AIMC Topic: Metals, Heavy

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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...
Adsorption Charcoal Metals, Heavy Artificial Intelligence
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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...
Metals, Heavy Titanium Nickel Corrosion Mass Spectrometry Autopsy Prostheses and Implants Humans Robotics Cobalt Aluminum Chromium Ions
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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 ...
Metals, Heavy Machine Learning Reproducibility of Results Charcoal Humans Artificial Intelligence
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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...
Humans China Zea mays Environmental Pollution Metals, Heavy Environmental Monitoring Support Vector Machine Principal Component Analysis
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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...
Metals, Heavy Flatfishes Bioaccumulation Environmental Biomarkers Muscle, Skeletal Machine Learning Nonlinear Dynamics Animals Linear Models Liver Environmental Monitoring Europe Water Pollutants, Chemical
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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 Metals, Heavy India Humans Groundwater Risk Assessment Environmental Monitoring Deep Learning
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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...
Neural Networks, Computer Artificial Intelligence Australia Metals, Heavy Bays
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Heavy metals in submicronic particulate matter (PM) from a Chinese metropolitan city predicted by machine learning models.

Chemosphere Jul 8, 2020
The aim of this study was to establish a method for predicting heavy metal concentrations in PM (aerosol particles with an aerodynamic diameter ≤ 1.0 μm) based on back propagation artificial neural network (BP-ANN) and support vector machine (SVM) me...
Pneumonia, Viral China Cities Environmental Monitoring Computer Simulation Metals, Heavy Seasons Wind Particulate Matter Meteorological Concepts Humans Aerosols SARS-CoV-2 Neural Networks, Computer Betacoronavirus Environmental Exposure Support Vector Machine Pandemics Coronavirus Infections Air Pollutants COVID-19 Air Pollution
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Optimized Multimetal Sensitized Phosphor for Enhanced Red Up-Conversion Luminescence by Machine Learning.

ACS combinatorial science Apr 26, 2020
In this research, machine learning including the genetic algorithm (GA) and support vector machine (SVM) algorithm is used to solve the "low up-conversion luminescence (UCL) intensity" problem in order to find the optimal phosphor with enhanced red U...
Metals, Heavy Metals, Alkali Luminescent Measurements Luminescence Humans Optical Imaging Machine Learning
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