International Journal of Advance Scientific Research
type: channel
description: 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
publisher: Professor Emma Franke
language: en
publication name: International Journal of Advance Scientific Research
issn: 2750-1396
items: https://sciencebring.com/index.php/ijasr/article/view/1249
items: Principles of Responsible Automation in Distribution Management: Aligning Operational Success with Equity
items: Modern Approaches to Sustainable Portfolio Governance: Computational Innovation and Analyst Expertise
items: Data-Centric Asset Health Management across Automated Manufacturing Networks: A Pathway to Enhanced Output Efficiency
items: Scientific and Methodological Foundations of Using Simulation Models in Natural Geographical Research
items: Machine-Learning Guided Medication Selection Frameworks Incorporating Socioeconomic Influence Factors
items: Adaptive Reliability Enhancement Through Failure Retrospection and AI-Driven Reasoning in Federated Corporate Computing Environments
items: Stablecoins And National Currency Stability: Impacts and Regulatory Mechanisms in The Digital Financial System
items: Investigation of The Influence of The Norin Ges Cascade on The Eastern Interconnection Load Profile
items: Improving the Quality of Internal Surfaces of Cylindrical Parts by Plastic Deformation
items: Fibrous Waste Processing Technology
items: Researching the Properties of Cotton Fiber
items: Genetic and Morphological Correlates of External Facial Development: An Ancestor–Descendant Analysis
items: Radiographic Assessment of Cranial Pit Characteristics in the Dogra Community: A Population-Based Observational Investigation
items: Knowledge and Compliance Regarding Clinical Refuse Handling Among Diverse Oral Health Personnel: A Facility-Oriented Investigation
items: Investigation of Methods for Extending the Service Life of Dies Made of SKD 11 Steel
items: Digital Resources and Online Platforms for Enhancing Students’ Independent Learning Competence: A Comprehensive Analysis and Classification
items: Improving the Machining Efficiency of Mold Guide Grooves Using A Separable-Insert Method: An Experimental Comparison of Machining Time, Tool Wear, And Surface Roughness
items: Advanced Machine Learning and Deep Learning Architectures for Enhanced Early Diagnosis and Risk Stratification of Neurodegenerative Diseases in Precision Healthcare
items: Possibilities Of Using Thermal Affected Zones And Special Welding Methods In Repairing Machine Parts And Metal Structures By Welding
items: Intelligent Energy Flow Management In Battery–Supercapacitor Systems
items: Methodological Approaches to Adapting the Smart City Concept to Fergana City
items: 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
items: Climate-Adaptive Small Architectural Forms For Public Spaces In Hot-Arid Urban Conditions Of Uzbekistan
items: Effective Hybrid Algorithm For Generating Large Prime Numbers
items: Models and Algorithms for Identifying Important Pages Based on User Behavior Analysis on Websites
items: Predictive Intrusion Limitation in Virtualized Environments via Self-Evolving Misguidance Frameworks
items: Autonomous Risk Mitigation across Multi-Tenant Platforms through Artificial Intelligence–Based Diversion Techniques
items: Improving Computational Sustainability Frameworks in Economic and Monetary Sectors Using Anticipatory Data Evaluation Models
items: Evolutionary Parameter Selection with Layered Computational Analytics in Biological Diagnosis Prediction

https://sciencebring.com/index.php/ijasr/article/view/1249
type: item
title: 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
description: 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 evalua…
subject: Articles
  topic: section
subject: Neonatal intensive care unit
  topic: keywords
subject: disinfectant efficacy
  topic: keywords
subject: antimicrobial resistance
  topic: keywords
creator: Dr. Ahmad Hidayat
rights: Copyright (c) 2026 Dr. Ahmad Hidayat https://creativecommons.org/licenses/by/4.0
date: 2026-07-01
publication date: 2026-07-01
starting page: 1
ending page: 10

Principles of Responsible Automation in Distribution Management: Aligning Operational Success with Equity
type: item
description: 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 a…
subject: Articles
  topic: section
subject: Responsible Automation
  topic: keywords
subject: Distribution Management
  topic: keywords
subject: AI Ethics
  topic: keywords
subject: Algorithmic Fairness
  topic: keywords
creator: Lukas Schneider
rights: Copyright (c) 2026 Lukas Schneider https://creativecommons.org/licenses/by/4.0
date: 2026-06-29
publication date: 2026-06-29
starting page: 94
ending page: 104

