International Journal of Advance Scientific Research
International Journal of Advance Scientific Research ISSN - 2750-1396 Last Submission:- 25th of Every Month Frequency: 12 Issues per Year (Monthly) https://portal.dnb.de/opac.htm https://zdb-katalog.de/title.xhtml?idn=1244864870
Investigating Advanced Computational Planning Methods for Enhancing Execution Quality and Financial Performance
Advanced computational planning methods have emerged as a strategic enabler for improving execution quality and financial performance across technology-driven organizations. The increasing complexity of modern software systems, project management environments, and intelligent decision-support mechanisms has highlighted the limitations of conventional planning approaches that often rely on static resource allocation, sequential decision-making, and isolated performance metrics. Contemporary computational intelligence, cognitive complexity analysis, and aspect-oriented software engineering provide opportunities to design planning models capable of adapting to changing operational requirements while maintaining execution efficiency and cost effectiveness. This study investigates advanced computational planning methods by integrating principles of computational intelligence, cognitive complexity measurement, software modularization, inheritance-based design, and artificial intelligence-supported resource allocation into a unified conceptual research framework. The research adopts a qualitative analytical methodology based exclusively on established literature in computational intelligence and aspect-oriented software engineering. Existing studies are synthesized to examine how computational planning contributes to execution quality through improved modularity, complexity management, intelligent resource scheduling, and adaptive decision-making. Particular attention is given to the relationship between planning quality and financial outcomes, demonstrating how optimized execution reduces project delays, minimizes operational risk, improves resource utilization, and enhances organizational productivity. The study further examines cognitive complexity metrics as an important mechanism for evaluating planning efficiency within software-intensive environments. The role of AI-powered resource allocation is also explored as an emerging extension of computational planning capable of improving project efficiency and cost optimization (Philip, 2024). The findings indicate that organizations employing intelligent computational planning frameworks achieve superior execution consistency through better coordination between architectural design, software modularity, and dynamic resource management. Computational intelligence facilitates predictive planning by incorporating learning mechanisms capable of responding to uncertainty and evolving project conditions. Furthermore, aspect-oriented programming contributes to execution quality by reducing cross-cutting complexity, thereby improving maintainability and supporting scalable planning structures. The research demonstrates that financial performance is influenced not only by operational efficiency but also by the quality of planning decisions that govern software architecture, project scheduling, and organizational resource allocation. The study contributes to the growing body of knowledge by presenting an integrated perspective connecting computational intelligence, software engineering principles, and financial performance within a unified planning framework. The proposed conceptual model provides researchers and practitioners with insights into designing adaptive planning strategies capable of improving execution effectiveness while supporting sustainable organizational performance.
Copyright (c) 2026 Dr. Hassan Ahmed Moussa https://creativecommons.org/licenses/by/4.0
The Role of The Master Plan in Tourism Development: A Case Study of The Kuvasoy City Center and Riverside Area
This study investigates the role of the master plan in promoting regional and sustainable tourism development through the case study of the development master plan for the Kuvasoy city center and riverside area. The research demonstrates the significance of the master plan in functional zoning, the phased development of tourism infrastructure, the coordination of public and private investments, the regeneration of public spaces, and the diversification of the urban economy. Based on the planning indicators presented in the project documentation, the implementation strategy for the period 2026–2035, the functional structure of an 850-ha planning area, an investment portfolio valued at USD 98.7 million, and the expected socio-economic outcomes were comprehensively analyzed. The implementation of the master plan is projected to generate approximately 4,550 permanent jobs and between 960 and 1,630 temporary construction jobs. In addition, the project is expected to contribute approximately USD 48 million directly to the city's gross product and up to USD 94 million to the regional gross product through indirect and induced economic effects. The findings demonstrate that a master plan should not be regarded merely as a spatial scheme for locating tourism facilities but rather as an integrated planning instrument that combines spatial development, investment management, environmental sustainability, and the interests of the local community within a unified strategic framework. Tourism market positioning scenarios further indicate that the annual number of visitors could increase from approximately 18,000 to 70,170 following the successful implementation of the proposed development strategy.
Copyright (c) 2026 Sardorkhon S. Sodiqkhonov https://creativecommons.org/licenses/by/4.0
Pedagogical and Psychological Foundations of Developing Students' Critical Thinking in A Digital Learning Environment
This article examines the pedagogical and psychological foundations underlying the development of critical thinking among students within a digital learning environment. The rapid expansion of digital educational resources, online platforms, and artificial-intelligence-based tools has transformed the conditions under which independent, analytical, and evidence-based thinking is formed. Drawing on research produced within Uzbek pedagogical and psychological science as well as broader international scholarship, the study analyzes the theoretical models of critical thinking, the cognitive and motivational mechanisms that support its formation, and the didactic conditions of the digital classroom that either stimulate or hinder its growth. The authors argue that effective development of critical thinking in digital settings requires a coordinated combination of constructivist teaching strategies, problem-based and project-based instruction, purposeful reflection, and psychologically safe learning conditions. The article concludes with practical recommendations for organizing digital learning activities that strengthen students' capacity for reasoned judgment, evaluation of information, and independent decision-making.
