Vol. 8 No. 05 (2026): Volume 08 Issue 05

Modern passenger vehicles rely on distributed electronic control architectures in which the Engine Control Module (ECM) and Body Control Module (BCM) play central roles in powertrain management, ignition authorization, and body-function coordination. Understanding the internal electrical organization of these modules is essential for diagnostic engineering, electronic system integration, and development of auxiliary vehicle-control solutions.

This study presents a comparative structural analysis of ECM and BCM systems implemented in Chevrolet Tracker and Chevrolet Onix vehicles equipped with the common 1.2 L turbocharged engine platform. The research was conducted through detailed connector-level and pin-level investigation of ECM connector groups X1 and X2 and BCM connector groups X1–X8. Particular attention was given to ignition-related signals, communication pathways, relay-control circuits, and body-condition monitoring functions relevant to engine-start authorization.

The results demonstrate a high degree of architectural similarity between both vehicle platforms, confirming the use of a unified General Motors electronic framework. At the same time, several model-specific differences were identified, including clutch-position sensing, brake-signal architecture, selective sensor-reference redistribution, and BCM adaptation associated with rear body-function implementation. These findings confirm the feasibility of developing unified auxiliary control solutions while accounting for limited model-dependent electrical variations.

Pharmaceutical supply chains represent one of the most operationally demanding environments for artificial intelligence deployment, requiring continuous demand forecasting, expiration tracking, procurement automation, and multi-location coordination under conditions that permit minimal margin for error. This paper presents the design, implementation, and real-world deployment outcomes of Restocks AI, an autonomous multi-agent AI platform engineered to replace manual pharmaceutical supply chain operations at national scale. The system integrates predictive inventory engines, multi-agent decisioning frameworks, distributed task-processing infrastructure, and real-time data pipelines to autonomously manage end-to-end supply chain operations across geographically dispersed pharmacy networks. The platform was deployed across two pharmaceutical organizations in Uzbekistan spanning a combined 427 pharmacy locations across all 14 administrative regions, including one of the country's largest private pharmacy networks (150+ locations) and the largest government-operated pharmaceutical institution (277 locations). Deployment results demonstrated 70% reduction in manual operational workload, 30% acceleration of replenishment cycles, 65% reduction in pharmaceutical expiration waste, 70% reduction in stockout incidents, and a combined financial impact of 812 billion Uzbek sum. These results demonstrate that autonomous multi-agent AI systems can effectively manage pharmaceutical supply chains at national scale, offering a replicable architecture with significant implications for healthcare infrastructure, public health outcomes, and the broader application of multi-agent AI in critical industries.

The rapid evolution of healthcare digitalization has accelerated the development of intelligent human digital twin ecosystems that integrate artificial intelligence, Internet of Things (IoT) infrastructures, and cyber-physical healthcare systems. Human digital twin architectures represent virtual replicas of human physiological, behavioral, and clinical conditions capable of supporting predictive analytics, adaptive treatment planning, rehabilitation optimization, and personalized medicine. This research investigates advanced AI-driven human digital twin architectures that combine smart IoT networks with cyber-physical intelligence to enhance healthcare monitoring, clinical decision-making, and adaptive rehabilitation systems. The study critically synthesizes existing research related to digital twin healthcare infrastructures, sensor-enabled medical environments, AI-assisted medical modeling, and cyber-physical healthcare engineering. A research-oriented methodological framework is proposed to explain the interaction among data acquisition layers, AI processing engines, edge-cloud coordination, and adaptive rehabilitation analytics modules. The paper further evaluates the operational capabilities, limitations, and implementation barriers of intelligent digital twin healthcare ecosystems. Findings indicate that the integration of generative artificial intelligence, sensor fusion, cloud-edge orchestration, and cyber-physical intelligence significantly improves predictive treatment accuracy, remote rehabilitation monitoring, patient-specific simulation, and healthcare system responsiveness. However, challenges related to data interoperability, cybersecurity, privacy governance, computational scalability, and ethical accountability remain major concerns for practical implementation. The study contributes a comprehensive conceptual and analytical framework for future healthcare digital twin systems while emphasizing the need for secure, scalable, and intelligent architectures capable of supporting next-generation precision medicine and adaptive rehabilitation environments.

