Fair East Publishers: E-Journals
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Factors affecting the effectiveness of internal control systems in small and medium enterprises operating in the trade and service sector in Binh Duong Province
No full textThis study aims to identify the factors affecting the effectiveness of internal control systems (ICS) in small and medium-sized enterprises (SMEs) operating in the trade and service sector in Binh Duong Province, Vietnam. The key factors examined include risk assessment, control environment, information and communication, control activities, and monitoring. The survey of 155 samples and multivariate regression analysis indicate that four main factors influence ICS effectiveness: risk assessment, control environment, control activities, and monitoring, with the control environment being the most impactful. Based on these findings, the study proposes management implications to enhance ICS for SMEs, including the development and implementation of appropriate ICS systems. This contributes to improved management efficiency and aids SMEs in achieving their operational, reporting, and compliance objectives.
Keywords: Internal Control Systems, Trade Enterprises, Effectiveness, And Binh Duon
A trust-building model for financial advisory services in Nigeria’s investment sector
No full textThe financial advisory sector in Nigeria, while pivotal for guiding investors and enhancing market efficiency, faces significant challenges related to trust. This paper presents a comprehensive trust-building model aimed at addressing the trust deficit within Nigeria’s investment sector. The model proposes a multifaceted approach to enhance trust through improved transparency, accountability, regulatory reforms, and client education. Trust is a crucial element in financial advisory services, influencing investor confidence and market stability. In Nigeria, historical issues such as regulatory inconsistencies, information asymmetry, and instances of financial misconduct have eroded trust between advisors and clients. The proposed trust-building model emphasizes four key areas: enhancing transparency and accountability, implementing regulatory reforms, improving financial literacy among investors, and leveraging technology. Transparency can be achieved through open disclosure of fees, commissions, and potential conflicts of interest, alongside standardized reporting mechanisms. Regulatory reforms are essential for strengthening oversight and enforcing ethical standards. Improving financial literacy involves educating clients about investment options and risks, empowering them to make informed decisions. Technology plays a critical role by offering real-time updates and personalized advice through fintech platforms and AI-driven tools. Case studies from both developed and developing markets highlight successful trust-building strategies, providing valuable insights for the Nigerian context. For instance, countries with robust regulatory frameworks and advanced technological integration offer practical examples of how transparency and accountability can be effectively implemented. Nigerian case studies demonstrate instances where trust-building initiatives have led to increased investor confidence and market stability. Despite the promising aspects of the model, challenges such as institutional resistance, economic instability, and entrenched industry practices may hinder implementation. Overcoming these barriers requires a collaborative effort from regulators, financial advisors, and investors. The model's anticipated impact includes greater investor confidence, market growth, and a more stable investment environment. By addressing the core issues affecting trust and fostering a culture of transparency and accountability, the proposed model aims to transform Nigeria’s financial advisory sector, ultimately contributing to its long-term development and stability.
Keywords: Trust-Building, Financial Advisory, Nigeria, Investment Secto
Combining parental controls and educational programs to enhance child safety online effectively
No full textThe increasing use of the internet by children poses significant safety risks, necessitating the development of comprehensive solutions to ensure their protection in the digital environment. This paper explores the effectiveness of combining parental controls with educational programs as a strategy to enhance child safety online. Parental controls, including content filtering, time management, and activity monitoring, provide a first line of defense by limiting exposure to harmful content and online predators. However, relying solely on these controls is insufficient, as children must also be equipped with the knowledge and skills to navigate the internet safely. Educational programs focused on digital literacy and online behavior complement parental controls by fostering critical thinking, responsible use of technology, and an understanding of potential risks. When integrated, parental controls and educational programs create a holistic approach that addresses both the technical and behavioral aspects of online safety. By empowering children through education, they become active participants in their own protection, reducing reliance on parental monitoring and fostering long-term, responsible digital habits. This combined approach also strengthens the parent-child relationship, as parents become active collaborators in guiding their children’s online experiences. The paper highlights case studies that demonstrate the success of integrating parental controls with educational initiatives, underscoring the importance of collaboration between parents, educators, and technology developers. Additionally, it identifies challenges in implementation, such as balancing protection with privacy and fostering engagement from both children and parents. The findings suggest that a synergistic model, combining technical safeguards with educational empowerment, is essential for effectively safeguarding children online.
