Fair East Publishers: E-Journals
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Leveraging agricultural certificates (Mugharasah) for ethical finance in the South Asian food chain: A pathway to sustainable development
No full textAgriculture is a cornerstone of South Asia's economy and food security, yet it faces challenges such as financial exclusion and inefficiencies in traditional financing models. This study explores the potential of Mugharasah (Agricultural Certificates), a Shariah-compliant ethical financing tool rooted in Islamic finance principles like Mudarabah and Musharakah, to address these issues. Mugharasah emphasizes risk-sharing and equitable profit distribution, offering an alternative to interest-based loans and rigid microfinance structures. This research evaluates Mugharasah's role in enhancing financial inclusion, improving food security, and promoting sustainable agricultural practices. A mixed-methods approach, including surveys and comparative analyses, highlights Mugharasah's advantages in terms of flexibility, reduced financial burdens on smallholder farmers, and its alignment with sustainability goals. While findings reveal no significant statistical differences between Mugharasah and microfinance regarding risk-sharing or sustainability perceptions, Mugharasah demonstrates potential as a more equitable and performance-linked financing model. Policy recommendations advocate for government support, regulatory frameworks, and tailored financial products to integrate Mugharasah into South Asia's agricultural landscape, fostering a pathway toward ethical finance and sustainable development.
Keywords: Mugharasah, Ethical Finance, Islamic Finance, Sustainable Agriculture, Food Supply Chain, Financial Inclusion, South Asia
Infrastructure and Regulatory Barriers to AI Supply Chain Systems in Nigeria vs. the U.S.
No full textThe integration of Artificial Intelligence (AI) into supply chain systems promises to revolutionize logistics, inventory management, demand forecasting, and real-time decision-making. However, the successful deployment of AI technologies is heavily dependent on robust infrastructure and a conducive regulatory environment. This explores the contrasting infrastructure and regulatory barriers to AI-driven supply chain systems in Nigeria and the United States, representing a developing and a developed economy, respectively. In Nigeria, major infrastructure challenges include inadequate broadband connectivity, unreliable power supply, limited data center availability, and poorly maintained transport and logistics networks. These issues hinder the real-time data collection and processing required for effective AI deployment. Furthermore, the digitalization of supply chains remains minimal, and access to structured datasets is limited. On the regulatory front, Nigeria faces a lack of clear AI governance frameworks, weak data protection laws, inconsistent customs processes, and an underdeveloped standardization ecosystem. In contrast, the United States benefits from advanced digital infrastructure, including widespread 5G coverage, high-capacity data centers, and integrated transport systems equipped with IoT technologies. Regulatory frameworks in the U.S. are more developed, with emerging AI-specific guidelines, data privacy laws such as the CCPA and HIPAA, and standardized compliance mechanisms. However, even in the U.S., challenges persist in harmonizing AI regulations across states and balancing innovation with ethical concerns. The disparity between Nigeria and the U.S. highlights the need for tailored strategies to overcome barriers. While the U.S. continues to refine its regulatory oversight and invest in AI innovation, Nigeria must prioritize foundational infrastructure development, policy reforms, and capacity building to enable AI integration. Understanding these comparative barriers is essential for policymakers, investors, and supply chain stakeholders aiming to harness AI’s full potential in both contexts.
Keywords: Infrastructure, Regulatory Barriers, AI, Supply Chain Systems, Nigeria, U.S
Resilient futures: Navigating climate change with sustainable non-farming livelihoods in Nadowli-Kaleo districts
Get PDFThis study investigates the effects of climate change on non-farming livelihoods in the Nadowli-Kaleo District. The philosophical stand is interpretivist with qualitative research design using interviews to collect data from 10 non-farmers in the district. The findings reveal that climate change has some effects on non-farm livelihood strategies, leading to decreased income. It also demonstrates that climate change has some effects on non-farm livelihood employment leading to decreased employment opportunities and thus, exacerbating food insecurity, poverty rates, and existing socio-economic challenges. Practical implications underscore the need for comprehensive and inclusive policies to support non-farm livelihoods. The Ministry of Environment and Forestry Department should enforce stricter deforestation regulations and promote soil conservation techniques, including sustainable forestry and farming methods, with support from local environmental groups. Governmental authorities should prioritize infrastructure improvements to enhance resilience against climate change, such as upgrading road networks and flood defenses. Urban planning agencies and public works departments should integrate climate resilience into development plans. Thus, this research provides valuable insights emphasizing the importance of targeted interventions and policies to enhance resilience and sustainability.