The modern nail service industry is increasingly focused on safety, efficiency, and preservation of the natural nail structure. Traditional material removal techniques often involve aggressive filing, which may lead to damage of the nail plate, including thinning, sensitivity, and reduced adhesion of subsequent coatings.

This article presents the Zero-Base Safe Removal™ method — a proprietary system of controlled material removal designed to eliminate direct mechanical contact between the drill bit and the natural nail plate. The method is based on the principle of preserving a residual layer of artificial material, which functions as a protective and stabilizing element.

The paper outlines the conceptual framework, scientific rationale, technical principles, and practical application of the method, emphasizing its role in improving safety standards and professional practices within the nail industry.

Over-the-air (OTA) software updates are essential in modern software-defined vehicles, enabling continuous feature delivery, security patching, and system optimization [1], [3]. However, ensuring the reliability of OTA workflows in automotive embedded environments is challenging due to intermittent connectivity, power-state transitions, and stringent cybersecurity and safety requirements [5], [6], [10], [11]. This paper presents a reliability-focused design for OTA software update systems on Android-based automotive embedded platforms, emphasizing failure handling, recovery mechanisms, system integrity validation, and staged orchestration. The proposed architecture incorporates checkpoint-based progress tracking, modular update delivery, cryptographic verification, and deterministic recovery workflows. The design specifically addresses interruptions such as network disruptions, process restarts, and suspend-to-RAM (STR) events [3], [6], [7], [9], [12]. This framework provides a practical approach for constructing resilient OTA workflows that utilize both platform-level update support and application-layer recovery logic.

Department of Electrical and Electronics Engineering, Federal University of Technology, Akure, This research assessed persistent unavailability electrical energy problem of Local Government Service Commission (LGSC) Alagbaka in Akure, Ondo State, Data were collected from Benin Electricity Distribution Company (BEDC), Akure and LGSC log books Alagbaka. Frequency and duration of electricity supply were obtained from the BEDC and LGSC log books. Results revealed that the existing public utility and existing generators sources feed the LGSC; the LVP and associated CBs of the LGSC network are functioning very well; the average public supply is averagely 4hrs per each working day as against the required 8hrs per day amounting to 50% availability through the year; total connected load is 113.4 kVA. Conclusively, a proposed generator or inverter capacity is designed to 79.4 kVA considering appropriate standards of demand load, diversity factor and future expansion; solar inverter is recommended for the LGSC operation due to its overall benefits existing literature established in this research; and 80 kVA solar inverter is recommended for the LGSC operation.  Load survey should be carried out before sizing of a generator/Inverter for any edifice. Appropriate standards should be considered in sizing a power supply unit source.

The rapid expansion of e-commerce ecosystems has intensified the need for highly responsive logistics systems capable of minimizing delivery delays, inventory costs, and operational inefficiencies. Cross-docking has emerged as a strategic logistics mechanism that reduces warehousing duration by enabling direct transfer of products from inbound transportation to outbound distribution channels. However, increasing order variability, fragmented transportation networks, and fulfillment complexity create significant operational challenges for cross-docking implementation within e-commerce supply chains. This research develops an intelligent logistics optimization model for cross-docking operations aimed at improving goods distribution efficiency across e-commerce fulfillment centers. The study integrates logistics performance theories, data envelopment analysis principles, scheduling optimization approaches, and multimodal transportation coordination models to construct a scalable framework for intelligent distribution management. The research synthesizes previous studies on port logistics, warehouse benchmarking, transportation scheduling, operational efficiency, and supply chain performance to identify critical optimization variables affecting cross-docking performance. The proposed model incorporates dynamic scheduling, real-time routing coordination, dock synchronization, transportation resource allocation, and predictive demand integration. Findings indicate that intelligent cross-docking systems significantly reduce handling time, transportation redundancy, and fulfillment delays while enhancing throughput capacity and supply chain responsiveness. The study further demonstrates that the integration of decision-support systems and efficiency benchmarking methodologies can substantially improve operational resilience in high-volume e-commerce environments. The research contributes theoretically by extending logistics optimization frameworks into digitally integrated fulfillment systems and practically by proposing an operational model suitable for modern e-commerce distribution infrastructures.

