Luminis Applied Science and Engineering (LUMIN) (ISSN: 3030-1831) is a peer-reviewed, open-access journal that illuminates innovative research and practical applications across diverse fields of science and engineering. Committed to fostering knowledge that bridges theory and practice, LASE serves as a platform for groundbreaking studies, technological advancements, and interdisciplinary insights that address complex global challenges.
Management Models for Providing Various Preventive Treatments
Preventive healthcare has become an essential component of modern healthcare systems due to increasing chronic diseases and rising healthcare expenditures. Effective management models for preventive treatment programs allow healthcare institutions to improve service delivery and reduce long-term medical costs. Digital transformation, technological innovation, and interdisciplinary cooperation have significantly influenced healthcare management strategies. Modern research emphasizes the importance of integrated management systems that combine digital infrastructure, policy coordination, and data-driven decision-making. This study analyzes different management models that support preventive treatment services and evaluates their role in improving public health outcomes.
Copyright (c) 2026 Luminis Applied Science and Engineering https://creativecommons.org/licenses/by/4.0
Evaluation of Alternative Tests for Measuring Vo2max Compared to the Gold Standard CPET Treadmill Test
Accurate assessment of the body's oxygen consumption is essential for the effective management of endurance in athletes. Peak oxygen uptake (VO₂max) remains the primary indicator for evaluating aerobic capacity and exercise efficiency. Although the gold standard for measuring VO₂max is the Cardiopulmonary Exercise Test (CPET) on a treadmill, it is resource-intensive, time-consuming, and requires precise calibration, making it less accessible in many settings. This study aims to evaluate alternative VO₂max testing protocols—namely, the CPET velo test, Astrand cycle ergometer test, Chester Step Test, and Submaximal Cycle Ergometry—with a focus on practicality, efficiency, and reliability. The objective is to determine whether these alternatives can offer comparable accuracy to CPET while being easier and more cost-effective to implement. A mixed-method research design was applied, combining experimental procedures and statistical regression analysis. Five participants with diverse physical activity backgrounds completed various tests, although some data was excluded due to technical limitations. Results indicated that none of the alternative tests consistently matched the CPET treadmill outcomes. The Astrand test showed identical VO₂max values in one participant, but significant discrepancies were observed in others. The findings confirm that the CPET treadmill test remains the most valid and reliable method for measuring VO₂max. While alternative submaximal tests may serve in general fitness assessments, they lack the precision required for high-stakes evaluation. Moreover, the study highlights the importance of considering participants' lifestyle and activity familiarity when selecting appropriate testing protocols. Future research should further explore hybrid models that balance scientific rigor with practical applicability.
Adaptive AI-Driven Learning Systems for Personalized Student Engagement and Performance
Adaptive AI-driven learning systems personalize instruction by estimating learner state and dynamically selecting content, feedback, and pacing to improve mastery and engagement. This paper synthesizes peer-reviewed evidence on adaptive learning, intelligent tutoring, knowledge tracing, educational data mining, and recommender systems, and proposes an applied engineering framework suitable for deployment in higher-education STEM contexts. We ground personalization in classic student modeling (knowledge tracing) and modern sequence modeling (deep knowledge tracing), and integrate a multidimensional view of engagement to avoid reducing “engagement” to simple clickstream metrics. We then present a modular, service-oriented system architecture encompassing data ingestion, learner modeling, pedagogical decisioning, explainability, monitoring, and governance controls. A prototype evaluation is conducted using a simulation-based testbed (explicitly illustrative, not empirical) with synthetic learners and skills. Across 600 simulated learners and 25 skills over 120 learning steps, an adaptive policy improves average mastery (fraction of skills mastered at threshold) compared to non-adaptive paging and random sequencing, with markedly higher rates of reaching “80% mastery.” The results also show that naive optimization may widen outcome gaps across learner subgroups, motivating fairness-aware objectives and human-in-the-loop controls. Ethical, privacy, and accessibility requirements are addressed through risk management practices, differential privacy–compatible training options, transparent explanations, and WCAG-aligned interface design.
