Fair East Publishers: E-Journals
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Secure by design - enhancing software products with AI-Driven security measures
No full textAs cyber threats continue to evolve in scale and complexity, traditional reactive security measures no longer suffice. This study explores the integration of AI-driven security within the Secure by Design framework as a forward-looking approach to building inherently secure digital products across industries. Rather than treating security as an afterthought, Secure by Design embeds protective mechanisms—such as encryption, predictive analytics, and real-time threat detection—throughout the product development lifecycle.
This research employs quantitative design, surveying 203 professionals from sectors including finance, software development, agriculture, and construction. It investigates the adoption, effectiveness, and challenges of AI-powered security measures, using machine learning algorithms to analyze key security features. The findings reveal that encryption, predictive security, and automated response systems are the most impactful components in strengthening product security. The model achieved a strong performance with an accuracy of 79%, though challenges such as false positives and integration complexity persist.
Despite growing awareness, many organizations still address security reactively, with only 14.8% incorporating it during the design phase. Barriers such as limited awareness, cost, and complexity continue to slow adoption. However, 74.9% of respondents express openness to deeper AI integration in future product developments, highlighting optimism about its potential.
This study reinforces the need for a proactive shift in security practices, where AI not only supports real-time threat detection but also future-proofs products in an increasingly hostile cyber landscape. By embedding AI into the design phase, organizations can reduce attack surfaces, comply with regulatory demands, and build stakeholder trust. Future research should explore industry-specific implementations, autonomous AI systems in low-tech environments, and the scalability of cross-sector security frameworks.
Keywords: Secure by Design, AI-Driven Security, Encryption, Predictive Threat Detection, Machine Learning, Product Development
Zero-trust implementation for secure & reliable container architecture
No full textContainerized architecture has revolutionized application development and deployment, offering significant benefits such as scalability, portability, and resource efficiency. However, these environments also introduce unique security, and reliability challenges that traditional security models, reliant on perimeter defenses, struggle to address. This research explores the implementation of Zero-Trust security principles in containerized architectures to enhance both security and reliability. Through a comprehensive qualitative literature review, the study examines key strategies such as explicit verification, least privilege access, micro-segmentation, and continuous monitoring. The findings indicate that Zero-Trust principles provide a robust framework for mitigating security risks and improving the performance and scalability of containerized applications. By reducing the attack surface and ensuring that only authenticated and authorized entities can access the system, Zero-Trust minimizes the likelihood of successful attacks, thereby enhancing application reliability. The study also highlights the need for further empirical research to validate the effectiveness of Zero-Trust strategies in real-world implementations. This research offers valuable insights for practitioners and researchers, providing practical guidelines for adopting Zero-Trust security in containerized environments.
Keywords: Docker, Containerization, Zero-Trust, Micro Service, Risk Management Framework
Integration of extended reality (XR) for oncology pharmacist training in chemotherapeutic compounding and risk mitigation
No full textThe increasing complexity of oncology pharmacotherapy, particularly in the safe handling and compounding of chemotherapeutic agents, necessitates advanced training methodologies that ensure both efficacy and risk reduction. This review explores the integration of Extended Reality (XR)—encompassing Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR)—into the educational frameworks for oncology pharmacists. XR technologies present a transformative opportunity to simulate high-risk clinical environments, enabling immersive, interactive, and replicable training scenarios that are otherwise impractical or hazardous in real-life settings. The paper critically evaluates current XR-based training programs and their efficacy in enhancing cognitive retention, procedural accuracy, and hazard recognition during chemotherapeutic compounding. Furthermore, it discusses the role of XR in improving adherence to safety protocols, reducing contamination risks, and mitigating occupational exposure to hazardous drugs. Emphasis is placed on empirical evidence from recent studies demonstrating improved learner outcomes and behavioral competence among pharmacists trained with XR modules compared to traditional didactic and observational methods. Additionally, the paper highlights implementation challenges, including technological costs, content standardization, and regulatory considerations. Finally, the review outlines strategic recommendations for integrating XR into pharmacy curricula and continuing professional development programs, ultimately aiming to elevate safety standards and patient care quality in oncology settings.
Keywords: Extended Reality (XR), Oncology Pharmacist Training, Chemotherapeutic Compounding, Risk Mitigation, Immersive Simulation
Influence of entrepreneurship education on youth employment in Juba City, South Sudan
Full text linkThis study seeks to examine the influence of entrepreneurship education on youth employment in Juba City with the aim to establish the relationship between the entrepreneurship education and youth employment in Juba City. The study adopted a quantitative research approach using a descriptive survey design. A simple random sampling technique was used to determine a sample size of 357 using a Sloven’s formula from a target population of youths who underwent entrepreneurship education. Data were collected using a structured questionnaire from 292 respondents (81.8% respond rate, 19.2% were unreturned and badly filled) and analyzed using Statistical Package for Social Sciences (SPSS) version 25.0. to determine the correlation between the variables. The result indicated no significant relationship between entrepreneurship education and youth employment generation in Juba City. It is therefore, recommended that policy makers and educators to use multidimensional approaches to address youth unemployment problems in Juba City.
