AIMC Topic: Water

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Predicting the evolution of number of native contacts of a small protein by using deep learning approach.

Computational biology and chemistry
Native contacts (NCs) are one of the most vital parameters in order to define the resemblance of a protein conformation with its native state. Prediction of number of native contacts in a protein is useful in protein folding mechanism. In this work, ...

Temporal Prediction of Paralytic Shellfish Toxins in the Mussel Using a LSTM Neural Network Model from Environmental Data.

Toxins
Paralytic shellfish toxins (PSTs) are produced mainly by (formerly ). Since 2000, the National Institute of Fisheries Science (NIFS) has been providing information on PST outbreaks in Korean coastal waters at one- or two-week intervals. However, a d...

Application of NIRs coupled with PLS and ANN modelling to predict average droplet size in oil-in-water emulsions prepared with different microfluidic devices.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
In this study, the potential of microfluidic systems with different microchannel geometries (microchannel with teardrop micromixers and microchannel with swirl micromixers) for the preparation of oil-in-water (O/W) emulsions using two different emuls...

An Ambidextrous STarfish-Inspired Exploration and Reconnaissance Robot (The ASTER-bot).

Soft robotics
As more roboticists are turning to Nature for design inspiration, it is becoming increasingly apparent that multisystem-level investigations of biological processes can frequently lead to unexpected advances in the development of experimental researc...

Revolutionizing Membrane Design Using Machine Learning-Bayesian Optimization.

Environmental science & technology
Polymeric membrane design is a multidimensional process involving selection of membrane materials and optimization of fabrication conditions from an infinite candidate space. It is impossible to explore the entire space by trial-and-error experimenta...

Localizing Perturbations in Pressurized Water Reactors Using One-Dimensional Deep Convolutional Neural Networks.

Sensors (Basel, Switzerland)
This work outlines an approach for localizing anomalies in nuclear reactor cores during their steady state operation, employing deep, one-dimensional, convolutional neural networks. Anomalies are characterized by the application of perturbation diagn...

Formulation and physicochemical stability of oil-in-water nanoemulsion loaded with α-terpineol as flavor oil using Quillaja saponins as natural emulsifier.

Food research international (Ottawa, Ont.)
Alpha-terpineol (α-TOH) is a promising monoterpenoid detaining several biological activities. However, as a volatile molecule, the incorporation of α-TOH within formulated products poses several challenges related to its stability. In this sense, nan...

Performance comparison of deep learning and machine learning methods in determining wetland water areas using EuroSAT dataset.

Environmental science and pollution research international
Wetlands are critical to the ecology because they maintain biodiversity and provide home for a variety of species. Researching, mapping, and conservation of wetlands is a challenging and time-consuming process. Because they produce temporal and geogr...

Digital Twins of the Water Cooling System in a Power Plant Based on Fuzzy Logic.

Sensors (Basel, Switzerland)
In the search for increased productivity and efficiency in the industrial sector, a new industrial revolution, called Industry 4.0, was promoted. In the electric sector, power plants seek to adapt these new concepts to optimize electric power generat...

Anomaly Detection of Water Level Using Deep Autoencoder.

Sensors (Basel, Switzerland)
Anomaly detection is one of the crucial tasks in daily infrastructure operations as it can prevent massive damage to devices or resources, which may then lead to catastrophic outcomes. To address this challenge, we propose an automated solution to de...