Copyright (c) 2026 Alimova Zarina Yulchibayevna, Mukimova Mohichehra Maksudovna, Mukimova Mohigul Maksudovna https://creativecommons.org/licenses/by/4.0
Smart Digital Modeling and Autonomous Analytics in Evolving Industry 5.0 Operational Frameworks
The transition from Industry 4.0 toward Industry 5.0 represents a significant transformation in industrial operational philosophy, where intelligent automation is increasingly combined with human-centered approaches, adaptive decision-making, and collaborative technological ecosystems. Smart digital modeling and autonomous analytics have emerged as critical capabilities for enabling this transformation by providing organizations with dynamic representations of physical processes, predictive insights, and intelligent operational control. This research examines the conceptual foundations, technological mechanisms, and strategic implications of integrating digital modeling approaches with autonomous analytics within evolving Industry 5.0 operational frameworks. The study adopts a conceptual research methodology based exclusively on the analysis and synthesis of existing literature related to digital twins, cyber-physical systems, Industry 4.0 enablers, big data analytics, and intelligent industrial architectures. The research investigates how smart digital models function as computational representations of industrial assets, processes, and environments, while autonomous analytics enables continuous interpretation of operational data for improved decision-making. The study explores model-driven development approaches, digital twin classifications, industrial simulation methodologies, and data-driven intelligence mechanisms. The findings indicate that smart digital modeling provides organizations with enhanced capabilities for system understanding, process optimization, and predictive operational management. Digital twins enable organizations to simulate industrial behavior, evaluate alternative strategies, and improve resource utilization before implementing changes in real environments (Kritzinger et al., 2018; Jones et al., 2020). Autonomous analytics further strengthens these capabilities by transforming large-scale industrial data into actionable intelligence, supporting adaptive and proactive decision processes (Xu and Duan, 2019). The research highlights that Industry 5.0 operational frameworks require a balanced integration of technological intelligence and human expertise. While autonomous analytics can improve efficiency and accuracy, effective governance, data quality management, cybersecurity protection, and human-centered design remain essential challenges. Digital transformation approaches based on artificial intelligence and digital twinning demonstrate potential for improving project execution, operational coordination, and strategic decision-making (Philip, 2024). This research contributes a conceptual understanding of how smart digital modeling and autonomous analytics can support future industrial ecosystems. The study concludes that Industry 5.0 success depends on developing intelligent operational frameworks where digital models, analytical capabilities, and human collaboration collectively enhance industrial resilience, sustainability, and innovation.
Copyright (c) 2025 Dr. Emmanuel Bangui https://creativecommons.org/licenses/by/4.0
Comparative Evaluation of Mucosal Healing Response Associated with Alternative Implant Components: A Randomized Investigation
The Peri-implant soft tissue integration represents a decisive factor in determining the long-term biological stability and clinical success of implant-supported rehabilitation. The interaction between implant components and surrounding mucosal tissues influences inflammatory regulation, epithelial attachment, connective tissue organization, and the maintenance of a stable peri-implant seal. This research paper presents a comparative evaluation framework for assessing mucosal healing responses associated with alternative implant components through a randomized investigation model. The study is conceptually positioned at the intersection of biomaterial performance evaluation, clinical outcome assessment, and evidence-based implantology. The investigation examines how variations in implant component characteristics influence biological adaptation and soft tissue behavior during the healing phase. The proposed randomized evaluation approach integrates clinical observation parameters with analytical comparison methods to determine differences in peri-implant mucosal responses. The methodology incorporates structured participant allocation, standardized assessment protocols, biological response evaluation, and comparative interpretation of healing outcomes. The research framework emphasizes the importance of controlling variability in implant component selection and evaluating biological responses through reproducible measures. Recent investigations into peri-implant soft tissue behavior demonstrate that abutment materials and component design can influence tissue compatibility and healing characteristics. Ingole et al. (2026) evaluated peri-implant soft tissue responses associated with different abutment materials through a randomized controlled clinical trial, highlighting the relevance of material-dependent biological interactions in implant therapy. The present investigation extends this perspective by developing a broader comparative framework for evaluating alternative implant components and their relationship with mucosal healing. The findings indicate that implant component characteristics may contribute significantly to variations in soft tissue adaptation, with differences observed in healing quality, tissue stability, and inflammatory response patterns. The study highlights the importance of integrating biological considerations into implant component selection rather than relying exclusively on mechanical performance criteria. The research contributes to a more comprehensive understanding of peri-implant tissue behavior and provides a foundation for future investigations involving optimized implant component design, personalized treatment planning, and improved long-term implant outcomes.
Copyright (c) 2026 Dr. Thandiwe Mokoena https://creativecommons.org/licenses/by/4.0
Determination of The Flow Regime in A Wet Dust and Gas Cleaning Apparatus
The article experimentally determines the definition of the flow mode for cleaning dusty and toxic harmful gases generated during industrial technological processes in wet dust collection and gas cleaning devices.
Abdullajonova Mubinakhon Sharabiddin qizi
Copyright (c) 2026 Ergashev Nasimbek Akhmadjonovich, Abdullajonova Mubinakhon Sharabiddin qizi https://creativecommons.org/licenses/by/4.0
Cognitive AI Neural Framework Operating on Distributed Monetary Recordkeeping Infrastructure: Instantaneous Deception Detection, Monetary Hazard Forecasting
The increasing complexity of distributed financial ecosystems has intensified the need for intelligent systems capable of real-time fraud detection, cognitive reasoning, and monetary hazard forecasting. Traditional financial monitoring frameworks rely heavily on rule-based auditing and delayed analytical pipelines, which are insufficient for detecting high-velocity cyber-financial anomalies and adaptive fraud behaviors. This paper proposes a Cognitive AI Neural Framework (CAINF) operating on distributed monetary recordkeeping infrastructure designed to enable instantaneous deception detection and predictive financial risk assessment. The framework integrates deep neural architectures, time-series feature embedding, and distributed ledger-like recordkeeping mechanisms to create a continuous learning environment for financial intelligence. Prior studies demonstrate the effectiveness of deep learning in volatility forecasting (Chen et al., 2023) and ensemble-based predictive models for system-level risk mitigation (Stefenon et al., 2022). Additionally, neural optimization techniques have been applied successfully to financial risk prediction in digital economies (Li et al., 2023), highlighting the feasibility of AI-driven financial reasoning systems. The proposed CAINF extends these works by embedding cognitive inference layers that simulate behavioral anomaly detection, supported by cyber-risk classification frameworks (Curti et al., 2023). Furthermore, it incorporates neuro-adaptive learning principles inspired by cognitive performance and behavioral regulation studies (Alhola & Polo-Kantola, 2007), as well as decision fatigue and restoration theories (Dalton Smith, 2019), to improve model robustness under high-load financial environments. A key component of the framework is its integration with cloud-based accounting intelligence systems, aligning with deep learning-enhanced financial infrastructures for fraud detection and real-time risk prediction (Kodela et al., 2026). This integration enables continuous transactional verification, probabilistic deception scoring, and predictive hazard mapping. The findings synthesized in this research indicate that cognitive AI-based distributed systems significantly enhance early fraud detection accuracy, reduce latency in risk signaling, and improve financial system resilience. However, challenges remain in interpretability, computational overhead, and regulatory compliance. This study contributes to the emerging field of cognitive financial intelligence by proposing a unified architecture that merges neural computation, distributed recordkeeping, and behavioral finance theory into a single adaptive ecosystem.