Keywords: Climate Change, Sustainability, Non-Farm Livelihoods, Nadowli-Kaleo
ESG data quality in multitier supply networks: Graph-based provenance and reconciliation methods
Get PDFEnvironmental, Social, and Governance (ESG) reporting in global supply chains is increasingly scrutinized for accuracy, comparability, and accountability. However, the quality of ESG data is undermined by opacity in multitier supplier relationships, fragmented data collection practices, and inconsistent standards. This paper proposes a graph-based framework for improving ESG data quality across multitier supply networks, integrating provenance tracking, reconciliation algorithms, and cross-tier validation protocols. Using mixed-methods analysis combining case studies from manufacturing, energy, and consumer goods sectors with computational experiments, the study demonstrates that graph-based provenance can significantly reduce data inconsistencies while improving auditability. The research contributes to ESG reporting scholarship by aligning supply network complexity with computational approaches, and to practice by providing managers and regulators with actionable methods to reconcile ESG disclosures across tiers. Findings reveal that firms deploying graph-based reconciliation improve verifiability of ESG metrics by up to 32% compared to conventional spreadsheet- or silo-based approaches. Implications span corporate governance, sustainable procurement, and regulatory compliance, highlighting graph-driven provenance as a scalable strategy for robust ESG reporting.
Keywords: ESG Reporting, Supply Networks, Data Quality, Provenance, Reconciliation, Graph Methods
Development communication and youth empowerment model for next-generation Agripreneurs
Get PDFThe urgent need to transform agriculture into a dynamic, youth-driven sector requires innovative approaches that integrate communication strategies with empowerment frameworks. This study proposes a Development Communication and Youth Empowerment Model for Next-Generation Agripreneurs, designed to address barriers faced by young people in adopting agribusiness as a viable career path. Traditional extension and training methods often overlook the centrality of youth aspirations, digital engagement, and participatory learning, resulting in low uptake of agricultural opportunities. The proposed model emphasizes the strategic use of development communication to inspire, inform, and influence youth toward agripreneurship while simultaneously building capacity through empowerment mechanisms. It integrates three interrelated components: Information Dissemination, which leverages digital media, peer networks, and storytelling to reshape perceptions of agriculture; Capacity Development, which provides technical, financial, and entrepreneurial skills through participatory training, mentorship, and experiential learning; and Enabling Environment, which ensures supportive policies, access to finance, markets, and technological infrastructure. By linking communication processes with empowerment outcomes, the model facilitates behavioral change, enhances self-efficacy, and nurtures innovation among young agripreneurs. It also recognizes the role of social capital, cultural narratives, and intergenerational dialogue in shaping youth identity and agency within agribusiness. Importantly, the model addresses gender and inclusivity gaps by ensuring equal access to opportunities and highlighting the voices of marginalized groups. Policy implications suggest that governments, development agencies, and private stakeholders should adopt communication-centered empowerment strategies to reposition agriculture as attractive, profitable, and sustainable for the youth. Practical applications include using social media campaigns to showcase agripreneurial success stories, establishing mentorship platforms, and designing incentive-based programs that align with youth motivations. By fostering participation, innovation, and resilience, the Development Communication and Youth Empowerment Model provides a roadmap for cultivating a new generation of agripreneurs capable of driving food security, rural development, and economic transformation in the face of global challenges.
