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.
The Influence of Muddy Water Flow in Pipelines on the Operating Efficiency of Pumps and Changes in Hydraulic Parameters
The article analyzes the hydraulic and energy-related problems that arise during the pumping of muddy water flows at pumping stations of the Amu Darya and its associated irrigation systems. The influence of sediment particles on flow parameters, specific hydraulic resistance, and pump operating efficiency is theoretically substantiated based on the law of conservation of energy. Formulas for determining head losses for finely and coarsely dispersed muddy mixtures, as well as the concept of critical velocity, are presented. Using a specific example, the head characteristics of a pipeline system under clean water and muddy water conditions are compared, and the increase in electrical energy consumption at a turbidity level of 5% is calculated. The obtained results are of practical significance for selecting optimal operating modes of pumps and reducing energy consumption.
muddy flow, pumping station, hydraulic resistance, pressure pipeline, head loss, critical velocity, energy consumption, irrigation system
Copyright (c) 2026 Innovative: International Multidisciplinary Journal of Applied Technology (2995-486X)
Optimization of Operating Modes of Pumping Stations Equipped With Parallel-Operated Centrifugal Pumps
The article examines the issues of reducing head losses and optimizing electricity consumption in pumping stations operating in parallel pump mode. A method is proposed to increase energy efficiency by reducing the hydraulic resistances of the pipeline system for parallel-operated pumps and selecting the pipe diameter at an economically optimal value. Using the example of three D1600–90 pumps, capital and operational costs were analyzed for various pipe diameters, and optimal options were identified. The calculations show that correct selection of the pipe diameter ensures that the pumps operate within their optimal working range and allows a significant reduction in electricity consumption over the season.
Geometric Modeling of Detected Objects in Ultrasound Imaging
Ultrasound imaging is widely used in medical diagnostics due to its safety, real-time capability, and cost-effectiveness. Nonetheless, images produced by ultrasound are prone to speckle noise, low contrast and sharp delimiting boundary thus making it hard to interpret objects accurately. In this study, it is proposed that geometric modeling approach will be used to model objects found in ultrasound images as a result of contour detection and segmentation in ultrasound. The study combines edge based contour extraction and region based segmentation in order to localize anatomical structures, and pathological regions. The identified contours are also used to form three-dimensional and two-dimensional geometric representations of objects in the two- and three-dimensional space respectively. The framework proposed allows more detailed description of the structure, and quantitative analysis of medical images. It is proved by experimental analysis that the contour detection together with the segmentation is beneficial in enhancing both the localization boundaries and the geometric consistency of modeled objects. The methodology has found application in diagnosis, treatment planning and medical image analysis systems which are computer aided. The results highlight the potential of geometric modeling as a tool for enhancing the interpretability of ultrasound data.
Numerical Analysis of Nonlinear Oscillations in Viscously Damped Systems with Finite Degrees of Freedom
Viscously damped finite degrees of freedom nonlinear oscillators appear in a variety of engineering and physical applications, such as vibration control, structural dynamics, and energy dissipation systems. Indeed, classical linear models can provide useful approximations however they do not capture amplitude responsible behavior, internal resonances or transient responses associated with the nonlinear effects of in any time limited time series data. While earlier works highlighted analytical solutions for simple cases or resonant steady state responses, very few numerical studies have addressed the strongly nonlinear case and multi degree of freedom interactions subject to viscous damping. In response to this dearth of research, this paper provides a systematic framework for numerical analysis of nonlinear oscillatory responses in viscously-damped finite-dimensional systems. The governing nonlinear differential equations are presented and discretized in state space form based on Newtonian mechanics. High order numerical integration schemes are used to carry out time domain simulations, which are followed by phase space analysis, frequency response analysis, and parametric investigations into how much damping coefficients and nonlinear stiffness terms affect dynamic response. We find strong departures from the linear predictions including frequency shifts that depend on amplitude, nonlinear decay rates, and mode coupling that becomes more pronounced with higher levels of nonlinearity and damping. Through numerical experiments, we show how viscous damping is crucial for the stabilization or suppression of complex oscillatory regimes, which also modifies the transient response properties. The results shortly elucidate the interplay within the complex non-linear behaviours of such damped systems, thus contributes significantly towards the design, optimization and control of practical mechanical and structural systems where precise predictions of oscillatory response is fundamental.