Artificial Intelligence in Transit Corridor Management: Economic Efficiency Prospects of the Zangezur Corridor
Artificial Intelligence (AI) is increasingly transforming transit corridor management by enhancing operational efficiency, enabling predictive logistics, and optimizing cross-border trade flows. The Zangezur Corridor, as a strategically important transport route connecting Azerbaijan with regional and global markets, offers substantial opportunities for AI-driven modernization. The integration of intelligent transport systems, real-time data analytics, automated customs procedures, and predictive risk assessment mechanisms can significantly reduce operational costs, shorten delivery times, and improve reliability in freight movement. Moreover, AI-based coordination platforms can strengthen institutional cooperation among regional stakeholders and enhance supply chain transparency. The digitalization of corridor infrastructure is expected to improve resilience, sustainability, and long-term economic competitiveness. By leveraging AI technologies in transport planning, monitoring, and logistics management, the Zangezur Corridor can evolve into a technologically advanced transit hub, contributing to economic diversification and sustainable growth in the South Caucasus region.
From Fiscal Extraction to Entrepreneurial Acceleration: A Next-Generation Theoretical Architecture for Tax Policy Formation in the Business Sphere
In modern market economies, tax policy is simultaneously a fiscal backbone of the state and a decisive institutional signal shaping business behavior, investment horizons, and the quality of the entrepreneurial climate. Yet, in many transitioning systems, tax reforms frequently outpace the development of coherent theoretical frameworks, producing instability, compliance costs, and incentives for informality. This article systematizes the theoretical foundations of tax policy formation in the business sphere by integrating classical, neoclassical, Keynesian, and institutional perspectives and linking them to practical mechanisms of tax administration, special tax regimes, and incentive design. Using scientific abstraction, structural-functional analysis, theoretical generalization, and comparative analysis, the study clarifies how fiscal, regulatory, and stimulating models interact in real policy mixtures and how stability, transparency, and administrative simplicity condition business responses. The results propose an integrated conceptual architecture for “balanced tax policy” that aligns budget sustainability with entrepreneurial development through predictable rules, targeted incentives, and efficient administration. The discussion emphasizes institutional quality and risk-management logic as key determinants of compliance, competitiveness, and long-term growth.
Cultivation Conditions and Optimization Strategies for Antibiotic-Producing Microorganisms: Focus on Actinomycetes
Antibiotics remain indispensable for treating bacterial infections, yet accelerating antimicrobial resistance is reducing their clinical effectiveness and increasing the demand for reliable, high-yield production. Because most major antibiotics originate from microbial secondary metabolism, understanding and controlling cultivation conditions is essential for both laboratory reproducibility and industrial-scale fermentation. This review analyzes cultivation and process factors that determine antibiotic productivity in antibiotic-producing microorganisms, with emphasis on actinomycetes (especially Streptomyces). The findings highlight how undefined (complex) media can support growth but reduce comparability, whereas defined media improve control and interpretation of physiological responses. Carbon and nitrogen sources strongly shape pathway activation through catabolite and nitrogen regulation, while pH and temperature influence morphology, enzyme activity, and the timing of entry into idiophase. Evidence also supports roles for lipid and fatty-acid components in selected systems, as well as precursor feeding strategies and feedback inhibition (retroinhibition) that can either enhance or suppress biosynthesis depending on pathway control. Inoculum age, mycelial state, aeration/hydrodynamics, and typical fermentation durations (often multi-day) further determine yield stability. Optimizing these upstream variables can improve productivity and batch-to-batch reproducibility and supports future research integrating omics-guided optimization and metabolic engineering.
Regular Almost-Periodic Functions and the Concept of Periodicity
The present paper investigates the concept of regular almost-periodic functions and their fundamental properties as a natural generalization of classical periodic functions. Special attention is paid to the notion of ε-periods and their role in describing quasi-periodic behavior on the real line. The study clarifies the mathematical essence of almost periodicity in the sense of Bohr and highlights the importance of relative density of ε-periods. Key structural properties of regular almost-periodic functions are discussed, emphasizing their stability under algebraic operations and translations. The relevance of these functions in the theory of differential equations and in the modeling of physical and technical systems is also briefly addressed. The results demonstrate that regular almost-periodic functions constitute an effective analytical tool for describing complex processes that cannot be adequately modeled using classical periodic functions.
