Innovative: International Multidisciplinary Journal of Applied Technology (2995-486X)
Innovative: International Multidisciplinary Journal of Applied Technology (IMJAT) with U.S ISSN 2995-486X is a peer-reviewed, open-access scientific journal dedicated to publishing original research and innovative developments across a broad spectrum of applied and technical sciences. With its growing reputation, IMJAT continues to be a valuable platform for academics, researchers, engineers, and industry professionals seeking to publish impactful research that advances applied science and technology on a global scale. We are pleased to announce that IMJAT has been officially recognized by the Higher Attestation Commission (OAK) of the Republic of Uzbekistan for meeting the criteria required for technical sciences publications. As per OAK Decision No. 368/4 dated March 6, 2025, the journal is now listed as a recommended scientific publication for “05.00.00 – Technical Sciences, making it a valid outlet for PhD and DSc researchers to publish their dissertation-related scientific results. JOURNAL INFORMATION Journal title : Innovative: International Multidisciplinary Journal of Applied Technology (IMJAT) Initials : Innovative Abbreviation : Int.Multi.Jour. Applied. Techno. Frequency : Monthly Published DOI : Publisher Association : Antis Publishing House Online ISSN : ISSN 2995-486X (Online) | Innovative | The ISSN Portal Collaboration Institution : Kokand State University Publisher : Multi Journals Press Citation Analysis : IEEE Reference Style Accreditation : - Abstracting and Indexing : Click here
Transition From Monitoring to Observability in Modern Service Assurance Through an Empirical Evaluation of Current Practices
Traditional IT checking is failing as a result of the growing complexity systems, exacerbated by the introduction of distributed IT systems, cloud computing, and microservices. Observability is a new comprehensive assurance methodology that adapts monitoring practices to include the analysis of metrics, and traces to enhance visibility and system resilience. A one-time quantitative survey design was applied to a sample of 155 IT professionals based in the USA use cloud infrastructure and service assurance. A five-point Likert-scale questionnaire was the primary method of data collection. The analysis of the data was conducted using descriptive statistics, correlation, and structural statistics using IBM SPSS Statistics 29.0. System Reliability achieved the highest average score (4.02), followed by Observability Capability (3.95). Monitoring Capability achieved the lowest mean score (3.71). Application Performance Monitoring was the most common practice, at 31.6%, while 34.8% of respondents reported partial deployment of observability. The highest correlation was found between Observability Capability and Service Assurance Performance (r = 0.781). In the structural analysis, Monitoring Capability, at least, positively impacted Observability Capability (β = 0.653). The model incorporated the structural equation and reported that the model explained 68.4% of the variance of Service Assurance Performance (R² = 0.684). The analysis conducted showed that observability significantly improves incident response, system reliability, and service assurance performance. This study suggests that the integration of observability should be the primary focus increase the speed at which issues are resolved, in conjunction with improving the reliability of digital services.
Copyright (c) 2023 Md Mofasel Hossain, Afsara Tasnim Shama (Author)
PHARMACOLOGICAL EFFICACY AND TOXICOLOGICAL SAFETY ASSESSMENT OF INTROVIT ES 100 ORAL SOLUTION IN LAYING HENS
Oxidative stress, immunosuppression and reduced productivity remain major constraints in intensively managed poultry flocks. This study evaluated the pharmacological efficacy and toxicological safety of Introvit ES 100, an oral vitamin E–selenium complex, in laying hens under industrial rearing conditions. Thirty clinically healthy hens of comparable age and body weight were allocated to three groups (n = 10 each): an untreated control group, and two experimental groups receiving the preparation via drinking water at 1 mL/L (Group I) and 2 mL/L (Group II) for 30 consecutive days. Haematological, biochemical, antioxidant, immunological, growth, egg-production, feed-conversion, survivability, and hepato-renal histomorphological parameters were assessed, together with an economic-efficiency analysis. Introvit ES 100 produced dose-dependent increases in erythrocyte count, haemoglobin, total protein, superoxide dismutase, glutathione peroxidase and catalase activities, together with reductions in malondialdehyde, AST and ALT, all statistically significant relative to control (p < 0.05). Phagocytic activity, immunoglobulin concentration and the stress-resistance index improved in treated groups, egg production rose by up to 14.3 %, feed-conversion ratio improved by up to 56 %, and survivability reached 100 % versus 90 % in controls. Histological examination revealed no degenerative, necrotic or inflammatory changes in liver or kidney of treated birds. The findings indicate that Introvit ES 100, applied at the recommended oral doses, is a safe and pharmacologically effective antioxidant–immunomodulatory agent for use in commercial poultry production.
