Jangipur sub-division of Murshidabad district is a major hub of beedi industry in West Bengal and also in India. In Jangipur sub-division about seventy (70) percent of the workers involved in the Beedi industry are women and children. Major Beedi factories in the state are located in Aurangabad and Dhuliyan areas of Jangipur sub-division and this area is home to the largest number of beedi workers. They have various problems related to wages, job security, and health issues. Due to the production of tobacco products, the number of diseases among workers in this region is high. School dropouts are high among children as they are engaged in beedi production from an early age. The workers are duped in various ways by the Munsis and beedi companies. As most of the workers are unorganized and poverty is a constant companion of the people in this region and there are no major industries in this district, the economic base of this region is dependent on beedi industry; they cannot voice their demands for fear of losing their jobs. The State and Central Government have various developmental schemes for the workers but these schemes reach them with very few benefits.

The article is devoted to the study of the essence and signs of asset securitization, as well as determining its place in the legal system of Ukraine. It is determined that in recent years, when the national economy is at the stage of active search for means to attract large-scale investments, asset securitization is considered not only a financial mechanism, but also a strategic legal category. Making changes to the rights of claim in liquid securities allows business entities to mobilize resources, which plays a critically important role in the restoration of the housing and infrastructure sector. However, the legal significance of this process is still debatable, repeatedly creating legal uncertainty between the usual assignment of the right of claim (cession) and factoring. In general, the need to study asset securitization from the point of view of legal science is associated with comprehensive changes in the financial system of Ukraine, the need to attract investments for the purpose of post-war economic recovery and the dynamic development of digitalization. It is worth noting that the development of the digital economy leads to the emergence of new challenges, in particular the need for a legal definition of securitization of virtual assets and the use of «blockchain technologies». In turn, this requires a reassessment of the features of securitization. Securitization should be considered a complex legal institution, which includes an interdisciplinary complex of legal norms and stock market law. Therefore, a clear understanding of its place will make it possible to unify legal norms and ensure stable development of the capital market. We have determined that asset securitization in Ukraine should be considered the process of transforming future cash flows from assets (in particular, loans) into securities, which allows the originator (creditor) to obtain financing by selling rights to such flows to investors through a specially created institution (SPV). It has been established that securitization should be considered not only as an economic process, but also as a complex legal structure, which involves the assignment of the right of claim to a specially created institution (SPV) and the subsequent issuance of securities. The main features of securitization are identified, in particular, the separation of the originator’s property pool; the presence of a special entity (SPV); the change of rights of claim into financial instruments.

Internet of Things has become very essential technological tool and gaining wide acceptability globally due to the convenience, economy and effectiveness it adds to various life operations into which it is being applied. This study reviews how IoT has evolved till date and taking a look at the future of this technology. The objectives of the study include examining the technology upon which IoT was developed, various fields into which it has been implemented, its impacts and applications in essential fields, advantages and disadvantages of IoT in several fields of life. IoT has greatly impacted the manufacturing industries, agriculture and sports. IoT market will experience inconceivable increase in the future in that it is expected to be hybridized with artificial intelligence (AI) to facilitate effective security, domestic services and smart cities; with 6G/7G technology for effective telecommunications; with robotic technology for space and marine explorations. Security and interference issues across the complex network of things are some of the anticipated challenges but since no technology stands alone, others such AI will help to mitigate the challenges. Therefore, it is obvious that IoT will make all fields more efficient by ensuring security, enhanced productivity, education and boosting economy, leading to a smart world.

The sustainability nexus of cultured meat: Integrating environmental, social, and technological-economic insights.

