Results 71 to 80 of about 82,607 (197)

Probability Closure and Risk Geometry in the Gaasu Galaxy Spectrum (F5) Winning-Path Mathematics

Hisashi Suga
openalex   +1 more source

A Preface and an Introductory Article to the Re-edition of the Work of Platon Sergeevich Poreckiy «Solving General Tasks in Probability Theory by Using Mathematical Logic»

, 2015
Igor A. Ryabinin, Alexandr Vladimirovich Strukov   +1 more
openalex   +2 more sources

Deep Learning‐Assisted Coherent Raman Scattering Microscopy

Advanced Intelligent Discovery, EarlyView.
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu, Rujia Liu, Xinyan Zhang, Xun Chen, Shuhua Yue   +4 more
wiley   +1 more source

The Head-Toes-Knees-Shoulders Task as a Screening Tool for Kindergarten-Level Achievement. [PDF]

Behav Sci (Basel)
Korucu I, Duncan RJ, Kenny SA, Gonzales CR, Ahmadi A, Karing JT, McClelland MM.   +6 more
europepmc   +1 more source

Using School Choice to Connect to Mathematics Learning in a Statistics and Probability Course for K-8 Pre-service Teachers

, 2018
Ha Nguyen, Heidi Eisenreich
openalex   +2 more sources

A Machine Learning Model for Interpretable PECVD Deposition Rate Prediction

Advanced Intelligent Discovery, EarlyView.
This study develops six machine learning models (k‐nearest neighbors, support vector regression, decision tree, random forest, CatBoost, and backpropagation neural network) to predict SiNx deposition rates in plasma‐enhanced chemical vapor deposition using hybrid production and simulation data.
Yuxuan Zhai, Wenrui Wang, Rui Chen, Tong Yu, Xiaojuan Zheng, Hua Shao, Dashan Shang, Ling Li, Lado Filipovic   +8 more
wiley   +1 more source

The sobol sensitivity analysis of the pressure, stresses, and displacement arising from poroelastic modelling of hard mechanical systems. [PDF]

Sci Rep
Asghari H, Miller L, Penta R, Melnikov A, Spitas C, Merodio J.   +5 more
europepmc   +1 more source

Proposal for Developing Learning Materials Focusing on Modified Problem-Posing Using a Probability Game to Promote Understanding of the Problem Structure in Basic Engineering Mathematics [PDF]

Masanori Fukui, Masakatsu Kuroda, Peter Chew, Yuji Sasaki   +3 more
openalex   +1 more source

Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

Advanced Intelligent Discovery, EarlyView.
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang, Xinyi Chen, Xu Ye, Yulin Yang, Bin Ai   +4 more
wiley   +1 more source

A two-group kinetic wealth model with wealth-gap drift and non-Maxwellian kernels. [PDF]

PLoS One
Sun R.
europepmc   +1 more source