Copyright (c) 2026 Shakhzod Doniyorov (Author)
ELECTRODE MATERIALS FOR ELECTROCOAGULATION OF CARWASH WASTEWATER
Carwash wastewater (CWW) is generated in large volumes, typically 150–600 L per vehicle, and carries high, variable loads of surfactants, oil and grease, suspended solids and turbidity (often exceeding 10³ NTU), together with dissolved salts and traces of heavy metals, making its treatment and reuse a priority for water conservation. Electrocoagulation (EC), in which coagulants are generated in situ by anodic dissolution while cathodically evolved hydrogen drives flotation, is a compact, low-reagent option well suited to such decentralized sources. However, comparisons of electrode materials remain scattered across dissimilar effluents, and the roles of the cathode and of the metallurgical composition of iron are seldom treated systematically. This review critically consolidates the metals reported as EC anodes, namely Al (Al), iron (Fe), zinc, magnesium, copper, stainless steel, titanium and graphite, together with the influence of cathode material and key operating factors, in order to justify electrode selection for CWW. Across the literature, EC typically removes 75–98% of chemical oxygen demand and 80–99% of turbidity, with Al and Fe dominating practice owing to their low cost, availability, non-toxicity and high ionic valence. Al generally affords superior turbidity, color and surfactant removal at lower energy but higher sludge and a risk of residual metal, whereas Fe is often faster and cheaper yet imparts residual color. Other metals are effective only for specific pollutants. The cathode is shown to govern flotation, pH evolution and passivation, and the carbon and impurity content of iron electrodes is identified as an under-explored factor. Al and Fe, alone or as Al/Fe hybrids, emerge as the pragmatic choice for CWW treatment and reuse in line with Sustainable Development Goal 6.
Copyright (c) 2026 Innovative: International Multidisciplinary Journal of Applied Technology https://creativecommons.org/licenses/by/4.0
A WEBCAM-BASED RECOGNITION SYSTEM FOR THE UZBEK FINGERSPELLING ALPHABET
For individuals with hearing or speech impairments, sign language serves as the primary channel of natural connection with society. To date, work on the automated recognition of Uzbek sign language remains scarce, and an openly available dataset for this purpose is essentially nonexistent. This paper presents a system that recognizes the Uzbek fingerspelling alphabet using an ordinary webcam. Hand movements are tracked with the MediaPipe Hands tool; letters represented by static hand poses are classified using a Random Forest classifier, while letters expressed through motion are recognized using an LSTM neural network. Trained on a manually collected dataset of 1,523 static frames and 468 motion sequences, the system recognized static letters with 99.11% accuracy and dynamic letters with 65.96% accuracy. The results indicate that the recognition of stable hand poses can be achieved with high reliability, whereas the recognition of temporally evolving gestures remains a considerably more complex scientific and technical challenge.
Copyright (c) 2026 Sevinch Jovlieva (Author)
EVALUATION OF WCAG CRITERIA IN DIGITAL LEARNING PLATFORMS USING A HYBRID AUDIT APPROACH
Ensuring digital accessibility in e-learning platforms is a primary requirement for creating equal educational opportunities for all students. This study examines the practical effectiveness of a hybrid audit approach that combines automated software tools, specifically Axe-core and Google Lighthouse, with manual keyboard testing. As a practical testing ground, a prototype of the DigiLab learning platform developed by the author was used as the research object. While automated scanners are highly useful for the rapid detection of static code errors, their technical algorithms are limited to identifying only about 20 to 25 percent of overall WCAG standard violations. Full evaluation of interactive functionality and real user experience requires dynamic testing beyond mere source code analysis, a limitation openly acknowledged by the tool developers in their official documentation. Manual testing conducted using a standard keyboard successfully revealed these hidden semantic and navigational errors that the software had overlooked. After resolving all identified issues, the platform's initial Lighthouse score increased from 68 to 95 points, while Axe-core recorded zero errors in the final audit. The experiment demonstrates that to achieve authentic digital inclusion in educational resources, automated assessments must be systematically complemented by manual human verification.
Investigation of Gas Concentrations Emitted From Chemical Reagents Used in Polyurethane Production
This study investigates the composition and concentration of gases generated during the heating, polycondensation, curing, crystallization, and recycling processes of liquid diisocyanates, polyether polyols, and chain extenders used in polyurethane synthesis within the temperature range of 60–120 °C. The polycondensation kinetics and thermochemical transformations of both virgin and recycled polyurethane formulations were examined. The concentrations of volatile organic compounds (VOCs), isocyanate vapors, and other gaseous emissions released into the workplace atmosphere during the technological process were determined using ANT-3M and GC-8000 gas analyzers. The measured gas concentrations were compared with the maximum permissible concentrations (MPCs) established for workplace air, and their hazard classes were determined in accordance with GOST 12.1.007–76. Based on the experimental findings, an innovative technological approach and a multi-stage gas purification system were developed to capture, neutralize, and minimize the release of hazardous gaseous emissions into the atmosphere during polyurethane synthesis and recycling. The proposed technology is expected to improve the environmental safety of polyurethane production, ensure compliance of workplace air quality with occupational safety standards, reduce harmful emissions, and significantly enhance the environmental and technological efficiency of polyurethane manufacturing processes.