Modern Approaches to Sustainable Portfolio Governance: Computational Innovation and Analyst Expertise
type: item
description: 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…
subject: Articles
  topic: section
subject: Sustainable portfolio governance
  topic: keywords
subject: computational finance
  topic: keywords
subject: artificial intelligence
  topic: keywords
subject: system dynamics
  topic: keywords
creator: Ingrid Falkenberg
rights: Copyright (c) 2026 Ingrid Falkenberg https://creativecommons.org/licenses/by/4.0
date: 2026-06-29
publication date: 2026-06-29
starting page: 84
ending page: 93

Data-Centric Asset Health Management across Automated Manufacturing Networks: A Pathway to Enhanced Output Efficiency
type: item
description: 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 signific…
subject: Articles
  topic: section
subject: Asset Health Management
  topic: keywords
subject: Predictive Maintenance
  topic: keywords
subject: Industrial Automation
  topic: keywords
subject: Machine Learning
  topic: keywords
creator: Ri Jong-Ho
rights: Copyright (c) 2026 Ri Jong-Ho https://creativecommons.org/licenses/by/4.0
date: 2026-06-27
publication date: 2026-06-27
starting page: 74
ending page: 83

Scientific and Methodological Foundations of Using Simulation Models in Natural Geographical Research
type: item
description: 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.
subject: Articles
  topic: section
subject: Natural geography
  topic: keywords
subject: simulation model
  topic: keywords
subject: geographical research
  topic: keywords
creator: Qudratov Shahboz
rights: Copyright (c) 2026 Qudratov Shahboz https://creativecommons.org/licenses/by/4.0
date: 2026-06-22
publication date: 2026-06-22
starting page: 66
ending page: 73
doi: 10.37547/ijasr-06-06-08

Machine-Learning Guided Medication Selection Frameworks Incorporating Socioeconomic Influence Factors
type: item
description: 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 p…
subject: Articles
  topic: section
subject: Machine Learning
  topic: keywords
subject: Medication Selection
  topic: keywords
subject: Socioeconomic Factors
  topic: keywords
subject: Clinical Decision Support Systems
  topic: keywords
creator: Dr. Valeria Méndez
rights: Copyright (c) 2025 Dr. Valeria Méndez https://creativecommons.org/licenses/by/4.0
date: 2025-12-31
publication date: 2025-12-31
starting page: 148
ending page: 160

Adaptive Reliability Enhancement Through Failure Retrospection and AI-Driven Reasoning in Federated Corporate Computing Environments
type: item
description: 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…
subject: Articles
  topic: section
subject: Federated computing
  topic: keywords
subject: self-healing systems
  topic: keywords
subject: failure retrospection
  topic: keywords
subject: large language models
  topic: keywords
creator: Dr. Priya Nair
rights: Copyright (c) 2026 Dr. Priya Nair https://creativecommons.org/licenses/by/4.0
date: 2026-04-30
publication date: 2026-04-30
starting page: 102
ending page: 111

Stablecoins And National Currency Stability: Impacts and Regulatory Mechanisms in The Digital Financial System
type: item
description: 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.
subject: Articles
  topic: section
subject: Stablecoins
  topic: keywords
subject: monetary sovereignty
  topic: keywords
subject: currency stability
  topic: keywords
creator: Yakubova Sh.Sh.
creator: Qoryogdiyev O.A.
rights: Copyright (c) 2026 Yakubova Sh.Sh., Qoryogdiyev O.A. https://creativecommons.org/licenses/by/4.0
date: 2026-06-08
publication date: 2026-06-08
starting page: 51
ending page: 59
doi: 10.37547/ijasr-06-06-06

Investigation of The Influence of The Norin Ges Cascade on The Eastern Interconnection Load Profile
type: item
description: 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.
subject: Articles
  topic: section
subject: Hydropower cascade
  topic: keywords
subject: load profile
  topic: keywords
subject: peak shaving
  topic: keywords
creator: J.D. Kholbayev
creator: G’. M. Ergashev
creator: S. Abdurashidov
rights: Copyright (c) 2026 J.D. Kholbayev, G’. M. Ergashev, S. Abdurashidov https://creativecommons.org/licenses/by/4.0
date: 2026-06-13
publication date: 2026-06-13
starting page: 60
ending page: 65
doi: 10.37547/ijasr-06-06-07

