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5 Results

Type: News

Section: Research > Methods/Tools

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Neural Networks Predict Material Structures, Behaviors Faster

A recent interview on Phys.org with Argonne National Laboratory Engineer Mark Messner explores how neural networks in AI could be used to predict material structures, given preferred properties, some 2,000+ times faster than standard simulations.

After simulating more than 100 prescriptions, a correlation between the actual cleansing rate and the simulation results of how dirt is absorbed was found, according to research from the companies.

System Developed for Predicting Makeup Removal Performance of Cleansing Agents

After simulating more than 100 mixtures, a group of researchers from Kirin Holdings, FANCL and Keio University identified a correlation between the actual cleansing rate and simulated results of how dirt is absorbed.

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Kao Predicts 86 Changes in Skin with Age, Menstrual Cycle and More

Kao Corp. has designed a technique using AI and RNA from skin lipids to predict 86 constantly changing skin attributes. Results indicate shifts in skin moisture, transparency and glycosylation with age and during the menstrual cycle.

The Pistoia Alliance is a not-for-profit organization working to lower barriers to innovation in life science and healthcare R&D.

Survey Predicts AI, Blockchain, Emerging Tech will Receive Most Funding

In a recent survey, respondents predicted that emerging technologies including AI and blockchain, and startup biotech will be the biggest contributors to life science innovation post-pandemic.

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Study Reveals Factorial Model to Predict O/W Emulsion Stability

A study published in the Journal of Pharmacy & Pharmacognosy Research confirmed an approach to predict the physical stability and quality of o/w cosmetic emulsions using full factorial design.

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