five areas, namely, the strategic importance of UI engineering, the main pillars of effective interfaces, technical solutions involving responsive and accessible design, performance optimization as a user experience requirement, and scalability of UI architecture to expand applications. By combining the results of research and experience in the industry, the article illustrates how consideration of interface design has a direct influence on adoption rates, technical maintenance needs, and the cost of operations. It intersects the field of technical and human aspects of the discussion, covering the cognitive aspects of the user experience, component-based architectures, accessibility requirements, and empirical validation practices. Developers can develop applications that meet the needs of users and technical sustainability criteria when they consider UI development as an engineering challenge and not just a matter of aesthetics.
AI-Driven Compliance Automation in Banking: A Hybrid Model Integrating Natural Language Processing and Knowledge Graphs
Maintaining regulatory compliance while detecting ever more complex fraud patterns via conventional rules-based systems presents unmatched difficulties for the financial services sector. The incorporation of understandable artificial intelligence approaches with hybrid architectures integrating knowledge graphs and natural language processing to automate compliance and fraud detection in banking is discussed in this article. Machine learning models show superior performance to conventional detection methods, but their black-box character goes against transparency and explainability regulations. Using transformer-based language models and heterogeneous graph neural networks, the hybrid design extracts semantic patterns from textual transaction data while encoding domain knowledge via structured knowledge representations. Using SHAP and attentional mechanisms, human-interpretable explanations that satisfy legislative obligations can be created while keeping identification accuracy. Regulatory compliance frameworks, including the GDPR and Basel Committee guidelines, provide openness requirements, yet execution issues with regard to clarity, specificity, adversarial robustness, and computational overhead persist. Deploying reliable artificial intelligence systems for financial compliance calls for balancing the conflicting needs of stakeholder trust, traceability performance, and operational efficiency by means of well-thought-out governance systems and multi-modal explainability strategies.
Low-Latency Communication Framework for Enterprise Server Simulation
The article discusses a shared-memory-based tooling framework that facilitates firmware validation processes in enterprise-grade server environments. The framework enables low-latency communication between test harnesses and simulation environments, such as Wind River Simics, by overcoming the key weaknesses of conventional simulator interfaces: command-line and socket-based communications. The architecture uses a file that is memory-mapped and can be accessed by both the simulator and the client application, with powerful synchronisation, abstraction of the command, and autonomous performance. Evaluation of the performance of IBM Power firmware validation workflows can be shown to achieve significant benefits in execution time with respect to the traditional methodology, with direct applications to memory controller configuration, service processor execution, and power state validation. The ability of the framework to be flexible to a wide variety of simulation environments, such as QEMU and Gem5/SystemC environments, demonstrates that it is a more general method of minimising inter-process communication latency. Combination with continuous integration/continuous delivery pipelines also increases the usefulness of the framework to enterprise firmware validation, allowing full automated testing and faster development cycles, and increasing system reliability of mission-critical applications.
Ensuring Data Privacy and Compliance in Healthcare Analytics
Healthcare organizations today face the critical challenge of harnessing the transformative power of data analytics while maintaining absolute commitment to patient privacy and regulatory compliance. This article examines the complex landscape where healthcare innovation intersects with privacy protection, exploring how organizations can successfully navigate regulatory requirements such as HIPAA while pursuing data-driven insights that improve patient outcomes. The article begins by establishing the fundamental importance of patient trust and the severe consequences that can result from privacy breaches, including financial penalties, reputational damage, and erosion of the patient-provider relationship. Through a comprehensive examination of technical safeguards, process-oriented protections, and organizational governance strategies, the article demonstrates that effective privacy protection requires a multi-layered approach encompassing data anonymization techniques, encryption protocols, access controls, and staff training programs. Real-world case studies illustrate how healthcare institutions have successfully implemented privacy-preserving analytics frameworks that enable collaborative research, support clinical decision-making, and drive operational improvements without compromising patient confidentiality. The article extends to emerging technologies and future considerations, addressing challenges posed by artificial intelligence, Internet of Things devices, and cross-institutional data sharing initiatives. Key findings emphasize that privacy protection and analytical innovation are not mutually exclusive objectives, but rather complementary elements that together strengthen healthcare delivery systems. The article concludes that organizations adopting privacy-by-design principles, establishing robust governance frameworks, and maintaining transparent communication with patients will be best positioned to realize the full potential of healthcare analytics while preserving the trust that forms the foundation of effective patient care.
Beyond Imitation: Neuroscience-Inspired Architectures for Reasoning, Memory, and Abstraction
The intersection of artificial intelligence and neuroscience offers revolutionary potential for designing machines beyond existing constraints of pattern matching towards realizing true reasoning and comprehension. This work explores how insights from predictive coding, hippocampal episodic memory, and prefrontal executive control in the biological domain can be applied to hybrid architectures blending neural and symbolic computation. The suggested framework combines content-addressable memory systems for fast episodic encoding and access, goal-conditioned controllers acting over abstract program spaces, and self-supervised world models using predictive coding for counterfactual reasoning. Training schedules drawing on biological development switch between passive viewing and active searching, allowing systems to extract maximum information from sparse data without overfitting. Evaluation paradigms transcend standard accuracy measures to evaluate compositional generalization, causal reasoning, and transfer learning ability that distinguish true intelligence. The resulting architectures show radical advances in sample efficiency, needing orders of magnitude fewer training data than standard transformer models while generalizing better out-of-distribution. These developments imply that the integration of neuroscientific principles allows qualitatively different learning dynamics that reflect biological intelligence, providing avenues to artificial general intelligence that learns and reasons in essentially human-compatible means.
Automating CDISC Data Transformation: A Statistical Programmer's Guide
Within the pharmaceutical industry, there is an increasing pressure on pharmaceutical companies to submit clinical trial data that fulfill the strict regulatory requirements and operate within tight timeframes and limited resources. The manual generation of CDISC-conformant datasets is still resource-intensive, subject to error, and implicates the generation process as the complexity of a trial increases. Transformative solutions are provided in automation frameworks based on the use of SAS macros and R scripts that are applied to develop metadata-driven development, modular architecture, and dynamic code generation that is dynamic. These frameworks save radically programming time and, at the same time, enhance the data quality metrics, lengths of CDISC conformance, cross-dataset consistency, and specification compliance dimensions. Practical applications show efficiency improvements that allow an organization to handle non-proportional program resource demands. The automation migration needs organizational dedication, tactical planning, and up-front investment in the formation of sound structures, broad metatag designing, and validation mechanisms. Pharmaceutical corporations and educational medical facilities have demonstrated that automation has enabled quicker study completion schedules, lower operational expenses, enhanced regulatory standards, and better contentment of programmers. The Hybrid SAS-R workflows are based on the synergistic use of platform strengths, where regulatory familiarity is provided by SAS, and modern programming capabilities are provided by R. Techniques of performance optimization, such as parallel processing, incremental updates as well and effective data structures make ensure that the frameworks can be scaled easily with the increase of the data volumes. Among the success factors, one can identify the initiation of focused pilot implementations, investment in metadata quality, emphasis on validation, documentation, creation of cross-functional collaboration, and formal governance. Automation will enable statistical programmers to become strategic consultants and not merely tactical code generators, and enable the intellectual power to develop novel analytical techniques and strategic advice to clinical teams that assist in generating the evidence needed to make regulatory decisions.
SwiftUI Architecture Patterns for Financial Applications: A Comprehensive Analysis
This comprehensive article examines the architectural patterns and implementation strategies that make SwiftUI particularly well-suited for financial application development in the post-pandemic digital banking era. The article explores how SwiftUI's declarative programming paradigm fundamentally transforms the development of financial user interfaces by enabling automatic state synchronization and reducing code complexity compared to traditional imperative approaches. Through an article on advanced state management techniques utilizing property wrappers such as @State, @StateObject, and @EnvironmentObject, the article demonstrates how financial applications can maintain complex data hierarchies while ensuring real-time synchronization across multiple views. The integration of reactive programming patterns through the Combine framework addresses critical challenges in processing high-frequency financial data streams, enabling sophisticated features like real-time portfolio valuation and continuous risk monitoring. Furthermore, the analysis reveals how SwiftUI's architecture inherently enhances security through immutable state design and controlled data flow mechanisms, providing robust protection against common attack vectors in mobile banking applications. The framework's seamless integration with iOS platform security features, including biometric authentication and hardware-based encryption, creates a comprehensive security architecture suitable for protecting sensitive financial data. This article establishes that SwiftUI's modern architecture, combining declarative syntax, reactive programming capabilities, and built-in security features, positions it as an optimal framework for developing next-generation financial applications that meet contemporary user expectations for performance, security, and user experience.
Dynamic Intent-Aware URSP: Advancing Adaptive Network Slicing for 5G and Beyond
The 3GPP network slicing standard allows mobile operators to provide differentiated services using the common 5G infrastructure when using User Equipment Route Selection Policy mechanisms. The modern versions of URSP are still largely static and do not provide any contextual flexibility to deal with dynamic application needs, enterprise guidelines, and efficiency aspects of energy consumption. To overcome these limitations, the Dynamic Intent-Aware URSP Framework introduces intent recognition, hierarchical policy coordination, and sustainability-conscious metrics to URSP descriptors through the use of artificial intelligence. The framework facilitates cross-slice adaptation and service-level agreement assurance with descriptors of URSP that are extended to represent the user intent, enterprise policy, and energy-efficiency requirements. Simulation outcomes show that the quality-of-service stability, compliance with service-level agreement, and energy saving are higher than the baseline URSP approaches. The contribution bridges the gap between intent-driven networking and 3GPP-defined slicing architectures to progress autonomous service delivery in next-generation networks.
Ethically Aligned Personalization: Reimagining Large-Scale AI Through Distributed Systems for an Inclusive Digital Society
The large-scale application of artificial intelligence to personalization has become a digital infrastructure that is required, which is fundamentally changing the way individuals access information, services, and opportunities. Democracy in access may be enabled by such systems, but in society, there is a fundamental conflict between maximizing engagement and achievement. The current personalization systems are characterized by prioritizing instant interactions to the detriment of diversity in information and equal accessibility, resulting in the mentioned disparities between demographic groups, which are reported. This article suggests that distributed systems, the technical basis that makes large-scale personalization possible, could be re-architectured to include ethical concerns not as a feature but as a central element of the architecture. Through exploring the duality of AI personalization, creating an ethical system around distributed architectures, exploring regulatory needs, and demonstrating responsible systems, a direction can be seen towards personalization systems that are both optimizing and ethical in nature. These architectures combine fairness checks, cultural diversity, governance structures, and multi-objective optimization to build personalization infrastructures that broaden, not reduce, human capabilities and social cohesion.
Current Signal-Based Fault Classification Using MFCC-DWT Feature Fusion and AI Techniques in IPMSM used in Electrical Vehicle
Permanent magnet synchronous motor (PMSM) is known as one of the most promis- ing machines for electric vehicle (EV) propulsion due to its high torque density, efficiency and excellent speed regulation. However, motor faults may seriously affect the performance, safety, and reliability of the system. Traditional methods of fault detection cannot provide satisfactory performance in the aspects of accuracy, adap- tation to dynamic working condition and real-time performance. To address these issues, in this paper, we propose a hybrid fault classification framework by combining features from MFCC and wavelet transform. Comprehensive information from the time and frequency domains is combined together in the proposed method, which improves the discriminator for distinguishing the fault types better. The tests were conducted on dataset in two cases: with MFCC only and the combined version using two classifiers catboost and random forest. Finally, the obtained results are very encouraging with 97.10 % in the first case and 86.00 % in the second case.
Optimization of Diagrid Angle in Different Shaped Diagrid Structural System by Dynamic Analysis in Seismic Zone
The increasing demand for tall buildings in seismically active regions has necessitated innovative structural systems that combine architectural aesthetics with superior seismic performance. Diagrid structural systems have emerged as a promising solution, offering enhanced lateral load resistance through their triangulated geometric configuration. This research investigates the optimization of diagrid angles across various building geometries—square, rectangular, circular, and hexagonal—subjected to seismic loading conditions in high-risk zones. Through comprehensive dynamic analysis using finite element modeling, this study evaluates the structural behavior of diagrid systems with varying angles (ranging from 35° to 75°) under earthquake loads corresponding to Zone V seismic conditions. The research employs ETABS software for modeling and analysis, examining critical parameters including story drift, displacement, base shear, and acceleration responses. Results indicate that optimal diagrid angles vary significantly with building geometry, with square and rectangular structures performing best at 65-70°, while circular configurations show superior performance at 55-60°. The findings reveal that properly optimized diagrid angles can reduce lateral displacement by up to 35% and improve overall structural efficiency by 28% compared to conventional configurations. This research contributes valuable insights for structural engineers and architects designing tall buildings in earthquake-prone areas, providing evidence-based guidelines for selecting appropriate diagrid angles based on building shape and seismic requirements.
Hyperparameter metaheuristic optimization technique (using salp swarm algorithm) For (Bert transformer) In sentiment analysis. (IMDb dataset)
Sentiment recognition is a complex task in natural language processing (NLP), and needs training models to handle bulk volumes of data such as IMDb dataset (English movie reviews) with hesitating linguistic trends, posing some considerable computational difficulties.The proposed framework incorporates Bidirectional Encoder Representations Transformers (BERT) and Salp Swarm Algorithm (SSA) to optimize hyperparameters in sentiment analysis using IMDb dataset.Salp swarm intelligence of (SSA) is conducted to optimize the learning rate, batch size, dropout rate, and number of attention heads.Comparative analysis has been conducted against 4 state-of-the-art algorithms (Grid Search, Particle Swarm Optimization, Improved SSA, WOA-AdaBoost) indicating the effectiveness of the proposed SSA-BERT model.The model shows an accuracy of )99.5% (on IMDb dataset performing better than Grid Search (95.5%), Particle Swarm Optimization (PSO) (96%), and Improved SSA (ISSA) (98.5%) as well as the WOA-AdaBoost (99%). Statistical analysis has been conducted using a T-test to prove the model’s superiority.The proposed model achieved a )99.0% (accuracy on the fifth epoch, and an overall accuracy of (99.5%).
Electronic and Optical Properties of FeNi2Mn and FeNi2Cr Alloys Using the Density Function Theory
Iron and nickel alloys attract great interest from researchers due to their distinctive properties and thus their use in many technological and practical applications. The structural, electronic and optical properties of the FeNi2Mn and FeNi2Cr alloys were studied using the first-principles planar wave method compatible with the ultra-soft pseudo potential scheme under the density functional theory (DFT). The calculated equilibrium lattice constant for FeNi3 is very close with other available results. It turns out that the large contribution of d electrons in the total electronic density of states is dominant, which in turn affects the electronic and magnetic properties of these alloys. The electronic band structure, total and partial electron density were analyzed and it was concluded that the FeNi2Cr alloy has a greater magnetic, the absorption coefficient, optical conductivity, and refractive index of the two alloys were calculated and analyzed, and based on that, they will be used in the appropriate technological applications.
Factors Affecting Response Time of the Saudi Red Crescent Ambulances
The response time of the Saudi Red Crescent ambulances is influenced by a multitude of factors that can significantly impact the efficiency of emergency medical services. One primary factor is the geographical layout of urban and rural areas, which affects accessibility and travel time. Urban environments may present challenges such as traffic congestion and road conditions, while rural areas may have longer distances to cover with fewer available routes. Additionally, the availability of resources, including the number of ambulances, trained personnel, and medical equipment, plays a crucial role in determining how quickly an ambulance can be dispatched and reach a patient. Effective communication systems and coordination with local hospitals also contribute to optimizing response times, ensuring that emergency medical teams are well-prepared upon arrival. Another critical factor affecting response time is the time taken for emergency calls to be processed and dispatched. The efficiency of the dispatch center, including the use of technology and protocols for prioritizing calls, can either expedite or delay the response. Furthermore, public awareness and education regarding emergency response procedures can influence the time it takes for individuals to seek help and report emergencies. In some cases, delays may arise from non-emergency calls that divert resources away from critical situations. Addressing these factors through improved infrastructure, enhanced training for dispatch personnel, and public education initiatives can lead to more timely and effective responses from the Saudi Red Crescent ambulances, ultimately improving patient outcomes in emergency situations.
