Results 161 to 170 of about 3,984,242 (310)

Development of a Computerized Adaptive Assessment and Learning System for Mathematical Ability Based on Cognitive Diagnosis. [PDF]

J Intell
Zhang Y, Zhang L, Zhang H, Wu X.
europepmc   +1 more source

A didactic study of introduction of statistic concepts in their relations with the probability in teaching of mathematics in Vietnam

, 2009
Thu Huong Vu Nhu
openalex   +1 more source

Application of Neural Networks for Advanced Ir Spectroscopy Characterization of Ceria Catalysts Surfaces

Advanced Intelligent Discovery, EarlyView.
A novel convolutional neural network architecture enables rapid, unsupervised analysis of IR spectroscopic data from DRIFTS and IRRAS. By combining synthetic data generation with parallel convolutional layers and advanced regularization, the model accurately resolves spectral features of adsorbed CO, offering real‐time insights into ceria surface ...
Mehrdad Jalali, Lachlan Caulfield, Eric Sauter, Alexei Nefedov, Chengwu Yang, Christof Wöll   +5 more
wiley   +1 more source

Analyzing the impact of a discounted parameter on the reduction of collision time in Brownian particle trajectories. [PDF]

Sci Rep
El-Hadidy MAA, Alraddadi R.
europepmc   +1 more source

The Mathematical Model for Predicting the Probability of Minimal Change Nephrotic Syndrome Using Clinical Parameters

, 2005
Hye‐Suk Han, Hye‐Young Kim, Su-In Yoon, Ja-Chung Goo, Byeong-Uk Kim, Sung‐Soon Park, Ki-Ok Jeung, Soon Kil Kwon, Hoen Kim, Jae-Ho Earm   +9 more
openalex   +1 more source

The Application of Probability Statistics in Higher Mathematics [PDF]

, 2016
Yugui Tang
openalex   +1 more source

Artificial Intelligence‐Driven Insights into Electrospinning: Machine Learning Models to Predict Cotton‐Wool‐Like Structure of Electrospun Fibers

Advanced Intelligent Discovery, EarlyView.
Electrospinning allows the fabrication of fibrous 3D cotton‐wool‐like scaffolds for tissue engineering. Optimizing this process traditionally relies on trial‐and‐error approaches, and artificial intelligence (AI)‐based tools can support it, with the prediction of fiber properties. This work uses machine learning to classify and predict the structure of
Paolo D’Elia, Giovanna Pappalardo, Aldo R. Boccaccini, Liliana Liverani   +3 more
wiley   +1 more source

Modelling Like an Experimentalist. [PDF]

Ecol Lett
Drake JM.
europepmc   +1 more source

Random discrete groups of Möbius transformations : probabilities and limit set dimensions : a thesis presented in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Mathematics at Massey University, Albany, New Zealand

, 2017
Graeme K O'Brien
openalex   +1 more source

CrossMatAgent: AI‐Assisted Design of Manufacturable Metamaterial Patterns via Multi‐Agent Generative Framework

Advanced Intelligent Discovery, EarlyView.
CrossMatAgent is a multi‐agent framework that combines large language models and diffusion‐based generative AI to automate metamaterial design. By coordinating task‐specific agents—such as describer, architect, and builder—it transforms user‐provided image prompts into high‐fidelity, printable lattice patterns.
Jie Tian, Martin Taylor Sobczak, Dhanush Patil, Jixin Hou, Lin Pang, Arunachalam Ramanathan, Libin Yang, Xianyan Chen, Yuval Golan, Xiaoming Zhai, Hongyue Sun, Kenan Song, Xianqiao Wang   +12 more
wiley   +1 more source