Health management for aircraft system using Bayesian probability model

2016 ◽  
Author(s):  
W. Feng Wei ◽  
Z. C. Pei ◽  
D. D Hu
Keyword(s):  
Health Management ◽  
Probability Model ◽  
Bayesian Probability ◽  
Aircraft System

Production of Landslide Susceptibility Map using Bayesian Probability Model

2015 ◽  
Vol 4 (2) ◽  
pp. 16-33 ◽  
Author(s):  
Halil Akıncı ◽  
Ayşe Yavuz Özalp ◽  
Mehmet Özalp ◽  
Sebahat Temuçin Kılıçer ◽  
Cem Kılıçoğlu ◽  
...  
Keyword(s):  
Land Use ◽  
Land Use Planning ◽  
Landslide Susceptibility ◽  
Probability Model ◽  
Bayesian Probability ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Model Parameters ◽  
Operating Characteristics ◽  
Landslide Susceptibility Map

Artvin is one of the provinces in Turkey where landslides occur most frequently. There have been numerous landslides characterized as natural disaster recorded across the province. The areas sensitive to landslides across the province should be identified in order to ensure people's safety, to take the necessary measures for reducing any devastating effects of landslides and to make the right decisions in respect to land use planning. In this study, the landslide susceptibility map of the Central district of Artvin was produced by using Bayesian probability model. Parameters including lithology, altitude, slope, aspect, plan and profile curvatures, soil depth, topographic wetness index, land cover, and proximity to the road and stream were used in landslide susceptibility analysis. The landslide susceptibility map produced in this study was validated using the receiver operating characteristics (ROC) based on area under curve (AUC) analysis. In addition, control landslide locations were used to validate the results of the landslide susceptibility map and the validation analysis resulted in 94.30% accuracy, a reliable outcome for this map that can be useful for general land use planning in Artvin.

scholarly journals A Bayesian Probability Model Can Simulate the Knowledge of Soybean Rust Researchers to Optimize the Application of Fungicides

2019 ◽  
Vol 10 (4) ◽  
pp. 37-51
Author(s):  
Gregory Vinícius Conor Figueiredo ◽  
Lucas Henrique Fantin ◽  
Marcelo Giovanetti Canteri ◽  
José Carlos Ferreira da Rocha ◽  
David de Souza Jaccoud Filho
Keyword(s):  
Literature Review ◽  
Bayesian Network ◽  
Knowledge Engineering ◽  
Probability Model ◽  
Soybean Rust ◽  
Bayesian Probability ◽  
Yield Reduction ◽  
Test Cases ◽  
Validation Process ◽  
Asian Soybean Rust

Asian rust is the main soybean disease in Brazil, causing up to 80% of yield reduction. The use of fungicides is the main form of control; however, due to farmer's concern with outbreaks many unnecessary applications are performed. The present study aims to verify the usefulness of a probability model to estimate the timing and the number of fungicides sprays required to control Asian soybean rust, using Bayesian networks and knowledge engineering. The model was developed through interviews with rust researchers and a literature review. The Bayesian network was constructed with the GeNIe 2.0 software. The validation process was performed by 42 farmers and 10 rust researchers, using 28 test cases. Among the 28 tested cases, generated by the system, the agreement with the model was 47.5% for the farmers and 89.3% for the rust researchers. In general, the farmers overestimate the number. The results showed that the Bayesian network has accurately represented the knowledge of the expert, and also could help the farmers to avoid the unnecessary applications.

Reduction of Enteropathogenic Yersinia in the Pig Slaughterhouse by Using Bagging of the Rectum

2010 ◽  
Vol 73 (12) ◽  
pp. 2161-2168 ◽  
Author(s):  
RIIKKA LAUKKANEN ◽  
JUKKA RANTA ◽  
XIAOJIN DONG ◽  
MARJAANA HAKKINEN ◽  
PILAR ORTIZ MARTÍNEZ ◽  
...  
Keyword(s):  
Yersinia Pseudotuberculosis ◽  
Probability Model ◽  
Production Capacity ◽  
Bayesian Probability ◽  
Credible Intervals ◽  
High Production ◽  
Enteropathogenic Yersinia ◽  
Contamination Routes ◽  
Carcass Contamination ◽  
Farm Types

To evaluate the effectiveness of bagging of the rectum in mitigating the contamination of carcasses with enteropathogenic Yersinia at the slaughterhouse and to estimate the hidden prevalences of these pathogens in different farm types and capacities, samples from pigs, carcasses, and slaughterhouse environment were collected, and a Bayesian probability model was constructed. In addition, the contamination routes were studied with molecular typing of the isolated strains. According to the model, bagging of the rectum reduced carcass contamination significantly with pathogenic Yersinia enterocolitica, but not with Yersinia pseudotuberculosis, and alone it was insufficient to completely prevent the carcass contamination with enteropathogenic Yersinia. The hidden prevalence of pathogenic Y. enterocolitica was higher at high production capacity than it was in low production capacity, but the 95% credible intervals overlapped. Slaughterhouse environments can contaminate carcasses with enteropathogenic Yersinia, but the plausible main contamination source is the pig carrying the pathogen.

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