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.
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.
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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.
Examining the Importance of Education Management with the Approach of Building a Culture of Commerce and E-Economy in Afghan Society
The growth and development of Afghanistan's communication system, despite being mountainous, has been very rapid. It is now growing at a rapid pace using modern technologies, and more than 80% of the country is covered by GSM l telecommunications. In Afghanistan, more than 80,000 people own telephones, and 80 times this number also own mobile phones, and most of the people's payments are made via mobile phones. A new service that has emerged in Afghanistan is the electronic payment service for people via mobile phones, which is discussed in this article. Due to its security and people's trust in it, it has attracted a large wave of people. More than 2,400,000 people in Afghanistan are Internet users. Afghanistan has made significant progress in the field of telecommunications, which is confirmed by the activities of 6 private and government telecommunications companies. This article attempts to discuss education and culture building for the development of Afghanistan's e-commerce and economy, given the importance of culture building and education for everyone in Afghanistan, overcoming the illiteracy crisis and becoming an innovative country in the region, along with improving the quality of life and educational progress.
The Role of Enzymes in Modern Medicine: Advances, Applications, and Future Directions
Enzymes play a critical role in modern medicine, serving as essential biological catalysts in therapeutic and diagnostic applications. This article explores the use of enzymes in enzyme replacement therapy (ERT), pharmaceutical drug development, and clinical diagnostics. The advancements in biotechnology have led to the development of engineered enzymes with improved stability and efficiency, addressing challenges such as enzyme degradation, immunogenicity, and production costs. Recent innovations, including enzyme immobilization, nanotechnology-based delivery systems, and CRISPR-engineered enzymes, have significantly enhanced the scope of enzyme-based treatments. Despite existing limitations, ongoing research continues to refine enzyme therapies, making them more accessible and effective. This study highlights the transformative impact of enzymes in medicine and discusses future directions for optimizing their clinical applications.
A Review of Digital Human Resource Management: Transforming HR Practices Through Technology
Digital Human Resource Management is a digital transformation in human resource practices and processes through the use of electronic media, mobile, analytics and information technology to make human resource management more efficient. In other words, digital human resource management basically performs or manages all human resource work using soft technologies, applications and the internet. Digitalization or digital transformation is something that all organizations must bring to be efficient and relevant in the future. Otherwise, they will be left behind by other organizations in the market industry. This study reviews several studies and discusses the concept and various aspects of digital human resource management. This study mainly relied on secondary data. The findings of this study are important for organizations to help them effectively implement digital human resource management and thereby improve their performance. Furthermore, the findings can help researchers by providing a basis for understanding the impact of digital human resources on organizational performance.
Genetic Engineering and Its Ethical Implications: A Bioethical Perspective
Genetic engineering has revolutionized the fields of medicine, agriculture, and biotechnology, offering unprecedented control over DNA modification. The development of CRISPR-Cas9 and other gene-editing tools has enabled precise genetic alterations, raising both scientific opportunities and ethical dilemmas. This paper explores the methodologies of germline and somatic gene editing, the applications of gene drives in conservation, and the corporate influence on genetically modified organisms (GMOs). Ethical concerns regarding human genetic modification, ecological risks, and economic inequalities are critically examined. Additionally, the study highlights bioethical principles, regulatory frameworks, and global governance approaches aimed at ensuring the responsible use of genetic engineering. The findings emphasize the need for adaptive regulations, interdisciplinary collaboration, and public engagement to balance scientific progress with ethical responsibility. While genetic engineering holds immense potential, its future depends on the establishment of equitable, ethical, and sustainable policies that align with human rights and ecological integrity.
Review And Importance of International Legal Dimensions of The Use of Artificial Intelligence in Space Technologies
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 to perform 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 governments 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 drafted. 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 it.