Device Tree-Aware Diagnostics Framework for Portable and Scalable Platform Health Monitoring
Contemporary server platforms exhibit significant hardware diversity in communication interfaces, including I2C, I3C, SPI, and eSPI, traditionally requiring hardcoded board-specific diagnostics implementations that introduce substantial technical debt through reduced portability and increased maintenance burden. Using the flattened device tree standard and the libfdt library, the Device Tree-Aware Diagnostics Framework overcomes these constraints by dynamically extracting platform-specific data at runtime, letting diagnostics applications run across several hardware setups free of adjustment. Three main subsystems make up the framework architecture: an interface abstraction level offering uniform access to Heterogeneous communication buses and a fault detection engine running configurable health monitoring algorithms. Deployment across multiple platform variants demonstrates enhanced portability through the elimination of platform-specific code, improved maintainability via reduced cyclomatic complexity, and negligible performance overhead during diagnostic operations. The framework establishes a practical solution for scalable platform health monitoring in heterogeneous computing environments while promoting declarative hardware description methodologies that separate configuration from operational logic.
Reclaiming Performance: The Strategic Role of C++ in High-Volume Financial Transaction Systems
The financial technology industry faces unprecedented computational complexity requiring transaction processing systems that respond at microsecond time frames and process millions of operations in a second. This article puts C++ in a position of strategic technology foundation for high-performance financial systems, as opposed to seeing it as legacy infrastructure to be replaced. New C++ standards have revolutionized the language in profound ways with features like smart pointers, move semantics, parallel algorithms and coroutines, effectively solving long-standing criticisms while maintaining the deterministic execution behavior that is so critical for latency-sensitive financial transactions. The architectural development towards microservices and cloud-native deployments from monolithic traditional systems first went in favor of higher-level languages with high-speed development cycles, but with accelerating growth in transaction volumes and greater algorithmic trading complexity, underlying constraints in managed runtime environments have been exposed. Today's financial workloads in the form of high-frequency trading, real-time payment, risk computation, and compliance necessitate performance properties that system programming alone can provide via direct control over hardware and deterministic resource utilization. This article illustrates how C++ offers unmatched support for memory management, parallelism and hardware-level optimization while supporting complete continuity with contemporary DevOps protocols and cloud paradigms. The strategic placement of C++ in thoughtfully architected layered systems allows financial institutions to optimize performance for mission-critical transaction processing pipelines, all while taking advantage of higher-level languages to implement orchestration, business logic, and user interface aspects, thus producing balanced technology environments that maximize operational efficiency and developer productivity without giving up competitive edge.
Securing Salesforce Ecosystems: Cloud-Native Threat Detection and Automated Defenses in Enterprise Development
The rapid adoption of Salesforce as a cloud-based enterprise platform has expanded opportunities for digital transformation while simultaneously increasing exposure to sophisticated cyber threats. This study investigates the effectiveness of cloud-native threat detection and automated defense mechanisms in securing Salesforce ecosystems across diverse industries. Using a mixed-methods approach that combined system log analysis, controlled attack simulations, and surveys from 210 Salesforce professionals, the research evaluated detection accuracy, false positive and false negative rates, incident response times, downtime, compliance scores, and industry-specific variations. Results revealed that AI-driven detection achieved the highest performance, with superior accuracy, precision, and recall compared to rule-based and anomaly-based methods. Fully automated defenses significantly reduced response times and downtime while maximizing containment success and data protection. Moreover, multi-factor authentication mitigated risks associated with integration complexity, and compliance adherence strongly correlated with higher defense effectiveness. The findings contribute to the limited body of Salesforce-specific security research and provide actionable insights for enterprises seeking to safeguard sensitive data and workflows in increasingly interconnected cloud environments.
Healthcare Services Evolution in Saudi Arabia An Overview and Future Trends
The evolution of healthcare services in Saudi Arabia has undergone significant transformations over the past few decades, reflecting the nation's dedication to improving the health and well-being of its citizens. Initially focused on basic healthcare needs, the sector has expanded tremendously to include advanced medical technologies, specialized treatments, and comprehensive healthcare systems. The government's Vision 2030 initiative has been pivotal in this transformation, prioritizing diversification and modernization of the healthcare infrastructure. Health services have shifted from a predominantly public sector model to a more integrated system involving private sector participation, thereby enhancing efficiency and accessibility. Key advancements include the establishment of new hospitals, the implementation of electronic health records, and the promotion of telehealth services, particularly in response to the COVID-19 pandemic. Looking ahead, the future of healthcare services in Saudi Arabia is positioned for continuous improvement and innovation, driven by technological integration and data-driven practices. As the country focuses on enhancing patient outcomes and preventative care, trends such as personalized medicine, artificial intelligence in diagnostics, and the expansion of telemedicine are expected to gain momentum. Additionally, the growing emphasis on public health initiatives, mental health, and chronic disease management reflects a holistic approach to healthcare that prioritizes preventive measures. The government’s commitment to investing in healthcare education and workforce development will also play a critical role in sustaining these advancements, ensuring that Saudi Arabia builds a resilient, effective healthcare system that meets the needs of its population well into the future.
AI Archaeology: Extracting Strategic Intelligence from Decommissioned Machine Learning Models
AI Archaeology represents a transformative discipline that systematically extracts strategic intelligence from decommissioned machine learning models to inform future development initiatives. This comprehensive framework addresses the critical gap between substantial model development investments and the limited effort allocated to post-deployment intelligence gathering. The archaeological perspective transforms retired models from simple cleanup operations into valuable learning opportunities that reveal performance patterns, failure mechanisms, and environmental interactions previously unexplored in production settings. Through systematic failure pattern identification, organizations can categorize recurring degradation modes that remain invisible during individual model assessments. Feature impact intelligence provides unprecedented insights into temporal stability dynamics, revealing how individual features contribute to system resilience or degradation over extended operational periods. Drift pattern documentation establishes evidence-based monitoring protocols that optimize resource allocation while maintaining system reliability across diverse operational contexts. Anti-pattern recognition enables systematic documentation of recurring failure modes, converting expensive mistakes into institutional knowledge that prevents repetition across development teams and project cycles. Knowledge management systems facilitate the transformation of archaeological insights into actionable organizational intelligence, creating living knowledge bases that grow more valuable through continued documentation efforts. The archaeological framework enables organizations to develop more resilient machine learning systems through evidence-based decision-making rather than theoretical assumptions, ultimately improving system longevity and reducing maintenance overhead through systematic intelligence extraction from historical deployments.
Analytics Data Sanitization Framework for Event Tracking Systems
Contemporary digital ecosystems produce enormous behavioral data sets with event tracking systems that guide strategic choices and refine user experience. Nevertheless, the process of gathering analytics data creates inherent conflicts between obtaining useful insights and safeguarding sensitive personal data from improper disclosure. Analytics systems that are fed raw event data pose significant privacy threats by having personally identifiable data, financial credentials, authentication tokens, or health records unwittingly sent to third-party services. The repercussions of poor data management go beyond technical breakdowns to include regulatory sanctions, legal liabilities, and loss of user trust that effectively compromise organizational longevity. This article offers a detailed framework for the application of analytics data sanitization as a core architectural element, as opposed to an afterthought in system development. The architecture includes systematic enumeration of sensitive data elements in event payloads, creation of transformation rules balancing privacy protection and analytical utility, and deployment of centralized processing frameworks that support consistent enforcement in all analytics integrations. Cryptographic hashing, partial masking, and complete replacement transformation techniques cover various situations where data has legitimate analytical purposes or is pure risk with no value. Centralized sanitization layers provide single points of control where transformation logic is kept consistent, avoiding erratic implementations across distributed system elements. Organizations that follow robust sanitization frameworks gain regulatory compliance, limit data breach exposure, maintain analytical capabilities, and uphold consumer trust by being capable of demonstrating privacy commitments.
Strategic HR System Integration During M&A: A Framework for Large-Scale Cloud Migration and Workforce Consolidation
Human resources system integration in mergers and acquisitions is a high-impact, multifaceted task that directly impacts the employee experience, regulatory compliance, and operational continuity of organizations. This article bases actionable advice on real-world experience from Abbott Laboratories' $25 billion acquisition of St. Jude Medical, the largest acquisition in Abbott's history thus far, which formally established Abbott as a major player in the $30 billion cardiovascular device market. The integration undertaking involved the strategic migration of approximately 18,000 employees and 10,000 contingent workers across 50+ countries from St. Jude Medical's legacy SAP system to Abbott's cloud-based Workday Human Capital Management platform, all while ensuring uninterrupted payroll continuity and strict regulatory compliance in each jurisdiction. Typical challenges faced during this transition included complex dual-system synchronization demands between SAP and Workday environments, intricate non-English language data conversion requirements, sophisticated ETL processing using Lavastorm technology for data extraction and transformation, and multi-stakeholder alignment among geographically scattered teams. This article offers a systematized human resources system integration framework, outlines key best practices and avoidance pitfalls, and offers authoritative knowledge derived from partnering with strategic Workday implementation specialists including the benefits of phased deployment methods and real-time bi-directional integration processes. Underpinned by real-world implementation proof involving one of the medical device industry's most complex HR system consolidations, these guidelines are intended to enable organizations to obtain integrated HR platforms, significantly mitigate operational risk exposure, and create foundations for scalable post-acquisition expansion.
Agentic ai-driven enterprise architecture: a foundational framework for scalable, secure, and resilient systems
Agentic AI introduces a new paradigm in enterprise architecture by enabling autonomous, communicative, and goal-driven agents that operate across distributed systems. Building upon generative AI, agentic frameworks deliver scalable, self-orchestrating architectures capable of optimizing performance, fortifying cybersecurity, and enhancing operational resilience. This paper presents a foundational architecture that integrates Agentic AI principles into enterprise systems, offering a unified approach to intelligent orchestration, security, and adaptability. This paper explores the convergence of agentic and generative AI through recent frameworks, emphasizing Agent-to-Agent (A2A) communication protocols, intent-based coordination via the Agent Communication Protocol (ACP), and integration with LLM-powered reasoning engines. Agentic architectures automate complex workflows, detect and respond to cyber threats, and simulate failure scenarios through decentralized, intelligent agents. The proposed framework incorporates event-driven communication, vectorized memory, and optional blockchain-backed verification to support trust, transparency, and traceability across agents. The result is a composable, adaptive infrastructure that redefines how enterprise systems achieve agility, security, and continuity. When implemented with robust governance and AI oversight, Agentic AI powered by A2A emerges as a transformative force for high-performance, resilient enterprise design.
Real-Time Graph-Based Anomaly Detection for Capital Markets Using Stream Processing
The growing complexity and speed of trading activities in capital markets have rendered rule-based anomaly detection systems incapable of following real-time monitoring. The paper examines the model of integrating graph-based modeling and stream processing architecture on financial transactions as an effective framework to detect anomalous behaviors of financial transactions. Via the representation of the market entities and their relations as dynamic graphs and the application of the machine learning model, leveraging a graph neural network to them, it is possible to detect market anomalies, like fraud, market manipulation, and insider trading, with a more in-depth understanding of the context and a faster pace. Stream processing engines (e.g., Apache Kafka, Flink) facilitate large-scale throughput, low-latency data ingestion, whereas graph forms describe non-linear as well as dynamic relationships between brokers, traders, and instruments. In the paper, architectural, machine-learning, and compliance-based deployment criteria needed to operationalize such systems are discussed. It also covers more complex subjects, such as cross-market graph correlation, federated learning, and explainability in high-stakes settings. Results indicate that graph-based real-time anomaly detection systems bring a dramatic improvement in scalability, accuracy, and compliance, as well as represent the first essential step towards proactive financial market surveillance
Explainable deep learning based adaptive malware detection framework to improve interpretability
The development of advanced evasion strategies that make conventional signature-based approaches useless presents great difficulties for modern malware detection. This paper suggests an explainable deep learning-based adaptive malware detection system meant to improve detection accuracy and offer interpretable insights on its decision-making process at once. Our method uses a hybrid neural architecture combining convolutional and recurrent layers to extract both stationary and behavioural aspects from possibly dangerous executables. Through constant learning systems that change the detection settings as new malware variants develop, the model responds to growing threats. Our integrated explainability layer—which uses local interpretable model-agnostic explanations (LIME) and attention visualization approaches to clarify the particular traits and patterns that impact categorization decisions—is the key novelty. Experimental results reveal that our technique achieves a 97.3% detection rate on zero-day samples while maintaining a false positive rate below 0.5%. The given explanations help security experts to grasp detection rationales, confirm results, and create more successful countermeasures. This interpretability feature helps to solve the "black-box" issue sometimes connected with deep learning solutions in cybersecurity and promotes more confidence and acceptance in corporate security settings.
Enhanced Data Security Through NFC-Based Storage Architecture in Modern Payment Credentials: Evolution, Implementation, and Implications
Secure NFC-based data storage represents a pivotal advancement in payment card technology, enabling the protection of sensitive information through embedded chips rather than physical printing. This article explores the core concepts of NFC integration in payment systems, emphasizing standards compliance and fraud mitigation. By storing account details, CVV codes, and expiration dates within encrypted NFC chips accessible via authenticated applications or portals, this approach minimizes exposure to physical theft and skimming attacks. The discussion covers historical development from magnetic stripe cards to contactless systems, technical frameworks including ISO 14443 and EMV specifications, and real-world applications in retail and transit sectors. Implications include enhanced user control, reduced fraud rates, and alignment with regulatory requirements for data security. This concept not only bolsters transaction integrity but also supports broader societal goals of secure digital economies.
Copilot Impact Studies: Measuring Productivity, Trust, and Skill Evolution in Enterprise Developer Teams
The incorporation of artificial intelligence-driven coding assistants in enterprise software development is a paradigmatic shift in the way development teams think and implement technical solutions. This article examines the many-sided effects of AI copilot technology on developer productivity, trust calibration, and skill development in different enterprise contexts. By using a mixed-methods paradigm that integrates quantitative performance data with qualitative data derived from developer experiences, findings show a multifaceted reality where productivity improvements occur in an uneven pattern across task types and levels of experience. Although routine implementation tasks reveal hn≤icated that subjects who had been given an AI assistant tended to generate incorrect and insecure solutions to cryptography problems, with those who were provided with the assistant writing substantially less secure code (p = 0.05) and tending to be more confident in their insecure solutions (p < 0.001) [2]. Additionally, the enterprise environment provides special considerations related to security, compliance, and intellectual property that distinguish it from open-source or startup environments on which much of the current research has been performed.
Cloud HR Technology in the Airline Industry: Addressing Regulatory Complexity Through Enterprise Design
This article examines how cloud HR platforms address the complex workforce management challenges within the airline industry. Through focused sections on regulatory compliance architectures, integration models, mobile enablement strategies, and implementation considerations, the article shows how advanced HR systems support aviation-specific requirements while ensuring operational flexibility. Case implementations illustrate the transition from administrative record-keeping to strategic workforce optimization, delivering tangible benefits including reduced compliance incidents, accelerated crew qualification processing, and enhanced operational continuity during disruptions. The article provides practical guidance for airlines transitioning to cloud HR platforms, balancing enterprise standardization with the specialized needs of aviation workforce management.
Intelligent Customer Acquisition Modeling via Campaign Channel Attribution: A Machine Learning Approach
The concept of data-driven marketing demands that companies maximize customer acquisition in multi-channel ecosystems. The old attribution models, i.e., last-click or linear models, simplify consumer journeys and fail to provide accurate ROI. Attribution is provided through machine learning, which marks a paradigm shift in approach. It relies on past data and algorithms, combined with contextual information, to improve accuracy. This enables better prediction of acquisition likelihood and more efficient budget allocation. The paper discusses customer acquisition modeling based on machine learning with campaign channel attribution. It explains the underlying principles and explores its real-world applications. The discussion also addresses key constraints and outlines perspectives for future use. In addition, the paper highlights how AI can further enhance effectiveness, personalization, and strategic decision-making in digital markets.
