Results 121 to 130 of about 279,412 (303)

High‐Throughput Data Generation and Transfer Learning Enabled Microstructure‐Property Integrated Design of Nickel‐Based Powder Metallurgy Superalloy

Advanced Science, EarlyView.
An integrated transfer learning framework integrates CALPHAD simulations, diffusion‐multiple experiments, and literature data to predict long‐term microstructural stability and short‐term mechanical properties of Ni‐based powder metallurgy superalloys. Based on these model predictions, a high‐performance, low‐density alloy, USTB‐PM750, is designed from
Zixin Li, Hongtao Zhang, Zichao Peng, Yuheng Zhang, Qian Lv, Yihuan Cao, Xuqing Wang, Huadong Fu, Jianxin Xie   +8 more
wiley   +1 more source

Shadow‐Calibrated Stereo Vision for Colorimetric Sweat Analysis

Advanced Science, EarlyView.
By establishing a mathematical model that reconstructs 3D structures through geometric features of object shadows under controlled illumination, and combining it with Convolutional Neural Network‐based 2D image analysis for volumetric calibration, this work enables highly accurate 3D morphological reconstruction.
Ting Xiao, Yuwen Yan, Miaorong Lin, Jiahui Chen, Jianxin Meng, Xiang Cui, Peng Zhang, Fengyu Li   +7 more
wiley   +1 more source

Clustering Multivariate Normal Distributions [PDF]

, 2009
Frank Nielsen, Richard Nock
openaire   +1 more source

Boosting Sensory Nerve‐to‐Bone Interactions Enhances Hedgehog Mediated Calvarial Bone Repair

Advanced Science, EarlyView.
Boosting sensory nerve activity via TrkA agonism strongly accelerates calvarial bone repair in adult mice. Furthermore, single‐cell RNA sequencing and neuron–bone interactome analyses identify these sensory neurons as a direct neural source of Hedgehog pathway ligands. Consequently, these ligands drive osteoblast differentiation of skeletal progenitors,
Zhao Li, Xin Xing, Beicheng Du, Myles Zhou, Austin Z. Chen, Mary Archer, Chunbao Rao, Manyu Zhu, Masnsen Cherief, Aaron W. James   +9 more
wiley   +1 more source

Repeated Selection with Multivariate Normal Distribution [PDF]

, 1966
5 pages, 1 article*Repeated Selection with Multivariate Normal Distribution* (Wang, Ying Y.) 5 ...
Wang, Ying Y., Cornell University. Dept. of Biological Statistics and Computational Biology., Cornell University. Biometrics Unit., Cornell University. Dept. of Biometrics.   +3 more
core  

The centred parametrization for the multivariate skew-normal distribution

, 2008
For statistical inference connected to the scalar skew-normal distribution, it is known that the so-called centred parametrization provides a more convenient parametrization than the one commonly employed for writing the density function. We extend the
ARELLANO VALLE R. B, Arellano Valle, Reinaldo B., Arellano-Valle, Reinaldo B., AZZALINI, ADELCHI   +3 more
core   +1 more source

A High‐Throughput Live Imaging Platform to Investigate Circuit‐Dependent Regulation of Circadian Rhythms in Brain Tissue

Advanced Science, EarlyView.
Biological rhythms coordinate physiology, from genes to behavior. Study of circadian rhythms in brain tissue is constrained by limited throughput and spatial and temporal information quality. A new platform for high‐throughput, long‐term multiplexed fluorescent live imaging of circadian rhythms in brain slices is introduced.
Marco Ferrari, Natalie Ness, Julieta Acosta, Marco Brancaccio   +3 more
wiley   +1 more source

On the performance of the new minimax shrinkage estimators for a normal mean vector

Demonstratio Mathematica
This paper explores new classes of estimators for a multivariate normal mean (MNM) with an unknown variance and evaluating their performance based on the risk relative to the balanced loss function (BLF).
Benkhaled Abdelkader, Alahmadi Amani, Hamdaoui Abdenour, Terbeche Mekki   +3 more
doaj   +1 more source

A Characterization of the Multivariate Normal Distribution

The Annals of Mathematical Statistics, 1962
Ghurye, S. G., Olkin, Ingram
openaire   +2 more sources

Data‐Driven Modeling of Composition–Processing–Microstructure Relations for Recycled Aluminum Cast Alloys

Advanced Science, EarlyView.
Interpretable machine learning reveals how composition and processing govern the formation and microstructural burden of Fe‐rich intermetallic compounds in recycled Al–Si–Fe–Mn alloys. By separating morphology selection from morphology‐conditioned burden partitioning, this framework shows that identical Fe contents can yield different intermetallic ...
Jaemin Wang, Waleed Mohammed, Dierk Raabe   +2 more
wiley   +1 more source