The telecommunications services market faces essential challenges in an increasingly flexible and customer-adaptable environment. Research has highlighted that the monopolization of the spectrum by one operator reduces competition and negatively impacts users and the general dynamics of the sector. This article aims to present a proposal to predict the number of users, the level of traffic, and the operators’ income in the telecommunications market using artificial intelligence. Deep Learning (DL) is implemented through a Long-Short Term Memory (LSTM) as a prediction technique. The database used corresponds to the users, revenues, and traffic of 15 network operators obtained from the Communications Regulation Commission of the Republic of Colombia. The ability of LSTMs to handle temporal sequences, long-term dependencies, adaptability to changes, and complex data management makes them an excellent strategy for predicting and forecasting the telecom market. Various works involve LSTM and telecommunications. However, many questions remain in prediction. Various strategies can be proposed, and continued research should focus on providing cognitive engines to address further challenges. MATLAB is used for the design and subsequent implementation. The low Root Mean Squared Error (RMSE) values and the acceptable levels of Mean Absolute Percentage Error (MAPE), especially in an environment characterized by high variability in the number of users, support the conclusion that the implemented model exhibits excellent performance in terms of precision in the prediction process in both open-loop and closed-loop.

This study developed a specific scale to measure the impact of extrinsic motivations on students’ decisions to pursue online graduate programs at business schools in Latin America. Using a mixed-methods approach, the research proceeded in three stages. In the first stage, the construct was defined by identifying key extrinsic factors motivating students to enroll in online graduate programs, followed by the creation and initial validation of the scale in Colombia. The second stage involved testing the scale in Chile to determine its cross-cultural applicability. In the third stage, the scale’s predictive validity was confirmed, demonstrating its effectiveness in explaining how extrinsic motivations influence students’ intentions to enroll in online graduate programs. The findings indicate that the scale, composed of five dimensions—Cost Reduction, Ability to Study from Any Location, Control Over Learning Pace, Flexibility to Balance Study and Work, and Avoiding Commuting Time—is a reliable predictor of student preferences and intentions in online graduate education. The final scale includes 25 items across these dimensions, measuring extrinsic factors through items related to flexibility, time savings, and global accessibility. Validation in two Latin American countries confirms the scale’s relevance across diverse cultural contexts, enhancing its applicability within the region. This study provides empirical evidence that extrinsic motivation is a key determinant of students’ intentions to enroll in online programs in developing countries. It confirms that extrinsic motivations reflect a preference for flexible learning options compatible with students’ lifestyles and professional needs, linked to their beliefs about time management, professional advancement, and career opportunities associated with earning a graduate degree.

This qualitative research aimed to study the effectiveness of the local health constitution in controlling the spread of COVID-19. It reports the role of local communities, government agencies, and healthcare providers in implementing and enforcing local health constitutions and how their engagement can be improved to enhance surveillance. We also reported factors that influence compliance and strategies for improving compliance. We also evaluated the long-term sustainability of local health institutions beyond the pandemic. The population and sample group consisted of key members of the local health constitution teams at the provincial, sub-district, and village levels in the rural area of Ubon Ratchathani. Participants were purposively selected and volunteered to provide information. It included health science professionals, public health volunteers, community leaders, and local government officials, totaling 157 individuals. The study was conducted from December 2022 to September 2023. Our research shows that local health constitutions can better engage and educate communities to actively participate in pandemic surveillance and prevention. This approach is a learning experience for responding to emergencies, such as new infectious diseases that may arise in the future. This simplifies the work of officials, as everyone understands the guidelines for action. Relevant organizations contribute to disease prevention efforts, and there is sustainable improvement in work operations.

One of the most important ways to achieve the goals stipulated by the Paris (2015) Agree-ment on climate change is to solve a two-fold task: 1) the adsorption of CO2 by the forest communities fcom the atmosphere during global warming and 2) their adaptation to these climate changes, which should ensure the effectiveness of adsorption itself. Report presents the regional experience of the numerical solution of this task. Calculations of the carbon balance of forests in the Oka-Volga River basin were carried out for global forecasts of moderate and extreme warming. The proposed index of labile elastic-plastic stability of forest ecosystems, which characterizes their succession-restorative po-tential, was used as an indicator of adaptation. A numerical experiment was conducted to assess the effect of the elastic-plastic stability of forest formations and the predicted climatic conditions on the carbon balance. In the upcoming 100-year forecast period, the overall stability of forest formations should increase, and to the greatest extent with extreme warming. Accordingly, one should expect a significant increase in the ability of boreal forests to ab-sorb greenhouse gases. It is determined unambiguous picture of a significant increase in the adsorption capacity of boreal forests with a rise in their regenerative potential.