Results 71 to 80 of about 29,745 (305)

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

Physics‐Constrained Constitutive Learning of Rate‐Limiting Timescales for Efficient Hydrogen‐Based Direct Reduction for Green Steel Making

Advanced Science, EarlyView.
A conversion‐resolved constitutive framework is developed for the hydrogen‐based direct reduction of iron oxide pellets. Effective reaction and transport timescales are inferred directly from measured trajectories and mapped against operating conditions, pellet architecture, and composition. The analysis reveals how late‐stage transport control emerges
Anurag Bajpai, Barak Ratzker, Pasquale Cavaliere, Dierk Raabe   +3 more
wiley   +1 more source

Asymptotic Efficiency of the OLS Estimator with Singular Limiting Sample Moment Matrices [PDF]

This paper presents a time series model that has an asymptotically efficient ordinary least squares (OLS) estimator, irrespective of the singularity of its limiting sample moment matrices.
Yoshimasa Uematsu
core  

FOS3D: A Fluorescence‐Enabled Toolkit for Characterizing a Three‐dimensional Osteosarcoma Model

Advanced Science, EarlyView.
FOS3D describes fluorescent (F) osteosarcoma (OS) cells in a tri‐dimensional (3D) model. The study comprises three phases: development, where biofabrication parameters are tuned to achieve cytocompatibility and tumor‐specific mechanical properties in cell‐laden gelatin methacryloyl constructs; validation, where whole‐well fluorescence reading is ...
William Humble, Wiktor Zywicki, Enrico Lucarelli, Ania Naila Guerrieri, Francesca Taraballi, Gianluca Cidonio, Claudia Di Bella, Carmine Onofrillo, Andrea J. O'Connor, Serena Duchi   +9 more
wiley   +1 more source

Preliminary Test Estimation for Parallel 2-Sampling in Autoregressive Model

Stats
The purpose of this paper is to discuss the problem of estimation and testing the equality of two autoregressive parameters of two first-order autoregressive processes AR(1), where for each process, the observations are made at different time points. The
Syed Ejaz Ahmed, Arsalane Chouaib Guidoum, Sara Bendjeddou   +2 more
doaj   +1 more source

Comparing Tests of Autoregressive Versus Moving Average Errors in Regression Models Using Bahadur's Asymptotic Relative Efficiency, [PDF]

The purpose of this paper is to use Bahadur's asymptotic relative efficiency measure to compare the performance of various tests of autoregressive (AR) versus moving average (MA) error processes in regression models.
Michael McAleer, C. R. McKenzie
core  

Low‐Power Control Of Resistance Switching Transitions in First‐Order Memristors

Advanced Electronic Materials, EarlyView.
Joule losses are a serious concern in modern integrated circuit design. In this regard, minimizing the energy necessary for programming memristors should be handled with care. This manuscript presents an optimal control framework, allowing to derive energy‐efficient programming voltage protocols for resistance switching devices. Following this approach,
Valeriy A. Slipko, Alon Ascoli, Fernando Corinto, Yuriy V. Pershin   +3 more
wiley   +1 more source

Hybrid Monte Carlo simulation with Fourier acceleration of the N = 2 principal chiral model in two dimensions

Physics Letters B
Motivated by the similarity to QCD, specifically the property of asymptotic freedom, we simulate the dynamics of the SU(2) × SU(2) model in two dimensions using the Hybrid Monte Carlo algorithm.
Roger Horsley, Brian Pendleton, Julian Wack   +2 more
doaj   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

A Numerical Comparison for a Discrete HIV Infection of CD4+ T-Cell Model Derived from Nonstandard Numerical Scheme

Journal of Applied Mathematics, 2013
A nonstandard numerical scheme has been constructed and analyzed for a mathematical model that describes HIV infection of CD4+ T cells. This new discrete system has the same stability properties as the continuous model and, particularly, it preserves the
Mevlüde Yakıt Ongun, İlkem Turhan
doaj   +1 more source