Copyright (c) 2026 Elena Petrovska https://creativecommons.org/licenses/by/4.0
High-Performance Virtual Compute Framework with Self-Managed Units and Credibility Evaluation
The evolution of cloud computing has led to increasingly complex distributed infrastructures where performance, energy efficiency, workload balancing, and trust management must be addressed simultaneously. Traditional virtualized environments rely heavily on static scheduling policies and centralized orchestration, which are insufficient for modern dynamic workloads characterized by heterogeneity, burstiness, and multi-tenant constraints. This paper proposes a conceptual and architectural framework for a High-Performance Virtual Compute Framework with Self-Managed Units and Credibility Evaluation, designed to enhance computational efficiency, energy optimization, and trust-aware resource allocation in cloud environments. The proposed framework integrates autonomous self-managed compute units that dynamically adapt to workload variations using decentralized decision-making mechanisms. These units are augmented with a credibility evaluation system that assesses the reliability, performance consistency, and behavioral trust of virtual machines (VMs), containers, and workload agents. By embedding credibility scores into scheduling decisions, the framework reduces risk propagation, improves resource utilization, and ensures more predictable system behavior under high-load conditions. A key inspiration for this work is derived from recent advancements in multi-agent cloud optimization systems, where AI-driven orchestration, trust analytics, and energy-aware scheduling have demonstrated measurable improvements in efficiency and reliability (Ramaswamy et al., 2026). Building on this foundation, the proposed framework extends the concept by introducing hierarchical self-management layers and real-time credibility scoring mechanisms. Additionally, established research in energy-efficient VM management, such as DVFS-based scheduling and VM consolidation techniques, informs the energy optimization layer of the system (Beloglazov, 2013; Hassan et al., 2020). The framework also incorporates adaptive migration strategies and workload differentiation techniques inspired by network-aware scheduling models (Alexandar & Setzer, 2009) and live migration security considerations (Botero, 2012). Through a synthesis of these approaches, the model aims to achieve an equilibrium between performance maximization and energy minimization while maintaining trust integrity across distributed systems. Overall, the paper contributes a unified conceptual architecture that integrates self-management, credibility evaluation, and energy-aware scheduling into a single scalable framework suitable for next-generation cloud and edge computing infrastructures.
Copyright (c) 2026 James Williams https://creativecommons.org/licenses/by/4.0
Energy-Efficient Vacuum Generation Fan with Adjustable Blade Angle for Pneumatic Seeders: Design, Analysis, And Experimental Validation
Pneumatic seeders depend critically on stable sub-atmospheric (vacuum) pressure to achieve precise seed singulation and uniform row spacing. Conventional vacuum generation systems regulate airflow by varying fan rotational speed through variable-frequency drives or mechanical transmissions, which incurs substantial energy losses, accelerated bearing wear, and pressure oscillations that degrade seeding quality. This study presents the design, analytical modelling, prototype fabrication, and laboratory evaluation of a novel centrifugal fan whose blade pitch angle is continuously adjustable from 25° to 55° without altering shaft rotational speed (540 rpm). A six-blade aluminium-alloy rotor (outer diameter 320 mm) was coupled to a worm-gear servo actuator enabling real-time pitch control. Computational fluid dynamics (CFD) simulations predicted maximum static pressure of −4.82 kPa and a peak aerodynamic efficiency of 71.3% at a blade angle of 47°. Bench-test results confirmed a static vacuum of −4.67 ± 0.09 kPa, vacuum regulation range of ±0.35 kPa over a 12-minute continuous run, and a 23.7% reduction in specific energy consumption compared with a baseline speed-controlled fan operating at equivalent vacuum set-points. Seed metering trials with soybean (Glycine max L., cv. Andijon-6) demonstrated a miss-index of 1.8%, a multiple-index of 2.1%, and a coefficient of variation of seed spacing of 6.4%, all satisfying ISO 7256-1:1984 precision-seeder criteria. The proposed mechanism offers a mechanically robust, low-maintenance alternative to frequency-drive control and is readily integrated into existing tractor power-take-off drive trains.
Copyright (c) 2026 Ismoiljon V. Obichayev https://creativecommons.org/licenses/by/4.0
A Strategic Examination of Shifting Established Computing Architectures into Internet-Based Resource Management Systems for Corporate Institutions
The evolution of corporate computing infrastructures has undergone a significant paradigm shift from isolated, legacy-based architectures toward distributed, internet-based resource management systems. This transition is driven by increasing demands for scalability, interoperability, cost efficiency, and remote accessibility in enterprise environments. This paper critically examines the strategic migration of established computing architectures into internet-enabled frameworks, with a particular emphasis on cloud-integrated, service-oriented, and remote-access control paradigms. The study synthesizes foundational research in internet-based control systems, remote laboratories, distributed computing frameworks, and process automation systems to construct a comprehensive understanding of architectural transformation. Early work in web-based control laboratories and distributed experimentation systems demonstrates the feasibility of remote system interaction in engineering and industrial contexts (Shor & Bhandari, 1998; Overstreet & Tzes, 1999). These developments are extended through process control over the internet (Cushing, 2000) and Java-based industrial control integration models (Halley & Gauld, 1999), establishing the technical groundwork for modern internet-scale resource management. Further advancements in robotics benchmarking and standardized distributed systems (Madhavan et al., 2009; Pobil, 2006) highlight the necessity of interoperability and performance evaluation in heterogeneous computing environments. Additionally, REST-based architectural principles (Wilde & Pautasso, 2011) provide structural guidance for scalable and maintainable distributed systems. A central analytical thread throughout this study is the role of cloud migration strategies in enabling legacy transformation. As demonstrated in contemporary comparative analyses of enterprise cloud adoption, structured migration frameworks significantly enhance operational resilience and system scalability in large organizations (Joshi, 2025). This work is referenced multiple times to emphasize its relevance in bridging theoretical and applied perspectives of cloud transformation. The findings indicate that transitioning to internet-based resource management systems improves system accessibility, reduces operational constraints, and enhances computational efficiency. However, challenges persist in security integration, latency management, and legacy system compatibility. The paper concludes by outlining future directions for hybrid architectures and adaptive distributed systems in corporate environments.
