Environmental Health

Air pollution forecasting based on wireless communications: review.

The development of contemporary artificial intelligence (AI) methods such as artificial neural networks (ANNs) has given researchers around the world new opportunities to address climate change and air quality issues. The small size, low cost, and low power consumption of sensors can facilitate obtaining the values of polluting gases in the atmosphere. However, several problems with using air poll...

Prediction and model evaluation for space-time data.

Evaluation metrics for prediction error, model selection and model averaging on space-time data are understudied and poorly understood. The absence of independent replication makes prediction ambiguous as a concept and renders evaluation procedures developed for independent data inappropriate for most space-time prediction problems. Motivated by air pollution data collected during California wildf...

Developing machine learning approaches to identify candidate persistent, mobile and toxic (PMT) and very persistent and very mobile (vPvM) substances based on molecular structure.

Determining which substances on the global market could be classified as persistent, mobile and toxic (PMT) substances or very persistent, very mobile...

Air-to-land transitions: from wingless animals and plant seeds to shuttlecocks and bio-inspired robots.

Recent observations of wingless animals, including jumping nematodes, springtails, insects, and wingless vertebrates like geckos, snakes, and salamand...

New drugs and stock market: a machine learning framework for predicting pharma market reaction to clinical trial announcements.

Pharmaceutical companies operate in a strictly regulated and highly risky environment in which a single slip can lead to serious financial implication...

Electrochemical Impedance Spectroscopy-Based Sensing of Biofilms: A Comprehensive Review.

Biofilms are complex communities of microorganisms that can form on various surfaces, including medical devices, industrial equipment, and natural env...

Improved delineation model of a standard 12-lead electrocardiogram based on a deep learning algorithm.

BACKGROUND: Signal delineation of a standard 12-lead electrocardiogram (ECG) is a decisive step for retrieving complete information and extracting sig...

Photothermal Conversion of Hydrogel-Based Biomaterial.

Traditional energy from fossil fuels like petroleum and coal is limited and contributes to global environmental pollution and climate change. Developi...

Automatic Myocardial Contrast Echocardiography Image Quality Assessment Using Deep Learning: Impact on Myocardial Perfusion Evaluation.

OBJECTIVE: The image quality of myocardial contrast echocardiography (MCE) is critical for precise myocardial perfusion evaluation but challenging for...

The impact of artificial intelligence on pollution emission intensity-evidence from China.

Artificial intelligence (AI) is a crucial component of sustainable economic development and an indicator of the next wave of technological progress. T...

Analysis of Friction Noise Mechanism in Lead Screw System of Autonomous Vehicle Seats and Dynamic Instability Prediction Based on Deep Neural Network.

This study investigated the squeal mechanism induced by friction in a lead screw system. The dynamic instability in the friction noise model of the le...

Efficient reduction of Cr(VI) by guava (Psidium guajava) leaf extract and its mitigation effect on Cr toxicity in rice seedlings.

Hexavalent chromium (Cr(VI)) is a toxic element that has negative impacts on crop growth and yield. Using plant extracts to convert toxic Cr(VI) into ...

Machine Learning in Environmental Research: Common Pitfalls and Best Practices.

Machine learning (ML) is increasingly used in environmental research to process large data sets and decipher complex relationships between system vari...

Long-lead streamflow forecasting using computational intelligence methods while considering uncertainty issue.

While some robust artificial intelligence (AI) techniques such as Gene-Expression Programming (GEP), Model Tree (MT), and Multivariate Adaptive Regres...

A stacking ensemble classifier-based machine learning model for classifying pollution sources on photovoltaic panels.

Solar energy is a very efficient alternative for generating clean electric energy. However, pollution on the surface of solar panels reduces solar rad...

Unsupervised Learning-Based WSN Clustering for Efficient Environmental Pollution Monitoring.

Wireless Sensor Networks (WSNs) have been adopted in various environmental pollution monitoring applications. As an important environmental field, wat...

Phytochemical Screening, HPTLC Fingerprinting and Evaluation of In Vitro Cytotoxic Activity of Stem Bark Extracts of O (L.) Vent. Against Human Cervical Cancer Cells.

a well-known traditional medicinal plant which is used to alleviate various kinds of diseases in Asia. The study aimed to identify bioactive compound...

DeepSea is an efficient deep-learning model for single-cell segmentation and tracking in time-lapse microscopy.

Time-lapse microscopy is the only method that can directly capture the dynamics and heterogeneity of fundamental cellular processes at the single-cell...

Machine Learning-Based Hazard-Driven Prioritization of Features in Nontarget Screening of Environmental High-Resolution Mass Spectrometry Data.

Nontarget high-resolution mass spectrometry screening (NTS HRMS/MS) can detect thousands of organic substances in environmental samples. However, new ...

Genetic Susceptibility to Atrial Fibrillation Identified via Deep Learning of 12-Lead Electrocardiograms.

BACKGROUND: Artificial intelligence (AI) models applied to 12-lead ECG waveforms can predict atrial fibrillation (AF), a heritable and morbid arrhythm...