Indicators of Quality Assessment of Services in the Business Sector
Economically developed countries are characterized by the leading position of the service sector in socio-economic life. This is confirmed by the increase in the number of people engaged in this field, the increase in the volume and range of services offered. Among the main trends inherent in the modern service sector, the following can be noted. First, the further expansion of the service sector, including the emergence of service functions and an increase in their special share in the overall activities of industrial enterprises: after-sales service, brand trade and other forms of approaching the enterprise to consumers. products. Secondly, service functions are included in the composition of products as an important component that provides competitive advantages, for example, the presence of a menu of various service functions in modern goods such as televisions, telephones, computers. Thus, the traditional concept of a product is expanding, going beyond the boundaries of direct interaction between the supplier and the consumer and reflecting the results of the supplier's internal activities to meet the requirements of the consumer. Thirdly, there is a further differentiation of service levels that characterize the capabilities and needs of customers. For example, it is necessary to note homogeneous services offered separately for professionals and amateurs, wealthy and underprivileged people, as well as exclusive services distinguished by high standards and speed of service. Fourth, significant changes have occurred in the legislation, primarily in terms of expanding the rights of customers in relations with the service sector, where the issues of protecting the rights of consumers of products and services play the role of a powerful incentive ensuring their quality. In general, it can be concluded that the level of development of the service sector is one of the most important features of the development of modern society - its social orientation. A similar position is found in the concept of a post-industrial society proposed by foreign researchers, where the advantage of the service sector over the manufacturing sector is also noted.
Motivation plays a central role in human resource management because it directly shapes employee performance, commitment, and retention. As a core managerial function, motivation enables an enterprise to accomplish organizational tasks through a structured system of incentives and supportive measures. A well-designed motivation system requires managers to apply relevant motivational theories, which help explain how employees’ behavior can be influenced and guided more effectively. Importantly, these theories also demonstrate that financial rewards are not the only drivers of work behavior: employees differ in their needs, expectations, and values, which makes it necessary to employ varied motivational instruments to sustain engagement and reduce turnover. In recent years, attention to motivation as a management function has grown considerably. Many organizations now prioritize motivational practices in order to strengthen effectiveness and improve overall productivity. The successful operation of any institution depends largely on the quality of its workforce and the professionalism of managerial leadership. For this reason, enterprises must develop improved systems of motivation and encouragement that stimulate both the collective team and each individual employee to work efficiently toward shared objectives. Sustainable success is typically achieved by leaders who recognize that people are the main driving force of organizational development. In this context, motivation can be understood as the process of encouraging employees toward productive and goal-oriented activity.
Investigation of the Chemical Basis of the Temperature- and Environment-Dependent Color Change of Cobalt(II) Ions
The temperature- and environment-dependent color change of cobalt(II) ions is a chemical process closely related to the structural characteristics and electronic behavior of the complexes they form. In aqueous media, the Co²⁺ ion predominantly forms the octahedral, pink-colored [Co(H₂O)₆]²⁺ complex. In this complex, the d-electrons of the cobalt ion split into specific energy levels under the field effect created by the water ligands, and the wavelength of the absorbed light results in the appearance of the pink color. As the temperature increases, the detachment of water molecules from the complex becomes easier, and the chloride ions present in the system form the tetrahedral, blue-colored [CoCl₄]²⁻ complex with Co²⁺. Since the ligand field splitting in this complex differs from that in the octahedral one, the absorption spectrum also changes, and consequently the solution turns blue. The endothermic nature of the equilibrium causes the reaction to shift to the right with increasing temperature, favoring the formation of the blue complex. The ionic strength of the medium, particularly the concentration of Cl⁻, significantly affects the rate and stability of this transition. In certain cases, pH may also influence the stability of the complexes. This color change is one of the remarkable processes that illustrates the fundamental principles of coordination chemistry and holds particular importance in the study of cobalt(II) ions.