Copyright (c) 2025 Shodiev Asliddin Faxriddin ugli, Muhiddinov Bahodir Fahriddinovich (Author)
EFFECT OF 1,4-BUTANEDIOL CHAIN EXTENDER ON THE REACTION MECHANISM OF POLYURETHANE FORMATION
This study investigates the polymerization mechanism of 1,4-butanediol, used as a chain extender in the synthesis of polyurethane based on 4,4′-methylenediphenyl diisocyanate (4,4′-MDI), as well as the effect of the diisocyanate concentration on the macromolecular structure and physicomechanical properties of the polymer. During the reaction, the hydroxyl (–OH) groups of 1,4-butanediol react with the isocyanate (–NCO) groups of 4,4′-MDI to form urethane (–NHCOO–) linkages, resulting in polymer chain extension, an increase in the content of hard segments, and improved phase organization of the polymer structure. The effects of 1,4-butanediol (3–8 wt%) and 4,4′-MDI (60–75 wt%) on the polymerization kinetics, macromolecular chain growth, and crosslink density were systematically evaluated. The results demonstrated that compositions containing 4–6 wt% 1,4-butanediol and 68–70 wt% 4,4′-MDI promoted the formation of a homogeneous polyurethane macromolecular structure, providing high mechanical strength, optimum elasticity, and excellent thermal stability. Deviations from these composition ranges resulted in an increased content of residual isocyanate (–NCO) groups, non-uniform formation of the three-dimensional polymer network, and increased brittleness of the material. These findings demonstrate that optimizing the contents of the chain extender and diisocyanate is an effective approach for controlling the molecular architecture and enhancing the performance characteristics of polyurethane materials.
A SIMULATOR INDEPENDENT DYNAMIC EFFECTIVENESS-NTU MODEL FOR HEAT-EXCHANGER PERFORMANCE PREDICTION UNDER FLOW, TEMPERATURE, AND FOULING DEPENDENT OPERATION
Fouling is the dominant cause of the progressive loss of thermal duty in industrial heat exchangers, yet the design and monitoring of these units still rely largely on constant fouling factors that ignore the dependence of deposition on flow rate and surface temperature. This work develops a self-contained, simulator-independent mathematical model that couples the steady-flow energy balance, the logarithmic-mean-temperature-difference (LMTD) relation and the effectiveness–number-of-transfer-units (ε–NTU) formulation to an asymptotic, Kern–Seaton-type fouling law whose deposition and removal terms are written as explicit functions of tube velocity and film temperature. The overall heat-transfer coefficient, the effectiveness and the duty are advanced in time by integrating a single ordinary differential equation for the fouling resistance, with the thermal field solved quasi-steadily at each instant. Closed-form normalized sensitivity coefficients are derived for the effectiveness, the duty and the overall coefficient with respect to NTU, mass flow rate, inlet temperature difference and fouling resistance, and a dimensionless fouling-degradation number Θ = Uc Rf* is introduced to collapse the asymptotic performance loss onto a single group. The framework is verified against the exact ε–NTU/LMTD duty identity and against the analytical solution of the fouling law, and is then exercised on a thermodynamically consistent counterflow shell-and-tube case study. At the design velocity the model predicts a 20% reduction in the overall coefficient, a 10.7% loss of duty and a 30-day fouling time constant, and it reproduces the experimentally reported inverse-square dependence of that time constant on velocity. An uncertainty analysis quantifies the amplification of measurement error near a close temperature approach. The resulting model is transparent, computationally trivial and readily embedded in performance-monitoring and cleaning-schedule tools.
Copyright (c) 2026 Hassanin M. Ali (Author)
THEORETICAL PREREQUISITES FOR ADAPTING TRADITIONAL BAKERY TECHNOLOGY TO THERAPEUTIC AND DIETARY PRODUCTION
The traditional bread-making scheme evolved for wheat dough whose structure is built on a gluten framework, and it is not optimal once functional ingredients are introduced and thermolabile components must be preserved. This paper sets out the theoretical prerequisites for adapting the traditional technological scheme to the requirements of therapeutic and dietary bakery production. The scheme is treated as a controllable system of stages in which the functional load creates specific deviations from the standard regime that call for compensating technological solutions, subject to the principle of minimal technological intervention. Critical adaptation points are identified at raw-material preparation (pre-treatment of functional ingredients), dough mixing (choice of the dough-making method and compensation of gluten weakening), fermentation (sourdough and acidity control), baking (gentle, zone-differentiated regimes for thermolabile components) and the final stages (post-baking addition of probiotics and barrier packaging). Gluten-free production is considered as a special, fundamental case in which a system of hydrocolloids replaces the structural functions of gluten. The safety and guaranteed functionality of the products are addressed through HACCP-based quality management. The proposed adaptation closes the theoretical loop between recipe design and the practical manufacturability of functional bakery products.
