The power factor in electrical power systems is of paramount importance because of the influence on the economic cost of energy consumption as well as the power quality requested by the grid. Low power factor affects both electrical consumers and suppliers due to an increase in current requirements for the installation, bigger sizing of industrial equipment, bigger conductor wiring that can sustain higher currents, and additional voltage regulators for the equipment. In this work, we present a technique for predicting power factor variations in three phase electrical power systems, using machine learning algorithms. The proposed model was developed and tested in medium voltage installations and was found to be fairly accurate thus representing a cost reduced approach for power quality monitoring. The model can be modified to predict the variation of the power factor, taking into account removable energy sources connected to the grid. This new way of analyzing the behavior of the power factor through prediction has the potential to facilitate decision-making by customers, reduce maintenance costs, reduce the probability of injecting disturbances into the network, and above all affords a reliable model of behavior without the need for real-time monitoring, which represents a potential cost reduction for the consumer.

In recent years, machine and deep learning models have attracted significant attention for electricity price forecast in global wholesale electricity markets. Yet, a predominant focus on point forecast in most parts of literature limits the practical application of these models due to the absence of uncertainty quantification. In this study, we first perform an analysis of the electricity price trends in the Mexican wholesale electricity market to determine the influence of key variables. Using independent component analysis and wavelet coherence analysis, we were able to identify primary determinants influencing locational marginal electricity prices. Subsequently, we applied four different models covering the most important algorithms proposed in the literature for electricity price forecast. Our findings revealed that the most accurate forecasting results were achieved using a deep learning‐based method with a decision tree‐based model trailing closely. Finally, we incorporate conformal prediction for uncertainty quantification by calculating the prediction intervals with a target coverage level of 95%. The conformal prediction intervals provide a more comprehensive view of the possible future scenarios, enhancing economic efficiency, risk management, and decision‐making processes. This is particularly important because of the dynamic nature of electricity markets, where prices are strongly influenced by multiple factors.

This article describes the synthesis of Cd $_{x}$ Zn $_{1-}$ $_{x}$ S quantum dots prepared using the successive ionic layer adsorption and reaction method and incorporated into a photovoltaic device. The Cd $_{x}$ Zn $_{1-}$ $_{x}$ S quantum dots exhibit good optical and electrical properties. The photovoltaic device has the configuration TiO $_{2}$ /Cd $_{0.75}$ Zn $_{0.25}$ S $_{1}$ /ZnS. A photoconversion efficiency of 3.6% was obtained with this device. This efficiency corresponds to a 16% relative increment compared with a reference sample with the configuration TiO $_{2}$ /Cd $_{1}$ Zn $_{0}$ S $_{1}$ /ZnS. The improvement is associated with an increment in the open-circuit voltage (V $_{oc}$ ) from 0.517 to 0.725 V. The corresponding short-circuit current density (J $_{sc}$ ) was reduced from 12.15 to 11.66 mA cm−2. Electrochemical impedance spectroscopy analyses confirm that the behavior of the device was due to a recombination rate reduction obtained as a result of surface passivation between the TiO $_{2}$ layer and the Cd $_{x}$ Zn $_{x-1}$ S QDs interface.

Artificial Intelligence in Education is expanding rapidly, yet the adaptation of chatbots to specific reading-comprehension levels remains underexplored. This mixed-methods study presents Lectobot, a conversational agent designed to provide personalized scaffolding across three levels of reading comprehension (literal, inferential, and critical). First, we conducted a diagnostic assessment with first-year undergraduates (N = 58) using validated instruments: COMPLECsec (reading comprehension), EMA (Academic Motivation Scale), and MARSI (Metacognitive Strategies). Non-parametric analyses (Kolmogorov–Smirnov; Mann–Whitney U with Benjamini–Hochberg adjustment) indicated wide heterogeneity in comprehension (median global accuracy ≈ 55%) and a predominance of extrinsic motivation, with selective use of problem-solving strategies. These findings informed design rules for Lectobot (text selection, adaptive task difficulty, and strategy prompts). In a five-week implementation with a focus group (n = 8), semi-structured interviews were transcribed and coded in MAXQDA, guided by the Technology Acceptance Model (perceived usefulness and ease of use). Students perceived Lectobot as useful for text understanding and synthesis and moderately easy to use; reported difficulties were mainly technical (access and session continuity), leading to actionable design improvements. We discuss ethical and practical implications for personalized scaffolding in higher education and outline avenues for larger-scale evaluations and broader grade levels.

Background: The relationship between selfie addiction and narcissism in students at the Universidad Autónoma de Zacatecas (UAZ, Autonomous University of Zacatecas), Mexico, was analysed. The aim of this study was to identify the narcissistic factors associated with students’ selfie taking addiction. Methods: To identify the relationship between narcissism and selfie addiction, the Narcissistic Personality Inventory (Adapted NPI-40) and the Psychometric Selfie Addiction Scale (PSAS) were used. A structural equation model (SEM) was employed. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used for structural validation. EFA and CFA confirmed adequate fit and reliability indicators regarding the robust unidimensional structure of the PSAS. Results: The SEM results indicated significant associations between the latent dimensions of the narcissistic traits of superiority, authority, need for admiration, exhibitionism, and exploitation and selfie addiction. This research provides structural and psychometric evidence for the relationship between narcissism and selfie addiction in the Mexican student population, integrating Adapted NPI-40 and PSAS, as well as a parsimonious explanatory structural equation model that demonstrates the relationship between different narcissistic traits and social media practices. Conclusions: The results provide a basis for developing digital literacy interventions for young people and future cross-validations.