Fair East Publishers: E-Journals
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SOCIOECONOMIC DETERMINANTS OF MENTAL HEALTH AND SUBSTANCE USE: A REVIEW AND CONCEPTUAL SOLUTIONS FOR PUBLIC HEALTH POLICY
Get PDFThe review provides a concise summary of the outlined paper on Socioeconomic Determinants of Mental Health and Substance Use, focusing on a review of relevant factors and proposing conceptual solutions for public health policy. This paper examines the intricate relationship between socioeconomic determinants and mental health, as well as substance use disorders, emphasizing their significant impact on individuals' well-being and the broader public health landscape. Through a comprehensive review, it elucidates how factors such as income, education, employment, and housing conditions shape patterns of mental health outcomes and substance use behaviors. The analysis underscores the pervasive influence of socioeconomic disparities, highlighting the disproportionate burden borne by marginalized communities. Low income, limited educational opportunities, unstable employment, and inadequate housing are identified as critical determinants exacerbating mental health issues and fostering substance use vulnerabilities. Furthermore, the paper offers conceptual solutions for public health policy aimed at addressing these socioeconomic determinants. It advocates for multifaceted approaches that target systemic inequalities, including income support programs, accessible education initiatives, strengthened mental health services, and comprehensive substance use prevention and treatment strategies. Emphasizing the importance of supportive housing environments, the proposed solutions prioritize holistic interventions that address the root causes of mental health disparities and substance use challenges. This paper underscores the imperative for policymakers to prioritize socioeconomic equity in crafting effective public health policies. By addressing the underlying determinants outlined herein, policymakers can foster a more inclusive and resilient society, where individuals have equitable access to resources and opportunities for optimal mental health and well-being.
Keywords: Socioeconomic, Mental Health, Substance Use, Public Health, Policy, Revie
ETHICAL IMPLICATIONS OF AI IN FINANCIAL DECISION – MAKING: A REVIEW WITH REAL WORLD APPLICATIONS
Get PDFThis study delves into the ethical implications of Artificial Intelligence (AI) in financial decision-making, exploring the transformative impact of AI technologies on the financial services sector. Through a comprehensive literature review, the research highlights the dual nature of AI's integration into finance, showcasing both its potential to enhance operational efficiency and decision accuracy and the ethical challenges it introduces. These challenges include concerns over data privacy, algorithmic bias, and the potential for systemic risks, underscoring the need for robust ethical frameworks and regulatory standards. The study emphasizes the importance of a multidisciplinary approach to AI development and deployment, advocating for collaboration among technologists, ethicists, policymakers, and end-users to ensure that AI technologies are aligned with societal values and ethical principles. Future directions for research are identified, focusing on the development of adaptive ethical guidelines, methodologies for embedding ethical principles into AI systems, and the investigation of AI's long-term impact on market dynamics and consumer behaviour. This research contributes valuable insights into the ethical integration of AI in finance, offering recommendations for ensuring that AI technologies are utilized in a manner that is both ethically sound and conducive to the advancement of the financial services industry.
Keywords: Artificial Intelligence, Financial Decision-Making, Ethical Implications, Algorithmic Bias, Data Privacy, Regulatory Standards, Multidisciplinary Approach
LEVERAGING WORKFORCE ANALYTICS FOR SUPPLY CHAIN EFFICIENCY: A REVIEW OF HR DATA-DRIVEN PRACTICES
Get PDFThis study explores the pivotal role of HR analytics in enhancing supply chain management (SCM) efficiency, marking a significant shift from traditional HR practices to data-driven decision-making. The main objective was to bridge the gap between HR analytics and SCM performance, highlighting the transformative potential of integrating advanced data analysis in workforce management to optimize supply chain operations. Employing a systematic literature review and content analysis methodology, this research meticulously analyzed peer-reviewed articles, conference papers, and scholarly books from prominent databases. The search strategy was comprehensive, incorporating a broad spectrum of relevant keywords to ensure the inclusion of significant studies that address the integration of HR analytics into SCM. Key findings underscore the transformative power of HR analytics in SCM, revealing that data-driven HR practices significantly contribute to operational efficiency, strategic decision-making, and enhanced competitive advantage. Advanced analytical tools, such as AI, machine learning, and predictive analytics, have been identified as critical in optimizing workforce performance and aligning human capital with supply chain objectives. The study concludes with actionable recommendations for practitioners, leaders, and policymakers, emphasizing the importance of robust data governance, cross-functional collaboration, and the development of analytics competencies. It also outlines emerging challenges and opportunities in leveraging workforce analytics, suggesting directions for future research, particularly in exploring the strategic impacts of HR analytics on supply chain resilience and sustainability. This research contributes to the growing body of knowledge on the strategic integration of HR analytics in supply chain management, offering a roadmap for organizations seeking to enhance supply chain efficiency through data-driven HR practices.