Improving the Quality of Internal Surfaces of Cylindrical Parts by Plastic Deformation
type: item
description: 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.
subject: Articles
  topic: section
subject: Cylindrical parts
  topic: keywords
subject: internal surface
  topic: keywords
subject: roller burnishing
  topic: keywords
creator: Sh.N. Fayzimatov
creator: R.B. Khabibullayev
rights: Copyright (c) 2026 Sh.N. Fayzimatov, R.B. Khabibullayev https://creativecommons.org/licenses/by/4.0
date: 2026-06-12
publication date: 2026-06-12
starting page: 36
ending page: 50
doi: 10.37547/ijasr-06-06-05

Fibrous Waste Processing Technology
type: item
description: 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.
subject: Articles
  topic: section
subject: Fiber
  topic: keywords
subject: fiber waste
  topic: keywords
subject: fiber suitable for spinning
  topic: keywords
creator: Shodmankulov Alisher Mamatkulovich
creator: Yuldasheva Mavlyuda Toramurodovna
rights: Copyright (c) 2026 Shodmankulov Alisher Mamatkulovich, Yuldasheva Mavlyuda Toramurodovna https://creativecommons.org/licenses/by/4.0
date: 2026-06-11
publication date: 2026-06-11
starting page: 30
ending page: 35
doi: 10.37547/ijasr-06-06-04

Researching the Properties of Cotton Fiber
type: item
description: 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.
subject: Articles
  topic: section
subject: Selection cotton
  topic: keywords
subject: cotton fiber properties
  topic: keywords
subject: compact yarn properties
  topic: keywords
creator: Shodmankulov Alisher Mamatkulovich
creator: Yuldasheva Mavlyuda Toramurodovna
rights: Copyright (c) 2026 Shodmankulov Alisher Mamatkulovich, Yuldasheva Mavlyuda Toramurodovna https://creativecommons.org/licenses/by/4.0
date: 2026-06-11
publication date: 2026-06-11
starting page: 18
ending page: 29
doi: 10.37547/ijasr-06-06-03

Genetic and Morphological Correlates of External Facial Development: An Ancestor–Descendant Analysis
type: item
description: 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 variabi…
subject: Articles
  topic: section
subject: Facial Development
  topic: keywords
subject: Craniofacial Morphology
  topic: keywords
subject: Genetic Inheritance
  topic: keywords
subject: Ancestor–Descendant Analysis
  topic: keywords
creator: Dr. Chinedu Okafor
rights: Copyright (c) 2024 Dr. Chinedu Okafor https://creativecommons.org/licenses/by/4.0
date: 2024-12-31
publication date: 2024-12-31
starting page: 349
ending page: 359

Radiographic Assessment of Cranial Pit Characteristics in the Dogra Community: A Population-Based Observational Investigation
type: item
description: 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 o…
subject: Articles
  topic: section
subject: Cranial pit morphology
  topic: keywords
subject: Dogra population
  topic: keywords
subject: radiographic analysis
  topic: keywords
subject: sellar region
  topic: keywords
creator: Dr. Arman Petrosyan
rights: Copyright (c) 2024 Dr. Arman Petrosyan https://creativecommons.org/licenses/by/4.0
date: 2024-06-30
publication date: 2024-06-30
starting page: 195
ending page: 205

Knowledge and Compliance Regarding Clinical Refuse Handling Among Diverse Oral Health Personnel: A Facility-Oriented Investigation
type: item
description: 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 …
subject: Articles
  topic: section
subject: Clinical refuse management
  topic: keywords
subject: biomedical waste
  topic: keywords
subject: oral health personnel
  topic: keywords
subject: compliance behavior
  topic: keywords
creator: Dr. Lukas Weber
rights: Copyright (c) 2024 Dr. Lukas Weber https://creativecommons.org/licenses/by/4.0
date: 2024-09-30
publication date: 2024-09-30
starting page: 59
ending page: 69

Investigation of Methods for Extending the Service Life of Dies Made of SKD 11 Steel
type: item
description: 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.
subject: Articles
  topic: section
subject: SKD 11 steel
  topic: keywords
subject: wear resistance
  topic: keywords
subject: heat treatment
  topic: keywords
creator: Sh.N. Fayzimatov
creator: L.X. Yusufjonov
rights: Copyright (c) 2026 Sh.N. Fayzimatov, L.X. Yusufjonov https://creativecommons.org/licenses/by/4.0
date: 2026-06-09
publication date: 2026-06-09
starting page: 10
ending page: 17
doi: 10.37547/ijasr-06-06-02

