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.

Indonesia, an emerging archipelagic nation, possesses abundant natural resources spanning marine, land (including forests and water sources), and diverse biological riches. The agricultural sector emerges as a pivotal driver of growth across the country, exhibiting extensive distribution. Consequently, there is an urgent imperative for comprehensive research to bolster and optimize the performance of this sector. This study aims to meticulously analyze and scrutinize macroeconomic variables aimed at enhancing Indonesia’s agricultural sector. Through the utilization of a dynamic panel model, the study zeroes in on crucial variables: economic growth in the agricultural sector, farmer terms of exchange, human development index, population density, inflation, average daily wages, and lagged economic growth data from each province in Indonesia. The best model for dynamic panel testing, employing both First Difference Generalized Method of Moments (FD-GMM) and Generalized Method of Moments System (SYS-GMM) approaches, is identified as the SYS-GMM model. This model exhibits unbiased and consistent estimation, as evidenced by the Arellano-Bond (AB) test and Sargan test results. The analysis conducted using this selected model reveals notable findings. Lagging agricultural sector performance, human capital measured by the Human Development Index (HDI), and farmers’ exchange rates are found to significantly and positively influence the economic growth of the agricultural sector. Conversely, inflation exerts a significant and negative impact on sectoral growth. However, wage levels and population density do not demonstrate a significant partial effect on the economic growth of the agricultural sector.

Our study is based on the premise that every crisis has historical precedents and antecedents. First, we analyze past crises, beginning with the experiences of the Dutch tulip bulb crisis. Then, we review major cataclysms, such as World War I, the Spanish flu crisis, the Great Depression of 1929–1933, World War II and the subsequent transition to socialism, the 1973 oil shock, the regime change of 1989, and the 2008–2009 global financial crisis from both general and corporate perspectives. Throughout history, periods of crisis have alternated with phases of development. During times of crisis, people’s behavior changes as they search for solutions and support. This pattern is evident across all levels of economic activity, where governments, organizations, and individuals do their utmost to achieve a quick recovery. Sometimes, they look to external aid, forgetting that lessons from the past may provide guidance for crisis management. Without claiming to be exhaustive, we have identified points worthy of consideration. Our goal is to offer guidance for business organizations, complemented by thoughts addressed to individuals and governments alike. Organizations must pay attention to the first signs of crises and either proceed according to a pre-developed fitting strategy or revise it according to specific circumstances. They cannot avoid the consequences, but they can mitigate the negative effects.

This study addresses the impact of the tourism sector on poverty, poverty depth, and poverty severity in Indonesia, focusing on the micro-level dynamics in the province. Despite numerous tourism destinations, their strategic contribution to regional progress remains underexplored. The motivation stems from the need to comprehend the nuanced relationship between tourism and poverty at both the national and local levels, with specific attention to the untapped potential at the province level in Indonesia. We hypothesize that a higher tourism sector GRDP will be inversely correlated with poverty levels, and the inclusion of a Covid-19 variable will reveal a structural impact on poverty dynamics. Employing a Panel Regression Model, secondary data from the Central Statistics Agency (BPS) spanning 2011–2020 is utilized. A panel data regression equation model, including CEM, FEM, and REM, is employed to analyze the intricate relationship between tourism and poverty. The findings demonstrate a negative correlation between higher tourism sector GRDP and the number of poor people. The Covid-19 variable, considered a structural break, reveals a significant association between increased cases and elevated poverty and severity across Indonesian provinces. This study contributes a micro-level analysis of tourism’s role, emphasizing its impact at the provincial level. The findings underscore the need for strategic initiatives to harness the untapped potential of tourism in alleviating poverty and promoting regional progress.

This study focused on the topic of competences and challenges faced by university teachers in Ecuadorian higher education. The objective of this study was to identify the essential competences that university teachers must possess to confront the current challenges in the Ecuadorian educational field. A mixed research methodology was utilized. A concurrent triangulation design (DITRIAC) was applied. The data collection technique was through documentary study and focus groups. Eight experts in Ecuadorian higher education participated as key informants. Among the findings, there was a consensus on 7 key competences (disciplinary mastery, pedagogical competences, technological skills, research and continuous updating, critical thinking development, ethical and social commitment, flexibility and adaptability to change). It was concluded that Ecuadorian higher education requires teaching professionals who not only master their disciplines and possess advanced pedagogical and technological skills, but who are also leaders in research, promoters of critical thinking, and exemplify ethical commitment and adaptability.