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Non-pharmaceutical interventions (NPIs), such as lockdowns, travel restrictions, and social distancing mandates, play a critical role in controlling the spread of infectious diseases by shaping human mobility patterns. Using COVID-19 as a case study, this research investigates the relationships between NPIs, mobility, and the effective reproduction number (R-t) across 13 European countries. We employ XGBoost regression models to estimate missing mobility data from NPIs and missing R(t )values from mobility, achieving high accuracy. Additionally, using clustering techniques, we uncover national distinctions in social compliance. Northern European countries demonstrate higher adherence to NPIs than Southern Europe, which exhibits more variability in response to restrictions. These differences highlight the influence of cultural and social norms on public health outcomes. In general, our analysis reveals a strong correlation between NPIs and mobility reductions, highlighting the immediate impact of restrictions on population movement. However, the relationship between mobility and R(t )is weaker and more nuanced, reflecting the time delays involved, as changes in mobility take time to influence transmission rates. These results underscore the interdependence of restrictions, mobility, and disease spread while demonstrating the potential for data-driven approaches to guide policy decisions. Our approach offers valuable insights for optimizing public health strategies and tailoring interventions to diverse cultural contexts during future health crises.

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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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