Results 131 to 140 of about 15,893 (296)

Sustainable Innovation and Impact on Triple Bottom Line Performance: Are Innovation Types Simple Mediators?

Business Strategy and the Environment, EarlyView.
ABSTRACT This study investigates the way sustainable innovation, conceptualized as a second‐order construct integrating sustainable orientation and innovation culture, impacts triple bottom line (TBL) performance. It also examines the mediating roles of product, process, organizational, and marketing innovations.
Nuno Fernandes Crespo, Lucas Bonacina Roldan   +1 more
wiley   +1 more source

GastroNet: A robust attention‐based deep learning and cosine similarity feature selection framework for gastrointestinal disease classification from endoscopic images

CAAI Transactions on Intelligence Technology, EarlyView., 2023
Abstract Diseases of the Gastrointestinal (GI) tract significantly affect the quality of human life and have a high fatality rate. Accurate diagnosis of GI diseases plays a pivotal role in healthcare systems. However, processing large amounts of medical image data can be challenging for radiologists and other medical professionals, increasing the risk ...
Muhammad Nouman Noor, Muhammad Nazir, Imran Ashraf, Nouf Abdullah Almujally, Muhammad Aslam, Syeda Fizzah Jilani   +5 more
wiley   +1 more source

Data‐driven simulation of crude distillation using Aspen HYSYS and comparative machine learning models

The Canadian Journal of Chemical Engineering, EarlyView.
Integrated Aspen HYSYS–machine learning framework for predicting product yields and quality variables. Abstract Crude oil refining is a complex process requiring precise modelling to optimize yield, quality, and efficiency. This study integrates Aspen HYSYS® simulations with machine learning techniques to develop predictive models for key refinery ...
Aldimiro Paixão Domingos, Heleno Bispo, Thiago Vaz da Costa, Sérgio Mauro da Silva Neiro   +3 more
wiley   +1 more source

A hidden Markov model and reinforcement learning‐based strategy for fault‐tolerant control

The Canadian Journal of Chemical Engineering, EarlyView.
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, Debaprasad Dutta, Simant R. Upreti   +2 more
wiley   +1 more source

Normalising Flows for Bayesian Gravity Inversion

, 2023
Gravity inversion is a commonly applied data analysis technique in the field of geophysics. While machine learning methods have previously been explored for the problem of gravity inversion, these are deterministic approaches returning a single solution ...
Toland, Karl, Rakoczi, Henrietta, Prasad, Abhinav, Messenger, Christopher, Hammond, Giles   +4 more
core  

On numerical implementation of α-stable priors in Bayesian inversion [PDF]

, 2023
In this thesis, we introduce numerical approximations of Levy α-stable random field priors for Bayesian inversion. The α-stable processes are well-studied in stochastic process literature, and they can be potentially formulated as discretization ...
Suuronen, Jarkko
core  

Safety soft sensor development for pilot‐scale ilmenite electric arc furnace using long short‐term memory‐based architecture

The Canadian Journal of Chemical Engineering, EarlyView.
Abstract Ilmenite electric arc furnaces (EAFs) are used for smelting titanium‐iron oxide ore at high temperatures generated by electrical arcs to produce titanium slag and pig iron. As these units are pushed to their limits, ensuring safe and reliable operation becomes challenging.
Antony Gareau‐Lajoie, Djogap F. Chrysler J., Daniel Rodrigues, Marie‐Ève Gosselin, Moncef Chioua   +4 more
wiley   +1 more source

Bayesian inverse ensemble forecasting for COVID‐19

Canadian Journal of Statistics, EarlyView.
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

Dielectric Parameter Estimation at Ka-Band using Bayesian Inversion Method

, 2020
This paper presents the estimation of dielectric material properties such as relative permittivity and thickness, using the well-known Bayesian inversion method.
Abbas, Syed Muzahir, Syed Muzahir Abbas, Bernasconi, Giancarlo, Dev, Kapal, Gentili, G. Guido, G. Guido Gentili, Shahid, Saleem, Giancarlo Bernasconi, Saleem Shahid, Kapal Dev   +9 more
core   +1 more source

A partial envelope approach for modelling multivariate spatial‐temporal data

Canadian Journal of Statistics, EarlyView.
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, Wenbo Wu, Victor De Oliveira, Keying Ye   +3 more
wiley   +1 more source