Copyright (c) 2026 Kurbanov M. T. (Author)
NEXT-GENERATION POWER SYSTEMS: INTEGRATING SUSTAINABLE ENERGY SOURCES WITH ADVANCED DISTRIBUTION AND COMPENSATION STRATEGIES
Power systems of the next generation are a transformative advance in generating, distributing, and managing electric energy, with greater integration of sustainable resources into grid systems for improved reliability and efficiency. The current research investigates the building blocks, integration schemes, remuneration techniques, advantages, challenges, and applications of contemporary power systems. Global energy infrastructure is transitioning from centralized fossil-fuel-based grid systems to decentralized, intelligent, and renewable-based (DIW) systems. Some key enabling technologies are renewable energy resources (solar, wind, hydroelectric, and biomass), along with advanced energy storage systems coupled with the smart grid and hybrid energy design. They are the solution to harness all renewable energy intermittency while reducing system oscillation and increasing the flexibility of operations. According to the study, real-time monitoring enabled by Distributed Energy Resource Management Systems (DERMS), Advanced Distribution Management Systems (ADMS), and optimization tools based on artificial intelligence are critical in predicting maintenance and facilitating dispatch. Moreover, reactive power compensation, voltage regulation, and Flexible AC Transmission Systems (FACTS) are two needs of the day for better power quality and grid stability under high renewable penetration. Nevertheless, issues like high initial capital expenditure (CapEx), technological complexity, limited skilled workforce, and regulatory bottlenecks still obstruct scaling. Case studies from the industrial sector and pilot renewable energy projects show both promise and limits to current implementation efforts, underscoring the need for strong stakeholder engagement across scales and flexible policy adaptations.
efficient energy systems by integrating renewables
SYNERGISTIC ADVANCEMENTS IN SMART GRID TECHNOLOGIES: A REVIEW OF PROTECTION, OPTIMIZATION, AND RENEWABLE INTEGRATION
The smart grid is a development of modern power systems, integrating digital communication, intelligent control, and advanced optimization to improve efficiency, reliability, and sustainability. This shift from conventional centralized grids to decentralized, data-driven infrastructures enable bidirectional energy flow, real-time monitoring, and adaptive decision-making. This transformation, however, also poses key challenges surrounding cybersecurity breaches, concerns over system and geolocation stability, and complexity in the management of energy resources locally. The research studies highlight the development of advanced protection techniques, which include smart adaptive schemes that can adjust and respond to dynamic grid conditions, as well as comprehensive multi-layer cybersecurity systems specifically developed to deal with risks such as those from ransomware attacks, insider threats, and network overloads. Moreover, the paper also elaborates on various intelligent optimization techniques, which developed like as linear programming, heuristic algorithms, genetic algorithms (GA), particle swarm optimization (PSO), and ant colony optimization (ACO), which are necessary for improving energy management and operation efficiency. Machine learning and real-time analytics enhance smart grid functionality by enabling predictive energy management, load forecasting, voltage control, and fault detection being some of them thereby strengthening performance. It also discusses the integration of renewable energy sources, where it stresses hybrid energy systems that utilize both conventional and renewable generation, explaining the role of energy storage technologies, demand response mechanisms, or any other tool to stabilize variable generation.
Copyright (c) 2026 Sayed Sufia Sumi, Anwar Hakim Tamim (Author)
EFFICIENCY OF THE SPRINKLER IRRIGATION METHOD FOR THE SOYBEAN VARIETY "NAFIS"
To address growing food security demands and optimize agricultural water management under typical irrigated grey soils of the Tashkent region, this study evaluates the efficiency of the sprinkler irrigation method for the "Nafis" soybean variety. Comprehensive analyses of key soil physical properties—such as bulk density, total porosity, and water permeability—were conducted alongside detailed field experiments. The research investigates the impact of scientifically grounded irrigation scheduling, individual watering norms, and seasonal irrigation rates on plant growth, development, and grain productivity. The experimental results demonstrate that optimized sprinkler irrigation significantly improves crop yield and water-use efficiency compared to traditional furrow irrigation methods. Specifically, the proposed optimal irrigation regime reduces water consumption while achieving a higher grain yield per hectare. These findings establish that sprinkler irrigation is a highly viable, water-saving, and sustainable agricultural strategy for arid and semi-arid regions facing increasing water scarcity challenges, offering a reliable technological framework for improving regional crop production and resource management.
Copyright (c) 2026 Innovative: International Multidisciplinary Journal of Applied Technology (2995-486X)
Development of a Full Mathematical Model for Evaluating the Performance of an Electrically Driven Ring Spinning Machine under Drive Failure and Motor Speed Reduction
The operational efficiency of modern ring spinning machines is strongly influenced by the technical condition of the electric drive system. Temporary drive failures and reductions in motor rotational speed directly affect production stability, yarn quality, and machine productivity. Existing mathematical approaches generally evaluate these factors independently or employ simplified linear relationships, which restricts the accuracy of technological performance prediction under real operating conditions. This study develops a comprehensive second-order mathematical model for evaluating the performance of an electrically driven ring spinning machine by simultaneously considering the duration of electric drive failure and the percentage reduction in motor rotational speed. The experimental investigation was organized according to a second-order orthogonal design based on Kono planning. Two independent variables were investigated using nine experimental design points with twelve randomized replications at each point. The obtained experimental data were processed through regression analysis to establish linear, quadratic, and interaction effects between the selected technological factors. The adequacy and statistical reliability of the developed model were verified using Cochran's homogeneity criterion, Student's significance test, and Fisher's adequacy criterion at a confidence probability of 95%. The statistical analysis confirmed that the proposed regression model adequately represents the experimental observations and accurately describes the nonlinear behaviour of the spinning process under varying electric drive operating conditions. The developed mathematical model makes it possible to quantitatively predict productivity changes resulting from simultaneous variations in drive failure duration and motor speed reduction. The obtained response surface provides a practical tool for selecting rational operating conditions that minimise productivity losses while improving the reliability of electrically driven spinning machines. The proposed modelling approach may serve as a theoretical basis for intelligent monitoring, adaptive control, and optimisation of spinning machine operation in modern textile production systems.