The article is dedicated to the transformation of dimensional inspection in precision manufacturing through the integration of machine vision systems and coordinate measuring machines. Relevance is determined by increasing geometric complexity of components and the need to maintain micron-level accuracy under high production rates. The work describes the structural reorganization of inspection processes into distributed measurement systems where sensing, validation, and control are interconnected. Special attention is paid to data fusion mechanisms, calibration strategies, and the redistribution of measurement uncertainty across heterogeneous sensing layers. The work sets itself the task of explaining how integrated inspection architectures improve both efficiency and measurement reliability. Analytical review and conceptual interpretation of recent scientific studies are used to solve it. Such sources have been studied in terms of measurement architectures, in-line metrology, optical coordinate systems, and uncertainty propagation models. The conclusion shows that integration does not merge technologies into a single method but restructures them into a hierarchical measurement infrastructure combining speed, coverage, and traceable accuracy. The article will be useful for researchers and engineers working with advanced inspection systems in precision manufacturing environments.

The aim is to reduce the energy consumption of nodes and maximise the lifetime of the sensor network. The original LEACH (Low Energy Adaptive Clustering Hierarchy) protocol selects the head cluster based on random number generation and does not consider the residual energy of the nodes. In this paper, we propose a modified head cluster selection algorithm and cluster formation algorithm. Cluster formation is based on the nearest neighbour distance algorithm, which is an improvement over the k-means algorithm for forming the clustering structure. The simulation results show that the proposed protocol has improved the occurrence of nodes in a non-working state, the percentage of alive nodes, the residual energy, and the throughput of the sensor network.

The article examines an architectural model for securing microservice systems in a Kubernetes environment, integrating mechanisms for federated authentication, secrets management, network isolation, and secure data evolution, leveraging OAuth 2.1, PKCE, Keycloak, Vault, and LDAP. This is motivated by the increasing attack surface of cloud‑native environments, exacerbated by container ephemerality, flat cluster networking, and fragmented access domains. In such environments, a compromised container can attack other services, steal credentials, and intercept traffic․ The paper thus seeks to develop and validate an integrated architectural approach to secure the Kubernetes microservice ecosystem under the Zero Trust security model․ The main contribution of the article is formalizing an end-to-end security pattern for the IAM domain․ This pattern is formed by combining several technologies (PKCE, mTLS‚ Vault PKI‚ External Secrets Operator‚ and Liquibase) into a single model․ It is concluded that the combination of an API Gateway, Keycloak with LDAP, short-lived secrets, and separated privileges for applications and migrations reduces the risk of token interception, secret leakage, and lateral movement within the cluster at an acceptable infrastructure cost. The article will be useful for IT architects, DevSecOps engineers, information security specialists, and cloud platform developers.

The global retail supply chain ecosystem has been restructuring with the digital transformation driven by demand volatility, geopolitical instability (recent tariff and counter tariff effects), and consumer preference. The U.S. retail faces challenges like persistent inflation squeezing consumer spending, skilled labor shortages, operational inefficiency and supply chain volatility. Traditional retailers are struggling to manage inventory, adapt to shifting consumer demands for omnichannel experiences, and cope with high operational costs and increased security risks. Therefore, the integration of IoT (Internet of Things) sensing, BI (Business Intelligence) analytics, and robotic automation is crucial here in improving U.S. retail supply chain efficiency and strengthen resilience. 

