AIMC Topic: Ammonia

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Predicting the adsorption of ammonia nitrogen by biochar in water bodies using machine learning strategies: Model optimization and analysis of key characteristic variables.

Environmental research
Biochar adsorption technology has been widely used to remove ammonia nitrogen from water bodies. However, existing methods for predicting adsorption efficiency often lack sufficient accuracy and practical usability. This study evaluated eight machine...

Ammonia and ethanol detection via an electronic nose utilizing a bionic chamber and a sparrow search algorithm-optimized backpropagation neural network.

PloS one
Ammonia is widely acknowledged to be a stressor and one of the most detrimental gases in animal enclosures. In livestock- and poultry-breeding facilities, a precise, rapid, and affordable method for detecting ammonia concentrations is essential. We d...

SERS-AI Based Detection and Bioanalysis of Malodorous Components in Kitchen Waste.

Analytical chemistry
The prevention and control of odor gas generated from kitchen waste are significant missions in research on environmental pollution. Because of the high complexity and variability of kitchen waste, the development of a suitable technique with high se...

Optimizing the early-stage of composting process emissions - artificial intelligence primary tests.

Scientific reports
Although composting has many advantages in treating organic waste, many problems and challenges are still associated with emissions, like NH, CO and HS, as well as greenhouse gases such as CO. One promising approach to enhancing composting conditions...

Predicting ammonia emissions and global warming potential in composting by machine learning.

Bioresource technology
The amounts of gases emitted from composting are key to evaluating global warming potential (GWP). However, few methods can accurately predict the quantities of relevant gas emissions. In this study, three developed machine-learning models were used ...

Gated SPECT-Derived Myocardial Strain Estimated From Deep-Learning Image Translation Validated From N-13 Ammonia PET.

Academic radiology
RATIONALE AND OBJECTIVES: This study investigated the use of deep learning-generated virtual positron emission tomography (PET)-like gated single-photon emission tomography (SPECT) for assessing myocardial strain, overcoming limitations of convention...

Ppb-Level Ammonia Sensor for Exhaled Breath Diagnosis Based on UV-DOAS Combined with Spectral Reconstruction Fitting Neural Network.

ACS sensors
Ammonia (NH) in exhaled breath (EB) has been a biomarker for kidney function, and accurate measurement of NH is essential for early screening of kidney disease. In this work, we report an optical sensor that combines ultraviolet differential optical ...

Fast real-time monitoring of meat freshness based on fluorescent sensing array and deep learning: From development to deployment.

Food chemistry
A fluorescent sensor array (FSA) combined with deep learning (DL) techniques was developed for meat freshness real-time monitoring from development to deployment. The array was made up of copper metal nanoclusters (CuNCs) and fluorescent dyes, having...

On-Demand Optimization of Colorimetric Gas Sensors Using a Knowledge-Aware Algorithm-Driven Robotic Experimental Platform.

ACS sensors
Synthesizing the best material globally is challenging; it needs to know what and how much the best ingredient composition should be for satisfying multiple figures of merit simultaneously. Traditional one-variable-at-a-time methods are inefficient; ...

Identification of pollution source and prediction of water quality based on deep learning techniques.

Journal of contaminant hydrology
Semi-arid rivers are particularly vulnerable and responsive to the impacts of industrial contamination. Prompt identification and projection of pollutant dynamics are crucial in the accidental pollution incidents, therefore required the timely inform...