Scaling Digital Transformation: Leadership Strategies for Cloud and Network Infrastructure Modernization
Digital transformation requires visionary leadership to guide infrastructure modernization throughout organizations. The current technology landscape challenges executives to craft integrated approaches combining cloud systems, programmable networks, and advanced security frameworks. Effective transformation harmonizes technical progress with cultural evolution, establishing conditions where creative solutions coexist with reliable operations. Real-world results demonstrate how infrastructure choices strengthen business flexibility, endurance, and competitive standing. Forward-thinking leaders build lasting advantages through balanced governance structures, expanded organizational abilities, and heightened security consciousness. Companies mastering these transitions secure exceptional advantages, transforming the technology backbone from an expense item to a competitive weapon. Modernization builds platforms enabling business expansion while accelerating adaptation to market shifts, creating superior experiences for customers and returns for stakeholders. When executives prioritize business results above technical specifications, infrastructure renewal elevates from routine maintenance to strategic necessity, establishing firms for enduring prosperity within digital economies. Effective transformations reshape organizational capabilities, enabling rapid development, stronger adaptability during disruptions, and tailored customer services. Leading organizations recognize that infrastructure decisions determine product release timing, business adaptability, and information security—factors now essential for market leadership. Such advantages become more crucial as technology shifts competition patterns throughout economic sectors. Companies mastering infrastructure modernization seize emerging possibilities while rivals face technical constraints limiting available strategic choices. Modernized infrastructure provides strategic freedom to pursue innovative business models, enter adjacent markets, and respond decisively to disruptive forces. The technology foundation increasingly serves as either an accelerator or a barrier to business transformation initiatives across all functional areas.
The Red Crescent's Triage Framework for Crush Injury Cases at Public Events
Mass gatherings (MGs) present a significant risk for crush disasters, where the resulting crush injuries can lead to the lethal Crush Syndrome (CS). Standard trauma triage systems are ill-equipped to identify patients at risk for CS, who may appear stable initially but succumb to systemic complications like hyperkalemic cardiac arrest and acute kidney injury post-extrication. This review examines the evidence-based triage framework developed by the Red Crescent to address this critical gap in prehospital emergency care. A comprehensive literature review was conducted, analyzing peer-reviewed publications, disaster medicine reports, and official guidelines from humanitarian organizations. The development, structure, and application of the Red Crescent's protocol were evaluated with a focus on its pathophysiological rationale and operational practicality. The Red Crescent's framework is a product of a systematic methodology involving evidence synthesis, multi-disciplinary expert consultation, and field validation. It modifies the primary survey by integrating a "Mechanism" (M) component to identify prolonged compression. The secondary survey emphasizes rapid limb assessment for key indicators of compartment syndrome—palpable tenseness and sensory deficit—over late signs like pulselessness. Triage prioritization is directly linked to specific interventions; patients with a significant crush mechanism and signs of neurological compromise or myoglobinuria (detected via point-of-care urine dipstick) are categorized as Immediate (Red) and require aggressive, pre-emptive intravenous fluid resuscitation to prevent CS. The protocol is designed for seamless integration within the broader mass casualty incident response system, leveraging the Red Crescent's extensive volunteer network and logistical capabilities. The Red Crescent's crush injury triage framework represents a critical advancement in disaster medicine. By providing a structured, evidence-based tool for the early identification and prioritization of patients at risk for Crush Syndrome, it empowers responders to prevent predictable, delayed mortality in mass gathering settings. Its widespread adoption and integration into public event medical planning are essential for improving survival outcomes in these complex emergencies.
Geographic Access Audit Dashboard: Monitoring Oracle HCM Cloud Authentication Events via RESTful API Integration
Cloud-based human capital management systems have revolutionized workforce data management while simultaneously introducing complex security challenges that extend beyond traditional perimeter-based protection models. This article presents a comprehensive framework for implementing external access auditing capabilities for Oracle HCM Cloud through RESTful API integration with Oracle Access Management. The article examines Oracle HCM Cloud's multi-layered security architecture, including role-based access controls, job-level privileges, and data-level security mechanisms, while analyzing the strategic trade-offs between restrictive IP whitelisting approaches and comprehensive audit logging methodologies. A detailed technical specification of the two-tier audit data extraction process is provided, demonstrating how statistical count retrieval followed by paginated event data collection optimizes bandwidth utilization and processing efficiency. The article evaluates Oracle Integration Cloud as an enterprise-grade middleware orchestration platform, comparing its capabilities with those of alternative tools and justifying its selection for mission-critical automation workflows. Implementation best practices are established for scheduling automated data collection cycles, designing dimensional data warehouse schemas to support temporal and geospatial analysis, and developing visualization dashboards that enable anomaly detection and compliance reporting. The integration of audit data with security information and event management systems is discussed as a mechanism for elevating isolated access logs into comprehensive security intelligence. This framework enables organizations to maintain visibility into geographic and temporal access patterns occurring outside native application boundaries, supporting proactive threat detection, forensic investigation capabilities, and regulatory compliance requirements in distributed enterprise environments.
Best Practices for Securing Cloud-Native Payment Architectures: A Practical Guide for Financial Institutions
This article examines the security challenges and compliance requirements facing financial institutions transitioning to cloud-native payment architectures. It explores the expanded attack surface created by distributed microservices, containerization vulnerabilities, and multi-cloud complexity while providing actionable guidance for implementing zero-trust security frameworks specifically designed for payment environments. The article addresses practical implementation of least-privilege policies, continuous authentication, and service mesh technologies to secure distributed payment systems. Additionally, it outlines regulatory compliance strategies for containerized payment applications, with particular focus on PCI DSS requirements, data sovereignty considerations, strong customer authentication mandates, and operational resilience frameworks. Through examination of real-world implementation cases and emerging technologies, the article establishes a comprehensive security model balancing innovation with appropriate risk management for cloud-native payment infrastructures.
VXLAN EVPN on Next-Generation Switches: Modern Data Center Network Architecture
Digital extensible LAN generation mixed with ethernet vpn to manipulate aircraft has emerged as a transformative solution for cutting-edge data center networking challenges, addressing the fundamental boundaries of traditional layer 2 switching in virtualized and multi-tenant environments. The exponential growth of cloud computing and the acceleration of multicloud adoption styles have created extraordinary needs for scalable community virtualization technology that may support vast community segmentation, even as it maintains gold standard performance characteristics. Vxlan EVPN leverages BGP as an advanced manipulation plane mechanism, enabling distributed knowledge of and marketing of endpoint information throughout fabric architectures without requiring centralized management factors or flood-and-learn mechanisms that plague conventional bridging protocols. The technology presents considerable expansion in community identifier capability through its improved addressing scheme, representing a sizeable development past the limitations of conventional VLAN implementations. Superior implementations make use of hardware-based encapsulation and decapsulation abilities in modern switching platforms, making sure that overlay networking functionality does not compromise forwarding performance, even as it allows obvious layer 2 connectivity throughout the infrastructure. The dispensed nature of EVPN manages plane operations gives inherent redundancy and scalability via general BGP mechanisms, with sophisticated commercial course techniques that optimize convergence conduct in large-scale deployments. Integration of routing and bridging capability through distributed anycast gateway configurations permits the most desirable forwarding paths for inter-subnet communication while keeping the operational advantages of dispersed routing architectures, which could scale horizontally throughout multiple physical locations.
Assessing the Quality of Urban Life in New Algerian Cities: The Case of Sidi Abdellah
This study assesses the level of urban quality of life in the new city of Sidi Abdellah, Algeria. It addresses the central research question: do modern urban communities represented by new cities meet the standards of quality urban life for their residents? The research investigates whether these emerging urban centres act as attractive hubs that fulfil residents' needs and foster both residential and spatial satisfaction. The theoretical framework identifies the essential human and environmental needs that enhance the overall quality of life. On the applied side, the study employs multiple contemporary urban design indicators and evaluation methods to measure and analyse urban quality of life. The objective is to establish a localised Algerian reference framework or code for assessing urban quality of life. The findings reveal that improving quality of life in new cities requires active community participation within sustainable regulatory frameworks and the adoption of a unified measurement approach capable of evaluating quality-of-life standards in high-density urban environments.
Augmented Intelligence in Enterprise Content Management: Human-in-the-Loop Systems and Conversational Interfaces for Enhanced Content Retrieval and Workflow Integration
Business entities throughout various industries gather substantial collections of documents, communications, contractual materials, and administrative records, necessitating robust organizational and access frameworks. Traditional management approaches depend on human-driven classification, keyword-based location methods, and rigid procedural architectures that struggle amid rapidly escalating information volumes. Computational intelligence provides significant automation capabilities for document operations, yet fully automated systems create vulnerability in regulated contexts where errors produce serious consequences. Augmented intelligence frameworks address this tension by combining machine processing power with human judgment, framing technology as an enhancement rather than a replacement of professional expertise. Automated classification generates suggested categories and metadata that knowledgeable personnel verify before adoption, improving accuracy while decreasing repetitive effort. Intelligent redaction identifies potentially protected information requiring expert review and approval before release, maintaining control over legally significant choices. Voice and text-based conversational systems allow staff to retrieve materials, launch approval processes, and generate summaries using natural language queries instead of complex navigation paths. Insurance operations provide concrete evidence of practical benefit through streamlined policy handling, faster claims processing, and seamless connections between content systems and underwriting platforms. Deployment outcomes show meaningful productivity improvements while maintaining accuracy standards. Achieving an effective balance between automation benefits and oversight requirements demands careful framework construction, determining which decisions operate autonomously and which require human involvement, supported by ongoing monitoring of system performance and user experience measures. Intelligent redaction mechanisms identify potentially sensitive materials requiring human examination and authorization before document distribution, preserving oversight in legally consequential decisions. Conversational interfaces utilizing voice recognition and natural language interpretation enable personnel to locate documents, initiate approval sequences, and produce content summaries through intuitive spoken or typed requests rather than navigating elaborate menu architectures. Insurance sector implementations demonstrate tangible value through automated policy document processing, accelerated claims handling, and cross-platform integration connecting content repositories with underwriting and adjudication infrastructure. Implementation results indicate substantial productivity enhancements alongside sustained accuracy benchmarks. Balancing automation advantages against oversight necessities requires deliberate framework design specifying which determinations machines manage autonomously and which demand human participation, supported by continuous performance monitoring and user satisfaction assessment.
Edge Computing Explained: Why It Matters for Real-Time Location Services
Edge computing represents a transformative paradigm shift in computational architecture, fundamentally altering how location-based services operate by processing data closer to its source rather than relying solely on distant cloud infrastructure. This comprehensive article explores the architectural principles, implementation considerations, and strategic advantages of edge computing specifically within the context of location-aware applications. By minimizing the physical and network distance data must travel, edge computing significantly enhances responsiveness, reliability, and efficiency—critical factors for applications where spatial context directly influences functionality and user experience. The article examines diverse use cases across urban mobility, emergency services, retail environments, autonomous vehicles, and healthcare, where edge-enabled location intelligence has demonstrated substantial improvements in operational capabilities. Technical challenges, including data synchronization, resource constraints, security considerations, and networking complexities, are explored alongside implementation strategies. Rather than positioning edge and cloud as competing models, the article emphasizes their complementary nature, advocating for hybrid architectures that strategically distribute computational workloads based on specific application requirements to maximize system performance while addressing inherent limitations.
Next-Generation Cryptographic Security in Multi-Cloud Enterprises: AI-Enhanced Data Privacy, Protection, and Threat-Resilient Automation
Enterprise architectures with multiple clouds have brought unprecedented complexity to cryptographic security management, demanding out-of-the-box solutions that go beyond the old perimeter-based protection frameworks. The coming together of artificial intelligence and innovative cryptographic methods in protecting distributed cloud environments places high emphasis on homomorphic encryption, quantum-resistant cryptography, and self-response attack mechanisms. Cryptographic protocols integrated with machine learning models bring about dynamic key management, anomaly detection in real time, and adaptive security states reacting to changing threat profiles. Modern multi-cloud ecosystems require cryptographic frameworks that preserve data secrecy across disparate platforms with varied regulatory requirements across multiple jurisdictions. Modern advancements in AI-boosted cryptographic architecture show promise in safeguarding enterprise data across cloud borders, but there remain crucial challenges in deploying threat-resilient automation systems. Effective next-generation security deployments demand effortless collaboration among cryptographic primitives, smart automation layers, and complete governance frameworks that balance technical and organizational aspects of multi-cloud security. Zero-trust design principles remove implicit trust assumptions by using continuous verification measures, while federated learning methods facilitate collaborative security optimization without centralizing the sensitive operational information. Quantum key distribution and blockchain-based key management are new technologies that hold the promise of overcoming current constraints in multi-cloud cryptographic deployments.
AI-Enhanced Platform Engineering: Revolutionizing CI/CD Pipelines Through Intelligent Automation
The article describes how artificial intelligence has changed the field of platform engineering in the context of continuous integration and continuous delivery (CI/CD) pipelines. Intelligent, self-healing systems with the ability to optimise themselves autonomously are the next paradigm shift in the integration of AI capabilities that changes the process of manual management to a more intelligent and self-aware system. It is an investigation of how machine learning algorithms can be used to improve test selection, provide high-level monitoring of pipelines, and allow autonomous failure remediation. The article reveals key concerns to be considered by organisations that are exploring AI-enhanced platform engineering, such as data quality requirements, model selection approaches, and incorporating existing toolchains. It also explores new trends that are set to transform software delivery ecosystems, such as generative AI to define the infrastructure, federated learning with engineering teams, and end-to-end optimization at the entire software delivery lifecycle. Through a combination of the results of various studies, this article will give a general overview of how AI is transforming the field of platform engineering and give a basis for what is to be expected in the evolution of intelligent automation to achieve software delivery.
Building the Future of Automotive Commerce: A Deep Dive into API-Orchestrated Unified Access Platforms
The automotive industry faces unprecedented transformation as traditional vehicle ownership models converge with emerging mobility solutions, necessitating fundamental shifts in digital infrastructure and business strategies. This comprehensive technical exposition presents an API-orchestrated unified access platform that seamlessly integrates retail, lease, and subscription models into a single intelligent ecosystem, addressing the fragmentation and inefficiencies plaguing current automotive commerce systems. The platform leverages microservices architecture, event-driven design patterns, and artificial intelligence to create a flexible, scalable solution that enables real-time inventory management, dynamic pricing, and personalized customer experiences across multiple ownership paradigms. Through sophisticated orchestration layers and standardized API interfaces, the architecture facilitates seamless integration with Original Equipment Manufacturers, financial institutions, and third-party service providers while maintaining security, compliance, and performance at scale. The implementation demonstrates how domain-driven design principles, combined with cloud-native deployment strategies and advanced data processing pipelines, can transform siloed automotive operations into cohesive digital ecosystems. The platform's intelligent decision engines utilize machine learning algorithms to optimize inventory allocation, predict customer preferences, and automate complex workflows, resulting in significant operational efficiencies and enhanced revenue opportunities. By breaking down traditional barriers between different business models and enabling fluid transitions between ownership options, the unified platform positions automotive organizations to capitalize on evolving mobility trends while delivering the seamless, personalized experiences modern consumers demand.
The First Hour Golden Hour Management of a Penetrating Trauma Patient by the Red Crescent
In the realm of emergency medical response, particularly in the context of penetrating trauma, the "Golden Hour" refers to the critical time frame within the first hour following an injury. Red Crescent teams are trained to prioritize rapid assessment, stabilization, and transport of penetrating trauma patients to optimize outcomes. During this crucial period, medical personnel conduct immediate interventions, including airway management, control of hemorrhage, and assessment for life-threatening injuries. The use of tourniquets, pressure dressings, and intravenous fluid resuscitation are essential components of their protocol, aimed at minimizing blood loss and ensuring that vital signs remain stable until definitive care can be provided in a hospital setting. The Red Crescent's approach emphasizes not only medical intervention but also efficient communication and coordination within their teams and with other emergency services. By ensuring that all responders are informed of the trauma patient's status and needs, the Red Crescent can streamline transport and surgical intervention. Additionally, first responders focus on assessing the mechanism of injury, which can provide invaluable insight into potential complications and necessary surgical interventions required upon arrival at the hospital. This comprehensive strategy not only enhances survival rates but also fosters a culture of rapid response and effective trauma management within the Red Crescent teams, ultimately contributing to better outcomes for patients in critical conditions.
