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Ground Reaction Forces and Joint Moments Predict Metabolic Cost in Physical Performance: Harnessing the Power of Artificial Neural Networks.
Appl Sci (Basel)
June 2024
Department of Biomechanics and Center for Research in Human Movement Variability, University of Nebraska at Omaha, Omaha, NE 68182, USA.
Understanding metabolic cost through biomechanical data, including ground reaction forces (GRFs) and joint moments, is vital for health, sports, and rehabilitation. The long stabilization time (2-5 min) of indirect calorimetry poses challenges in prolonged tests. This study investigated using artificial neural networks (ANNs) to predict metabolic costs from the GRF and joint moment time series.
View Article and Find Full Text PDFDeveloping a brain inspired multilobar neural networks architecture for rapidly and accurately estimating concrete compressive strength.
Sci Rep
January 2025
Faculty of Civil Engineering, Damascus University, Damascus, Syria.
Concrete compressive strength is a critical parameter in construction and structural engineering. Destructive experimental methods that offer a reliable approach to obtaining this property involve time-consuming procedures. Recent advancements in artificial neural networks (ANNs) have shown promise in simplifying this task by estimating it with high accuracy.
View Article and Find Full Text PDFCost analysis of hidden hepatitis D virus infection in Spain.
Gastroenterol Hepatol
January 2025
Pharmacoeconomics & Outcomes Research Iberia (PORIB), Madrid, España. Electronic address:
Introduction: A significant percentage of patients coinfected with hepatitis B virus (HBV) and hepatitis D virus (HDV) are undiagnosed. Coinfected patients progress to advanced liver disease faster than HBV monoinfected patients, thereby consuming more healthcare resources. The aim was to perform an analysis to determine the cost of hidden HDV infection in Spain.
View Article and Find Full Text PDFDistributed Representations for Cognitive Control in Frontal Medial Cortex.
J Cogn Neurosci
December 2024
In natural and artificial neural networks, modularity and distributed structure afford complementary but competing benefits. The former allows for hierarchical representations that can flexibly recombine modules to address novel problems, whereas the latter can benefit from less constrained training, potentially uncovering fruitful statistical regularities. Here, we investigate these competing demands in the context of human sequential behavior.
View Article and Find Full Text PDFThe hidden cost of digital learning: a cross-sectional study assessing the prevalence of computer vision syndrome (CVS) among medical students in Jordan.
BMJ Open
January 2025
Department of Special Surgery, The Hashemite University Faculty of Medicine, Zarqa, Jordan.
Objective: Increased screen time has resulted in widespread computer vision syndrome (CVS) in the digital era. Medical students, who rely heavily on digital screens, are particularly at risk. Our study aims to evaluate the prevalence of CVS among Jordanian medical students, which is a gap in existing knowledge.
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