Environmental Health

A three-dimensional marine plastic litter real-time detection embedded system based on deep learning.

Marine plastic pollution has emerged as a significant ecological and biological issue impacting global marine ecosystems. To develop real-time cleaning systems for marine plastic litter, we implemented a three-dimensional marine plastic litter real-time detection (3D-MPLRD) system that incorporates deep learning techniques. Specifically, image quality assessment and enhancement are applied to miti...

Machine learning models for water safety enhancement.

Humans encounter both natural and artificial radiation sources, including cosmic rays, primordial radionuclides, and radiation generated by human activities. These radionuclides can infiltrate the human body through various pathways, potentially leading to cancer and genetic mutations. A study was conducted using random sampling to assess the concentrations of radioactive isotopes and heavy metals...

Elucidating the Mechanism of VVTT Infection Through Machine Learning and Transcriptome Analysis.

The vaccinia virus (VV) is extensively utilized as a vaccine vector in the treatment of various infectious diseases, cardiovascular diseases, immunode...

Predicting On-Road Air Pollution Coupling Street View Images and Machine Learning: A Quantitative Analysis of the Optimal Strategy.

Integrating mobile monitoring data with street view images (SVIs) holds promise for predicting local air pollution. However, algorithms, sampling stra...

High-resolution groundwater storage anomalies in the Middle and Lower Yangtze River Basin of China using machine learning fusion of in-situ wells, satellite gravity and hydrological model.

Groundwater plays a key role in the water cycle and is used to meet industrial, agricultural, and domestic water demands. High-resolution modeling of ...

XIS-PM: A daily spatiotemporal machine-learning model for PM in the contiguous United States.

Air-pollution monitoring is sparse across most of the United States, so geostatistical models are important for reconstructing concentrations of fine ...

Identification and information acquisition of high-value construction solid waste combined millimeter-wave radar and convolutional neural networks.

The accumulation of construction solid waste (CSW) leads to the waste of land resources and environmental pollution, becoming a significant social pro...

Unlocking Antimicrobial Peptides: In Silico Proteolysis and Artificial Intelligence-Driven Discovery from Cnidarian Omics.

Overcoming the growing challenge of antimicrobial resistance (AMR), which affects millions of people worldwide, has driven attention for the explorati...

A spatial interpolation method based on 3D-CNN for soil petroleum hydrocarbon pollution.

Petroleum hydrocarbon pollution causes significant damage to soil, so accurate prediction and early intervention are crucial for sustainable soil mana...

Understanding the low-temperature drying process of sludge with machine learning in a sewage-source heat pump drying system.

Heat pump drying technology based on sewage heat source is an eco-friendly sludge drying method. It can effectively reduce the pollution of natural wa...

Real-time detection and monitoring of public littering behavior using deep learning for a sustainable environment.

With the global population surpassing 8 billion, waste production has skyrocketed, leading to increased pollution that adversely affects both terrestr...

A noninvasive hyperkalemia monitoring system for dialysis patients based on a 1D-CNN model and single-lead ECG from wearable devices.

This study aimed to develop a real-time, noninvasive hyperkalemia monitoring system for dialysis patients with chronic kidney disease. Hyperkalemia, c...

Using machine learning models to predict the dose-effect curve of municipal wastewater for zebrafish embryo toxicity.

Municipal wastewater substantially contributes to aquatic ecological risks. Assessing the toxicity of municipal wastewater through dose-effect curves ...

A machine learning framework for short-term prediction of chronic obstructive pulmonary disease exacerbations using personal air quality monitors and lifestyle data.

Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous disease with a variety of symptoms including, persistent coughing and mucus production...

Machine learning-integrated droplet microfluidic system for accurate quantification and classification of microplastics.

Microplastic (MP) pollution poses serious environmental and public health concerns, requiring efficient detection methods. Conventional techniques hav...

W70: a probiotic with capacity to enhance ammonia assimilation in the rumen.

Ruminants excrete both undigested and excess consumed nitrogen (N) through feces and urine, which results in the waste of feed protein and environment...

Application of supervised learning models for enhanced lead (II) removal from wastewater via modified cellulose nanocrystals (CNCs).

Heavy metal ions are acknowledged to impact the environment and human health adversely. CNCs are effective materials for removing heavy metal ions in ...

DeepDrug as an expert guided and AI driven drug repurposing methodology for selecting the lead combination of drugs for Alzheimer's disease.

Alzheimer's Disease (AD) significantly aggravates human dignity and quality of life. While newly approved amyloid immunotherapy has been reported, eff...

Predicting drug combination side effects based on a metapath-based heterogeneous graph neural network.

In recent years, combined drug screening has played a very important role in modern drug discovery. Generally, synergistic drug combinations are cruci...

Analysis and prediction of atmospheric ozone concentrations using machine learning.

Atmospheric ozone chemistry involves various substances and reactions, which makes it a complex system. We analyzed data recorded by Switzerland's Nat...