The development of artificial intelligence (AI) and machine learning (ML) has become a new disruptive technology in precision medicine, which allows the provision of tailored treatment plans with the help of sophisticated data analytics, multimodal assimilation, and predictive modelling. It is an empirical review of the recent literature on AI and ML in the context of precision medicine, specifically how they can be used to combine patient genomics, lifestyle data, and clinical records to provide personalised care delivery. The review covers the new advances in genomics, clinical diagnostics, biomarker discovery, personalised therapeutics, and answers questions on the issues of critical data security in healthcare and ethical concerns. Randomised controlled trials, systematic reviews, and massive implementations provide evidence indicating that AI can transform patient care by means of precision based on data, but show that stringent security systems and ethics governance are needed.

In recent years, . This creates a dangerous environment in which people live in fear. In today’s environment, the issue of home security is a cause of concern. The conventional systems used for intruder detection are very costly, and there is also a possibility of false alarms. The problem of false alarms is eliminated by using OpenCV and a mobile phone to develop a system that can effectively detect an intruder by eliminating the movements of objects that are also moving. When an intruder is detected, the system sends an alert to the user through an email, and the video is also stored in the local storage.

Background Modern hospital environments require wireless communication systems that ensure electromagnetic interference (EMI) compliance, privacy, and high throughput for mission-critical applications, such as telemetry, medical imaging, and Electronic Health Record (EHR) synchronization. Traditional RF-based wireless systems are susceptible to EMI, limited spectrum availability, and security issues. Direct-Modulated Laser (DML)-based Light Fidelity (LiFi) offers a promising alternative by leveraging the visible spectrum for high-speed, interference-free communication in terms of intended optical emissions. Methods The optimized configuration achieves BER < 10⁻⁹, SNR ≈ 74.94 dB, and Q ≈ 18.84 at 25 m, surpassing hospital reliability thresholds (BER < 10 -9 ; Q > 6). Launch powers ≥ +5 dBm are required beyond ~15 m, modulation indices of 0.8–1.0 yield higher Q across distances, narrow beam divergences (1–2 mrad) maintain stronger SNR, and receiver apertures of 4–6 mm provide a balance between light collection and noise. Results The optimized configuration achieves BER well below commonly cited hospital-grade analytical reliability thresholds (BER < 10⁻⁹), SNR ˜ 74.94 dB, and Q ˜ 18.84 at 25 m, surpassing hospital reliability thresholds (BER < 0 -9 Q > 6). Launch powers = +5 dBm are required beyond ~15 m, modulation indices of 0.8–1.0 yield higher Q across distances, narrow beam divergences (1–2 mrad) maintain stronger SNR, and receiver apertures of 4–6 mm provide a balance between light collection and noise in a best-case, well-aligned configuration. Conclusions This paper introduces a four-parameter DML-LiFi optimization framework unique to a given hospital, which offers a theoretical explanation of link-budget feasibility and parameter sensitivity to idealized indoor environment. These results indicate an upper-bound performance study, and not a demonstration of deployment-ready reliability, and are meant to be used in future experimental and system-level studies that focus on mobility, line-of-sight blockage, ambient-light-induced shot noise, electromagnetic interference pickup, and eye-safety constraints in hospital settings.

Secure and reliable operations with information are needed for the sustainability of a context-aware mobile open learning environment. Students' attendance prediction can be maximized to optimize resource usage, enhance operational effectiveness, and increase confidence in the institution. The challenge is that conventional statistical techniques don't take into account temporal, contextual, and resource security issues in a decentralized mobile learning setting. This paper presents a model that provides the background for LSTM networks (Long Short-Term Memory) in the context of realtime student attendance prediction. The model can capture an intricate time series while preserving confidential student data, and possessing a touch of privacy-protected lightweight mobile streams keeps the model's ability to interface intact. In the evaluation of the model within the framework, it is evident that the model exhibits satisfactory performance (training: accuracy = 99%, precision = 98%, recall = 99%, F1 = 98%). Furthermore, the model's performance has been sustained even in the presence of adversarial attacks, data silos, and data exfiltration. The MAX LOAD behavior is considered good support for the viability of the model and as supportive evidence that AI-based prediction in the mobile environment can be undertaken with reasonable confidence. This effort addresses the task of reasonably integrating predictive modeling into the problem of obtaining appropriate measures of security for use in modeling, towards intelligent mobile learning systems that are adaptive, sustainable, private yet trusted, and hence maximize institutional trust in digital learning spaces.