Copyright (c) 2026 Nazirjon Aripov, Bekmurod Tojiyev (Author)
ANALYSIS OF CALCULATION RESULTS OF VIBRATION OF BELT CONVEYOR ROLLER MECHANISMS
The article presents the determination of the roller mechanisms of the belt conveyor according to the vibrations of the outer shell, taking into account the values of the technological load and friction force. The laws of the displacement of the outer shell of the part acting as a sliding support used in the roller mechanism, the change in the vibration speed are obtained. Their recommended values are based on the analysis of the parameters and changes in the modes of movement of the roller, as well as the graphs of the dependence of the technological resistance of the transported product on the parameters of the roller.
Copyright (c) 2026 Jumaev Akbarjon Sayfullaevich, Abdurakhmanova Muattar Musurmakulovna (Author)
ARTIFICIAL INTELLIGENCE FOR POWER QUALITY OPTIMIZATION IN RENEWABLE ENERGY CIRCULATION RELATIONS
Background: The rising adoption of renewable power systems throughout present-day distribution systems has produced various obstacles which affect the delivery of stable power with high quality. The irregular operation of solar and wind power systems creates multiple problems which include voltage changes and harmonic interference and unstable power grid frequency. Methods: The study used a quantitative cross-sectional survey framework to assess AI systems optimize power quality for renewable energy networks which serve American power distribution systems. A total of 185 respondents, including engineers, utility operators, and energy researchers, participated in a structured questionnaire. Pearson correlation along with descriptive statistics based on percentage analysis to study the connection between AI adoption rates and power system performance indicators. Results: AI technology produces a 26% rise in fault detection precision and creates a 20% improvement in load distribution performance and voltage network stability. Machine Learning stands as the most popular AI method which receives 28% of usage while Neural Networks follow with 22% usage. AI adoption creates strong positive connections with power quality and system reliability and fault detection efficiency according to correlation analysis results r = 0.78, 0.74, and 0.81 respectively. The deployment of this technology faces three main obstacles which include expensive installation costs that affect 25% of users and 22% of users struggle to find qualified personnel and 15% of users need to handle security threats. Conclusion: Artificial Intelligence plays a crucial role in enhancing power quality and reliability in renewable energy distribution networks.
Copyright (c) 2026 Md Shahdat Hossain, Farabi Hasan Chadni, Md Atiqur Rahman (Author)
DETERMINATION OF ANALYTICAL DEPENDENCE AND NUMERICAL CALCULATION OF TORQUE AND DISPLACEMENT OF VIBRATION OF THE SUPPORT OF AN INTERNAL COMBUSTION ENGINE
This article examines the analytical determination of torque and numerical calculation of vibration displacement of a mobile vehicle's internal combustion engine mount. An analytical relationship between engine torque, mount stiffness, damping coefficient, and vibration displacement is developed. Key mathematical modeling methods based on differential equations of motion, numerical solution methods, and computer simulation are presented. The results obtained allow for improved accuracy in predicting dynamic loads, reducing engine vibration levels, increasing the service life of mounts, and enhancing the reliability of mobile vehicles.
Copyright (c) 2026 Matmurodov Farkhod Matqurbonovich (Author)
A MODEL FOR THE INTEGRATION OF MULTI-SOURCE DATA AND AUTOMATIC QUALITY CONTROL IN EMERGENCY-SITUATION MONITORING
This article proposes a model for converting multi-source heterogeneous data into a single format and automatically controlling its quality in the monitoring of emergency situations (ES). The model covers five sources (seismic, hydrometeorological, satellite, IoT-sensor, and institutional) and performs range checking, missing-value and duplicate detection, unit normalization, statistical outlier detection (IQR), and domain-specific reliability checks. The model was tested on real open data — 267 records from the USGS earthquake catalog: 88.4% of records were recognized as valid, while 31 records (including 29 of low reliability — azimuthal gap > 180°) were automatically flagged. The results show that the model performs identically on real and simulated data and provides clean, ready input for a forecasting model.
