International Bulletin of Applied Science and Technology
International Bulletin of Applied Sciences and Technology - (ISSN-2750-3402) is an open access international peer reviewed german journal. IBAST publishes research monthly frequency basis. we explore your research worldwide by international Database indexings. we provide all articles crossref metadata link, google scholar, microsoft academic, Metadata Indexing and much more worldwide international indexings.
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UPCYCLING ASOSIDA ZAMONAVIY LIBOS KOLLEKSIYALARINI YARATISH
Mazkur maqolada zamonaviy moda sanoatida keng qo‘llanilayotgan upcycling (qayta ishlash orqali qiymatni oshirish) tamoyillari, uning ekologik va iqtisodiy ahamiyati hamda ushbu yondashuv asosida zamonaviy libos kolleksiyalarini yaratish metodikasi tahlil qilinadi. Tadqiqotda eskirgan yoki foydalanilmay qolgan matolar, tayyor kiyimlar va tikuvchilik chiqindilaridan yangi, estetik va tijorat jihatdan raqobatbardosh liboslar yaratish bosqichlari, dizayn yechimlari hamda O‘zbekiston tikuvchilik-trikotaj sanoatida upcycling texnologiyasini joriy etish istiqbollari yoritiladi. Muallif tomonidan amaliy tavsiyalar va kolleksiya yaratish algoritmi taklif etiladi.
upcycling, ekomoda, qayta ishlangan matolar, barqaror dizayn, libos kolleksiyasi, tikuvchilik chiqindilari, zero waste, moda sanoati, ijodiy dizayn.
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
ЕNHАNCING ЕFFICIЕNCY, RЕLIАBILITY, АND SUSTАINАBILITY IN THЕ ЕNЕRGY SЕCTOR THROUGH THЕ АPPLICАTION OF DIGITАL TЕCHNOLOGIЕS
Thе аrticlе ехаminеs thе implеmеntаtion аnd dеvеlopmеnt of digitаl tеchnologiеs in thе еnеrgy sеctor. It discussеs thе trаnsition from trаditionаl mаnаgеmеnt systеms to thе concеpt of digitаl еnеrgy, thе intеgrаtion of big dаtа, аutomаtеd control аlgorithms, аnd cloud аnd fog computing to еnhаncе thе еfficiеncy, rеliаbility, аnd sаfеty of еnеrgy infrаstructurе. Digitаl еnеrgy systеms еnаblе rеmotе monitoring аnd control, аdvаncеd diаgnostics аnd prognostics, аs wеll аs cybеrsеcurity, аllowing for rаpid dеtеction of еmеrgеnciеs аnd minimizаtion of humаn еrrors. Spеciаl аttеntion is givеn to thе importаncе of nаtionаl tеchnologiеs аnd locаlly dеvеlopеd softwаrе аnd hаrdwаrе solutions to еnsurе tеchnologicаl sovеrеignty аnd systеm rеsiliеncе. Thе аrticlе highlights thе rolе of digitаl solutions in improving thе еfficiеncy, sаfеty, аnd еnvironmеntаl sustаinаbility of еnеrgy systеm opеrаtions.
Digitаl еnеrgy, еnеrgy sеctor, smаrt grids, rеmotе monitoring, аdvаncеd diаgnostics, prеdictivе mаintеnаncе, cybеr-physicаl systеms, Intеrnеt of Things (IoT), fog computing, еnеrgy systеm еfficiеncy, cybеrsеcurity, intеgrаtion of rеnеwаblе еnеrgy sourcеs, tеchnologicаl sovеrеignty.
ALKALINE WATER ELECTROLYSIS FOR HYDROGEN PRODUCTION IN HIGH-IRRADIANCE REGIONS: STATE OF THE ART, EXPERIMENTAL EVIDENCE, AND PROSPECTS FOR UZBEKISTAN
The combination of falling photovoltaic costs and rising policy pressure on carbon-intensive hydrogen production has created a compelling economic and environmental case for solar-powered alkaline water electrolysis (AEL). This review synthesises experimental data from four peer-reviewed publications produced at Andijan State Technical Institute (ASTI) [3–6] alongside seventeen international studies to examine the state of knowledge on electrolyte chemistry, electrode materials, and photovoltaic power integration. The ASTI dataset covers a 5-order-of-magnitude range of current density (3 mA cm⁻² to 500 mA cm⁻²), two electrode substrates (316L stainless steel and Ni-Mo alloy), and both direct and MPPT-mediated PV coupling. Across this parameter space, three design principles emerge as robust: (i) 30 wt% KOH at 60–80°C maximises ionic conductivity and electrode durability; (ii) the anode is the primary energy bottleneck on bare 316L, contributing 45% of total voltage loss and representing the highest-return upgrade target; and (iii) MPPT control captures 12–24% additional solar energy relative to direct coupling. Applying these principles to the Andijan solar resource, a 5 kW PV system can yield 160–180 kg of emission-free hydrogen per year at a levelised cost of $5–8 kg⁻¹, with a clear technical roadmap to below $5 kg⁻¹ through electrode coating and current density scaling.
alkaline water electrolysis, solar hydrogen, KOH electrolyte, SS316L electrode, NiFe-LDH, MPPT control, LCOH, Central Asia.