Copyright (c) 2025 Luminis Applied Science and Engineering https://creativecommons.org/licenses/by/4.0
Engineered Inorganic Adsorbents for Water Remediation: From Surface Modification to Selective Heavy-Metal Capture
Inorganic adsorbent materials have become key tools in water purification and industrial wastewater treatment due to their chemical stability, low cost, and strong adsorption performance. This article reviews the adsorption behavior of major inorganic adsorbent groups—zeolites (clinoptilolite), metal oxide nanoparticles (Fe₂O₃ and MnO₂), and functionalized silica—focusing on their structural features, surface chemistry, and pollutant–surface interactions. The main adsorption mechanisms include ion exchange, electrostatic attraction, surface complexation, and, in the case of MnO₂, oxidation-related processes. Comparative analysis indicates that surface modification significantly improves selectivity and capacity, particularly for heavy metal ions such as Pb²⁺, Cd²⁺, and Cr species. Isotherm and kinetic interpretations suggest that modified oxides and amine-functionalized silica often follow Langmuir-type behavior with pseudo-second-order kinetics, supporting chemically controlled adsorption. Regeneration studies highlight that many inorganic adsorbents maintain practical efficiency over multiple cycles, strengthening their sustainability and real-world applicability. Overall, modified inorganic nanomaterials show strong potential for long-term use in pollutant removal, especially when designed for selectivity, reusability, and performance under realistic wastewater conditions.
The Position and Classification of Hydrogen in the Periodic Table: A Comparative and Conceptual Analysis
Hydrogen occupies a unique and controversial position in the periodic table due to its simple atomic structure and diverse chemical behavior. Although it is traditionally placed at the top of Group 1, hydrogen displays properties that resemble not only alkali metals but also halogens, making its classification problematic. This ongoing debate highlights the limitations of rigid group-based classification within the periodic system. The purpose of this study is to examine the position of hydrogen in the periodic table by analyzing its atomic structure, chemical reactivity, and bonding behavior in comparison with different element groups. The research employs a comparative analytical method, focusing primarily on similarities and differences between hydrogen, alkali metals, and halogens, as well as considering alternative classification models proposed in modern chemistry. The findings indicate that hydrogen cannot be fully integrated into any single group due to its ability to exhibit both metallic and nonmetallic characteristics and to form compounds with varying oxidation states. The study concludes that hydrogen should be regarded as a unique element with a special or independent position in the periodic table, reflecting its exceptional chemical nature and theoretical significance.
Molecular and Cellular Mechanisms of Wound Healing in Animals: Biological Dynamics and Therapeutic Innovations
Wound healing in animals is a highly complex and dynamic biological process that involves coordinated cellular, molecular, and tissue-level responses aimed at restoring structural and functional integrity after injury. This study examines the biological regulation of wound healing in animals, with particular emphasis on the sequential phases of hemostasis, inflammation, proliferation, and remodeling. Clinical and experimental observations were conducted on farm animals with traumatic, surgical, purulent, and necrotic wounds to evaluate healing dynamics under different treatment approaches. The application of bioactive collagen-based dressings and platelet-rich plasma (PRP) therapy significantly reduced microbial contamination, enhanced fibroblast activity, promoted angiogenesis, and accelerated epithelialization compared with conventional antiseptic treatments. Histological and microbiological findings confirmed improved tissue organization and faster wound closure in the experimental group. The results highlight the importance of understanding wound healing biology for optimizing therapeutic strategies and demonstrate the potential of innovative bioactive treatments in improving clinical outcomes in veterinary practice.
Substances Migrating from Plastic Products into the Human Body and Their Effects on Human Health
This study examines the toxicological effects of substances migrating from commonly used plastic products into the human body, with particular emphasis on endocrine-disrupting chemicals (EDCs) such as bisphenol A (BPA) and phthalates. Based on a comprehensive review of international scientific literature published between 2002 and 2021, the research analyzes the sources of human exposure, metabolic pathways, and biological mechanisms through which these chemicals affect human health. The findings indicate that both BPA and phthalates can exert significant biological effects even at very low exposure levels, particularly during critical developmental periods such as pregnancy and early childhood. Evidence suggests that perinatal exposure to BPA may alter fetal epigenetic programming, increasing the risk of metabolic disorders, cardiovascular diseases, and developmental abnormalities later in life. Phthalates, on the other hand, are strongly associated with anti-androgenic effects and reproductive dysfunction. The study highlights ongoing regulatory challenges related to low-dose exposure and non-monotonic dose–response relationships, emphasizing the need for stricter control measures, increased public awareness, and effective strategies to reduce human exposure to these chemicals.
