Diego Carmona Fernández

STEM outreach evaluations often rely on full-sample averages and general enjoyment metrics, overlooking how specific pedagogical factors relate to students without a consolidated vocation. This study evaluates the “Desayuna Ingeniería” (Breakfast Engineering) STEM outreach programme to identify intervention variables associated with perceived increases in pre-university students’ vocational interest in industrial engineering. In the retrospective survey (N = 584), 48.8% of participants reported increased interest after the visit. Focusing specifically on the target subsample of students with low or moderate prior interest, who represent the “undecided” group (n = 408), binary logistic regression models examined how active workshop participation, overall workshop satisfaction, and mentor gender configuration were associated with perceived vocational change. Active workshop participation showed a positive predictive trend that did not reach the prespecified significance threshold (p = 0.056), overall workshop satisfaction showed no statistically significant association (p = 0.697), and evidence regarding mentor gender configuration was inconclusive. Exploratory gender-stratified analyses suggested that perceived increases were primarily associated with baseline interest in female students, and with baseline interest alongside a positive, although non-significant, trend for active participation (p = 0.052) in male students. Brief interventions may benefit from prioritising hands-on involvement, whereas fostering girls’ baseline STEM motivation at earlier school stages remains a theoretical hypothesis for future longitudinal studies.

Distribution networks are operating under increasingly variable conditions as renewable generation and electric vehicles become more widespread. Maintaining reliable and secure operation in this setting requires timely information about the electrical state of the network, yet DSSE remains difficult to implement when measurement coverage is sparse, communication infrastructure is limited, and feeder configurations are complex enough to compromise observability. This review examines how current DSSE methods deal with these conditions, including high R/X ratios, three-phase unbalance, and limited instrumentation. More than one hundred studies are considered, spanning optimization-based estimators and related mean-squared-error formulations, Kalman-filter-based dynamic approaches, artificial intelligence techniques, and centralized and distributed estimation schemes. Their reported advantages, limitations, and application areas are assessed in the context of practical deployment requirements. The review also considers directions that combine physical network models with neural networks, improve pseudo-measurement generation, account for dynamic and stochastic behavior, and distribute the estimation process through approaches such as Bayesian consensus filtering and the alternating direction method of multipliers. Comparative tables summarize the main characteristics of these methods, while the discussion gives particular attention to real-time implementation, cybersecurity, and operational constraints encountered in modern distribution networks.

Argentina’s expanding lithium industry exposes a constitutional tension between provincial control over natural resources, environmental protection, and Indigenous peoples’ rights. Although the National Constitution recognizes a healthy environment and Indigenous participation, implementation depends on fragmented regulatory institutions, access to environmental information, and timely remedies. This article examines how Argentina’s federal structure mediates conflicts among lithium development, Indigenous self-determination, and water protection. It employs doctrinal legal research and qualitative analysis of constitutional provisions, legislation, international instruments, judicial materials, and peer-reviewed scholarship. The article develops the concept of constitutional asymmetry in extractive governance: a mismatch between the formal recognition of rights and the institutional capacity required to make them effective. The analysis identifies four dimensions—normative, institutional, informational, and remedial—and explains how they may interact when mining permits, environmental assessments, consultation processes, and judicial review operate separately. It argues that provincial ownership does not displace constitutional and international obligations, and that project-by-project assessment may be inadequate where impacts accumulate across shared hydrological systems. The article proposes a rights-based framework linking cumulative water assessment, meaningful prior consultation, accessible environmental information, intergovernmental coordination, and effective review. It does not claim that all Argentine lithium projects violate rights; rather, it provides a framework for evaluating whether governance arrangements make constitutional guarantees practically enforceable. It concludes that the legitimacy of lithium development should be assessed through rights protection and accountability, not only economic benefits or formal compliance.

