Abstract
The increasing complexity of modern power systems, driven by renewable energy integration, distributed energy resources, and evolving operational requirements, has intensified the need for advanced optimization tools capable of ensuring secure and economical system operation. Security-Constrained Optimal Power Flow (SCOPF) is a fundamental tool used to ensure system security under N-1 contingency scenarios. This paper proposes a novel nonlinear programming (NLP)-based multi-period SCOPF formulation that enhances computational efficiency and scalability while preserving solution accuracy. The proposed approach explicitly incorporates load shifting, energy storage systems (ESSs), and renewable generation with reactive power injection, addressing the operational needs of modern, renewable-dominated grids. The methodology is validated using IEEE 9-, 30-, 118-, and 300-bus systems, demonstrating consistent scalability across network sizes. Unlike conventional approaches that rely on linearization or DC approximations to reduce computational burden, the proposed NLP framework directly addresses the full nonlinear AC power flow equations. Simulation results indicate that the method effectively handles the non-convexities of the problem, ensuring superior solution accuracy and strict adherence to voltage and reactive power constraints. These results confirm that the proposed framework offers a reliable and robust tool for secure power system operation, capable of managing the complex dynamics of renewable penetration without compromising physical power systems modelling fidelity.

Abstract
Background: This study aimed to validate the content of a game focused on clinical supervision in nursing, with the collaboration of experts, and to assess its usability alongside a group of nurses. The development of SUPERVISE (R) was grounded in theories of Experiential Learning, Self-Determination, Constructivist, and Social Cognitive.Methods: A mixed study design was used. In the first phase, the content of the game was validated with the participation of experts using a modified e-Delphi method. In the second phase, the usability of SUPERVISE (R) was tested with nurses.Results: In the first phase, the content of the game was validated by 36 experts, reaching a consensus = 95.4% on the 128 questions on which the game was based. In the second phase, the SUPERVISE (R) game was tested and evaluated by 39 nurses. It showed good usability and with a System Usability Scale score = 79.4 (above the cut-off of 68) and was recognised as an effective teaching strategy.Conclusion: This study highlights the importance of combining rigorous content validation with practical evaluation to develop effective gamified educational tools for nursing practice.

Abstract
As Artificial Intelligence (AI) systems increasingly permeate sensitive domains such as finance, healthcare, and media, ensuring their ethical deployment has become a central concern for researchers, policymakers, and practitioners. Current auditing tools often assess isolated principles, such as fairness or explainability, lacking a comprehensive view of the ethical risks involved. This paper presents a multidimensional framework for ethical evaluation of AI systems, designed to support responsible AI governance and alignment with the United Nations Sustainable Development Goals (SDGs). The proposed approach enables the simultaneous analysis of key ethical dimensions, including fairness, bias, explainability, robustness, transparency, and legal compliance. We demonstrate the applicability of this tool through one extensive case study: a credit scoring system, considered high-risk under the AI Act. This work contributes to operationalizing responsible AI governance, providing insight for policymakers, regulators, and practitioners to ensure ethical, legally compliant, and socially responsible AI deployment.

Abstract
Heating, Ventilation, and Air Conditioning (HVAC) systems represent a major share of building energy consumption, creating both operating cost and emissions reduction challenges for energy communities. This work proposes an optimisation-based management framework for a community of buildings, integrating HVAC operation with renewable generation, battery storage, and demand-side flexibility. The methodology employs a mixed-integer linear programming model to coordinate thermal and electrical energy flows, considering indoor comfort constraints, equipment dynamics, and market price signals. The framework is validated through a case study using real demand, weather, and market data, comparing baseline and optimised operation under varying seasonal conditions. Results demonstrate significant reductions in total operating cost and peak demand of the energy community, alongside improved usage of renewable generation and reduced reliance on the grid, without compromising thermal comfort. The proposed approach highlights the potential of coordinated HVAC scheduling in energy communities as a pathway toward more cost-efficient and sustainable building operation.

Abstract
Acoustic Event Detection and Classification (AEDC) aims to identify and classify specific audio events within audio signals. AEDC has applications in various fields, including security systems, scene monitoring, smart hospitals, environmental monitoring, and more. The process of AEDC typically involves steps that include audio signal processing to extract relevant features from the input, a machine learning model to recognise patterns in the extracted features and a classifier to detect events. Recent research on AEDC has increasingly focused on features based on the frequency distribution of the Mel-frequency cepstral coefficients (MFCCs). In this study, the feature extraction is performed based on Cochleogram, which involves the analysis of audio signals using Gammatone filters. Cochleogram features are inspired by the human cochlea, part of the inner ear responsible for converting sound vibrations into electrical signals sent to the brain. A two-dimensional (2D) feature is extracted from the Cochleogram using Welchs spectral density estimation and then converted into a frequency spectrum. The frequency distribution of different cochleogram filter banks is then used as a one-dimensional (1D) feature. The proposed classification method uses a 1D Convolutional Neural Network (CNN), which is less complex than traditional 2D CNNs. The proposed method was evaluated using the URBAN-SED dataset, and its performance was compared against the related state-of-the-art methods. The results showed the competitiveness of the cochleogram over Mel-based features such as MFCC in AEDC if the deep learning algorithm is properly designed and trained.

Abstract