Analysis of Toxic Substances in the Composition of Cosmetic Products
This study provides a critical analysis of potentially toxic substances commonly found in cosmetic products, with a particular focus on parabens, phthalates, formaldehyde-releasing agents, surfactants, and fragrance components. Using a systematic literature review approach, the research examines the functional roles of these substances in cosmetic formulations and evaluates their possible health effects associated with long-term and repeated exposure. Evidence from recent toxicological and epidemiological studies indicates that certain cosmetic ingredients may act as endocrine disruptors, contribute to skin irritation and allergic reactions, and pose cumulative health risks through dermal absorption. The study also highlights existing challenges related to insufficient labeling transparency and limited consumer awareness, which hinder informed decision-making. Furthermore, current industry trends toward safer alternatives, including natural and plant-based ingredients, are discussed as promising strategies for reducing health risks. The findings emphasize the need for improved regulatory oversight, clearer labeling practices, and increased public awareness to promote safer cosmetic use and encourage the development of health-conscious cosmetic formulations.
AI-Enhanced Analytical Processing in Data Warehouses: Methods, Tools, and Decision Support
Abstract; The convergence of artificial intelligence (AI) and data warehousing is revolutionizing analytical processing and decision support. By integrating machine learning (ML), predictive models, and automated intelligence into traditional online analytical processing (OLAP) systems, organizations gain deeper insights and faster, more accurate forecasts. This review examines current methods for embedding AI in data warehouses, surveys tools and platforms that support AI-driven analytics, and evaluates the impact on decision support. We describe data integration processes (ETL/ELT), common AI/ML techniques (supervised/unsupervised learning, NLP, predictive analytics), and emerging capabilities such as automated query optimization and anomaly detection. Case studies show that AI-enabled warehouses improve forecasting accuracy and data exploration, though challenges remain in data quality, cost, and workforce skills. Comparative analysis of recent literature confirms that advanced analytical algorithms and big-data technologies significantly enhance managerial decision-making by consolidating disparate data and enabling real-time, predictive insights (Ismaili & Besimi, 2024; Kopczewski et al., 2025). We conclude by discussing best practices and future trends – including cloud-based AI services and self-optimizing architectures – that will further empower business intelligence (BI) in the era of data-driven decision support.
Impact of Theatre and Cinema Culture on Economy
Theatre and cinema have traditionally been regarded as cultural and artistic pursuits; however, their economic significance has gained increasing recognition in recent decades. This study explores the multifaceted economic contributions of these sectors, focusing on their roles in revenue generation, employment, tourism, and urban development. Employing a mixed-methods approach, the research integrates quantitative data from global and national economic databases with qualitative analysis from policy documents and industry reports. Results show that theatre and cinema industries contribute substantially to national GDPs, support millions of jobs worldwide, and stimulate economic activity across adjacent sectors such as hospitality, retail, and technology. The analysis also highlights the growing impact of digital transformation and streaming platforms, particularly in the wake of the COVID-19 pandemic. In addition, cultural tourism driven by theatrical landmarks and filming locations has proven to be a significant source of regional income. The findings underscore the need for governments and policymakers to treat these industries not merely as entertainment, but as strategic economic assets. This paper concludes with recommendations for public investment, policy development, and international cooperation aimed at sustaining and expanding the economic impact of theatre and cinema in both developed and developing economies.
Mathematical and Statistical Methods in the Application Fields of Data Mining Technology
In today’s digital age, enormous volumes of data are generated every moment. Data mining leverages mathematical and statistical methods—alongside machine learning, database management, and data visualization—to extract valuable knowledge from these large data sets. Its techniques are applied successfully in diverse areas, including business, government, healthcare, science, and sports, with use cases such as database marketing, fraud detection, retail analytics, credit scoring, astronomy, and molecular biology. Mathematics provides the backbone of these processes through statistics, optimization, linear algebra, probability theory, and other fields. Together, mathematical and statistical methods enable efficient preprocessing, accurate modeling, reliable forecasting, and optimization. Their integration makes large‑scale data easier to process and analyze, ultimately supporting informed decision‑making across multiple domains.
