Environmental Health

Bioinspired Smart Triboelectric Soft Pneumatic Actuator-Enabled Hand Rehabilitation Robot.

Quantitative assessment for post-stroke spasticity remains a significant challenge due to the encountered variable resistance during passive stretching, which can lead to the widely used modified Ashworth scale (MAS) for spasticity assessment depending heavily on rehabilitation physicians. To address these challenges, a high-force-output triboelectric soft pneumatic actuator (TENG-SPA) inspired by...

Colorectal cancer classification using weakly annotated whole slide images: Multiple instance learning optimization study.

Colorectal cancer (CRC) is considered one of the most deadly cancer types nowadays. It is rapidly increasing due to many factors, such as unhealthy lifestyles, water and food pollution, aging, and medical diagnosis development. Detecting CRC in its early stages can help stop its growth by providing the necessary treatments, thereby saving many people's lives. There are various tests that doctors c...

Enhancing air quality predictions in Chile: Integrating ARIMA and Artificial Neural Network models for Quintero and Coyhaique cities.

In this comprehensive analysis of Chile's air quality dynamics spanning 2016 to 2021, the utilization of data from the National Air Quality Informatio...

Evaluation of machine learning models for predicting xerostomia in adults with head and neck cancer during proton and heavy ion radiotherapy.

BACKGROUND AND PURPOSE: Few studies have examined the factors associated with xerostomia during proton and carbon ion radiotherapy for head and neck c...

Predictive modeling of air quality in the Tehran megacity via deep learning techniques.

Air pollution is a significant challenge in metropolitan areas, where increasing amounts of air pollutants threaten public health and environmental sa...

Understanding the impacts of drought on peanuts L.): exploring physio-genetic mechanisms to develop drought-resilient peanut cultivars.

Peanut is a vital source of protein, particularly in the tropical regions of Asian and African countries. About three-quarters of peanut production oc...

Measuring water pollution effects on antimicrobial resistance through explainable artificial intelligence.

Antimicrobial resistance refers to the ability of pathogens to develop resistance to drugs designed to eliminate them, making the infections they caus...

Polymer Biodegradation in Aquatic Environments: A Machine Learning Model Informed by Meta-Analysis of Structure-Biodegradation Relationships.

Polymers are widely produced and contribute significantly to environmental pollution due to their low recycling rates and persistence in natural envir...

Cognitive load detection through EEG lead wise feature optimization and ensemble classification.

Cognitive load stimulates neural activity, essential for understanding the brain's response to stress-inducing stimuli or mental strain. This study ex...

Improving groundwater quality predictions in semi-arid regions using ensemble learning models.

Groundwater resources constitute one of the primary sources of freshwater in semi-arid and arid climates. Monitoring the groundwater quality is an ess...

Machine Learning-Enhanced Bacteria Detection Using a Fluorescent Sensor Array with Functionalized Graphene Quantum Dots.

Pathogenic bacteria are the source of many serious health problems, such as foodborne diseases and hospital infections. Timely and accurate detection ...

How do multi-faceted environmental policies enhance the production efficiency of enterprises?-Mechanisms discovery based on machine learning algorithms.

Carbon neutrality has gained considerable attention globally, and the impact of environmental policy on businesses has been extensively studied. Howev...

Forecasting O and NO concentrations with spatiotemporally continuous coverage in southeastern China using a Machine learning approach.

Ozone (O) is a significant contributor to air pollution and the main constituent ofphotochemical smog that plagues China. Nitrogen dioxide (NO) is a s...

Assessing the environmental determinants of micropollutant contamination in streams using explainable machine learning and network analysis.

Even at trace concentrations, micropollutants, including pesticides and pharmaceuticals, pose considerable ecological risks, and the increasing presen...

Predictive modeling of diazinon residual concentration in soils contaminated with potentially toxic elements: a comparative study of machine learning approaches.

The widespread use of pesticides, including diazinon, poses an increased risk of environmental pollution and detrimental effects on biodiversity, food...

MOF-Based Biomimetic Enzyme Microrobots for Efficient Detection of Total Antioxidant Capacity of Fruits and Vegetables.

Green and efficient total antioxidant capacity (TAC) detection is significant for healthy diet and disease prevention. This work first proposed the co...

Unraveling Microplastic Effects on Gut Microbiota across Various Animals Using Machine Learning.

Microplastics, rapidly expanding and durable pollutant, have been shown to significantly impact gut microbiota across a spectrum of animal species. Ho...

Spatiotemporal analysis of airborne pollutants and health risks in Mashhad metropolis: enhanced insights through sensitivity analysis and machine learning.

The study delved into an extensive assessment of outdoor air pollutant levels, focusing specifically on PM, SO, NO, and CO, across the Mashhad metropo...

A Comparative Analysis of Explainable Artificial Intelligence Models for Electric Field Strength Prediction over Eight European Cities.

The widespread propagation of wireless communication devices, from smartphones and tablets to Internet of Things (IoT) systems, has become an integral...

Achieving pollution abatement and carbon reduction synergistically: How can industrial robots play a role?

Intelligent manufacturing and green development are pivotal issues in China's pursuit of high-quality economic growth. As the core carrier of artifici...