Periodical publishing
A research study has proposed a machine learning-based framework for classifying occupational stress levels among nurses using physiological time-series data collected from wearable sensors. The study aimed to develop an effective method for monitoring stress levels in nurses, which can support proactive workforce management and improve care quality. The researchers
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Researchers at Yildiz Technical University in Istanbul, Turkiye, have developed a wearable sensor-based gait analysis system that can provide real-time feedback data to therapists and patients with gait disorders, such as those associated with Parkinson's disease. The system uses wearable sensors to evaluate kinematic, kinetic, and physiological parameters
Periodical publishing
Investigators from Technical University of Sofia have published a new report on the application of artificial intelligence in high-risk environments, specifically focusing on virtual reality and machine learning. The research aims to help prepare students and professionals with electrical safety, emergency response, and fire safety. The study explores the use
Periodical publishing
Researchers from the University of Florida have developed a machine learning framework to classify and identify special nuclear materials using scintillation detectors. This study highlights the challenges in discriminating between different levels of enrichments, weak radiation signals, and complex self-shielding effects. The researchers used supervised learning algorithms, including Random Forest,
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Researchers from Xi'an University of Science and Technology have developed a time risk level early warning model that can predict and prevent rock bursts in coal mines. According to the study, the model combines Bayesian optimization algorithm with long-term and short-term memory network, allowing it to accurately warn
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A recent study published in the Journal of Engineering has showcased the efficacy of a machine learning algorithm in mapping the humus horizon thickness (HHT) in Russian soils. The research, conducted by the Russian Academy of Sciences, utilized the Random Forest algorithm implemented on the Google Earth Engine cloud online
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South African researchers have proposed a hybrid RVM-WT-AdaBoostRT-RF framework for predicting power outages in the country's power grid. The framework leverages machine learning algorithms to accurately forecast outages, which is crucial for efficient power grid operations and effective business strategies. The researchers claim that their framework outperforms traditional
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Researchers from the Beijing University of Technology have made a significant breakthrough in the field of silicon solar cells, introducing an oxygen-doping strategy to enhance their efficiency and stability. According to a recent study published in the journal Advanced Functional Materials, this innovative approach has shown remarkable results, retaining up
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Researchers from the Central South University of Forestry and Technology have made a significant breakthrough in the production of hydrogen, a clean energy carrier. By designing a Zn-doped CoP heterostructure catalyst anchored on MXene-engineered nickel foam, they have achieved exceptional alkaline hydrogen evolution reaction (HER) activity and superior overall water
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Researchers from Karpagam Institute of Technology, India, have made significant advancements in the field of artificial intelligence by developing a machine learning model to monitor seawater temperature variations and their effects on desalination efficiency. This breakthrough allows for more efficient and resilient management of desalination plants, especially in climate-sensitive coastal
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Current research from the National Institute of Forestry, Mexico, has shown that machine learning techniques can significantly improve the accuracy of aboveground biomass estimation in temperate forests. The study modelled AGB in central Mexico using active and passive remote sensing data combined with machine learning techniques (Random Forest and XGBoost)
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Researchers at the Graduate University of Advanced Technology have developed an innovative approach to monitor water quality and detect dead zones using satellite imagery and machine learning algorithms. The study employed seven AI models to estimate dissolved oxygen levels in Long Island Sound, New York, and found that the Extreme
