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Less is more? New take on machine learning helps us

Researchers from Tokyo Metropolitan University have enhanced super-resolution machine learning techniques to study phase transitions. They identified key features of how large arrays of interacting particles behave at different temperatures by simulating tiny arrays before using a convolutional neural network to generate a good estimate of what a larger array would look like using correlation configurations. The massive saving in computational cost may realize unique ways of understanding how materials behave.

Next-gen electric vehicle batteries: These are the questions we still need to answer

When to release free and paid apps for maximal revenue

Cheap alloy rivals expensive platinum to boost fuel cells

 E-Mail IMAGE: Synthesis diagram of Ni5.2WCu2.2 and a Ni5.2WCu2.2 electrode of size 3×10cm2 obtained in this way. view more  Credit: QIN Shuai et al. As the cleanest renewable energy, hydrogen energy has attracted special attention in the research. Yet the commercialization of traditional proton exchange membrane fuel cells (PEMFCs), which consume hydrogen and produce electricity, is seriously restricted due to the chemical reaction of PEMFCs cathode largely relies on expensive platinum-based catalysts. A solution is to change the acidic electrolyte of PEMFCs to alkaline. Such fuel cells are called anion exchange membrane fuel cells (AEMFCs), and they allow for the use of cheaper metal elements like Co, Ni or Mn to design electrocatalysts.

A non-invasive procedure allows obtaining archaeological information without excavating

 E-Mail An international archaeological study, led by researchers from the Culture and Socio-Ecological Dynamics (CaSEs) research group at Pompeu Fabra University, has advanced in the understanding and preservation of archaeological sites and in improving their analysis and surveying, thanks to the application of pXRF (portable X-ray fluorescence analysis) to anthropogenic sediments in Africa. It is a rapid, inexpensive, non-invasive procedure, which enables generating an additional archaeological record from the anthropogenic deposit by analysing chemical elements, combined with geostatistics. It is a rapid, inexpensive, non-invasive procedure, which enables generating an additional archaeological record from the anthropogenic deposit by analysing chemical elements, combined with geostatistics.

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