Examining the Use of Artificial Intelligence in Space Technologies, Taking Into Account the Dimensions of International Law
With the remarkable developments in space technologies, artificial intelligence has gradually been used instead of humans in decision-making. Artificial intelligence has the ability to think logically, manage its own actions, and correct decisions in the event of changes in external conditions. New smart space technologies are being developed with the aim of performing various space activities such as processing space data and information, removing space debris, extracting natural space resources, and exploring without human intervention. However, the regulation of the activities of space actors, especially private actors, and the supervision of these activities by states in the use of these types of technologies has become one of the new issues in the field of international space law. Since the obligations of States within the framework of international space law are explained on the basis of human behavior, in the face of monitoring the performance of intelligent space technologies and compensation for damage resulting from their performance, the question arises as to whether the existing international space regulations on the international responsibility of States for monitoring space activities and compensation for damage, which are based on human behavior, can also be applied to the use of these technologies, or should the regulations be A new space law should be developed. With a broad interpretation of Articles 6 and 7 of the 1967 Outer Space Treaty regarding the responsibility of States for monitoring space activities and also the responsibility for compensation for damage, these provisions can still be considered applicable. Nevertheless, it seems that the development of new international space regulations could be an important step in better defining and recognizing the responsibility of states to monitor the use of intelligent space technologies by space actors and to compensate for damages resulting from them.
Advancements in Neural Implants: A Systematic Review of Neuralink-Enabled Brain-Machine Communication
This systematic review examines recent advancements in neural implants with a focus on Neuralink-enabled brain-machine communication systems. By integrating ultra-flexible electrode threads, high-channel wireless data transmission, and robotic surgical implantation, Neuralink represents a significant leap in neural interface technology aimed at restoring movement, communication, and sensory perception for individuals with severe neurological impairments. The review situates Neuralink within the broader landscape of brain-machine interface (BMI) development, comparing its approach to competing technologies such as Synchron’s endovascular Stentrode, Blackrock Neurotech’s Utah array, and Paradromics’ high-bandwidth systems. Key design considerations—including biocompatibility, signal stability, power management, and data security—are explored, alongside the ethical and regulatory challenges associated with invasive neurotechnology. Early clinical trials, including Neuralink’s first-in-human implant, demonstrate the feasibility of enabling mind-controlled computer interaction in paralyzed individuals. This paper also highlights sustainability dimensions, such as the development of minimally invasive techniques, long-term implant durability, and inclusive access to assistive neurotechnology. By synthesizing recent peer-reviewed studies and clinical milestones, this review outlines the current state, emerging challenges, and future directions of neural implant systems that bridge human cognition and digital systems—positioning them as a critical component in the evolution of sustainable and intelligent healthcare technologies.
An Overview of the Challenging Paths Ahead for the Future of Artificial Intelligence Law
Today, artificial intelligence, using its capabilities such as text and image processing, has many applications in education, health and wellness, career development, personal affairs, and even law. Among its applications are the analysis of raw data for research, content production, disease detection and identification, and marketing. Therefore, artificial intelligence is progressing at a rapid pace, and its monitoring is visible in our lives and work and in the not-so-distant future, it will cover an important part of our lives. Therefore, it is important to understand the paths and challenges facing the future of artificial intelligence law. In this regard, this research discusses the relevant field of artificial intelligence law, and also considers what kind of regulatory issues artificial intelligence projects at the national and international levels can add to the current digital law discourse. Relying on the literature on artificial intelligence law, this research points out other problems related to the subjectivity of artificial intelligence law and the real personality of artificial intelligence that have not yet been recognized in digital law.
Human Capital and Digital Skills as Drivers of Firm-Level Competitiveness in Azerbaijan’s Transition Economy
As Azerbaijan continues its economic transition, the development of human capital and acquisition of digital skills have emerged as vital factors shaping firm-level competitiveness. This article examines the ways investments in education and digital competencies enhance productivity, foster innovation, and improve market positioning among Azerbaijani firms. Drawing from national statistics, international case studies, and relevant theoretical frameworks, this study highlights the evolving role of digital transformation within the country's post-Soviet economy. It also offers strategic recommendations and policy implications aimed at cultivating sustainable competitive advantages through human capital and digitalization.
