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
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
Intelligent Energy Flow Management In Battery–Supercapacitor Systems
This paper presents an intelligent energy flow management approach for hybrid battery–supercapacitor systems used in autonomous photovoltaic power plants. The study focuses on peak load smoothing, inverter current stabilization, reduction of battery degradation, and optimization of reactive power distribution. Mathematical modeling and comparative analysis were performed for different operational scenarios. Results demonstrate that the proposed hybrid architecture improves system efficiency, decreases voltage deviations, and extends battery lifetime.
Copyright (c) 2026 B.B. Boynazarov, B.T. Jabborov https://creativecommons.org/licenses/by/4.0
Methodological Approaches to Adapting the Smart City Concept to Fergana City
The Smart City concept has become one of the leading paradigms of contemporary urban development, combining digital governance, intelligent infrastructure, environmental monitoring, data-based decision-making and human-centered urban services. However, international Smart City models cannot be directly transferred to regional cities without methodological adaptation, because each urban context has its own socio-economic, infrastructural, cultural, regulatory and environmental conditions. This paper examines methodological approaches to adapting the Smart City concept to Fergana city, Uzbekistan. The study is based on qualitative analysis of international Smart City practices, contextual interpretation of Uzbekistan’s digital transformation agenda, and the development of an adaptive framework for local implementation. The proposed methodology includes five interconnected stages: analysis of international models, assessment of local urban conditions, identification of priority Smart City directions, development of adaptive implementation tools, and formulation of key performance indicators for monitoring progress. Special attention is given to the integration of Digital Twin technologies, BIM, GIS, IoT-based monitoring, citizen participation mechanisms, and the conceptual development of the UzSmart Building Standard as a local evaluation tool for smart buildings. The paper argues that Fergana’s Smart City transformation should be implemented as a phased, human-centered and context-sensitive process rather than as a direct replication of foreign models. The proposed methodological approach can support urban planners, local authorities and researchers in developing an adaptive Smart City model for regional cities of Uzbekistan.
Copyright (c) 2026 Shokhrukh Obitov, Doniyor Nurmatov https://creativecommons.org/licenses/by/4.0
Investigation Into The Correlation Between Intake Air Temperature And Fuel Consumption In Chevrolet Cobalt Vehicles Equipped With A 1.5-Litre B15d2 Engine Under Winter Operational Conditions
This research is specifically aimed at determining and evaluating the precise correlation that exists between the intake air temperature entering the engine and the subsequent fuel consumption rates of Chevrolet Cobalt passenger cars during winter operational periods. Over the course of this investigation, the multi-faceted effects of ambient environmental temperatures, along with the thermal regime of the internal combustion engine, on overall fuel economy were systematically examined utilizing comprehensive analytical methods and mathematical modelling techniques. The empirical and theoretical outcomes obtained demonstrate that under low-temperature conditions, fuel consumption increases significantly and substantially, which is primarily driven by the extended engine warm-up durations required and the inherently increased density of the cold intake air.
Copyright (c) 2026 N.I. Otabayev, A.B. Luqmonjonov https://creativecommons.org/licenses/by/4.0
Climate-Adaptive Small Architectural Forms For Public Spaces In Hot-Arid Urban Conditions Of Uzbekistan
The increasing intensity of heat stress in urban environments has made climate-responsive design one of the key priorities of contemporary architecture and urban planning. This issue is particularly relevant for Uzbekistan, where hot-arid climatic conditions, high summer temperatures, solar radiation, and rapid urban development create significant challenges for the comfort and usability of public spaces. Small architectural forms, including shading structures, benches, pavilions, pergolas, modular landscape elements, drinking fountains, and urban furniture, can play an important role in improving thermal comfort and enhancing the functional quality of public spaces. This study examines climate-adaptive small architectural forms as design instruments for improving public spaces in the hot-arid urban conditions of Uzbekistan. The research is based on a comprehensive methodology that includes literature review, comparative analysis, classification, and design-oriented evaluation. The study identifies the main types of small architectural forms that can mitigate heat stress through shading, ventilation, material selection, vegetation integration, water elements, and ergonomic placement. The findings show that climate-adaptive small architectural forms can improve pedestrian comfort, increase the usability of public spaces, support sustainable urban design, and contribute to the formation of more resilient urban environments. The study concludes that the integration of shading, greenery, reflective materials, modular design, and context-sensitive placement should become a key principle in the design of public spaces in Uzbekistan.
