American Journal of Applied Science and Technology

Many Participatory One Differential In The Game Persecution To Pursue

Umrzakov Nodirbek — 2025-12-10

This at work we look at the differential game in space. Our goal chaser and fugitive between in relationships Local run away to leave issue solution enough condition seeing This problem is convex. compact edge at points to normal has hyperbolic U set with from other general to the point has not to be This lemma is the converse. hypothesis way with proven.

Private Debt, PIK Financing, and Evolution in Leveraged Transaction Structures: Theoretical Foundations, Contractual Design, and Empirical Implications

John M. Aldridge — 2025-12-04

This article examines the contemporary architecture of private debt and leveraged finance with a concentrated focus on pay-in-kind (PIK) instruments, unitranche solutions, and nonbank direct lending. Building from foundational theories of incomplete contracts and adverse selection, the study synthesizes legal, contractual, and market microstructure perspectives to explain why PIK instruments and unitranche debt have proliferated in sponsor-backed leveraged buyouts and direct lending markets. The manuscript reviews the mechanics of PIK notes, holdco PIK structures, unitranche bifurcation, covenant-lite features, and the role of reputation in financing outcomes. It situates these instruments within a risk-transfer framework considering bank skin-in-the-game, rollover risk, and market freezes. The article then articulates a structured methodology for analyzing the value tradeoffs inherent to flexible payment instruments and bundled debt solutions using descriptive comparative techniques, counterfactual reasoning, and quasi-experimental evidence synthesized from the literature. Descriptive results illuminate channels through which PIKs extend runway while potentially eroding long-term value, how unitranche structures reallocate amortization and default risk, and how nonbank direct lenders reshape covenant design and monitoring intensity. The discussion integrates theoretical implications for corporate governance, agency costs, and systemic risk, and offers a set of testable propositions for future empirical research. Limitations and avenues for extension are considered, including data constraints, endogeneity concerns, and regulatory evolutions. Overall, the paper argues that contemporary leveraged finance instruments reflect rational contract innovation responding to liquidity premia, investor heterogeneity, and shifting bank–nonbank intermediation, but they also create concentrated tail risks that warrant closer empirical scrutiny and prudent underwriting practice. (Words: 285)

Chemical Processing Of Aznek Ore Deposits: Modern Analytical Approaches For Determining The Composition Of Primary Components

Kudiyarova Ayimkhan — 2025-12-13

The effective chemical processing of Aznek ore deposits requires precise identification of their primary mineral and elemental components using advanced analytical techniques. This study presents a comprehensive evaluation of modern spectroscopic, microscopic, and wet-chemical methods applied to the characterization of Aznek ores. High-resolution analytical tools including X-ray fluorescence (XRF), inductively coupled plasma mass spectrometry (ICP-MS), electron probe microanalysis (EPMA), Raman spectroscopy, and X-ray diffraction (XRD) were employed to determine major elements, trace impurities, mineral phases, and microstructural heterogeneity. The integrated methodological approach provided highly accurate, reproducible data that serve as a scientific basis for predicting ore behavior during hydrometallurgical and pyrometallurgical processing. The findings demonstrate that modern multi-modal analysis enhances technological decision-making, increases metal recovery efficiency, and supports the development of environmentally sustainable processing strategies. This work underscores the critical role of advanced chemical characterization in optimizing the beneficiation and industrial utilization of Aznek ore resources.

Drafting System Of A Spinning Machine

S.N. Yarashov — 2025-12-10

In this article, a spinning machine drafting device is used in the textile industry. It contains three grooved cylinders and three rollers with an elastic coating, pressed to the cylinders by a spring load. The grooves of the last third cylinder are made symmetrically in the form of a chevron with angles α1 and α2 selected in accordance with the coefficient of friction of the material on the grooves 3 or.

Working Conditions Of Employees In Electroplating Shops

A.M. Lazarenkov — 2025-12-12

The results of a comprehensive assessment of working conditions at workplaces in electroplating shops are presented, and the determining factors of the production environment are identified. The working conditions of the main professions are compared with regulatory values. It has been established that, when performing a comprehensive assessment of working conditions for employees in electroplating shops (sections), it is necessary to take into account the equipment and hand tools used, as well as the duration of exposure near operating equipment.

