Results 121 to 130 of about 7,236,752 (305)
MathematicsWe study strong laws of large numbers in a non-linear framework based on conditional sub-additive expectations and conditional sub-additive capacities. Using an axiomatic approach to conditional sub-additive expectation, we establish a conditional Hájek ...
Nyanga Honda Masasila, István Fazekas
doaj +1 more source
Implications of Transient Negative Capacitance Effect in Ferroelectric Polarization Dynamics
Advanced Electronic Materials, EarlyView.Transient voltage artifacts observed during ferroelectric switching are shown to originate from measurement circuitry rather than intrinsic negative capacitance. By correlating switching current, time scale, and series resistance, this work establishes practical design rules for reliable pulse‐switching experiments and circuit integration of ...
Marin Alexe
wiley +1 more source
Applied Economic Perspectives and Policy, EarlyView.ABSTRACT Amid rising food and fertilizer prices, understanding farmers' policy preferences is critical for effective crisis response. We use best‐worst scaling experiment to assess Kenyan mobile‐owning crop farmers' preferences for government support under high and normal price scenarios.
Mywish K. Maredia +4 more
wiley +1 more source
Annals of Gastroenterological Surgery, EarlyView.Threshold‐optimized machine learning models using routine clinical and laboratory data in 623 adults undergoing appendectomy. Logistic regression (AUC = 0.765) and random forest (AUC = 0.785) were the best‐performing models for appendicitis detection and complicated appendicitis prediction, respectively.
Ivan Males +8 more
wiley +1 more source
A modified Kolmogorov-Smirnov test for normality
In this paper we propose an improvement of the Kolmogorov-Smirnov test for normality. In the current implementation of the Kolmogorov-Smirnov test, a sample is compared with a normal distribution where the sample mean and the sample variance are used as ...
Zerom, Dawit, Turel, Ofir, Drezner, Zvi
core
Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning
Advanced Intelligent Discovery, EarlyView.This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley +1 more source
Computing the Two-Sided Kolmogorov-Smirnov Distribution
We propose an algorithm to compute the cumulative distribution function of the two-sided Kolmogorov-Smirnov test statistic D_n and its complementary distribution in a fast and reliable way.
Pierre L'Ecuyer, Richard Simard
core
Turbulence characterisation for Astronomical Observatories [PDF]
, 2012 Atmospheric turbulence has two effects in astronomy; (i) the broadening of the point spread function due to phase fluctuations limiting the resolution of imaging and (ii) producing intensity fluctuations known as scintillation.
SHEPHERD, HARRY,WILLIAM +1 more
core
Advanced Intelligent Systems, EarlyView.Physics‐Informed Neural Networks (PINNs) provide a framework for integrating physical laws with data. However, their application to Prognostics and Health Management (PHM) remains constrained by the limited uncertainty quantification (UQ) capabilities.
Ibai Ramirez +4 more
wiley +1 more source
Advanced Intelligent Systems, EarlyView.Single‐cell Spatial Transcriptomics Analysis and Denoising Engine is introduced as a unified deep learning framework that jointly performs denoising, clustering, and gene prioritization in spatial transcriptomics. By integrating linear and nonlinear representations within a dual‐channel architecture, it improves robustness and accuracy, uncovers ...
Yaxuan Cui +11 more
wiley +1 more source