Nonlinear oscillations, viscous damping, finite degrees of freedom, numerical simulation, nonlinear dynamics, time domain analysis, phase space analysis, mode coupling, vibration control, structural dynamics
Copyright (c) 2025 Innovative: International Multidisciplinary Journal of Applied Technology (2995-486X)
STAGES OF ESTABLISHING A QUALITY MANAGEMENT SYSTEM IN THE CONSTRUCTION MATERIALS INDUSTRY AND MANAGEMENT MECHANISMS
This article comprehensively examines the theoretical foundations of establishing a quality management system in the construction materials industry, the stages of its implementation, and the mechanisms for its management. During the study, they analyze the influence the quality management system exerts on efficiency in production, product quality, and the competitiveness of the enterprises. The results of the analysis show that the gradual introduction of a quality managing system in accordance with the requirements of the ISO 9001 standard helps to ensure stability of product quality, decrease production costs, while increasing customer satisfaction. In addition, the main mechanisms of quality management system governance— including process-based management, a risk-oriented approach, internal audits, corrective and preventive actions, and continuous improvement methods—are scientifically substantiated.
REVISITING LOW DOSE RADIOBIOLOGY: MECHANISTIC INSIGHTS AND FUTURE DIRECTIONS
Low-dose radiobiology focuses on the biological effects of ionizing radiation at absorbed doses that are less than about 100 mGy or low dose rates (less than 10 mGy/h -1). These exposures are common, and they include the diagnostic imaging, nuclear medicine, radiotherapy scatter, occupational and environmental exposures, radon exposures and space travel. Although widespread, the biological effects of low dose radiation (LDR) have not been fully defined and they persistently troubles the current radiation risk assessment systems. Linear no-threshold (LNT) radiation protection has been the basis upon which radiation protection has depended over decades, wherein a proportional relationship exists between dose and stochastic risk and there exists no safe dose. Despite the fact that LNT has offered a conservative and practical regulatory foundation, increasing experimental and mechanistic information suggests that it is not sufficiently comprehensive of biological responses in the low-dose area. Recent developments in the field of molecular biology, epigenetics, microdosimetry, and systems biology show that LDR can trigger adaptive signaling, non-targeted, and long-lasting epigenetic reprogramming without causing a corresponding DNA damage. The responses tend to be qualitative as opposed to high-dose effects being extended in a linear manner. This is a critical review of the empirical and biological basis of the LNT paradigm against modern mechanistic data, a comparison with currently evolving non-linear dose response models, and epigenetic regulation as the key determinant of low dose effects. Combining historical information with the current experimental and clinical data, we will seek to answer the current controversies, and provide a way forward in terms of biologically informed but pragmatically feasible models of low-dose radiation risk assessment.
The Internet of things (IoT) refers to the billions of physical devices around the world that are now connected to the Internet, all collecting and sharing data. It is a paradigm where physical objects, devices, and machines are embedded with sensors, software, and network connectivity to collect, exchange, and act upon data. The logistics sector is one of the domains where IoT has emerged as a formidable force, propelling innovation, efficiency, and transformation. IoT in logistics represents the integration of physical devices within the logistics and transportation industry network that communicate and exchange data without human intervention. IoT enables devices like sensors, vehicles, and equipment to connect over the Internet, creating a more dynamic, responsive, and interconnected logistics environment. In this paper, we will dig deep into the applications, benefits, and future trends of IoT in the logistics sector.
