Results 51 to 60 of about 58,914 (295)

Chebyshev collocation treatment of Volterra–Fredholm integral equation with error analysis

Arabian Journal of Mathematics, 2019
This work reports a collocation algorithm for the numerical solution of a Volterra–Fredholm integral equation (V-FIE), using shifted Chebyshev collocation (SCC) method. Some properties of the shifted Chebyshev polynomials are presented.
Youssri Hassan Youssri, R. Hafez
semanticscholar   +1 more source

Mapping the Innovation DNA of Agribusiness Firms: A Multi‐Method Analysis of Strategic Capabilities and Performance

Agribusiness, EarlyView.
ABSTRACT Innovation is essential for competitiveness in agribusiness facing dynamic environments. This study examines how market orientation, marketing, relational, and social capabilities influence innovation performance. Using data from 751 Spanish firms and a multi‐method approach that integrates Structural Equation Modeling (PLS‐SEM), Necessary ...
Beatriz Corchuelo Martínez‐Azúa, Alvaro Dias   +1 more
wiley   +1 more source

The recurring collocation of vreiðr and vega in Old Norse poetry

Manuscript and Text Cultures
Poetry in early Germanic vernaculars exhibits variations on a metrical form predicated on a pattern of alliterating stressed syllables linking two halves of a line (in contrast to syllabic metres in which scansion requires a fixed number of syllables ...
James Parkhouse
doaj   +1 more source

A meshless local collocation method for time fractional diffusion wave equation

Computers and Mathematics with Applications, 2019
In this manuscript, we present a radial basis function based local collocation method for solving time fractional diffusion-wave equation. The advantage of the local collocation method is only the discretization points located in each sub-domain ...
Alpesh Kumar, Akanksha Bhardwaj, B. Kumar   +2 more
semanticscholar   +1 more source

AI in chemical engineering: From promise to practice

AIChE Journal, EarlyView.
Abstract Artificial intelligence (AI) in chemical engineering has moved from promise to practice: physics‐aware (gray‐box) models are gaining traction, reinforcement learning complements model predictive control (MPC), and generative AI powers documentation, digitization, and safety workflows.
Jia Wei Chew, Ronnie Andersson, Thomas Bierweiler, Patrik Ryttestal, Torsten Wik   +4 more
wiley   +1 more source

Sensortoolkit—A Python Library for Standardizing the Ingestion, Analysis, and Reporting of Air Sensor Data for Performance Evaluation

Sensors
Open-source software tools designed specifically for evaluating and reporting air sensor performance are limited. The available tools do not provide a means for summarizing the sensor performance using common statistical metrics and figures, nor are they
Menaka Kumar, Samuel G. Frederick, Karoline K. Barkjohn, Andrea L. Clements   +3 more
doaj   +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

Computable error bounds with improved applicability conditions for collocation methods

International Journal of Mathematics and Mathematical Sciences, 1998
This paper is concerned with error bounds for numerical solution of linear ordinary differential equation using collocation method. It is shown that if the differential operator is split in different operator forms then the applicability conditions for ...
A. H. Ahmed
doaj   +1 more source

RAMS: Residual‐Based Adversarial‐Gradient Moving Sample Method for Scientific Machine Learning in Solving Partial Differential Equations

Advanced Intelligent Discovery, EarlyView.
We propose a residual‐based adversarial‐gradient moving sample (RAMS) method for scientific machine learning that treats samples as trainable variables and updates them to maximize the physics residual, thereby effectively concentrating samples in inadequately learned regions.
Weihang Ouyang, Min Zhu, Wei Xiong, Si‐Wei Liu, Lu Lu   +4 more
wiley   +1 more source

English language students’ productive and receptive knowledge of collocations

ExELL (Explorations in English Language and Linguistics), 2014
The importance of collocations in second language learning has been recognized in the past few decades. There have been numerous studies in L2 acquisition research that investigated how the knowledge and use of collocations at different levels of ...
Begagić Mirna
doaj   +1 more source