(1) Many children in schoolyards are excluded from social interactions with peers on a daily basis. For these excluded children, schoolyard environments often contain features that hinder, rather than facilitate, their participation. These features may include lack of appropriate play equipment, overcrowded areas, or insufficient supervision. These can generate negative situations, especially for children with special needs-such as attention deficit or autism-which includes 10% of children worldwide. All children need to be able to participate in their social environment in order to engage in social learning and development. For children living with a condition that limits access to social learning, barriers to schoolyard participation can further inhibit this. Given that much physical development also occurs as a result of schoolyard play, excluded children may also be at risk for reduced physical development. (2) However, empirically examining schoolyard environments in order to understand existing obstacles to participation requires huge amounts of detailed, precise information about play behaviour, movement, and social interactions of children in a given environment from different layers around the child (physical, social, and cultural). Recruiting this information has typically been exceedingly difficult and too expensive. In this preliminary study, we present a novel sensor data-driven approach for gathering information on social interactions and apply it, in light of schoolyard affordances and individual effectivities, to examine to what extent the schoolyard environment affects children's movements and social behaviours. We collected and analysed sensor data from 150 children (aged 5-15 years) at two primary special education schools in the Netherlands using a global positioning system tracker, proximity tags, and Multi-Motion Receivers to measure locations, face-to-face interactions, and activities. Results show strong potential for this data-driven approach to examine the triad of physical, social, and cultural affordances in schoolyards. (3) First, we found strong potential in using our sensor data-driven approach for collecting data from individuals and their interactions with the schoolyard environment. Second, using this approach, we identified and discussed three schoolyard affordances (physical, social, and cultural) in our sample data. Third, we discussed factors that significantly impact children's movement and social behaviours in schoolyards: schoolyard capacity, social use of space, and individual differences. Better knowledge on the impact of these factors could help identify limitations in existing schoolyard designs and inform school officials, policymakers, supervisory authorities, and designers about current problems and practical solutions. This data-driven approach could play a crucial role in collecting information that will help identify factors involved in children's effective movements and social behaviour.
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http://dx.doi.org/10.3390/children9081177 | DOI Listing |
Front Genet
December 2024
Department of Statistics, Federal University of São Carlos (UFSCar), São Carlos, Brazil.
Introduction: Cardiometabolic diseases, a major global health concern, stem from complex interactions of lifestyle, genetics, and biochemical markers. While extensive research has revealed strong associations between various risk factors and these diseases, latent confounding and limited causal discovery methods hinder understanding of their causal relationships, essential for mechanistic insights and developing effective prevention and intervention strategies.
Methods: We introduce anchorFCI, a novel adaptation of the conservative Really Fast Causal Inference (RFCI) algorithm, designed to enhance robustness and discovery power in causal learning by strategically selecting and integrating reliable anchor variables from a set of variables known not to be caused by the variables of interest.
Int J Med Robot
December 2024
Division of Transplantation, Department of Surgery, University of Illinois at Chicago, Chicago, Illinois, USA.
Background: Machine learning has emerged as a potent tool in healthcare. A decision tree model was built to improve the decision-making process when determining the optimal choice between an open or robotic surgical approach for kidney transplant.
Methods: 822 patients (OKT) and 169 (RKT) underwent kidney transplantation at our centre during the study period.
Alzheimers Res Ther
December 2024
University of Pompeu Fabra (UPF), Barcelona, Spain.
Background: Cerebrospinal fluid (CSF) biomarkers of synaptic dysfunction, neuroinflammation, and glial response, complementing Alzheimer's disease (AD) core biomarkers, have improved the pathophysiological characterization of the disease. Here, we tested the hypothesis that the co-expression of multiple CSF biomarkers will help the identification of AD-like phenotypes when biomarker positivity thresholds are not met yet.
Methods: Two hundred and seventy cognitively unimpaired adults with family history (FH) of sporadic AD (mean age = 60.
Pharmaceut Med
December 2024
Medical Affairs Department, AstraZeneca Farmacéutica Spain, C. del Puerto de Somport 21-23, Fuencarral-El Pardo, 28050, Madrid, Spain.
Introduction: The rapidly evolving healthcare landscape has prompted Medical Affairs (MA) departments within pharmaceutical companies to transition from their traditional role as information providers to becoming strategic partners in the healthcare ecosystem. Responding to the increasing complexity of patient needs and stakeholder dynamics within Spain's national health system, this shift emphasizes the importance of aligning MA functions with broader healthcare goals. Effective transformation requires in-depth assessments of stakeholder trends and expectations.
View Article and Find Full Text PDFPLoS One
December 2024
Insight Centre for Data Analytics, Dublin City University, Dublin, Ireland.
Physics informed neural networks have been gaining popularity due to their unique ability to incorporate physics laws into data-driven models, ensuring that the predictions are not only consistent with empirical data but also align with domain-specific knowledge in the form of physics equations. The integration of physics principles enables the method to require less data while maintaining the robustness of deep learning in modelling complex dynamical systems. However, current PINN frameworks are not sufficiently mature for real-world ODE systems, especially those with extreme multi-scale behavior such as mosquito population dynamical modelling.
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