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bayesian network model to distinguish between intentional attacks and accidental technical failures:a case study of floodgates

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Cybersecurity 2021年第1期4卷 442-460页

作者： Sabarathinam Chockalingam Wolter Pieters AndréTeixeira Pieter van Gelder Faculty of Technology Policy and ManagementDelft University of TechnologyDelftThe Netherlands Department of Risk Safety and SecurityInstitute for Energy TechnologyHaldenNorway Behavioural Science Institute Radboud UniversityNijmegenThe Netherlands Department of Electrical Engineering Uppsala UniversityUppsalaSweden

Water management infrastructures such as floodgates are critical and increasingly operated by Industrial Control Systems(ICS).These systems are becoming more connected to the internet,either directly or through the corporate *** makes them vulnerable to *** behaviour in floodgates operated by ICS could be caused by both(intentional)attacks and(accidental)technical *** operators notice abnormal behaviour,they should be able to distinguish between those two causes to take appropriate measures,because for example replacing a sensor in case of intentional incorrect sensor measurements would be ineffective and would not block corresponding the attack *** the previous work,we developed the attack-failure distinguisher framework for constructing bayesian network(BN)models to enable operators to distinguish between those two causes,including the knowledge elicitation method to construct the directed acyclic graph and conditional probability tables of BN *** a full case study of the attack-failure distinguisher framework,this paper presents a BN model constructed to distinguish between attacks and technical failures for the problem of incorrect sensor measurements in floodgates,addressing the problem of floodgate *** utilised experts who associate themselves with the safety and/or security community to construct the BN model and validate the qualitative part of constructed BN *** constructed BN model is usable in water management infrastructures to distinguish between intentional attacks and accidental technical failures in case of incorrect sensor *** could help to decide on appropriate response strategies and avoid further complications in case of incorrect sensor measurements.

关键词： bayesian network DeMorgan model Intentional attack Probability elicitation Safety Security Technical failure Water management

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bayesian network-based survival prediction model for patients having undergone post-transjugular intrahepatic portosystemic shunt for portal hypertension

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World Journal of Gastroenterology 2024年第13期30卷 1859-1870页

作者： Rong Chen Ling Luo Yun-Zhi Zhang Zhen Liu An-Lin Liu Yi-Wen Zhang Department of Infectious Diseases Second Affiliated Hospital of Chongqing Medical UniversityChongqing 400016China

BACKGROUND Portal hypertension(PHT),primarily induced by cirrhosis,manifests severe symptoms impacting patient *** transjugular intrahepatic portosystemic shunt(TIPS)is a critical intervention for managing PHT,it carries risks like hepatic encephalopathy,thus affecting patient survival *** our knowledge,existing prognostic models for post-TIPS survival in patients with PHT fail to account for the interplay among and collective impact of various prognostic factors on ***,the development of an innovative modeling approach is essential to address this *** To develop and validate a bayesian network(BN)-based survival prediction model for patients with cirrhosis-induced PHT having undergone *** The clinical data of 393 patients with cirrhosis-induced PHT who underwent TIPS surgery at the Second Affiliated Hospital of Chongqing Medical University between January 2015 and May 2022 were retrospectively *** were selected using Cox and least absolute shrinkage and selection operator regression methods,and a BN-based model was established and evaluated to predict survival in patients having undergone TIPS surgery for *** Variable selection revealed the following as key factors impacting survival:age,ascites,hypertension,indications for TIPS,postoperative portal vein pressure(post-PVP),aspartate aminotransferase,alkaline phosphatase,total bilirubin,prealbumin,the Child-Pugh grade,and the model for end-stage liver disease(MELD)*** on the above-mentioned variables,a BN-based 2-year survival prognostic prediction model was constructed,which identified the following factors to be directly linked to the survival time:age,ascites,indications for TIPS,concurrent hypertension,post-PVP,the Child-Pugh grade,and the MELD *** bayesian information criterion was 3589.04,and 10-fold cross-validation indicated an average log-likelihood loss of 5.55 with a standard deviation of *** model’s acc