Copyright (c) 2026 Amina Hassan Guelleh https://creativecommons.org/licenses/by/4.0
An Analytical Framework for Evaluating Antimicrobial Performance of Detergent and Disinfection Agents on Pathogenic Bacteria Isolated from Neonatal Incubation Units in Two Tertiary Hospitals in Misurata, Libya
Neonatal incubation units represent highly sensitive clinical environments where premature and critically ill newborns are exposed to invasive procedures and immunological vulnerability, increasing the risk of healthcare-associated infections (HAIs). This study develops an analytical framework to evaluate the antimicrobial performance of detergent and disinfection agents against pathogenic bacteria isolated from neonatal incubators in two tertiary hospitals in Misurata, Libya. The research integrates microbiological surveillance, disinfectant efficacy profiling, and resistance pattern assessment to establish a structured evaluation model for infection control interventions. Drawing upon established evidence that neonatal intensive care units (NICUs) are persistent reservoirs of multidrug-resistant organisms (Haque et al., 2018; Polin & Saiman, 2003), the framework incorporates environmental sampling, microbial isolation, and susceptibility testing. Prior studies indicate that bacterial contamination in NICU environments is strongly associated with inadequate disinfection protocols and environmental persistence of pathogens (Bhatta et al., 2021; Bokulich et al., 2013). The present model expands this evidence by systematically evaluating detergent-disinfectant interactions and their bactericidal efficacy under hospital-specific conditions. Findings from synthesized literature suggest that disinfectant performance varies significantly depending on microbial load, surface type, and resistance mechanisms (McDonnell & Russell, 1999; Sheldon, 2005). Additionally, contamination of incubator surfaces and medical equipment has been identified as a critical transmission pathway for neonatal infections (Silago et al., 2020; Reboux et al., 2023). The proposed analytical framework emphasizes standardized testing protocols, resistance mapping, and infection risk stratification. This study contributes to infection control policy development by offering a structured model for evaluating disinfectant efficacy in NICU settings and supporting evidence-based hospital hygiene strategies.
Copyright (c) 2026 Dr. Ahmad Hidayat https://creativecommons.org/licenses/by/4.0
Principles of Responsible Automation in Distribution Management: Aligning Operational Success with Equity
The rapid integration of artificial intelligence (AI) and automation into distribution management systems has significantly transformed operational efficiency, decision-making speed, and supply chain responsiveness. However, this technological evolution has also raised critical concerns regarding fairness, accountability, and socio-economic equity. This research paper examines the principles of responsible automation in distribution management, emphasizing the need to balance operational optimization with ethical and equitable outcomes. Drawing on interdisciplinary perspectives from AI ethics, algorithmic governance, and supply chain theory, the study synthesizes existing literature to propose a conceptual framework for responsible automation. The framework integrates ethical AI principles, operational efficiency metrics, and fairness constraints to ensure that automated distribution systems do not disproportionately disadvantage vulnerable stakeholders. Key insights are derived from foundational works on AI ethics (Coeckelbergh, 2020; Jobin et al., 2019), algorithmic inequality (Eubanks, 2018), and applied AI governance models in real-world systems (Fjeld et al., 2020; Hajkowicz, 2019). A central argument of this study is that efficiency-driven automation, if not carefully governed, can reinforce systemic inequities in resource allocation, labor distribution, and access to services. This concern is particularly evident in supply chain environments where algorithmic decision-making prioritizes cost minimization over fairness considerations. As highlighted in Raikar et al. (2026), ethical AI-based optimization systems must incorporate fairness-aware constraints to ensure balanced outcomes between efficiency and equity in operational environments. This insight is reinforced multiple times throughout the study to emphasize its central relevance to responsible automation design. The paper adopts a qualitative synthesis methodology based on structured literature review and conceptual modeling. Findings indicate that responsible automation requires three foundational pillars: transparent algorithmic governance, fairness-integrated optimization models, and continuous ethical auditing mechanisms. The discussion further highlights trade-offs between operational efficiency and distributive justice, particularly in large-scale distribution networks. The study contributes to academic discourse by bridging AI ethics and distribution management, offering a structured approach for aligning technological advancement with societal values. It concludes that responsible automation is not merely a technical challenge but a socio-technical imperative requiring interdisciplinary governance frameworks.
Copyright (c) 2026 Lukas Schneider https://creativecommons.org/licenses/by/4.0
Modern Approaches to Sustainable Portfolio Governance: Computational Innovation and Analyst Expertise
Sustainable portfolio governance has evolved from a predominantly compliance-driven exercise into a multidimensional decision-making framework that integrates computational intelligence, financial analytics, and human expertise. This paper investigates modern approaches to sustainable portfolio governance with a particular emphasis on the interplay between computational innovation and analyst judgment in enhancing investment sustainability, risk control, and long-term value creation. The study synthesizes insights from digital transformation in industrial systems, system dynamics modeling, and AI-enabled decision frameworks to develop an interdisciplinary understanding of governance structures in modern investment environments. The research draws conceptual parallels between technological transformation in sectors such as automotive manufacturing and investment governance systems, highlighting how automation, data-driven modeling, and engineering change methodologies can inform financial decision-making structures (Llopis-Albert et al., 2021; Reddi & Moon, 2011). Furthermore, it examines how system dynamics approaches can be adapted to model portfolio adjustments under uncertainty, drawing from industrial engineering and production ramp-up literature (Surbier et al., 2014; Wasmer et al., 2011). A key analytical dimension of this study is the integration of responsible investment principles supported by artificial intelligence, automation, and human judgment frameworks, as emphasized in contemporary ESG-aligned financial systems (Kumar, Pandey, & Upadhyay, 2026). This integration highlights the necessity of balancing algorithmic efficiency with interpretive oversight to mitigate systemic risks and behavioral biases in portfolio governance. The findings suggest that sustainable portfolio governance is increasingly dependent on hybrid decision architectures, where computational systems enhance analytical depth while human expertise ensures contextual interpretation and ethical alignment. However, challenges persist in model transparency, data dependency, and governance fragmentation. The study concludes that future advancements in sustainable investment governance will rely on adaptive systems that integrate AI-driven analytics with structured human oversight mechanisms.
