AIMC Topic: Metals, Heavy

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Automation in arthrosis research.

SLAS technology Oct 25, 2021
Widespread medical studies require the analysis of suitable sample numbers to discover certain effects. Arthrosis treatment with hip and knee joint endoprostheses introduces multiple materials into the human body. Metal-containing implants may releas...
Automation Metals, Heavy Humans Osteoarthritis Autopsy Robotics
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Detection of heavy metal lead in lettuce leaves based on fluorescence hyperspectral technology combined with deep learning algorithm.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Oct 6, 2021
The feasibility analysis of fluorescence hyperspectral imaging technology was studied for the detection of lead content in lettuce leaves. Further, Monte Carlo optimized wavelet transform stacked auto-encoders (WT-MC-SAE) was proposed for dimensional...
Algorithms Technology Deep Learning Least-Squares Analysis Lactuca Metals, Heavy Lead Plant Leaves
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Artificial intelligence (AI) applications in adsorption of heavy metals using modified biochar.

The Science of the total environment Aug 18, 2021
The process of removal of heavy metals is important due to their toxic effects on living organisms and undesirable anthropogenic effects. Conventional methods possess many irreconcilable disadvantages pertaining to cost and efficiency. As a result, t...
Artificial Intelligence Metals, Heavy Adsorption Charcoal
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Semi-Automated Determination of Heavy Metals in Autopsy Tissue Using Robot-Assisted Sample Preparation and ICP-MS.

Molecules (Basel, Switzerland) Jun 23, 2021
The endoprosthetic care of hip and knee joints introduces multiple materials into the human body. Metal containing implant surfaces release degradation products such as particulate wear and corrosion debris, metal-protein complexes, free metallic ion...
Cobalt Ions Metals, Heavy Prostheses and Implants Autopsy Corrosion Mass Spectrometry Robotics Titanium Nickel Chromium Humans Aluminum
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Performance evaluation of nanotubular halloysites from weathered pegmatites in removing heavy metals from water through novel artificial intelligence-based models and human-based optimization algorithm.

Chemosphere Jun 3, 2021
The efforts of this study aimed to evaluate the feasibility of the nanotubular halloysites in weathered pegmatites (NaHWP) for removing heavy metals (i.e., Cd, Pb) from water. Furthermore, two novel intelligent models, such as teaching-learning-based...
Artificial Intelligence Algorithms Water Humans Metals, Heavy Clay
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Predicting the sorption efficiency of heavy metal based on the biochar characteristics, metal sources, and environmental conditions using various novel hybrid machine learning models.

Chemosphere Mar 9, 2021
Heavy metals in water and wastewater are taken into account as one of the most hazardous environmental issues that significantly impact human health. The use of biochar systems with different materials helped significantly remove heavy metals in the ...
Humans Artificial Intelligence Machine Learning Metals, Heavy Charcoal Reproducibility of Results
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A spectral characteristic analysis method for distinguishing heavy metal pollution in crops: VMD-PCA-SVM.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Mar 9, 2021
Exploring the characteristics and types of heavy metal pollution in crops has important implications for food security and human health. In this study, a method for distinguishing heavy metal-polluted elements in corn leaves was proposed. Based on th...
China Environmental Pollution Environmental Monitoring Metals, Heavy Principal Component Analysis Support Vector Machine Zea mays Humans
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A Machine Learning Approach in Analyzing Bioaccumulation of Heavy Metals in Turbot Tissues.

Molecules (Basel, Switzerland) Oct 14, 2020
Metals are considered to be one of the most hazardous substances due to their potential for accumulation, magnification, persistence, and wide distribution in water, sediments, and aquatic organisms. Demersal fish species, such as turbot (), are acce...
Flatfishes Bioaccumulation Environmental Biomarkers Linear Models Metals, Heavy Machine Learning Muscle, Skeletal Nonlinear Dynamics Water Pollutants, Chemical Liver Animals Europe Environmental Monitoring
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Effectiveness of groundwater heavy metal pollution indices studies by deep-learning.

Journal of contaminant hydrology Sep 23, 2020
Globally, groundwater heavy metal (HM) pollution is a serious concern, threatening drinking water safety as well as human and animal health. Therefore, evaluation of groundwater HM pollution is essential to prevent accompanying hazardous ecological i...
Water Pollutants, Chemical Humans Metals, Heavy Deep Learning Groundwater India Environmental Monitoring Risk Assessment
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Prediction of sediment heavy metal at the Australian Bays using newly developed hybrid artificial intelligence models.

Environmental pollution (Barking, Essex : 1987) Sep 16, 2020
Hybrid artificial intelligence (AI) models are developed for sediment lead (Pb) prediction in two Bays (i.e., Bramble (BB) and Deception (DB)) stations, Australia. A feature selection (FS) algorithm called extreme gradient boosting (XGBoost) is propo...
Australia Bays Neural Networks, Computer Metals, Heavy Artificial Intelligence
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