Symmetric Encryption Algorithm Based On A One-Way Function Extended By Parametric Algebra

Khudoykulov Zarifjon — 2025-12-09

A key concern in information security within communication systems is the necessity of employing robust symmetric encryption algorithms to safeguard large volumes of data. Designing stable and secure symmetric encryption algorithms remains one of the most challenging problems in cryptology. This paper introduces a methodology to enhance symmetric cryptographic systems by utilizing a one-way function-based encryption algorithm, offering a level of security comparable to the discrete logarithm problem in finite fields.

Chitosan-Based Biocomposites Integrated With Nanotechnology: A Comprehensive Scientific Review

Nurutdinova Feruza Muidinovna — 2025-12-14

Chitosan-based biocomposites have emerged as a highly versatile class of sustainable materials due to their biodegradability, biocompatibility, and chemical versatility. The integration of nanotechnology into chitosan matrices has led to significant advancements in mechanical performance, antimicrobial properties, thermal stability, and biological functionality. This extended review provides an in-depth examination of the structural characteristics of chitosan, the molecular mechanisms that govern its interactions with diverse nanomaterials, and the synthesis methodologies used for preparing advanced nanobiocomposites. Particular attention is given to polymer–nanofiller interfacial chemistry, nanoparticle dispersion behavior, physicochemical characterization techniques, and multifunctional applications across biomedical, pharmaceutical, environmental, agricultural, food packaging, and industrial fields. The review also analyzes safety considerations, toxicity issues, production challenges, and future research prospects. The discussion demonstrates that nanotechnology not only enhances the intrinsic properties of chitosan but also expands its application potential for next-generation smart materials, controlled-release systems, biosensors, and environmentally friendly technologies.

Multimodal Approaches To Assessing Liver Disease And Cardiovascular Risk Based On Ultrasound Imaging And Clinical Biomarkers: A Review Of The Scientific Literature

Akhram Nishanov — 2025-12-11

Liver diseases, particularly NAFLD and NASH, and cardiovascular diseases are serious global health risks. Noninvasive diagnostic methods, particularly ultrasound elastography (2D SWE, pSWE, ARFI) and clinical biomarkers (AST, ALT, FIB 4, NFS, CRP, lipid profile), are effective tools for assessing liver fibrosis and predicting CVD risk. A multimodal approach, with the integration of AI and deep learning, increases diagnostic accuracy and allows for individualized patient risk stratification. This review article presents the effectiveness of multimodal approaches based on ultrasound images and biomarkers and a review of the scientific literature.

Ecological State Of Groundwater In The “Avval” Township Of Fergana District

Xolmatova Surayyo — 2025-12-09

This article is devoted to assessing the ecological state of groundwater in the territory of Avval town. The paper examines the hydrogeological conditions of the area, factors influencing groundwater formation, anthropogenic impacts, sources of pollution, and the results of ecological monitoring. Laboratory analyses showed that some groundwater quality indicators deviate from sanitary standards, revealing, in particular, a potential risk related to nitrate levels.

Personal Characteristics Of The Ideal Coach

Salimov Utkirjon Shaidullaevich — 2025-12-14

The personality of the coach is increasingly recognized as a key factor determining not only athletic performance but also the personal and social development of athletes. The concept of the ideal coach goes beyond technical competence and includes a complex system of moral, psychological, pedagogical, and communicative characteristics. The purpose of this review article is to systematize and critically analyze contemporary scientific approaches to the study of personal characteristics of the ideal coach. The review is based on interdisciplinary research in sport psychology, pedagogy, and coaching science published between 2010 and 2024. Particular attention is paid to value-based orientations, moral qualities, leadership styles, emotional stability, and communicative competence as core elements of the coach’s personality. The analysis demonstrates that an ideal coach is characterized by a balance between demandingness and humanism, authority and empathy, emotional regulation and pedagogical optimism. The article also highlights the role of coaching philosophy as an integrative framework that shapes professional behavior, interpersonal relationships, and long-term athlete development. The findings of this review suggest that personal characteristics of the coach should be considered a strategic resource in athlete preparation systems and in the professional education and continuous development of coaches.