Guidewire Technology in the Insurance Domain: A Career Perspective on Skills, Opportunities, and Market Dynamics
This article examines the career opportunities and skill requirements in Guidewire technology implementation within the insurance industry, analyzing the convergence of technical expertise and domain knowledge that creates unique professional advantages. The article explores how Guidewire's integrated platform, consisting of PolicyCenter, ClaimCenter, and BillingCenter, has become fundamental to insurance digital transformation, driving unprecedented demand for skilled professionals who can bridge insurance operations and technology implementation. Through analysis of market dynamics, implementation patterns, skill requirements, and global career trajectories, the article reveals how the complexity of enterprise-wide Guidewire deployments, combined with continuous platform evolution and AI integration, has created a sustained talent shortage that positions qualified professionals for exceptional career opportunities. The article demonstrates that success in this field requires a unique combination of insurance domain expertise, technical proficiency in Guidewire configuration and programming, commitment to continuous learning, and adaptability to evolving project methodologies, with professionals who master these competencies commanding premium compensation and enjoying diverse global opportunities across insurance companies, consulting firms, and independent practice.
Filter Integrity Testing in Biopharmaceuticals: A Comprehensive Review of Methods, Challenges, and Future Directions
Filter Integrity Testing (FIT) serves as a cornerstone of sterility assurance in pharmaceutical manufacturing, validating sterilizing-grade filters' performance throughout their lifecycle. This article examines FIT methodologies, equipment selection, and implementation challenges specific to biopharmaceuticals and advanced therapeutics. The article explores critical timing considerations for testing, non-destructive verification methods, and the complexities of closed-system cell therapy manufacturing. It addresses regulatory interpretation disparities while highlighting industry harmonization initiatives aimed at standardizing practices. The emergence of digital quality systems, computational modeling, and artificial intelligence applications demonstrates the evolution of FIT toward more predictive, data-driven approaches. Through critical analysis of technical limitations and case studies, this article provides a framework for implementing robust integrity testing strategies that balance regulatory compliance, operational efficiency, and patient safety in increasingly complex therapeutic modalities.
Real-Time Predictive Maintenance with ML-Enhanced IoT Sensor Data Processing
Introduction of machine learning (ML) and Internet of Things (IoT) sensor data has transformed the concept of predictive maintenance (PdM) allowing the industries to shift to real-time and intelligent decision-making systems rather than reactive ones.This review examines the modern real time PdM landscape, highlighting the relevant ML approaches, architectures, deployment models, and applications. The work is a synthesis of the results of the last ten years, comparing such algorithms as Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Echo State Networks and evaluates them in the conditions of the real world when it is necessary to consider the time of work. The concept of a hybrid edge-cloud architecture has been put forward to meet the requirement of low-latency inference, scalability, and data privacy. The review ends with the named challenges including model interpretability, unlabeled data, and cybersecurity and provides the directions promising to be successful in the future, including federated learning, explainable AI, and adaptive transfer learning. The presented insights can be a guide to researchers, practitioners, and policy-makers who want to create resilient and intelligent maintenance infrastructures during the Industry 4.0 era.
Telecom for Humanity: AI-Driven BSS Platforms for Digital Equity and Accessibility
Digital exclusion remains a pervasive barrier to economic participation, education, healthcare access, and social inclusion for billions globally. Artificial intelligence-driven Business Support Systems within telecommunications represent a transformative opportunity to advance digital equity and accessibility through automated, affordable, and inclusive service delivery. This research examines the convergence of AI technologies with BSS architectures to address affordability barriers, operational inefficiencies, and service accessibility challenges confronting underserved populations. The global Digital BSS market, valued at $7.9 billion in 2024, demonstrates an 11.8% compound annual growth rate toward $15.4 billion by 2030, driven substantially by cloud-native deployments and AI integration. Key findings reveal that AI-driven customer service automation manages 80% of routine inquiries, achieves 87.2% positive user acceptance, and reduces revenue leakage by 30-40% within the first implementation year. Concurrently, 2.6 billion individuals remain offline globally in 2024, with 1.8 billion residing in rural areas where deployment costs exceed urban equivalents by 200-300%. This synthesis establishes that AI-enabled BSS platforms deliver measurable improvements in affordability, service velocity, fraud prevention, and multilingual accessibility, positioning telecommunications as a critical enabler of humanitarian outcomes and societal advancement.
Collaborative Intelligence: The Human-AI Synergy Matrix Across Critical Enterprise Domains
This article explores the critical paradigm shift from AI automation to collaborative intelligence across enterprise domains. Implementation data reveals that human-AI teams outperform both AI-only and human-only approaches. In complex tasks, demonstrating that the future lies in partnership, not replacement. Through extensive analysis of deployments in healthcare, manufacturing, and scientific research, the article examines how collaborative approaches leverage complementary strengths—AI's computational power and pattern recognition paired with human contextual judgment and ethical reasoning—to achieve superior outcomes. The article provides actionable frameworks for measuring collaboration effectiveness, balancing efficiency with safety considerations, calculating ROI in high-stakes environments, and implementing transparent governance systems. Organizations implementing these collaborative intelligence principles demonstrate higher productivity metrics compared to those pursuing pure automation strategies, while reducing decision errors through continuous learning feedback loops. By synthesizing cross-sectoral implementation experiences, the article delivers practical insights for organizations seeking to implement effective human-AI partnerships while addressing domain-specific requirements, regulatory constraints, and outlining essential future competencies such as AI fluency, transparency design principles, and research agendas for next-generation collaborative systems.
Human-Centered Artificial Intelligence: Designing Systems with Empathy and Ethics
The rapid advancement of artificial intelligence technologies has necessitated a fundamental paradigm shift toward Human-Centered Artificial Intelligence (HCAI) that prioritizes empathy, ethical considerations, and societal impact over purely technical performance metrics. HCAI emerges as a comprehensive framework addressing multifaceted challenges by systematically prioritizing human values, ethical considerations, and societal needs throughout entire AI development lifecycles.Contemporary implementations demonstrate that organizations adopting HCAI principles consistently achieve superior outcomes compared to traditional performance-focused approaches. The EMPATHIC framework, encompassing Equity, Meaningfulness, Privacy, Accountability, Transparency, Human Agency, Inclusivity, and Compassion, provides an integrated approach to ethical AI design that balances technological capability with human values.Implementation strategies emphasize participatory design methodologies, agile ethics integration, and comprehensive evaluation frameworks that capture both technical performance and human-centered outcomes. Despite significant progress, persistent challenges include resource constraints, measurement complexity, and the automation paradox, which requires careful design of interaction patterns that maintain human expertise while effectively leveraging AI capabilities.Future directions encompass adaptive ethical reasoning systems, empathetic AI development, collective intelligence mechanisms, and evolving regulatory frameworks that will significantly influence the trajectory of HCAI advancement.
Transforming Capital Adequacy Assessment: The Role of Artificial Intelligence in Comprehensive Capital Analysis and Review
The extensive capital review and analysis process is an integral regulatory framework guaranteeing financial institutions are adequately capitalized with buffers during times of economic duress. Conventional practices in annual stress testing exercises are strongly based on manual data consolidation, static econometric model formats, and time-consuming documentation practices, taking up significant institutional resources while opening up scope for errors and inconsistencies. The advent of artificial intelligence technologies offers revolutionary possibilities to reengineer capital adequacy evaluation processes on several fronts. Machine learning techniques provide for reconciling data automatically and assuring quality, compressing preparation schedules while increasing data accuracy. Generative artificial intelligence architectures allow for the design of elaborate stress scenarios beyond the usual regulatory boundaries, incorporating intricate macroeconomic interactions and institution-specific weaknesses. Sophisticated predictive models using gradient boosting and neural network topologies exhibit better forecasting precision for credit losses and revenues in stressed scenarios. Natural language processing tools expedite the generation of technical reports and regulatory narratives, whereas robotic process automation provides consistent populating of templates and intelligent validation. Distributed ledger technologies integrated with capabilities to continuously monitor turn episodic compliance exercises into real-time resilience frameworks, enabling real-time management of capital. The effective incorporation of artificial intelligence in regulatory stress testing calls for vigilant consideration of model transparency, explainability requirements, and governance rules that guarantee supervisory acceptability while upholding the quintessential goals of financial solidity and stakeholder safeguarding in times of economic turmoil.
Semantic–Lexical Fusion: Improving Retrieval Accuracy for AI-Driven Knowledge Systems
Large language models increasingly rely on external knowledge retrieval to generate accurate, context-aware responses. Dense vector representations enable semantic similarity matching but struggle with exact term identification, structured metadata constraints, and domain-specific identifiers common in enterprise environments. This framework integrates dense embedding-based retrieval with token-level inverted indexing to address these limitations. The architecture employs dual indexing structures, a unified query decomposition layer, and a weighted scoring mechanism that combines cosine similarity with BM25 relevance signals. Field-level filtering and access control mechanisms ensure compliance with organizational constraints while maintaining semantic generalization. Performance characterization across production-scale deployments demonstrates that hybrid architectures achieve 60-150ms query latency with 1.0-2.0GB memory footprint per million documents, while pure semantic approaches require 768MB-1.5GB and pure lexical systems consume 200-500MB. Index construction analysis reveals vector encoding demands 15-50 hours for million-document collections compared to minutes for inverted indices, though batch processing strategies mitigate operational impact. Sensitivity analysis across weighting parameters identifies optimal semantic-lexical balance at α = 0.6, achieving peak F1 score of 0.847 and demonstrating 15-30% accuracy improvements over single-paradigm methods. Evaluation across open-domain corpora (MS MARCO, Natural Questions) and enterprise document collections demonstrates improved precision and recall, particularly for queries containing product identifiers, user entities, and structured filters. The system supports retrieval-augmented generation pipelines, conversational interfaces, and knowledge-grounded chatbots where both conceptual relevance and deterministic matching are essential. Results indicate that fusing complementary retrieval paradigms provides robust performance across diverse query types while maintaining interpretability and production-grade reliability for AI-driven applications. Cost analysis for enterprise deployments reveals 50-80% infrastructure premium for hybrid systems compared to single-approach implementations, justified by reduced hallucinations, improved user satisfaction, and decreased support escalations in operational AI applications.
Process and Digital Transformation in Industrial Manufacturing: A Strategic Framework
Business production agencies that produce precision contraptions, control systems, and automation solutions face exceptional working pressures because of fragmented delivery chains, converting consumer expectations, and increasing convergence of technologies. Conventional operating models related to reactive maintenance practices, manual inspection cycles, and siloed information architectures are increasingly restricting organizational responsiveness while raising the level of operational risk profiles. This article explores how incorporated transformation via system optimization and virtual enablement builds a sustainable, aggressive advantage. Technique transformation resolves center workflow inefficiencies by using systematic re-engineering, lean, and cost movement optimization before the deployment of the era. Virtual transformation employs interlinked sensing technologies, cloud analytics platforms, and intelligence-based algorithms that create new cost propositions through predictive maintenance, remote diagnostics, and outcomes-based provider delivery. Thorough evaluation confirms that effective transformation calls for integrated intervention across strategic, technological, human capital, and organizational ranges instead of independent technology adoption. Manufacturing excellence implementations illustrate better overall performance through condition-based protection practices, real-time optimization of resources, and quality management structures. Customer-centric service delivery models utilize remote diagnostic competencies and subscription-based monitoring services that turn transactional relationships into ongoing partnerships aimed at the success of operational outcomes.
Digital Transformation in Healthcare Technology: Modernizing Legacy Expense Management Systems
The healthcare technology market is under pressure to redesign its old systems that are not very scalable, have integration issues, and are not very efficient in manual processing. This article is a case study of the change of a legacy healthcare expense management system to a cloud-native microservices platform in a large financial services company. The project substituted a single-facade legacy screen scraper system with the medical expense management application based on the use of recent technologies such as Java/J2EE, Spring Boot, REST/FHIR APIs, and Microsoft Azure. The transformation delivered impressive results in accuracy of automated file processing, a decrease in manual intervention, an improvement in performance, and user engagement. The exploration looks into implementation methodology, technical architecture choices, challenges, measurable results, and lessons learned that can be used in similar digital transformation programs in healthcare technology platforms. The case reveals that well-considered modernization of the legacy can provide significant gains even with the already-present challenges, and it can be used to build premises of continuous innovation with better agility, scalability, and developer productivity. Some of its critical success factors came out, such as architectural choices that took into account cloud-native microservices, strict engineering practices, agile-based practices, and continuous people investment in terms of skills, change management, and team morale. With the future of healthcare technology being value-based care and interoperability standards, consumer-centered experiences, the platforms founded on a modern architectural basis will become a more and more distinguishing feature between the market leaders and organizations that are still bound by the shackles of legacy.
High-Frequency Trading Systems: Advancements in Ultra-Fast Computing for Enhanced Market Efficiency
High-frequency trading (HFT) engines have radically transformed the world of financial markets by leading the way in terms of ultra-fast computing systems. This article discusses how custom hardware architectures, software optimisation methods, and machine-learning algorithms have significantly reduced the data-processing latency in trading settings. The milliseconds to nanosecond execution capabilities have changed the microstructure of the market, having improved the price-discovery mechanisms and liquidity-provisions in a variety of markets. Although these technological advancements have led to a reduction in the bid-ask spreads and enhanced market efficiency, they have, at the same time, caused serious problems in relation to market stability in the case of extreme events. The article examines regulatory reaction to such worries, such as circuit-breaker structures and principle-based structures that trade innovations and systemic-risk issues. Ethical issues relating to speed benefits and fair access to the market are resolved by exploring the alternative market designs, which seek to offset pure technological benefits and maintain efficient price discovery.
The Evolution of Time Series Analysis: Beyond Traditional Forecasting
The field of time series forecasting has undergone a profound transformation, evolving from traditional statistical foundations to sophisticated deep learning innovations. Modern neural networks and machine learning models now offer enhanced capabilities for capturing complex patterns and non-linear relationships, often surpassing conventional approaches. Key architectural advancements, such as attention mechanisms and transformer architectures, have revolutionized the processing of sequential data. Concurrently, the emergence of automated machine learning (AutoML) and explainable AI (XAI) has significantly streamlined model development and improved interpretability. These developments hold particular significance for domains requiring multi-dimensional analysis and real-time predictions, where advanced architectures excel at discerning intricate relationships between variables while maintaining computational efficiency.
SSVSM-An SEO-SEM model to improve E-commerce product visibility
In the fast-paced world of e-commerce, especially on platforms like Amazon, standing out with your products is essential for attracting customers and boosting sales. Often, businesses rely on traditional Search Engine Optimization (SEO) and Search Engine Marketing (SEM) separately, which can lead to less-than-ideal outcomes. This paper introduces a new model called the Strategic SEO and SEM Visibility Model (SSVSM), which combines the best of both SEO and SEM to improve product visibility and rankings. The model uses a mix of on-page and off-page SEO techniques along with SEM strategies like pay-per-click (PPC), retargeting, and display ads, all while continuously optimizing in real-time. A case study was performed on Amazon.com, comparing products that were optimized using standard SEO/SEM methods (Group A) against those using the SSVSM hybrid model (Group B). The evaluation focused on key performance indicators such as ranking improvements, impression growth, conversion rates, and precision. The results showed that Group B outperformed Group A significantly, achieving a 4-page ranking boost, a 35% increase in impressions, a 20% conversion rate, and a precision rate that was four times higher than the control group. These findings confirm that the SSVSM model effectively targets the right users while maximizing both organic and paid traffic results. This research offers a practical and statistically backed framework for digital marketers and e-commerce businesses looking to enhance their visibility strategies in ever-changing online markets.
