This study aims to explore the implications of imported electrical equipment in Indonesia, analysing both short-term and long-term impacts using a quantitative approach. The research focuses on understanding how various economic factors, such as domestic production, international pricing, national income, and exchange rates, influence the country’s import dynamics in the electrical equipment sector. Employing an Error Correction Model (ECM) for regression analysis, the study utilises time-series data from 2007 to 2021 to delve into the complex interplay of these variables. The methodology involves a comprehensive analysis using the Augmented Dickey-Fuller and Phillips-Perron tests to assess the stationarity of the data. This approach ensures the robustness of the ECM, which is employed to analyse the short-term and long-term effects of the identified variables on electrical equipment imports in Indonesia. The results reveal significant relationships between these economic factors and import levels. In the short term, imports are shown to be sensitive to changes in domestic economic conditions and international market prices, while in the long term, the country’s economic growth, reflected through GDP, emerges as a significant determinant. The findings suggest that Indonesia’s electrical equipment import policies must adapt highly to domestic and international economic changes. In the short term, a responsive approach is required to manage the immediate impacts of market fluctuations. The study highlights the importance of aligning import strategies with broader economic growth and environmental sustainability goals for long-term sustainability. Policymakers are advised to focus on enhancing domestic production capabilities, reducing import dependency, and ensuring that environmental considerations are integral to import policies. This study contributes to understanding import dynamics in a developing country context, offering valuable insights for policymakers and industry stakeholders in shaping strategies for economic growth and sustainability in the electrical equipment sector. The findings underscore the need for a balanced, data-driven approach to managing imports, aligning short-term responses with long-term strategic objectives for Indonesia’s ongoing development and industrial advancement.

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.

The need for forest products, agricultural expansion, and dependency on biomass for the household energy source has largely influenced Ethiopia’s forest resources. Consequently, the country lost its forest resources to less than 6% until the millennium. In this study, quantitative and qualitative historical data analysis was employed to understand the socioeconomic benefits of large dam construction to Ethiopia and downstream countries. Moreover, remotely sensed data was also used to analyze the trends of vegetation cover change in the Nile catchment since the commencement of the dam; focusing on areas where there are high settlement and urban areas. It was identified that Ethiopia has one of the lowest electricity consumption per capita in Africa; about 91% of the source of household energy supply depends on fuelwood today and more than 55.7% of the population does not have access to electricity. The normalized difference vegetation index result shows an increment of vegetation area in the Nile catchment and a reduction of no vegetation area from 2011–2021 by 37.1%; which is directly related to the protection of the dam catchment for its sustainability in the last decade. The hydroelectric dam construction has prospects of multi-benefit to Ethiopia and downstream countries either through the direct benefit of hydropower energy production, related socioeconomic values, and reducing risks of destructive flood from Ethiopian highlands. Generally, it explains the reason why to not say ‘No’ to the reservoir as it is an ever more vital tool for fulfilling growing energy demand and supporting ecological stability.

With the characteristics of resisting business cycle, mitigating cash flow, and improving portfolio resilience, special assets usually enter a highly active period in the economic downturn cycle, and gradually become an effective asset allocation means in the transition phase of the business cycle. This article aims to analyze the importance of the development of China's special asset investment industry in the context of high-quality economic development, and explore how to introduce market-oriented mechanisms to build primary and secondary markets for special assets, in order to improve the effective allocation of market resources and maximize returns.