Copyright (c) 2026 Razokova Mahfiza Khabibovna, Absalamov Rashid Amirovich (Author)
COMPARATIVE ANALYSIS OF METHODS FOR INCREASING THE CAPACITY OF FIBER-OPTIC COMMUNICATION LINES AND EVALUATION OF A SINGLE-FIBER TRANSMISSION METHOD USING Y-SPLITTERS
The continuous growth of broadband traffic and the limited availability of optical fibers in existing fiber-optic communication networks require the development of cost-effective methods for increasing transmission capacity. This paper presents a comparative analysis of conventional approaches, including deployment of new fiber-optic communication lines, wavelength-division multiplexing (WDM), and transmission rate enhancement, together with a proposed method based on converting dual-fiber optical transmission systems into a single-fiber mode using passive optical Y-splitters. A mathematical model based on signal-to-noise ratio (SNR), bit error rate (BER), and optical power budget analysis is developed. The obtained results demonstrate that the proposed method increases effective fiber utilization by 1.8–2 times while maintaining transmission quality within standardized limits. The approach is particularly suitable for rural and regional communication networks characterized by limited budgets and a shortage of available optical fibers.
Copyright (c) 2026 Kiyamov Rakhmatullo Ruziyevich (Author)
DESIGNING MODERN CLOTHING USING LOCAL TEXTILE MATERIALS BASED ON SUSTAINABLE DEVELOPMENT PRINCIPLES
This article investigates the design of modern garments using local textile materials based on sustainable development principles. The ecological, economic, and aesthetic advantages of locally produced fabrics were analyzed, and their potential application in contemporary fashion design was evaluated. The study proposes environmentally friendly design approaches that integrate national textile traditions with modern fashion requirements. The results demonstrate that the use of local materials contributes to sustainable production, cultural preservation, and increased competitiveness in the light industry sector.
Copyright (c) 2026 Mamatkulova Kunduzkhon Umarovna, Resi Sepsilia Elvera (Author)
Artificial Intelligence-Driven Cyber Threat Detection for Protecting Critical Digital Infrastructure
Background: Energy grids and healthcare systems and financial networks and transportation systems more vulnerable to sophisticated cyber threats. The signature-based detection systems which rule-based cybersecurity methods use no longer provide sufficient protection against advanced persistent threats and zero-day exploits and AI-driven cyberattacks. Methods: We conducted their investigation through a data-based system which used simulated data that demonstrated actual conditions of essential infrastructure systems which included energy and healthcare and financial institutions. The dataset includes 500 entries which contain network traffic information and system operation patterns and transaction data elements that show both regular and harmful system activities. Random Forest and Gradient Boosting and Long Short-Term Memory (LSTM) and Auto encoder models through their performance on accuracy and precision and recall and F1-score and detection latency and error rates. Result: This finding demonstrate that AI-powered systems deliver better results than conventional methods because they produce accuracy percentages which range from 92% to 97%. Auto encoder model which uses deep learning techniques achieved the best results with 96.7% accuracy and maintained a false positive rate of 3.5%. The system achieved threat detection times which dropped by more than 90% to provide organizations with the ability to detect threats in real time. Conclusion: The models demonstrated outstanding performance when they needed to identify complex cyber threats included advanced persistent threats and zero-day attacks.
Copyright (c) 2023 Chowdhury Amin Abdullah, Md Jahidul Islam Ridoy (Author)
FORECASTING ROAD TRAFFIC CAPACITY USING MATHEMATICAL MODELING OF TRAFFIC FLOW DYNAMICS
The rapid growth of urbanization, motorization, and transportation demand has significantly increased the complexity of traffic management in modern cities. Accurate forecasting of road traffic capacity has therefore become a crucial task for transportation engineers, urban planners, and policymakers seeking to improve mobility, reduce congestion, and optimize infrastructure utilization. This study presents a mathematical modeling framework for predicting road traffic capacity based on fundamental traffic flow dynamics. The proposed approach employs the classical relationship between traffic flow, traffic density, and average vehicle speed and integrates an exponential speed–density function to describe nonlinear traffic behavior under varying congestion conditions. The mathematical model enables the determination of critical traffic density and maximum traffic throughput through analytical optimization of the flow–density relationship. Numerical simulations were conducted using a wide range of traffic density values to evaluate the model’s predictive performance and investigate congestion formation mechanisms. The simulation results demonstrate that traffic flow increases with density up to a critical threshold, beyond which traffic instability and congestion effects emerge, causing a reduction in operational efficiency. Furthermore, the model successfully reproduces the nonlinear characteristics of real traffic systems and provides physically interpretable estimates of roadway capacity. The findings indicate that the proposed modeling approach can serve as an effective tool for short-term and medium-term traffic forecasting, transportation planning, and intelligent transportation system applications. The model is computationally efficient, analytically transparent, and easily adaptable to various road network conditions. In addition, the framework creates opportunities for integration with real-time traffic monitoring technologies, sensor networks, and artificial intelligence algorithms. The proposed methodology contributes to the advancement of data-driven traffic management strategies and provides a practical foundation for improving road network performance, reducing congestion, and supporting sustainable urban transportation development.