Keywords: Parental Controls, Child Safety Online, Digital Literacy, Educational Programs, Internet Safety, Online Behavior, Content Filtering, Internet Monitoring, Parental Engagement, Online Risks
Current state of tourism human resources in Bac Ninh Province, Vietnam
Get PDFIn recent years, the development of the tourism industry has been contributing to promoting economic restructuring, while preserving and promoting the value of cultural heritage and natural resources; creating many jobs, improving people's lives; accelerating the process of international integration, promoting the image of the country and people of Vietnam. To continue promoting the achievements of the tourism industry, it is necessary to promote all resources of the industry, approach new trends and transform appropriately in the context of the impact of the current epidemic situation. In particular, developing tourism human resources, especially high-quality ones, is a direction in line with the trend of international economic integration. Human resources play a key role in determining the success or failure of tourism businesses. However, developing a high-quality human resource team for tourism is still a difficult problem.
Keywords: Human Resources, Tourism, Human Resource
Developing a green economy towards sustainability: Research in Vietnam in the context of digital transformation
No full textIn recent years, the Party and State have paid great attention to promoting green economy, circular economy and sustainable development. Thereby, many policies have been issued to orient economic development towards sustainability, environmental protection, climate change adaptation, improving investment quality and efficiency, and focusing on attracting high-quality projects. However, in reality in Vietnam, the trend of green economic development is only at the starting point, the green economic development process is still lacking in synchronization and facing barriers in terms of capital, human resources, science and technology resources... In the face of environmental and social consequences, from developing the brown economy, countries have gradually shifted to a green economy - an economy that cares about happiness, social justice and the environment in addition to economic goals. Vietnam is no exception to this trend. Green economic development has been of interest to the Party and the State since the early years of the Doi Moi period and has achieved certain successes. However, the construction of a green economy in Vietnam still faces many limitations due to many reasons, such as: lack of capital resources, labor quality, science and technology level not meeting requirements... This poses synchronous and long-term solutions with each specific step towards a green economy. The article reflects the current situation and solutions for green economic development in Vietnam.
Keywords: Sustainability, Green Economy, Digital Transformation
Implementing fair lending practices: Advanced data analytics approaches and regulatory compliance
No full textImplementing fair lending practices is crucial for financial institutions to ensure equal access to credit and comply with regulatory requirements. Advanced data analytics approaches offer powerful tools for detecting and mitigating potential biases in lending decisions. This paper provides a comprehensive framework for leveraging advanced data analytics techniques to enhance fair lending practices and maintain regulatory compliance. The review begins by outlining the importance of fair lending and the role of advanced data analytics in achieving this goal. It then discusses the regulatory landscape governing fair lending and the risks associated with non-compliance. The paper emphasizes the significance of data collection, management, and security in implementing fair lending practices. Next, it delves into advanced data analytics techniques such as predictive modeling, machine learning, text mining, and geospatial analysis for identifying and addressing potential biases in lending practices. The importance of establishing a fair lending framework, developing robust risk assessment methodologies, and implementing model validation procedures is highlighted. Furthermore, the review emphasizes the need for continuous monitoring and reporting of fair lending performance, as well as engaging with regulatory agencies to ensure compliance. Case studies and best practices are presented to illustrate successful implementations of advanced analytics for fair lending. In conclusion, the paper underscores the ongoing commitment required to maintain fair lending practices and regulatory compliance in the evolving financial landscape. It also discusses future trends and developments in fair lending and data analytics.