This paper addresses the regulatory challenges and political debates surrounding platform work in Europe, with a particular focus on the legal and institutional context of Hungary. It also reviews evolving definitions of platform work, highlighting the tension between traditional employment classifications and emerging forms of digital work. The analysis addresses the economic and social implications of platform work, including issues of worker protection, social security, and collective representation. In this context, the proposed EU Directive on improving working conditions in platform work (2024) can be considered a significant legislative development aimed at harmonizing employment standards in this atypical type of employment relationship, enhancing transparency, and expanding the scope of collective workers' rights. Furthermore, the study attempts to review some national approaches taken by member states, highlighting various strategies. It concludes with an overview of future legislative prospects, the need for institutional capacity building, and the importance of inclusive stakeholder engagement to ensure that regulatory reforms translate into tangible improvements in working conditions on the ground to achieve greater social protection for platform workers.

The study examines the role of Artificial Intelligence (AI) in enhancing teachers’ productivity and classroom management. For this purpose, a “Systematic Literature Review” of twenty-five (25) research papers was selected to analyse the phenomena. To analyse the data, three major themes were developed: a) AI and Teachers' productivity, b) AI and classroom management, and c) barriers and challenges teachers face when using AI tools and systems. To give the theoretical foundation, the Technology Acceptance Model (TAM), the Technological Pedagogical Content Knowledge (TPACK), and the Resource-Based View (RBV) were applied. The findings showed that AI enhances overall Teachers' productivity through advancements in administrative tasks, facilitation of lesson planning and design, content creation, and performance and workload evaluation, ultimately saving time. Moreover, the adoption of AI can enhance classroom management through automation, monitoring and assessment, behavioral tracking, and greater efficiency in the teaching and learning process. However, integration of AI in classroom settings remains challenging for teachers due to insufficient infrastructure, teachers’ resistance, a lack of technical training and skills, data privacy and security issues, ethical considerations, and inadequate institutional support. It can be recommended that implementing various types of training and awareness programs for teachers to enhance their competencies in using AI tools and systems in classroom settings.

Metaverse is a paradigm where people can interact in virtual environments using current technologies. The Metaverse is being advanced by artificial intelligence (AI) as it develops. These technologies have the power to significantly change how people communicate and live. Examples of these integrations include automating repetitive operations, creating customized user experiences based on their preferences and behaviors, and freeing up resources for more intricate and creative endeavors. The roles of artificial intelligence (AI) and other cutting-edge technologies in the Metaverse, such as the Internet of Things (IoT), virtual reality (VR), augmented reality (AR), extended reality (XR), and natural language processing (NLP), are comprehensively reviewed in this study. A systematic literature review was used to successfully achieve the goals of this study. In addition to addressing the research topics, this systematic evaluation of the literature aims to improve the understanding of security and privacy issues in metaverse for suggestive solutions. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline were used in this work to ensure transparency. Despite being hailed as a promising new technology, early experiences in Metaverse suggest that security procedures need to be strengthened and issues addressed for fulfilling its promises. Hence, this work attempts to address Metaverse Security issues by leveraging on zero-trust principles where Zero Trust Architecture (ZTA) frameworks for Metaverse are examined with the aim of enhancing security in the applications of metaverse.