The logistics sector involves complex operations that require a high level of precision and efficiency. In recent years, the use of big data has revolutionized the logistics industry, providing companies with valuable insights and analytics that can help improve their operations. Logistics companies generate massive amounts of data daily, including shipment tracking, inventory levels, delivery schedules, and customer feedback. In logistics, big data analytics involves leveraging extensive and complex data sets to derive insights, make informed decisions, and enhance transportation processes. The goal is to extract valuable information and patterns from this data to improve operational efficiency, reduce costs, enhance customer service, and enable strategic decisions. The impact of big data on logistics is hard to overestimate. This paper will discuss the role of big data analytics in the logistics sector.
Mathematical Modeling of The Vertical Launch and Operation of An Aircraft Missile Gyroscope
The vertical launch system has been widely adopted to land- and sea-based guided missile systems, because of its excellent operation flexibility, quick response capability and effective utilization of the space volume over the deck. In this paper the advantages and technical characteristics of the vertical missile launch systems are presented and it is analyzed a process of inclination (turning) with respect to a missile necessary for an entrance in guidance trajectory. Special emphasis is placed on the role of the gyroscopic control in maintaining stable angle motion during important phases of flight initiation. A mathematical model is developed for the motion of a guided missile that takes place between vertical launch and turn on torque. The model considers the forces and moments acting on a missile control system, control forces generated by impulse thrusters, inertia conditions, mass change as a result of fuel depletion and location of points of application. In addition, the angular response of a three directional gyroscope subjected to fixed and linearly moving bases is mathematically represented with generalized coordinates. The derived equations include extra damping and restoring moment due to aerodynamic pressure, gravitational effect of the ground terrain, and external disturbances. Both correctable and uncorrectable gyro spin configurations are examined, so that the model may be used over the full envelope of missile guidance platforms. The formulated identifiability conditions establish a solid foundation for the structure design, which can be incorporated in authors’ future research on intelligent missile control system algorithms to promote guidance accuracy, stability and survivability in the stages of vertical launch and initial maneuvering.
Standardization and Quality Assurance of Non-Autoclaved Aerated Concrete
Standardization and the Improvement of Non autoclaved Aerated Concrete Quality in Modern Construction: A Review In the introduction, the authors explain that conditions of large-scale production of construction units, coupled with regulatory reforms in the field of building materials, have made great demands on the production of lightweight, cost effective, and energy efficient materials. In long-lasting experience with aerated concrete, this gap of knowledge had been settled but not on the basis of an applied methodology standardised in regard to the use of chemical additives, mainly carboxymethyl cellulose, as well as for their effects on physical-mechanical properties in non autoclaved production systems. It uses a blended study design, one of experimental and regulatory based. Concrete compositions were made from Portland cement, sand, aluminum powder, and a three-percent aqueous solution of carboxymethyl cellulose. Sample preparation, curing, and testing procedures were performed following related GOST standards to determine their density, compressive strength, contents of moisture as well as structural properties. A comparative study of conventional composition and modified composition mixtures. The results confirm that carboxymethyl cellulose can be used as an additive to enhance pore uniformity, pore structure strength development characteristics and overall stability of the material. The results verify that, among different additive concentrations, three percent provides the optimal performance, which results in a higher concrete grade, better density control, and higher thermal efficiency without need for autoclaving. The final results clarify that the controlled application of carboxymethyl cellulose in the production of non-autoclaved aerated concrete delivers both practical and economic advantages. Such results are significant in relation to sustainable construction practices, regulatory compliance, and development of lightweight readily applicable building materials for industrial scale.
Advantages of standardization of building materials
CMC (Carboxymethylcellulose) GOST 25845-89
Analysis Of Primary Tillage Plate Harrows
Considering that the world's primary tillage area is 4.2 billion hectares, the development of high-quality, energy-efficient and resource-efficient soil tillage machines and tools is one of the important tasks. This article analyzes mechanical vibrations, their use in primary tillage, active and passive oscillating working bodies, soil tillage technical means with oscillating working bodies, and the impact of oscillating plate tines on the traction resistance of the plow and the quality of work.
