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Aalto Materials Digitalisation Platform (AMAD) – opening new possibilities for data sharing and collaboration between research groups

‘As a researcher, I've found AMAD to be an invaluable tool for collaborating with others and for data processing in research projects,’ says Juha Koivisto, Research Fellow, Department of Applied Physics. Juha is not the only person who feels that the Aalto Materials Digitalisation Platform (AMAD) opens new possibilities for data sharing and collaboration within and between research groups. ....

Juha Koivisto , Department Of Chemistry , Department Of Applied Physics , Aalto It Services , Aalto University , Research Fellow , Aalto Materials Digitalisation Platform , Postdoctoral Researcher , Applied Physics , Matthias Stosiek , Matteo Iannacchero , Doctoral Researcher , Materials Science , Bayesian Optimisation , Molecular Massively Parallel Simulator , All Aalto ,

AI typeface shapeshifts for speedy reading

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theiet.org - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from theiet.org Daily Mail and Mail on Sunday newspapers.

Constantin Rothkopf , Tu Darmstadt Centre For Cognitive Science , Cognitive Science , Times New Roman , Times New , Bayesian Optimisation , German Wiki , Professor Constantin Rothkopf , Human Factors , Computing Systems , அறிவாற்றல் அறிவியல் , முறை புதியது ரோமன் , முறை புதியது , ஜெர்மன் விக்கி , மனிதன் காரணிகள் , கணினி அமைப்புகள் ,

"Coupling machine learning with 3D bioprinting to fast track optimisati" by Kalani Ruberu, Manisha Senadeera et al.

Abstract
3D bioprinting, a paradigm shift in tissue engineering holds a promising perspective for regenerative medicine and disease modelling. 3D scaffolds are fabricated for subsequent cell seeding or incorporated directly to the bioink to create cell-laden 3D constructs. A plethora of factors relating to bioink properties, printing parameters and post print curing play a significant role in the optimisation of the printing process. Although qualitative evaluation of printability has been investigated largely, there is a paucity of studies on quantitative approaches to assess printability. Hence, this study explores machine learning as a novel tool to evaluate printability quantitatively and to fast track optimisation of extrusion printing in achieving a reproducible 3D scaffold. Bayesian Optimisation, a machine learning method has been employed for optimising 3D bioplotting with a scoring system established to assess the printability of gelatin methacryloyl (GelMA) and hyalu ....

3d Bioprinting , Bayesian Optimisation , Elatin Methacryloyl , Yaluronic Acid Methacrylate , Machine Learning ,