Digital Resources and Online Platforms for Enhancing Students’ Independent Learning Competence: A Comprehensive Analysis and Classification
type: item
description: 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.
subject: Articles
  topic: section
subject: Digital resources
  topic: keywords
subject: independent learning competence
  topic: keywords
subject: online learning platforms
  topic: keywords
creator: Y.R. Azzamov
rights: Copyright (c) 2026 Y.R. Azzamov https://creativecommons.org/licenses/by/4.0
date: 2026-05-31
publication date: 2026-05-31
starting page: 118
ending page: 124
doi: 10.37547/ijasr-06-05-13

Improving the Machining Efficiency of Mold Guide Grooves Using A Separable-Insert Method: An Experimental Comparison of Machining Time, Tool Wear, And Surface Roughness
type: item
description: 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.
subject: Articles
  topic: section
subject: Mold die
  topic: keywords
subject: guide groove
  topic: keywords
subject: separable insert
  topic: keywords
creator: D.A. Akbarov
creator: Y. Y. Khusanov
rights: Copyright (c) 2026 D.A. Akbarov, Y. Y. Khusanov https://creativecommons.org/licenses/by/4.0
date: 2026-05-31
publication date: 2026-05-31
starting page: 125
ending page: 133
doi: 10.37547/ijasr-06-05-14

Advanced Machine Learning and Deep Learning Architectures for Enhanced Early Diagnosis and Risk Stratification of Neurodegenerative Diseases in Precision Healthcare
type: item
description: 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.
subject: Articles
  topic: section
subject: Machine Learning
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Neurodegenerative Diseases
  topic: keywords
subject: Early Diagnosis
  topic: keywords
creator: Kojo Frimpong
rights: Copyright (c) 2026 Kojo Frimpong https://creativecommons.org/licenses/by/4.0
date: 2026-06-01
publication date: 2026-06-01
starting page: 1
ending page: 9

Possibilities Of Using Thermal Affected Zones And Special Welding Methods In Repairing Machine Parts And Metal Structures By Welding
type: item
description: 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.
subject: Articles
  topic: section
subject: welding
  topic: keywords
subject: heat affected zone (HAZ)
  topic: keywords
subject: machine parts
  topic: keywords
creator: B.J. Khursanov
rights: Copyright (c) 2026 B.J. Khursanov https://creativecommons.org/licenses/by/4.0
date: 2026-05-22
publication date: 2026-05-22
starting page: 104
ending page: 109
doi: 10.37547/ijasr-06-05-11

Intelligent Energy Flow Management In Battery–Supercapacitor Systems
type: item
description: 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.
subject: Articles
  topic: section
subject: Battery-supercapacitor system
  topic: keywords
subject: intelligent control
  topic: keywords
subject: hybrid energy storage
  topic: keywords
creator: B.B. Boynazarov
creator: B.T. Jabborov
rights: Copyright (c) 2026 B.B. Boynazarov, B.T. Jabborov https://creativecommons.org/licenses/by/4.0
date: 2026-05-23
publication date: 2026-05-23
starting page: 110
ending page: 117
doi: 10.37547/ijasr-06-05-12

Methodological Approaches to Adapting the Smart City Concept to Fergana City
type: item
description: 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.
subject: Articles
  topic: section
creator: Shokhrukh Obitov
creator: Doniyor Nurmatov
rights: Copyright (c) 2026 Shokhrukh Obitov, Doniyor Nurmatov https://creativecommons.org/licenses/by/4.0
date: 2026-05-20
publication date: 2026-05-20
starting page: 87
ending page: 103
doi: 10.37547/ijasr-06-05-10

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
type: item
description: 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.
subject: Articles
  topic: section
subject: Chevrolet Cobalt
  topic: keywords
subject: winter operation
  topic: keywords
subject: engine thermal regime
  topic: keywords
creator: N.I. Otabayev
creator: A.B. Luqmonjonov
rights: Copyright (c) 2026 N.I. Otabayev, A.B. Luqmonjonov https://creativecommons.org/licenses/by/4.0
date: 2026-05-20
publication date: 2026-05-20
starting page: 77
ending page: 86
doi: 10.37547/ijasr-06-05-09

