The improper disposal of litter by tourists poses a significant threat to tourism destinations worldwide, including in Indonesia. To mitigate marine litter, promoting eco-friendly behavior (EFB) among tourists is essential. This study applies the extended Theory of Planned Behavior (TPB), which posits that an individual’s behavior is driven by their attitudes, subjective norms, and perceived behavioral control, to better understand the factors influencing eco-friendly behavioral intentions. In this research, ecological consciousness and ecological knowledge were added to the traditional TPB framework to gain deeper insights into tourist behavior. Data were collected through a structured questionnaire from 876 visitors to Lake Singkarak, Indonesia. The findings demonstrate that the inclusion of ecological consciousness and ecological knowledge significantly enhances the predictive power of the TPB model in explaining eco-friendly behavioral intentions. Based on these results, raising public awareness, improving government management, and enhancing the quality of lake attractions are recommended to encourage responsible tourism. These measures can reduce litter and conserve lake habitats, ultimately contributing to the sustainability of tourism in the region.

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.

Contract workers are the direct victims of casualization but beyond that, the effects they suffer transcend to their families and the larger society. The study examined the effects of casualization on the contract workers of banks in Sokoto, Nigeria. The primary methods of gathering data for the study were in-depth and key informant interviews, with sixty individuals who were specifically chosen. Following content analysis, the gathered data were presented narratively with verbatim quotations. According to the study, there are a number of negative effects of casualization, such as low wages that contribute to a low standard of living and the inability of employees and their families to adequately meet their basic needs, the arbitrary termination of casual employees without cause, and the lack of a claim for work-related injuries or diseases in the event of an accident or death. The overall inference is that the temporary employees are working in appallingly subpar conditions. The study suggests that in order to raise the living standards of their temporary employees, banks should provide welfare packages. Additionally, because inflation is on the rise, contract employees’ compensation should be reviewed upward.

Infectious diseases often occur, especially as diseases such as COVID-19 have claimed many lives in the years between 2019–2021. That’s why it’s called COVID-19, considering that this infectious disease outbreak started in 2019, and its consequences and effects are devastating. Like other countries’ governments, the Indonesian government always announces the latest data on this infectious disease, such as death rates and recoveries. Infectious diseases are transmitted directly through disease carriers to humans through infections such as fungi, bacteria, viruses and parasites. In this research, we offer a contagious illness monitoring application to help the public and government know the zone’s status so that people are more alert when travelling between regions. This application was created based on Web Application Programming Interface (API) data and configured on the Google Map API to determine a person’s or user’s coordinates in a particular zone. We made it using the prototype method to help users understand this application well. This research is part of the Automatic Identification System (AIS) research, where the use of mobile technology is an example of implementation options that can be made to implement this system.