Researchers introduced a novel approach to cardiovascular health monitoring by integrating a stroke-volume allocation (SVA) model with wearable sensors. Through comprehensive experiments and clinical trials, they demonstrated the effectiveness and accuracy of this model in assessing vascular age and detecting cardiovascular diseases, offering a promising solution for real-time monitoring and early intervention.
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A recent study in Scientific Reports introduces a stochastic SEIRS model to dissect the intricate dynamics of worm transmission in wireless sensor networks (WSNs). By integrating environmental randomness, the research uncovers critical insights into malware propagation, paving the way for proactive control strategies to fortify network security against malicious attacks.
Researchers in Buildings journal propose the CEACE method, integrating environmental indicators like CF, EF, WF, and CDW to assess sustainability in residential construction in Andalusia, Spain. Analyzing 15 diverse projects, the method offers holistic evaluation, identifies environmental impacts, and suggests improvements, highlighting its effectiveness in promoting sustainable construction practices.
Researchers from the UK demonstrate the efficacy of self-supervised learning for human activity recognition using a vast wearable dataset. By addressing convergence issues and evaluating multi-task self-supervision, they showcase robust models with superior representation quality across diverse populations and activity types.