Results 131 to 140 of about 39,022 (281)

Monotone Regression

, 2004
This is an entry for The Encyclopedia of Statistics in Behavioral Science, to be published by Wiley in 2005.
openaire   +2 more sources

Deciphering Short‐Range Order in 2D Transition Metal Dichalcogenides: From Origin to Multi‐Scale Property Modulation

Advanced Science, EarlyView.
Short‐range order in 2D transition metal dichalcogenides is revealed as a new design paradigm. Driven by chemical affinity and atomic size, it governs properties across scales. Weak ordering tunes site‐resolved magnetism and d‐band centers, while strong ordering eliminates gap states to open band gaps.
Hanyu Liu, Linggang Zhu, Jian Zhou, Zhimei Sun   +3 more
wiley   +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

Optimal Rates of Statistical Seriation

, 2016
Given a matrix the seriation problem consists in permuting its rows in such way that all its columns have the same shape, for example, they are monotone increasing.
Flammarion, Nicolas, Mao, Cheng, Rigollet, Philippe   +2 more
core   +1 more source

Harnessing Phase Separation for the Development of High‐Performance Hydrogels

Advanced Science, EarlyView.
ABSTRACT Hydrogels are indispensable for the development of next‐generation bioelectronics, soft robotics, and biomedical devices, where their mechanical properties determine performance and reliability. Among strategies to enhance hydrogel mechanics, phase separation enables controlled heterogeneity resulting in gel networks that are reinforced by ...
Yue Shao, Yiming Ma, Baihao Shao, Molly M. Stevens   +3 more
wiley   +1 more source

Linking Plant Metabolomics with Fungal Functional Dynamics Reveals a Noncanonical S‐R‐C Adaptive Trajectory

Advanced Science, EarlyView.
Using field‐based holo‐omics, we demonstrate that developmental shifts in sorghum leaf metabolomes drive a noncanonical fungal succession from stress tolerators (S) through ruderals (R) to competitors (C). Antifungal metabolites in young leaves select for S strategists with expanded genomes, transient maltose pulses during flowering favor fast‐growing ...
Peilin Chen, John W. Taylor, Cheng Gao
wiley   +1 more source

GreenMix-pareto: Uncertainty-aware, physics-guided multi-objective optimization of low-carbon concrete mix designs

Ain Shams Engineering Journal
Concrete mix design increasingly requires balancing mechanical performance with climate and resource impacts. Most existing data-driven studies either optimize a single target (often strength) or report point predictions without calibrated uncertainty ...
Tarek Salem Abdennaji, Amel Ksibi, Aymen Flah, Vandna Batra, Rupesh Kumar Tipu   +4 more
doaj   +1 more source

Chitosan‐Carbon Dot Composite Materials Form a Leaf Surface Barrier to Mitigate the Enrichment and Invasion of Nanoplastics: From Leaf Interface to Systemic Response

Advanced Science, EarlyView.
Foliar spraying of CS‐CDs can form a film on the leaves of Brassica rapa, effectively reducing the enrichment and absorption of PS in the leaves, while increasing the biomass and nutrient content of the plants. In addition, CS‐CDs can also enrich the interfoliar microbial community and activate the plant's own defense metabolic pathways.
Beibei Zhao, Mei Li, Mengjiao Fan, Chuanhuan Liu, Jie Jiang, Jia Song, Yingzhu Liu   +6 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

Decoupling Intrinsic Molecular Efficacy From Platform Effects: An Interpretable Machine Learning Framework for Unbiased Perovskite Passivator Discovery

Advanced Science, EarlyView.
This study establishes an interpretable machine learning framework that disentangles the intrinsic molecular efficacy of passivators from experimental platform effects—enabling unbiased, high‐throughput discovery of effective perovskite surface modifiers.
Jing Zhang, Ziyuan Li, Shan Gao, Zhen Zhu, Jing Wang, Xiangmei Duan   +5 more
wiley   +1 more source