THE IMPACT OF ERGONOMIC APPROACHES ON EFFICIENCY IN THE OCCUPATIONAL SAFETY SYSTEM
The paper analyzes inventive approaches of ergonomics in improving occupational safety and labor productivity in the system of providing occupational safety. The impact of ergonomic working conditions on occupational risk of injury and illness, work fatigue, and performance measures are evaluated using theoretical as well as applied research. Features of assessment of ergonomic conditions are offered and the statistical processing of results of realization of the offers is carried out. The results revealed that the inclusion of ergonomics in the occupational safety system can contribute to a considerable increase in labor safety and work productivity.
On Semi Strong and Semi Weak Sets in Topological Spaces
We invest.igate the conc.ept of semi-.strong (s-s.trong) and semi.-weak (s-.weak) sets in topolo.gical spa.ces. We beg.in by giv.ing defini.tions, some exam.ples and fundam.ental characte.ristics for the two sor.ts of sets. The two sets are contr.asted, and the.ir lin.ks are descr.ibed and stud.ied. In addi.tion, a cla.ss of funct.ions usi.ng semi str.ong and semi weak sets has been discov.ered in topological spa.ces. Additi.onally, s-st.rong and s-w.eak sets are indepe.ndent and can.not ever be compa.rable.
Analysis of Heat Transfer Dynamics in Biological Tissues During Thermal Therapy and Development of a Three-Dimensional Computational Model for Real-Time Optimization of Thermal Device Parameters
Thermal therapies namely, thermal ablation, hyperthermia, and laser play an important role in the treatment of various medical conditions especially, malignancies focusing on tumor/tumor-like tissues. These therapies require careful temperature control to ensure effective treatment while avoiding damage to adjacent healthy tissue. Optimisation of thermal therapies requires accurate bioheat transfer modelling of soft biological tissues. Heat transfer in soft tissues is dictated by thermal conduction as well as by perfusion and metabolic processes, therefore, a reliable thermal model is crucial for a successful treatment outcome. Although substantial contributions also exist for modeling heat propagation in biological tissues, it is still difficult to optimize parameters of thermal therapies in real time, especially for heterogeneous tissue behavior and surrounding heterogeneous environments during the treatment. This study is based on the development of a novel three-dimensional computational model for predicting heat transfer dynamics in biological tissues during thermal therapeutic processes, which can help optimize thermal device parameters to achieve better treatment responses in real time. A three-dimensional bioheat transfer model was created and compared to experimental temperature distributions under various thermal therapy conditions, comparing them with a high degree of accuracy. The model could reproduce the thermal response of tissues during microwave ablation and laser therapy. Their model includes online optimization algorithms to provide the clinician with a means to adjust the parameters of a thermal device to account for changing tissue responses to stay within safe and effective bounds. Our model serves as a strong basis to optimize thermal therapies, which will benefit patients by increasing the accuracy of heating delivery and reducing collateral effect on the adjacent healthy tissues.
Analysis of Lightgbm Model Accuracy and Validation Strategies for Predicting Oil and Gas Probability Maps
The accuracy of the LightGBM model in forecasting oil and gas probability maps had been evaluated based on MapOil analytic system. Integration of geological, geophysical, satellite and topographic map datasets in single spatial processing pipeline through MapOil streamline feature extraction, and probability assessment for each grid cell over exploration areas. The leaf-wise growth and advanced gradient boosting optimization criteria of LightGBM are capable of capturing the complex nonlinear relationships associated with subsurface properties. Differences among the various validation strategies are also examined to compare the robustness of predictions. Standard random cross-validation along with spatially informed cross-validation approaches, such as block and region-based validation, were utilized. Validation in space results in reduced information leakage and more realistic performance statistics, leading to a more informative generalisation of the model on untested areas. In summary, the application of LightGBM in combination with MapOil platform forms a powerful methodology for creating high-precision oil and gas probability maps which are able to improve the data driven decision making for hydrocarbon exploration.