PHOTOVOLTAIC-DRIVEN ALKALINE ELECTROLYSIS AS A PATHWAY TO DECENTRALISED GREEN HYDROGEN: A CRITICAL REVIEW
Decentralised green hydrogen generation through photovoltaic-driven alkaline water electrolysis (AEL) is attracting growing research interest as the cost of solar electricity approaches parity with grid power across much of the developing world. This paper critically examines the published evidence on three pillars of system performance: the thermodynamic and kinetic roles of the electrolyte; the catalytic and durability properties of electrode materials available at low cost; and the power-conditioning strategies required to interface a variable photovoltaic source with the largely ohmic load of an AEL stack. Four experimental papers from Andijan State Technical Institute (ASTI) [3–6] are analysed within the context of seventeen international studies to identify design principles applicable to high-irradiance, resource-limited settings. The review concludes that 30 wt% KOH, 316L stainless steel electrodes operated below 10 mA cm⁻², and MPPT-equipped power converters together constitute a technically sound and economically accessible entry configuration, capable of yielding 160–180 kg H₂ yr⁻¹ from a 5 kW solar array under Uzbekistan conditions. Pathways to reduce the levelised hydrogen cost below $5 kg⁻¹ are discussed.
green hydrogen, alkaline electrolyzer, photovoltaic coupling, KOH electrolyte, 316L stainless steel, MPPT, levelised cost of hydrogen, Uzbekistan.
In the process of ionization, positive ions appear between the electrodes and move towards the cathode. Due to the difference in mobility of electrons and ions, during the movement of the electron beam to the anode, the positive ions remain in the place where they appeared. It is known that due to many ionizers in nature (space particles, radioactive radiation, ultraviolet radiation of the sun), free electrons continuously appear between the electrodes. A continuous avalanche of electrons occurs under the influence of the voltage placed between the electrodes
electrodes, discharge, ionization, charge, voltage, dielektr, gas, amper, transformation, energy
STRUCTURAL COMPONENTS OF FAMILY DISCOURSE
This article examines the concept of family discourse, its structural components, and its sociolinguistic features within the Uzbek family. It analyzes the role of social positions, hierarchical statuses, and norms of verbal behavior in family communication. The study highlights the relationships of dominance and subordination in the speech of roles such as father, mother, child, and mother-in-law/daughter-in-law, as expressed through forms of address and emotive-pragmatic tools.
family discourse, social status, speech behavior, linguistic communication, hierarchy, dominance, forms of address, pragmatics, Uzbek family, social role.
THE SOCIO-PEDAGOGICAL NECESSITY OF FORMING SPIRITUAL IMMUNITY IN STUDENTS BASED ON THE TRANSFORMATION OF DOGMATIC TEACHING ELEMENTS
This article provides a scientific and theoretical justification for the socio-pedagogical necessity of fostering spiritual immunity among students amidst globalization, the information age, and digital transformation. The study analyzes opportunities to develop students' independent thinking, critical reasoning, conscious attitude toward values, and spiritual stability by transforming elements of traditional dogmatic education through modern pedagogical approaches. Furthermore, the restructuring of dogmatic educational elements in alignment with the requirements of the modern educational paradigm is substantiated as a crucial pedagogical factor in building spiritual immunity. The author elucidates the socio-pedagogical essence of this process and highlights its practical significance.
spiritual immunity, dogmatic education, transformation, student, pedagogical transformation, value, critical thinking, spiritual security, higher education.
ZAMONAVIY TA’LIM STANDARTLARI VA KOMPETENSIYAVIY YONDASHUV
Ushbu maqolada professional ta’lim jarayonida zamonaviy ta’lim vositalari imkoniyatlaridan foydalanishning pedagogik jihatlari bayon etilgan. Shuningdek, zamonaviy ta’lim vositalarining professional ta’lim jarayonidagi o‘rni va ahamiyati yoritib berilgan. Zamonaviy ta’lim vositalari samaradorligini ta’minlaydigan didaktik tamoyillar tahlil qilingan. Jumladan, mavjudlik, ilmiylik, tasviriylik, onglilik va ijodiy faollik, tizimlilik, ta’limni hayot va amaliyot bilan bog‘lash, bilimlarni o‘zlashtirishning mustahkamligi va o‘quvchilarning kognitiv kuchlarini rivojlantirish kabi tamoyillar keltirib o‘tilgan.Bundan tashqari maqolada kompetensiyaviy yondashuvlarga asoslangan ta’limning bir qator jihatlari ko’rib o’tilgan. Xususan kasbiy, amaliy kompetentlik turlari va ularning rivojlanish bosqichlari haqida fikr yuritilgan.