Keywords: HR Analytics, Supply Chain Management, Data-Driven Decision-Making, Workforce Optimization
The dynamics of language shifts in migrant communities: Implications for social integration and cultural preservation
Get PDFThis study examines language shifts within migrant communities, focusing on the social and cultural implications of adopting a new dominant language while maintaining heritage languages. It explores the impact of these dynamics on community cohesion, identity, and intergenerational communication. The paper also discusses the role of educational and social policies in supporting multilingualism and cultural diversity as assets in the integration process. This paper delves into the multifaceted dynamics of language shifts within migrant populations and examines their profound implications for social integration and cultural preservation. Migrant communities undergo a complex linguistic adaptation process influenced by various factors, including historical contexts, socio-economic conditions, and government policies. The phenomenon of language shift, wherein migrants gradually adopt the dominant language of the host country while potentially losing proficiency in their native language, represents a fundamental aspect of this adaptation process. This paper explores the nuanced interplay between linguistic assimilation and cultural retention among migrants, highlighting the intricate mechanisms through which language shapes their sense of belonging and cultural identity. Language is a gateway to social integration, facilitating interactions with the host society and enhancing access to employment, education, and public services. However, the pursuit of linguistic assimilation often entails challenges such as discrimination, linguistic barriers, and feelings of alienation, posing significant obstacles to migrants' integration efforts. Moreover, the erosion of native languages within migrant communities can engender a sense of loss and disconnection from cultural heritage, raising concerns about preserving linguistic diversity and intergenerational transmission of cultural knowledge. This paper examines the role of language policies and educational initiatives in shaping language dynamics within migrant communities. Government interventions, ranging from language proficiency requirements for citizenship to bilingual education programs, profoundly influence migrants' linguistic trajectories and cultural adaptation experiences.
Keywords: Language, Communities, Social Integration, Cultural Preservation, Policies, Multiculturalism
Developing legal frameworks for successful engineering, procurement, and construction projects
Get PDFDeveloping robust legal frameworks is crucial for the success of Engineering, Procurement, and Construction (EPC) projects. These projects, characterized by their complexity and substantial financial investment, demand clear and comprehensive legal structures to ensure smooth execution, mitigate risks, and resolve disputes efficiently. A cornerstone of successful EPC projects is well-defined contractual agreements. These contracts must delineate the scope of work, time schedules, costs, and quality expectations. Essential clauses should address risk allocation, clearly specifying the responsibilities and liabilities of all parties involved. This reduces ambiguities and potential conflicts, fostering a collaborative project environment. Regulatory compliance forms another critical component of the legal framework. EPC projects must adhere to industry standards, environmental regulations, and safety protocols. Compliance ensures not only the project's legitimacy but also its sustainability and public acceptance. Non-compliance can lead to severe legal repercussions, financial losses, and reputational damage. Effective contract management is integral to maintaining the integrity of EPC projects. This involves meticulous contract drafting and review, with a focus on clarity and detail to prevent misunderstandings. Continuous contract administration is necessary to monitor performance, manage changes, and address disputes promptly. Proper termination and closeout procedures, including final inspections and documentation of lessons learned, are essential to conclude projects successfully. Risk management is another pivotal aspect. Identifying, assessing, and mitigating risks—be they technical, financial, or legal are fundamental to the project's success. Developing risk mitigation strategies, securing appropriate insurance, and maintaining contingency plans can safeguard against unforeseen challenges. Dispute resolution mechanisms are vital for handling conflicts that may arise during the project lifecycle. Negotiation and mediation offer informal resolution methods, while arbitration provides a more structured approach. Litigation, although a last resort, remains an option for resolving intractable disputes. Developing legal frameworks for EPC projects involves a comprehensive approach encompassing well-structured contracts, regulatory compliance, effective contract management, robust risk management, and efficient dispute resolution. Such frameworks not only ensure the project's successful completion but also enhance its sustainability, safety, and stakeholder satisfaction.