Numerical Analysis of Slope Stability under Surcharge Effects Using Convex Crossover Optimization
An important problem in geotechnical engineering is slope stability analysis, yet it remains highly complex due to the nonlinear nature of failure mechanisms. The evaluation requires determining the critical failure surface (CFS) and the minimum factor of safety (FOS), which poses a challenging optimization problem. In this study, the limit equilibrium method (LEM), based on the Fellenius approach, is applied to assess slope stability under varying surcharge conditions, including surcharge intensity, position, and width. Results show that higher surcharge intensity combined with reduced position distance and width significantly undermines slope stability. Traditional optimization algorithms are often inadequate for such problems, as they rely on initial guesses and may converge only to local optima. To address this, a Multi-Parametric Genetic Algorithm (MPGA) with a Convex Crossover (CC) operator is developed. The CC operator parameters are calibrated using benchmark test functions, while a time-dependent decay factor is incorporated to improve variable interaction and prevent premature convergence. The proposed MPGA consistently identifies more accurate critical failure surfaces and provides lower safety factors compared to conventional approaches. Overall, the method demonstrates superior efficiency and robustness, offering a reliable tool for slope stability optimization under surcharge conditions.
Standards-Driven Available to Promise Architecture: Transforming Enterprise Commerce Through Reliable Inventory Promises
The article introduces an available to promise (ATP) reference architecture based on standards that are intended to overcome important inventory visibility issues in enterprise commerce. The architecture provides a concerted, event-based strategy that provides customer-facing inventory promises to be reliable of the real sellable inventory. Through rigorous design rules and governance, the organizations can get rid of differences across channels, lower the number of orders canceled, and increase the conversion rates dramatically. The model reinforces a single line of truth that is based on business engine tables and not on guesswork or mass computing. The architecture yields a set of consistent and reliable promises to all customer touchpoints by exercising disciplined filtering of inventory locations, stringent subtraction of near-term demand, and using confirmed future receipts. The implementation fundamentally changes the inventory visibility, which used to be a collection of fragmented estimates into a unified and reliable system that improves customer experience and operational efficiency. The article outlines implementation plans, performance measurement models, and improvement points in the future, taking into consideration the architectural trade-offs and governance demands.
Artificial Intelligence in the Internet of Medical Things (IoMT) for Holistic Diabetic Care: Outcomes, Architecture, and Industrial Impact
Diabetes management experiences significant advancement as Artificial Intelligence combines with Internet of Medical Things platforms, creating continuous surveillance systems. Individuals gain access to forecasting tools and automated control of metabolic parameters. These systems influence several organ networks, including pancreatic, hepatic, renal, and peripheral components, producing improved glucose regulation and timely detection of emerging complications. Automated processes refine insulin delivery, achieving greater precision than conventional manual adjustment protocols. Patients exhibit improved outcomes through these technologies, demonstrating fewer acute events and maintaining glucose stability throughout daily periods. Food and pharmaceutical industries derive measurable advantages as patient data shapes formulation decisions and operational strategies. Nutrition labeling evolves from standardized indices toward individualized response metrics reflecting personal glycemic patterns. Cloud computing infrastructure processes continuous device data while maintaining confidentiality requirements and regulatory compliance. Food manufacturers adjust products based on observed glucose responses, whereas pharmaceutical operations modify production processes and distribution systems according to utilization patterns. Conventional scheduled clinical encounters transition toward continuous personalized monitoring, accommodating individual metabolic profiles and behavioral patterns. Clinical evidence generated through these systems influences product development across sectors, establishing data-driven connections between patient outcomes and manufacturing decisions. The integrated ecosystem positions real-world effectiveness data as foundational input for therapeutic optimization and industry innovation.
The Future of Legacy Modernization: AI-Augmented Strategies in Healthcare and Finance
Healthcare and financial sectors rely on critical operations, which rely on legacy systems where critical functions, such as claims processing and patient records, are performed. Although these systems have reliability, they usually do not cope with modern needs to scale and integrate clouds. The introduction of Artificial Intelligence and machine learning into the development of modernization methods is a groundbreaking strategy that will boost its performance, workflow improvement, and the shift to cloud environments. Modernization that uses AI is used to code-analyze, refactor, migrate data, and optimize processes and guarantee regulatory adherence to standards such as HIPAA, GDPR, PCI-DSS, and SOX. In medicine, AI can detect inefficiencies in the old systems and provide predictive analytics to provide better care to patients. In finance, it is used to improve fraud detection and simplify compliance processes. Such strategic alignment between legacy knowledge and AI possibilities gives companies the ability to expand infrastructure services plus speed up the process of digital transformation, improve the decision-making process, and provide more secure services. The strategy offers a process that enables a systematic movement of old systems into smart and adaptable platforms whilst striking a balance between innovation and stability in operations.
Intelligent Test Data Generation for Conversational AI Systems: A Comprehensive Review
Conversational AI systems are now part of various industries, requiring efficient testing methods to guarantee reliability, accuracy, and user satisfaction at production levels. Intelligent test data generation has become a vital part of developing and assessing these systems, counteracting the core issues present due to the nature of natural language as well as changing user interactions. This in-depth survey explores existing methodologies for creating efficient test datasets that mimic actual conversations and corner cases with the help of cutting-edge machine learning, natural language processing, and automation technologies. The shift from basic rule-based chatbots to high-end neural dialogue systems has revolutionized the testing arena with the need for systems capable of dealing with contextual comprehension, multi-turn dialogue, emotional undertones, and specialized domain vocabulary across a variety of languages and cultural backgrounds. Classical software testing practices are found wanting for the probabilistic and context-based nature of conversational AI, resulting in enormous system validation gaps. The review delves into different generation strategies involving rule-based approaches, data augmentation methods, generative models, adversarial testing, and user simulation platforms. Modern quality assurance issues include semantic coherence verification, pragmatic appropriateness assessment, cultural sensitivity validation, scalability needs, domain adaptation challenges, and privacy issues. Future directions place focus on human-in-the-loop integration, context-sensitive generation abilities, cross-lingual and multimodal data generation, and ongoing testing frameworks that evolve according to changing system capabilities.
Automating UVM Frameworks Using Artificial Intelligence and Machine Learning for Complex SoC Verification
The exponential growth in System-on-Chip complexity has created unprecedented challenges in functional verification, where traditional Universal Verification. The article approaches the struggle to maintain efficiency and thoroughness against increasingly heterogeneous architectures integrating diverse processing elements, accelerators, and high-speed interconnects. This technical article presents an Artificial Intelligence and Machine Learning-driven framework that fundamentally transforms UVM verification workflows by embedding intelligent automation, adaptive learning, and autonomous decision-making capabilities throughout the verification lifecycle. The proposed system leverages multiple AI paradigms, including Reinforcement Learning algorithms implementing Proximal Policy Optimization and Deep Q-Networks for adaptive stimulus generation that learns optimal testing strategies through interaction with designs under verification, supervised ensemble learning models combining gradient boosting and neural networks for predictive coverage trajectory forecasting, and unsupervised learning techniques employing Variational Autoencoders with density-based clustering for automated failure triage and root cause inference. Implementation on production-grade FPGA-based SoC environments featuring high-speed network controllers, storage interfaces, and interconnect fabrics demonstrates substantial improvements across multiple dimensions. The framework achieves significant acceleration in coverage closure timelines, dramatic reduction in debug effort through intelligent failure categorization and automated root cause summarization, and notable decrease in computational resource consumption while maintaining or exceeding verification quality metrics compared to traditional manual methodologies. The modular architecture ensures extensibility to emerging verification challenges, including mixed-signal validation, formal property checking, power-aware simulation, and security verification, with the incorporation of Explainable AI techniques providing transparency into automated decision-making processes essential for safety-critical and certified environments. Transfer learning policies allow policies trained on similar designs to serve well on new verification platforms with little or no additional training needs, which is many times faster to deploy than training policies in semiconductor product lines. The framework is a paradigm shift of tool-assisted verification to AI-assisted autonomous verification systems that continuously learn, evolve, and optimize through project lifecycles, which puts intelligent automation as a necessary feature of maintaining semiconductor innovation and first-silicon success in successive generations of more and more complex System-on-Chip implementations.
WebAssembly's Expanding Role in Frontend Development: Transforming Web Application Capabilities
This in-depth technical article traces the development of WebAssembly from a compilation target to a core technology in frontend development. It discusses WebAssembly's fundamental architecture with a focus on its binary instruction format that supports near-native speeds of execution in browser environments, along with security via structured control flow and memory safety features. The article outlines WebAssembly's growing set of features, such as SIMD operations for data parallelism at the data level, thread support for leveraging multi-core processors, and WASI for formalized system interfaces beyond the browser. It examines implementation spaces where WebAssembly has had real impact: gaming with rich rendering features, scientific visualization with real-time data handling, development environments with sophisticated code analysis, and productivity software with advanced document management. Technical details for best-in-class integration patterns are examined alongside new capabilities such as garbage collection support, the Component Model for standardized module interaction, and enhanced security features. Throughout, the article emphasizes how WebAssembly is inherently revolutionizing web application development through the enabling of capabilities heretofore the province of native software.
Healthcare Analytics Transformed: Leveraging AI/ML for Predictive Fraud Detection within Clinical BI Platforms
Healthcare fraud detection has undergone a fundamental transformation through the integration of Artificial Intelligence and Machine Learning technologies within clinical Business Intelligence platforms. Traditional rule-based detection systems show significant limitations in identifying sophisticated fraudulent activities due to their static parameters and inability to adapt to evolving fraud patterns. Modern AI-driven frameworks use advanced algorithms, including gradient boosting machines, random forest algorithms, and deep neural networks, to process vast volumes of healthcare data with superior accuracy and reduced false positive rates. These predictive models incorporate comprehensive training methodologies using extensive historical claims databases. This enables the identification of hidden anomalies and suspicious patterns that conventional systems frequently miss. Technical implementation includes seamless data pipeline infrastructures, real-time processing architectures, and dynamic risk scoring systems that enable immediate decision-making for claim processing workflows. Governance frameworks ensure regulatory compliance with HIPAA, HITECH, and state-specific requirements while maintaining algorithmic transparency and comprehensive audit trails.Future technological developments include natural language processing integration for unstructured data evaluation, graph analytics for network-based fraud identification, and federated learning architectures enabling privacy-preserving collaborative model development across distributed healthcare networks.
Data-Centric CRM Architecture for Smart Manufacturing Ecosystems: A Framework for Lifecycle-Driven Customer Engagement
Introduction of Industry 4.0 has radically restructured the manufacturing environment by connecting machines, streams of real-time data, and intelligent production systems, but the old platforms of customer relationship management remain independent of the intelligence in the architecture to provide active customer relationships in a lifecycle fashion. This model solves the crucial disengagement between operating systems and CRM potential by suggesting a detailed data-centric CRM design that has been tailored to the intelligent manufacturing ecosystem. Grounded in Socio-Technical Systems Theory, Digital Ecosystem Design, and the Resource-Based View, the framework conceptualizes a five-layer architecture encompassing data ingestion from IoT sensors and enterprise systems, integration of operational and customer intelligence through master data management, predictive analytics modules for proactive service interventions, CRM workflow translation of insights into engagement actions, and governance mechanisms ensuring quality, compliance, and trust. Four conceptual propositions establish theoretical relationships between architectural characteristics and organizational outcomes, linking data quality and integration completeness to predictive service accuracy, architectural integration to lifecycle engagement capabilities, predictive intelligence deployment to governance requirements, and socio-technical alignment to implementation effectiveness. The framework extends CRM scholarship into manufacturing contexts where operational data convergence creates novel engagement possibilities, positions CRM architecture as ecosystem infrastructure enabling controlled data exchange across manufacturing networks, and emphasizes that competitive advantage flows from integration architecture completeness rather than individual system sophistication. Strategic implications also emphasize data integration capabilities as important resources, lifecycle-dependent engagement as a particular orientation that needs organizational change, governance as a strategic enabler that ensures trust and transparency, and technical capabilities as the socio-technical fit that enhances performance. Managerial advice consists of creating data infrastructure before beginning to employ analytics, concentrating on two-way data movement between operational and CRM systems, and setting governance next to analytical skills, deliberately investing in organizational alignment, and executing in a modular, iterative implementation plan.
Metadata-Centric Orchestration for Cloud-Native ETL Pipelines
Cloud-native data environments running on distributed architectures are severely challenged when classic Extract-Transform-Load orchestration patterns depend on static Directed Acyclic Graph structures, which do not support dynamic data dependencies, schema change, and heterogeneous source system integration. Contemporary data platforms handling data from hundreds of heterogeneous sources are burdened with increasing operational complexity as pipeline logic hard-coded in applications forms maintenance bottlenecks and governance hurdles. The metadata-driven orchestration pattern overcomes these limitations by decoupling control logic from application code into versioned metadata stores that act as centralized sources of truth for pipeline specifications. Everything configurable, such as source connections, transformation rules, data quality constraints, dependency relationships, and lineage mappings, gets declaratively defined through structured metadata schemas independent of the execution fabric. Orchestration engines query metadata repositories at runtime to build dynamic execution plans sensitive to real-time system conditions and upstream data availability trends. Technology deployments use Apache Airflow as a task orchestrator, dbt framework as an SQL-based transformer, and OpenLineage standards for end-to-end lineage tracking across distributed processing environments. The metadata layer also serves as an observability and governance platform that supports end-to-end traceability, reproducibility, and impact analysis during workflow execution. Empirical implementations in multi-tenant data platforms illustrate dramatic decreases in pipeline maintenance overhead and faster recovery from schema drift events. Cross-functional coordination is greatly enhanced as abstraction of metadata separates transformation logic from infrastructure code, allowing business rules to be defined by data analysts without requiring proficiency in intricately complex orchestration frameworks. Metadata-based orchestration lays grounding capabilities towards self-adaptive data pipelines, combining data engineering, governance, and observability under concerted architectural frameworks.
Quantum-Inspired Optimization for Cloud Database Query Processing
Cloud database platforms face considerable processing limitations when managing complex relational queries that involve intricate joins and layered optimization challenges. Quantum computing techniques offer groundbreaking solutions by integrating quantum principles into conventional hardware systems.These advanced methodologies implement tensor network architectures, simulated annealing processes, and quantum-influenced sampling procedures to examine multiple query execution routes concurrently, substantially minimizing processing demands for elaborate analytical operations. Assessment findings indicate remarkable performance enhancements, with quantum-inspired optimization producing significant acceleration improvements for specialized analytical processes involving extensive graph relationships and combinatorial data configurations. These enhancements show particular effectiveness for complex queries that typically pose challenges to traditional database optimization methods. Deployment strategies focus on smooth integration with current SQL optimization systems while maintaining compatibility with existing relational database infrastructures. Technical implementation requires advanced hardware acceleration capabilities and strategic workload identification procedures that allow organizations to optimize performance advantages. Integration obstacles include maintaining compatibility with current optimization frameworks, optimizing resource distribution, and systematically evaluating query patterns appropriate for quantum-inspired processing methods. Database architects and performance engineers gain valuable insights into how these quantum-inspired approaches constitute substantial evolutionary progress beyond conventional parallel processing methods. These techniques create fundamental principles for advanced database optimization while maintaining operational compatibility with current relational database management systems, delivering scalable solutions for complex analytical processing demands across diverse cloud computing environments.
Impact of Prehospital Care Provided by Emergency Medical Services on Patient Survival in Trauma Cases
The impact of prehospital care provided by Emergency Medical Services (EMS) on patient survival in trauma cases is a critical area of study in emergency medicine. Research has consistently shown that timely and effective prehospital interventions—such as advanced airway management, hemorrhage control, and rapid transport to trauma centers—significantly improve survival rates among trauma patients. The golden hour concept highlights the importance of early medical intervention, where rapid and appropriate treatment in the field can stabilize patients and improve outcomes. Effective communication between EMS and receiving hospitals is also vital, enabling trauma teams to prepare for incoming patients and streamline the transition to definitive care. In addition to immediate medical interventions, the training and experience of EMS providers play a pivotal role in influencing patient outcomes. The implementation of evidence-based protocols and continuous education ensures that EMS personnel are equipped to handle a variety of traumatic injuries effectively. Moreover, system factors such as response times, resource availability, and regional trauma care systems further affect the quality of prehospital care. Understanding these dynamics is essential for developing strategies to enhance prehospital trauma care, ultimately leading to improved patient survival rates in emergency situations.
