Results 121 to 130 of about 419,951 (288)

Wetting‐Driven Transfer of Flexible Ultra‐Thin Temperature Sensors

Advanced Electronic Materials, EarlyView.
A wetting‐driven transfer approach enables ultra‐thin temperature sensors to be transferred from a super‐hydrophilic donor substrate onto rigid, curved, and biological receiver substrates. By combining controlled wetting with a conformal Parylene carrier layer, the method preserves stable thermal sensing performance while expanding the integration of ...
Hafiza Faiqa Maqsood, Annelot Nijkoops, Fahimeh Masoumi, Giuseppe Cantarella, Niko Münzenrieder, Riccardo Zamboni   +5 more
wiley   +1 more source

Farmers' Preferences for Gene Editing Crops and Influencing Factors

Applied Economic Perspectives and Policy, EarlyView.
ABSTRACT Gene editing (GE) is gaining momentum worldwide, but limited data on UK farmers' preferences hinders our understanding of its potential impact amid deregulation debates. Based on a survey of 200 English arable farmers, we employ a Latent Class Analysis and Multinomial Logit regressions to investigate current preferences for GE crops.
Bertolozzi‐Caredio Daniele, Vigani Mauro   +1 more
wiley   +1 more source

Sparse Polynomial Interpolation and Testing [PDF]

, 2016
Interpolation is the process of learning an unknown polynomial f from some set of its evaluations. We consider the interpolation of a sparse polynomial, i.e., where f is comprised of a small, bounded number of terms.
Arnold, Andrew
core  

Data‐Driven High‐Throughput Volume Fraction Estimation From X‐Ray Diffraction Patterns

Advanced Intelligent Discovery, EarlyView.
Long exposure times and the need for manual evaluation limit the use of X‐ray diffraction in high‐throughput applications. This study presents a data‐driven approach addressing both issues. HiVE (a method for High‐throughput Volume fraction Estimation) performs composition estimation for high‐noise XRD patterns produced using polychromatic emission ...
Hawo H. Höfer, André Orth, Robert Wang, Ben Breitung, Simon Schweidler, Jasmin Aghassi‐Hagmann, Markus Reischl   +6 more
wiley   +1 more source

On the interpolation in linear normed spaces using multiple nodes

Journal of Numerical Analysis and Approximation Theory, 2015
In the papers [2], [3], [4], [6], [7] we indicated a method of extending the notion of interpolation polynomial for the case of a non-linear mapping \(f : X \to Y\) where \(X\) and \(Y\) are linear spaces with special structures.
Adrian Diaconu
doaj   +2 more sources

Comparison of Two Interpolation Methods for Resampling Center of Mass Velocity Data [PDF]

Sport Mont, 2016
Data interpolation methods are highly useful for estimating missing values. Another usage of these methods is resampling the measured data. In the field of biomechanics sometimes researchers have to deal problems related to data acquisition rate or ...
Uğur Ödek
doaj  

On Polynomial Interpolation Of Two Variables

, 2003
Polynomial interpolation of two variables based on points that are located on multiple circles is studied. First, the poisedness of a Birkhoff interpolation on points that are located on several concentric circles is established.
Borislav Bojanov, Yuan Xu
core   +1 more source

Comparison of DeePMD, MTP, GAP, ACE and MACE Machine‐Learned Potentials for Radiation‐Damage Simulations: A User Perspective

Advanced Intelligent Discovery, EarlyView.
The authors evaluated six machine‐learned interatomic potentials for simulating threshold displacement energies and tritium diffusion in LiAlO2 essential for tritium production. Trained on the same density functional theory data and benchmarked against traditional models for accuracy, stability, displacement energies, and cost, Moment Tensor Potential ...
Ankit Roy, Ram Devanathan, Sarah Allec, Giridhar Nandipati, Andrew M. Casella, David J. Senor, Duane D. Johnson, Ganesh Balasubramanian, Ayoub Soulami   +8 more
wiley   +1 more source

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

Polynomial interpolation and approximation in C^d

, 2012
We give a survey of the state of the art of the theory of multivariate polynomial ...
Bloom T., Calvi J. P., BOS, LEONARD PETER, Levenberg N.   +3 more
core   +1 more source