Results 171 to 180 of about 17,736 (248)

Consumer Adoption of Internet of Things

open access: yesJournal of Consumer Behaviour, Volume 24, Issue 2, Page 673-693, March 2025.
ABSTRACT The Internet of Things (IoT), a pivotal technology in enhancing user connectivity, faces a paradox: its widespread potential yet limited consumer adoption. This study addresses this dichotomy by synthesizing a large‐scale meta‐analytic structural equation modeling (MASEM) and hierarchical linear meta‐analysis (HiLMA) of 2736 effect sizes from ...
Wagner Junior Ladeira   +6 more
wiley   +1 more source

Ensemble‐based soil liquefaction assessment: Leveraging CPT data for enhanced predictions

open access: yesCivil Engineering Design, Volume 7, Issue 1, Page 23-35, March 2025.
Abstract This study focuses on predicting soil liquefaction, a critical phenomenon that can significantly impact the stability and safety of structures during seismic events. Accurate liquefaction assessment is vital for geotechnical engineering, as it informs the design and mitigation strategies needed to safeguard infrastructure and reduce the risk ...
Arsham Moayedi Far, Masoud Zare
wiley   +1 more source

Beyond the blank page: Frequentist and Bayesian perspectives on risk prediction algorithms. [PDF]

open access: yesWorld J Gastrointest Oncol
Tustumi F   +2 more
europepmc   +1 more source

Uncertainty Calibration in Molecular Machine Learning: Comparing Evidential and Ensemble Approaches

open access: yesChemistry – A European Journal, EarlyView.
Raw uncertainty estimates from deep evidential regression and deep ensembles are systematically miscalibrated. Post hoc calibration aligns predicted uncertainty with true errors, improving reliability and enabling efficient active learning and reducing computational cost while preserving predictive accuracy.
Bidhan Chandra Garain   +3 more
wiley   +1 more source

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

open access: yesThe 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   +2 more
wiley   +1 more source

A‐optimal model‐based design of experiments for processes with uncertain inputs

open access: yesThe Canadian Journal of Chemical Engineering, EarlyView.
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

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