kompetentlik, baholash, ma’lumot, o’qituvchi, o’quvchilar, munosabat, shaxsiy, amaliy, ijtimoiy, tashabbuskor, professional ta’lim, kasbiy tayyorlash, ta’lim vositalari, innovatsion ta’lim vositalari, an’anaviy o‘qitish vositalari, o‘quv-uslubiy ta’minot.
Integrated Reward-Guided Neural Intelligence Framework For Efficient Settlement Lag Management In Commercial Logistics Funding
The rapid expansion of commercial logistics networks has increased the complexity of financial settlement processes, where delayed payments, inventory uncertainty, transportation dependencies, and capital flow interruptions create significant operational inefficiencies. Traditional logistics financing systems generally rely on predefined rules and static optimization methods that struggle to adapt to dynamic market conditions, changing transaction patterns, and heterogeneous supply chain structures. This research proposes an Integrated Reward-Guided Neural Intelligence Framework (IRGNIF) designed to improve settlement lag management by combining adaptive decision intelligence, reinforcement-based optimization, and neural learning mechanisms. The proposed framework integrates transaction monitoring, logistics state analysis, reward-driven decision adjustment, and predictive financial optimization to reduce settlement delays and enhance capital circulation efficiency. The conceptual foundation of the framework is derived from decision optimization approaches in logistics, inventory management, and intelligent financial systems. Previous research has demonstrated that integrated transportation and inventory decision models can improve operational coordination by balancing resource allocation and timing constraints (CAO Xue ming et al., 2006). Similarly, inventory-oriented optimization studies emphasize the importance of adaptive strategies for managing uncertainty and improving supply chain responsiveness (Fleischmann et al., 2002). Building upon these principles, this study introduces a neural intelligence architecture where financial settlement decisions are continuously refined through feedback-based learning. The proposed methodology consists of four major components: logistics-financial data acquisition, neural state representation, reward-guided settlement optimization, and adaptive decision execution. The framework evaluates transaction conditions, payment risks, inventory movement, and transportation status to generate optimized settlement recommendations. The approach is particularly relevant for commercial logistics funding environments where delayed settlements can affect supplier liquidity, inventory availability, and overall network stability. Recent research on hybrid reinforcement and deep learning models for payment delay optimization highlights the potential of combining predictive intelligence with adaptive learning mechanisms for improving financial transaction efficiency (D. SinghJatav et al., 2025). The expected findings indicate that reward-guided neural optimization can reduce settlement uncertainty by dynamically adjusting financial decisions according to operational conditions. The framework contributes to existing logistics-finance research by integrating computational intelligence with settlement management rather than focusing only on transportation or inventory optimization. However, limitations remain regarding data quality, model interpretability, computational requirements, and adaptation across different industrial sectors. Future research can extend the framework through real-time financial ecosystems, explainable artificial intelligence mechanisms, and large-scale industrial validation.
Copyright (c) 2026 Dr. Vikram Singh https://creativecommons.org/licenses/by/4.0
Collaborative Machine Intelligence Approach For Streamlining Invoice Processing In Business Capital Management
The increasing adoption of artificial intelligence (AI) has transformed business capital management by enabling intelligent systems for improving financial efficiency and decision-making. Invoice processing remains a critical operational activity but faces challenges such as manual verification, delayed approvals, inconsistent data processing, and limited integration between automation and human expertise. This research proposes a Collaborative Machine Intelligence (CMI) approach that combines AI-driven automation with human financial judgment to improve invoice management. The study uses a conceptual research methodology based on existing literature related to human-machine collaboration, generative artificial intelligence, intelligent systems, and financial optimization. The proposed framework integrates intelligent invoice acquisition, AI-based document analysis, collaborative validation, predictive capital management, and continuous learning mechanisms. Unlike traditional automation approaches focused on replacing human activities, CMI emphasizes cooperation where machines handle data-intensive tasks while humans provide contextual interpretation and strategic decisions. The findings suggest that CMI can improve invoice processing speed, payment accuracy, cash-flow visibility, and capital allocation efficiency. However, successful implementation requires addressing challenges related to data quality, transparency, organizational readiness, and human-machine interaction. This research contributes to intelligent financial management by presenting a collaborative framework that extends beyond conventional automation toward sustainable AI-enabled business transformation.
