In recent years, Vietnam has achieved great achievements in the implementation of economic growth, which has contributed to reducing poverty and is highly appreciated by the international community. Although Vietnam has made remarkable achievements in reducing poverty and meeting the requirements of sustainable development, there are still many challenges and work to be done. Vietnam needs to continue to push ahead to improve the quality of life for the poorest, reduce the development gap between regions, and strengthen its response to climate change and the environment. This study uses a qualitative method to analyze the current situation of poverty reduction in Vietnam. The article also uses analytical, synthetic, logical, and historical methods to clarify the results and limitations of poverty reduction. The value of the research helps the Vietnamese government to be aware of the results and limitations of poverty reduction and suggests scientific and timely solutions to implement poverty reduction work in Vietnam.

This study aims at exploring the direct impact of positive mental health through 6 factors on quality of life among students with disabilities and diabetes at Saudi universities, as well as the moderating impact of physical fitness on all direct relationships among all variables of the study. Employing a quantitative research methodology, using self-administered surveys distributed to a sample of students with disabilities and diabetes at numerous Saudi Arabian universities. 468 completed surveys were received and subjected to statistical analysis, using PLS-SEM, and the study uncovered significant positive direct relationships between all positive mental health sub factors and quality of life among students. Additionally, the study revealed that physical fitness acts as a moderator in all direct relationships These findings offer valuable insights for universities, in order to develop and implement psychological support and academic adjustments policies ensuring students have access to health and wellness programs, and engage local communities in the creation of policies that can help students with disabilities.

This study aims to analyze connectivity or accessibility between regions in Wakatobi islands, both within and between islands, to understand the available transportation network. Based on an understanding of the dynamics of connectivity, it is expected to provide a solid foundation for the development of more efficient and sustainable transportation infrastructure in the future. A combination of qualitative and quantitative approaches is used to explore data more comprehensively and accurately. The two primary airports and several ports are still insufficient in enhancing connectivity for both the residents and tourists within the archipelago. Improving road, sea, and air transportation networks is a necessity and expectation to improve connectivity between regions. An analysis of accessibility potential provides an overview of transportation costs and expensive and long travel fares. There are several needs that need to be met in the form of the revitalization of local ports, the development of the concept of Air Buses between crossing ports, optimizing routes between airports, and the implementation of Bus/BRT (Bus Rapid Transit) on each island with feeder lines. Furthermore, the development of connectivity in Wakatobi must consider various alternative modes of transportation, increasing service frequencies, and developing supporting infrastructure. This conclusion is the basis for the preparation of a holistic and sustainable connectivity development plan in the Wakatobi archipelago.

In the Indian context, financial planning for salaried individuals has gained increased importance due to economic fluctuations, rising living costs, and the need for robust retirement planning. Despite its importance, there is limited research on the specific factors that influence financial decision-making among salaried employees in India. Understanding these determinants is essential for developing effective strategies to enhance financial well-being among employees. This study explores the key factors influencing financial decision-making among employees, including financial goals, emergency savings, retirement planning, budgeting, financial confidence and literacy, financial stress, use of tax-saving instruments, income level, risk tolerance, and debt levels. A sample of 549 employees from diverse sectors in Uttar Pradesh participated in this research, highlighting the critical aspects of personal financial management that impact financial well-being. The study used a questionnaire-based survey to gather data on factors affecting financial decision-making. Descriptive statistics, correlation, and regression analyses were employed to identify significant predictors. The results reveal that financial literacy, access to resources, attitudes toward retirement planning, and cultural norms significantly influence financial decisions. Additionally, income level, job stability, and social support are crucial in shaping employees’ financial planning. The study recommends enhancing employees’ financial decision-making by offering financial education programs, budgeting tools, retirement planning assistance, debt management programs, tax planning workshops, financial counselling services, and employer match programs for retirement savings. These initiatives aim to boost financial literacy and confidence, enabling employees to make informed financial decisions and improve their financial well-being.

Although various actors have examined the user acceptance of e-government developments, less attention has so far devoted to the relationship between attitudes of certain commuter groups against digital technologies and their intention to engage in productive time-use by mobile devices. This paper aims to fill this gap by establishing an overall framework which focuses on Hungarian commuters’ attitudes toward e-government applications as well as their possible demands of developing them. Relying on a representative questionnaire survey conducted in Hungary in March and April 2020, the data were examined by a machine learning and correlations to identify the factors, attitudes and demands that influence the use of mobile devices during frequent commuting. The paper argues that the regularity of commuting in rural areas, as well as the higher levels of qualification and employment status in cities show a more positive, technophile attitude to new ICT and mobile technologies that strengthen the demands for digital development, with special regard to optimising e-government applications for certain types of commuting groups. One of the main limitations of this study is that results suggest a picture of the commuters in a narrow timeframe. The findings suggest that developing e-government applications is necessary and desirable from both of the supply and demand sides. Based on prior scholarly knowledge, no research has ever analysed these correlations in Hungary where commuters are among the European citizens who spend extensive time with commuting.

The present study focuses on improving Cognitive Radio Networks (CRNs) based on applying machine learning to spectrum sensing in remote learning scenarios. Remote education requires connection dependability and continuity that can be affected by the scarcity of the amount of usable spectrum and suboptimal spectrum usage. The solution for the proposed problem utilizes deep learning approaches, namely CNN and LSTM networks, to enhance the spectrum detection probability (92% detection accuracy) and consequently reduce the number of false alarms (5% false alarm rate) to maximize spectrum utilization efficiency. By developing the cooperative spectrum sensing where many users share their data, the system makes detection more reliable and energy-saving (achieving 92% energy efficiency) which is crucial for sustaining stable connections in educational scenarios. This approach addresses critical challenges in remote education by ensuring scalability across diverse network conditions and maintaining performance on resource-constrained devices like tablets and IoT sensors. Combining CRNs with new technologies like IoT and 5G improves their capabilities and allows these networks to meet the constantly changing loads of distant educational systems. This approach presents another prospect to spectrum management dilemmas in that education delivery needs are met optimally from any STI irrespective of the availability of resources in the locale. The results show that together with machine learning, CRNs can be considered a viable path to improving the networks’ performance in the context of remote learning and advancing the future of education in the digital environment. This work also focuses on how machine learning has enabled the enhancement of CRNs for education and provides robust solutions that can meet the increasing needs of online learning.