The survival of the human community depends on the agricultural industry. A number of actions have been taken to increase agricultural yield. Nevertheless, the loss of agriculture is caused by severe weather and frequent pest infestation. In a situation like this, integrating cutting-edge technologies like sophisticated sensors with the Internet of Things (IoT) could increase agricultural output and reduce financial loss. Research has been done all around the world that has adequately shown how integrated IoT-smart sensors may be used to monitor environmental elements that are important for crop growth, such as temperature, humidity, moisture, and soil composition. Automated sensors are also used to measure greenhouse gasses like carbon dioxide, methane, etc. In order to help farmers decide how much fertilizer to apply to their fields, smart farming also makes it possible to measure the amount of nitrogen in the soil. Unmanned aerial vehicles and some IoT-enabled devices are helpful for precise monitoring of pest attacks and related illnesses in agricultural vegetation. Although smart farming offers a lot of potential, there are certain drawbacks, such as high implementation costs, data security issues, and farmers' lack of adequate digital literacy. Future IoT-enabled smart farming may be made easier by special economic policies, data encryption, and digital literacy. The goal of this review is to give readers a thorough overview of the major advancements and new trends. By emphasizing the advantages of incorporating smart sensors and cutting-edge technologies, it hopes to educate farmers, researchers, and policymakers about best practices, present issues, and future possibilities. It seeks to promote the ongoing use and application of new technology while facilitating the shift towards more intelligent, efficient, and sustainable farming methods.

Kyrgyzstan and Uzbekistan are sovereign states of Central Asia, whose relations have developed since Soviet times due to complex historical, economic, and geographical factors. After the collapse of the Soviet Union in 1991, both countries declared independence. However, during this period, border issues, disputes related to water resources, and threats to territorial security complicated relations. This article is dedicated to analyzing security issues in interstate relations between Kyrgyzstan and Uzbekistan from 1991 to 2006. The article systematically analyzes the security aspects of Kyrgyzstan-Uzbekistan relations in the post-Soviet era from 1991 to 2006. Ensuring security for Kyrgyzstan is an important process of protecting Kyrgyz statehood, preserving the national identity of the people, and further sustainable, consistent development of the country to maintain security in the region at an appropriate level. Factors contributing to the security dynamics during the period from 1991 to 2006 included: the creation of regional architectures, regional economic interests, and confidence-building diplomatic negotiations. The aim of the article is to study the causes, consequences, and impact of these issues on regional stability. Despite the challenges, security policy laid the groundwork for discoveries and a new security culture for development.

Artificial intelligence (AI) is revolutionizing the financial industry through automation, ‎enhanced client interactions, and data analytics. Its primary functions include automating ‎banking processes to optimize productivity and reduce human error, improving decision-making ‎in asset management and loan evaluations, and facilitating faster transaction processing. AI ‎enhances client experience by personalizing recommendations and providing 24/7 support ‎via chatbots. Additionally, AI-driven analytics offer insights into consumer behavior and help ‎financial firms tailor services. The study also examined the implications of AI from an employee ‎perspective. The study found that, according to the survey among employees, 27% of Fintech ‎workers are female and 73% male, with senior employees comprising 55% of the workforce. ‎Most workers are in payment-related industries (57%), while awareness of AI is high, as 73% ‎are extremely familiar with it. Over 73% of companies use AI, mainly for customer service and ‎risk assessment, though 35% report a lack of qualified workers as a barrier. Employees believe ‎AI improves time management (85%), but have mixed views on productivity benefits. Ethical ‎concerns about data privacy are acknowledged, and 63% feel AI can manage customer ‎inquiries effectively, while 85% believe AI could significantly transform the Fintech sector. The ‎study opined that Gender differences were found to significantly influence the challenges ‎organizations face in implementing AI technologies, followed by factors such as organizational ‎transformation, customer experience, usage and adoption of AI in fintech operations, familiarity ‎with AI technologies, industry segment, and job position. However, AI has its drawbacks and ‎restrictions in finance, such as data security and privacy issues. To reduce these risks, AI ‎systems must be fair and transparent, with ethical standards and routine audits being used to ‎detect and correct biased results‎.