This article focuses on developing framework by integrating IoT sensing, BI analytics, and robotic automation to enhance efficiency and resilience in U.S. retail supply chains. The framework enables retailers with comprehensive knowledgebase on how to capture quality data through IoT devices, transforming streaming signals into predictive and prescriptive insights using BI system, and executing optimized responses autonomously by integrating robotic platforms. This integration will facilitate retail operations in evaluating performance impacts across stock/inventory accuracy, fulfillment speed, downtime reduction, forecast precision, and disruption recovery. The findings demonstrate that integrated IoT–BI–robotics architectures represent a strategic pathway for strengthening U.S. retail competitiveness and supply chain resilience.

The rapid evolution of smart urban ecosystems has intensified the demand for adaptive digital twin frameworks capable of integrating heterogeneous urban data, supporting intelligent analytics, and optimizing real-time infrastructure operations. Conventional smart city systems often struggle with fragmented data architectures, insufficient interoperability, cybersecurity vulnerabilities, and limited scalability across dynamic urban environments. This research proposes a comprehensive adaptive digital twin framework designed to support secure data fusion, intelligent decision-making, and infrastructure optimization within interconnected urban ecosystems. The study synthesizes existing research on digital twins, cyber-physical systems, urban intelligence, cloud-enabled architectures, Internet of Things integration, and secure infrastructure management. The proposed framework incorporates multi-layer data acquisition, edge-cloud intelligence, cybersecurity mechanisms, predictive analytics, and adaptive optimization modules to enable resilient urban management. The methodology combines theoretical modeling, comparative literature synthesis, and architectural analysis to evaluate the functional effectiveness of adaptive digital twins in smart city contexts. Findings indicate that secure digital twin ecosystems significantly improve operational responsiveness, predictive maintenance accuracy, infrastructure efficiency, and urban sustainability while reducing system fragmentation and decision latency. However, challenges associated with interoperability, governance, computational complexity, and ethical data management remain critical barriers to implementation. The study contributes a structured research-driven architecture that advances theoretical and practical understanding of adaptive digital twins for next-generation urban ecosystems.

This article considers the comprehensive evaluation of flight designs used in drum dryers and the justification of a new three-section flight design. For the analysis, straight radial, inclined, 90° right-angled, 120° angled, hook-shaped, two-segment, optimized two-segment, three-segment, inclined-flight, notched/opening and curved/notched flight designs were selected. These designs were chosen on the basis of real technical solutions reported in literature and patent sources and were evaluated according to their ability to lift the material, redistribute it, disperse it into the gas stream, direct it along the drum, intensify heat and mass transfer, and limit aerodynamic resistance. The comprehensive analysis was performed using a heatmap, radar diagram, 3D surface plot and moisture-profile graphs. The results show that simple straight radial and inclined flights can effectively perform some individual functions, but their ability to simultaneously lift, redistribute and disperse the material into the gas flow is limited. The three-segment design and the proposed three-section flight have high compatibility coefficients in zones 2–4 of the drum and are therefore evaluated as the most suitable structural solutions for intensifying the drying process.

The increasing complexity of modern socio-technical and industrial ecosystems has intensified the demand for intelligent, adaptive, and scalable risk governance mechanisms. Traditional risk management approaches are increasingly insufficient in addressing dynamic uncertainties across sectors such as construction, healthcare, finance, and public infrastructure. This research proposes a unified AI-driven framework for multi-sector risk governance that integrates predictive analytics, machine learning, and explainable AI to enhance organizational resilience in complex systems. Building upon prior advancements in artificial intelligence applications across domain-specific risk environments (Abioye et al., 2021), this study synthesizes interdisciplinary literature to develop a cross-sectoral governance architecture. The proposed framework integrates data ingestion layers, predictive modeling engines, risk intelligence modules, and decision governance interfaces to enable real-time risk detection, classification, and mitigation.