Copyright (c) 2026 Ilhom Ergashev, Akhmedova Nazokat https://creativecommons.org/licenses/by/4.0
Effective Hybrid Algorithm For Generating Large Prime Numbers
This article addresses the problem of generating large prime numbers. Due to the high computational complexity of traditional methods, their efficiency remains low. Therefore, the paper analyzes existing approaches and proposes a new hybrid algorithm, based on algebraic and probabilistic tests. The proposed method demonstrates the ability to generate prime numbers quickly and reliably. The results are compared across different bit lengths (256, 512, 1024, 2048 bits) using experimental data, and the efficiency of the algorithm is presented. The research outcomes have practical significance for generating secure keys in public-key cryptosystems (e.g., RSA).
Copyright (c) 2026 Xudoykulov Z. T., Jabbarov N. A., Jabborov A. A. https://creativecommons.org/licenses/by/4.0
Models and Algorithms for Identifying Important Pages Based on User Behavior Analysis on Websites
This article investigates modern models and algorithms for identifying important web pages based on the analysis of user behavior on websites. The study employs Web Usage Mining techniques, user session analysis, page transition probabilities, and clustering methods. The proposed model analyzes users’ navigational activities within a website and enables the identification of the most important pages. The model integrates Markov Chain, PageRank, K-means, and DBSCAN algorithms. Experimental results demonstrated high efficiency in identifying important pages based on user behavior analysis.
Copyright (c) 2026 Husanov Sherzod Abdimannonovich https://creativecommons.org/licenses/by/4.0
Predictive Intrusion Limitation in Virtualized Environments via Self-Evolving Misguidance Frameworks
Virtualized computing environments have become foundational to modern distributed systems, enabling scalable, elastic, and cost-efficient infrastructure sharing. However, their inherent abstraction layers introduce expanded attack surfaces that are increasingly exploited through adaptive intrusion strategies, lateral movement techniques, and virtualization-aware malware. Traditional intrusion detection systems (IDS) and prevention mechanisms remain largely reactive and are insufficient for dynamic threat landscapes characterized by concept drift, polymorphic attacks, and multi-vector intrusion attempts. This paper proposes a predictive intrusion limitation framework based on self-evolving misguidance mechanisms designed to proactively reduce intrusion success probability in virtualized environments. The framework integrates machine learning–driven anomaly prediction, behavioral profiling, and adaptive deception orchestration to redirect malicious activities into controlled decoy execution spaces. Unlike conventional IDS architectures, the proposed system does not solely focus on detection; instead, it anticipates intrusion likelihood and dynamically restructures the attack surface to limit adversarial progression. The methodology incorporates ensemble-based predictive modeling inspired by stacking architectures (Gupta et al., 2021), probabilistic cybersecurity risk quantification (Algarni et al., 2021), and concept drift adaptation mechanisms (Gama et al., 2014). A self-evolving misguidance engine continuously refines deception policies using reinforcement learning feedback loops and real-time network telemetry. The system is evaluated conceptually under multi-tenant virtualization scenarios, including hypervisor-level attacks, container escape attempts, and cross-VM communication exploits. Results indicate that predictive misguidance significantly reduces intrusion progression depth, limits lateral movement probability, and increases attacker resource consumption. Furthermore, integration of adaptive learning mechanisms ensures robustness against evolving adversarial strategies. Ethical and operational constraints are addressed through alignment with responsible AI principles and functional safety standards (ISO 26262; ISO 21448). The study contributes a novel paradigm in cybersecurity defense by shifting from detection-centric models to predictive intrusion limitation through adaptive deception and intelligent misdirection. It further demonstrates how AI-driven self-evolving frameworks can enhance resilience in highly virtualized, cloud-native ecosystems.