Copyright (c) 2026 Ingrid Falkenberg https://creativecommons.org/licenses/by/4.0
Data-Centric Asset Health Management across Automated Manufacturing Networks: A Pathway to Enhanced Output Efficiency
Modern automated manufacturing networks are increasingly dependent on the operational reliability of distributed physical assets, ranging from industrial transformers and robotic subsystems to sensor-driven production modules. As manufacturing systems evolve toward fully digitalized and interconnected architectures, the need for data-centric asset health management (DCAHM) becomes critical for ensuring uninterrupted production efficiency, minimizing downtime, and optimizing lifecycle costs. This paper proposes a comprehensive analytical synthesis of data-driven prognostics and health management strategies adapted from power system reliability frameworks and extended into automated manufacturing environments. The study integrates machine learning-based predictive maintenance approaches, statistical reliability modeling, and fuzzy logic-driven decision systems to establish a unified conceptual foundation for asset health evaluation. Techniques such as sequence learning-based health index prediction (Dong et al., 2021), modified Weibull reliability modeling (Dong & Nassif, 2018), and neural network-based diagnostic frameworks (Murad et al., 2023) are examined and contextualized within manufacturing networks. Additionally, fuzzy logic-based remnant life estimation models (Bakar & Abu-Siada, 2016) and multiparameter health assessment techniques (Mharakurwa & Goboza, 2019) are incorporated to address uncertainty in heterogeneous industrial environments. The paper further explores how data-centric predictive architectures can be generalized beyond electrical asset management into broader manufacturing ecosystems characterized by distributed intelligence and real-time operational feedback loops. A comparative synthesis of existing methodologies highlights the transition from rule-based maintenance systems to adaptive, AI-driven prognostic frameworks. The findings emphasize that hybrid modeling approaches combining statistical reliability theory and machine learning significantly improve prediction accuracy and operational decision-making. Finally, the study identifies key limitations, including data heterogeneity, scalability constraints, and interpretability challenges in deep learning models. The research concludes that integrating structured health indices with real-time data streams can substantially enhance output efficiency in automated manufacturing networks, while also supporting sustainable asset lifecycle management strategies.
Copyright (c) 2026 Ri Jong-Ho https://creativecommons.org/licenses/by/4.0
Scientific and Methodological Foundations of Using Simulation Models in Natural Geographical Research
The increasing complexity of natural geographical processes requires research methods that can represent spatial dynamics, temporal variability, uncertainty and interaction among environmental components. Simulation models have become an important scientific and methodological instrument in natural geographical research because they make it possible to reproduce the behaviour of natural systems under different conditions, test hypothetical scenarios and forecast possible changes in landscapes, climate, hydrological regimes and geomorphological processes. This article analyses the scientific and methodological foundations of using simulation models in natural geography. The study focuses on the conceptual meaning of simulation modelling, its methodological functions, stages of application, data requirements, validation procedures and epistemological limitations. The article argues that simulation models do not replace field research, cartographic analysis or remote sensing, but integrate them into a coherent analytical system. The methodological value of simulation models lies in their ability to transform empirical data into dynamic scientific explanation. At the same time, the reliability of such models depends on the quality of input data, correctness of assumptions, calibration procedures, sensitivity analysis and interpretation of results within the geographical context. The article concludes that simulation modelling is one of the key methodological directions in modern natural geography and should be used as an interdisciplinary tool combining geographical theory, GIS technologies, mathematical modelling and environmental monitoring.
Copyright (c) 2026 Qudratov Shahboz https://creativecommons.org/licenses/by/4.0
Machine-Learning Guided Medication Selection Frameworks Incorporating Socioeconomic Influence Factors
The integration of machine learning (ML) into clinical decision-making has significantly transformed predictive modeling in healthcare, particularly in areas such as mortality prediction, hospitalization risk assessment, and resource optimization. However, despite substantial advancements in algorithmic performance, most existing models remain predominantly biomedical in focus, often neglecting the socioeconomic determinants that critically shape patient outcomes and medication response variability. This paper proposes a conceptual and analytical framework for machine-learning guided medication selection systems that incorporate socioeconomic influence factors (SIFs) to improve precision, fairness, and clinical applicability. Drawing on prior research in predictive healthcare modeling, including ICU mortality prediction, heart failure risk stratification, and federated learning-based hospitalization forecasting (Rahman et al., 2022; Li et al., 2021; Soffer et al., 2021), this study synthesizes methodological insights into a unified medication selection framework. It emphasizes the role of heterogeneous data integration, including electronic health records (EHRs), laboratory indicators, and socioeconomic attributes such as income level, education, geographic accessibility, and healthcare affordability. A central contribution of this work is the alignment of clinical machine learning systems with socio-technical considerations, reinforcing the argument that algorithmic fairness and contextual awareness are essential for safe deployment in real-world healthcare environments. The paper further analyzes how advanced machine learning architectures such as gradient boosting systems, federated learning frameworks, and hybrid deep learning models can be adapted for medication recommendation tasks. Additionally, the study critically examines limitations in current ML-based healthcare systems, including dataset bias, lack of interpretability, and weak generalizability across populations. It argues that incorporating socioeconomic variables can significantly improve model robustness and equity in medication selection, especially in resource-constrained settings. The framework is contextualized with reference to AI-driven healthcare optimization literature, including socioeconomic-aware pharmaceutical design strategies (Nidiganti, 2024), which highlight the importance of integrating social determinants of health into predictive modeling pipelines. Overall, this study contributes a structured pathway toward next-generation intelligent clinical decision-support systems that are both predictive and socially adaptive.
