HIV prevention trials typically randomize thousands of participants to active or control intervention arms, with regular (e.g. monthly) clinic visits over one or more years of follow-up. Because HIV infection rates are often lower than 3 per 100 person-years even in high prevalence settings, tens of thousands of clinic visits may take place before the number of infections required to achieve adequate study power has been observed. In addition to clinical outcomes, the multitude of study visits provides an opportunity to assess adherence and related participant behaviors in great detail. These data may be used to refine counseling messages, gain insight into patterns of behavior, and perform supporting analyses in an attempt to obtain more precise estimates of treatment efficacy. Exploratory analyses were performed to assess how our understanding of participant behaviors and their relationships to biological outcomes in two recent prevention trials might have been impacted had the frequency of routine behavioral data collection been reduced from monthly to just months 1, 3, 6, 9, and 12. Results were comparably informative in the reduced case, suggesting that unnecessarily extensive amounts of routine behavioral data may be collected in these trials.
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Sci Rep
December 2024
School of Electrical Engineering, Vellore Institute of Technology, Chennai, 600127, India.
Spherical tanks have been predominantly used in process industries due to their large storage capability. The fundamental challenges in process industries require a very efficient controller to control the various process parameters owing to their nonlinear behavior. The current research work in this paper aims to propose the Approximate Generalized Time Moments (AGTM) optimization technique for designing Fractional-Order PI (FOPI) and Fractional-Order PID (FOPID) controllers for the nonlinear Single Spherical Tank Liquid Level System (SSTLLS).
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December 2024
Health Services Research and Pharmacoepidemiology Unit, Foundation for the Promotion of Health and Biomedical Research of Valencia Region (FISABIO), Avenida Cataluña, 21, 46020, Valencia, Spain.
Improvement of post-stroke outcomes relies on patient adherence and appropriate therapy maintenance by physicians. However, comprehensive evaluation of these factors is often overlooked. This study assesses secondary stroke prevention by differentiating patient adherence to antithrombotic treatments (ATT) from physician-initiated interruptions or switches.
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December 2024
Computer Science Department, Saarland University, Saarbrücken, Germany.
Estimating the numbers and whereabouts of internally displaced people (IDP) is paramount to providing targeted humanitarian assistance. In conflict settings like the ongoing Russia-Ukraine war, on-the-ground data collection is nevertheless often inadequate to provide accurate and timely information. Satellite imagery may sidestep some of these challenges and enhance our understanding of the IDP dynamics.
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December 2024
School of Electrical Engineering, Aalto University, P.O. Box 15500, Aalto, FI-00076, Finland.
Engineering plastics are finding widespread applications across a broad temperature spectrum, with additive manufacturing (AM) having now become commonplace for producing aerospace-grade components from polymers. However, there is limited data available on the behavior of plastic AM parts exposed to elevated temperatures. This study focuses on investigating the tensile strength, tensile modulus and Poisson's ratio of parts manufactured using fused filament fabrication (FFF) and polyetheretherketone (PEEK) plastics doped with two additives: short carbon fibers (SCFs) and multi-wall carbon nanotubes (MWCNTs).
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December 2024
Department of Civil Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Deep learning models are widely used for traffic forecasting on freeways due to their ability to learn complex temporal and spatial relationships. In particular, graph neural networks, which integrate graph theory into deep learning, have become popular for modeling traffic sensor networks. However, traditional graph convolutional networks (GCNs) face limitations in capturing long-range spatial correlations, which can hinder accurate long-term predictions.
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