Anti-Symmetric Vibrations of a Free-Supported Three-Layer Elastic Plate
In the study, the head parts of the slopes of the selected three-tiered elastic plate are selected as search functions. Thus, a system of fifth order differential equations, which can be used to solve practical problems, has been created and mathematical operations are performed, and this system of equations can be eliminated by the Maple 12 program, which can occur in three-dimensional plate positions and tension graphs.
Fractal Analysis Using the Mass-Radius Method
Biological images often contain structural formations, especially those derived from neural tissues. It is known that neurons are divided into several types, but distinguishing these types remains a constant problem. The main feature of the fractal dimension is proposed in this article. The possibilities of performing fractal analysis using the mass-radius method are investigated below. The obtained results were analyzed based on the mass-radius method. In addition, the article presents the capabilities and characteristics of the software tool designed for calculating the mass-radius method.
Hydraulic Modeling of Vane Pump Parameters
An analysis of previous studies shows that performance optimization issues in three-dimensional impellers of vane pumps have been insufficiently explored. In a meridional plane, the vanes rotate from an axial to a radial direction in three dimensions. However, the flow studies are primarily limited to those near the impeller inlet and outlet. The goal of these studies is to improve the pump flow path to reduce impeller cavitation wear and improve operating parameters. The data obtained in the study were statistically processed, and based on them, pump design options with optimal parameters can be developed. The article presents key recommendations for studying transient processes using OPV and V-type pump units with an impeller diameter of Dr = 250 mm. The influence of cavitation on the pressure pulsation pattern was determined. Software studies were used to determine the compatibility of impeller modes, which are designed using formulas for high- and low-pressure zones, thereby increasing pump efficiency by 3-5%.
Machine Learning Forecasts of Spot Truckload Prices Using Operational Carrier Data: A Comparative Study of XGBoost and Benchmark Models
This article creates and assesses a supervX`ised machine learning model in order to forecast spot truckload rates at the shipment level, this paper. We create a feature set with lane information, shipment date, distance, and cargo weight using operational data from carriers for 2022–2024. The target is defined as freight cost in USD per load and rate per mile. A global mean model, a lane mean model, multiple linear regression, random forest, and an XGBoost ensemble are the five methods we benchmark after formulating the problem as a regression task.
Vision Transformers vs. Convolutional Networks for Colonoscopic Polyp Segmentation: A Systematic Review
Colorectal cancer (CRC) is the second most common cause of cancer-related death in the world, and early detection of adenomatous polyps with colonoscopy is important because it can prevent CRC. Nonetheless, traditional colonoscopy is still operator-dependent and with lesion-miss rates of up to 25% for small or flat lesions. Over the past decade, automated polyp detection and segmentation have come a long way with deep learning. CNNs like U-Net, UNet++ and ResUNet++ set strong baselines, however their low receptive field bound hindered generalization. More recently, vision transformers (ViTs) and hybrid CNN–transformer architectures have achieved state-of-the-art results by modeling long-range dependencies and incorporating global context. This systematic review examines more than 50 studies as the representative one available between 2015 and 2025, with specific attention to the results on Kvasir-SEG, CVC-ClinicDB, and ETIS-Larib benchmarks. Experiments demonstrate that while NFL-CNNs reach Dice scores of 0.85–0.90 on easier datasets, ViTs and hybrids almost always outperform motors, with the best models reaching 0.94 (NA-SegFormer) on Kvasir-SEG and 0.81 over ETIS. Our results illustrate the disruptive nature of attention-based approaches, closer to what was desired by clinical colleagues, and signal some of the stubborn open challenges relating to dataset availability, practical computational cost and high-quality clinical evidence. Advances in the future will likely center around the development of lightweight understandable and generalisable AI systems designed to provide real-time, clinically reliable polyp detection.