Copyright (c) 2026 Dr. Aino Virtanen Korhonen https://creativecommons.org/licenses/by/4.0
Advanced Decision-Oriented Neural Framework for Improving Financial Settlement Performance Across Distribution Networks
Financial settlement efficiency has become a critical factor in modern distribution networks due to increasing transaction complexity, interconnected supply chains, and the need for faster capital circulation. Traditional settlement mechanisms often depend on rule-based systems and delayed decision processes, which can create inefficiencies in payment coordination, liquidity management, and financial risk control. This research proposes an Advanced Decision-Oriented Neural Framework (ADNF) designed to improve financial settlement performance across distribution networks through intelligent decision-making, predictive analysis, and adaptive learning capabilities. The study adopts a conceptual research methodology based on the integration of neural intelligence, financial decision optimization, and digital financial management principles. The proposed framework combines transaction data processing, predictive settlement analysis, intelligent risk evaluation, and adaptive decision optimization to support efficient financial operations. Unlike conventional automation approaches that focus primarily on transaction execution, the proposed framework emphasizes decision-oriented intelligence, enabling systems to recommend optimal settlement strategies according to dynamic financial conditions. The theoretical foundation of this research is supported by studies examining financial development, digital financial services, banking efficiency, and intelligent payment optimization. Financial systems play an important role in economic efficiency by improving capital allocation and transaction effectiveness (Levine, 1999). Similarly, advanced learning-based approaches have demonstrated potential for optimizing payment-related decisions in supply chain finance (SinghJatav et al., 2025). The proposed framework identifies four major capabilities: intelligent transaction monitoring, neural-based settlement prediction, adaptive financial decision support, and continuous optimization. The findings indicate that an AI-driven decision framework can enhance settlement accuracy, reduce payment delays, improve liquidity visibility, and strengthen distribution network performance. However, implementation challenges remain, including data reliability, model transparency, regulatory requirements, and organizational readiness. This research contributes to the emerging field of intelligent financial management by presenting a decision-oriented neural approach for improving settlement performance. The framework provides theoretical and practical insights for organizations seeking efficient, adaptive, and data-driven financial settlement systems.
Copyright (c) 2026 Dr. Claire Martin Dubois https://creativecommons.org/licenses/by/4.0
THE ROLE AND IMPORTANCE OF ROBOTIZED TECHNICAL MEANS IN COMBAT ACTIONS
This article analyzes the importance and widespread use of robotic equipment (RTV) in the context of the ongoing military conflict between Ukraine and Russia and their impact on combat operations. In this regard, the strategies, tactical tasks, and directions for implementing robotic platforms of the NATO, Ukrainian, and Russian armies will be considered.
Smart Learning-Based Optimization Technique For Accurate Resource Planning Across Logistics Operations
The rapid evolution of logistics networks, driven by increasing market volatility, globalization, and the complexity of supply chain ecosystems, has created a significant demand for intelligent resource planning approaches capable of improving operational accuracy and adaptability. Traditional resource allocation methods in logistics often rely on historical trends, manual decision-making, and static optimization models, which are insufficient for handling dynamic transportation conditions, fluctuating demand patterns, and multi-dimensional operational constraints. This research presents a smart learning-based optimization technique for accurate resource planning across logistics operations by integrating data-driven intelligence, machine learning principles, predictive analytics, and adaptive optimization mechanisms. The proposed conceptual framework focuses on utilizing heterogeneous operational data, intelligent forecasting models, and continuous learning approaches to enhance resource utilization, reduce inefficiencies, and improve decision accuracy. The study develops a research-oriented optimization framework that combines multi-source data integration, intelligent prediction, and reinforcement-based decision mechanisms for logistics resource planning. The theoretical foundation of the framework is aligned with recent developments in intelligent construction management, spatial data fusion, deep learning applications, and supply chain optimization. Existing research demonstrates that information-based management systems and intelligent technologies significantly influence operational efficiency by improving data accessibility, automation capability, and decision support mechanisms (Liu, 2025; Xing, 2025). Furthermore, deep learning-based approaches for complex data environments provide opportunities for improved modeling accuracy and real-time operational analysis (Sun et al., 2024; Yang et al., 2025).
Copyright (c) 2026 Dr. Camila Martínez Herrera https://creativecommons.org/licenses/by/4.0
СУБКУЛЬТИВИРОВАНИЕ РАСТЕНИЙ ЧУБУШНИКА (PHILADELPHUS L.) В КУЛЬТУРЕ IN VITRO И ИХ EX VITRO АККЛИМАТИЗАЦИЯ В УСЛОВИЯХ РЕСПУБЛИКИ КАРАКАЛПАКСТАН
В статье рассматривается возможность размножения растений рода Чубушник способом микроклонирования, а так же оценка эффективности ex vitro акклиматизации растений чубушника (Philadelphus L.), выращенных в культуре in vitro, в условиях Республики Каракалпакстан.
