This study presents a numerical analysis of the deflection of a reinforced concrete building founded on sandy clay soil in the city of Douala. To conduct this research, two numerical simulations were performed. The first consisted of a simulation in MATLAB based on mathematical and mechanical models of the building and the soil. The second consisted of a finite element simulation of the building and the soil using COMSOL Multiphysics. The result obtained for the structural deflection after the MATLAB simulation was 0.0026 mm for elastic soil and 4.1 nanometers when the soil plasticity was taken into account. These results show that the rigidity of the structure and the elastoplastic nature of certain sandy clay soils play a very significant role in the stability of the building. Furthermore, the numerical simulation in COMSOL Multiphysics yielded a deflection of 2.2 mm for both elastic and elastoplastic soils. This is explained by the fact that, under steady-state conditions, the structure settles under its self-weight and live load, which, combined with the rigidity of the structure, resists large displacements and considerably reduces deflection. In addition, during the COMSOL simulation, the threshold stresses of the building were 0.16 MPa for elastic soil and 0.20 MPa for elastoplastic soil. These values represent limits beyond which structural deflection could cause damage to the frame.

Despite the transformative potential of artificial intelligence (AI) across multiple sectors, its deployment generates significant risks for human rights, exacerbates structural inequality, and produces outcomes of limited predictability. These dynamics make the development of robust ethical and legal governance frameworks both urgent and complex. Uzbekistan adopted Ethical Rules for the development, deployment, and use of AI-based solutions in 2026, marking an important step in national regulatory development. Yet questions of implementation, understood here as the formal incorporation of normative principles into binding legal instruments and the establishment of institutional enforcement mechanisms, remain largely unresolved. This paper employs a comparative legal analysis method, grounded in soft law theory and legal transplant theory, to examine how Uzbekistan's emerging AI governance framework aligns with international standards, including UNESCO's 2021 Recommendations, the OECD Principles on AI, and the European Union's Artificial Intelligence Act. The analysis identifies six structural gaps in Uzbekistan's current framework and proposes a pathway from declaratory principles toward enforceable, institutionally supported standards. The paper contributes to the emerging literature on AI governance in transitional legal systems and to broader debates on the conditions under which soft law instruments can effectively constrain technological risk.

The high growth rate in digital businesses has heightened issues around the world about the environmental footprint of information technology (IT) infrastructure, especially as the data centres, cloud services, and high-performance computing workloads are becoming a large contributor to increasing energy use and carbon emissions. Although the topic of sustainability has gained increased regulatory and corporate attention, there remains no set, data-based approaches to measure the ecological performance of the organizations with regard to the digital operations. The paper presents a holistic analytical model that combines sustainable IT infrastructure indicators, cloud resource optimization policy, and corporate sustainability key performance indicators (KPIs). Based on a mixed-methodology, which integrates empirical data related to industry benchmarking, cloud provider sustainability reporting, and already existing environmental reporting criteria, the framework provides a systematic approach to connect micro-level IT energy telemetry (including power usage effectiveness (PUE), server utilization rates, virtualization efficiency, and carbon intensity of workloads) to macro-level sustainability results, including reductions in greenhouse gas (GHG) emissions, integration of renewable energy, energy cost reductions, and improvement in ESG performance. Quantitative modeling based on real world data demonstrates how green data analytics can be used to aid in carbon-conscious scheduling, predict IT energy demand and optimizing allocation of cloud resources. The outcomes indicate that the incorporation of IT operating data with high-end analytics can improve greatly the level of transparency, the measurement precision, and the environmental responsibility of digital enterprises. The originality of the study is the cross-layered mapping of technical IT metrics on organizational sustainability KPIs, which can be replicated to achieve the net-zero digital transformation. The suggested framework offers practical information to businesses, policymakers, and cloud service providers interested in realizing sustainability goals in fast-changing digital realms.

The current financial regulatory frameworks, specifically the ‘Fundamental Review of the Trading Book’ (FRTB), require almost real-time disclosure of Expected Shortfall and Value at Risk measures of heterogeneous portfolios of equities, fixed-income securities, and complex over-the-counter derivatives. Switching from batch-based reporting to continuous intraday risk reporting poses significant computational challenges, especially when dealing with portfolios of over 100,000 positions with nonlinear sensitivities. This article introduces the Decentralized Risk Computation Grid (DRCG), a cloud-native design that uses stateful orchestration and sensitivity-aware sharding to decentralize portfolio decomposition to elastic worker clusters. In contrast to the classical stateless parallel models, the DRCG uses a warm-cache worker pattern that avoids unnecessary market data loading cycles, and tail latency is significantly lower, and yet deterministic recovery is achieved in the event of node failures. The framework is horizontally scalable, with sensitivity-based portfolio decomposition formalized mathematically and Byzantine fault-tolerance principles, without affecting audit compliance or data consistency. Empirical confirmation in distributed settings shows that state-conscious orchestration offers the resilience required in systemic risk surveillance in times of high market volatility and stress.

