Results 31 to 40 of about 3,751 (165)

Partial Identification in Matching Models for the Marriage Market

, 2020
We study partial identification of the preference parameters in models of one-to-one matching with perfectly transferable utilities, without imposing parametric distributional restrictions on the unobserved heterogeneity and with data on one large market.
Gualdani, Cristina, Sinha, Shruti
core   +2 more sources

The Ramsey Discounting Formula for a Hidden-State Stochastic Growth Process [PDF]

, 2012
The long term discount rate is critically dependent upon projections of future growth rates that are fuzzier in proportion to the remoteness of the time horizon.
Weitzman, Martin L.
core   +2 more sources

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

Civil 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

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

Artificial intelligence in enzyme catalysis: Emerging trends and applications in biocatalyst engineering

The Canadian Journal of Chemical Engineering, EarlyView.
Schematic representation of artificial intelligence approaches in enzyme catalysis, integrating bibliometric analysis, emerging research trends, and machine learning tools for enzyme design, prediction, and industrial biocatalytic applications. Abstract This study systematically explores the applications of artificial intelligence (AI) in enzyme ...
Misael Bessa Sales, Francisco Izaias da Silva Aires, Paulo Gonçalves de Sousa Junior, Calebe Da Rocha Silva, Emanuel Lino Rodrigues, Maria Cristiane Martins de Souza, José C. S. dos Santos   +6 more
wiley   +1 more source

Evaluation of software architecture using fuzzy colored Petri nets [PDF]

, 2013
Software Architecture (SA) is one of the most important artifacts for life cycle of a software system because it incorporates some important decisions and principles for the system development. On the other hand, developing the systems based on uncertain
Ali Harounabadi, Seyed Javad Mirabedini, Vahid Abroshan   +2 more
core  

Rockburst prediction based on data preprocessing and hyperband‐RNN‐DNN

Deep Underground Science and Engineering, EarlyView.
A data preprocessing workflow is proposed to address challenges in rockburst data analysis. Coupled algorithms preprocess the data set, and hyperband optimization is used to enhance RNN performance. Results show that preprocessing improves accuracy, while dense layers enhance model stability and prediction performance.
Yong Fan, Chenxi Yin, Guangdong Yang, Shengyong Ding, Bin Tian   +4 more
wiley   +1 more source

Dielectric Permeability of Forestry Depending on Environmental Parameters in Radio Frequency Monitoring [PDF]

, 2018
Introduction. New approach to forestry monitoring is presented. The relevance of the study is caused by the need to improve the forest management system based of modern information technologies. The paper demonstrates radio frequency monitoring as a most
Gazizov, A. M., Pobedinskiy, A. A., Pobedinsky, V. V., Sannikov, S. P., Газизов, А. М., Побединский, А. А., Побединский, В. В., Санников, С. П.   +7 more
core  

Real‐time lithology identification while drilling based on drill cuttings image analysis with ensemble learning

Deep Underground Science and Engineering, EarlyView.
A lithology identification while drilling method was developed, integrating an automated cuttings sampling system, a smart drilling rig, and an ensemble learning model. Underground trials achieved 97.42% accuracy in real‐time identification of cuttings lithology and composition, enhancing hazard management and supporting unmanned drilling technology in
Kun Li, Ting Ren, Ningping Yao, Jon Roberts, Haitao Song, Zhongbei Li, Xueqiu He, Liang Zhao   +7 more
wiley   +1 more source

Electricity Price Prediction Using Multikernel Gaussian Process Regression Combined With Kernel‐Based Support Vector Regression

Journal of Forecasting, EarlyView.
ABSTRACT This paper presents a new hybrid model for predicting German electricity prices. The algorithm is based on a combination of Gaussian process regression (GPR) and support vector regression (SVR). Although GPR is a competent model for learning stochastic patterns within data and for interpolation, its performance for out‐of‐sample data is not ...
Abhinav Das, Stephan Schlüter, Lorenz Schneider   +2 more
wiley   +1 more source