Synthesis, Structural Characterization, Spectroscopic Investigation And Antibacterial Activity Of Vanadium (Iv) And Vanadium(V) Complexes With Glycine And Glutamine Ligands

Muzafarov Farukh Ikhtiyorovich — 2025-12-11

A comprehensive investigation of vanadium (IV) and vanadium(V) coordination compounds with biologically relevant amino acid ligands, specifically glycine and glutamine, has been conducted. Four novel complexes were successfully synthesized and characterized through elemental analysis, infrared spectroscopy, ultraviolet-visible spectroscopy, and thermogravimetric analysis. The coordination behavior of these amino acids toward vanadyl (IV) and dioxovanadium(V) centers was systematically examined, revealing bidentate chelation through carboxylate and amino groups. Structural elucidation confirmed octahedral geometry for VO(II) complexes and distorted square pyramidal geometry for V(V) species. The antibacterial efficacy of synthesized complexes was evaluated against both Gram-positive (Staphylococcus aureus, Bacillus subtilis) and Gram-negative (Escherichia coli, Pseudomonas aeruginosa) bacterial strains using disc diffusion and minimum inhibitory concentration (MIC) determination methods. Results demonstrated significantly enhanced antimicrobial activity of metal complexes compared to free ligands, with MIC values ranging from 12.5 to 50 μg/mL. The vanadium(V)-glutamine complex exhibited the most potent antibacterial activity, particularly against S. aureus (MIC = 12.5 μg/mL). These findings contribute to understanding vanadium-amino acid coordination chemistry and suggest potential applications in developing novel antimicrobial agents.

The Influence Of Chemical Factors On The Synthesis Of Amino Acids And The Production Of Biologically Active Substances Based On Them

Kuzibay sKultayev — 2025-12-09

In the article formation of aminoalcohols and aminocompounds by Mannich reaction from acetylenic alcohols and phenylacetylene and also biological activity of halogenated derivatives of the synthesized aminocompounds have been inves -tigated. Acetylenic alcohols were synthesized by the reaction A.E. Favorsky and phenylacetylene was obtained from styrene. The influence of various factors (tem -perature, catalyst, time and nature of solvents) on the yield of aminoalcohols was studied. A theoretical analysis of the mechanism of formation of acetylenic amino- alcohols is given. The physico-chemical properties of the synthesized aminoalcohols and their yields were determined. The chemical structure of aminoalcohols has been confirmed by IR and PMR spectrums. The biological activity of compounds syn-thesized by chlorination and bromination of aminocompounds has been investigated.

Genetic Algorithm Optimization Of Neural Network Hyperparameters For Predicting Key Bits In The S-Aes Cipher

Boykuziev Ilkhom Mardanokulovich — 2025-12-13

Recent advances in machine learning have opened new directions in the cryptanalysis of lightweight block ciphers, particularly in the study of nonlinear components and key-dependent transformations. Building on prior work involving simplified cryptographic models such as Mini-AES and deep-learning-based attacks on lightweight ciphers, this study investigates the learnability of round-key bits in the Simplified Advanced Encryption Standard (S-AES). A structured dataset was generated by producing random 16-bit master keys and deriving their corresponding 48-bit subkey representations through the key-schedule algorithm. Additionally, two fixed plaintext blocks were encrypted under each key to construct three distinct training sets for the classification of the KPK_PKP, KFK_FKF, and KSK_SKS round-key bits. To examine the predictive potential of machine-learning models, Support Vector Machines (SVMs) were chosen as primary classifiers due to their robustness and proven ability to capture nonlinear decision boundaries even in limited training regimes. The Ray Tune optimization framework was employed to identify optimal SVM hyperparameters, leveraging distributed search mechanisms that have demonstrated superior performance compared with conventional optimizers such as HyperOpt and SMAC. Experimental evaluations conducted on datasets consisting of 1500 samples - split into 1200 training and 300 testing instances - reveal that certain round-key bits exhibit significantly higher learnability than others, indicating non-uniform structural leakage within the S-AES key schedule. The results highlight that optimized SVM configurations can achieve strong classification performance across multiple round-key bit positions, demonstrating the presence of machine-learnable relationships between plaintext–ciphertext mappings and internal subkey bits. These findings contribute to a deeper understanding of cryptanalytic vulnerabilities in lightweight block ciphers and confirm the growing relevance of machine-learning-driven approaches in modern symmetric cryptography research.

