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A pilot study for speech assessment to detect the severity of Parkinson's disease: An ensemble approach.
Comput Biol Med
December 2024
School of Engineering, RMIT University, Victoria, Australia. Electronic address:
Background: Changes in voice are a symptom of Parkinson's disease and used to assess the progression of the condition. However, natural differences in the voices of people can make this challenging. Computerized binary speech classification can identify people with PD (PwPD), but its multiclass application to detect the severity of the disease remains difficult.
View Article and Find Full Text PDFDFT-based computational investigation of the structural, electronic, and thermoelectric properties of transition-metal hydride VH.
J Mol Model
December 2024
Department of Physical and Numerical Sciences, Qurtuba University of Science and Information Technology, Peshawar, 25100, Pakistan.
Context: Vanadium hydride is of significant interest because of its potential applications in thermoelectric materials and hydrogen storage technologies. Understanding its structural, electronic, and thermoelectric properties is crucial for optimizing its performance in these applications. This study investigates these properties via density functional theory (DFT), revealing key insights into its stability and efficiency as a thermoelectric material.
View Article and Find Full Text PDFAlternative assessment of machine learning to polynomial regression in response surface methodology for predicting decolorization efficiency in textile wastewater treatment.
Chemosphere
December 2024
Department of Energy Systems Research, Ajou University, Suwon, 16499, Republic of Korea; Department of Environmental and Safety Engineering, Ajou University, Suwon, 16499, Republic of Korea. Electronic address:
This study investigated the potential of machine learning (ML) as a substitute for polynomial regression in conventional response surface methodology (RSM) for decolorizing textile wastewater via a UV/HO process. While polynomial regression offers limited adaptability, ML models provide superior flexibility in capturing nonlinear responses but are prone to overfitting, particularly with constrained RSM datasets. In this study, we evaluated decision tree (DT), random forest (RF), multilayer perceptron (MLP), and extreme gradient boosting (XGBoost) models with respect to a quadratic regression model.
View Article and Find Full Text PDFOptimal Cell Length for Exploration and Exploitation in Chemotactic Planktonic Bacteria.
Environ Microbiol
December 2024
School of Life Sciences, Joseph Banks Laboratories, University of Lincoln, Lincoln, UK.
Elongated morphologies are prevalent among motile bacterioplankton in aquatic systems. This is often attributed to enhanced chemotactic ability, but how long is best? We hypothesized the existence of an optimal cell length for efficient chemotaxis resulting from shape-imposed physical constraints acting on the trade-off between rapid exploration versus efficient exploitation of nutrient sources. To test this hypothesis, we evaluated the chemotactic performance of elongated cephalexin-treated Escherichia coli towards α-methyl-aspartate in a microfluidic device creating linear, stable and quiescent chemical gradients.
View Article and Find Full Text PDFDevelopment and validation of a machine learning-based predictive model for compassion fatigue in Chinese nursing interns: a cross-sectional study utilizing latent profile analysis.
BMC Med Educ
December 2024
Division of Science & Technology and Foreign Affairs, Chongqing Traditional Chinese Medicine Hospital, Chongqing, 400020, China.
Background: Compassion fatigue is a significant issue in nursing, affecting both registered nurses and nursing students, potentially leading to burnout and reduced quality of care. During internships, compassion fatigue can shape nursing students' career trajectories and intent to stay in the profession. Identifying those at high risk is crucial for timely interventions, yet existing tools often fail to account for within-group variability, limiting their ability to accurately predict compassion fatigue risk.
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