Copyright (c) 2026 Dr. Haruto Nakamura https://creativecommons.org/licenses/by/4.0
Autonomous Risk Mitigation across Multi-Tenant Platforms through Artificial Intelligence–Based Diversion Techniques
Multi-tenant computing platforms have become foundational to modern cloud-native ecosystems, enabling shared infrastructure utilization across heterogeneous users, applications, and services. While multi-tenancy enhances scalability and cost efficiency, it simultaneously introduces amplified security risks due to shared resource contention, cross-tenant interference, and expanded attack surfaces. Traditional isolation mechanisms and perimeter-based security models are increasingly insufficient to mitigate adaptive cyber threats that exploit tenant adjacency, workload co-location, and dynamic resource orchestration. This paper proposes an autonomous risk mitigation framework for multi-tenant platforms based on artificial intelligence–driven diversion techniques. The framework introduces adaptive traffic diversion, intelligent workload redirection, and deception-based risk absorption mechanisms designed to minimize exposure of critical tenant assets while preserving service continuity. The proposed system integrates machine learning–based risk scoring, behavioral anomaly detection, and reinforcement learning–enabled diversion policies to dynamically reconfigure traffic flows in response to evolving threat conditions. A game-theoretic and decision-theoretic model is developed to represent adversarial interactions between malicious actors and autonomous mitigation agents within multi-tenant environments. The framework further incorporates ethical constraints and safety requirements derived from autonomous system governance standards, ensuring alignment with responsible AI principles and regulatory expectations (Arkin, 2016; Jobin et al., 2019). The methodology is evaluated through theoretical simulation of cloud-based multi-tenant infrastructures under attack scenarios involving lateral movement, tenant isolation bypass attempts, and workload manipulation. Results indicate that AI-based diversion techniques significantly reduce successful cross-tenant attack propagation, improve isolation robustness, and enhance system survivability under coordinated adversarial pressure. The system demonstrates strong adaptability under concept drift conditions and evolving attack patterns (Gama et al., 2014). Furthermore, integration of reinforcement learning–driven deception strategies enhances real-time responsiveness and reduces mean exposure windows, consistent with prior findings in adaptive cyber deception research (Pesaramilli & Gudisa, 2025). The study contributes a unified framework for autonomous risk mitigation in multi-tenant systems, bridging cybersecurity, artificial intelligence, and distributed systems engineering. It further identifies limitations related to diversion detectability, computational overhead, and ethical constraints in autonomous decision-making systems.
Copyright (c) 2026 Dr. Rafael Souza https://creativecommons.org/licenses/by/4.0
Improving Computational Sustainability Frameworks in Economic and Monetary Sectors Using Anticipatory Data Evaluation Models
Computational sustainability has emerged as an interdisciplinary paradigm for addressing the increasing complexity of economic and monetary systems under environmental and energy constraints. This paper develops a computational sustainability framework integrating anticipatory data evaluation models within economic and monetary sectors to strengthen predictive governance, sustainable growth planning, and adaptive financial decision-making. Existing research has extensively examined the relationships among energy consumption, renewable energy adoption, carbon emissions, urbanization, and economic growth. However, limited attention has been devoted to integrating anticipatory computational systems with monetary governance mechanisms capable of evaluating sustainability risks in real time. The study adopts a conceptual and analytical research design supported by synthesis of empirical findings from existing literature. The proposed framework combines anticipatory analytics, sustainability indicators, machine learning-based forecasting, stochastic decision systems, and circular financial intelligence models. Particular emphasis is placed on the integration of artificial intelligence into sustainability-oriented financial systems through predictive analytics for de-risking green investments. The framework introduces a multilayer anticipatory architecture consisting of economic forecasting modules, renewable energy impact estimators, carbon-risk monetization systems, and adaptive policy optimization engines. The findings indicate that anticipatory computational systems significantly improve sustainability forecasting, reduce economic volatility associated with energy transitions, optimize monetary interventions, and strengthen green investment resilience. Furthermore, computational sustainability frameworks support policy synchronization between renewable energy systems, environmental governance, and macroeconomic stability. The study also demonstrates that anticipatory evaluation models improve institutional adaptability by enabling continuous assessment of sustainability indicators across interconnected sectors. This paper contributes to sustainability economics and computational governance by proposing an integrated framework suitable for implementation within financial institutions, central banking systems, and sustainability-oriented policy environments. The study concludes that anticipatory computational intelligence represents a transformative mechanism for sustainable economic governance in increasingly data-intensive and environmentally constrained global economies.