Federated Query Rewriting for Conversational AI: Privacy-Preserving, Cross-Channel Retrieval on Voice and Web
We present a federated framework for retrieval-grounded query rewriting that serves a single Dialogflow CX agent across telephony and web. Local, adapter-based rewriters are trained per tenant and aggregated with secure aggregation; optional (ε, δ)-differential privacy bounds the privacy loss. A channel-consistency regularizer aligns voice↔web rewrites using only local statistics. On embedding retrieval with Vertex AI Matching Engine, our best federated configuration improves nDCG@10 by 6.8% and MRR by 5.2% over a centralized baseline while keeping P95 latency ≤ 280 ms. We describe a privacy-accounted pipeline with Firestore DLP memory, Apigee X controls, and BigQuery/Cloud Trace evaluation, showing that federated optimization can enhance accuracy and robustness under non-IID traffic without centralizing transcripts.
Verifiable and Programmable Cross-Border Payments
The article introduces a verifiable API-centric solution to international payments that builds on existing infrastructure and overcomes key issues in the payment ecosystem of the world. Cross-border transactions are now experiencing obstacles such as fragmentation systems, discrepancies in regulations, and poor transparency, leading to delays, high costs, and limited access. The suggested architecture proposes an overlay based on layers that combine financial-grade access controls, harmonized ISO 20022 semantics, cryptographically verifiable payment receipts, and end-to-end observability. This design makes it possible to handle an improved payment verification without central bodies, it is more programmable with standardized interfaces, and it is compatible with existing networks such as SWIFT. The framework is advantageous on economic, inclusiveness, and regulatory levels by moving away from institutional claims to proofs, which are independently verifiable, and diverse implementation capacities and operational conditions can be met.
Enamino ester synthesis using a proton exchanged Algerian montmorillonite clay as acid eco-friendly catalyst
An efficient and straightforward methodology has been developed for the synthesis of β-enamino ester derivatives (3a-h). This transformation involves the direct condensation of alkyl or aromatic amines (2a-d) with β-ketoesters (1a-b), facilitated by protonated Algerian montmorillonite clay (MMT-H+) as a heterogeneous, eco-friendly catalyst. The protocol yields the target products in good to excellent yields.The key advantage of this method lies in the utilization of the MMT-H+ catalyst, which is notable for its natural abundance, low cost, and non-toxic nature. As a solid acid, it simplifies the work-up procedure and can be recovered and reused over several cycles without a significant decrease in its catalytic performance. This approach, characterized by its operational simplicity, cost-effectiveness, and environmental friendliness, represents a sustainable alternative for the preparation of these valuable synthetic intermediates.
An efficient and eco-friendly preparation of N-substituted acridine-1,8-dione derivatives using triethylamine as catalyst in ethanol medium
This work presents a straightforward, efficient, and environmentally benign method for synthesizing N-substituted acridine-1,8-dione derivatives (4a-h). The synthesis is achieved via a one-pot, three-component condensation of an aromatic aldehyde, dimedone, and p-toluidine.The protocol employs ethanol as a green solvent and triethylamine (TEA) as a simple, effective catalyst. This approach offers significant advantages over conventional methods, including operational simplicity and mild reaction conditions. The reaction proceeds efficiently, affording the target products in excellent yields of 86–91%.A key practical benefit is the simple work-up; pure products are easily isolated by filtration and recrystallization, eliminating the need for complex chromatography. This method, characterized by its high yields, minimal environmental impact, and practicality, provides a robust and scalable route to the biologically significant acridine-dione core.
AI for Predictive Maintenance in Engineering Systems
This comprehensive article examines the transformative impact of artificial intelligence on predictive maintenance strategies across various engineering systems, highlighting the evolution from traditional, reactive, and scheduled maintenance approaches toward sophisticated, data-driven methodologies. The article explores core technologies, including vibration analysis, thermal imaging, and sensor fusion techniques, that enable machine learning algorithms to detect equipment anomalies and predict failures with unprecedented accuracy. Through detailed examination of applications spanning industrial manufacturing, aviation, power generation, and transportation infrastructure, this article demonstrates how AI-enabled predictive maintenance systems significantly enhance operational reliability while reducing costs and improving safety outcomes. The article identifies key implementation methodologies, including anomaly detection algorithms, neural networks for pattern recognition, and real-time analytics platforms that process streaming sensor data to enable proactive maintenance decisions. While acknowledging substantial benefits such as extended equipment lifespan, optimized maintenance resource allocation, and enhanced safety performance, the article also addresses critical implementation challenges, including data quality issues, organizational change management requirements, and technical integration complexities with existing systems. The article reveals emerging trends toward autonomous maintenance systems, digital twin integration, and blockchain applications for data integrity, while identifying significant research opportunities in human-AI collaboration and ethical AI implementation. The article concludes that AI-driven predictive maintenance represents a paradigm shift that fundamentally alters equipment management practices across engineering disciplines, establishing new standards for operational excellence while addressing evolving sustainability and regulatory requirements in modern industrial environments.
Embedded Linux Crash Resilience for Autonomous Vehicles
This article examines the implementation of persistent crash logging infrastructure for autonomous vehicles running embedded Linux systems. It explores how proper failure capture mechanisms can transform system crashes from dangerous incidents into valuable learning opportunities. The article investigates the challenges of crash capture in embedded automotive environments, detailing the implementation of the Linux kernel's persistent storage (pstore) subsystem with the ramoops driver, and the enhancement of diagnostic capabilities through kdump integration. Through analysis of multiple studies and empirical data, the article demonstrates how this dual-layer approach to crash resilience significantly improves engineering efficiency, system reliability, and safety assurance. The paper demonstrates that a robust crash logging infrastructure allows organizations to solve once-undiagnosable failures, speed development cycles, and establish more cogent evidence-based safety arguments for regulatory approval. The paper demonstrates that continuous crash logging not only improves diagnostics for sophisticated failure modes but also delivers measurable benefits in development speed, certification processes, and overall system dependability in safety-critical autonomous vehicle deployments.
Event-Driven Architectures for Ultra-Low-Latency Systems: Design Principles, Reference Patterns, and Evaluation
Ultra-low-latency, event-driven systems have strict end-to-end latency demands in the range of microseconds or sub-milliseconds for applications in high-frequency trading, augmented realities, teleoperation, and real-time machine learning inference. Current applications require not only reductions in median latency performance, but also tight control on tail latency to meet service-level objectives and maintain operational safety. Recent advances in kernel-bypass networking (DPDK, AF_XDP), persistent RDMA semantics, FPGAs as fabrics, and deterministic scheduling provide powerful ways to achieve these demands, but there does not yet exist an integrated framework that covers all of these techniques. This article builds on five years of systems research and organizes the findings into a usable taxonomy that maps sources of latency, namely: network traversal, kernel overheads, serialization, scheduling delays, and distributed state management, to architectural levers and supporting systems. Three example architectures are shown: a single-node user-space architecture, a rack-scale PCIe/FPGA architecture, and a DRAM-assisted architecture. It also presents application-level implementation patterns for users to achieve similar latency outcomes, focusing on zero-copy paths, NIC offload, and placement that preserves locality. It also gives reproducible evaluation techniques associated with the implementation patterns in this development. Finally, case study examples from high-frequency trading and edge-based machine learning inference are presented in which single-digit microsecond latency is feasible while also explaining fundamental trade-offs among latency, maintainability, and hardware specialization.
Agentic Commerce: The Paradigm Shift from Human-Mediated to Autonomous AI-Driven Transactions in Digital Payment Systems
Agentic commerce represents a monumental change from human-to-human digital commerce in that the buying decisions that humans used to make are being supplanted in the future with automated and self-aware AI-driven commerce. The advent of agents based on large language models can now, to varying degrees, engage with users through relatively natural conversation to learn what they require, search across multiple platforms, negotiate prices, and autonomously handle the entire transaction process without human involvement. As a result, a completely new payment construct, authentication method, and security model are needed. Traditional banks and major technology companies, payment processors, card networks, and AI research organizations have begun working together to build the technical architecture that will allow agents to autonomously engage in commercial transactions. Numerous issues need to be resolved, including the appropriate way to verify AI agents acting on behalf of users, formalizing dispute resolution procedures, enforcing consumer protections, and addressing ethical concerns with machines making judgments about purchases on behalf of humans. The union of AI and fintech is an exciting space that will likely increase convenience and productivity, while demanding governments adapt their regulatory frameworks and the commercial ecosystem create trust that also fits the compliance model of the regulatory body in place.
Privacy-Preserving and Federated Learning for Regulated Data Ecosystems
Entities bound by rigorous information protection regulations encounter ongoing friction between extracting insights from dispersed repositories and upholding legal obligations. Traditional collaborative intelligence initiatives spanning organizational perimeters necessitate consolidating confidential records into centralized locations, thereby generating exposure risks and administrative burdens. Emerging cryptographic and distributed learning frameworks address this challenge by enabling model training directly on decentralized data sources without exposing raw records. This manuscript examines architectural blueprints enabling compliant joint intelligence development across medical networks, banking consortia, and similarly governed sectors. The paper synthesizes 120+ peer-reviewed studies and contrasts major privacy-preserving frameworks such as Secure Aggregation, Differential Privacy, and Homomorphic Encryption. Device-level and institutional-scale network configurations create communication substrates, whereas protected aggregation sequences block intermediate interception and withstand adversarial participant conduct. Privacy-calibrated randomization delivers quantifiable disclosure containment through controlled perturbation injection. Isolated processing domains, computation-preserving encryption schemes, and distributed cryptographic protocols furnish supplementary defense mechanisms exhibiting varied performance and precision characteristics. Administrative structures incorporating lineage documentation, authorization metadata handling, and cryptographically anchored verification records satisfy regulatory monitoring mandates. Vulnerability landscapes encompassing gradient extraction and membership detection necessitate specialized mitigation strategies and uniform assessment frameworks. The manuscript introduces a unified architectural taxonomy linking federated learning components with regulatory-compliance mechanisms, highlighting novel cross-disciplinary design patterns for secure data collaboration. Enduring obstacles persist in balancing confidentiality against utility at enterprise scale, validating protection workflows, and establishing sector-tailored benchmarks reconciling advancement with public confidence in vital intelligent infrastructure.
AI-Driven Data Placement in Distributed Storage: A Reinforcement Learning Framework for Workload Orchestration and QoS Optimization
Modern distributed storage systems are challenged by increasingly complex optimizations of data placement across heterogeneous resources, without compromising performance and quality of service. Traditional data placement strategies rely on static policies, which cannot adapt to dynamic workload patterns and continuously changing system conditions. This article presents a holistic reinforcement learning framework that makes intelligent data placement decisions in distributed storage. The framework implements a Deep Reinforcement Learning-based placement engine with state-of-the-art policy optimization, a workload characterization system using time-series analysis and anomaly detection, and a QoS-aware orchestration layer with real-time feedback mechanisms. Initial simulations and theoretical analysis indicate great improvements in access latency reduction, resource utilization, and QoS compliance compared to traditional placement strategies, while reducing operational costs substantially. These preliminary findings reveal promising effectiveness in proactive management of dynamic workloads across heterogeneous environments by reinforcement learning-based decision making to bridge critical gaps in existing storage management approaches, and provide a way forward toward more efficient and adaptive storage.
Platform Engineering for Data Teams in the Age of AI: Building Autonomous, Intelligent Data Infrastructure
The explosion of tools and disjointed workflows is posing a level of complexity to enterprise data infrastructure like never before, hindering agility in an organization. Platform engineering is a solution to such challenges, based on Internal Developer Platforms, which simplify infrastructure complexity by providing self-service and standard workflows. With Generative AI integrated into platform engineering, intelligent operating models are formed, with AI copilots generating infrastructure code, automatically enforcing governance, and anticipating failures before they strike. It is an AI-enhanced architecture that consists of five coordinated layers, including user interaction, AI intelligence, orchestration, infrastructure management, and observability layers. Context engines combine multi-dimensional data to drive specialist models to generate code, validate policies, and predict analytics. The empirical validation is showing significant gains in terms of deployment velocity, reliability, compliance with governance, cost optimization, and productivity of the developers. Organizations that adopt AI-enhanced platforms see transformational benefits in the reduction of incidents, consistency in policy enforcement, and operational efficiency, which confirms the use of the strategy as the basis of competitive advantage in data-driven markets.
AI-Enhanced ETL: Accelerating Data Quality and Transformation with Intelligent Automation
The development of Extract, Transform, Load (ETL) processes with the help of artificial intelligence provides a breakthrough to the old problems in data management. The present article will introduce an AI-Enhanced ETL framework, which transforms the data integration process from a fixed rule-based framework to dynamic and adaptive systems. The architecture uses natural language processing to infer the schema, reinforcement learning to perform optimized transformations, anomaly detection to manage quality, and knowledge graphs to provide awareness about the environment. Deployed in multi-cloud infrastructures, the framework has shown considerable benefits in the precision of the data, its efficiency in validation and processing speed, as well as minimizing the number of people who need it. This modular design with specific intelligent components allows self-learning capabilities, which are constantly enhanced due to the feedback during functioning. The explainable AI-based metadata-driven approach ensures transparency and governance, along with the ability to adopt gradually. This architectural paradigm makes ETL a maintenance-free field that becomes self-optimizing and provides speed of insights on the modern analytics workload.
A mobility-aware service migration technique in fog computing environments
Fog computing allows the utilization of resources near the Internet of Things (IoT) devices to serve various latency-sensitive applications. However, the mobility of users of IoT devices necessitates the migration of applications to maintain service continuity and quality of service (QoS). This study proposes a new migration technique that minimizes delay, network usage and energy consumption in the Fog network, providing a real-time user experience. An objective function-based decision-making approach is used to migrate the applications efficiently, guaranteeing service continuity and QoS. The proposed technique chose an appropriate fog node with sufficient resources by evaluating parameters like connection duration between the fog nodes and the users, resource availability, and application execution time at the fog nodes. The results indicate that the proposed approach has a remarkable improvement of up to 20% in average delay, 16% in network usage and 7% in energy consumption compared to the conventional approach. The number of migrations is also lowered by 18%, which is necessary to efficiently utilize limited fog node resources as each migration event consumes additional resources. The benefits of the proposed approach for the users are low latencies, low network usage, improved energy efficiency and better user experience.
Proposed Standards for Promoting the Concept of Sustainable Universities in the Kingdom of Saudi Arabia
Sustainability presents a significant challenge given the environmental, social, and economic challenges facing societies worldwide. Enhancing the environmental efficiency of university buildings requires continuous efforts and integrated collaboration. By adopting modern technologies and sustainable practices, universities can effectively contribute to environmental protection and support sustainability globally. A sustainable campus necessitates standards and requirements aimed at reducing indoor heat gain, conserving energy, water, and fuel, and lowering costs. Through these sustainable practices, the overall health of users can be improved, which is reflected in the quality of the educational process and related activities, as well as the expected positive impact on the behavior and performance of students and all staff within the sustainable campus. This research aims to study the standards and requirements for sustainable universities in the Kingdom of Saudi Arabia and discuss related points within the context of the university campus, while providing an analysis of what has been achieved, ongoing development efforts, and how to improve the carbon footprint performance of buildings. This was achieved through an analytical study of the standards and requirements for sustainable universities and a proposal for their application to Saudi universities. Quantitative analytical methodology was adopted, employing a pilot study using Design Builder version 7.0 to simulate a case study related to the private colleges in Buraidah. This simulation aimed to assess the feasibility of reducing the carbon footprint and promoting the application of university sustainability principles.