Intelligent Transportation Systems (ITS)
Copyright (c) 2026 Mamadaliev Foziljon Abdullaevich (Author)
CONSUMER-ORIENTED DESIGN STRATEGIES FOR MODERN LIGHT INDUSTRY PRODUCTS
This article discusses the importance of consumer-oriented design strategies in the development of modern light industry products. The study analyzes the main factors influencing the creation of competitive products, including consumer needs, ergonomic indicators, aesthetic requirements, and functional characteristics. Special attention is paid to the use of digital technologies, innovative design methods, and sustainable development principles in the product development process. The research results show that a deep analysis of consumer requirements contributes to improving product quality, expanding market opportunities, and strengthening the competitiveness of light industry enterprises. A consumer-oriented approach enables the creation of modern, comfortable, and highly demanded products that meet the requirements of a rapidly developing market. The findings indicate that the implementation of consumer-oriented design strategies serves as a critical factor in improving product innovation, increasing market adaptability, and ensuring sustainable growth within the light industry sector. Consequently, the research provides valuable theoretical and practical insights for designers, manufacturers, and industry stakeholders seeking to develop products that effectively respond to evolving consumer demands.
Leveraging Strategic Management Information Systems for Organizational Resilience: Integrating Artificial Intelligence, Financial Risk Mitigation, and Sustainable Marketing
Strategic Management Information Systems (SMIS) have emerged as critical tools for enhancing organizational resilience in an increasingly dynamic, technology-driven, and uncertain business environment. This study examines the integration of artificial intelligence (AI), financial risk mitigation strategies, and sustainable marketing practices within SMIS to strengthen organizational performance and long-term sustainability. The review highlights the role of SMIS in supporting strategic decision-making, resource optimization, risk assessment, and organizational adaptability through the effective management and analysis of information. The integration of enterprise systems such as Customer Relationship Management (CRM), Enterprise Resource Planning (ERP), and Risk Management Information Systems (RMIS) enables organizations to develop a comprehensive understanding of operational performance and potential risks. Artificial intelligence significantly enhances SMIS by improving data management, predictive analytics, decision support, and operational automation. AI-driven technologies facilitate real-time analysis of large datasets, enabling organizations to forecast trends, identify emerging risks, and optimize business processes. Predictive analytics further strengthens financial risk mitigation by providing proactive insights into market volatility, credit risk, and operational uncertainties. The study also emphasizes the growing importance of sustainable marketing, where AI supports environmentally responsible business practices through customer behavior analysis, personalized marketing, resource optimization, and supply chain efficiency. Organizational resilience is reinforced through effective leadership, governance, knowledge sharing, continuous learning, and strategic alignment. These factors enable organizations to anticipate, adapt to, and recover from disruptions while maintaining competitive advantage. The findings suggest that organizations adopting these integrated approaches are better positioned to navigate uncertainty and achieve long-term success in a rapidly evolving global marketplace.
Strategic Management Information Systems (SMIS)
Copyright (c) 2025 Md. Nazmul Haque, Md. Sohel Masud (Author)
Course Equations for Studying The Stability and Controllability of Wheeled Machines
The controllability and stability of self-propelled energy vehicles are key parameters that determine their effectiveness in agricultural production and safety during transportation work. This work examines the course equations of wheeled machines used to analyze their stability and controllability. The influence of steering, the features of hydraulic steering systems, and their influence on the dynamic characteristics of machines are being investigated. Mathematical models are presented that take into account the rolling angles of the wheels, the transverse tilt of the machine frame, and the correction factors reflecting the influence of external factors on the stability of the movement. The obtained results can be used to optimize the structural parameters of steering and improve the safety of wheeled vehicles during their operation on public roads.
Copyright (c) 2025 Tursunov Ibragim Samandarovich, Ibragimov Botir Dastamovich (Author)
Numerical Solution of a Nonlinear Fractional Diffusion Problem
This article constructs a numerical solution for a nonlinear diffusion equation with a time-fractional derivative. The considered problem is posed on a semi-infinite domain, where the diffusion coefficient is expressed through a power function of the solution. The boundary condition of the problem also has a nonlinear form. The time-fractional derivative is discretized by an L1-type fractional difference approximation, while the spatial operator is discretized by the finite difference method in conservative form. The nonlinear diffusion coefficient is taken from the previous time layer, leading to a semi-implicit scheme. As a result, a tridiagonal system of linear algebraic equations is obtained at each time layer. This approach simplifies the computational process, improves stability, and provides a convenient framework for practical implementation.
Copyright (c) 2026 Xolmurzayeva Nigora Abdumurotovna (Author)
MECHATRONIC CONTROL ALGORITHM FOR AN AUTOMOTIVE COOLING SYSTEM FAN
This paper investigates a mechatronic control algorithm aimed at improving the operational efficiency and optimizing the energy consumption of a passenger car cooling system fan. The negative impacts of the conventional ON/OFF (switching) method on system stability, electrical grid current surges, and the mechanical durability of motors are analyzed. To simultaneously ensure the operational economy and thermal stability of the system, an adaptive mechatronic control algorithm is considered, which modifies control parameters in accordance with the current operating modes of the engine. In the proposed mechatronic system, the fan voltage and rotation speed are controlled in a stepped and smooth manner according to temperature variations. The advantages of the proposed algorithm are evaluated for high ambient temperatures and heavy urban traffic conditions. The results indicate that mechatronic control allows for reducing mechanical loads on fan motors, saving electrical energy, and maintaining the engine temperature stably within the optimal operational range.