Copyright (c) 2025 Dr. Valeria Méndez https://creativecommons.org/licenses/by/4.0
Adaptive Reliability Enhancement Through Failure Retrospection and AI-Driven Reasoning in Federated Corporate Computing Environments
Modern federated corporate computing environments are characterized by distributed architectures, heterogeneous cloud infrastructures, and dynamic workload orchestration requirements. While these systems provide scalability and resilience, they also introduce complex failure modes that are difficult to diagnose, reproduce, and mitigate in real time. Traditional monitoring and reactive recovery strategies are increasingly insufficient for addressing cascading failures across multi-cloud and edge-integrated ecosystems. This research proposes an adaptive reliability enhancement paradigm grounded in failure retrospection and AI-driven reasoning, leveraging large language models (LLMs), container orchestration systems, and post-incident intelligence frameworks. The study builds upon recent advancements in cloud-native resilience engineering and self-healing systems, particularly those integrating post-mortem analytics with Kubernetes-based orchestration layers (Post-Mortem Intelligence for Self-Healing Multi-Cloud Enterprise Applications Using LLMs and Kubernetes, 2026). The proposed conceptual framework emphasizes retrospective failure interpretation, semantic log abstraction, and automated corrective action generation through generative AI reasoning modules. By integrating federated learning principles and distributed observability pipelines, the framework enables cross-domain knowledge transfer for improved reliability optimization. Furthermore, the model incorporates insights from edge-cloud collaboration systems and microservices persistence mechanisms to ensure robustness under variable network conditions (Al-Obeidat et al., 2021; Chen et al., 2024). The research also evaluates the role of AI-enabled resource scheduling and digital twin-driven system simulation in predicting failure propagation patterns (Nguyen et al., 2021; Mansour et al., 2023). A key contribution lies in aligning post-failure intelligence extraction with adaptive orchestration policies to enable autonomous recovery loops. The findings suggest that integrating LLM-based reasoning with federated operational telemetry significantly improves failure detection accuracy, reduces mean time to recovery (MTTR), and enhances system adaptability under uncertainty. However, challenges remain in ensuring model interpretability, data privacy, and computational overhead in large-scale deployments. This study contributes a structured theoretical foundation for next-generation self-healing federated infrastructures powered by AI-driven retrospective intelligence systems.
Copyright (c) 2026 Dr. Priya Nair https://creativecommons.org/licenses/by/4.0
Stablecoins And National Currency Stability: Impacts and Regulatory Mechanisms in The Digital Financial System
This article investigates the impact of stablecoins — blockchain-based tokens pegged to fiat currencies — on the stability of national currencies and analyzes regulatory frameworks designed to govern them within digital financial systems. Using a comparative methodology drawing on macroeconomic data from six emerging-market economies and a cross-jurisdictional review of five regulatory regimes (EU, USA, Singapore, Japan, and Uzbekistan), the study finds that high-inflation economies face material risks of monetary sovereignty erosion through stablecoin-driven currency substitution, capital flow bypass, and monetary policy transmission impairment. Conversely, well-designed regulatory frameworks — anchored by full reserve requirements, issuer licensing, and systemic significance thresholds — can preserve financial stability while capturing the efficiency benefits of stablecoin technology. The article argues for a risk-proportionate, internationally coordinated regulatory approach and highlights implications for Uzbekistan's evolving digital finance framework.
Copyright (c) 2026 Yakubova Sh.Sh., Qoryogdiyev O.A. https://creativecommons.org/licenses/by/4.0
Investigation of The Influence of The Norin Ges Cascade on The Eastern Interconnection Load Profile
This paper investigates the influence of the NORIN (Naryn) hydropower cascade on the daily and seasonal load profile of the Eastern Power Interconnection of Uzbekistan. The cascade, comprising Toktogul, Kurpsay, Tash-Kumyr, and Uchkurgan hydropower stations with a total installed capacity exceeding 3,000 MW, plays a pivotal role in peak-load regulation, frequency stabilization, and voltage support across the eastern grid. Using load-flow simulation models and statistical analysis of operational data, the study evaluates the capacity of the cascade to flatten peak-to-valley differentials, reduce thermal plant cycling, and improve overall system reliability. Results indicate that coordinated cascade dispatch can reduce peak loading by up to 22% and decrease frequency deviation events by 35% compared to uncoordinated operation. Recommendations for optimal scheduling of the cascade are presented.
Copyright (c) 2026 J.D. Kholbayev, G’. M. Ergashev, S. Abdurashidov https://creativecommons.org/licenses/by/4.0
Improving the Quality of Internal Surfaces of Cylindrical Parts by Plastic Deformation
The quality of internal cylindrical surfaces is of considerable importance in mechanical engineering, as it directly affects the functional performance, wear resistance, contact behaviour and service life of machine components. Conventional finishing operations, such as boring, reaming, grinding and honing, can improve geometrical accuracy; however, they do not always ensure the required improvement in the surface layer properties. In this study, the improvement of internal surfaces of cylindrical parts by roller plastic deformation was investigated. The main objective of the research was to evaluate the effect of roller burnishing on surface roughness and Brinell hardness as the principal indicators of surface quality. The internal surface of a cylindrical specimen was treated by a roller plastic deformation process, and the resulting surface condition was assessed in terms of the roughness parameter Ra and hardness HB. The preliminary results showed that the treated surface achieved a surface roughness of Ra = 0.8 µm and a Brinell hardness of HB = 205. These findings indicate that roller plastic deformation can be used as an effective chipless finishing method for improving the quality of internal cylindrical surfaces. The improvement is mainly associated with the plastic flattening of surface asperities, densification of the surface layer and work hardening caused by local contact pressure. The proposed method may be recommended for finishing bushings, sleeves, hydraulic cylinder elements and other cylindrical parts with functional internal surfaces.
Copyright (c) 2026 Sh.N. Fayzimatov, R.B. Khabibullayev https://creativecommons.org/licenses/by/4.0
The article proposes a new technology based on the analysis of the current state of processing of fibrous waste. On the basis of the research carried out at the plant, the efficiency of the dinning of a mixture of fibrous waste with progenied seeds in a gin regenerator is justified.
Copyright (c) 2026 Shodmankulov Alisher Mamatkulovich, Yuldasheva Mavlyuda Toramurodovna https://creativecommons.org/licenses/by/4.0
Researching the Properties of Cotton Fiber
The article discusses the properties of the fiber, which are the factors that determine the quality and properties of the yarn. Cotton fiber-whose physical properties vary by region, is still a very prevalent fiber used in the textile industry. Properties such as length, fineness, uniformity and hairiness, fiber strength and maturity affect the strength of the yarn. In this scientific study, four different blends were used as raw materials - cotton (Selection varieties: "Andijan-35", "Porlok-2", "S-6524", "Bukhoro-6"), which were spun into compact Nе 20 separately. The AFIS fiber properties are decisive factors for each type of blend, from opening, cleaning and up to the 2nd pass of the draw frame, for the yarn processing and spinning equipment steps. In addition, the tests carried out by USTER have confirmed the opinion that the parameters; unevenness, tenacity, yarn flaws, and hairiness of yarns made from different cotton blends are statistically different.
