Introduction: gamification is a playful strategy that has been used to motivate and engage people in achieving specific objectives. In the realm of education and mental health, gamification has emerged as a promising tool to enhance emotional and psychological well-being.Objetive: the aim of this paper was to conduct a scoping review of the literature to assess the effectiveness of artificial intelligence in gamification for promoting mental health among university students.Methods: an exhaustive search for scientific articles was conducted across different databases, and the PRISMA guidelines were used for its development. From an initial phase where 424 potentially relevant titles were found, 14 articles were selected for analysis. Results: the studies reviewed showed that gamification can enhance motivation and engagement, as well as improve self-esteem and self-efficacy among higher education students. Additionally, it was found that 50 % of the articles had funding, showcasing the interest of higher education institutions in enhancing their quality standards.Conclusions: the published paper indicated that gamification can be executed in any field of study driven by emerging technologies such as artificial intelligence. However, it's crucial to note that the effectiveness of gamification interventions may vary depending on various factors like game design quality, implementation context, and user characteristics

In this research, the design and simulation of a heat sink for photovoltaic panels were carried out using aluminum and copper, the most commonly used materials in heat dissipation systems. This heat sink consisted of fins that were tested both perforated and non-perforated to improve heat dissipation efficiency. This research stems from the need to reduce the temperature of photovoltaic panels during operation, as scientific evidence shows that photovoltaic panels experience a decrease in efficiency as the temperature increases, taking as a reference the temperature under standard test conditions. The simulations of photovoltaic panels with aluminum and copper fins, both perforated and non-perforated, followed a rigorous methodology. For validation, the simulation results were compared with field data, yielding a mean absolute percentage error of 1.71%. The findings indicate that copper fins reduced the temperature of the photovoltaic panel by 2.62 K, resulting in a 1.31% increase in efficiency. Similarly, aluminum fins reduced the temperature by 2.10 K, with a 1.05% increase in efficiency. Perforated copper fins achieved a temperature reduction of 3.07 K, increasing efficiency by 1.54%, while perforated aluminum fins reduced the temperature by 2.49 K, contributing to a 1.25% increase in efficiency.

The accurate dosing of polyaluminum chloride (PAC) is essential for achieving effective coagulation in drinking water treatment, yet conventional methods such as jar tests are limited in their responsiveness and operational efficiency. This study proposes a hybrid modeling framework that integrates artificial neural networks (ANN) with genetic algorithms (GA) to optimize PAC dosage under variable raw water conditions. Operational data from 400 jar test experiments, collected between 2022 and 2024 at the Yanahurco water treatment plant (Ecuador), were used to train an ANN model capable of predicting six post-treatment water quality indicators, including turbidity, color, and pH. The ANN achieved excellent predictive accuracy (R² > 0.95 for turbidity and color), supporting its use as a surrogate model within a GA-based optimization scheme. The genetic algorithm evaluated dosage strategies by minimizing treatment costs while enforcing compliance with national water quality standards. The results revealed a bimodal dosing pattern, favoring low PAC dosages (~4 ppm) during routine conditions and higher dosages (~12 ppm) when influent quality declined. Optimization yielded a 49% reduction in median chemical costs and improved color compliance from 52% to 63%, while maintaining pH compliance above 97%. Turbidity remained a challenge under some conditions, indicating the potential benefit of complementary coagulants. The proposed ANN–GA approach offers a scalable and adaptive solution for enhancing chemical dosing efficiency in water treatment operations.

The present research, using finite element method simulation, studies the heat dissipation of a fin-type passive cooling system installed on monocrystalline photovoltaic panels under different environmental conditions associated with altitude. For this purpose, three scenarios at different altitudes were analyzed: Manta (14 m.a.s.l.), Puyo (926 m.a.s.l.), and Ambato (2724 m.a.s.l.). A model simulated using the finite element method, validated in a previous investigation, was used to simulate these three cases. The model was meshed, and the boundary conditions used were obtained from meteorological data averaged over one year. The variables used in this stage were irradiance, ambient temperature, and wind speed in the time range from 08:00 to 17:00. The numerical model used in the simulation considered the mechanisms of conduction in the panel layers, mixed convection toward the surrounding air, and thermal radiation from the exposed surfaces. The results show that, in the city of Ambato, the heat sink presents its best thermal performance. Under conditions of minimum ambient temperature and solar irradiance, a maximum percentage reduction of 3.11% in the photovoltaic panel temperature was obtained, while under conditions of maximum ambient temperature and solar irradiance, the reduction reached 11.11%. This reveals that, when higher panel temperatures occur, the heat sink exhibits better performance. In general, the results showed a reduction in temperature when this heat dissipation mechanism was used. It is evident that the effectiveness of these systems depends not only on geometry or materials, but also on the atmospheric conditions associated with altitude. It is concluded that the heat dissipation capacity of passive cooling mechanisms is influenced by the meteorological conditions of the area, such as ambient temperature, solar irradiance, and wind speed, which may vary according to the altitude at which the system is located.

This study proposes to integrate mathematical modeling as an active methodology with a pedagogical approach oriented toward competency development. The objective is to use modeling activities as a bridge that connects problem-solving with the strengthening of cognitive competencies. The model contrasts with traditional methods, which typically focus on the execution of algorithms and the memorization of theorems and formulas, limiting mathematical learning to obtaining results without exploring their application to engineering problems or practical contexts. The research is conducted in an educational environment, where students could analyze, describe, formulate hypotheses, contrast them, reflect, argue, and communicate their ideas. The research design is quasi-experimental, descriptive-correlational, and the research employs scientific, inductive-deductive, and analytical-synthetic methods, basing the methodology on problem-solving in accordance with the progress of the Differential Equations course syllabus. To evaluate the proposal, techniques such as direct observation, teamwork, multiple-choice tests, and feedback were used with second-semester students. The results show that methodology promotes greater development of cognitive skills compared to the traditional approach based on mechanical problem-solving. It can be concluded that mathematical modeling allows students to develop cognitive skills such as critical thinking, creativity, and problem-solving through the analysis, synthesis, and evaluation of information.