The proliferation of data brought about by the Internet of Things (IoT) has revolutionized a number of industries, including manufacturing and healthcare. However, raw data is insufficient on its own. The true promise of the Internet of Things lies in its capacity to proactively address issues and streamline procedures through astute decision-making. For data analysis and decision-making, traditional Internet of Things systems typically need to rely on centralized cloud computing. This could lead to the introduction of latency, bandwidth limitations, and security issues, especially in applications that need real-time responses. This is where artificial intelligence (AI) enters the picture, creating a synergistic mix that could lead to a future full of efficient and autonomous systems. In contrast, the AI-powered Internet of Things allows edge devices to process data locally, allowing for faster and more accurate decision-making. Consider a smart factory that has sensors installed to keep an eye on the condition of the machines there. To identify likely tool failures, an artificial intelligence system integrated into the Internet of Things device may examine temperature readings, vibration patterns, and other relevant data. For analytical reasons, this is an alternative to transferring all of the sensor data to the cloud. By using this preventative approach, it is possible to do the right maintenance at the right time, preventing expensive downtime and increasing overall operational efficiency. The integration of artificial intelligence into Internet of Things (IoT) devices may yield a number of benefits, including increased efficiency, better safety and security, lower costs, and personalized experiences

ABSTRACT Xenophobic violence has become a persistent threat to human security in post-apartheid South Africa, disproportionately targeting African migrants. Despite its frequency and destabilising effects, this form of violence remains largely absent from national disaster risk management (DRM) frameworks, which continue to prioritise environmental hazards over socially driven risks. This article conceptualises xenophobia as a slow-onset, socially constructed disaster risk that undermines social cohesion and regional stability. Drawing on Integrated Threat Theory (ITT), the study explores the perceived drivers of xenophobic violence, including economic competition, cultural threat, and intergroup anxiety through survey data collected from 33 DRM practitioners and academics. The findings reveal a critical gap between current DRM practices and the need for inclusive, anticipatory approaches to identity-based violence. The article argues for the formal recognition of xenophobic violence within DRM systems, including its integration into risk assessments, early warning mechanisms, and public education strategies. By reframing xenophobia as a security and governance issue, the study contributes to a broader understanding of disaster risk in Africa and calls for more adaptive and socially responsive DRM frameworks that reflect the continent’s complex human security landscape.

ABSTRACT Healthcare is rapidly evolving with the integration of machine learning (ML) and edge computing, which enables real‐time data processing and improved patient care. Edge computing plays a critical role by reducing latency and enhancing data privacy, especially in patient monitoring systems. However, limitations such as device resource constraints and security issues persist. This study presents a systematic literature review (SLR) on using ML and edge computing in healthcare, identifying key benefits, challenges, and research trends. This SLR aimed to identify key benefits, challenges, and current research trends. We sourced relevant studies from databases such as IEEE Xplore, ScienceDirect, ACM Digital Library, and so forth. We applied inclusion and exclusion criteria. We also used the snowballing technique to find more relevant studies by checking selected papers' reference lists, ensuring we did not miss any important ones. Finally, 37 papers were selected and analyzed for their methodologies, algorithms, tools, frameworks, data sources, limitations, motivations, and challenges. Findings show a broad use of ML methods such as support vector machines, clustering, and deep learning, with a strong emphasis on data privacy and model performance; many studies employed federated learning and privacy‐preserving techniques to support real‐time decision‐making. Overall, ML and edge computing integration promise to transform healthcare, though challenges remain. Future research should address resource limitations, enhance ML models for edge environments, and develop standardized protocols. This article is categorized under: Application Areas > Health Care Technologies > Machine Learning Technologies > Cloud Computing

Timely risk prevention is a key factor in ensuring the sustainable development of an enterprise. Global changes in supply chains, coupled with local financial conditions, are currently among the main factors destabilizing business operations. This article examines the theoretical and methodological foundations of risk management at the enterprise level and proposes a management model based on G.B. Kleiner’s tetrad theory. Drawing on an analysis of various definitions of “risk management,” the author offers a definition that incorporates both process and systems approaches and addresses strategic and tactical management levels. A neosystemic paradigm based on the tetrad model was used to develop the risk management model within the economic security system, identifying three levels of tetrads and four subsystems . The proposed enterprise risk management model enables a systematic approach to addressing economic security issues. The study highlights the importance of an integrated approach to risk management that considers theoretical foundations, practical aspects, and methodological, universal, and local factors. The proposed model can be used to design strategies for enterprise and industry sector development, thereby contributing to the long-term sustainability of the economy.

