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PurposeThis study aims to present an innovative sandbox platform that implements a decision support system (DSS) to assess the sustainable development goals (SDGs) addressed at the municipal level. It intends to determine the relative importance of each SDG in municipalities and explore the synergies that can be discovered among them. Design/methodology/approachParticipatory action research is used to develop a DSS and an algorithm designated as discrete heavy fuzzy was also developed, which extends the Apriori algorithm to include discrete quantitative assessments of the level of SDG compliance by each project. A scenario consisting of three municipalities in Portugal (i.e. Porto, Loule and Castelo de Vide) was chosen to demonstrate the implementation of the sandbox platform and to interpret the observed results. FindingsThe results reveal significant differences in the typology of SDGs addressed by each municipality. It was found that municipal sustainable projects are strongly influenced by the contextual factors of each municipality. Porto has projects that address the first five SDGs. Loule appears projects that promote innovation, the fight against climate change and the development of sustainable cities. Castelo de Vida has initiatives related to innovation and infrastructure and decent work and economic growth. Research limitations/implicationsThis study provides knowledge about the relative importance of the SDGs in Portuguese municipalities and explores the synergies among them. The proposed sandbox platform fills the gaps of the ODSlocal Webtool by proposing a dynamic and interactive approach for the exploration of quantitative indicators regarding the implementation status of the SDGs established in the 2030 Agenda. Originality/valueThis study provides knowledge about the relative importance of the SDGs and the various synergies that exist between them considering the Portuguese municipalities. The sandbox platform presented and developed within this study allows filling the gaps of the ODSlocal Webtool that gathers essentially qualitative information about each project and offers a dynamic and interactive exploration with quantitative indicators of the implementation status of the SDGs established in the 2030 Agenda.

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Aims: Heart Failure (HF) is a global health challenge that is often associated with reduced left ventricular ejection fraction (EF). Current EF assessments rely on echocardiography exams performed by specialists. This study explores the feasibility of predicting EF using cardiac intervals derived from synchronous phonocardiography (PCG) and single-lead electrocardiography (ECG) recorded with a bimodal stethoscope. Methods: 84 pairs of synchronous PCG and ECG signals were collected from 42 patients. Signal pairs were categorized into three different EF groups: EF <40%, EF 40-49% and EF =50%. Results: Logistic regression revealed that the QS2 interval was a significant predictor of reduced ejection fraction, with p = 0.0186 for EF 40% and p = 0.0090 for EF 50%. QT interval showed no predictive value. The Kruskal-Wallis test showed significant group differences for QS2 (p=0.008) and S1S2 (p=0.009), but not for QT (p=0.299) or QS1 (p=0.673). Mann-Whitney U-test confirmed that QS2 and S1S2 intervals differed significantly between EF. © 2025 IEEE Computer Society. All rights reserved.

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Acoustic monitoring of road traffic events is an indispensable element of Intelligent Transport Systems to increase their effectiveness. It aims to detect the temporal activity of sound events in road traffic auditory scenes and classify their occurrences. Current state-of-the-art algorithms have limitations in capturing long-range dependencies between different audio features to achieve robust performance. Additionally, these models suffer from external noise and variation in audio intensities. Therefore, this study proposes a spectrogram-specific transformer model employing a multi-head attention mechanism using the scaled product attention technique based on softmax in combination with Temporal Convolutional Networks to overcome these difficulties with increased accuracy and robustness. It also proposes a unique preprocessing step and a Deep Linear Projection method to reduce the dimensions of the features before passing them to the learnable Positional Encoding layer. Rather than monophonic audio data samples, stereophonic Mel-spectrogram features are fed into the model, improving the model's robustness to noise. State-of-the-art One-dimensional Convolutional Neural Networks and Long Short-term Memory models were used to compare the proposed model's performance on two well-known datasets. The results demonstrated its superior performance by achieving an improvement in accuracy of 1.51 to 3.55% compared to the studied baselines.

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