Transforming Medical Device Supply Chains via Robotic Automation and SAP Integration: A Success Model for Medical Device Distribution at Jabil Inc
The combination of robotic process automation and enterprise resource planning systems is powerful in medical device distribution, meeting the dual imperatives of operational efficiency and regulatory compliance in highly regulated environments. The implementation at Jabil Inc. shows how strategic integration of automated picking systems with the SAP Warehouse Management architecture can achieve significant productivity improvements while ensuring full validation and traceability requirements mandated for FDA Class II and III medical devices. Technical architecture also features advanced middleware integration layers of heterogeneous robotic fleets and enterprise systems; full serialization layers that ensure full product lineage tracking; multi-layered audit trails to allow readiness to regulatory inspections. The validation procedures under the GAMP 5 framework and the FDA guidance documents are the procedures that ensure that automated processes retain the data integrity, quality control, and compliance requirements within system lifecycles. The effects of operations relate to a great enhancement in pick-and-pack performance, almost complete removal of mispicking, enhancement of inventory precision, better usage of space, and enhancement of customer service indications. The implementation provides a template that can be followed by organizations that have to maneuver their way through the mayhem of implementing automation in GxP-compliant settings, demonstrating that even the most stringent regulatory conditions can be employed together with the latest and most sophisticated technologies. In this manner, the combination of warehouse robotics, enterprise resource planning systems, and quality management platforms forms synergistic capabilities that actually improve both operational performance and regulatory adherence, challenging conventional assumptions regarding inherent tensions between efficiency and compliance objectives.
Ethical Hacking: Techniques and Legal Implications
Ethical hacking constitutes a scientific field of security assessment wherein authorized professionals employ adversarial strategies to identify and remediate vulnerabilities within an organization's computing infrastructure. The practice addresses escalating cybersecurity threats through proactively inspecting defensive postures from attacker perspectives while maintaining strict adherence to legal and ethical boundaries. Present-day organizations face sophisticated threat actors who constantly evolve exploitation methodologies to compromise sensitive systems, necessitating comprehensive security evaluation frameworks that mirror real-world attack scenarios. The article examines technical methodologies underlying authorized penetration testing operations, including reconnaissance strategies leveraging both passive intelligence gathering and active network enumeration, vulnerability assessment techniques using automated scanning tools and manual testing approaches, and exploitation frameworks utilizing deep reinforcement learning for automated attack path discovery. Legal issues surrounding ethical hacking activities prove particularly complex, as testing strategies closely parallel criminal intrusion strategies, with authorization serving as the primary distinguishing factor between legitimate security assessment and unauthorized access prosecutable under computer fraud statutes. Ethical responsibilities extend beyond statutory compliance to encompass professional obligations for minimizing operational disruption, protecting discovered vulnerabilities via responsible disclosure practices, and prioritizing organizational security enhancement over technical demonstration. The integration of security testing findings into risk management strategies enables organizations to prioritize remediation efforts based on exploitability factors and potential business impact, strengthening defensive capabilities against persistent cyber threats targeting critical infrastructure and sensitive information assets.
Seamless Integration of Edge-Cloud Computing and Distributed Artificial Intelligence: A Comprehensive Framework for Next-Generation Applications
Integration of edge computing with cloud infrastructure and distributed artificial intelligence yields a revolutionary paradigm that meets computational needs across contemporary data-intensive applications. Hierarchical architectures for processing arise through synergy in the edge-cloud framework, where proximity-based processing is used for latency-critical operations in tandem with cloud processing for elastic analytics and storage. Real-time response becomes feasible for applications that include autonomous vehicles, smart cities, industrial automation, and healthcare informatics with this integration. Collaborative model training over geographically distributed settings becomes possible using distributed AI techniques, specifically federated learning and data parallelism, without compromising data privacy and reducing communication overhead. Great technical challenges face these frameworks, such as resource heterogeneity, communication bottlenecks, the requirement of fault tolerance, and security issues. System efficiency and model convergence are greatly enhanced using sophisticated optimization schemes using gradient compression, hierarchical aggregation, and adaptive resource allocation. Next-generation applications' foundation infrastructure arises from the integration of edge-cloud architectures with distributed intelligence mechanisms, at the same time fulfilling strict latency requirements, privacy protection, and computational scalability demands.
Blockchain-Integrated Cyber Defense Mechanism for Cloud-Driven Financial Applications
On the financial sector, cloud computing has introduced a difference in terms of a scalable innovative and cost-effective service. Nevertheless, it has already caused an increase in cyber security risk - especially when sensitive financial information and compliance are involved. This paper empirically challenges key cybersecurity models of cloud-based financial applications both qualitatively and quantitatively. Comparing the various risk assessment models, CSA STAR based models are seen to offer 88 percent conformance to the standards lacks any form of economic measurements, as compared to Youssef CSRMF, which offers 90 percent conformance to the standards, and reaches all-time maximum business alignment score of 5/5. The blockchain-based TAB model simulations indicated that overlay-transparency-enhancing models including the TAB Model increase the trustworthiness by factor 4 by reducing the detection latency by 210ms and the detection latency by 480ms. Moreover, at the application level, layered API security solutions demonstrated significant improvement: a reduction of 120-40 security related incidents monthly in both basic and applied cases along the full-stack using AI/ML surveillance and the detection accuracy changed to 72 to 97. With the study on cost saving in place, installing structures which are based on SME is cheaper than installing hybrid enterprise-level structures which can cost between $200000 and 250000. The findings indicate that financial managers should consider a hybrid approach to cybersecurity at the expense of such factors as risk evaluation, transparency, and API-layered defenses to maintain the balance between operational efficiency, compliance, and resilience in clouds.
Managing ENT Emergencies, The Role of Nursing, Family Medicine, Psychiatry, Social Work, Emergency Services, and Medical Administration
Managing ENT (Ear, Nose, and Throat) emergencies requires a multidisciplinary approach that integrates the expertise of various healthcare professionals including nurses, family medicine practitioners, psychiatrists, social workers, emergency services personnel, and medical administrators. Nurses play a crucial role in the initial assessment and stabilization of patients, providing critical support through the collection of medical histories and vital signs, as well as administering treatments as prescribed. Family medicine practitioners offer continual care and primary assessments, ensuring that patients receive comprehensive management of their condition. Psychiatry is integral to address any mental health concerns that may arise in patients experiencing acute ENT issues, especially in cases where anxiety or trauma is involved. Social workers also contribute by assessing social determinants of health and facilitating access to necessary resources, thus ensuring a holistic approach to emergency care. Emergency services serve as the frontline responders in ENT emergencies, providing rapid assessment and transport to appropriate care facilities. Within the emergency department, collaboration among multidisciplinary teams is essential for effective patient management. Medical administration plays a pivotal role in creating protocols and maintaining quality of care standards to streamline the processes involved in ENT emergencies. Enhanced communication channels between all participants, alongside efficient resource allocation, dramatically improves patient outcomes in high-stress situations. The collaborative efforts of these diverse professionals not only address acute medical issues but also ensure a supportive environment for both patients and their families.
Ecological Assesment Of Flora And Fauna Following Dune Stabilization In The El Mesrane Region (Djelfa, Algeria)
Dune stabilization is wildly applied in arid and semi-arid regions of Algeria, representing one of the most effective ecological restoration strategies against desertification, yet its ecological outcomes remain scarce. This study evaluate the influence of dune fixation on soil properties, vegetation structure and faunal assemblages in The El Mesrane dune system (Djelfa, Algeria).Six stations, including three stabilized dunes and three adjacent mobile dunes were surveyed through soil analyses, floristic survey, and standardized sampling of invertebrates, reptiles, birds and small mammals. Stabilized sites showed improved edaphic conditions, along with higher species richness and diversity for all biological groups. Diversity indices (Shannon, Simpson and evenness) confirmed a more structured and balanced community in fixed dunes. Statistical analyses indicated significant differences between dunes types for both soil parameters and biodiversity metrics. These findings highlight the positive efficiency of dune fixation on habitat structure and biological diversity in arid steppe ecosystems of central Algeria.
Optimized Database Sharding Techniques for High-Performance MySQL Applications
With the ever-growing data-intensive applications, the classical monolithic MySQL databases are usually unable to satisfy the requirements of the high-throughput, low-latency, and real-time applications. The technique of horizontally dividing data in more than one database is called database sharding, though it has proven to be a strong tool to overcome these problems. The paper provides an extensive overview of optimized forms of database sharding that are specific to MySQL applications. It discusses the basic Sharding concepts, comparisons of various models, and advanced optimization techniques, i.e., consistent hashing, query-aware routing, dynamically re-sharding, and caching. The paper also introduces patterns of deployment of architecture that can be adapted to the cloud-native environment and provides comprehensive results of performance benchmarking that can be used to measure the benefits and drawbacks of diverse approaches. The paper will offer a roadmap to help the architects and engineers create scalable, reliable, and high-performance MySQL infrastructures by combining the existing best practices with scholarly work. The results highlight that sharding used and tuned properly can be of major benefit to a database in terms of performance, operational efficiency, and scalability of the system.
Interoperability-Driven Workflow Engines in Healthcare IT: Enhancing Care Coordination Through Standards-Based Automation
Global healthcare delivery systems are faced with considerable challenges due to technological fragmentation, wherein electronic health records, laboratory information systems, radiology platforms, and billing infrastructures exist as autonomous silos, not facilitating the smooth flow of information. The lack of connection creates huge delays in clinical decision-making, increases medical error risks during patient handoffs, and places tremendous administrative burdens on healthcare providers who are required to manually reconcile information on different disconnected platforms. Standards-based interoperability frameworks built on top of Fast Healthcare Interoperability Resources and Health Level Seven protocols have become key enablers of coordinated care delivery. Building on these interoperability foundations, workflow engines add advanced orchestration capabilities, automating intricate clinical and administrative workflows across discharge planning, medication reconciliation, and referral management. The paper analyzes architectural paradigms supporting interoperability-driven workflow engines, such as modular design principles facilitating vendor-agnostic communication and event-driven orchestration reacting dynamically to clinical cues. Infrastructure needs such as containerized microservices architecture and robust messaging mechanisms providing scalable deployment are thoroughly elaborated. Regulatory compliance frameworks controlling patient data privacy, in addition to ethical considerations tackling alert fatigue, algorithmic bias, and maintenance of clinical judgment, are explored in depth. The synthesis illustrates how workflow engines address persistent coordination failures in helping care continuity along complicated healthcare shipping chains with numerous specialties, corporations, and care settings.
Inherited Urbanism and Energy Transition: An Approach Based on Artificial Intelligence and GIS
This study aims to analyze the impact of urban planning in cities marked by an unequal distribution of neighbourhoods — particularly those that were colonized by foreign powers, such as the city of Sidi Bel Abbès in Algeria — on the current energy transition. The study area includes 277 rooftops suitable for solar panel installation and located near electricity distribution substations. The results revealed a disparity in annual individual photovoltaic energy production between neighborhoods once inhabited by colonizers and those designated for the indigenous population, a contrast still evident today due to the preservation of the same urban structure. Deep learning techniques (U-Net and Attention U-Net) were used to detect rooftops, yielding good results despite limited data availability. Geographic Information Systems (GIS) were also employed to assess the solar potential and analyze the proximity of rooftops to the distribution substations.
Assessing the Effect of Blood Perfusion on Prostate Cancer Hyperthermia Using a Bioheat Simulation Approach
The development of hyperthermia-based therapy for prostate cancer requires precise modeling of the thermal behavior of biological tissues. In order to simulate healthy tissue, this study creates a computational model of a prostate tumor that is shaped like a spherical area encircled by another concentric sphere. The investigation focuses on the effects of blood perfusion rate fluctuations on the temperature distribution and subsequent thermal damage in the surrounding tissue and tumor. The heating source is modeled as a collection of electric dipoles at the center of the tumor that are subjected to a microwave (2.45 GHz) alternating electromagnetic field. The temporal evolution of temperature and tissue necros is under varying perfusion rates is described using the Arrhenius damage model and the Pennes bioheat transfer equation. The findings contribute to the development of safer and more effective hyperthermia-based treatments for prostate cancer by offering a quantitative understanding of the effects of blood flow on heat diffusion and necrotic percentage
Design Verification of Multi-Chiplet AI Accelerators: Challenges and Solutions
The verification of multi-chiplet AI accelerators represents one of the most formidable challenges in contemporary semiconductor design, driven by the industry's transition from monolithic architectures to heterogeneous integration strategies that combine multiple specialized dies within a single package. This paradigm shift addresses fundamental limitations in Moore's Law scaling by enabling the integration of chiplets fabricated on different process nodes, optimized for specific functions such as computation, memory management, and input-output operations. However, this architectural evolution introduces unprecedented verification complexity stemming from the integration of billions of transistors across multiple dies, each containing intricate intellectual property blocks and subsystems that must communicate through sophisticated die-to-die protocols. The verification process must address not only functional correctness but also critical non-functional properties, including thermal management across stacked dies, signal integrity through vertical interconnects, power delivery network integrity, and timing predictability across asynchronous clock domains. Traditional monolithic verification approaches prove inadequate for these distributed systems, necessitating advanced methodologies that combine hardware-accelerated emulation platforms, distributed simulation techniques employing virtual channels, formal verification methods for protocol compliance, and intelligent clock gating strategies. Despite these sophisticated approaches, significant challenges persist, including substantial infrastructure costs, extended debugging cycles for distributed simulations that can span days or weeks, a combinatorial explosion of the verification space, and the complexity of achieving adequate coverage across all chiplets and operating modes. The semiconductor industry is responding with innovative solutions, including hybrid verification frameworks that strategically combine multiple methodologies, standardized chiplet interfaces that enable reusable verification components, advanced debugging tools with unified cross-chiplet waveform databases, and emerging artificial intelligence-driven techniques for intelligent testbench generation, coverage closure prediction, and verification planning optimization. These developments, while promising enhanced productivity and effectiveness, underscore that successful multi-chiplet verification requires not only technological innovation but also substantial capital investment, specialized expertise, and close collaboration between design and verification teams to deliver high-quality, bug-free AI accelerators that meet stringent performance and reliability requirements.
Serialization Systems: Advancing Pharmaceutical Equity and Pandemic Preparedness in Global Healthcare
The COVID-19 outbreak demonstrated the existence of fatal weaknesses in world pharmaceutical supply chains, unveiled the existence of far-reaching disparities in medicine and vaccine distribution between developed and developing countries. Serialization systems-technological tools that unite pharmaceutical products with distinctive identifiers become central in the way to mitigate healthcare disparities to improve pandemic preparedness. This pandemic revealed that the disjointed distribution channels, vaccine nationalism, and insufficient tracking infrastructure systematically marginalized vulnerable groups, leaving gaps in vaccination coverage that were sustained across pivotal periods of disease spread. There was the development of counterfeited pharmaceuticals during the crisis, which was especially high in areas where regulation was poor, leading to preventable deaths and eroding confidence in healthcare systems. The integration of blockchain technology, the Internet of Things, and digital health platforms presents promising opportunities to improve pharmaceutical traceability, and the pilot implementations of the technologies result in significant decreases in the rate of counterfeit drug penetration and the effectiveness of the supply chain. But there are serious obstacles to the implementation of serialization in resource-constrained environments, such as poor infrastructure, prohibitive costs of implementation, and large digital divides that threaten to contribute to any existing health inequities. Smaller pharmaceutical companies have a greater challenge than larger multinational corporations, which begs the question of how this may affect the affordability and accessibility of medicine in the low income markets. Implementing strategic plans should be done with attention to the accessibility, interoperability, and capacity building to ensure that serialization systems do not advance equity purposes, but instead, strengthen the gaps. The mechanisms of international cooperation, collaboration between the government and industry, and technological transfer can be critical in creating the infrastructure of a sustainable serialization that will be able to provide the routine distribution of pharmaceutical products and react quickly to an emergency situation in the future.
The Role of Artificial Intelligence in Cybersecurity: A Deep Learning Approach to Securing Digital Infrastructure
The goal of the research is to answer how deep learning techniques affect the accuracy of threat detection, threat response automation and the security of the digital infrastructure in general. These days, security measures can hardly defend against increasingly sophisticated cyber-attacks, including ransomware, phishing and even zero-day vulnerabilities. The rapid digitalization of modern-day industries, the increasing complexities of cyber threats have made the utilization of AI in cybersecurity imperative. As a set of traditional measures is unable to defend against these kinds of attacks, new emerging technologies such as deep learning are set to make an impact. AI and deep learning in particular, is set to make strides in the active, preventive and mitigative measures of cyber-attack threats. These goals achieved by employing a systematic literature review hybridized with deep learning model experimentation concerning the field of cybersecurity. Recent developments in the integration of AI in security measures reviewed, along with numerous neural networks CNNs, RNNs and GANs. It is implementing an experimental design and evaluation with a benchmark dataset pertaining to cybersecurity. The conclusion highlights that deep learning greatly improves threat detection mechanisms through automation in cybersecurity. Compared to conventional security systems, machine learning models offer higher mastery in identifying anomalies and produce fewer false alarms. There are still some obstacles like the complexity of computations, opposing threats and privacy issues. These findings suggest that AI-powered cybersecurity solutions greatly enhance the protection of national critical assets and infrastructures in a rapidly changing cyber environment.