Keywords: Legal Frameworks, EPC, Environmental Regulations, Risk Management
DEBT TRADING OF ENTERPRISES AND CREDIT INSTITUTIONS: A STUDY IN THE VIETNAMESE MARKET
Get PDFThe State Bank has issued Circular No. 18/2022/TT-NHNN amending and supplementing certain provisions of Circular No. 09/2015/TT-NHNN regulating the activities of buying and selling debts of credit institutions. Effective from February 9, 2023, Circular No. 18/2022/TT-NHNN is attracting the attention of many enterprises and banks in the process of implementation with new regulations. This article analyzes and clarifies the amendments and new supplements related to the trading of debts of credit institutions. The formation and development of the debt trading market, specifically bad debts of enterprises, are objective requirements in Vietnam today. The absence of a debt trading market is considered a major bottleneck in the current bad debt resolution process. According to the Economic Committee of the National Assembly, bad debts in the banking system in 2017 were below 3%, but in reality, bad debts throughout the economy are still high. However, the debt trading market still faces many limitations and requires adjustments to achieve higher efficiency. The activity of buying and selling debts of commercial banks in Vietnam is gradually becoming an objective necessity for the development of the economy. Debt trading is a very new field in Vietnam, but in essence, it is a fundamental economic activity in commercial banks. However, in recent years, the debt trading market has not truly developed, lacking competition among debt buyers, with low debt handling experience, and failing to meet market expectations with a large amount of bad debts to be resolved. This somewhat reduces the demand for debt trading of commercial banks with bad debts, hampering the development of the debt trading market of commercial banks.
Keywords: Debt Trading Market, Bad Debts, Vietnam, Commercial Banks
Urban farming and food supply: A comparative review of USA and African cities
No full textUrban farming plays a crucial role in addressing food security challenges in both developed and developing regions. This comparative review explores the dynamics of urban farming and its impact on food supply in two diverse contexts: the United States (USA) and African cities. The study investigates the unique challenges and opportunities faced by urban farming initiatives in these regions, considering factors such as infrastructure, socio-economic conditions, and agricultural practices. In the USA, urban farming has gained momentum as a sustainable solution to enhance local food production and reduce the carbon footprint associated with long-distance food transportation. The analysis delves into the policies, technological advancements, and community engagement strategies that have contributed to the success of urban farming in American cities. Additionally, the study examines the integration of rooftop gardens, vertical farming, and community-supported agriculture in the USA's urban landscape. Conversely, African cities grapple with distinct challenges in implementing and sustaining urban farming practices. The review explores the impact of rapid urbanization, limited resources, and diverse climatic conditions on the feasibility of urban agriculture in African contexts. Strategies such as community empowerment, micro-financing, and knowledge sharing are examined to understand their role in overcoming obstacles and fostering resilient urban farming systems.
Keywords: Urban Farming, Food Supply, Sustainable Agriculture, USA, African Cities, Urbanization, Community Engagement, Agricultural Practices, Food Security, Comparative Analysis
Enhancing fraud detection in accounting through AI: Techniques and case studies
Get PDFThe integration of artificial intelligence (AI) into accounting has significantly transformed the landscape of fraud detection. Traditional methods, while effective to some extent, often struggle with the increasing complexity and volume of financial data. AI, with its advanced analytical capabilities, offers a promising solution to these challenges. This review provides an overview of the techniques used in AI-driven fraud detection in accounting and highlights case studies that demonstrate the practical applications and benefits of these technologies. AI techniques for fraud detection in accounting primarily involve machine learning (ML), natural language processing (NLP), and data mining. Machine learning algorithms, such as supervised and unsupervised learning models, are employed to identify patterns and anomalies in financial data that could indicate fraudulent activity. Supervised learning involves training a model on a labelled dataset containing examples of both fraudulent and non-fraudulent transactions, enabling the model to learn the distinguishing features of fraud. Unsupervised learning, on the other hand, is used to detect anomalies without prior labeling, identifying outliers that deviate from the norm. Natural language processing (NLP) is utilized to analyze textual data, such as emails and financial documents, to uncover suspicious activities and hidden relationships. This is particularly useful in forensic accounting, where vast amounts of unstructured data must be examined for signs of fraud. Data mining techniques are also critical, enabling the extraction of useful information from large datasets and the identification of trends and patterns that may not be immediately apparent. Several case studies illustrate the effectiveness of AI in enhancing fraud detection in accounting. One notable example is the use of AI by major financial institutions to combat credit card fraud. By implementing ML algorithms, these institutions have significantly improved their ability to detect fraudulent transactions in real-time. The algorithms analyze transaction patterns and flag those that deviate from a customer's typical behavior, allowing for immediate investigation and action. Another case study involves a large multinational corporation that integrated NLP and data mining techniques into its internal audit processes. The company utilized AI to analyze thousands of financial documents and emails, uncovering a complex fraud scheme that had previously gone undetected. The AI system identified unusual communication patterns and financial discrepancies, leading to a comprehensive investigation and the eventual prosecution of the perpetrators. A further example is found in the public sector, where government agencies have employed AI to detect and prevent procurement fraud. By analyzing historical procurement data, AI systems can identify anomalies and potential red flags, such as unusually high bids or frequent contract awards to the same vendor. This proactive approach has enabled these agencies to save millions of dollars and improve the transparency and integrity of their procurement processes. The application of AI in fraud detection within accounting represents a significant advancement over traditional methods. Techniques such as machine learning, natural language processing, and data mining offer powerful tools for identifying and mitigating fraudulent activities. The case studies discussed highlight the practical benefits and successes achieved through AI-driven fraud detection, demonstrating its potential to enhance the accuracy, efficiency, and effectiveness of fraud prevention efforts. As the complexity and volume of financial transactions continue to grow, the role of AI in fraud detection will become increasingly vital. Continued advancements in AI technology, coupled with its integration into accounting practices, promise to further strengthen the fight against financial fraud, safeguarding the integrity of financial systems and promoting trust and confidence among stakeholders.