Keywords: Fair Lending, Advanced Data Analytics, Regulatory Compliance, Bias Detection, Predictive Modeling, Machine Learning, Text Mining, Geospatial Analysis
Enhancing energy production through remote monitoring: Lessons for the future of energy infrastructure
No full textThe growing demand for energy necessitates innovative solutions to enhance production efficiency while ensuring sustainability. Remote monitoring technologies, leveraging advancements in the Internet of Things (IoT), artificial intelligence (AI), and machine learning (ML), have emerged as pivotal tools in optimizing energy infrastructure. This paper explores the significant impact of remote monitoring on energy production, presenting lessons learned that can inform future infrastructure projects. By enabling real-time data collection and analysis, remote monitoring systems enhance operational visibility and decision-making capabilities across various energy sectors, including renewable energy, oil, and gas. The integration of IoT sensors in energy facilities allows for continuous monitoring of critical equipment and environmental conditions, facilitating early detection of potential failures and inefficiencies. This proactive approach significantly reduces downtime and maintenance costs while improving overall system reliability. The application of AI and ML algorithms further enhances the predictive capabilities of remote monitoring systems, enabling energy producers to anticipate equipment failures and optimize maintenance schedules. Case studies from wind farms, solar power plants, and traditional energy facilities illustrate the transformative benefits of remote monitoring. These examples highlight increased energy output, reduced operational costs, and improved safety measures. The paper also addresses challenges faced during the implementation of remote monitoring technologies, including data security concerns, integration with existing systems, and the need for skilled personnel to interpret data effectively. Additionally, the findings emphasize the importance of a strategic framework for adopting remote monitoring solutions in energy infrastructure. Recommendations include fostering collaboration between IT and operational teams, investing in staff training, and continuously evolving technology to meet emerging energy demands. In conclusion, remote monitoring technologies offer a promising pathway for enhancing energy production and sustainability. By leveraging the lessons learned from current implementations, energy companies can better prepare for the future, ensuring that infrastructure remains resilient, efficient, and responsive to the changing energy landscape.
Keywords: Remote Monitoring, Energy Production, Internet Of Things (Iot), Artificial Intelligence (AI), Machine Learning (ML), Infrastructure Optimization, Predictive Maintenance, Energy Efficiency, Sustainabilit
Disaster recovery framework for ensuring SME business continuity on cloud platforms
Get PDFDisaster recovery (DR) is a critical component of ensuring business continuity, especially for Small and Medium-sized Enterprises (SMEs) that rely heavily on cloud platforms for their operations. SMEs face unique challenges, including limited financial and technical resources, making it essential to develop a disaster recovery framework that is both cost-effective and robust. This proposes a disaster recovery framework that minimizes downtime and data loss, leveraging the capabilities of cloud platforms to ensure continuous business operations. The proposed framework focuses on three key objectives viz reducing Recovery Point Objectives (RPO), minimizing Recovery Time Objectives (RTO), and ensuring scalability. RPO refers to the amount of data that can be lost before causing significant harm to the business, while RTO measures the time it takes to restore operations after a disaster. The framework achieves these objectives through cloud-based replication and automated backup systems. Data replication across geographically distributed data centers ensures that a copy of the data is always available, while incremental backups reduce the potential for data loss, ensuring that SMEs can recover recent transactions and information with minimal disruption. Automation plays a central role in the disaster recovery process. Using tools like AWS Elastic Disaster Recovery or Azure Site Recovery, SMEs can implement automated failover procedures that trigger in the event of an outage. This automation significantly reduces manual intervention, decreasing the likelihood of human error while improving recovery speed. Furthermore, periodic testing of disaster recovery plans is incorporated to ensure preparedness, with simulations identifying any vulnerabilities in the DR strategy. By using a pay-as-you-go model for cloud resources, SMEs can scale their disaster recovery solutions as their operations grow, optimizing costs while maintaining flexibility. This framework provides a comprehensive, affordable solution for SMEs to safeguard their business continuity, protecting them from the potentially devastating impacts of data loss and downtime.
