Results 71 to 80 of about 32,246 (268)

Price Transmission and Leadership in the Global Poultry Market: Results From Parametric and Nonparametric Approaches

Agribusiness, EarlyView.
ABSTRACT Brazil and the United States account for more than 40% of global poultry exports, with China and South Korea among their major destination markets. This study examines price transmission and market linkages between Brazil and the United States using monthly poultry export price data from January 1990 to December 2024. It also assesses which of
Khondoker Abdul Mottaleb, Shaikh M. Rahman, Syed Imran Ali Meerza   +2 more
wiley   +1 more source

Design of Multi-Dimensional Recursive Systems through Pad'e Type Rational Approximation

Nonlinear Analysis, 2002
The results obtained in classical 1-D rational approximation are extended in this paper to rational approximation of M-D functions. A full analog of classical Montessus de Ballore theorem for the convergence of the rows of Pad´e’s tables is obtained.
Valeri V. Vavilov, Mikhail K. Tchobanou, Peter M. Tchobanou   +2 more
doaj   +1 more source

Automatic Determination of Quasicrystalline Patterns from Microscopy Images

Advanced Intelligent Discovery, EarlyView.
This work introduces a user‐friendly machine learning tool to automatically extract and visualize quasicrystalline tiling patterns from atomically resolved microscopy images. It uses feature clustering, nearest‐neighbor analysis, and support vector machines. The method is broadly applicable to various quasicrystalline systems and is released as part of
Tano Kim Kender, Marco Corrias, Cesare Franchini   +2 more
wiley   +1 more source

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source

Safe-RTSKF: A Safety-Constrained Recursive Two-Stage Kalman Filter for Robust Sensor State Estimation in Strongly Nonlinear Systems

Sensors
Robust state estimation is essential for sensor systems operating under strong nonlinearity, high uncertainty, and outlier-contaminated measurements. Recursive two-stage Kalman filtering provides an interpretable framework for systems with multiplicative
Yifeng Lin, Ziyu Liu, Chenglin Wen, Ke Ye, Yang-kwon Jeong, Shiwei Zhou, Xuehong Zheng   +6 more
doaj   +1 more source

An Efficient High-Order Time-Step Algorithm With Proportional-Integral Control Strategy for Semirecursive Vehicle Dynamics

IEEE Access, 2019
Dynamics of complex mechanical systems can be modeled and solved efficiently, often even in faster-than-real-time by employing semi-recursive formulations and their various versions.
Yongjun Pan, Saidi Xiang, Aki Mikkola
doaj   +1 more source

Recursive system identification by stochastic approximation [PDF]

Communications in Information and Systems, 2006
The convergence theorems for the stochastic approximation (SA) algorithm with expanding truncations are first presented, which the system identification methods discussed in the paper are essentially based on. Then, the recursive identification algorithms are respectively defined for the multivariate errors-in-variables systems, Hammerstein systems ...
openaire   +2 more sources

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

Recursive Identification for Dynamic Linear Systems from Noisy Input-Output Measurements

Journal of Applied Mathematics, 2013
Errors-in-variables (EIV) model is a kind of model with not only noisy output but also noisy input measurements, which can be used for system modeling in many engineering applications.
Dan Fan, Kueiming Lo
doaj   +1 more source

Large‐Scale Machine Learning to Screen for Small‐Molecule Senolytics

Advanced Intelligent Discovery, EarlyView.
A consistent workflow underpins all experiments in this study. A dedicated model‐selection dataset first identifies optimal hyperparameters for each algorithm. Models are then trained and rigorously evaluated on independent sets of molecules using the senolytic ratio SR. Comprehensive hyperparameter exploration across SMILES representations, task types,
Alexis Dougha, Jesus Gil, Pedro J. Ballester   +2 more
wiley   +1 more source