Accurate photovoltaic cell temperature estimation is critical for maximizing energy management and improving digital twin fidelity in building-integrated solar systems. Classical models, NOCT (Nominal Operating Cell Temperature), King, Skoplaki, and PVsyst/Faiman, provide a practical baseline but exhibit significant limitations when applied to complex, real-world scenarios. These static and linear approaches fail to capture dynamic thermal phenomena such as thermal inertia, nonlinear irradiance effects, and wind-temperature interactions. This paper presents an advanced physical model that incorporates thermal memory effects, sophisticated wind modeling, transient cloud-response mechanisms, and non-linear thermal dependencies. Parameter calibration was performed using a differential evolution algorithm, automatically optimizing the model fit to one year of experimental data from a 2.79 kW pilot installation at the University of Extremadura. The validation results demonstrate consistent improvements across all seasons: RMSE reductions of up to 4.9% and MAE reductions of up to 14.4% compared to classical approaches, with particularly pronounced gains during the summer and autumn. The methodology is readily transferable to diverse installations and climatic contexts, providing a robust framework for developing high-accuracy PV digital twins and enabling early fault detection and operational optimization.

Efficiently controlling and managing photovoltaic installations for self-consumption is key to making optimal use of these systems. The first step to achieving that control is modeling the generation of electricity by photovoltaic panels with digital twins. This paper presents an experimental evaluation of four photovoltaic power prediction models in a real-world environment using high-resolution, annual data from a university facility. A mathematical model and three neural network models (MLP, LSTM, and GRU) are compared under standardized metrics. The results demonstrate that the Multilayer Perceptron (MLP) achieves the highest overall accuracy and seasonal consistency. It outperforms the other three models, particularly in scenarios where the relationships between meteorological variables and power generation are predominantly static. However, the mathematical model maintains a competitive performance in conditions of low variability, such as in the summer and autumn. The seasonal analysis reveals the importance of selecting and adjusting models according to operational and climatic contexts. This study’s main contribution is identifying the MLP as the most efficient option for implementing digital twins in photovoltaic installation control. This facilitates real-time monitoring, optimization, and predictive maintenance and contributes to smart, sustainable energy management in buildings to align with climate neutrality guidelines.

Nowadays, due to the considerable increase in energy demand, the reduction of CO₂ emissions, the increase in energy efficiency and the lack of knowledge of the population in the current electricity system, both at residential and industrial levels, the need to implement measures that promote the common benefit of society has arisen. Photovoltaic energy has been gaining importance as a sustainable alternative to traditional energy sources, especially in Spain, where it is one of the most advantageous options thanks to its geographical location. In order to help consumers at household, SME or industrial levels to understand the complex electricity market, a new tool designed to optimize savings is presented. This application analyzes individual consumption patterns in detail and proposes savings strategies based on both tariffs and the implementation of solar photovoltaic installations. By integrating actual consumption data and PV generation estimations, the tool generates different customized scenarios, allowing the user to minimize their costs. The results of this study underline the importance of adopting renewable energy solutions, offering practical ideas for both policy makers and consumers.

This study explores sustainable practices in energy resource management, with the aim of optimizing energy consumption while maintaining quality of life. It focuses on energy efficiency in the residential sector of the autonomous community of Extremadura. The methodology involved collecting data from 50 households, using a computer tool to assess and classify energy efficiency levels. The results showed that homes built before 1994 had low energy ratings, whereas those built afterward demonstrated improvement, reflecting a growing environmental awareness. It was found that small investments, such as the installation of control devices and the adoption of conscious consumption habits, can significantly improve energy efficiency. The study highlights the importance of raising community awareness about energy efficiency and notes that, despite limitations in the data, the findings provide valuable insights for future interventions. In conclusion, energy efficiency is key to a sustainable future, and we all share the responsibility of improving it.

A neural network was developed using the Multilayer Perceptron (MLP) model to predict the darkness value of the night sky in rural areas. For data collection, a photometer was placed in three different rural locations in the province of Cáceres, Spain, recording darkness values over a period of 23 months. The recorded data were processed, debugged, and used as a training set (75%) and validation set (25%) in the development of an MLP capable of predicting the darkness level for a given date. The network had a single hidden layer of 10 neurons and hyperbolic activation function, obtaining a coefficient of determination (R²) of 0.85 and a mean absolute percentage error (MAPE) of 6.8%. The developed model could be employed in unpopulated rural areas for the promotion of sustainable astronomical tourism.