Copyright (c) 2026 Dr. Liina Vaher https://creativecommons.org/licenses/by/4.0
Evolutionary Parameter Selection with Layered Computational Analytics in Biological Diagnosis Prediction
Biological diagnosis prediction has become a central research domain in computational intelligence due to the rapid expansion of clinical datasets, genomic repositories, sensor-driven biomedical systems, and machine-assisted decision frameworks. Traditional diagnostic prediction mechanisms frequently encounter limitations associated with dimensional complexity, unstable feature representation, computational redundancy, and poor adaptability in heterogeneous medical environments. This research paper proposes a conceptual and analytical framework entitled Evolutionary Parameter Selection with Layered Computational Analytics (EPS-LCA) for biological diagnosis prediction. The framework integrates evolutionary optimization strategies, layered neural analytical structures, adaptive parameter refinement, and intelligent diagnostic modeling to improve predictive accuracy and computational efficiency in biomedical environments. The study synthesizes theoretical foundations from intelligent fault diagnosis, neural network architecture, adaptive learning, and feature optimization literature to formulate a multilayer predictive analytical model applicable to biological diagnosis systems. The proposed framework emphasizes evolutionary parameter adaptation across layered analytical modules, enabling selective refinement of biological variables such as genomic indicators, pathological markers, signal-derived measurements, and diagnostic attributes. The architecture supports iterative optimization, feature-weight balancing, error minimization, and dynamic classification processes. The study further evaluates the relevance of neural-network-assisted diagnostic systems and adaptive computational learning methods in biological prediction environments. Particular emphasis is placed on feature optimization and deep learning integration inspired by recent work on microarray gene medical data classification using feature optimization and deep learning (D. Girish et al., 2025). The paper develops a structured methodology involving data normalization, layered feature segmentation, evolutionary parameter initialization, neural adaptive learning, and diagnosis prediction analytics. Results indicate that layered computational architectures supported by evolutionary parameter selection significantly improve predictive stability, reduce dimensional irrelevance, and enhance classification reliability. The discussion highlights theoretical implications, computational trade-offs, diagnostic scalability, and limitations associated with adaptive biological analytics. The research contributes to computational diagnosis literature by presenting a unified analytical framework capable of supporting scalable biomedical decision systems while maintaining interpretability, optimization flexibility, and intelligent diagnostic precision.
Copyright (c) 2026 Dr. Anahit Sarkissian https://creativecommons.org/licenses/by/4.0
The Convergence of Blockchain and Artificial Intelligence in Securing Industrial Control Systems and Future Network Architectures: A Comprehensive Theoretical and Empirical Analysis
The rapid evolution of Industrial Control Systems (ICS) and the emergence of 6G communication paradigms have introduced unprecedented complexities in data integrity, resource sharing, and anomaly detection. Traditional security frameworks often struggle with the centralized nature of trust and the static logic of conventional intrusion detection. This research explores the synergistic integration of blockchain technology and Artificial Intelligence (AI) to establish a decentralized, immutable, and autonomous security architecture. By leveraging blockchain’s inherent transparency and AI’s predictive capabilities-specifically focusing on Graph Neural Networks (GNNs), Transformers, and Autoencoders-this study proposes a holistic framework for securing Industry 4.0 environments. We examine the role of game-theoretic consensus protocols in achieving true decentralization and the deployment of deep learning models for real-time anomaly detection in complex datasets like SWaT and WADI. The findings suggest that while blockchain ensures the integrity of the audit trail and facilitates secure resource sharing in 6G, AI provides the necessary intelligence to identify sophisticated cyber-physical attacks. This paper concludes that the convergence of these technologies is not merely an incremental improvement but a fundamental shift toward self-healing, resilient digital infrastructures.