Abstract Background Depression, as a common mental illness, is characterized by symptoms such as low mood, slow thinking, and decreased interest. Severe depression patients are at risk of suicidal ideation. The suicidal behavior of depression patients is a serious social security issue. The expansion of suicide includes suicide contagion or suicide imitation, which not only has a profound impact on individuals and families, but also poses a threat to social order and public safety. At the same time, with the development of Internet technology, suicidal behaviors of patients with depression have expanded. However, there is a certain degree of ambiguity in the current laws regarding the attribution of expanded acts of suicide. In most cases, it is difficult for the law to attribute responsibility to the act of suicide itself. The purpose of the study is to explore the influencing factors of suicide risk in patients with depression and provide guidance for the current relevant legal construction. Methods The study focused on 139 patients diagnosed with depression at a mental health center in a certain city from May 2022 to April 2025. The study distributed questionnaires to participants and analyzed their depression symptoms and suicide risk using the Patient Health Questionnaire (PHQ-9), the Brief Version of Borderline Symptom List (BSL-23), and the Columbia Suicide Severity Rating Scale (C-SSSRS). After completing the statistics, use SPSS software for independent sample testing and regression analysis. Results The experimental results showed that the average score of depression patients on the PHQ-9 scale was 19.78 points, with a standard deviation of 5.25 points. The data shows that the overall depression symptoms of patients are moderate to severe. The average score of participants on the borderline personality trait scale is 46.53 points, with a standard deviation of 23.53 points, indicating that the overall level of self acceptance in patients with depression is in a moderate state. Meanwhile, the suicidal ideation of patients with depression is positively correlated with the severity of depression, with a correlation coefficient of 0.52 and a significance level of p<.05. The positive correlation coefficient between suicidal ideation and borderline personality traits is 0.24, with a significance level of p<.05. In addition, there was a significant negative correlation between suicidal ideation and self acceptance dimensions in patients with depression, with a correlation coefficient of -0.21 and a significance level of p<.05. Discussion The experimental results indicate that suicidal ideation in depression is directly proportional to the severity of depression and inversely proportional to the degree of self acceptance. Research suggests that in addition to establishing a suicide crisis intervention mechanism and providing emergency intervention for patients with suicidal tendencies, it is also necessary to strengthen legal accountability, improve legislation related to suicide participation, regulate excessive exaggeration in online media, and reduce the imitation and expansion of suicidal behavior. The limitation of the study is that the survey subjects are limited to a single area. In future research, expand the survey subjects in different regions and analyze differentiated intervention pathways in different cultural, social, and legal environments.

Technological advancements, economic prospects, and climate change have intensified discussions on governance solutions for aligning social and economic relations among governmental bodies at all levels, corporations, businesses, industries, and local populations, including indigenous Arctic communities. As a result, the Arctic governance system is evolving, influenced by repeated calls on the international stage to maintain the Arctic as a “zone of peace.” This governance is becoming more complex due to climate change and the responses of Arctic nations, each with strategic interests in the region. The article aims to identify the specific characteristics of sustainable development governance in the northern territories of Arctic nations in national and international contexts. The article considers cooperation among Arctic states in promoting the sustainable development of the Far North and the role of the Arctic Council in this process. Based on the case study “Concepts of the Arctic Policy among Arctic Nations for Sustainable Development”, the article concludes that the Arctic strategies of these nations should be grounded in multilateral cooperation on Arctic affairs. This approach would mitigate security issues in the region and reduce the risk of military conflicts by promoting transparency, predictability, stability, accountability, and pragmatic collaboration.