Keywords: Entrepreneurship Education, Youth Employment, Unemployment
Determinants of coffee production and its marketing: Case of Abe Dongoro District, Oromia Regional State, Ethiopia
Full text linkCoffee, Ethiopia's largest export crop is the backbone of the Ethiopian economy. However, Ethiopia has not yet fully exploited its position as the producer of some of the best coffees in the world. Currently, Ethiopian export coffee performance has decline. Due to this, the study analyzed the determinants of coffee production and marketing in the study area. The objectives of this study were to examine the determinants of coffee production and marketing of smallholders in Abe Dongoro District. Accordingly, multi stage sampling procedure was employed to select the sample households. Four parent associations were randomly selected by using random sampling method and finally, 225 sample respondents were selected from the sampling frame based on probability proportional to size (PPS).Cobb-Douglas production function was employed to analyze the determinants of coffee production, while logit model analyzed the determinants of coffee marketing. Cobb-Douglas production function result showed that active family labor, cultivated land size, compost, pesticide and pruning of coffee plant were positive and significantly affect coffee production and age of coffee plant negative and significantly affect coffee production. The results of logistic regression indicated that education level of household heads, access of training, access of information positive and significantly affect the participation of coffee marketing while transportation costs negative and significantly affect coffee marketing. Moreover, policy consideration has to be given by government to those significant variables which have a potential impact in determining farmer's production and marketing of coffee.
Keywords: Production, Marketing, Logit Model, Cobb-Douglas Production Function
Economics of aging population in India: Implications for pensions and social security systems
Full text linkIndia is going through a rapid demographic change, with an increasing older population due to rising life expectancy and lower fertility rates. This transformation poses enormous economic concerns, particularly in pension systems, social security, healthcare, and labor market dynamics. By 2050, the proportion of India's population aged 60 and more is predicted to rise from 10% to 20%, putting a pressure on the country's present social assistance system. While the aging population offers issues, it also provides opportunity for change and innovation, particularly in the areas of developing sustainable pension systems, enhancing healthcare for the elderly, and resolving unemployment concerns. This research investigates the economic implications of an aging population in India, with a focus on the effectiveness and sustainability of the country's pension systems, social security mechanisms, healthcare infrastructure, and employment policies. It reviews the current state of India's pension system, which largely excludes informal sector workers, and the strain on social security programs that are ill-equipped to meet the needs of an expanding elderly population. This research offers a set of policy recommendations aimed at expanding pension coverage, improving elder care, and addressing unemployment. The key recommendations include extending pension schemes to informal sector workers, investing in healthcare infrastructure, and creating inclusive labor markets that provide opportunities for both older workers and youth. By adopting these reforms, India can address the economy.
Key words: Aging Population, Social Security, Economy, Pension, India, Employment. 
Closing the loop: Overcoming behavioral and institutional barriers to recycled material adoption in electronics supply chains through multi-stakeholder governance
Full text linkA linear economy fueled by increasing demand creates large amounts of e-waste and depletes limited resources, posing a major risk to the global electronics industry. Despite the urgent need for environmental action and corporate sustainability goals, high-quality recycled materials remain underused. This ongoing gap is driven by complex behavioral barriers, such as risk and loss aversion, and strong bias toward maintaining the status quo among suppliers and OEMs. It is worsened by institutional issues like fragmented regulations, misaligned incentives along the supply chain, and a lack of standardized systems for traceability and quality assurance. This study aims to break this deadlock. We analyze the causes of inertia through a rigorous mixed-methods approach, including a global survey of 150 electronics companies, in-depth interviews with 20 key stakeholders (OEMs, Tier 1-3 suppliers, recyclers, policymakers), and apply institutional theory (examining conflicting logics) and behavioral economics (developing incentive-compatible nudges). Our findings reveal that supplier lock-in contracts (43% of firms), concerns about performance risk (62%), and inconsistent regulations that hinder circularity investments are the main barriers. Importantly, we offer practical solutions after identifying these issues. We demonstrate how innovative multi-stakeholder governance systems, like independently verified certification schemes combined with behavioral nudges, can reduce perceived risks by 35% and better align incentives. The study introduces and evaluates collaborative "circularity compacts"—agreements with shared risk pools, common technology funds, and digital traceability tools like blockchain and Digital Product Passports. These compacts provide a feasible way to close the profit and circularity gap by fostering trust, transparency, and flexible collaboration. Industry leaders, governments, and students aiming to transform electronics supply chains from linear liabilities into circular opportunities will find these insights invaluable.
