Enhanced and idiosyncratic neural representations of personally typical scenes. [PDF]
Proc Biol SciWang G, Chen L, Cichy RM, Kaiser D.
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Redescription of Tachyura(s. str.)ferrugata (Reitter, 1895) (Coleoptera, Carabidae), with the comments on the availability of varieties described by Johan Reinhold Sahlberg in Tachyina. [PDF]
ZookeysKopecký T, Bezděk J, Mattila J.
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Formability Prediction Using Machine Learning Combined with Process Design for High-Drawing-Ratio Aluminum Alloy Cups. [PDF]
Materials (Basel)Hwang YM, Ho TH, Huang YF, Chen CM.
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A benchmark for Rey-Osterrieth complex figure test automatic scoring. [PDF]
HeliyonGuerrero-Martín J +4 more
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Editorial: Analysing emotional labor in the service industries: consumer and business perspectives, volume II. [PDF]
Front PsycholYoo WS, Park J, Back KJ.
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Material efficiency at the component level: how much metal can we do without? [PDF]
Philos Trans A Math Phys Eng SciAllwood JM, Music O.
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Illustrator-anatomist-clinician triad based pedagogical model in anatomy education: An auxiliary resource amidst lack of hands-on human dissection. [PDF]
Anat Sci EducCreze M, Ghosh SK.
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The optimisation deep-draw clearance design for deep-draw dies [PDF]
The International Journal of Advanced Manufacturing Technology, 1998 In this paper, the modelling of deep-drawing processing using neural networks is established. The relationships between process parameter (material thickness, punch diameter, die-cavity diameter and materials-clearance ratio) and deep-drawing performance (the dimensional error of diameter and cylinder) are created, based on a neural network.
J. C. Lin, Ching-Chih Tai
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