At present, supporting e-learning with interactive virtual campuses is a future goal in education. Models that measure the levels of acceptance, performance, and academic efficiency have been recently developed. In light of the above, we carried out a study to evaluate a model for which architecture design, configuration, metadata, and statistical coefficients were obtained using four Learning Management Systems (LMSs). That allowed us to determine reliability, accuracy, and correlation, using and integrating the factors that other researchers have previously used, only using isolated models, such as Anxiety–Innovation (AI), Utility and Use (UU), Tools Learning (TL), System Factors (SF), Access Strategies (AS), Virtual Library (VL), and Mobile Use (MU). The research was conducted over one year in nine groups. The results from an LMS Classroom, architecturally and configuration-wise, had the highest level of performance, with an average of 73% when evaluated using statistical coefficients. The LMS Classroom had a good acceptance and a greater impact: SF, 82%, AI, 80%, and VL, 43%, while out of the seven factors, those with the most significant impact on academic efficiency were TL, 80%, VL, 82%, and MU, 85%.

This study evaluates the energy performance of a BOGE C 22-2 oil-injected rotary screw compressor under real industrial conditions. Using direct measurements with a power quality analyzer and thermodynamic modeling, key performance indicators such as compression work, mass flow rate, compressor efficiency, and motor efficiency were determined. The results revealed actual efficiencies of 27–48%, significantly lower than the expected 60–70% for this type of equipment, mainly due to partial-load operation and low airflow demand. A low power factor of approximately 0.72 was also observed, caused by a high share of reactive power consumption. To address these inefficiencies, the study recommends the installation of an automatic capacitor bank to improve power quality and the integration of a secondary variable speed compressor to enhance performance under low-demand conditions. These findings underscore the importance of assessing compressor behavior in real-world environments and implementing techno-economic strategies to increase energy efficiency and reduce industrial electricity consumption.

The growing prevalence of anxiety, depression, and stress among students highlights the urgent need for school-based strategies that promote psychological well-being and timely intervention. This study explores the use of artificial intelligence (AI) as a scalable and data-driven tool to support institutional mental health initiatives in higher education. Using synthetic and real datasets derived from validated psychometric instruments (the Beck Anxiety Inventory (BAI), Beck Depression Inventory (BDI), and the Perceived Stress Scale (PSS-14)), we trained and evaluated 32 deep neural network architectures for the early detection of emotional distress. Optimized three- and four-layer dense models achieved classification accuracies exceeding 95%, demonstrating the feasibility of deploying AI-based screening tools in educational settings. Beyond prediction, this approach can support counselors and educators in identifying at-risk students and informing proactive, school-based interventions to improve mental health and resilience in post-pandemic academic environments.

Se propone un método basado en la distancia de Hellinger y la función de densidad de probabilidad de señales (Photo Response Non-Uniformity [PRNU]) para identificar el dispositivo de captura de imágenes digitales. El método asocia una imagen digital no modificada (imagen en disputa) con uno de un conjunto de dispositivos de captura candidatos, al comparar la huella digital insertada en la imagen en disputa contra la base de datos creada con las huellas digitales de los dispositivos de captura. El método propuesto se implementó en MatlabTM para demostrar su funcionamiento, luego se comparó en un caso de estudio controlado contra dos métodos diferentes: uno propuesto por Goljan et al. (2009) con un porcentaje de similitud promedio del 100% y el otro por Quintanar-Reséndiz et al. (2021) con el 99.35%, y para el método propuesto resultó ser de 97.68%.

Nowadays the use of mobile applications has contributed in different areas of engineering, the following project presents the development of a mobile application for the chemistry area, which allowed the effective management of substances following the Official Mexican Norm NOM-018-STPS-2015. The utilized method for the development of the application is scrum; which is an agile framework that allows collaboration, self-organization, early delivery, transparency beyond others; the workgroup was formed by students and teachers of the divisions of technologies of the information and communication and pharmaceutical and industrial chemistry. The initiative of implementing a mobile application for devices with an android operating system, with an API greater or equal to 4.4 allowed a highly effective, optimizing the management of the control of chemical substances used by students and teachers, Streamlining the process of returning the laboratory supplies and guaranteeing the fulfillment of the learning process in the laboratories of the technological university of San Juan Del Rio, it was implemented with students of Industrial and pharmaceutical chemistry engineering, where 86% of the users affirmed to be satisfied with the mobile application.

The information and communication technologies-ICT, have more use with the implementation of Learning Management System-LMS; The present study was carried out to evaluate the use of four LMS in a university, to identify acceptance and performance, through the student's inputs, the LMS used: Edmodo, Classroom, Schoology and Moodle, the methodology consisted of 4 stages; 1) LMS selection, 2) conFiguretion LMS, 3) evaluation of acceptance factors and 4) calculation of statistical coefficients. The results obtained from the four LMS, Google Classroom in its conFiguretion has the highest level of performance, with an average of 73%; while for the statistical coefficients; Of seven factors evaluated for the level of acceptance, those with the greatest preference for the learners were System Factors FS (82%), Anxiety and innovationAI (80%) and Virtual Library BV (43%).