Keywords: Fraud, Detection, Accounting, Artificial Intelligence, Case Studies
HARNESSING DATA ANALYTICS FOR SUSTAINABLE BUSINESS GROWTH IN THE U.S. RENEWABLE ENERGY SECTOR
Get PDFThe U.S. renewable energy sector is at the forefront of addressing global environmental challenges, with a growing emphasis on sustainability and clean energy alternatives. This review explores the pivotal role of data analytics in fostering sustainable business growth within this dynamic industry. As renewable energy sources continue to gain prominence, leveraging data analytics becomes imperative for companies aiming to optimize operations, enhance efficiency, and contribute to long-term ecological objectives. Data analytics serves as a powerful tool for harnessing insights from vast and complex datasets generated by renewable energy systems. This review delves into the ways in which organizations can extract actionable intelligence from data, enabling informed decision-making and strategic planning. From predictive maintenance of renewable infrastructure to real-time monitoring of energy production, data analytics empowers businesses to streamline processes and mitigate operational risks. Furthermore, the review sheds light on the role of data analytics in promoting innovation and research within the U.S. renewable energy sector. By analyzing patterns, trends, and historical data, organizations can identify areas for technological advancements, thereby accelerating the development of cutting-edge solutions. This proactive approach not only contributes to sustainable business growth but also positions the sector as a driver of innovation in the broader landscape of clean energy technologies. The review also highlights the challenges and opportunities associated with the integration of data analytics in the renewable energy domain, considering factors such as data security, regulatory compliance, and the need for skilled professionals. The synthesis of data-driven insights with sustainability objectives not only enhances the competitiveness of businesses in the U.S. renewable energy sector but also aligns them with global efforts to address climate change and achieve a more sustainable energy future.
Keywords: Data, Business, USA, Renewable Energy, Sustainability, Review
OPERATIONAL READINESS AND ASSURANCE (OR&A) IN ASSET INTEGRITY: STRATEGIES FOR EXCELLENCE IN FACILITY MANAGEMENT
No full textOperational readiness and assurance (OR&A) play a pivotal role in ensuring the integrity and reliability of assets within the oil and gas sector. This review explores methodologies and best practices aimed at achieving excellence in facility management, with a focus on meticulous operational readiness and assurance within the asset integrity framework. The review delves into the critical importance of OR&A in mitigating operational risks, enhancing safety, and maximizing asset performance throughout their lifecycle. It examines the key components of effective facility management, including maintenance planning, risk assessment, compliance management, and emergency preparedness. Furthermore, the review highlights the methodologies and strategies employed to ensure operational readiness and assurance, emphasizing the significance of proactive maintenance, robust inspection protocols, and data-driven decision-making. It discusses the role of advanced technologies such as predictive analytics, condition monitoring systems, and digital twins in enhancing operational efficiency and asset reliability. Moreover, the review underscores the importance of a holistic approach to facility management, encompassing people, processes, and technology. It examines case studies and industry examples that demonstrate successful implementation of OR&A strategies, showcasing tangible benefits such as improved uptime, reduced downtime, and optimized resource utilization. Overall, this review underscores the critical role of operational readiness and assurance in asset integrity management within the oil and gas sector. By adopting meticulous facility management practices and leveraging advanced technologies, organizations can enhance operational excellence, mitigate risks, and achieve sustainable performance across their asset portfolios.
Keywords: OR&A, Asset Integrity, Strategies, Excellence, Facility Management