Abstract
Portable, pocket-sized laboratories offer a cost-effective means for students to conduct control experiments outside the classroom. Broad access to such laboratories can help bridge the gap between theoretical knowledge and practical application. The Temperature Control Laboratory (TCLab) is one such portable kit that has been effectively utilized for teaching and learning control engineering. Building on experience with TCLab since 2018, we propose a unified experiment focused on PID control. This experiment was integrated into a Modeling and Control Engineering course for Biomedical Engineering undergraduates at UTAD. The students' feedback indicates strong interest and underscores the value of this handson experience. Copyright (c) 2025 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract
The increasing integration of Artificial Intelligence (AI) into Building Energy Management Systems (BEMS) is revolutionizing energy optimization by enabling real-time monitoring, predictive analytics, and automated control. While these advancements improve energy efficiency and sustainability, the opacity of AI models poses challenges in interpretability, limiting user trust and hindering widespread adoption in operational decisionmaking. Ensuring transparency is crucial for aligning AI insights with building performance requirements and regulatory expectations. This paper presents EI-Build, a novel Explainable Artificial Intelligence (XAI) framework designed to enhance the interpretability of intelligent automated BEMS. EI-Build integrates multiple XAI techniques, including Shapley Additive Explanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), Anchors, Partial Dependence Plots, Feature Permutation Importance, and correlation-based statistical analysis, to provide comprehensive explanations of model behavior. By dynamically tailoring the format and depth of explanations, EI-Build ensures that insights remain accessible and actionable for different user profiles, from general occupants to energy specialists and machine learning experts. A case study on photovoltaic power generation forecasting applied to a real BEMS context evaluates EI-Build's capacity to deliver to deliver both global and local explanations, validate feature dependencies, and facilitate cross-comparison of interpretability techniques. The results highlight how EI-Build enhances user trust, facilitates informed decision-making, and improves model validation. By consolidating diverse XAI methods into a single automated framework, EI-Build represents a significant advancement in bridging the gap between complex AI energy models and real-world applications.

Abstract
Social entrepreneurship is crucial for sustainable development as it blends innovative business models with a focus on economic, social and environmental impact. This synergy can potentially accelerate progress towards the sustainable development goals, creating a more equitable and sustainable future. This study aims to explore this phenomenon by carrying out a systematic review of literature. It is adopted the PRISMA framework to identify 54 relevant studies in this field. The findings characterize the evolution of articles in this field, the number of citations, the relationship between key terms, and the respective clusters. Moreover, seven contributions of social entrepreneurship for sustainable development are identified. Finally, the role of technology in promoting and supporting the interconnection between social entrepreneurship and sustainable development is explored. This study is relevant to enhance understanding of how technology supports social entrepreneurship and helps social entrepreneurs to achieve sustainable development goals.

Abstract
Accurate demand forecasting is essential for retail operations as it directly impacts supply chain efficiency, inventory management, and financial performance. However, forecasting retail time series presents significant challenges due to their irregular patterns, hierarchical structures, and strong dependence on external factors such as promotions, pricing strategies, and socio-economic conditions. This study evaluates the effectiveness of Transformer-based architectures, specifically Vanilla Transformer, Informer, Autoformer, ETSformer, NSTransformer, and Reformer, for probabilistic time series forecasting in retail. A key focus is the integration of explanatory variables, such as calendar-related indicators, selling prices, and socio-economic factors, which play a crucial role in capturing demand fluctuations. This study assesses how incorporating these variables enhances forecast accuracy, addressing a research gap in the comprehensive evaluation of explanatory variables within multiple Transformer-based models. Empirical results, based on the M5 dataset, show that incorporating explanatory variables generally improves forecasting performance. Models leveraging these variables achieve up to 12.4% reduction in Normalized Root Mean Squared Error (NRMSE) and 2.9% improvement in Mean Absolute Scaled Error (MASE) compared to models that rely solely on past sales. Furthermore, probabilistic forecasting enhances decision making by quantifying uncertainty, providing more reliable demand predictions for risk management. These findings underscore the effectiveness of Transformer-based models in retail forecasting and emphasize the importance of integrating domain-specific explanatory variables to achieve more accurate, context-aware predictions in dynamic retail environments.

Abstract
Artificial Intelligence has recently expanded across various applications. Machine Learning, a subset of Artificial Intelligence, is a powerful technique for identifying patterns in data to support decision making and managing the increasing volume of information. Simultaneously, Digital Twins have been applied in several fields. In this context, combining Digital Twins, Machine Learning, and Smart Buildings offers significant potential to improve energy efficiency and operational effectiveness in building management. This review aims to identify and analyze studies that explore the application of Machine Learning and Digital Twins for operation and energy management in Smart Buildings, providing an updated perspective on these rapidly evolving topics. The methodology follows the PRISMA guidelines for systematic reviews, using Scopus and Web of Science databases. This review identifies the main concepts, objectives, and trends emerging from the literature. Furthermore, the findings confirm the recent growth in research combining Machine Learning and Digital Twins for building management, revealing diverse approaches, tools, methods, and challenges. Finally, this paper highlights existing research gaps and outlines opportunities for future investigation.

Abstract
Rapidly evolving scientific and technological advances are introducing both exciting and disruptive educational tools. However, they also present new challenges in engaging and motivating students, particularly in courses with a strong mathematical foundation like control engineering. The first class of any course offers a prime opportunity to make a lasting impression that encourages active learning. This paper addresses the following question: How can the first feedback control class be transformed into a unique and memorable event that leaves a positive impact on students for the remainder of the course, and perhaps, ambitiously, for their lives? Copyright (c) 2025 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)