AIMC Topic: Gases
Clear Filters Showing 61 to 70 of 85 articles
Machine Learning and Bioinformatics Models to Identify Pathways that Mediate Influences of Welding Fumes on Cancer Progression.
Scientific reports
Feb 17, 2020
Welding generates and releases fumes that are hazardous to human health. Welding fumes (WFs) are a complex mix of metallic oxides, fluorides and silicates that can cause or exacerbate health problems in exposed individuals. In particular, WF inhalati...
Machine Vision Methods, Natural Language Processing, and Machine Learning Algorithms for Automated Dispersion Plot Analysis and Chemical Identification from Complex Mixtures.
Analytical chemistry
Jul 29, 2019
Gas-phase trace chemical detection techniques such as ion mobility spectrometry (IMS) and differential mobility spectrometry (DMS) can be used in many settings, such as evaluating the health condition of patients or detecting explosives at airports. ...
Automatic gas detection in prostate cancer patients during image-guided radiation therapy using a deep convolutional neural network.
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Jun 21, 2019
PURPOSE: The detection of intestinal/rectal gas is very important during image-guided radiation therapy (IGRT) of prostate cancer patients because intestinal/rectal gas increases the inter- and intra-fractional prostate motion. We propose a deep conv...
Performance assessment of gas-phase toluene removal in one- and two-liquid phase biotrickling filters using artificial neural networks.
Chemosphere
Jun 8, 2019
The main aim of this work is to study gas-phase toluene removal in one- and two-liquid phase biotrickling filters (O/TLP-BTF) and model the BTF performance using artificial neural networks (ANNs). The TLP-BTF was operated for 60 d in the presence of ...
Investigation of a rapid infrared heating assisted mineralization of soybean matrices for trace element analysis.
Food chemistry
Dec 19, 2018
A fast sample preparation procedure based on use of infrared (IR) assisted heating for mineralization of soybean derived samples has been developed for their subsequent multielement analysis by inductively coupled plasma optical emission spectrometry...
Improving the environmental impact of palm kernel shell through maximizing its production of hydrogen and syngas using advanced artificial intelligence.
The Science of the total environment
Dec 19, 2018
Fossil fuel depletion and the environmental concerns have been under discussion for energy production for many years and finding new and renewable energy sources became a must. Biomass is considered as a net zero CO energy source. Gasification of bio...
Leachate generation rate modeling using artificial intelligence algorithms aided by input optimization method for an MSW landfill.
Environmental science and pollution research international
Dec 3, 2018
Leachate is one of the main surface water pollution sources in Selangor State (SS), Malaysia. The prediction of leachate amounts is elementary in sustainable waste management and leachate treatment processes, before discharging to surrounding environ...
Forecasting the spatiotemporal variability of soil CO emissions in sugarcane areas in southeastern Brazil using artificial neural networks.
Environmental monitoring and assessment
Nov 21, 2018
Carbon dioxide (CO) is considered one of the main greenhouse effect gases and contributes significantly to global climate change. In Brazil, the agricultural areas offer an opportunity to mitigate this effect, especially with the sugarcane crop, sinc...
Functional Nanoparticles-Coated Nanomechanical Sensor Arrays for Machine Learning-Based Quantitative Odor Analysis.
ACS sensors
Aug 15, 2018
A sensing signal obtained by measuring an odor usually contains varied information that reflects an origin of the odor itself, while an effective approach is required to reasonably analyze informative data to derive the desired information. Herein, w...
Comparison of Machine Learning Models for Hazardous Gas Dispersion Prediction in Field Cases.
International journal of environmental research and public health
Jul 10, 2018
Dispersion prediction plays a significant role in the management and emergency response to hazardous gas emissions and accidental leaks. Compared with conventional atmospheric dispersion models, machine leaning (ML) models have both high accuracy and...
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