чубушник, микроклональное размножение, in vitro, питательная среда WPM (Woody Plant Medium), ризонегез, акклиматизация ex vitro.
Autonomous Decision Intelligence System Delivering Superior Predictive Performance Across Distribution Resource Planning
Distribution resource planning has evolved into a strategic function that requires intelligent decision-making capabilities capable of responding to dynamic market conditions, uncertain demand, and increasingly complex logistics networks. Conventional forecasting approaches often struggle to process large-scale operational data and adapt to rapidly changing supply chain environments, resulting in suboptimal inventory allocation, delayed deliveries, and increased operational costs. The emergence of autonomous decision intelligence integrates artificial intelligence, predictive analytics, and adaptive learning mechanisms to support real-time planning decisions with minimal human intervention. This study investigates the design and application of an Autonomous Decision Intelligence System (ADIS) for improving predictive performance in distribution resource planning. The proposed framework combines data acquisition, feature engineering, predictive modeling, autonomous decision optimization, and continuous feedback mechanisms to enhance planning efficiency across interconnected distribution networks. The research adopts a conceptual methodology supported by an analytical review of existing literature and theoretical foundations associated with intelligent forecasting and autonomous decision-making systems. Special emphasis is placed on predictive intelligence, adaptive optimization, and continuous learning capabilities that enable organizations to improve forecasting precision under uncertain operational conditions. The proposed architecture illustrates how integrated decision intelligence can synchronize inventory management, warehouse operations, transportation scheduling, and demand planning while minimizing forecasting errors and improving resource utilization. Recent developments in deep reinforcement learning further demonstrate that adaptive decision models significantly improve forecasting accuracy in supply chain optimization by continuously learning from operational environments (Viswanathan et al., 2025). The study concludes that autonomous decision intelligence represents an important advancement in modern distribution resource planning by integrating predictive analytics with adaptive optimization strategies. The proposed framework provides both theoretical and managerial insights for organizations seeking to modernize supply chain planning processes while establishing a foundation for future empirical validation and intelligent logistics research.
Copyright (c) 2026 Dr. Priyanshi Dev https://creativecommons.org/licenses/by/4.0
RAQOBATBARDOSH MARKETING STRATEGIYALARINI SHAKLLANTIRISH ORQALI KORXONA FAOLIYATI SAMARADORLIGINI OSHIRISH
Ushbu maqolada raqobatbardosh marketing strategiyalarini shakllantirishning nazariy-metodologik asoslari yoritilgan. Korxonaning bozordagi raqobat mavqeini mustahkamlashda SWOT-tahlil, farqlanish (differensiatsiya) va pozitsionerlash usullarining ahamiyati ko'rsatib berilgan. Raqobatbardosh strategiyalarni tanlash mezonlari, ularni amalga oshirishda uchraydigan muammolar tahlil qilingan hamda korxona faoliyati samaradorligini oshirish bo'yicha ilmiy-amaliy tavsiyalar ishlab chiqilgan.
raqobatbardoshlik, marketing strategiyasi, SWOT-tahlil, differensiatsiya, pozitsionerlash, bozor ulushi, korxona samaradorligi.
SCIENTIFIC ANALYSIS AND FUTURE POSSIBILITIES OF SOLAR PANELS
This article presents a scientific analysis of the working principles, energy efficiency, and future development prospects of solar panels. The physical foundations of the photovoltaic phenomenon, different types of solar panels, and their environmental and economic advantages are discussed. In addition, the efficiency of solar panels under the climatic conditions of Uzbekistan was studied using the examples of Tashkent, Samarkand, and Navoi regions, while the effects of temperature and solar radiation were analyzed. The article also examines the prospects of perovskite and tandem technologies, energy storage systems, and intelligent monitoring systems based on artificial intelligence.
solar panels, solar energy, efficiency, renewable energy, photovoltaic phenomenon, lithium-ion and sodium-ion batteries, silicon panels, solar radiation, artificial intelligence, carbon dioxide.