Keywords: Disaster Recovery, SME Business, Cloud Platforms, Review
Machine learning applications of network security enhancement: review
No full textMachine learning (ML) is being used to improve intrusion detection mechanisms and identification in cyber security. Network data volume scaling (with the help of Machine learning) — Automated analysis and pattern recognition for large amounts of network-data, thereby detection of anomalies / potentially malicious activities that escape current rule-based techniques. By training ML models on historical data, these models can learn benign network behavior as well the anomalies in them that may result from malicious activities. The purpose of this essay/report is to begin taking a look under the hood at how ML can be used for security threat detection and analysis in-networking on an ongoing basis. This paper covers the automation of ML algorithms to enhance network security. Given the current state of electronic threats and their evolution, traditional security methods are typically insufficient. ML can analyze large volumes of data, learn patterns from it and this makes it suitable to complement network defense mechanisms. It then discusses different ML applications in network cybersecurity: intrusion detection, anomaly detection, spam and malware analysis; which the paper characterizes. It analyzes the potential, benefits and constrains of major ML methods in network security like supervised learning; unsupervised learning and reinforcement-learning. Finally, this paper represents recent progress in the use and impact of ML techniques along with case studies.The paper discusses the existing difficulties in the field such as the necessity for datasets and the vulnerability of machine learning models to adversarial attacks.T he paper also highlights avenues for exploration by focusing on developing scalable security solutions based on machine learning that are resilient and flexible.The goal of this examination is to offer both researchers and industry professionals valuable perspectives into the opportunities and obstacles linked to utilizing machine learning, in the domain of network security. ML methods have potential to improve network security by addressing the challenges posed by the increasing cyber threats that traditional security measures struggle to combat effectively over time. One key strength of ML lies in its capacity to analyze datasets and identify intricate patterns efficiently. The research paper delves into applications of ML in enhancing network security. The list covers security tools like Intrusion Detection Systems (IDS) examining malware and phishing attempts as well as anomalies in network activity and user behavior analysis (UEBA). The study explores both supervised and unsupervised learning methods. How they are used for quick threat detection and response in real time scenarios.You will find case studies and recent developments that showcase the implementation and effectiveness of these strategies.In addition the article delves into the obstacles linked to using machine learning techniques in network security including the necessity, for datasets, The paper's goal is to give an enlightening summary to scholars and practitioners about how machine learning can be applied to network security in order to provide solutions that are robust, adaptive, and scalable. To this end, it touches on several relevant aspects. One is the threat posed by adversarial attacks on the sorts of models that are likely to be used in this context. Another is the imperative, deriving from both adversarial threat and model drift, that models needed in this context be available in a form usable for continuous update.
Keywords: Network Security, machine learning (ML), Intrusion Detection Systems (IDS), entity behavior analytics (UEBA)
Aligning oil and gas industry practices with sustainable development goals (SDGs)
Get PDFThe alignment of oil and gas industry practices with Sustainable Development Goals (SDGs) is imperative for fostering a sustainable future. This abstract provides an overview of the strategies and challenges associated with this alignment. The oil and gas industry plays a significant role in global energy supply, economic development, and geopolitical dynamics. However, its operations often have adverse environmental, social, and economic impacts, making alignment with SDGs essential. Challenges in aligning industry practices with SDGs include the environmental degradation caused by extraction activities, social and economic disparities in oil-producing regions, and regulatory complexities. To address these challenges, strategies such as reducing carbon footprints, transitioning to renewable energy sources, engaging with local communities, protecting human and indigenous rights, and fostering economic diversification are crucial. Case studies of companies successfully aligning with SDGs highlight best practices and lessons learned. Impact assessments demonstrate the positive outcomes of aligned practices on environmental conservation, social well-being, and economic development. Recommendations include policy reforms, industry guidelines, and stakeholder collaboration to facilitate broader adoption of sustainable practices. In conclusion, aligning oil and gas industry practices with SDGs is essential for achieving sustainable development goals globally. This abstract calls for concerted efforts from industry stakeholders, policymakers, and civil society to create a more sustainable future.
Keywords: Oil, Gas, Industry Practices, Sustainable Development Goals (SDGs)