Results 111 to 120 of about 9,081 (239)
Common fixed point theorems for ( T , g ) F -contraction in b-metric-like spaces. [PDF]
Yu D, Chen C, Wang H.
europepmc +1 more source
Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning
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
Fixed Point Theorems for Random Lowersemi-continuous Mappings
We prove a general principle in Random Fixed Point Theory by introducing a condition named ( ) which was inspired by some of Petryshyn's work, and then we apply our result to prove some random fixed points theorems, including generalizations of some ...
Morales ClaudioH +2 more
doaj
Overcoming the Nyquist Limit in Molecular Hyperspectral Imaging by Reinforcement Learning
Explorative spectral acquisition guide automatically selects informative spectral bands to optimize downstream tasks, outperforming full‐spectrum acquisition. The selected hyperspectral data are used for tasks such as unmixing and segmentation. BandOptiNet encodes selection states and outputs optimal bands to guide spectral acquisition. Recent advances
Xiaobin Tang +4 more
wiley +1 more source
Fixed point theorems on multi valued mappings in b-metric spaces. [PDF]
Maria Joseph J +2 more
europepmc +1 more source
This review aims to provide a broad understanding for interdisciplinary researchers in engineering and clinical applications. It addresses the development and control of magnetic actuation systems (MASs) in clinical surgeries and their revolutionary effects in multiple clinical applications.
Yingxin Huo +3 more
wiley +1 more source
Coupled fixed point theorems in G b -metric space satisfying some rational contractive conditions. [PDF]
Khomdram B, Rohen Y, Singh TC.
europepmc +1 more source
Predicting Performance of Hall Effect Ion Source Using Machine Learning
This study introduces HallNN, a machine learning tool for predicting Hall effect ion source performance using a neural network ensemble trained on data generated from numerical simulations. HallNN provides faster and more accurate predictions than numerical methods and traditional scaling laws, making it valuable for designing and optimizing Hall ...
Jaehong Park +8 more
wiley +1 more source
Rapid Assignment of Chemical Shifts From Crystal Structures in Solid‐State NMR
Chemical shift assignment in solids is a long and tedious process that relies on complex 1D and 2D NMR experiments. With prior knowledge of the 3D structure, this process can be significantly sped up by a Bayesian probabilistic assignment approach based on predicted chemical shifts.
Ruben Rodriguez‐Madrid +2 more
wiley +2 more sources
Some fixed point theorems in generating space of b-quasi-metric family. [PDF]
Kumari PS, Sarwar M.
europepmc +1 more source

