Abstract
Manual selection of real estate properties can pose considerable challenges for agents since it needs a careful balance of various factors to satisfy client requirements while also manoeuvring through the complexities of the market. Although automated valuation models are widely used to estimate property market values, they are not designed to support property recommendation tasks. To address this gap, filteringbased recommendation methods have been explored, including collaborative and content-based approaches. However, these methods face several limitations in the real estate domain. This paper proposes a recommendation methodology designed to identify houses that closely resemble a given property, allowing agents to select the best matches based on geographical and physical characteristics. To assess the performance of the proposed methodology, we employ a range of evaluation metrics that measure different aspects of the model's effectiveness in ranking and recommending relevant items. The findings suggest that, while geographic features may slightly influence ranking behaviour, the model is capable of producing diverse and relevant recommendations consistently.

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
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
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
This study examines the role of online health communities in Brazil dedicated to cannabis treatments for chronic diseases as platforms for evidence-based activism. Using a mixed-methods approach, the research combines qualitative analysis with computational techniques, including Latent Dirichlet Allocation (LDA) topic modeling, to analyze six online groups from WhatsApp and Facebook. Key themes emerging from the analysis include treatment per pathology, treatment effects, access barriers, peer support, and advocacy efforts. The findings reveal how these communities act as epistemic networks, where patients and caregivers co-produce knowledge by sharing personal experiences and engaging in dialogue with healthcare professionals. This study highlights how online health communities transform experience sharing into structured evidence, enabling collective action to address barriers such as limited access to cannabis-based treatments. It underscores the potential of digital platforms to empower patients, foster collaboration with healthcare professionals, and influence health governance.

Abstract
Online health communities enable patients and caregivers to share experiences, seek advice, and collaboratively generate knowledge about treatments and condition. However, accessing relevant information often proves challenging due to platform limitations like insufficient search functionalities. A previous study identified key topics discussed in Brazilian online health groups centered on cannabis treatments for chronic diseases. Building on these findings, this study introduces a proof-of-concept chatbot designed to enhance access to the collective knowledge within these communities. The chatbot prototype, built using Google Dialogflow, was tailored to provide contextually relevant, accurate, and user-friendly responses. A user study involving 38 participants evaluated its performance, showing high user satisfaction, task completion rates, and trust in the information provided. The results highlight the chatbot's potential enhance knowledge accessibility, promote patient engagement, and support evidence-based activism by organizing and disseminating community-generated content effectively.