AN INTEGRATED APPROACH TO CREATING ENVIRONMENTAL SUSTAINABILITY AND A HEALTHY WORKPLACE IN UZBEKISTAN
The research investigates the role of an integrated approach in promoting environmental sustainability and ensuring healthy working conditions within the industrial sectors of Uzbekistan. While legislation at the national level and international standards (including ISO 14001 and ISO 45001) encourage safer and greener production, a review of the available literature reveals limited analytical attention to how they can be applied together in the workplace. The study considers air quality, noise map, climatological conditions and waste treatments in typical enterprises by application of environmental monitoring, hygienic assessment and risk-analysis methods. While, findings show, general air pollution in Uzbekistan has slightly decreased in recent years, some industrial areas frequently violate recommended limits for particulate matter and emissions, continuing to pose dangers to the health of workers. The outcomes also show that environmental safety indices in particular areas are beneath the customary 0.7 with the need of stricter hygienic controls and present-day technologies. The study concludes that if ecological management systems, occupational hygiene, and safety systems are integrated well it may lead to better workplace well-paying and it may cause operational efficiency. It will also encourage the promotion of real-time monitoring, energy-saving technologies and alignment of national standards with international best practices towards the sustainable industrial development of Uzbekistan.
integrated approach, labor protection, ISO 14001, ISO 45001, Republic of Uzbekistan.
Harmonization of Discrepant Data: A Solution to the Computational Models for Data Collection in the Tertiary Institutions
Data visualization, interoperability, analysis, and business decisions face a significant challenge as a result of the growing volumes of heterogeneous data being produced by agencies and institutions in the education sector. Stakeholders in the education sector should implement Harmonization of Heterogeneous Data-set as a critical solution. The legacy data that is analyzed and is intended to be used in decision support systems and analytical applications is imported from various data sources with different data types and database architectures and structures. All of these data need to be harmonized for the intended business solutions and growth. Therefore, the Support Vector Machine (SVM) algorithm for Heterogeneous Data Harmonization technique has emerged as the most effective method for creating high-quality data intended to enhance the governance and usefulness of its purpose across the enterprise. The goal of the research was to create and refine a support vector machine-based heterogeneous data harmonization solution for enterprise databases. A data harmonization technique was developed with the integration of harmonization tools using a support machine learning algorithm, and the work was implemented using the Java Script (JS) development environment. The study looked at existing data production techniques on various active databases. To accomplish its goals, this work used the Rapid Application Development (RAD) system methodology. The system's AI machines were tested and trained using both structured and unstructured data imported from Microsoft Excel applications, thanks to the Supervised Machine Learning procedures. There were 10,990 different data sets that were used for training and testing. Testing was conducted on 8,393 (70%) datasets, while 2,597 (30%) were used for training. The outcomes demonstrate that the system was successful in redefining the data headers and column dimensions as a means of coordinating the pull of data imported into the system.
ANALYSIS OF LOCAL WOOL PRODUCTION STATUS AND MAIN PHYSICAL AND MECHANICAL CHARACTERISTICS OF LOCAL WOOL
The increasing need for natural textile fibers has reignited interest in wool production in the Republic of Uzbekistan, where today, Karakul, Jaidari, Hisar and Edilbay sheep are the main sources of raw wool. These breeds and their origin and distribution have been well documented, though there are few data on the physico-mechanical properties of their fibre effective factors including the environmental and processing factors to wool quality. This work fills this gap by investigating local wool post primary processing with respect to structure, dimensions, contaminant levels and technological performance. In spring and autumn, wool samples were taken from Karakul and Jaidari sheep and investigated under controlled conditions of shearing, moisture, drying and sorting. Results reveal significant differences of plant residues, mineral impurities and oil-sweat among breeds and over seasons. They showed that the total waste in spring wool was about 19–20%, with higher levels of mineral impurities in Jaidari wool (coarser structure of wool) and higher content of plant residues in Karakul wool (a larger content of fine tivit fibers). Contamination was generally lower during autumn shearing. The implication is that wool cleanness and yield are dramatically influenced by breed characteristics, feeding conditions and the timing of shearing. Findings emphasize the need to adopt optimized shearing schedules; finer sorting and combing technologies; and more efficient breeding management practices to maximize the clean, high-quality wool yield for textiles in Uzbekistan.
