AIMC Topic: Oxygen

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Machine Learning Driven Synthesis of Cobalt Oxide Entrapped Heteroatom-Doped Graphitic Carbon Nitride for Enhanced Oxygen Evolution Reaction.

PloS one
Developing highly efficient electrocatalysts for the oxygen evolution reaction is hindered by sluggish multi-electron kinetics, poor charge transfer efficiency, and limited active site accessibility. Transition metal-based electrocatalysts, such as c...

Oxygen Uptake Prediction for Timely Construction Worker Fatigue Monitoring Through Wearable Sensing Data Fusion.

Sensors (Basel, Switzerland)
The physical workload evaluation of construction activities will help to prevent excess physical fatigue or overexertion. The workload determination involves measuring physiological responses such as oxygen uptake (VO) while performing the work. The ...

Synergistic effects of environmental factors on benthic diversity: Machine learning analysis.

Water research
This study examines the water environmental factors of the Cangshan stream and benthic animal communities by using random forest, gradient boosting decision tree, and support vector machine models to analyze the complex response mechanisms of benthic...

Oxygen targets in critically ill patients: from pathophysiology to population enrichment strategies.

Medical gas research
Oxygen supplementation is widely used to enhance oxygen delivery and to treat or prevent hypoxia; however, it requires careful management to avoid the harmful effects of excessive oxygen exposure. Both hyperoxia (inspiratory oxygen fraction exceeding...

Addressing model discrepancy in a clinical model of the oxygen dissociation curve.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Many mathematical models suffer from model discrepancy, posing a significant challenge to their use in clinical decision-making. In this article, we consider methods for addressing this issue. In the first approach, a mathematical model is treated as...

Water quality parameters-based prediction of dissolved oxygen in estuaries using advanced explainable ensemble machine learning.

Journal of environmental management
The dissolved oxygen (DO) is crucial for the ecological health of estuaries and bays. However, human activities, land-sea interactions, and the unclear impact mechanisms of water quality parameters (WQPs) pose challenges to DO prediction. Water quali...

Automated mitochondrial oxygen consumption (mitoVO) analysis via a bi-directional long short-term memory neural network.

Journal of clinical monitoring and computing
Monitoring in vivo mitochondrial oxygen tension (mitoPO) enables the measurement of mitochondrial oxygen consumption (mitoVO), providing deeper insights into the skin's mitochondrial environment. However, current mitoVO analysis often relies on manua...

Temporal and spatial feature extraction using graph neural networks for multi-point water quality prediction in river network areas.

Water research
Deep learning methods have demonstrated strong capabilities in capturing nonlinear relationships for water quality prediction, yet existing studies predominantly focus on individual monitoring sites while neglecting pollutant spatial dynamics. To add...

Prediction of river dissolved oxygen (DO) based on multi-source data and various machine learning coupling models.

PloS one
Too low a concentration of dissolved oxygen (DO) in a river can disrupt the ecological balance, while too high a concentration may lead to eutrophication of the water body and threaten the health of the aquatic environment. Therefore, accurate predic...

Geometry-encoded molecular dynamics enables deep learning insights into P450 regiospecificity control.

Scientific reports
Cytochrome P450 1A2, as many isoenzymes, can generate multiple metabolites from a single substrate. A loose coupling between substrate binding and oxygen activation makes possible substrate reorientations at the active site prior to catalysis. In the...