Climate-Adaptive Small Architectural Forms For Public Spaces In Hot-Arid Urban Conditions Of Uzbekistan
type: item
description: 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.
subject: Articles
  topic: section
subject: Climate-adaptive design
  topic: keywords
subject: small architectural forms
  topic: keywords
subject: hot-arid climate
  topic: keywords
creator: Ilhom Ergashev
creator: Akhmedova Nazokat
rights: Copyright (c) 2026 Ilhom Ergashev, Akhmedova Nazokat https://creativecommons.org/licenses/by/4.0
date: 2026-05-19
publication date: 2026-05-19
starting page: 62
ending page: 76
doi: 10.37547/ijasr-06-05-08

Effective Hybrid Algorithm For Generating Large Prime Numbers
type: item
description: 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).
subject: Articles
  topic: section
subject: Prime number
  topic: keywords
subject: primality test
  topic: keywords
subject: Miller–Rabin,
  topic: keywords
subject: random number generation
  topic: keywords
subject: cryptography
  topic: keywords
creator: Xudoykulov Z. T.
creator: Jabbarov N. A.
creator: Jabborov A. A.
rights: Copyright (c) 2026 Xudoykulov Z. T., Jabbarov N. A., Jabborov A. A. https://creativecommons.org/licenses/by/4.0
date: 2026-05-18
publication date: 2026-05-18
starting page: 47
ending page: 61

Models and Algorithms for Identifying Important Pages Based on User Behavior Analysis on Websites
type: item
description: 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.
subject: Articles
  topic: section
subject: Web user behavior
  topic: keywords
subject: Web Usage Mining
  topic: keywords
subject: user navigation
  topic: keywords
subject: important page identification
  topic: keywords
creator: Husanov Sherzod Abdimannonovich
rights: Copyright (c) 2026 Husanov Sherzod Abdimannonovich https://creativecommons.org/licenses/by/4.0
date: 2026-05-18
publication date: 2026-05-18
starting page: 43
ending page: 46
doi: 10.37547/ijasr-06-05-06

Predictive Intrusion Limitation in Virtualized Environments via Self-Evolving Misguidance Frameworks
type: item
description: 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. …
subject: Articles
  topic: section
subject: Virtualized environments
  topic: keywords
subject: intrusion prediction
  topic: keywords
subject: cyber deception
  topic: keywords
subject: machine learning security
  topic: keywords
creator: Dr. Haruto Nakamura
rights: Copyright (c) 2026 Dr. Haruto Nakamura https://creativecommons.org/licenses/by/4.0
date: 2026-03-31
publication date: 2026-03-31
starting page: 180
ending page: 190

Autonomous Risk Mitigation across Multi-Tenant Platforms through Artificial Intelligence–Based Diversion Techniques
type: item
description: 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…
subject: Articles
  topic: section
subject: Multi-Tenant Systems
  topic: keywords
subject: Artificial Intelligence Security
  topic: keywords
subject: Risk Mitigation
  topic: keywords
subject: Traffic Diversion
  topic: keywords
creator: Dr. Rafael Souza
rights: Copyright (c) 2026 Dr. Rafael Souza https://creativecommons.org/licenses/by/4.0
date: 2026-03-31
publication date: 2026-03-31
starting page: 168
ending page: 179

Improving Computational Sustainability Frameworks in Economic and Monetary Sectors Using Anticipatory Data Evaluation Models
type: item
description: 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 e…
subject: Articles
  topic: section
subject: Computational sustainability
  topic: keywords
subject: anticipatory data evaluation
  topic: keywords
subject: economic systems
  topic: keywords
subject: monetary governance
  topic: keywords
creator: Dr. Liina Vaher
rights: Copyright (c) 2026 Dr. Liina Vaher https://creativecommons.org/licenses/by/4.0
date: 2026-03-31
publication date: 2026-03-31
starting page: 147
ending page: 167

Evolutionary Parameter Selection with Layered Computational Analytics in Biological Diagnosis Prediction
type: item
description: 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 d…
subject: Articles
  topic: section
subject: Biological diagnosis prediction
  topic: keywords
subject: evolutionary parameter selection
  topic: keywords
subject: computational analytics
  topic: keywords
subject: neural networks
  topic: keywords
creator: Dr. Anahit Sarkissian
rights: Copyright (c) 2026 Dr. Anahit Sarkissian https://creativecommons.org/licenses/by/4.0
date: 2026-04-30
publication date: 2026-04-30
starting page: 87
ending page: 101