Implementation of Artificial Intelligence Technologies in Accounting and Taxation Based on Expert Assessment Methods
This study focuses on the analysis of artificial intelligence (AI) applications in accounting and taxation, based on expert evaluations. A review of scientific literature has been conducted to explore the nature of artificial intelligence and to identify its core functions in this domain. Illustrative examples include software solutions such as Robotic Process Automation (RPA), RobBee accounting robots, and the DavrOn virtual assistant. The findings demonstrate that the adoption of AI and robotic technologies substantially contributes to the modernization of accounting practices and tax administration.
Study of the Effects of Ionizing Radiation and Physical Factors on Vegetative Growth and Pest Resistance in Some Plant Species
Ionising radiation and other physical factors have a strong impact on vegetative growth and pest resistance in various plant species. This influences agriculture and ecosystem management by determining the suitability of species for contaminated areas, space missions and development under suboptimal and variable conditions. Ionising radiation from radioactive materials, nuclear accidents, cosmic rays and material processing induces morphological, physiological and biochemical changes, establishing the tolerance window of a species. However, constraints prevent reliable extrapolation across conditions or rigorous evaluation of plant behaviour under combinations of gamma and X-rays. This determines the extent of irradiation during transport missions and defines 'safe' exposure in sensitive environments. Studies are being conducted to investigate growth and pest resistance under mono- and multiple-factor treatments in three species exposed to different physical parameters and specific doses of gamma and X-rays. Experimental specifications focus on controllable, pseudo-exhaustive factors within a 100-day timeframe. Treatments encompass several combinations of variables for each plant, producing a diverse dataset that can include non-irrigation factors in statistical models and establish standardised analytical protocols according to sample origin. Ionising radiation and other physical factors can have a significant impact on the vegetative growth and pest resistance of various plant species. This influences agriculture and ecosystem management by determining the suitability of species for contaminated areas, space missions and development under suboptimal and variable conditions. Ionising radiation from radioactive materials, nuclear accidents, cosmic rays and material processing induces morphological, physiological and biochemical changes, establishing the tolerance window of a species. However, constraints prevent reliable extrapolation across conditions or rigorous evaluation of plant behaviour under combinations of gamma rays, ultraviolet light and X-rays. This determines the extent of irradiation during transport missions and defines 'safe' exposure in sensitive environments. Elements released by irradiated soil and substrates can also harm vegetation, and elevated gamma field levels raise questions about species tolerance to external radioactivity and internal contamination.
APPLYING PRINCIPLES OF GENERAL PHYSICS TO DESIGN EFFECTIVE STRATEGIES FOR PLANT DISEASE PREVENTION AND ENHANCEMENT OF AGRICULTURAL PRODUCTION
The significant impact of ionizing radiation and physical factors on vegetative growth and pest resistance of various plant species underlies the development of their ability to exist and grow in contaminated areas, space conditions and unfavourable agricultural environment. Exposure to radiation from cosmic rays, nuclear accidents, or radioactive substrates causes the morphological, physiological, and biochemical changes that shapes species-specific tolerance windows, but previous studies failed to reliably extrapolate radiations and physical conditions such as temperature, humidity, and light. While radiation effects are well documented, the combined influence of multiple physical factors with γ- and X-ray exposures, and their role in growth–defense trade-offs across species with different architectural and physiological traits have not been well characterized. This research examines vegetative development, morphological and anatomical responses, photosynthetic performance and pests naturally occurring at the most susceptible entities of the targeted plant species under controlled doses of ionizing radiation coupled with modulated physical factors, such as mechanical stress and soil differences. Results show variable effects on biomass accumulation or suppression depending on the tested species, as well as structural alterations of stems and leaves, induced efficiency of chlorophyll, radiation-triggered defensive pathways leading to secondary metabolites production and pest pressure reduction. Evidence of growth defense trade-offs, with some species increasing resistance but at the cost of reduced vegetative growth. This study systematically integrates the effects of radiation and other abiotic components of the environment–showing how combined effects of abiotic inputs alter growth trajectories, resistance traits, as well as patterns of physiological allocation. These findings provide a basis for enhancing crop resilience, pest-management strategies, and the potentially relevant agricultural and ecological practices in radiation-affected or stress-microbe intensive systems.