Methodologically, this research employs a conceptual system design approach supported by comparative literature synthesis across construction, financial systems, healthcare, and cloud-based infrastructures. The framework emphasizes interoperability, scalability, and transparency through explainable AI mechanisms and adaptive learning models. Findings suggest that unified AI-driven governance significantly enhances predictive accuracy, reduces operational uncertainty, and improves strategic decision-making efficiency. However, limitations include data heterogeneity, model interpretability constraints, and ethical governance challenges. The study contributes to theoretical advancements in AI-enabled risk governance and offers practical implications for multi-sector digital transformation strategies. Future research directions include hybrid human-AI governance models and domain-specific optimization of predictive risk architectures.

The current data warehousing infrastructure heavily depends on the ETL (Extract, Transform, Load) process to deliver timely and accurate information to support analytics and decision-making processes. Nevertheless, conventional ETL scheduling methodologies, such as AutoSys and Linux-based scripting, tend to be rigid, rule-based, and inflexible to handle dynamic workload management, thereby leading to wastage of resources, delays, and inefficiencies. This paper novel study of AutoCortex, an autonomous job orchestration framework which improves ETL scheduling efficiency through intelligent, adaptive and self-optimizing mechanisms.The AutoCortex framework combines Linux shell scripting, AutoSys job scheduling, decision models to facilitate real-time monitoring, predictive scheduling and automated dependency of complex ETL workflows. The AutoCortex framework will utilize historical ETL execution information, system resource utilization and job dependencies to dynamically manage ETL job priorities, optimize execution sequences, and eliminate performance bottlenecks. AutoCortex will also include fault tolerance mechanisms such as automated recovery, alerting and self-healing to enhance system reliability.

This article examines the role of telecommunication technologies in the modern digital era and their impact on social, economic, and technological development. The rapid advancement of digital communication systems, including mobile networks, fiber-optic communication, satellite technologies, and wireless internet, has significantly transformed global connectivity. The study highlights the importance of telecommunication infrastructure in supporting digital transformation, smart technologies, e-commerce, дистанционное education, and healthcare services. Furthermore, the paper discusses current challenges such as cybersecurity threats, network stability, and the need for continuous technological innovation. The research concludes that modern telecommunication technologies are essential for sustainable development and the creation of an interconnected digital society.

Objective. We present an applied machine-learning framework for scheduling cleaning crews across distributed public-restroom fleets, replacing industry-standard fixed-interval and threshold-triggered rules with a predict-then-optimize pipeline that couple’s usage forecasting, a latent dirtiness state model, and travel-aware preemptive dispatch.

Methods. On a calibrated synthetic city of 50 restroom units across five location types — using a non-homogeneous Poisson usage generator with weather and event bursts and a detour-scaled Euclidean travel matrix — we benchmark five machine-learning contenders (seasonal-naive + greedy, LightGBM quantile forecaster + greedy, Cox proportional-hazards + greedy, Cox PH + OR-Tools mini-VRP, and a rolling-horizon variant) against fixed-interval and per-type usage-threshold baselines, plus a perfect-future-usage oracle. All numbers are produced by a single reproducible script (6 seeds, 2-week train / 4-week evaluation, with 95% confidence intervals and paired -tests).

Results. At the nominal crew budget (220 h/wk, 3 crews), LGBM+Greedy reduced hours above threshold per unit per week from 19.4 (FI-12h) to 1.83 — a 91% reduction (, paired) — using only 79% of the budget. A 3-hour oracle matches LGBM+Greedy within noise, indicating that short-horizon usage prediction is saturated at this fleet scale. Cox proportional-hazards variants under-perform the fixed-interval baseline. A generator–model mismatch stress test (Hawkes-style tails,  drift, spill shocks) preserves the ordering.

Conclusion. A simple usage-forecast-driven greedy dispatcher dominates more sophisticated routing-based alternatives at this fleet scale and provides an engineering baseline for a real-world pilot. A complete reproducible Python implementation accompanies this manuscript as supplementary material.