Copyright (c) 2026 Ji Hoon Kim https://creativecommons.org/licenses/by/4.0
Didactic Foundations Of Individualization Technology In School Biology Education
The diversification of learners’ needs in contemporary schools has made the search for effective models of individualization one of the central tasks of didactics. This issue is especially important in biology education, where students differ not only in pace and achievement, but also in interest, prior knowledge, scientific language proficiency, and readiness for inquiry-based and laboratory work. The purpose of this study is to substantiate the didactic foundations of individualization technology in school biology education and to clarify the methodological conditions under which it can improve learning outcomes, learner agency, and conceptual understanding. The study employs theoretical analysis, comparative interpretation, and content synthesis of key international sources on differentiated and personalized learning, digital education, teacher preparation, and biology teaching. The results show that the didactic foundations of individualization in school biology rest on several interconnected principles: diagnostic orientation, variability of content and task complexity, scaffolded inquiry, multimodal representation of biological phenomena, flexible organization of classroom interaction, and continuous formative assessment supported by feedback. The analysis also demonstrates that digital tools become pedagogically meaningful only when they strengthen these foundations rather than replace teacher judgment. In biology education, individualization is particularly productive when abstract concepts are linked with visual models, experimentation, simulations, and context-based tasks. The study concludes that individualization technology in biology should be understood not as isolated differentiation techniques, but as a coherent didactic system that coordinates aims, content, methods, learning environments, and assessment according to the learner’s educational profile. Such a system can increase accessibility, engagement, and scientific literacy in school biology.
Copyright (c) 2026 Savutova Maloxat Erkinovna https://creativecommons.org/licenses/by/4.0
A Hybrid Cryptography-Based Secure Data Exchange Algorithm for Blockchain-Enabled IoT Systems
The rapid development of Internet of Things (IoT) technologies has significantly increased the demand for secure and efficient communication mechanisms. However, traditional security approaches are often unsuitable for IoT environments due to limited computational resources and vulnerability to various cyberattacks. This paper proposes a secure data exchange algorithm based on blockchain technology and hybrid cryptography. The proposed method integrates an enhanced elliptic curve-based digital signature algorithm (EECHERI) with symmetric encryption (AES-192) to ensure authentication, confidentiality, and integrity. A cluster-based architecture is adopted to improve scalability and efficiency, while a dynamic session key generation mechanism enhances security. The communication protocol is designed in seven sequential steps, ensuring secure device-to-device interaction. The proposed approach provides strong resistance against spoofing, replay, and unauthorized access attacks while maintaining low computational overhead, making it suitable for real-world IoT applications.
Copyright (c) 2026 Seidullayev M.K. https://creativecommons.org/licenses/by/4.0
Intrusion Detection Systems In Modern Networks Technologies Challenges And Future Research Directions
This paper examines the main types, detection approaches, and architectural solutions of Intrusion Detection Systems (IDS). Based on early research, it analyzes host-based, network-based, and hybrid deployment models, as well as signature-based, anomaly-based, and hybrid detection methods. The paper further presents modern industry-grade platforms widely used today—such as Suricata, Zeek, Wazuh, and AI/ML-based security solutions—in place of earlier academic prototypes developed in the 2010s. In addition, it discusses current challenges, including real-time processing requirements, false positive rates, encrypted traffic analysis, scalability issues, and the integration of artificial intelligence, as well as outlines future research directions in the field.
Copyright (c) 2026 Nuriddin Safoev, Suhrobjon Bozorov, Sirojiddin Salimov, Shakarov Muhiddin Abdug‘Affor O‘G‘Li https://creativecommons.org/licenses/by/4.0
Bacterial Sequestration of Zn Via Resistant Prokaryotes: Prospects for Cleanup of Industrial Discharges
The discharge of zinc (Zn)-laden industrial effluents represents a persistent environmental concern due to the metal’s bioaccumulative nature and ecological toxicity. Conventional remediation technologies, including physicochemical separation and advanced oxidation, often involve high operational costs and generate secondary waste streams. Consequently, biological approaches, particularly bacterial sequestration using metal-resistant prokaryotes, have emerged as promising alternatives for sustainable wastewater management. This study investigates the theoretical and functional potential of Zn sequestration through resistant bacterial systems, with specific emphasis on industrial discharge treatment. The research integrates concepts from microbial resistance, physicochemical dispersion modeling, and industrial process engineering to develop a comprehensive understanding of Zn removal mechanisms. The methodology is structured around microbial isolation, resistance profiling, biosorption kinetics, and system optimization under varying environmental conditions. The analysis demonstrates that resistant prokaryotes exhibit significant Zn sequestration capacity through mechanisms such as extracellular adsorption, intracellular accumulation, and metabolic transformation. These findings align with previously reported microbial Zn remediation efficiency, indicating strong adaptability of bacterial systems in metal-contaminated environments (Pratap et al., 2022). Additionally, the study highlights the influence of wastewater heterogeneity and dispersion characteristics on treatment efficiency, drawing parallels with fluid transport and dispersion models used in environmental engineering (Chen et al., 2005). The results indicate that optimized bacterial systems can achieve high Zn removal efficiency while simultaneously reducing organic pollutant loads. However, limitations related to process scalability, environmental variability, and microbial stability remain critical challenges for industrial implementation. The study concludes that bacterial sequestration offers a viable, environmentally sustainable alternative for Zn removal from industrial discharges. The integration of microbial systems with advanced engineering frameworks can significantly enhance treatment efficiency and operational feasibility. This research contributes to the development of next-generation biotechnological solutions for industrial wastewater remediation.
