Abstract
Higher Education Institutions (HEIs) face significant challenges in managing and integrating diverse Information System (ISs) that support academic, administrative, and strategic operations. As digital transformation advances, the need for seamless interoperability and data-driven governance becomes increasingly crucial. This study provides a comprehensive analysis of the ISs Ecosystem (ISE) in HEIs, emphasizing the importance of system integration, Business Intelligence (BI) solutions, and Decision Support Systems (DSS) in fostering efficient, data-driven decision-making. By examining a real-world case study of the University of Tras-os-Montes and Alto Douro (UTAD), this research validates the role of BI in transforming fragmented information landscapes into cohesive digital environments. The findings demonstrate that successful BI adoption requires well-defined governance structures, seamless data flow, and alignment with institutional objectives. Additionally, the study underscores the strategic impact of interoperability, highlighting how institutions can enhance institutional intelligence, streamline decision-making processes, and improve operational efficiency through an integrated BI ecosystem. The insights contribute to ongoing discussions on digital transformation in higher education, offering a scalable framework for HEIs seeking to transition from isolated systems to an interoperable and intelligent data ecosystem. The paper also explores emerging trends such as AI-driven analytics and predictive modelling, outlining potential pathways for HEIs to further optimize their decision-support infrastructures.

Abstract
The increasing deployment of individual and collective self-consumption systems is reshaping Energy Management Systems (EMSs) under evolving regulatory frameworks. This paper presents a techno-economic comparison between a centralized EMS and a decentralized EMS for flexible resources dispatching and sharing under collective self-consumption schemes. The centralized EMS is formulated as a Mixed-Integer Non-Linear Programming (MINLP) optimization problem, whereas the decentralized EMS employs a rule-based algorithm that requires no information exchange among members. Both strategies have been evaluated under the Spanish regulatory framework, a) using fixed allocation coefficients and b) introducing improvements borrowed from the Portuguese regulation, selected as a benchmark due to its advanced regulatory maturity. For the case of ex-ante allocation coefficients computation, an optimization-based methodology is proposed combining Mixed-Integer Linear Programming (MILP) with data clustering techniques. Results indicate that both EMS architectures achieve comparable energetic performance. The centralized EMS achieves the highest levels of self-consumption, self-sufficiency and energy sharing, particularly when proportional allocation coefficients are used, while the decentralized EMS performs closely. From an economic perspective, the centralized EMS provides the highest cost reductions, while the decentralized EMS yields lower economic savings but with significantly less computational effort, with runtimes up to eighteen times shorter. These findings highlight a clear trade-off between economic optimality and computational efficiency, positioning decentralized EMS solutions as a scalable and privacy-preserving alternative for individual self-consumers transitioning to collective self-consumption schemes in evolving regulatory frameworks.

Abstract
This study proposes a comprehensive framework integrating eXplainable Artificial Intelligence (XAI) techniques with clustering-based context extraction to enhance energy consumption forecasting in modern office buildings. By leveraging explanation vectors derived from state-of-the-art XAI methods such as SHAP and LIME, our framework identifies latent operational contexts from sensor data aggregated at 15-min intervals. These contexts enable the tailoring of predictive models through feature augmentation, context-specific training, and transfer learning strategies, thereby improving forecasting accuracy compared to conventional approaches. To identify the best-performing models for each context, hyperparameter optimization via grid search is employed across multiple algorithmsincluding Gradient Boosting, Random Forest, and K-Nearest Neighbors. Extensive experiments demonstrate that context-aware models significantly outperform baseline methods, achieving up to a 7% improvement in the coefficient of determination (R-2) and a marked reduction in error metrics. Our findings underscore the importance of integrating XAI with data-driven modeling to enhance predictive performance and model interpretability, which are critical for practical energy management and decision-making in complex building environments.

Abstract
The production of insects for both human and animal consumption has seen an interest increase in recent years. Tenebrio molitor, a beetle species whose larval stage was considered by the European Commission safe for human consumption, represents a promising candidate for large-scale production. Efficient production of T. molitor requires the separation of its different metamorphic stages to optimize productivity and reduce cannibalism. Additionally, dead pupae must be removed to prevent contamination and ensure the healthy development of the remaining individuals. Currently, the identification of dead pupae relies primarily on visual inspection based on colour changes, as dead individuals tend to exhibit surface melanization. However, this method becomes inefficient in large-scale operations. This study presents a benchmark of hyperspectral imaging (HSI) and thermal imaging as alternative sensing technologies for automated dead pupae identification. Hyperspectral data acquired in the near-infrared range (900-1700 nm) enabled accurate discrimination between dead and live pupae using both a PLS-DA and a Logistic Regression model, achieving F1-Scores above 90 % for both classes. Furthermore, key wavelengths (903 nm and 1259 nm) were identified and used to develop a normalized difference index (NDI) capable of distinguishing pupae health status using only two spectral bands. Thermal imaging revealed consistent temperature differences, with dead pupae presenting approximately 1-3 % lower temperatures than live pupae. The proven reliability and precision of the proposed sensing technologies validate their use in contributing to the development of scalable and reliable solutions for quality control in the insect farming industry.

Abstract

Abstract