Results 111 to 120 of about 375,405 (243)
Optimal model‐based design of experiments for parameter precision: Supercritical extraction case
Abstract This study investigates the process of chamomile oil extraction from flowers. A parameter‐distributed model consisting of a set of partial differential equations is used to describe the governing mass transfer phenomena in a cylindrical packed bed with solid chamomile particles under supercritical conditions using carbon dioxide as a solvent ...
Oliwer Sliczniuk, Pekka Oinas
wiley +1 more source
Analysis of liquid sloshing frequencies in a partially filled 3D rectangular tank
This research investigates liquid sloshing in a 3D rigid rectangular tank. The impact of rigid baffle on sloshing frequencies has been studied. The mathematical modal has been developed using potential theory. The boundary value problem has an analytical
Narveen Kumar, Neelam Choudhary
doaj +1 more source
A hidden Markov model and reinforcement learning‐based strategy for fault‐tolerant control
Abstract This study introduces a data‐driven control strategy integrating hidden Markov models (HMM) and reinforcement learning (RL) to achieve resilient, fault‐tolerant operation against persistent disturbances in nonlinear chemical processes. Called hidden Markov model and reinforcement learning (HMMRL), this strategy is evaluated in two case studies
Tamera Leitao +2 more
wiley +1 more source
A‐optimal model‐based design of experiments for processes with uncertain inputs
Abstract Model‐based design of experiments (MBDoE) techniques are tools for selecting experimental conditions that enable accurate parameter estimation for mechanistic models. Most MBDoE approaches assume that the selected experimental conditions will be implemented perfectly, without uncertainties in the independent variables.
Bright Ofori +3 more
wiley +1 more source
Asymptotic properties of cross‐classified sampling designs
Abstract We investigate the family of cross‐classified sampling designs across an arbitrary number of dimensions. We introduce a variance decomposition that enables the derivation of general asymptotic properties for these designs and the development of straightforward and asymptotically unbiased variance estimators.
Jean Rubin, Guillaume Chauvet
wiley +1 more source
Bayesian inverse ensemble forecasting for COVID‐19
Abstract Variations in strains of COVID‐19 have a significant impact on the rate of surges and on the accuracy of forecasts of the epidemic dynamics. The primary goal for this article is to quantify the effects of varying strains of COVID‐19 on ensemble forecasts of individual “surges.” By modelling the disease dynamics with an SIR model, we solve the ...
Kimberly Kroetch, Don Estep
wiley +1 more source
The in the given article presents one of the options of the practical implementation of methodical features interactive-informational approach in teaching at profile level algebra and the beginnings of mathematical analysis high school students.
U V Sadovnichy, R M Turkmenov
doaj
A goodness‐of‐fit test for regression models with discrete outcomes
Abstract Regression models are often used to analyze discrete outcomes, but classical goodness‐of‐fit tests such as those based on the deviance or Pearson's statistic can be misleading or have little power in this context. To address this issue, we propose a new test, inspired by the work of Czado et al.
Lu Yang +2 more
wiley +1 more source
A partial envelope approach for modelling multivariate spatial‐temporal data
Abstract In the new era of big data, modelling multivariate spatial‐temporal data is a challenging task due to both the high dimensionality of the features and complex associations among the responses across different locations and time points.
Reisa Widjaja +3 more
wiley +1 more source
Self‐Similar Blowup for the Cubic Schrödinger Equation
ABSTRACT We give a rigorous proof for the existence of a finite‐energy, self‐similar solution to the focusing cubic Schrödinger equation in three spatial dimensions. The proof is computer‐assisted and relies on a fixed point argument that shows the existence of a solution in the vicinity of a numerically constructed approximation.
Roland Donninger, Birgit Schörkhuber
wiley +1 more source