SOLAR-DRIVEN ALKALINE WATER SPLITTING FOR GREEN HYDROGEN GENERATION: ROLE OF ELECTROLYTE COMPOSITION, ELECTRODE SELECTION, AND PHOTOVOLTAIC POWER MANAGEMENT
Three student research projects conducted at Andijan State Technical Institute are unified in this paper to deliver a system-level perspective on solar-powered alkaline hydrogen generation. The first project (Ibrohimov O.B.) screened five alkaline solutions — KOH at two concentrations, NaOH at two concentrations, and K₂CO₃ — with respect to ionic conductivity and long-term electrochemical stability. The second project (Yunusov Kh.F.) characterised five electrode configurations, ranging from conventional 316L stainless steel to a bespoke Ni-Mo cathode / NiFe-oxide anode pairing. The third project (Keldiboyev I.I.) quantified how maximum-power-point-tracking (MPPT) control shapes the annual hydrogen output of a grid-independent photovoltaic (PV) electrolyzer. Taken together, the data establish that a 5 kW PV installation operating under Andijan irradiance conditions, filled with 30 wt% KOH, and fitted with Ni-Mo | NiFe composite electrodes plus MPPT control, can deliver 160–180 kg of emission-free hydrogen annually.
green hydrogen, alkaline electrolysis, potassium hydroxide, Ni-Mo electrode, NiFe oxide, overpotential, MPPT, photovoltaic integration, Uzbekistan.
SOLAR-POWERED ALKALINE WATER ELECTROLYSIS FOR GREEN HYDROGEN GENERATION: A LITERATURE REVIEW
This paper presents a focused review of recent advances in solar-photovoltaic-coupled alkaline water electrolysis (AEL) for green hydrogen production, with particular attention to experimental findings from the authors' own research group at Andijan State Technical Institute (ASTI). Four publications from ASTI are synthesised alongside the broader international literature to map the current state of knowledge across three interrelated sub-topics: electrolyte selection and operating parameter optimisation, electrode material performance and durability, and photovoltaic-electrolyzer power integration. Key findings are that 30 wt% KOH at 60–80°C is the near-universal optimum electrolyte condition; that 316L stainless steel provides commercially adequate durability (corrosion rate < 0.01 mm yr⁻¹) but leaves substantial headroom for efficiency improvement through advanced electrode coatings; and that maximum-power-point-tracking (MPPT) converters raise system efficiency by 12–24% relative to direct photovoltaic coupling. Gaps in knowledge, particularly regarding long-term outdoor durability in continental climates and techno-economic scaling, are identified and directions for future work are proposed.
alkaline electrolysis, green hydrogen, potassium hydroxide, 316L stainless steel, MPPT, photovoltaic integration, Uzbekistan, literature review.
INVESTIGATION OF THE CHARACTERISTICS OF FRUIT PRESERVATION TECHNOLOGY IN AN IONIZED ENVIRONMENT
This article investigates the fundamental principles and technological processes of fruit preservation in an ionized environment and their effects on fruit quality and shelf life. Research shows that ionizing radiation significantly reduces microbial contamination, slows down the rotting process, and preserves the external appearance and beneficial properties of fruits. The article also analyzes the efficiency, energy-saving aspects, and environmental safety of storage in ionized conditions. The results obtained are of great importance for improving long-term fruit preservation technologies.
Ionization, fruit preservation, food safety, microflora, radiation technology, shelf life, environmental safety.
Identification Of Concealed User Profiles Via Modern Similarity-Based Classification Approaches
The increasing complexity of digital ecosystems has resulted in the generation of massive volumes of heterogeneous user data, making traditional profile identification approaches insufficient for discovering hidden behavioral structures. Modern applications require intelligent analytical mechanisms capable of identifying concealed user profiles that are not explicitly represented through conventional attributes. This research explores similarity-based classification approaches as a framework for discovering latent user characteristics through advanced data representation, pattern recognition, and classification strategies. The study examines how computational similarity measures, machine learning-based classification, and behavioral pattern analysis can contribute to identifying hidden relationships among users. The research develops a conceptual framework for concealed user profile identification by integrating principles of similarity analysis, clustering-based discovery, and intelligent classification. Existing studies related to customer segmentation, vulnerability detection, deep learning, and graph-based representation learning are critically analyzed to understand how hidden patterns can be extracted from complex datasets. Jatav et al. (2025) demonstrated that advanced clustering techniques can uncover latent behavioral patterns by identifying meaningful similarities among customer groups, providing a foundation for understanding concealed profile discovery through data-driven methods. The proposed analytical perspective considers user profile identification as a multi-stage process involving feature extraction, similarity measurement, classification modeling, and continuous refinement. Similarity-based approaches enable systems to compare complex behavioral and structural characteristics rather than relying only on predefined categories. Research in automated vulnerability detection further illustrates how machine learning and deep representation methods can identify hidden characteristics within software-related data structures (Russell et al., 2018; Harzevili et al., 2023). Similarly, graph-based deep learning approaches demonstrate the importance of structural relationships in discovering complex patterns (Zhou et al., 2019; Cheng et al., 2022). The findings indicate that similarity-based classification approaches provide significant potential for identifying concealed user profiles by improving pattern discovery, personalization, and analytical decision-making. However, challenges related to data quality, model interpretability, computational complexity, and security limitations remain important considerations. The research contributes a theoretical framework explaining how modern similarity-driven approaches can transform hidden behavioral information into meaningful user profiles while maintaining analytical reliability.
