Environmental Health

Advancements and Applications of Artificial Intelligence in Pharmaceutical Sciences: A Comprehensive Review.

Artificial intelligence (AI) has revolutionized the pharmaceutical industry, improving drug discovery, development, and personalized patient care. Through machine learning (ML), deep learning, natural language processing (NLP), and robotic automation, AI has enhanced efficiency, accuracy, and innovation in the field. The purpose of this review is to shed light on the practical applications and pot...

Deep learning assists early-detection of hypertension-mediated heart change on ECG signals.

Arterial hypertension is a major risk factor for cardiovascular diseases. While cardiac ultrasound is a typical way to diagnose hypertension-mediated heart change, it often fails to detect early subtle structural changes. Electrocardiogram(ECG) represents electrical activity of heart muscle, affected by the changes in heart's structure. It is crucial to explore whether ECG can capture slight signa...

Machine learning-assisted source tracing in domestic-industrial wastewater: A fluorescence information-based approach.

An emergency water pollution incident poses a significant risk to the proper functioning of wastewater treatment plants, particularly in domestic-indu...

Sex dimorphism and hormesis response to polystyrene microplastic exposure in kinematics and metabolism of Drosophila model based on deep learning.

The emergence of microplastics (MPs) has become a significant focus of environmental pollution, prompting widespread concern regarding its potential t...

Artificial Intelligence-Enhanced Analysis of Genomic DNA Visualized with Nanoparticle-Tagged Peptides under Electron Microscopy.

DNA visualization has advanced across multiple microscopy platforms, albeit with limited progress in the identification of novel staining agents for e...

A novel ensemble approach with deep transfer learning for accurate identification of foodborne bacteria from hyperspectral microscopy.

The detection of foodborne bacteria is critical in ensuring both consumer safety and food safety. If these pathogens are not properly identified, it c...

A novel interpretable machine learning and metaheuristic-based protocol to predict and optimize ciprofloxacin antibiotic adsorption with nano-adsorbent.

The existence of antibiotics in water sources poses substantial hazards to both the environment and public health. To effectively monitor and combat t...

Predicting plateau atmospheric ozone concentrations by a machine learning approach: A case study of a typical city on the southwestern plateau of China.

Atmospheric ozone (O) has been placed on the priority control pollutant list in China's 14th Five-Year Plan. Due to their unique meteorological condit...

Leveraging machine learning for sustainable cultivation of Zn-enriched crops in Cd-contaminated karst regions.

Karst soils often exhibit elevated zinc (Zn) levels, providing an opportunity to cultivate Zn-enriched crops. (meanwhile) However, these soils also fr...

Hypoxia extreme events in a changing climate: Machine learning methods and deterministic simulations for future scenarios development in the Venice Lagoon.

Climate change pressures include the dissolved oxygen decline that in lagoon ecosystems can lead to hypoxia, i.e. low dissolved oxygen concentrations,...

Hourly PM concentration prediction for dry bulk port clusters considering spatiotemporal correlation: A novel deep learning blending ensemble model.

Accurate prediction of PM concentrations in ports is crucial for authorities to combat ambient air pollution effectively and protect the health of por...

Machine learning-assisted laccase-like activity nanozyme for intelligently onsite real-time and dynamic analysis of pyrethroid pesticides.

The intelligently efficient, reliable, economical and portable onsite assay toward pyrethroid pesticides (PPs) residues is critical for food safety an...

Machine learning prediction of stalk lignin content using Fourier transform infrared spectroscopy in large scale maize germplasm.

Lignin has been recognized as a major factor contributing to lignocellulosic recalcitrance in biofuel production and attracted attentions as a high-va...

Co-exposure to microplastics and soil pollutants significantly exacerbates toxicity to crops: Insights from a global meta and machine-learning analysis.

Environmental contamination of microplastics (MPs) is ubiquitous worldwide, and co-contamination of arable soils with MPs and other pollutants is of i...

Does COVID-19 lockdown matter for air pollution in the short and long run in China? A machine learning approach to policy evaluation.

This paper leverages a data-driven two-step approach to effectively evaluate the effects of COVID-19 lockdown on air pollution in both the short and l...

Explainable machine learning for predicting diarrhetic shellfish poisoning events in the Adriatic Sea using long-term monitoring data.

In this study, explainable machine learning techniques are applied to predict the toxicity of mussels in the Gulf of Trieste (Adriatic Sea) caused by ...

Synergistic biochar and Serratia marcescens tackle toxic metal contamination: A multifaceted machine learning approach.

Metal contamination in soil poses environmental and health risks requiring effective remediation strategies. This study introduces an innovative appro...

FGTN: Fragment-based graph transformer network for predicting reproductive toxicity.

Reproductive toxicity is one of the important issues in chemical safety. Traditional laboratory testing methods are costly and time-consuming with rai...

Identifying the habitat suitability of Pteris vittata in China and associated key drivers using machine learning models.

Pteris vittata (P. vittata) possesses significant potential in remediating arsenic (As) soil pollution. Understanding the habitat suitability of P. vi...

Enhancing shipboard oil pollution prevention: Machine learning innovations in oil discharge monitoring equipment.

Maritime operations face significant challenges in environmental stewardship, particularly in managing oil discharges from tankers as mandated by the ...