Keywords: Development Communication, Youth Empowerment, Agripreneurship, Participatory Learning, Digital Media, Agricultural Transformation, Innovation, Capacity Building, Enabling Environment, Sustainability
Interpretable deep learning-based analysis for liquidity risk forecasting in American banks
Get PDFThis study examines the use of interpretable deep learning and traditional machine learning models for forecasting liquidity risk in American banks. It compares the performance of models such as Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRUs), Convolutional Neural Networks (CNNs) with traditional approaches like Random Forest, Logistic Regression, K-Nearest Neighbors (KNN), and Gradient Boosting in terms of accuracy, precision, and ROC AUC. The study emphasizes the application of the Local Interpretable Model-agnostic Explanations (LIME) technique to enhance the interpretability of predictions. The results indicate that while deep learning models provide superior accuracy and precision, LIME's integration offers valuable insights into the decision- making processes, crucial for regulatory compliance and informed risk management
Keywords: Interpretable Deep Learning, traditional Machine Learning
Machine learning and Blockchain approaches for enhancing fraud prevention in financial transactions
Get PDFFinancial fraud continues to threaten the integrity of digital financial systems, with traditional rule-based detection methods increasingly ineffective against evolving tactics. Recent advances suggest that integrating Machine Learning (ML) and Blockchain Technology may provide a robust solution. This study explores how this hybrid approach can improve fraud detection in financial transactions by enhancing accuracy, transparency, and response efficiency. The study adopted a quantitative research design using a publicly available dataset of over 2,500 financial transactions. Features included transaction amounts, account behavior, login attempts, and timestamps. Supervised machine learning models—Random Forest and Support Vector Machine (SVM)—were applied to classify transactions as fraudulent or legitimate. The models were trained, optimized, and evaluated using metrics such as accuracy, precision, recall, and F1-score. Descriptive analysis revealed fraud accounted for only 6.8% of transactions, confirming significant class imbalance. The Random Forest model outperformed the SVM, achieving 99.9% accuracy, precision, recall, and F1-score. TransactionAmount, TransactionDuration, and CustomerOccupation were found to be the most influential predictors. The integration of blockchain was identified as vital for secure, immutable data storage, enabling real-time auditability and enhancing the trustworthiness of the machine learning process. The combination of machine learning’s predictive power with blockchain’s immutable ledger creates a highly effective fraud detection framework. Random Forest was identified as the superior model in terms of both performance and reliability for this application. Financial institutions should adopt integrated ML-blockchain systems to strengthen fraud prevention, ensure transaction transparency, and support regulatory compliance. This study contributes to the advancement of intelligent and secure financial systems by offering empirical evidence of the value of this hybrid approach.
Keywords: Fraud Detection, Machine Learning, Blockchain, Random Forest, Financial Transactions, Cybersecurity, Anomaly Detection
Navigating the complexities of ethical AI and Algorithmic accountability in modern technological practices
Get PDFNavigating the complexities of ethical AI and algorithmic accountability in modern technological practices presents a multifaceted challenge that intersects with numerous domains including technology, law, ethics, and society. As artificial intelligence systems become increasingly integrated into various aspects of our lives, ensuring they operate ethically and accountably becomes imperative. At the heart of this issue lies the need for clear ethical guidelines to govern the development and deployment of AI systems. These guidelines must address a range of ethical considerations such as fairness, transparency, accountability, privacy, and bias mitigation. Stakeholders, including governments, industry leaders, researchers, and ethicists, must collaborate to establish robust frameworks that balance innovation with ethical responsibility. Fairness and bias mitigation are particularly critical aspects of ethical AI. AI systems are prone to inheriting biases present in the data they are trained on, leading to discriminatory outcomes. Addressing this requires careful data collection, preprocessing, and algorithm design to minimize bias and ensure equitable outcomes for all users. Transparency is another essential element of ethical AI. Users must understand how AI systems make decisions that affect them, particularly in high-stakes domains such as healthcare, criminal justice, and finance. Explainable AI techniques aim to make AI algorithms more interpretable, enabling users to understand the rationale behind decisions and identify potential biases or errors. Algorithmic accountability is closely related to transparency and involves mechanisms for holding AI systems and their developers accountable for their decisions and actions. This requires establishing clear lines of responsibility and liability in cases where AI systems cause harm or produce undesirable outcomes. Legal frameworks must evolve to address the unique challenges posed by AI, including issues of liability, consent, and data protection. Educating AI developers, policymakers, and the general public about the ethical implications of AI is essential for fostering a culture of responsible AI development and use. Ethical AI should not be viewed as a constraint on innovation but rather as a necessary foundation for building trust in AI systems and ensuring their long-term societal benefit. Navigating the complexities of ethical AI and algorithmic accountability requires a concerted effort from all stakeholders to establish clear guidelines, mitigate biases, ensure transparency, and enforce accountability. By prioritizing ethical considerations in AI development and deployment, we can harness the transformative potential of AI while minimizing its risks to society.
