Results 61 to 70 of about 3,566,037 (267)

Unifying Composition and Process Design: A Heterogeneous Graph Neural Network for Discovering High‐Performance Cu Alloys

Advanced Science, EarlyView.
By overcoming the fixed‐path limitations of conventional machine learning, a heterogeneous graph neural network fundamentally reconstructs material data representation. Integrating variable processing sequences with intrinsic elemental features, this framework enables exploratory optimization across high‐dimensional spaces.
Jie Yin, Qian Lei, Wei Yan, Yue Li, Tong Xie, Shuang Zhou, Chong Wang, Bram Hoex, Qiang Long, Caoyang Jiang, Min Song, Zhou Li, Zhangwei Wang   +12 more
wiley   +1 more source

The global distribution and transmission limits of lymphatic filariasis: past and present

Parasites & Vectors, 2014
Background Lymphatic filariasis (LF) is one of the neglected tropical diseases targeted for global elimination by 2020 and to guide elimination efforts countries have, in recent years, conducted extensive mapping surveys. Documenting the past and present
Jorge Cano, Maria P Rebollo, Nick Golding, Rachel L Pullan, Thomas Crellen, Anna Soler, Louise A Kelly- Hope, Steve W Lindsay, Simon I Hay, Moses J Bockarie, Simon J Brooker   +10 more
doaj   +1 more source

The sequence of boosted decision trees.

, 2022
The first (top) figure (Tree 0) is the first boosted decision tree from the XGBoost model. The next (Tree 1) is the second boosted decision tree from the XGBoost model. The next tree (Tree 2) is the third boosted decision tree from the XGBoost model. The
Xiaqing Jiang (13175627), Daniel S. Evans (8182770), Jessica Jacobson (7187699), Kyoung Min Kim (9137503), Steven R. Cummings (8806028), Warren Browner (378300)   +5 more
core   +1 more source

High‐Throughput Screening and Interpretable Machine Learning for Rational Design of Bimetallic Catalysts for Methane Activation

Advanced Science, EarlyView.
ABSTRACT Methane's efficient catalytic removal is vital for sustainable development. Bimetallic catalysts, though promising for methane activation, pose a design challenge due to their complex compositional space. This work introduces an integrated framework that combines high‐throughput density functional theory (DFT) and interpretable machine ...
Mingzhang Pan, Tian Zhang, Jiawei Dong, Yubao Xie, Kaifeng Zhong, Wei Guan, Haiqiao Wei, Changcheng Fu, Yaqiong Su   +8 more
wiley   +1 more source

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

Machine learning-based prediction of stand biomass using multi-source environmental data in the Hulunbuir Mixed Forests, Inner Mongolia

Trees, Forests and People
Accurate estimation of forest aboveground biomass (AGB) is vital for understanding ecosystem productivity and carbon dynamics, especially in complex mixed forests.
Yaxiong Zheng, Yongjie Yue, Runhong Gao
doaj   +1 more source

Appendix D. Native American variables (NAVs) selected by each species distribution modeling (SDM) algorithms, and variable importance in boosted regression tree (BRT) models (including NAVs).

, 2016
Native American variables (NAVs) selected by each species distribution modeling (SDM) algorithms, and variable importance in boosted regression tree (BRT) models (including NAVs)
Stephen J. Tulowiecki (2933748), Chris P. S. Larsen (2933751)   +1 more
core   +1 more source

Physics‐Informed Machine Learning for Sustainable Alloy Design: Toward a Recyclable Unified Q&P Steel

Advanced Science, EarlyView.
A physics‐informed property‐bridging framework links high‐throughput hardness screening to tensile performance in quenching and partitioning steels. By transferring metallurgically guided representations across properties, a single alloy composition is designed to achieve multiple strength grades through heat‐treatment tuning alone, offering a ...
Xiaolu Wei, Yong Li, Chenchong Wang, Lingyu Wang, Xiang Song, Keming Mao, Yu Zhang, Wei Xu   +7 more
wiley   +1 more source

The total mean deviance explained, mean residual deviance, estimated coefficient of variation (CV) of deviance, and the number of trees in each of the Boosted Regression Tree models.

, 2017
The total mean deviance explained, mean residual deviance, estimated coefficient of variation (CV) of deviance, and the number of trees in each of the Boosted Regression Tree models.
April E. Reside (239127), Erin M. Graham (3769474), Atte Moilanen (279666), Jeremy VanDerWal (291945)   +3 more
core   +1 more source

Flexoelectricity in Photoconversion: Fundamentals, Materials, and Outlooks

Advanced Science, EarlyView.
Mechanical bending of a flexible cantilever induces a strain gradient in the photoactive material. The resulting flexoelectric field couples with photovoltaic and photoconductive effects, modulating charge generation, separation, and collection. A comparative analysis of oxide perovskites, halide perovskites, and two‐dimensional materials is presented,
Xiang Huang, Feng Li, Rongkun Zheng
wiley   +1 more source