Copyright (c) 2026 Emily Carter https://creativecommons.org/licenses/by/4.0
Ustoz-kamolot ko’zgusi. Bu shaxs biz uchun ham ta’lim, ham tarbiya beruvchidir. Ana shunday zahmatkash insonlar tasviri badiiy asarlarda ham keng yoritilgan. Ushbu maqolada pedagog yoki ustoz xatti-harakati, xarakter xususiyatlari haqida so‘z boradi.
pedagog, ta’lim, tarbiya usullari, axloq, kamolot, dars, tarbiyasi og‘ir o‘quvchilar, kompozitsiya.
IMPROVING MECHANISMS FOR DEVELOPING 4C (4K) COMPETENCIES IN A DIGITAL EDUCATIONAL ENVIRONMENT
This article provides a scientific and pedagogical analysis of improving the systemic and organizational mechanisms for developing 4C (4K) competencies — critical thinking, creativity, communication, and collaboration — among learners within a digital educational environment. Unlike individual digital tools and methods, the current state of systemic mechanisms that ensure sustainable and large-scale development of these competencies — curriculum integration mechanisms, assessment and monitoring mechanisms, teacher preparation mechanisms, and infrastructure provision mechanisms — is examined, and their limitations and directions for improvement are substantiated from a scientific and practical perspective.
4C competencies, digital educational environment, systemic mechanism, curriculum integration, assessment mechanism, teacher preparation, monitoring, 21st-century skills, digital infrastructure, education management.
THE RELATIONSHIP BETWEEN INDIVIDUAL STYLE AND SOCIAL LANGUAGE UNITS IN LITERARY DISCOURSE
This article examines the relationship between individual style and social language units in literary discourse, arguing that literary meaning is co-constructed at the intersection of idiolectal choices and socially shared linguistic configurations. The study synthesizes insights from stylistics, sociolinguistics, discourse analysis, and systemic-functional linguistics to frame “individual style” as a pattern of motivated selections and “social language units” as repertoires including register, sociolect, and genre-linked norms. Methodologically, it combines qualitative discourse analysis with corpus-informed profiling of narratorial and character speech, triangulating collocational tendencies, appraisal resources, transitivity patterns, and indexical cues across a purposive sample of contemporary Anglophone fiction. Results indicate that authors’ stylistic signatures are legible not apart from but through social language units they orchestrate: narrative voice calibrates registers for stance management; character idiolects weave sociolectal markers to perform identity; and style-shifting across scenes functions as a discourse-level resource for heteroglossia and plot dynamics. The discussion articulates implications for theory (style as social action), method (mixed-methods stylistics), and pedagogy (teaching style via register awareness), while acknowledging limits of genre scope and language coverage. The article contributes a model in which “individual style” is best understood as a selective reconfiguration of social language units, yielding an integrated account of variation, meaning, and effect in literary discourse.
style; idiolect; sociolect; register; heteroglossia; dialogism; indexicality; stylistics; discourse; corpus
SOCIOLECTS THAT CREATE INDIVIDUAL SPEECH IN LITERARY DISCOURSE
This article explores how sociolects function as resources for crafting individual speech in literary discourse, focusing on the ways writers stylize social voices to build character identity, world-building, and narrative realism. Drawing on sociolinguistics and discourse analysis, the study conceptualizes sociolects as indexical repertoires that invoke social personae, positions, and communities of practice within fictional worlds. We integrate corpus-assisted stylistics with qualitative micro-analysis to examine markers such as lexicon, morphosyntax, represented phonology, code-switching, and jargon. Results show that authors strategically orchestrate sociolectal cues to differentiate idiolects, manage stance, and produce heteroglossic textures that guide reader inference. The discussion situates these findings within current debates on stylization, indexicality, and critical discourse analysis, arguing that sociolectal representation both reflects and reshapes social stratification, authenticity norms, and language ideologies. The article concludes with implications for literary linguistics, translation, and pedagogy, advocating transparent, reflexive approaches to sociolectal portrayal that balance aesthetic aims with ethical responsibility.
sociolect, idiolect, literary discourse, stylization, indexicality, heteroglossia, character voice, stance, register, code-switching
ON THE HISTORY OF THE DEVELOPMENT OF MILITARY MODELING
This article examines the main stages in the emergence and development of military modeling from ancient times to the beginning of the twentieth century. It analyzes the approaches to military modeling employed by outstanding military commanders, investigates the features of the first professional military conflict simulation system, Kriegsspiel, and considers the contribution of twentieth-century military scholars to the development of the theory of military modeling.
military science, modeling, planning, simulation, force ratio, analysis, enemy disposition, scenario, commander training, terrain.
