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Metals

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Preclinical validation of a novel deep learning-based metal artifact correction algorithm for orthopedic CT imaging.

Journal of applied clinical medical physics
PURPOSE: To validate a novel deep learning-based metal artifact correction (MAC) algorithm for CT, namely, AI-MAC, in preclinical setting with comparison to conventional MAC and virtual monochromatic imaging (VMI) technique.

Fungal bioleaching of metals from WPCBs of mobile phones employing mixed Aspergillus spp.: Optimization and predictive modelling by RSM and AI models.

Journal of environmental management
In the present study, optimization and prediction models for fungal bioleaching for effective metal extraction from waste printed circuit boards (WPCBs) of mobile phones were developed employing central composite design (CCD) of response surface meth...

Development of new materials for electrothermal metals using data driven and machine learning.

PloS one
After adopting a combined approach of data-driven methods and machine learning, the prediction of material performance and the optimization of composition design can significantly reduce the development time of materials at a lower cost. In this rese...

Artificial intelligence-based prediction model for the elemental occurrence form of tailings and mine wastes.

Environmental research
With the advent of the second industrial revolution, mining and metallurgical processes generate large volumes of tailings and mine wastes (TMW), which worsens global environmental pollution. Studying the occurrence of metal and metalloid elements in...

Soft Robots with Plant-Inspired Gravitropism Based on Fluidic Liquid Metal.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Plants can autonomously adjust their growth direction based on the gravitropic response to maximize energy acquisition, despite lacking nerves and muscles. Endowing soft robots with gravitropism may facilitate the development of self-regulating syste...

Machine learning-driven prediction of phosphorus removal performance of metal-modified biochar and optimization of preparation processes considering water quality management objectives.

Bioresource technology
Developing an optimized and targeted design approach for metal-modified biochar based on water quality conditions and management is achievable through machine learning. This study leveraged machine learning to analyze experimental data on phosphate a...

Evaluating the efficacy of vermicomposted products in rain-fed wetland rice and predicting potential hazards from metal-contaminated tannery sludge using novel machine learning tactic.

Chemosphere
The study assessed the ecotoxicity and bioavailability of potential metals (PMs) from tannery waste sludge, alongside addressing the environmental concerns of overuse of chemical fertilizers, by comparing the impacts of organic vermicomposted tannery...

Potential prediction and coupling relationship revealing for recovery of platinum group metals from spent auto-exhaust catalysts based on machine learning.

Journal of environmental management
As hazardous waste, the massive generation of spent auto-exhaust catalysts (SACs) puts enormous pressure on environmental management, but provides a rare opportunity for platinum group metals (PGMs) recycling. In this study, machine learning (ML) met...

Bioinspired Liquid Metal Based Soft Humanoid Robots.

Advanced materials (Deerfield Beach, Fla.)
The pursuit of constructing humanoid robots to replicate the anatomical structures and capabilities of human beings has been a long-standing significant undertaking and especially garnered tremendous attention in recent years. However, despite the pr...

The Relationship Between Metal Exposure and HPV Infection: Evidence from Explainable Machine Learning Methods.

Biological trace element research
HPV is a ubiquitous pathogen implicated in cervical and other cancers. Although vaccines are available, they do not encompass all subtypes. Meanwhile, metal exposure may elevate the risk of HPV infection and amplify its carcinogenic potential, but st...