Modern artificial intelligence and high-performance computing systems face critical bottlenecks in inter-chip communication as computational capabilities continue to advance beyond the limits of traditional copper-based interconnects and board-edge optical modules. Co-packaged optics emerges as a transformative solution by integrating photonic components directly within processor packages, dramatically reducing electrical path lengths and enabling unprecedented bandwidth densities while lowering energy consumption per transmitted bit. This article presents a comprehensive hybrid integration architecture that combines two-and-a-half-dimensional and three-dimensional packaging techniques to co-locate photonic chiplets with compute dies on silicon interposers. The article leverages micro-ring resonator-based wavelength division multiplexing on silicon photonic platforms to achieve high aggregate throughput while maintaining compatibility with advanced logic manufacturing processes. Through detailed co-design of packaging structures, electrical-optical interfaces, thermal management systems, and control algorithms, the architecture addresses key technical challenges that have historically impeded photonic integration efforts. Validation through multi-level simulations and physical prototypes demonstrates feasibility for rack-scale optical connectivity meeting the demanding requirements of distributed machine learning workloads. The article examines critical parameters that affect the timing of commercial adoption, such as manufacturing yield, serviceability, the need for standardization, and economic trade-offs. Results indicate that hybrid co-packaged optics architectures provide viable pathways for sustaining bandwidth scaling in next-generation data center fabrics serving artificial intelligence applications.

Digital Twin technology has become one of the key component of Industry 4.0 and smart manufacturing systems. The concept enables real-time synchronization between physical industrial assets and their virtual representations. This research investigates the architecture, mathematical models, and predictive algorithms used in Digital Twin systems for industrial automation. A comprehensive mathematical framework based on dynamic system modeling, data-driven analytics, and predictive maintenance algorithms is proposed. Experimental analysis demonstrates that the implementation of Digital Twin technology improves monitoring accuracy, reduces downtime, and increases production efficiency. The proposed system integrates sensor networks, industrial IoT, cloud computing, and machine learning algorithms to optimize industrial operations.

This article presents a systematic analysis of the geological structure and hydrogeological characteristics of the Isfara underground water deposit, which constitutes an integral part of the Fergana hydrogeological basin. The study provides a detailed examination of the Quaternary period sediments of the region, specifically the stratigraphy and lithological composition of the Sokh, Tashkent, Mirzachol, and Syrdarya complexes. The results of long-term scientific investigations concerning the hydrodynamic regime of the Isfara River alluvial fan, the recharge sources of groundwater, and the discharge balance are comprehensively summarized.

The article examines Human-in-the-Loop (HITL) architectures for automated decision-making systems deployed in enterprise operations and regulated domains. The topic’s relevance follows from the rapid adoption of high-capacity models alongside stricter requirements for accountability, traceability, explainability, and risk control. The paper’s novelty lies in formalizing a taxonomy of intervention modes and linking engineering choices to operational metrics rather than to model accuracy alone. The study identifies four recurring intervention patterns—pre-emptive review, confidence-based routing, asynchronous audit, and exception handling—and specifies their placement within the decision pipeline. The analytical basis relies on a comparative synthesis of documented production deployments in finance, healthcare, and corporate operations, focusing on throughput, decision quality, latency, and per-case processing cost. The results indicate a non-linear trade-off between automation rate and decision quality and show that optimal thresholding depends on risk asymmetry and governance constraints. Practical recommendations address uncertainty calibration, reviewer interface design, and closed-loop feedback capture for continuous improvement. The overall objective is to provide a deployment-oriented framework for selecting HITL patterns and tuning escalation thresholds in high-stakes settings.

This article proposes an improved regenerator design for cleaning fibrous material. A drawback of existing analogues is that when the material is conveyed by an air stream, a portion of it passes in transit to the outlet pipe, which leads to a decrease in cleaning efficiency and a large amount of cotton pieces entering the waste. In the proposed device, an auger installed in the upper part of the main saw drum forcibly moves the material along the drum's axis, directing it to the re-cleaning zone and ensuring the regeneration process.

During the past couple of years, rapid development of edge computing and cloud technologies has allowed for the implementation of hybrid models that offload computation to edge devices as well as cloud platforms. This article explores the design decisions in the implementation of hybrid models, specifically focusing on offloading processing to the cloud, and addresses the necessary trade-offs between latency and privacy. We begin by contrasting edge and cloud computing, highlighting the advantages of hybrid systems in enhancing scalability, real-time processing, and flexibility. Significant architectural concerns, such as model partitioning and offloading rules, are addressed in the context of the dynamic nature of edge and cloud environments. Latency is a fundamental concern that influences the effectiveness of hybrid systems, especially in applications involving real-time processing. We explore how to minimize latency through edge caching, adaptive algorithms, and local computation for enhanced system performance. Privacy becomes an issue when handling sensitive data on the edge and the cloud. In this paper, we present privacy-preserving mechanisms, such as data anonymization, encryption, and federated learning, to secure user information while leveraging the computational power of the cloud. By performance metric evaluation, such as latency, precision, and scalability, we compare hybrid model deployment with cloud-only and edge-only deployment. We concluded the paper by outlining challenges experienced in hybrid deployment, including network limitations and model complexity, and introduce future work ideas on further enhancing edge to cloud computation balance. This paper offers a thorough examination of deploying hybrid models and offers real-world architectural advice on how to maximize system performance without exacerbating latency and privacy concerns.