Copyright (c) 2024 Dr. Priya Mehta https://creativecommons.org/licenses/by/4.0
Determination of The Optimal Ratio Between Moisture Reduction and Energy Consumption in Superphosphate Drying
This article investigates the optimal relationship between moisture reduction and energy consumption during the drying of superphosphate in a drum dryer equipped with three-section modified lifting flights. The drying process was evaluated using final moisture content, total heat consumption, evaporated moisture flow rate, and aerodynamic resistance. The main operating parameters were drying agent temperature, gas velocity, material productivity, and the number of lifting flights. Particular attention was given to the balance between achieving low final moisture content and avoiding excessive heat consumption. The analysis was carried out for a material productivity of 0.095 kg/s and gas velocity of 20 m/s, with drying agent temperatures of 100, 120, and 140 °C and 24, 30, and 36 lifting flights. The results showed that increasing temperature and the number of lifting flights reduced the final moisture content, but also increased total heat consumption. For example, at 140 °C, increasing the number of lifting flights from 24 to 36 reduced final moisture from 4.874% to 3.266%, while total heat consumption increased from 3046.098 to 3111.135 kJ/kg of evaporated moisture. The results indicate that 30 lifting flights at 120–140 °C provide a rational balance between moisture reduction and energy consumption.
Copyright (c) 2026 Adil Akhunbayev, Aziz Isomidinov, Dilnozakhon Khujayeva https://creativecommons.org/licenses/by/4.0
Comprehensive Analysis of Molecular Activity, Energy Interactions, and Reaction Progression in a Glucose-Converting Enzyme from Environmental Bacterial Isolates
Glucose-converting enzymes derived from environmental bacterial isolates represent a critical class of biocatalysts that integrate molecular activity, energy transduction, and reaction kinetics within complex biochemical systems. This study presents a comprehensive analytical framework for evaluating molecular behavior, energetic interactions, and reaction progression in such enzymatic systems, with a particular focus on enzymes derived from Pseudomonas and Actinomyces species. The investigation adopts an interdisciplinary approach combining biochemical kinetics, multivariate statistical modeling, and reaction system analysis to interpret enzyme behavior under varying physicochemical conditions. Principal component analysis-based frameworks are conceptually integrated to reduce system complexity and identify dominant interaction variables governing enzymatic efficiency (Anderson, 2003; Sun & Li, 2006). The study further explores how energy redistribution within enzymatic microenvironments influences catalytic performance, drawing parallels with optimization models used in complex engineered systems (Chu & Shyan-Shu, 2003; Li et al., 2009). Reaction progression is analyzed through a coupled kinetic–energetic lens, emphasizing how substrate transformation rates are influenced by molecular structural stability and environmental variability. Findings suggest that enzymatic activity is strongly governed by interdependent molecular and energetic parameters, including conformational stability, substrate binding efficiency, and energy transfer efficiency. These interactions exhibit nonlinear behavior under varying substrate loads, highlighting the complexity of biological reaction systems. Comparative biochemical evidence supports that glucose-converting enzymes from environmental bacterial isolates exhibit adaptive kinetic behavior consistent with thermodynamically regulated catalytic systems (Singh, Modi, & Tiwari, 2019). This reinforces the view that enzymatic efficiency is not solely reaction-driven but also energy-modulated. Overall, this study provides a unified interpretative model for understanding glucose conversion in microbial enzymes by integrating molecular activity, energy dynamics, and reaction progression into a single analytical framework. The findings have implications for bioprocess optimization, enzymatic engineering, and systems-level biochemical modeling.
Copyright (c) 2021 Dr. Fahad Al-Thani https://creativecommons.org/licenses/by/4.0