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
Analyzing time series of counts often encounters the challenge of missing data, which can significantly hinder the accuracy and reliability of statistical models. This study addresses this issue by employing Poisson first-order integer-valued au-toregressive (PoINAR) models in conjunction with the Gibbs sampler with data augmentation. This method is particularly effective as it accounts for both the mechanisms behind missing data and the intrinsic serial correlation within the time series. Two distinct approaches to data augmentation are explored and compared in this work and illustrated using both simulated and real data. © 2026 Author(s).

Abstract
Atlantic salmon populations have declined in many regions and are affected by several natural and anthropogenic factors throughout their lives. We investigated the role of environmental drivers and the effect of dam construction on the trend in catches of spawning adults of a migratory population currently at risk. For this purpose, we examined the salmon catches from 1914 to 2020 in the Minho River (NW Portugal, SW Europe), located at the southern limit of this species' distribution. There was a decline in catches over time with an inverse and significant relationship between the trend in catches and lagged temperature. Delayed effects of this type may indicate temperature influences on survival during early life history stages. Similarly, the trend in catches decreased with the increasing number of dams. A forecast model built for the period before the construction of the first major dam in this river (before 1955), including lagged temperature, resulted in a decreasing trend in the number of catches. This demonstrates that catches would have declined due to temperature effects even without dam construction. This does not diminish the role of dams in the observed decline; rather, it reveals that temperature-driven declines would have occurred independently. Nonetheless, efficient management and conservation of this imperiled population require further detailed biological information on the number of returning spawning adults and salmons' survival throughout their life cycle.

Abstract
Background: – In the pursuit of understanding current improvements that enhance nursing care leveraging emerging technologies, this study focused on answering “How has artificial intelligence been integrated into electronic health records, with an emphasis on nursing practice?” Methods: – This scoping review was conducted after the methodology proposed by the Joanna Briggs Institute and structured according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews. The study protocol was registered on the Open Science Framework platform (DOI: 10.17605/OSF.IO/D96TY). Searches were performed across 7 databases, in addition to grey literature and manual reference screening. Results: – A total of 74 studies were included. A variety of artificial intelligence technologies were identified, particularly traditional supervised learning and natural language processing. Artificial intelligence contributed to clinical decision-making, risk anticipation, workload reduction through documentation automation, and the enhancement of documentation quality by improving its accuracy, completeness, and consistency. Discussion: – The adoption of these technologies demonstrates promising potential to optimize nursing documentation, support clinical decisions, and strengthen patient safety, thereby promoting a more efficient and evidence-based nursing practice. However, effective implementation requires attention to data quality, interoperability, and increased active engagement of nurses in the development and use of such technologies.