Keywords: AI, Ethical, Algorithms, Accountability, Technology, Review
A comprehensive review of renewable energy integration for climate resilience
Get PDFThe integration of renewable energy sources into existing energy systems is imperative for enhancing climate resilience and mitigating the adverse impacts of climate change. This comprehensive review explores the multifaceted aspects of renewable energy integration for climate resilience, focusing on key challenges, strategies, and opportunities. By analyzing the latest research findings and case studies, this review offers insights into the diverse approaches to renewable energy integration and their implications for enhancing climate resilience at local, regional, and global scales. The review begins by examining the significance of renewable energy integration as a crucial component of climate resilience strategies. It delves into the interconnectedness between renewable energy deployment and climate adaptation, highlighting the potential of renewable energy systems to enhance energy security, reduce greenhouse gas emissions, and build community resilience to climate-related hazards. Subsequently, the review explores the challenges associated with renewable energy integration, including intermittency, grid stability, and socioeconomic considerations. It discusses innovative solutions and technological advancements aimed at overcoming these challenges, such as energy storage technologies, smart grid systems, and demand-side management strategies. Moreover, the review investigates the role of policy frameworks, regulatory mechanisms, and financial incentives in facilitating renewable energy integration for climate resilience. It assesses the effectiveness of various policy instruments and governance structures in promoting renewable energy deployment and fostering climate-resilient energy systems. Furthermore, the review highlights the importance of stakeholder engagement, community involvement, and capacity-building initiatives in ensuring the successful implementation of renewable energy projects. It emphasizes the need for inclusive and participatory approaches to renewable energy planning and decision-making, particularly in vulnerable and marginalized communities. Overall, this comprehensive review provides valuable insights into the complex dynamics of renewable energy integration for climate resilience. By synthesizing existing knowledge and identifying emerging trends, it informs policymakers, practitioners, and researchers about the opportunities and challenges associated with leveraging renewable energy as a key strategy for building climate resilience in a rapidly changing world.
Keywords: Renewable Energy, Integration, Climate, Resilience, Policy
Capacity building through training and mentorship of clinical research teams across global sites
Get PDFCapacity building through targeted training and mentorship of clinical research teams has become a critical driver of sustainable quality and compliance in multi-site global clinical trials. The increasing complexity of studies, heightened regulatory expectations, and the globalization of research networks demand skilled, adaptable, and culturally competent professionals. This paper examines how structured training programs and mentorship initiatives can strengthen the capabilities of clinical research teams across diverse international settings, thereby enhancing trial integrity, patient safety, and data reliability. Training initiatives provide standardized knowledge on Good Clinical Practice (GCP), protocol adherence, data integrity, and ethical considerations, ensuring that site staff are adequately equipped to meet international regulatory requirements. Beyond initial instruction, continuous professional development programs foster adaptability to evolving technologies such as electronic data capture systems, remote monitoring tools, and risk-based quality management frameworks. By integrating case-based learning, simulation exercises, and competency assessments, these programs cultivate a culture of accountability and quality consciousness within research teams. Mentorship plays a complementary and transformative role, offering individualized guidance that builds confidence, critical thinking, and problem-solving skills. Experienced clinical research professionals can mentor junior investigators, coordinators, and data managers to navigate real-world challenges such as protocol deviations, patient recruitment bottlenecks, and cross-cultural communication issues. Mentorship also nurtures leadership pipelines within research organizations, creating resilient teams capable of sustaining operational excellence in complex, multi-country trials. Capacity-building frameworks that blend structured training with mentorship ensure alignment with both local contexts and global standards. They enable knowledge transfer across regions, reduce disparities in site performance, and build long-term sustainability in clinical trial infrastructure. Case studies from oncology, infectious disease, and rare disease trials demonstrate that investment in training and mentorship yields measurable improvements in recruitment efficiency, audit readiness, and patient engagement outcomes. In conclusion, training and mentorship represent synergistic strategies for advancing workforce competence, harmonizing global trial operations, and achieving sustainable compliance. By embedding these approaches into organizational culture, sponsors and research institutions can drive innovation, ensure ethical rigor, and reinforce public trust in clinical research worldwide.
Keywords: Capacity Building, Training, Mentorship, Clinical Research Teams, Global Sites, GCP, Regulatory Compliance, Workforce Development, Patient Safety, Sustainable Clinical Trials.