Containerized AutoML Services in Life Sciences for Omnichannel Analytics
Containerized Automated Machine Learning (AutoML) services are transforming omnichannel analytics in life sciences by enabling scalable, reproducible, and interoperable machine learning pipelines that unify data from diverse biomedical and operational sources. This review examines how containerization, through technologies such as Docker for environment encapsulation and Kubernetes for orchestration, supports the deployment of AutoML across distributed data environments, including clinical, genomic, and pharmacological channels. By decoupling model training from infrastructure, containerized AutoML systems facilitate cross-platform consistency and seamless integration of structured and unstructured data streams. Empirical evidence demonstrates that these systems achieve superior scalability, reproducibility, and interpretability compared with traditional monolithic approaches. Persistent challenges remain, particularly in ensuring domain-specific interpretability, safeguarding patient privacy, and achieving regulatory-grade interoperability. The review concludes with future research directions aimed at advancing adaptability, transparency, and regulatory compliance for omnichannel life-science analytics.
Real-Time AI-Driven Anomaly Detection in TV Ad Impression Logs Using Streaming Big Data Pipelines
The growth of the television advertising ecosystems in complexity and size has gradually increased the importance of real-time monitoring and analysis of ad impression logs. The existing traditional (batch-based) and fixed-point anomaly detection systems are not suitable in the context of the high-velocity, high-volume streaming television data. In this paper, the author introduces an in-depth discussion of the AI-powered anomaly detection algorithms that can be implemented in streams of big data pipelines to provide the accuracy, integrity, and adherence of TV ad impressions. Through the use of more sophisticated machine learning and deep learning algorithms, including autoencoders, LSTMs, and ensemble algorithms that are integrated into scalable frameworks, e.g., Apache Kafka and Flink, real-time insights may be obtained at the lowest possible latency. The paper also examines the nature of anomalies that are often witnessed in the ad impression log, deploying model strategies, and performance of the system based on measures like precision, recall, latency, and throughput. The paper is concluded with a discussion of the existing issues, such as data heterogeneity, concept drift, and explainability, and the forecast of future advances in federated learning, edge AI, and hybrid detection models. This convergent strategy illustrates how AI and streaming data systems have a transformative potential in improving the performance (both operational and financial) of the TV advertising sector.
AI-Driven Computer System Validation for Next-Gen GxP Compliance
As the field of artificial intelligence (AI) quickly infiltrates the life sciences and pharmaceutical industry, its disruptive quality in Good x Practice (GxP) compliance is increasingly becoming a plausible development particularly in the area of Computer System Validation (CSV). The traditional validation procedures that are rather inert, paper-based, and manual were not applicable in the world of agile development cycles, SaaS applications, and continuous system improvement. AI-Based CSV offers real-time risk evaluation, dynamic, intelligent automation, which is more efficient, precise, and in line with the regulations. The paper will look at the history of validation practices, the role of AI technologies, machine learning, and natural language processing, and the regulatory framework that is shifting to accommodate such a shift. It further examines these concerns as model explainability, data integrity, cybersecurity, and lifecycle governance, and offers a strategic outlook of AI as an initial tool for ensuring a continual validation. The paper also outlines the importance of AI in the next-generation GxP compliance and ensures data integrity in a more digitised regulatory environment in depth.
Advanced Timing Closure Techniques in Full Chip Integration of Adaptive SoCs
Meeting strict timing specifications in full-chip integration is becoming increasingly challenging with the growing adoption of adaptive system-on-chip (SoC) architectures. SoCs that incorporate programmable logic, Dynamic Voltage and Frequency Scaling (DVFS), and heterogeneous compute instances will require more advanced analysis methods as they are operating within the picosecond range. Such SoCs featuring programmable logic as well as Dynamic Voltage and Frequency Scaling (DVFS), and heterogeneous compute instances will need more powerful methods to analyze than the picoseconds range. This paper explores the extended timing closure techniques explicitly applied to full-chip implementations of adaptive SoCs, including Multi-Mode Multi-Corner (MMMC) analysis, hierarchical abstraction, and machine learning-aided path optimization. The issues of the Incremental Design Verification (IDV), Clock Domain Crossings (CDCs), and Advanced Formal Signoff (AFS) are given special concerns. Real-time design feedback is integrated with the capability of AI-based timing anomaly detection. It also highlights the application of physical-aware timing ECOs (Engineering Change Orders) and accumulated P&R flows as an example of improved closure efficacy. By using comprehensive case studies and empirical measurements, it shows that the provisional tools and techniques facilitate significant improvement of timing convergence, accuracy, performance predictability, and preparation of post-silicon validation. The findings are indicative of scalable and adaptive timing techniques that are increasingly gaining relevance to future SoC design, where timing design closure will have to assimilate both the static and dynamic system responses.
Study of the Influence of Different Welding Parameters on the Mechanical behaviour of Metallic Structures
Assessing the integrity of a structure involves demonstrating its ability to perform its mechanical functions under all normal or accidental stresses throughout its service life. In the gas safety sector, for major installations such as tanks or primary circuits, deterioration can be identified in several areas, such as cracks caused during the welding process. The aim is therefore to prove the mechanical strength of the structure in the event of this type of failure. We also aim to modify the strength of a structure when a crack is present and anomalies have been identified during an inspection. In this context, fracture mechanics theory provides the necessary tools to study cracked elements. The objective is to establish a fracture criterion that will make it possible to determine in advance the stress margins in normal or accident situations during operation. It is necessary to specifically characterise each type of fracture.
Frontend Performance Optimization of Large-Scale E-commerce Landing Pages: A Comprehensive Analysis
The frontend performance characteristics of large-scale e-commerce landing pages significantly impact user engagement, conversion rates, and revenue generation, yet systematic client-side optimization approaches remain underexplored in contemporary frontend development literature. This article examines the implementation of comprehensive frontend performance optimization strategies for high-traffic retail landing pages, demonstrating how systematic application of critical rendering path optimization, advanced client-side resource loading patterns, and modern browser capabilities can achieve substantial improvements in Core Web Vitals metrics and user experience indicators. The investigation reveals how strategic implementation of client-side rendering optimizations, intelligent frontend resource prioritization mechanisms, and progressive image loading systems enables significant landing page performance improvements in First Contentful Paint metrics and Largest Contentful Paint measurements. Advanced frontend API request optimization and intelligent client-side caching strategies demonstrate a meaningful reduction in network request overhead while maintaining responsive user interfaces. Performance benchmarking indicates that systematic frontend optimization implementations achieve improved Google Lighthouse performance scores while providing smooth user experiences across diverse device capabilities. Beyond technical metrics, organizational impact evaluations reveal that optimized frontend implementations generate notable improvement in user engagement rates and conversion velocity compared to baseline implementations. The article establishes that frontend performance optimization represents not merely a technical enhancement but a strategic business enabler that directly influences user experience and competitive positioning within the e-commerce ecosystem.
Risk-Based Alerting: Revolutionizing Cybersecurity Operations through Intelligent Threat Prioritization
There is a growing challenge on the cybersecurity scene because conventional security monitoring systems generate excessive alert levels that are beyond the analysis capability of humans. The alert fatigue poses a security lapse where real security threats slip in unnoticed, and security teams are overwhelmed by floods of notifications. Risk-Based Approach is a radical remedy as it moves past the volume-based to the intelligence-based security operations, contextual scoring systems of the security events are based on the potential impact and the probability of happening, where the security events are ranked by priority. The technology combines various sources of data, such as network traffic logs, authentication logs, endpoint behavior logs, and threat intelligence feeds, to create a complete threat context. Companies that deploy Risk-Based Alerting frameworks report significant operational gains, such as the reduction of false positives by a significant margin, the improvement of Mean Time to Detect critical threats, the improvement of Mean Time to Respond, and yielding significant returns to investment. Its architecture has advanced correlation engines that have machine learning functionality, which refines risk models in real time with historical incident data and new patterns of threats. Its implementation will involve proper planning that will include the assessment of the assets inventory, establishing the baseline, stakeholder interactions, and extensive training of security analysts. The quantifiable advantages go beyond direct proportionality savings into next-generation operational advantages to lower costs of breach, higher compliance posture, greater business continuity, and higher levels of analyst job satisfaction with lower turnover. Risk-Based Alerting is a paradigm shift to smart and sustainable cybersecurity operations that offer adaptive basics required to effectively safeguard against dynamic cyber threats.
When Middleware Heals Itself: The Rise of Autonomous Integration
This article explores the emergence of autonomous self-healing middleware as a transformative approach to enterprise integration resilience. Traditional middleware systems require manual intervention during failures, resulting in service disruptions, SLA violations, and business impact. Autonomous integration middleware addresses these limitations through sophisticated capabilities that detect anomalies, diagnose root causes, and implement remediation strategies without human intervention. The theoretical foundations span event-driven architectures, AI-powered anomaly detection, policy-based orchestration frameworks, distributed transaction management, and resilience patterns. Implementation architectures incorporate layered monitoring approaches, intelligent retry algorithms with alternate path selection, learning-based root cause analysis, and continuous feedback loops. Case studies across e-commerce, healthcare, and banking demonstrate significant improvements in service continuity, while future directions include predictive remediation, cross-system coordination, and ethical governance frameworks. The article establishes that autonomous integration represents not merely a technical evolution but a socio-technical transformation requiring balanced advancement across technical capabilities, organizational practices, and responsible implementation considerations.
Germination and Adaptive Responses of Moringa oleifera to Drought Stress: Insights into Morphological and Biochemical Mechanisms Under PEG-6000-Induced Conditions.
Moringa oleifera, a resilient species suited to arid and semi-tropical climates, was examined for its response to water stress induced by polyethylene glycol (PEG-6000) during germination and early growth. This study assessed the impact of different PEG-6000 concentrations (0, 4, 8, and 12 g/L) on germination rates, morphological traits, and biochemical parameters of the seedlings. Seeds showed remarkable germination capacity, reaching 100% at 12 g/L PEG-6000 concentration. However, water stress negatively affected plant development, reducing shoot length from 18 cm in the controls to 14 cm at the highest concentration, with similar declines in leaf area, leaf number, and collar diameter. Biochemical analyses indicated decreased levels of chlorophyll, soluble sugars, proline (down to 0.014 mg/mL at 12 g/L), and amino acids under stress conditions. These results suggest that while M. oleifera seeds are highly tolerant to water stress during germination, subsequent growth and biochemical functions are compromised, providing insights into their adaptability for cultivation in drought-prone areas.
Marine Bastadins as Potent ACAT1 Inhibitors: Integrated Molecular Docking and ADMET Profiling for Anticancer Drug Discovery
Acyl-CoA: cholesterol acyltransferase 1 (ACAT1) is a key enzyme in lipid homeostasis, catalyzing the esterification of cholesterol, a process closely associated with the metabolic reprogramming that supports tumor progression. In this study, twenty-two Bastadins, bromotyrosine-derived macrocyclic metabolites isolated from the marine sponge Ianthella basta, were evaluated as potential human ACAT1 inhibitors using molecular docking, ADME analysis, and toxicity predictions. These compounds were selected based on prior reports of their experimental ACAT1 inhibitory activity, underscoring their potential as lead scaffolds for enzyme modulation. Molecular docking was performed using PyRx (v0.8), and ADMET properties were evaluated with ADMETlab 3.0 and ProTox 3.0. Among the compounds, Bastadins 8, 10, 13, and 19 demonstrated the strongest affinities toward ACAT1 (-11.0 to -11.5 kcal/mol). Bastadin 13 exhibited the most stable complex formation (-11.5 kcal/mol), involving strong hydrogen bonds as well as π-π T-shaped and π-alkyl interactions with key residues, including His460 and Phe384. Similarly, Bastadin 19 displayed a high interaction energy (-11.4 kcal/mol), engaging in stable polar and hydrophobic contacts with residues such as His460, Trp420, Asn421, Phe254, and Tyr417. ADMET predictions indicated that both Bastadins 13 and 19 possess favorable pharmacokinetic properties, including enhanced intestinal absorption, metabolic stability, and low predicted toxicity. Overall, these compounds emerge as the most promising ACAT1 inhibitors, combining strong binding, robust interactions with critical residues, and favorable ADMET characteristics, offering a rational framework for the development of novel anticancer agents, and providing a solid basis for further experimental validation and optimization in targeted cancer therapy
A review on Adaptive and Hybrid Regulators Integration into UPQC for Optimal Power Quality with Grid Connected Renewable Sources and EV Charging Stations
In Renewable energy sources, primarily PV plants and wind farms, are increasingly replacing conventional power generation methods. To further reduce environmental pollution, traditional IC engine vehicles are being replaced by zero-emission EVs. The RSMs and charging circuits used in EVs rely on high-power electronic switches, which operate at high switching frequencies. These switching operations, along with rapid variations in power generation, introduce several power quality issues into the grid—most notably voltage sags, swells, and harmonics. Such disturbances can damage both grid-connected devices and power sources. To address these challenges, this paper proposes the integration of a UPQC between the grid-connected RSMs and EV charging stations. The UPQC controllers are enhanced using advanced control strategies featuring adaptive and hybrid regulators to ensure optimal power quality. The paper presents a detailed discussion on the proposed system’s design and the structure of the various controller regulators. This review explores the application of adaptive and hybrid regulators in UPQC systems to enhance their effectiveness in maintaining optimal PQ. It critically analyses various control methodologies, their adaptability to real-time grid conditions, and their coordination with renewable and EV-based systems. A comparative assessment is provided based on performance metrics such as response time, harmonic mitigation, voltage regulation, and system stability.
Integrating ai into enterprise java applications for secure high performance and scalable systems
The integration of Artificial Intelligence (AI) into enterprise Java applications is rapidly emerging as a transformative approach to building intelligent, secure, and scalable systems. Traditional enterprise applications, though robust, often lack the adaptive capabilities required to handle modern workloads such as predictive analytics, anomaly detection, and intelligent automation. This paper explores a framework for embedding AI within enterprise Java environments by leveraging contemporary machine learning libraries, cloud-native deployments, and microservice architectures. Emphasis is placed on achieving high performance and scalability while addressing critical security challenges, including data privacy, model integrity, and secure inference. Through a proposed reference architecture and a case study implementation, the paper evaluates performance benchmarks, security considerations, and scalability trade-offs. The findings highlight that AI-enabled enterprise Java applications can provide significant improvements in system intelligence and efficiency, provided that integration is carefully designed with attention to performance optimization and security governance.
Human-Centric Passwordless Authentication: Beyond Technology to Workforce Transformation
The modern enterprise authentication environment is confronted with unprecedented challenges derived from password-based security designs that introduce systemic risks with enormous operating expenses. Conventional password infrastructures create cascading security breaches via credential compromise events, while at the same time wasting organizational resources through support overhead and productivity degradation. The shift to passwordless authentication marks a seismic paradigm change toward human-oriented security design that focuses on user workflow incorporation as opposed to technology-driven deployments. Organizations need to implement persona-based authentication strategies that identify heterogeneous workforce needs among knowledge workers, frontline workers, and privileged users. Passwordless deployments need to be based on advanced collaborative frameworks under which Human Resources, Information Technology, and business unit leadership collaborate in strategic partnership. Authentication journey mapping methods allow for evidence-based technology selection that aligns specific authentication techniques with tested workflow requirements instead of forcing generic solutions on heterogeneous populations. Implementation failures are always due to poor lifecycle planning, especially in new employee onboarding and authenticator recovery situations. Phased deployment approaches with robust communication campaigns yield better adoption with reduced operational impact. The future of enterprise authentication requires dynamic multi-authenticator designs with contextual risk-based policies that facilitate transparent user experiences for everyday operations but dynamically increase security controls for high-risk situations.