Results 181 to 190 of about 2,493,559 (304)

Interpretable Machine Learning Framework for Nb─Si Based Alloy Design with Enhanced Fracture Toughness

Advanced Science, EarlyView.
An interpretable machine learning framework integrating SHAP and PDP analysis identifies critical design descriptors from 139 physicochemical features for Nb─Si alloys. The framework achieves <7% prediction error and guides the discovery of Nb38.5Ti38.5Si3Zr18V2 alloy with 22.791 MPa·m1/2 fracture toughness, breaking the 20 MPa·m1/2 barrier.
Dezhi Chen, Chao Xu, Jingyue Yu, Qi Wang, Hongze Fang, Shuo Yin, Turab Lookman, Ruirun Chen   +7 more
wiley   +1 more source

Synergizing machine learning and chemometrics with voltammetric fingerprinting for the rapid detection of <i>Piper retrofractum</i> adulteration. [PDF]

RSC Adv
Nikma SH, Afendi FM, Putra BR, Wahyuni WT.   +3 more
europepmc   +1 more source

Sliding Ferroelectricity Driven Spin‐Layertronics in Altermagnetic Multilayers

Advanced Science, EarlyView.
Integrating sliding ferroelectricity with altermagnetism enables nonvolatile electrical control of spin and layer degrees of freedom. In bilayer CuF2, interlayer translation reverses layer‐locked spin‐split bands, establishing a multifunctional “spin‐layertronic” platform.
Rui Peng, Guangxu Su, Yangyang Fan, Jiaan Li, Fanxin Liu, Yee Sin Ang   +5 more
wiley   +1 more source

Longitudinal associations between sedentary behavior types and mathematics ability mediated by externalizing and internalizing problems. [PDF]

NPJ Sci Learn
Guan K, Zhang Z, Liu Z, Pindus DM, Hillman CH, Yu Q, Kramer AF, Kuang J, Erickson KI, Herold F, Hallgren M, Heath M, Jiang R, Werneck AO, Xu X, Zou L.   +15 more
europepmc   +1 more source

Micro‐Galvanic Coupling Programs the Therapeutic Zinc Ion Window to Reconfigure Immune Cascades for Pro‐Regenerative Bone Healing

Advanced Science, EarlyView.
This study utilized alloy micro‐galvanic coupling design to regulate the release of essential elements, thereby programming immune responses and promoting regeneration. The sacrificial anodic process of Zn‐0.8Mg reduced Zn2+ release compared to the “large cathode‐small anode” coupling of Zn‐0.8Fe.
Chaoyang Sun, Bo Jia, Jiahui Shi, Shuang Li, Guo Bao, Dong Bian, Kai Chen, Junlong Tan, Xuenan Gu, Yan Guan, Yu Qin, Xinhua Qu, Xiaogang Wang, Yufeng Zheng, Hongtao Yang   +14 more
wiley   +1 more source

Sexual dimorphic pattern of renal transporters and channels in spontaneously hypertensive rats. [PDF]

Am J Physiol Cell Physiol
Xiong LI, Finch AM, Ralph DL, Anidu BS, Evans LC, McDonough AA.   +5 more
europepmc   +1 more source

Topics in J-fields and a diameter problem

, 1981
Thesis (M.Sc.) -- University of Adelaide, Dept.
McAuley, Michael J.
core  

A Single‐Cell Transcriptomic Atlas of the Ovine Rumen Microbiome Characterizes Lineage‐Specific Metabolic Shifts Associated with Host Heat Tolerance

Advanced Science, EarlyView.
An optimized single‐cell transcriptomic framework profiles over 60 000 cells to map the ovine rumen microbiome, partitioning the ecosystem into seven cross‐species functional clusters. In heat‐resistant hosts, a lineage‐specific metabolic shift in Anaerovibrio lipolyticus toward a highly glycolytic phenotype contributes to a “nutritional sparing ...
Sanbao Zhang, Qinyang Jiang, Junjie Ma, Jianwei Zou, Fan Wang, Feifei Lv, Yanna Huang, Yongcheng Wang, Ziye Xu   +8 more
wiley   +1 more source

On certain novel numerical and analytical solutions for the pure-cubic Schrödinger equation in optical fibers with Kerr nonlinearity. [PDF]

Sci Rep
Tariq KU, Khan R, Alsharidi AK, Almusallam N.   +3 more
europepmc   +1 more source

CauFinder: Steering Cell‐State and Phenotype Transitions by Causal Disentanglement Learning

Advanced Science, EarlyView.
CauFinder combines causal disentanglement modeling and network control to prioritize causal drivers of cell‐state transitions from observational transcriptomic data. The framework separates transition‐relevant signals from spurious associations, nominates intervention targets across biological and disease contexts, and identifies DAAM1 as an actionable
Chengming Zhang, Zexi Chen, Yuanxiang Miao, Zuolin Shen, Deyu Cai, Shijie Tang, Yun Xue, Weifeng Guo, Hongbin Ji, Jian Liu, Kazuyuki Aihara, Luonan Chen   +11 more
wiley   +1 more source