The Impact Of Hot Weather Conditions On The Efficiency Of Lesson Assessment During Outdoor Physical Education Lessons

Berdiev Gayrat Ulaboyevich — 2025-12-10

This article examines the impact of climatic factors on the effectiveness of physical education lessons conducted outdoors in three regions of Uzbekistan: Termiz, Fergana, and Karakalpakstan (Nukus). The analysis is based on meteorological indicators from the 2023–2024 academic year (air and soil temperature, humidity, precipitation, wind speed) and the physical fitness results of students in grades 5–9. Findings show that during warm spring and autumn periods, high temperatures and low humidity may lead to dehydration, fatigue, and reduced learning performance among students. The article provides methodological recommendations, including scheduling PE lessons during cooler hours, choosing climate-appropriate sportswear, and preventing heat-related health risks.

Developing A Question-Answering System In The Uzbek Language Based On The Xlm-Roberta Model

Khujayarov I.Sh. — 2025-12-12

This article presents the issue of testing the XML-RoBERTa model for generating questions and answers in the Uzbek language. In the study, the XML-RoBERTa model was adapted to the Uzbek language from a dataset consisting of context, question and answer pairs in the Uzbek language and, as a result, a model was developed to generate a fragment of the answer to the user's question from the context. ROUGE, EM (Exact Match) and F1 control metrics were used to determine the performance of the model.

A Framework for Transformative Pharmaceutical Supply Chains: Integrating Sustainability, Resilience, Ethics, and Technology

Dr. Mariana Kovács — 2025-12-09

This article develops an integrated, publication-ready theoretical and practical framework for sustainable pharmaceutical supply chain management that fuses environmental, social, and economic imperatives with emerging technologies and collaborative governance. Building on prior frameworks of sustainable supply chain management (Carter & Rogers, 2008; Closs, Speier & Meacham, 2011), supplier selection and triple bottom line evaluation (Ahi & Searcy, 2015; Govindan, Khodaverdi & Jafarian, 2013), and domain-specific studies in pharmaceuticals and healthcare logistics (Ding, 2018; Chaudhuri, 2015; Chen, Li & Wang, 2020), the article articulates an end-to-end model that emphasizes circularity, digital transparency, stakeholder co-creation, and public-health-aligned profit mechanisms (Dahan et al., 2010; Ding, Wang & Zheng, 2018). The paper synthesizes evidence on technological enablers — blockchain, big data analytics, Industry 4.0 advances — and social governance instruments to propose a coherent approach for measuring, implementing, and scaling sustainable practices across the pharmaceutical supply chain (Cole, Stevenson & Aitken, 2019; Barbosa et al., 2018; Ding, 2018). Methodologically, the work adopts a rigorous conceptual synthesis, comparative literature analysis, and construct-level triangulation of sustainability measurement instruments (Das, 2017; Shou et al., 2019). Results are presented as descriptive analyses and translatable managerial guidelines, showing how supplier evaluation, collaborative profit-allocation mechanisms, and digital tracking reduce waste and improve public health outcomes while maintaining commercial viability (Ding, Wang & Zheng, 2018; Chowdhury, 2025). The discussion interprets limitations, including measurement heterogeneity and governance complexity, and sets an agenda for empirical testing, simulation modeling, and policy experimentation. The conclusion offers concrete priorities for practitioners and policymakers to accelerate the transition to sustainable pharmaceutical supply chains that balance the triple bottom line and public-health imperatives.

The Role Of Chitosan And Its Biocomposites In Burn And Wound Healing

Rasulova Yulduz Zikrulloyevna — 2025-12-14

Chitosan, a biopolymer derived from chitin, has gained significant attention due to its biocompatibility, biodegradability, and wound healing properties. This article reviews the importance of chitosan and its biocomposites in burn and wound management, supported by statistical data and comparative analyses. Artificial datasets are used to visualize healing trends, antimicrobial activity, and comparative efficiency in clinical and experimental settings.