Copyright (c) 2026 A.I. Yangibayev, F.SH. Umerov (Author)
THE IMPACT OF INTEGRATING LEAN MANUFACTURING AND QUALITY MANAGEMENT SYSTEMS ON ENTERPRISE EFFICIENCY
This article investigates the implementation of a quality management system based on lean production principles and the assessment of its effectiveness. The study analyzes approaches aimed at reducing losses in production processes, ensuring efficient use of resources, and continuously improving quality. In particular, production processes were optimized based on 5S, Kaizen, value stream analysis, and standardized work methods. The results showed that integrating lean production elements with the quality management system helps reduce defect levels, decrease production costs, and improve management efficiency in enterprises.
Copyright (c) 2026 Boykishiyeva Dilnozakhon Abdusalomovna (Author)
MATHEMATICAL ANALYSIS OF THE R-PARAMETRIC OPERATION PRESENTED IN THE O'ZMST 286:2024 STANDARD
This article mathematically analyzes the R parametric multiplication operation introduced in the national standard of the Republic of Uzbekistan O'zMSt 286:2024 "Processes of generating and verifying electronic digital signatures". The theoretical foundations of the parametric multiplication and exponentiation operations presented in the standard are considered, and the computational processes generated on the basis of these operations are analyzed using examples. In particular, the commutativity of the parametric multiplication operation, the determination of the inverse element, and the exponentiation processes are mathematically considered. Additionally, the computational efficiency of the method recommended in the standard is analyzed during the calculation of large powers, and the possibility of optimizing this calculation is demonstrated. The research results determine that certain computational processes related to the R-parametric operation can be simplified.
Copyright (c) 2026 N. R. Zaynalov, U. B. Sharipova, B. Sh. Ibrohimov, O. A. Oydinov (Author)
MORPHOLOGICAL CHARACTERIZATION BY SCANNING ELECTRON MICROSCOPY (SEM) OF COMPOSITE ADSORBENTS DERIVED FROM PLANT HUSK WASTE: A BASIS FOR DEVELOPING ECO-FRIENDLY MATERIALS FOR POLLUTANT ADSORPTION
This study investigates the morphological features, obtained by scanning electron microscopy (SEM), of individual activated carbons prepared from agricultural and fruit-processing wastes apricot stone shells, walnut shells and grape seed shells and of their three-component composite adsorbent. The samples were pyrolysed at 600 °C in an inert atmosphere, chemically activated with KOH, and converted into a composite by mechanical mixing in an equal mass ratio (1:1:1) followed by a secondary heat treatment (300 °C). SEM analysis revealed that each precursor forms a distinct carbon skeleton: the apricot pyrolysate showed an open cellular (honeycomb-like) structure (cell density 400–600 cells/mm²), the walnut pyrolysate a dense sponge-like 3D-interconnected structure (800–1000 cells/mm²), and the grape pyrolysate a lamellar (plate-like) and “Swiss-cheese” morphology. KOH activation opened the cell walls in all samples and generated submicron pores (200–500 nm). The composite developed a hybrid “cheese–cellular–plate” morphology, absent in any individual component, together with a zone rich in uniformly sized (1–2 µm) circular pores, which morphologically confirms the synergistic effect of compositing. The resulting granular commercial form (0.3–0.8 mm) indicates the potential of the composite for industrial-scale application.
Copyright (c) 2025 Karimova Zarifa, Nurmurodov Tulqin (Author)
PHYSICO-CHEMICAL, TEXTURAL AND OPERATIONAL PROPERTIES OF CATALYSTS OBTAINED FROM LOCAL MINERAL RAW MATERIALS AND INDUSTRIAL TECHNOGENIC WASTE
The physico-chemical, textural, mechanical and operational properties of catalysts prepared from local mineral raw materials Oltintog’ and Angren kaolins, various grades of Navbahor bentonite, and Karmana opaque zeolite-bearing rocks together with industrial technogenic waste (metal-oxide active components) were studied. The supports were modified thermally, by acid treatment and hydrothermally; CuO, Bi2O3, NiO, CoO, ZnO, Cr2O3 and CdO were used as active components. The samples were characterized by IR spectroscopy, X-ray phase analysis (Rietveld method), scanning electron microscopy (SEM-EDS), a Shimadzu AGS-X mechanical testing machine, and water- and benzene-vapour adsorption isotherms (BET) measured on a McBain–Bakr balance. The results showed that the catalysts possess both Brønsted and Lewis acid sites, exhibit type IV adsorption isotherms, and have specific surface areas of 180–198 m2/g and crushing strengths of 44–60 kg/cm2. Bentonite-supported catalysts were distinguished by relatively higher specific surface area, mechanical strength and resistance to poisoning. The study substantiates the feasibility of converting cheap local raw materials and technogenic waste into value-added catalysts (a resource-efficient, circular approach).
Copyright (c) 2025 Umrzokov Abdulla, Mukhitdinov Boxodir, Vapoyev Xusniddin (Author)