Results 121 to 130 of about 1,423,830 (258)
Machine learning driven many‐objective moving horizon scheduling optimization
AIChE Journal, EarlyView.Abstract Industrial electrification can decarbonize chemical manufacturing, but it exposes operations to volatile electricity prices and carbon intensities. This work develops a machine learning‐enhanced many‐objective moving horizon scheduling framework that predicts objective correlation groupings from 48‐hour price and emission‐intensity profiles ...
Hongxuan Wang, Andrew Allman
wiley +1 more source
Parameter estimation in nonlinear AR–GARCH models [PDF]
This paper develops an asymptotic estimation theory for nonlinear autoregressive models with conditionally heteroskedastic errors. We consider a general nonlinear autoregression of order p (AR(p)) with the conditional variance specified as a general ...
Mika Meitz, Pentti Saikkonen
core
Embedded Morgan Fingerprints for More Efficient Molecular Property Predictions With Machine Learning
Advanced Intelligent Discovery, EarlyView.High dimensionality of Morgan fingerprints causes overfitting and slow training. Embedded Morgan Fingerprint (eMFP) compresses this encoding while keeping key structural information intact. Tested on five regression models across three databases for HOMO–LUMO gap prediction, eMFP outperforms standard fingerprints, training faster on large, high ...
Emilio Nuñez‐Andrade +4 more
wiley +1 more source
Nonparametric confidence bands in deconvolution density estimation [PDF]
Uniform confidence bands for densities f via nonparametric kernel estimates were first constructed by Bickel and Rosenblatt [Ann. Statist. 1, 1071.1095].
Bissantz, Nicolai +3 more
core
A Unifying Approach to Self‐Organizing Systems Interacting via Conservation Laws
Advanced Intelligent Discovery, EarlyView.The article develops a unified way to model and analyze self‐organizing systems whose interactions are constrained by conservation laws. It represents physical/biological/engineered networks as graphs and builds projection operators (from incidence/cycle structure) that enforce those constraints and decompose network variables into constrained versus ...
F. Barrows +7 more
wiley +1 more source
Pseudodifferential operators with generalized symbols and regularity theory
Electronic Journal of Differential Equations, 2005 We study pseudodifferential operators with amplitudes $a_varepsilon (x,xi)$ depending on a singular parameter $varepsilon o 0$ with asymptotic properties measured by different scales.
Claudia Garetto +2 more
doaj
Asymptotic theory of statistics form unit root test regressions when the alternative is a breaking-trend-stationary model [PDF]
We derive test regressions whose structure provides a link between tests for a unit root and tests on the nullity of the parameters associated with the regression's trend function.
Noriega-Muro, Antonio
core
Robot‐Assisted Measurement of the Critical Micelle Concentration
Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.The study introduces (SIMO) smart integrator for manual operations, a robotic platform for precise, repeatable determination of (CMC) critical micelle concentration in surfactants. SIMO reduces standard deviation by 80% compared to manual methods. Surfactant, dye, and diluent selection, robotic protocols, and data handling are detailed.
Vincenzo Scamarcio +3 more
wiley +1 more source
Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.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
Structure and Asymptotic Theory for Nonlinear Models with GARCH Errors [PDF]
Nonlinear time series models, especially those with regime-switching and conditionally heteroskedastic errors, have become increasingly popular in the economics and finance literature.
Michael McAleer +2 more
core