Artificial intelligence (AI) is a collection of various technologies and algorithms built to minimize human intervention and maximize results, saving time for businesses and customers. It is transforming ecommerce by enhancing customer experiences and optimizing business operations through data analysis, automation, and personalization. It is making an enormous impact on ecommerce businesses. It personalizes the shopping experience through dynamic recommendations and AI-powered chatbots, automates operations like customer service and supply chain management, and improves marketing with advanced customer segmentation and content generation. AI in ecommerce has revolutionized the industry, providing powerful tools to enhance customer experiences, streamline operations, and tackle various challenges. The significance of AI integration with ecommerce sites cannot be understated. This paper explores some of the essential AI tools transforming ecommerce.
RESEARCH ON THE MANUFACTURING TECHNOLOGY OF SPECIAL FOOTWEAR
In sectors such as medical, military, and industrial where the working environment is dangerous, special footwear is crucial to maintaining safety and comfort. With evolving industries, the need for specialized footwear to protect from mechanical, thermal, chemical, and electrical risks are on the rise. Integration of modern technologies in production methods such as CAD, 3D modeling, and smart Quality Control has become imperative. While considerable knowledge exists regarding special footwear technologies in developed economies, less attention has been drawn to the upgrading of production methods in transitional areas such as Central Asia. The purpose of this oeuvre is to study the technological processes that are used in the manufacturing of special shoes, and estimating the affect that modern interfacing, guidelines of ergonomic frameworks and the producing process have on their restoration. Use of materials such as polyurethane and thermoplastic polyurethane with improved ergonomic designs for better comfort and reduced fatigue among users are identified. The other highlights automation technologies for manufacturing, as well as conservation practices, integration of ag products and production technologies. This research expands the existing literature on sustainable and ergonomic manufacturing technology for special footwear, and the data generated would be valuable for Uzbekistan, located in Central Asia, and one of the countries in the region, having its textile and light industry sector to benefit from it. These results have implications on improving the international competitiveness of the special footwear sector through innovation in safety, comfort, and environmental sustainability, while serving as a model for the technological modernization in developing areas.
Artificial intelligence (AI) refers to the use of computers and technology to simulate intelligent behavior and critical thinking comparable to a human being. It is an interdisciplinary field that integrates computer science, mathematics, and related disciplines to create algorithms that can perform tasks conventionally restricted to human intelligence. It has the potential to transform healthcare and disrupt the field of medicine in significant ways. AI technologies such as machine learning, natural language processing, and robotics can help improve healthcare by enhancing medical services and research efficiency. Artificial intelligence techniques have the potential to be applied in almost every field of medicine. They have the potential to transform medicine. In this paper, you will explore the transformative impact of artificial intelligence on medicine.
A Unique Method for Concealing Information
The subject of steganography is the means and methods that ensure the concealment of the fact of message transmission. The historical roots of this discipline are very deep. With the development of technology, the methods have also changed. If previously invisible ink was used between the lines of ordinary text, then modern digital steganography consists of embedding data in digital objects with minimal distortion. This is possible due to the use of the redundancy of the container data or the peculiarities of human perception, which is unable to notice these changes. In light of the widespread use of text documents in organizational activities, methods of hiding data in a text container, in particular, in MS Word documents, are becoming especially relevant. Based on this, an MS Word document is considered as an information carrier here. MS Word documents have different parameters, by changing these parameters or properties, it is possible to achieve data embedding. In this article, we present steganography using invisible Unicode characters of the Space type, but with different encoding. /2