Results 101 to 110 of about 159,702 (261)

Who Are the Farmers Participating in a Carbon Sequestration Program? Results of a Discrete Choice Experiment in Germany

open access: yesAgribusiness, EarlyView.
ABSTRACT Agricultural soils offer great potential for carbon sequestration through humus formation. One way to motivate farmers to build up humus is through humus programs. These are still at an early stage of development, poorly explored, and the number of participating farmers is low. Our aim is to explain the heterogeneity of farmers' willingness to
Julia B. Block   +2 more
wiley   +1 more source

Failure Diagnosis Analysis of Medical Equipment Based on Fault Tree and Fuzzy Bayesian Network

open access: yesZhongguo yiliao qixie zazhi
ObjectiveTo enhance the reliability of medical equipment, this study aims to develop a failure cause diagnosis model and provide rational suggestions for efficient equipment use.
Ke ZHANG, Liang HUANG
doaj   +1 more source

Consumer Preferences for Craft Beer: The Interplay of Localness and Advertising Language

open access: yesAgribusiness, EarlyView.
ABSTRACT This study explores the influence of the language of the label, origin of production, and origin of brewing ingredients on Croatian consumers' preferences and willingness to pay for organic craft beer. Employing an online survey and a choice experiment among 223 Croatian alcohol consumers, we find that while there's a willingness to pay a ...
Marija Cerjak   +2 more
wiley   +1 more source

Association Between Liver Function Grade and Post‐Hepatectomy Liver Failure in Patients With Hepatocellular Carcinoma: A Latent Class Analysis

open access: yesAnnals of Gastroenterological Surgery, EarlyView.
We retrospectively analyzed clinical data from patients who underwent hepatectomy for hepatocellular carcinoma (HCC) using LCA‐based grading system. These findings provide a new risk stratification framework for the design of precision surgery to treat patients with HCC.
Ling Liu   +5 more
wiley   +1 more source

A multiscale Bayesian optimization framework for process and material codesign

open access: yesAIChE Journal, EarlyView.
Abstract The simultaneous design of processes and enabling materials such as solvents, catalysts, and adsorbents is challenging because molecular‐ and process‐level decisions are strongly interdependent. Sequential approaches often yield suboptimal results since improvements in material properties may not translate into superior process performance. We
Michael Baldea
wiley   +1 more source

Sequential Monte Carlo with likelihood tempering and parallel implementation for uncertainty quantification

open access: yesAIChE Journal, EarlyView.
Abstract Bayesian estimation enables uncertainty quantification, but analytical implementation is often intractable. As an approximate approach, the Markov Chain Monte Carlo (MCMC) method is widely used, though it entails a high computational cost due to frequent evaluations of the likelihood function.
Tatsuki Maruchi   +2 more
wiley   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

open access: yesAdvanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

Bayesian Optimization Guiding the Experimental Mapping of the Pareto Front of Mechanical and Flame‐Retardant Properties in Polyamide Nanocomposites

open access: yesAdvanced Intelligent Discovery, EarlyView.
Bayesian optimization enabled the design of PA56 system with just 8 wt% additives, achieving limiting oxygen index 30.5%, tensile strength 80.9 MPa, and UL‐94 V‐0 rating. Without prior knowledge, the algorithm uncovered synergistic effects between aluminum diethyl‐phosphinate and nanoclay.
Burcu Ozdemir   +4 more
wiley   +1 more source

The parieto-occipital cortex is a candidate neural substrate for the human ability to approximate Bayesian inference

open access: yesCommunications Biology
Adaptive decision-making often requires one to infer unobservable states based on incomplete information. Bayesian logic prescribes that individuals should do so by estimating the posterior probability by integrating the prior probability with new ...
Nicholas M. Singletary   +2 more
doaj   +1 more source

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