Keywords: Electronics Supply Chains, Adoption of Recycled Materials, Circular Economy, Behavioral Barriers, Institutional Voids, Multi-Stakeholder Governance, Risk Perception, Circularity Compacts, Incentive Alignment, Sustainable Electronics
Self-Learning autonomous cyber defense agents in AI-empowered security operations
Full text linkThe increasing scale, speed, and sophistication of cyber threats have outpaced traditional, human-centered security operations, prompting the development of self-learning autonomous cyber defense agents. These AI-empowered entities leverage machine learning, deep reinforcement learning, and adaptive decision-making to detect, analyze, and respond to cyber threats in real time without direct human intervention. By continuously monitoring diverse data streams such as network traffic, endpoint telemetry, and system logs these agents dynamically update their threat models, enabling rapid adaptation to evolving attack patterns, including zero-day exploits and advanced persistent threats (APTs). Unlike rule-based systems, self-learning agents refine their performance through iterative feedback loops, allowing for proactive threat hunting, predictive risk assessment, and autonomous mitigation actions such as traffic filtering, process isolation, or automated patch deployment. However, their deployment introduces complex operational, technical, and ethical challenges, including model drift, adversarial manipulation, explainability limitations, and potential overreach in automated decision-making. Integration into security operations centers (SOCs) requires robust orchestration with existing SIEM/SOAR platforms, real-time situational awareness, and human-in-the-loop oversight for high-impact actions to maintain accountability and compliance. The architecture of such agents often incorporates multi-agent systems for coordinated defense, enabling distributed detection and response across hybrid and cloud-native infrastructures. This paper presents an in-depth analysis of the design principles, learning mechanisms, and operational workflows underpinning self-learning autonomous cyber defense agents, alongside a discussion of performance metrics such as detection accuracy, false positive rates, time-to-mitigation, and adaptability to emerging threats. It further examines governance frameworks and regulatory considerations to ensure ethical deployment, resilience against adversarial AI attacks, and alignment with organizational risk appetites. By uniting adaptive AI with automated security orchestration, self-learning cyber defense agents represent a transformative leap in cyber resilience, offering the potential to outpace threat actors while reducing analyst workload and improving incident response efficiency. Yet, realizing their full potential demands careful balancing of autonomy, transparency, and human oversight to sustain operational trust and strategic control in AI-driven cybersecurity ecosystems.
Keywords: Self-Learning, Autonomous Agents, Cyber Defense, Artificial Intelligence, Machine Learning, Deep Reinforcement Learning, Security Operations, Threat Detection, Incident Response, Zero-Day Exploits, Advanced Persistent Threats, SIEM, SOAR, Explainable AI, Cyber Resilience
Privacy-First security models for AI-integrated identity governance in multi-access cloud and edge environments
Full text linkThe convergence of artificial intelligence (AI), multi-access edge computing (MEC), and cloud environments has transformed identity governance by enabling real-time decision-making and seamless access control across decentralized infrastructures. However, this evolution has also introduced complex challenges concerning data privacy, identity trust, and security. This review explores privacy-first security models that integrate AI for identity governance in hybrid cloud-edge architectures. It evaluates privacy-preserving techniques such as homomorphic encryption, federated learning, and zero-knowledge proofs, emphasizing their role in ensuring secure identity authentication, authorization, and auditability. The paper critically analyzes the limitations of conventional identity and access management (IAM) frameworks in dynamic, resource-constrained edge environments and proposes adaptive models that embed privacy by design. Furthermore, the review investigates the interplay between explainable AI (XAI) and policy enforcement for transparent and compliant identity governance. By synthesizing advancements in cryptographic methods, AI reasoning engines, and decentralized identity (DID) systems, the paper outlines a roadmap for building secure, scalable, and privacy-compliant identity infrastructures in the era of pervasive computing.
Keywords: Privacy-Preserving Identity Governance, AI-Driven Access Control, Multi-Access Edge Computing (MEC). Federated Identity Management, Explainable AI (XAI), Zero-Knowledge Proofs
Resilient infrastructure management systems using real-time analytics and AI-driven disaster preparedness protocols
Full text linkThis review explores the convergence of real-time analytics and artificial intelligence (AI) in strengthening resilient infrastructure management systems, particularly for disaster preparedness and response. As climate change and urbanization amplify infrastructure vulnerability, cities and critical systems require intelligent frameworks capable of anticipating, adapting to, and recovering from disruptions. The paper outlines how AI-powered data streams from sensors, digital twins, and geospatial platforms are transforming static infrastructure into self-monitoring, self-correcting networks. It discusses predictive models for hazard forecasting, risk detection, and automated decision-making protocols during emergencies. Emphasis is placed on early warning systems, dynamic resource allocation, and post-event impact analysis, all supported by AI and real-time dashboards. Use cases across transportation, energy, water, and healthcare systems are examined to illustrate the role of integrated AI in building infrastructure resilience. The paper concludes with a call for ethical AI governance, interoperable systems, and cross-sector collaboration to enable sustainable, intelligent infrastructure preparedness.
Keywords: Resilient Infrastructure, Real-Time Analytics, AI-Driven Disaster Preparedness, Risk Forecasting, Critical Infrastructure Management