Results 131 to 140 of about 40,132 (204)
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
ABSTRACT Using online job advertisement data improves the timeliness and granularity depth of analysis in the labor market in domains not covered by official data. Specifically, its variation over time may be used as an anticipator of official employment variations.
Pietro Giorgio Lovaglio +1 more
wiley +1 more source
Do robots boost productivity? A quantitative meta‐study
ABSTRACT This meta‐study analyzes the productivity effects of industrial robots. More than 1800 estimates from 85 primary studies are collected. The meta‐analytic evidence suggests that robotization has so far provided, at best, a small boost to productivity. There is strong evidence of publication bias in the positive direction.
Florian Schneider
wiley +1 more source
ABSTRACT We introduce a dynamic and stochastic interbank model with an endogenous notion of distress contagion, arising from rational worries about future defaults and ensuing losses. This entails a mark‐to‐market valuation adjustment for interbank claims, leading to a forward‐backward approach to the equilibrium dynamics whereby future default ...
Zachary Feinstein, Andreas Søjmark
wiley +1 more source
Influence of Observational Temperature Data Sets on ECS and TCR Estimates
Abstract Uncertainties in estimates of Equilibrium Climate Sensitivity (ECS) and Transient Climate Response (TCR) are influenced by observational temperature data sets. Variability exists not just among the data products, but also within the creation of each one.
Vikrant Sapkota +3 more
wiley +1 more source
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
A Survey for Deep Reinforcement Learning Based Network Intrusion Detection
This paper surveys deep reinforcement learning (DRL) for network intrusion detection, evaluating model efficiency, minority attack detection, and dataset imbalance. Findings show DRL achieves state‐of‐the‐art results on public datasets, sometimes surpassing traditional deep learning.
Wanrong Yang +3 more
wiley +1 more source
Ordered probit regression is used as a latent trait model, with age at death estimated from a Gompertz distribution. Combined with Bayesian Markov Chain Monte Carlo sampling, this approach eliminates the need for reference priors for transition ages or population parameters.
Nils Müller‐Scheeßel +2 more
wiley +1 more source
Bayesian Evaluation of Treatment Effect of Avelumab Plus Axitinib for Advanced Renal Cell Carcinoma
ABSTRACT Background Despite not achieving statistical significance, the JAVELIN Renal 101 trial indicated a potentially clinically relevant effect size (hazard ratio [HR], 0.88; 95% confidence interval, 0.75 to 1.04) on overall survival (OS) favoring avelumab plus axitinib over sunitinib for advanced renal cell carcinoma (aRCC).
Wataru Fukuokaya +9 more
wiley +1 more source
ABSTRACT The fiscal sustainability of healthcare systems is increasingly strained by aging populations with two competing hypotheses dominating the literature. The Red Herring Hypothesis suggests that healthcare expenditures are driven more by proximity to death than by chronological age, while the Steepening Hypothesis examines whether expenditures ...
Malene Kallestrup‐Lamb +2 more
wiley +1 more source