An Overview of International Order and Its Impact on International Political Economy
In the existing literature on international relations in the country, realism and the theoretical branches associated with this school enjoy high acceptance. Various factors have driven the country's elites towards a specific reading of realism. The author proceeds from the belief that realism encompasses a diverse set of different theories. The lack of complete knowledge of this set, the efforts and the inability to accurately understand the key concepts in this field, will lead to a superficial and inaccurate perception. The present study aims to address this deficiency with a new perspective and, if possible, provide an answer. With this description, the question that the present study attempts to answer is: What capacity does realism have to respond to today's new issues in the field of changes in the international system? In response to this question, it can be said that Robert Gilpin's reading of realism based on the cycle of hegemony (in international politics) alongside hegemonic stability (in international political economy) provides a suitable window for presenting a more or less narrative account of this issue (the transformation of the international political economy system). In this regard, the Solow economic model, which is used to strengthen, enrich, and attempt to quantify Gilpin's reading of realism, provides a framework for providing a better explanation of the transitions and changes in the system. The writing strategy of the previous paper is of the comparative type, and its research method is also of the basic, case-based, and static type. What has been examined in this study is the use of Robert Gilpin's theories in the fields of international politics and international political economy, which have been strengthened and enriched by Robert Solow's economic model to better explain the cycles and changes in the international political economy system.
Synthesis And Physical Properties of Cadmium Sulphur (Cds) Nanoparticles
In this article, we present a simple and less hazardous route for the synthesis of CdS nanoparticles. We use the coprecipitation method for the synthesis of CdS nanoparticles, and obtain nanoparticles by calcination at 400°C. The synthesized CdS nanoparticles were characterized by optical microscopy and X-ray diffraction. The uniqueness of hexagonal (Wurtzite) CdS was revealed by X-ray diffraction peaks. The article also presents a study of the optical and electrical properties of polymer suspensions containing carbon nanotubes and composites based on them. These suspensions were also used to obtain composite thin films. Carbon nanotubes are of great interest in science and technology due to the combination of outstanding mechanical and electrical properties, extremely large interfacial contact area, high aspect ratio and low mass density. The use of nanotube concentrations as fillers in various composites, even less than 1 wt.%, has a significant impact on the optoelectronic properties of the final devices, making their use in nanoelectronics applications promising.
Deep Reinforcement Learning Models for Traffic Flow Optimization in SDN Architectures
Deep reinforcement learning (DRL) has emerged as a promising approach to dynamic traffic engineering in software-defined networks (SDN). In this work, we evaluate three popular DRL agents—Deep Q-Network (DQN), Asynchronous Advantage Actor-Critic (A3C), and Proximal Policy Optimization (PPO)—on simulated SDN routing tasks. Using a Mininet emulated network with a Ryu controller and TensorFlow-based agents, we compare DRL models against traditional baselines (shortest-path routing and equal-cost multi-path (ECMP)). The DRL agents learn to select routes based on observed link loads and flow queues, with rewards reflecting combined throughput, latency, and packet loss. Our simulations show that all DRL methods significantly outperform fixed routing baselines: for example, a PPO-based agent reduced average flow latency by ≈20% and packet loss by ≈25% relative to shortest-path routing. PPO and A3C converged faster and to higher rewards than DQN, likely due to their on-policy and parallel learning designs. We provide a detailed comparison of algorithm characteristics, training stability, and network metric outcomes. The results highlight each model’s strengths: PPO’s stability and sample efficiency, A3C’s parallelism and multi-agent potential, and DQN’s simplicity. We critically discuss limitations such as training overhead and convergence variance. Finally, we outline future directions for improving real-world SDN traffic control with DRL, including transfer learning across topologies, online continual learning, and multi-agent coordination.
Chainsawing Cayley Trees: Markovian Methods in Tree Enumeration
We revisit Cayley’s classical result that there are nn−2 labeled trees on nn vertices (Cayley’s formula). We introduce a stochastic pruning process on this space of Cayley trees, which we term a Markov chainsaw. In this model, edges of a labeled tree are cut or reattached randomly over time, yielding a Markov chain on the space of forests. We derive rigorous results for this process: we prove it is irreducible and aperiodic on the forest state space, and we find its stationary distribution via detailed balance. In particular, the uniform spanning-tree case recovers Cayley’s count and relates to loop-erased random walks and Wilson’s algorithm. We also implement computational experiments (in Python/NetworkX) for small nn to illustrate convergence and mixing; empirical frequencies agree with our theoretical stationary laws. Our contributions tie together classical enumeration (e.g. Prüfer codes), Markov‐chain theory (coupling and convergence), and applications in random graph processes and network reliability.