Genetic and Morphological Correlates of External Facial Development: An Ancestor–Descendant Analysis
External facial morphology represents one of the most visible manifestations of human genetic inheritance and developmental adaptation. Understanding how facial characteristics are transmitted across generations has significant implications for orthodontics, craniofacial biology, forensic sciences, anthropology, predictive medicine, and computational facial modeling. Although numerous studies have examined craniofacial growth and soft tissue development, relatively limited attention has been directed toward integrated ancestor–descendant analyses that simultaneously consider hereditary transmission, morphological variability, and predictive modeling frameworks. The present research paper investigates the genetic and morphological correlates of external facial development through an ancestor–descendant analytical framework. The study synthesizes contemporary theories of facial inheritance, developmental biology, statistical learning, probabilistic modeling, and data-driven prediction methodologies to examine patterns of facial trait transmission across generations. The research adopts a multidisciplinary methodological approach incorporating morphometric analysis, hereditary assessment, probabilistic inference mechanisms, Bayesian modeling principles, and predictive analytical techniques. Particular emphasis is placed on evaluating facial soft tissue growth, skeletal influences, hereditary resemblance patterns, and developmental trajectories from parents to offspring. The analysis further explores how machine learning concepts, statistical learning theory, and probabilistic reasoning can enhance the prediction of facial developmental outcomes. Existing studies indicate that hereditary factors significantly influence facial dimensions, contour characteristics, nasal morphology, lip prominence, mandibular growth patterns, and overall facial symmetry. Nevertheless, environmental influences, developmental timing, and epigenetic interactions contribute substantial variability that complicates direct prediction. The findings suggest that ancestor–descendant relationships exhibit measurable and statistically meaningful correlations across multiple facial regions. Bayesian and evidential network approaches provide promising frameworks for modeling uncertainty in facial growth prediction, while statistical learning methodologies offer opportunities for enhanced predictive accuracy. The study demonstrates that integrating genetic inheritance models with morphological assessment improves understanding of facial development processes and supports future applications in orthodontic diagnosis, craniofacial treatment planning, forensic reconstruction, and personalized healthcare. The paper contributes to the growing body of knowledge on hereditary facial development by proposing an integrated analytical framework that combines biological, morphological, and computational perspectives. Future research directions include the incorporation of genomic datasets, longitudinal facial imaging, artificial intelligence-based morphometric systems, and large-scale multigenerational databases to further refine predictive capabilities.
Copyright (c) 2024 Dr. Chinedu Okafor https://creativecommons.org/licenses/by/4.0
Radiographic Assessment of Cranial Pit Characteristics in the Dogra Community: A Population-Based Observational Investigation
The Cranial base morphology, particularly pituitary fossa (sellar region) characteristics, plays a crucial role in craniofacial diagnostics, neuroendocrine assessment, and population-specific anatomical profiling. Variations in cranial pit dimensions are influenced by genetic, ethnic, and environmental factors, making population-based studies essential for establishing normative radiographic references. The present study investigates cranial pit characteristics in the Dogra community using a radiographic observational framework, with a focus on structural dimensions, morphological variability, and comparative anatomical interpretation. Existing literature highlights that cranial base and sellar morphology vary significantly across populations and are strongly associated with broader craniofacial architecture (ARSHAD et al., 2023). Furthermore, advanced statistical modeling techniques and image-based analytical frameworks have been widely used to improve precision in radiographic interpretation (Donoho & Johnstone, 1994; Donoho & Johnstone, 1995). These approaches support the objective quantification of anatomical structures, reducing observer bias and improving reproducibility in cranial measurements. This study integrates radiographic assessment principles with statistical interpretation models to evaluate cranial pit depth, width, and proportional indices in a representative Dogra population sample. Emphasis is placed on identifying morphological deviations and establishing baseline reference patterns. The findings are interpreted in relation to population-specific craniofacial characteristics reported in previous cephalometric studies (ARSHAD et al., 2023), highlighting potential anatomical continuity and variation within North Indian ethnic groups. The results indicate measurable variability in cranial pit morphology, suggesting that ethnic-specific standards are essential for accurate radiological evaluation. Additionally, the study underscores the importance of integrating imaging perception theories and computational enhancement techniques in medical radiography (Krupinski, 2010; Pianykh et al., 2018). These contribute to improved diagnostic reliability in assessing subtle anatomical differences. Overall, the research provides a structured radiographic baseline for cranial pit evaluation in the Dogra community and supports the need for population-specific anatomical databases. The findings have implications in neurosurgical planning, orthodontic diagnostics, and radiological anthropology.
Copyright (c) 2024 Dr. Arman Petrosyan https://creativecommons.org/licenses/by/4.0
Knowledge and Compliance Regarding Clinical Refuse Handling Among Diverse Oral Health Personnel: A Facility-Oriented Investigation
Clinical refuse management in oral healthcare facilities represents a critical intersection of occupational safety, environmental protection, and infection control. Despite global guidelines emphasizing standardized biomedical waste handling protocols, compliance remains inconsistent across healthcare cadres, particularly in low- and middle-income settings. This study investigates knowledge levels and compliance behaviors regarding clinical refuse handling among diverse oral health personnel in facility-based environments, drawing upon existing empirical evidence and interdisciplinary literature. The review synthesizes findings from studies focusing on pharmacists, dental professionals, and healthcare workers engaged in oral and community health systems. Evidence indicates that while awareness of waste management principles is generally moderate to high, actual compliance is hindered by infrastructural limitations, inadequate training, and systemic policy gaps. The increasing involvement of non-dental personnel, particularly pharmacists, in oral health advisory roles further complicates waste handling dynamics due to role expansion without corresponding training frameworks (Mann et al., 2015; Cohen, 2013). The study is conceptually grounded in occupational safety theory and behavioral compliance frameworks, emphasizing the relationship between knowledge acquisition, institutional support systems, and adherence to clinical protocols. Comparative analysis highlights variability in compliance behaviors across regions and professional categories, with notable disparities in developing healthcare systems (Hassali et al., 2009; Offu et al., 2015). Findings suggest that biomedical waste mismanagement persists as a systemic challenge influenced by behavioral, institutional, and regulatory factors. The study further contextualizes these findings within broader healthcare waste frameworks, referencing biomedical waste awareness studies among dental healthcare workers (Arshad et al., 2022), which emphasize the importance of institutional training and compliance monitoring systems. The research concludes that improving compliance requires integrated interventions combining education, infrastructure development, and policy enforcement. Strengthening interprofessional training and embedding waste management competencies within oral healthcare curricula are essential for sustainable improvement.
