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 paper presents the state of displacement of a multilayered composite laminate subjected to transverse static load with varying balance, symmetric and anti-symmetric angle-ply and cross-ply staking sequences. Higher-order shear deformation theory (HSDT) is considered in the finite element formulation of nine-noded isoparametric element with seven degrees of freedom at each node. The finite element formulation is transformed into computer codes. A convergence study is carried out first to obtain the optimal mesh size for minimizing the computational time. The maximum deflection at the center of plate for both fixed and simply supported edges is verified with reported literature and a good conformity is found. An attempt has been made to observe the minimum value of maximum deflection in the laminate for attaining the maximum strength of laminate with a suitable combination of stacking sequences with a constant volume of material.

This study explores the determinants of control loss in eating behaviors, employing decision tree regression analysis on a sample of 558 participants. Guided by Self-Determination Theory, the findings highlight amotivation (β = 0.48, p < 0.001) and external regulation (β = 0.36, p < 0.01) as primary predictors of control loss, with introjected regulation also playing a significant role (β = 0.24, p < 0.05). Consistent with Self-Determination Theory, the results emphasize the critical role of autonomous motivation and its deficits in shaping self-regulation. Physical characteristics, such as age and weight, exhibited limited predictive power (β = 0.12, p = 0.08). The decision tree model demonstrated reliability in explaining eating behavior patterns, achieving an R² value of 0.39, with a standard deviation of 0.11. These results underline the importance of addressing motivational deficits in designing interventions aimed at improving self-regulation and promoting healthier eating behaviors.

A comprehensive survey was conducted in 2012 and 2020 to assess the financial culture of Hungarian higher education students. The findings revealed that financial training effectiveness had not improved over time. To address this, a conative examination of financial personality was initiated by the Financial Compass Foundation, which gathered over 40,000 responses from three distinct age groups: Children, high school students, and adults. The study identified key behavioral patterns, such as excessive spending and financial fragility, which were prominent across all age groups. These results informed Hungary’s seven-year strategy to enhance financial literacy and integrate economic education into the National Core Curriculum. The research is now expanding internationally with the aim of building a comparative database. The study’s main findings highlight the widespread need for improved financial education, with more than 80% of adults demonstrating risky financial behaviors. The implications of these findings suggest the importance of early financial education and tailored interventions to foster long-term financial stability. The international expansion of this research will allow for the examination of country-specific financial behaviors and provide data-driven recommendations for policy development.

This study examined the correlations between highly entangled variables such as leadership, work environment, effective communication, reward fairness, and physical facilities for faculty members. The data was gathered from faculty members of educational institutions in Pakistan using a survey questionnaire, and the sampling method was purposive sampling. For this study, data was obtained from a varied group of education professionals from several places in Pakistan, each with a distinct degree of education and experience. This study demonstrates how independent factors affect faculty performance and can have a further impact on organizational productivity. The findings indicated that good organizational behavior had a considerable favorable influence on faculty performance. The paper reviews significant literature on the proposed factors and makes recommendations for further research.