Results 161 to 170 of about 1,208,874 (255)

To Be or Not to Be Eutectic: Anomalous Glass Forming Ability Governed by Melt Structural Complexity

Advanced Science, EarlyView.
Challenging the eutectic rule, congruently melting Ti–Cu alloys show greater glass‐forming ability than their eutectic counterparts. This arises from the congruent melt's structural complexity: higher cluster diversity and stronger inter‐cluster correlations from entropy–enthalpy competition.
Wei Chen, Xuerui Wei, Chengzhe Wang, Zhichao Lu, Fan Zhang, Yan Huang, Yibo Zhang, Liang Wang, Saichao Cao, Chunyin Zhou, Ke Yang, Zhendong Fu, Jinkui Zhao, Dongbai Sun, Fanqiang Meng, Zhou Zhou, Xun‐li Wang, Weihua Wang, Dong Ma   +18 more
wiley   +1 more source

Monte carlo assessment of advanced shielding materials for space-efficient radiotherapy vault design. [PDF]

J Appl Clin Med Phys
Koo B, Xu R, Glennie M, Shin J, Farrey K, Kim S, Sohn JJ.   +6 more
europepmc   +1 more source

Flooding‐Driven Greenhouse Gas Emissions From Global Upland Croplands

Advanced Science, EarlyView.
Temporary flooding of upland croplands releases nitrous oxide, carbon dioxide, and methane. Field measurements combined with a global literature synthesis yield a global estimate of 63.3 teragrams of carbon dioxide equivalent per year for diffusive emissions from flooded upland croplands.
Shuilian Bi, Zhifeng Yan, Zhengkui Ge, Si‐Liang Li, Guirui Yu, Cong‐Qiang Liu, Hanqin Tian, Pete Smith   +7 more
wiley   +1 more source

Melanin Bias in Cerebral fNIRS: A Monte Carlo Study Toward Equitable Optical Brain Sensing. [PDF]

Biosensors (Basel)
Althobaiti M.
europepmc   +1 more source

An AI‐Driven Framework for Directed Self‐Assembly Toward Design‐Technology Co‐Optimization

Advanced Science, EarlyView.
An artificial intelligence (AI)‐driven closed‐loop framework integrates experiment, three‐dimensional physics‐based simulation, and template design for directed self‐assembly (DSA). Guided by experimental data, AI‐assisted calibration accelerates model optimization, supports morphology prediction, and identifies template windows.
Caiwei Shang, Yanting Xu, Yue Zhang, Yan Zhang, Jianfeng Li, Ming Li, Longlin Lei, Wenda Bao, Weixuan Zeng, Shisheng Xiong   +9 more
wiley   +1 more source

Multi-fidelity Monte Carlo fusion for BNCT dose calculation using a dual-branch network. [PDF]

Med Phys
Li L, Hong X, Wang Y, Huang G, Wu Z, Xiao Y, Tanaka H, Watabe H.   +7 more
europepmc   +1 more source

An Explainable Multimodal Deep‐Learning Model Using Histopathological Image and Clinical Information for Prognosis Prediction in Colorectal Cancer: A Multi‐Center Cohort Study

Advanced Science, EarlyView.
This study develops a multimodal deep‐learning model that integrates clinical variables and histopathological images to predict survival outcomes in colorectal cancer. The multimodal model effectively stratifies patients into low‐ and high‐risk groups, demonstrating superior accuracy and generalizability across multiple independent cohorts.
Zhihan Jiang, Xiangchao Shi, Chenghan Su, Yao Ma, Rui Li, Menglei Chen, Jianxin Cui, Nicholas Hawkins, Robyn Ward, Xiangzhi Bai, Jitendra Jonnagaddala, Wei Fu, Xin Zhou   +12 more
wiley   +1 more source

Comparative Evaluation of Chelator‐Conjugated Peptides for Lead‐Labeled SSTR2‐Targeted Theranostics: Integrated Computational Modeling and Preclinical Validation

Advanced Science, EarlyView.
The study integrates density functional theory with radiochemical and biological validation to compare DOTA‐, PSC‐, and DOTAM‐based chelator‐conjugates for 203Pb/212Pb theranostic applications. Distinct differences in metal coordination, electrostatic properties, conformational flexibility, cellular uptake, biodistribution, and dosimetry are identified
Jeongyun Kim, Na Yeon Han, Ayman Muzammal, Jae Hun Ahn, Changkeun Im, Sujin Kim, Sairan Eom, Choong Mo Kang, Ku Kang, Yong Jin Lee, Kyo Chul Lee, Michael K. Schultz, Dongyoul Lee   +12 more
wiley   +1 more source

A lattice Monte Carlo model for amyloid fibril formation. [PDF]

Biophys Physicobiol
Okumura H, Hoai LT, Sompornpisut P, Itoh SG.   +3 more
europepmc   +1 more source

Efficient In‐Hardware Matrix–Vector Multiplication and Addition Exploiting Bilinearity of Schottky Barrier Transistors Processed on Industrial FDSOI

Advanced Electronic Materials, EarlyView.
ABSTRACT Machine learning and Artificial Intelligence (AI) tasks have stretched traditional hardware to its limits. In‐hardware computation is a novel approach that aims to run complex operations, such as matrix–vector multiplication, directly at the device level for increased efficiency.
Juan P. Martinez, Niladri Bhattacharjee, Yuxuan He, Giulio Galderisi, Brojogopal Sapui, Sara Mannaa, Ian O'Connor, Violetta Sessi, Peter Javorka, Thomas Mikolajick, Jens Trommer   +10 more
wiley   +1 more source