Basis Of Systematic Analysis And Optimal Solution Finding

B.J. Khursanov — 2025-12-13

This article discusses the role and importance of a systematic approach in the analysis of complex technological processes in the chemical industry. Due to the multi-stage, interconnected structure of the processes, scientific and practical approaches are proposed aimed at optimal management and increasing efficiency. The research describes methods for modeling and optimizing chemical reactors, heat and mass transfer devices, and processes with high resource consumption.

Application Of Neural Networks In Cryptanalysis: The Case Of The Vigenère Algorithm

Davlatov Mirzo-Ulugbek — 2025-12-09

This paper investigates the application of neural networks in cryptanalysis processes using the Vigenere cipher as a case study. Although the Vigenere cipher, one of the classical polyalphabetic substitution algorithms, was historically regarded as a strong cryptosystem, modern computational capabilities and artificial intelligence approaches help reveal its weaknesses. In this study, a neural network model was built using the PyTorch library, and experiments were conducted to reconstruct plaintext from ciphertext. The experimental results demonstrated that neural networks can learn statistical patterns inherent in the Vigenere cipher and are capable of partially automating the decryption process. This work highlights the potential of neural networks as a complement to classical cryptanalysis methods and proposes new approaches for evaluating the robustness of cryptosystems.

Gastronomic Tourism As A Factor In The Development Of Tourist Streets In A Megapolis (The Case Of Tashkent)

Rikhsieva Nodira Ravshan qizi — 2025-12-14

The article examines gastronomic tourism as one of the most dynamically developing areas of the modern travel industry, contributing to the formation of the tourist identity of megacities. Using the example of Tashkent, the study analyzes the influence of gastronomic tourism on the development of tourist streets, the enhancement of their economic and cultural attractiveness, as well as the role of gastronomic spaces in shaping the city's image. The authors emphasize that gastronomic tourism is becoming a factor in the sustainable development of the urban infrastructure and the local economy.

Improving Efficient Indexing Methods For Data Storage

Ikromov Husniddin Abduvaid o'g'li — 2025-12-11

This article examines modern database indexing methods and proposes new approaches to their optimization to improve data access speed and reduce storage and maintenance costs. Structures such as B-Tree and Hash indexes, as well as hardware-specific and hybrid methods adapted to large data volumes and workloads, are analyzed. Based on a theoretical analysis and a review of empirical research, recommendations are developed for selecting and combining index structures for various scenarios: transactional systems, big data warehouses, and machine learning systems. The obtained results significantly reduce retrieval time, improve scalability, and reduce index update overhead, making the proposed approaches relevant for modern distributed and highly loaded data storage systems.

Application Of FDM-Printed PLA Patterns In Investment Casting Using AL9-1 Aluminum Alloy

Muzaffarov Abdurashidkhon Abdulaziz ogli — 2025-12-09

This article explores the application of Fused Deposition Modeling (FDM) technology using polylactic acid (PLA) for the fabrication of sacrificial patterns within the investment casting process (Lost Pattern Casting). A comprehensive assessment was carried out on the mechanical properties and dimensional accuracy of castings made from AL9-1 aluminum alloy. 3D-printed models served as expendable patterns for creating gypsum-based ceramic molds. The geometric accuracy, surface roughness, and mechanical properties of the alloy after casting were evaluated. It was determined that the proposed approach enables dimensional tolerances of up to ±0.2 mm and surface roughness of approximately Ra ≈ 12 µm, without the formation of cracks in the mold. The results confirm the feasibility of using FDM printing in foundry technology for the production of small-batch and prototype parts.

Algorithm For Modeling The Process Of Information Exchange On Diet

Shikhnazarova Guzal Alisherovna — 2025-12-14

In today's digitalized healthcare environment, the task of effectively managing information exchange related to dietary planning and adjustments is particularly relevant. This paper examines an algorithm for modeling dietary information exchange based on the integration of data on actual food consumption, the nutrient composition of the diet, and the user's individual physiological characteristics. The proposed algorithm enables structured collection, processing, and analysis of dietary information, as well as the generation of personalized recommendations taking into account regulatory requirements and dynamically changing parameters. Algorithmic modeling improves the accuracy of dietary assessments, enhances interaction between users and healthcare professionals, and provides decision support in nutrition and healthcare systems.