The Impact of Employees' Cultural Intelligence on Hospital Organizational Culture
Background and Objective: Organizational culture, as an important and fundamental part of an organization, affects all aspects of the organization, including goal setting, strategy, individual behavior, organizational performance, motivation and job satisfaction, creativity and innovation, the level of employee participation in matters, and the like, so that successful organizations have a strong and effective organizational culture. Meanwhile, employees' enjoyment of cultural intelligence plays a significant role in strengthening organizational culture, and the relationship between these two variables has not yet been studied in hospitals in the country. In this regard, the present study aimed to investigate the relationship between cultural intelligence and its dimensions with organizational culture in Shahid Madani Hospital in Khorramabad. Materials and Methods: The present study is a descriptive-correlational and cross-sectional study. The statistical population studied in this study is all employees of Shahid Madani Hospital in Khorramabad. Accordingly, 160 employees were selected by random sampling using the Morgan table. Ang and Denison questionnaires were used to measure cultural intelligence and organizational culture, respectively, and finally the collected data were analyzed using structural equation modeling and SPSS 26 and LISREL software. Findings: The research findings showed that the relationship between cultural intelligence and its dimensions with organizational culture is significant. Among the dimensions of cultural intelligence, the cultural dimension has a stronger correlation with organizational culture with a coefficient of 0.65. Ethical considerations: All matters related to the informed consent of the participants in the study to fill out the questionnaire and the confidentiality of the responses were taken into account. Conclusion: The results showed that the ability of cultural intelligence to understand cultural factors outside the organization and to identify internal strengths and weaknesses is important. Therefore, considering the acquired nature of this ability, it is suggested that cultural intelligence be increased among hospital staff through formal training. This requires that individuals be aware of the characteristics of other cultures and, by reviewing their own cultural knowledge, provide the context for extensive interactions between other cultures.
Bioecological Characteristics of Plant Species Belonging to the Genus Tulipa H. Distributed in the Territory of Sharur District, Nakhchivan MR
The bioecological characteristics of some plant species of the genus Tulipa H.—Humped Black Grouse Tulip, Florensky's Tulip, Julia's Tulip, and Two-flowered Tulip—distributed in the territory of Sharur district, Nakhchivan AR, have been studied. Extensive information is provided on the habitat and distribution area, as well as the form and method of reproduction of these species. The lifespan of the stem, leaves, flower, petals, and stamens of these species is also described. Additionally, information is presented on the inclusion of these plants in the "Red Book" for conservation purposes.
Harnessing Hydrogen: The Next Step in Renewable Energy Evolution
The problem of anthropogenic climate change and its inextricable link to the current and future energy needs of our global society is the greatest challenge facing our planet. Hydrogen is increasingly acknowledged as a crucial element of a potential energy framework for the 21st century, providing a feasible solution for reducing environmental emissions, improving sustainability, and bolstering energy security. Hydrogen offers a low-impact alternative with potential uses in transportation, decentralized heat and power generation, and energy storage systems, which might substantially mitigate environmental damage on both local and global levels. Nonetheless, the shift from a fossil fuel-dependent energy framework to a hydrogen-based economy presents significant scientific, technological, and socio-economic obstacles. This paper seeks to clarify the forces propelling the worldwide interest in hydrogen energy and critically analyzes essential concerns influencing its future as a primary energy vector.
Adaptive Learning with AI: How Bots Personalize Foreign Language Education
The integration of artificial intelligence (AI) in language education has led to the emergence of adaptive learning systems that personalize instruction based on individual learners' needs. AI-powered chatbots and virtual tutors offer real-time feedback, interactive engagement, and accessibility, making foreign language learning more efficient. This paper explores the advantages and challenges of AI-driven adaptive learning in language acquisition. While AI enhances personalized learning paths, engagement through gamification, and 24/7 accessibility, it also presents limitations such as a lack of human interaction, authenticity issues, and challenges in assessing fluency and creativity. Additionally, ethical concerns arise regarding its integration into formal curricula and its potential over-reliance by learners. The article argues that AI should be viewed as a complementary tool rather than a replacement for human instruction. Future advancements in AI models, incorporating cultural awareness and emotional intelligence, may bridge some of these gaps, making AI-driven language learning even more effective. A balanced approach, where AI supplements rather than replaces traditional teaching, is essential to ensuring comprehensive and meaningful language acquisition.