When AI Hesitates: Methods for Identifying and Managing Model Uncertainty
Model uncertainty—often termed epistemic uncertainty—is a critical factor in the reliability of AI systems, especially in safety-critical domains such as healthcare, autonomous vehicles, and legal decision-making. This study examines methods to identify and quantify model uncertainty by combining a systematic literature survey with empirical modeling. We evaluate approaches including Bayesian neural networks (via variational inference), Monte Carlo Dropout, and deep ensembles on benchmark tasks (e.g., CIFAR-10 image recognition and MIMIC-III ICU mortality prediction). We measure performance using metrics such as classification accuracy, expected calibration error (ECE), predictive entropy, and 95% confidence intervals, illustrating results with tables and calibration curves. Key findings include: (1) Deep ensembles consistently produce the most reliable uncertainty estimates, yielding well-calibrated probabilities and superior identification of misclassified or out-of-domain examples. This leads to improved accuracy when decisions are restricted to high-confidence predictions. (2) MC Dropout offers a practical, lightweight proxy for Bayesian inference, but it often underestimates uncertainty for unfamiliar inputs and requires many stochastic forward passes to approximate the posterior. (3) Explicit Bayesian neural networks deliver theoretically grounded uncertainty bounds, but at high computational cost and with mixed empirical gains due to the difficulty of specifying priors. Our results clarify the trade-offs in accuracy, calibration, and computational complexity among these methods. We provide practical guidance for deploying uncertainty-aware AI systems—such as post-hoc calibration of model outputs and deferring low-confidence predictions to human experts or additional checks—to enhance safety and trust in critical applications.
New Spice Road, Regional Rival to the New Silk Road
With the Belt and Road Initiative, China is at the forefront of players seeking regional links and connectivity between Asia and Europe. However, China’s presence in this arena is not exclusive, and the competition for infrastructure across Eurasia is multifaceted. India is another Asian giant that has quietly entered the mega-infrastructure projects of this supercontinent, seeking to revive the dormant plan for a north-south international transport corridor. The New Spice Road is a metaphor that narrates India’s resurgence and its competition with China for control of the routes for the movement and transportation of goods and energy. The aim of this article is to explore India’s main motivations and objectives for the revival of the North-South International Transport Corridor (New Drug Route) after nearly 15 years of its inception. The article shows that India’s efforts to rebuild the North-South International Corridor and promote the New Drug Route are primarily focused on security goals and motivations, including confronting the changing situation in Jammu and Kashmir in Pakistan’s favor; denying opportunities for China's geopolitics-geostrategy in the Indian Ocean region; creating a gap between Pakistan and its neighbors, especially Afghanistan; and geopolitical linkage with its new partners in Central Asia and the South Caucasus through Iran.
North-South International Transport Corridor
Artificial Intelligence in Digital Management
Artificial Intelligence (AI) represents a transformative cluster of technologies that empower machines to perform complex tasks traditionally reliant on human cognition. These capabilities include natural language processing, data interpretation, real-time decision-making, and intelligent automation. In the context of digital management, AI is rapidly becoming a cornerstone for optimizing operations, enhancing strategic planning, and reshaping customer engagement. The integration of AI tools enables automation of repetitive functions, freeing human capital for higher-order tasks while improving organizational agility and decision-making accuracy. This paper explores the evolution of artificial intelligence, its societal and economic impact, and its central role in driving digital transformation across industries. It also addresses key pillars such as strategy, governance, infrastructure, and organizational culture, offering insight into how AI integration can revolutionize digital business management.
The Strategic Role of E-Commerce in Supply Chain Management
Nowadays, organizations have turned to using various tools to gain competitive advantage. One of these tools can be considered information technology in the field of trade. The increasing advances in information technology have been able to create strategic changes in e-commerce and consequently in supply chain management. This article, by examining research in the field of e-commerce and supply chain, explains the strategic role of e-commerce on supply chain management. E-commerce can be considered a platform for improving supply chain performance, because the use of this tool can lead to cost reduction, improved scheduling, and system coordination.