AIMC Topic: Hydrocarbons

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'Emulsion locks' for the containment of hydrocarbons during surfactant flushing.

Journal of environmental sciences (China)
Reversible double water in oil in water (W/O/W) emulsions were developed to contain subsurface hydrocarbon spills during their remediation using surfactant flushing. Double emulsions were prepared by emulsifying CaCl solutions in canola oil, and subs...

Single spectral imagery and faster R-CNN to identify hazardous and noxious substances spills.

Environmental pollution (Barking, Essex : 1987)
The automatic identification (location, segmentation, and classification) by UAV- based optical imaging of spills of transparent floating Hazardous and Noxious Substances (HNS) benefits the on-site response to spill incidents, but it is also challeng...

Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning.

Nature communications
Computational modeling of chemical and biological systems at atomic resolution is a crucial tool in the chemist's toolset. The use of computer simulations requires a balance between cost and accuracy: quantum-mechanical methods provide high accuracy ...

A Neural Network QSPR Model for Accurate Prediction of Flash Point of Pure Hydrocarbons.

Molecular informatics
The present study introduces a QSPR model to predict the flash point of pure organic compounds from diverse chemical families. We used the Maximum-Relevance Minimum-Redundancy (MRMR) as an efficient descriptor selection algorithm to select 20 the mos...

Prediction of bioavailability and toxicity of complex chemical mixtures through machine learning models.

Chemosphere
Empirical data from a 6-month mesocosms experiment were used to assess the ability and performance of two machine learning (ML) models, including artificial neural network (NN) and random forest (RF), to predict temporal bioavailability changes of co...

Moisture Damage Modeling in Lime and Chemically Modified Asphalt at Nanolevel Using Ensemble Computational Intelligence.

Computational intelligence and neuroscience
This paper measures the adhesion/cohesion force among asphalt molecules at nanoscale level using an Atomic Force Microscopy (AFM) and models the moisture damage by applying state-of-the-art Computational Intelligence (CI) techniques (e.g., artificial...

Adult fly age estimations using cuticular hydrocarbons and Artificial Neural Networks in forensically important Calliphoridae species.

Forensic science international
Blowflies (Diptera: Calliphoridae) are forensically important as they are known to be one of the first to colonise human remains. The larval stage is typically used to assist a forensic entomologists with adult flies rarely used as they are difficult...

A Flexible Approach for Human Activity Recognition Using Artificial Hydrocarbon Networks.

Sensors (Basel, Switzerland)
Physical activity recognition based on sensors is a growing area of interest given the great advances in wearable sensors. Applications in various domains are taking advantage of the ease of obtaining data to monitor personal activities and behavior ...

Genetic programming based quantitative structure-retention relationships for the prediction of Kovats retention indices.

Journal of chromatography. A
The development of quantitative structure-retention relationships (QSRR) aims at constructing an appropriate linear/nonlinear model for the prediction of the retention behavior (such as Kovats retention index) of a solute on a chromatographic column....

Pavement type and wear condition classification from tire cavity acoustic measurements with artificial neural networks.

The Journal of the Acoustical Society of America
Tire road noise is the major contributor to traffic noise, which leads to general annoyance, speech interference, and sleep disturbances. Standardized methods to measure tire road noise are expensive, sophisticated to use, and they cannot be applied ...