AIMC Topic: Water

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Shortwave infrared diffuse optical wearable probe for quantification of water and lipid content in emulsion phantoms using deep learning.

Journal of biomedical optics
SIGNIFICANCE: The shortwave infrared (SWIR, to 2000 nm) holds promise for label-free measurements of water and lipid content in thick tissue, owed to the chromophore-specific absorption features and low scattering in this range. water and lipid est...

Tri-objective optimization of a waste-to-energy plant with super critical carbon dioxide and multi-effect water desalination for building application based on biomass fuels.

Chemosphere
In the present research, an innovative biomass-based energy system for the production of electricity and desalinated water for building application is proposed. The main subsystems of this power plant include gasification cycle, gas turbine (GT), sup...

Deep learning-based Lorentzian fitting of water saturation shift referencing spectra in MRI.

Magnetic resonance in medicine
PURPOSE: Water saturation shift referencing (WASSR) Z-spectra are used commonly for field referencing in chemical exchange saturation transfer (CEST) MRI. However, their analysis using least-squares (LS) Lorentzian fitting is time-consuming and prone...

Optimization of a near-zero-emission energy system for the production of desalinated water and cooling using waste energy of fuel cells.

Chemosphere
In the present study, a biomass-based multi-purpose energy system that can generate power, desalinated water, hydrogen, and ammonia is presented. The gasification cycle, gas turbine, Rankine cycle, PEM electrolyzer, ammonia production cycle using the...

Prediction of microplastic abundance in surface water of the ocean and influencing factors based on ensemble learning.

Environmental pollution (Barking, Essex : 1987)
Microplastics are regarded as emergent contaminants posing a serious threat to the marine ecosystem. It is time-consuming and labor-intensive to determine the number of microplastics in different seas using traditional sampling and detection methods....

Building Machine Learning Small Molecule Melting Points and Solubility Models Using CCDC Melting Points Dataset.

Journal of chemical information and modeling
Predicting solubility of small molecules is a very difficult undertaking due to the lack of reliable and consistent experimental solubility data. It is well known that for a molecule in a crystal lattice to be dissolved, it must, first, dissociate fr...

Measuring the crop water demand and satisfied degree using remote sensing data and machine learning method in monsoon climatic region, India.

Environmental science and pollution research international
Supply of water is one of the most significant determinants of regional crop production and human food security. To promote sustainable management of agricultural water, the crop water requirement assessment (CropWRA) model was introduced as a tool f...

Water level prediction using soft computing techniques: A case study in the Malwathu Oya, Sri Lanka.

PloS one
Hydrologic models to simulate river flows are computationally costly. In addition to the precipitation and other meteorological time series, catchment characteristics, including soil data, land use, land cover, and roughness, are essential in most hy...

Daily reference evapotranspiration prediction for irrigation scheduling decisions based on the hybrid PSO-LSTM model.

PloS one
The shortage of available water resources and climate change are major factors affecting agricultural irrigation. In order to improve the irrigation water use efficiency, it is necessary to predict the water requirements for crops in advance. Referen...

A quality-checked and physics-constrained deep learning method to estimate material basis images from single-kV contrast-enhanced chest CT scans.

Medical physics
BACKGROUND: Single-kV CT imaging is one of the primary imaging methods in radiology practices. However, it does not provide material basis images for some subtle lesion characterization tasks in clinical diagnosis.