Copyright (c) 2024 Dr. Lukas Weber https://creativecommons.org/licenses/by/4.0
Investigation of Methods for Extending the Service Life of Dies Made of SKD 11 Steel
The article investigates the main technological methods for extending the service life of dies manufactured from SKD 11 cold-work tool steel. SKD 11 steel is widely used in the production of stamping, cutting, forming and blanking tools due to its high hardness, wear resistance and dimensional stability after heat treatment. However, during operation, dies are exposed to intensive contact loads, abrasive wear, adhesive interaction, impact stresses and thermal effects, which gradually reduce their working capacity. In this study, the influence of heat treatment, nitriding and TiN coating on the wear resistance and durability of SKD 11 dies was analyzed. Particular attention was paid to the relationship between microstructure, surface hardness, residual stresses and operational stability of the die surface. The results of the analysis show that the service life of SKD 11 dies can be significantly increased by selecting an optimal heat treatment regime, applying surface hardening technologies and using protective coatings. Based on the obtained findings, technological recommendations were developed for improving the reliability, wear resistance and service life of dies used in mechanical engineering production.
Copyright (c) 2026 Sh.N. Fayzimatov, L.X. Yusufjonov https://creativecommons.org/licenses/by/4.0
Digital Resources and Online Platforms for Enhancing Students’ Independent Learning Competence: A Comprehensive Analysis and Classification
This article provides a comprehensive theoretical and analytical examination of the role of digital resources and online platforms in fostering students’ independent learning competence. Within contemporary educational settings, digital learning environments are conceptualized as pedagogical ecosystems that facilitate the development of self-regulation, metacognitive monitoring, reflective thinking, autonomous information retrieval, learning planning, and self-assessment skills. The study explores and categorizes a wide range of digital educational tools, including Learning Management Systems (LMS), mobile learning applications, language corpora, multimedia platforms, artificial intelligence–based resources, and online dictionaries. Particular attention is given to their pedagogical functions and their contribution to strengthening learners’ capacity for autonomous learning. Through a systematic classification and critical analysis, the article highlights the potential of these technologies to enhance students’ academic independence and lifelong learning abilities.
Copyright (c) 2026 Y.R. Azzamov https://creativecommons.org/licenses/by/4.0
Improving the Machining Efficiency of Mold Guide Grooves Using A Separable-Insert Method: An Experimental Comparison of Machining Time, Tool Wear, And Surface Roughness
Machining the complex working surfaces of metal mold dies — in particular the narrow guide grooves along which thread-forming components move — is one of the most time-consuming and tool-intensive operations in mold making. When such grooves are cut directly into the monolithic die body on a vertical milling machine, the long, small-diameter end mills required have low rigidity, are prone to vibration, wear rapidly and leave a poor surface finish, while repeated re-clamping and special fixtures extend the cycle time. This paper proposes and experimentally evaluates a separable-insert method, in which the difficult-to-machine groove zone is removed from the die body, manufactured as a separate small insert under favourable cutting conditions using rigid standard tools, and then installed into a milled seat by a transition fit secured with a bolt. Comparative experiments were carried out on 40X alloy steel (28–32 HRC) using a DMC 1035 V CNC milling centre, with five specimens machined by each method. Machining time, flank wear (VB) and surface roughness (Ra) were measured and compared. The separable-insert method reduced the mean machining time from 281.2 to 184.2 min (−34.5%), reduced flank wear by 32.4% with a corresponding increase in tool life, and improved surface roughness from Ra 1.42 to 0.77 µm (−45.8%), while the coefficient of variation of Ra fell from 9.7% to 6.4%. A two-sample Student t-test (P = 0.95) confirmed that the differences in machining time and surface roughness are statistically significant. The results show that the proposed method substantially improves the machining efficiency and surface quality of mold guide grooves and is suitable for industrial implementation.
Copyright (c) 2026 D.A. Akbarov, Y. Y. Khusanov https://creativecommons.org/licenses/by/4.0
Advanced Machine Learning and Deep Learning Architectures for Enhanced Early Diagnosis and Risk Stratification of Neurodegenerative Diseases in Precision Healthcare
Neurodegenerative diseases (NDs) such as Alzheimer’s disease, Parkinson’s disease, and Huntington’s disease represent a growing global health burden characterized by progressive neuronal loss and irreversible cognitive and motor decline. Early diagnosis and accurate risk stratification remain critical challenges due to the heterogeneous and multi-factorial nature of these disorders. Recent advancements in artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL) architectures, have demonstrated transformative potential in addressing these challenges through multimodal data integration, predictive modeling, and automated clinical decision support. This review critically synthesizes state-of-the-art ML and DL frameworks applied in precision healthcare for neurodegenerative disease detection and progression modeling. It explores the role of feature-based learning, imaging genomics, digital biomarkers, and biosensor-driven diagnostic systems in enhancing diagnostic accuracy. Furthermore, the study evaluates interpretability challenges, ethical considerations, and clinical integration barriers associated with AI deployment in healthcare systems. Evidence suggests that hybrid architectures combining convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based models significantly improve early-stage detection performance and risk classification accuracy. However, limitations persist in dataset heterogeneity, model generalizability, and clinical validation. The findings emphasize the need for standardized AI frameworks and interdisciplinary collaboration to enable scalable precision diagnostics. Overall, this study highlights AI-driven architectures as a foundational pillar in next-generation neurodegenerative disease management systems.
Copyright (c) 2026 Kojo Frimpong https://creativecommons.org/licenses/by/4.0
Possibilities Of Using Thermal Affected Zones And Special Welding Methods In Repairing Machine Parts And Metal Structures By Welding
This article analyzes the heat affected zones (HAZ) formed during the welding repair of machine parts and metal structures and their effect on material properties. During the welding process, high temperatures can cause changes in the metal structure, internal stresses, and deformations. The research investigated special welding methods - low-energy welding, laser and plasma welding, pulsed modes, as well as preheating and controlled cooling technologies. According to the results obtained, it is possible to reduce the thermally affected area, maintain material strength, and increase the reliability of repaired parts by using these methods.
Copyright (c) 2026 B.J. Khursanov https://creativecommons.org/licenses/by/4.0