IMPROVING SOCIAL ACTIVITY COMPETENCE OF PROSPECTIVE TEACHERS THROUGH PROJECT-BASED AND REFLECTIVE LEARNING
This article provides a scientific and pedagogical analysis of developing the social activity competence of prospective teachers through project-based and reflective learning approaches. The concept of social activity competence and its structural components are examined. The practical potential of project-based learning oriented toward real social problems, along with the role of reflective learning in consciously drawing lessons from experience, are analyzed, and a step-by-step integrative model combining both approaches is substantiated from a scientific and practical perspective.
social activity competence, project-based learning, reflective learning, prospective teacher, civic consciousness, social responsibility, collaboration, experiential learning, professional training, competence-based approach.
DEVELOPING STUDENTS’ INDEPENDENT THINKING THROUGH THE CASE-METHOD APPROACH
This article examines the role of the case-method approach in developing students’ independent thinking skills. In modern education, fostering learners’ ability to analyze situations, make decisions, and think critically is considered a key objective. The case-method, as an interactive teaching strategy, provides real-life scenarios that encourage students to engage actively in problem-solving processes. The study highlights the effectiveness of this method in enhancing students’ intellectual abilities, creativity, and decision-making skills.
case-method, independent thinking, students, critical thinking, problem-solving, education, interactive learning
WAYS TO IMPROVE INVESTMENT EFFICIENCY IN THE TRANSITION TO A GREEN ECONOMY: INTERNATIONAL EXPERIENCE AND PROSPECTS FOR UZBEKISTAN
Ther article studies the theoretical and practical issues of increasing the efficiency of investment in the process of transition to a green economy. On the basis of experience of other countries, a comparative analysis of the methods applied by Germany, Japan, South Korea, China, Singapore, Finland, Australia, and Canada to attract and efficiently use green investments is carried out. The comparative method, statistical method, and system approach were used in the study of the effectiveness of green investment models. It was found that the development of green financial tools, the increase in investment in the renewable energy industry, the optimization of the PPP mechanism, and the application of the ESG principles in investment policy are the most important approaches to increase the efficiency of investments in Uzbekistan. On the basis of the international best practices, recommendations for the adaptation of successful green investment mechanisms in national economic conditions of Uzbekistan are given.
green economy, green investments, investment efficiency, Environmental, Social, and Governance (ESG), green finance, renewable energy, sustainable development, international experience.
Assessing Digital Intelligence-Based Planning Architectures for Improved Operational Outcomes and Resource Utilization
The increasing complexity of modern operational environments has created a demand for intelligent planning architectures capable of improving decision quality, resource utilization, and system-level performance. Traditional planning approaches based primarily on deterministic models and human expertise face limitations when managing highly dynamic, uncertain, and interconnected systems. This research paper examines digital intelligence-based planning architectures as an emerging paradigm for enhancing operational outcomes through artificial intelligence, automated decision support, reinforcement learning, distributed intelligence, and adaptive resource allocation mechanisms. The study analyzes how intelligent architectures integrate computational models, autonomous agents, and learning-based optimization methods to improve planning efficiency and operational resilience. The research adopts a conceptual analytical methodology based on synthesis of established studies related to automated air traffic management, intelligent control systems, reinforcement learning, and AI-driven resource optimization. Existing frameworks for automated arrival management, conflict resolution, distributed agent-based systems, and learning-based control are examined to identify their contribution toward intelligent planning architectures. The analysis demonstrates that digital intelligence enables improved prediction, real-time adaptation, and efficient allocation of constrained resources by transforming planning systems from static decision structures into dynamic learning environments. Findings indicate that intelligent planning architectures provide significant benefits in operational coordination, workload reduction, resource optimization, and decision accuracy. However, challenges remain regarding system integration, reliability, transparency, human-machine collaboration, and implementation barriers within complex socio-technical environments. The research highlights that successful adoption requires balancing automation capabilities with human oversight and developing architectures capable of explaining and adapting their decisions. The study contributes to understanding the theoretical and practical foundations of digital intelligence-based planning systems by connecting artificial intelligence methods with operational management requirements. Furthermore, it emphasizes the relevance of AI-powered resource allocation approaches in improving efficiency and cost optimization across complex operational domains (Philip, 2024). The findings provide a framework for future development of intelligent planning architectures that support sustainable, adaptive, and high-performance operational systems.
Copyright (c) 2026 Dr. Jean Baptiste Ilunga https://creativecommons.org/licenses/by/4.0