关键词： bayesian network Cirrhosis Portal hypertension Transjugular intrahepatic portosystemic shunt Survival prediction model

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bayesian network-based resilience assessment of interdependent infrastructure systems under optimal resource allocation strategies

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Resilient Cities and Structures 2024年第2期3卷 46-56页

作者： Jingran Sun Kyle Bathgate Zhanmin Zhang Center for Transportation Research The University of Texas at AustinAustinTX 78759USA Department of Civil Architecturaland Environmental EngineeringThe University of Texas at AustinAustinTX 78712USA

Critical infrastructure systems(CISs)play a key role in the socio-economic activity of a society,but are exposed to an array of disruptive events that can greatly impact their function and ***,understanding the underlying behaviors of CISs and their response to perturbations is needed to better prepare for,and mitigate the impact of,future *** is one characteristic of CISs that influences the extent and severity of the impact induced by extreme *** is often dissected into four dimensions:robustness,redundancy,resourcefulness,and rapidity,known as the“4Rs”.This study proposes a framework to assess the resilience of an infrastructure network in terms of these four dimensions under optimal resource allocation strategies and incorporates interdependencies between different CISs,with resilience considered as a stochastic *** proposed framework combines an agent-based infrastructure interdependency model,advanced optimization algorithms,bayesian network techniques,and Monte Carlo simulation to assess the resilience of an infrastructure *** applicability and flexibility of the proposed framework is demonstrated with a case study using a network of CISs in Austin,Texas,where the resilience of the network is assessed and a“what-if”analysis is performed.

关键词： Infrastructure resilience bayesian network Resilience assessment Infrastructure interdependency Resource allocation

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bayesian network revealing pathways to workplace innovation and career satisfaction in the public service

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Journal of Management Analytics 2020年第2期7卷 253-280页

作者： Warit Wipulanusat Kriengsak Panuwatwanich Rodney A.Stewart Stewart L.Arnold Jue Wang Logistics and Business Analytics Center of Excellence and School of Engineering and Technology Walailak UniversityNakhonsithammaratThailand School of Civil Engineering and Technology Sirindhorn International Institute of TechnologyThammasat UniversityPathum ThaniThailand School of Engineering and Built Environment Griffith UniversityQueenslandAustralia Nanyang Business School Nanyang Technological UniversitySingapore School of Social Sciences Nanyang Technological UniversitySingapore

This paper examined the innovation process in the Australian Public Service(APS)using a bayesian network(BN)founded on an empirically derived structural equation *** focus of the BN was to examine the impact of leadership style and organisational culture on workplace innovation and career satisfaction in the *** scenario analysis,the best combination of managerial actions for enhancing APS career satisfaction was *** results emphasise the benefit of encouraging management to adopt a transformational leadership style and instilling innovative culture in their *** addition,innovative culture was a key driver of workplace innovation,which served to improve the career satisfaction of APS *** are discussed to propose practical strategies for organisations wish to encourage innovation among employees.

关键词： innovation public sectors scenario analysis bayesian network structural equation model

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bayesian network-based risk evaluation model for the operational requirements of the China Railway Express under the Belt and Road initiative

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Transportation Safety and Environment 2022年第3期4卷 18-31页

作者： Fenling Feng Junjie Jia Ailan Liang Chengguang Liu School of Traffic and Transportation Engineering Central South UniversityChangsha 410075HunanChina Rail Data Research and Application Key Laboratory of Hunan Province Central South UniversityChangsha 410075HunanChina.

The operation of the China Railway Express features numerous links across several regions and countries. Effectively controlling therisks involved in the operation of the China Railway Express is crucial for ensuring safety and efficiency and promoting the sustainabledevelopment of the China Railway Express. The bayesian network-based risk-management model was built corresponding to theactual operation of the China Railway Express;the model used an advanced risk-management theory. The sensitivity analysis of riskfactors and bayesian network inference were realized using the expectation-maximization and clique-tree propagation *** the risk-checklistmethod, 17 risk-related factors were analysed on 17 nodes of the bayesian network fromthree perspectives—safety, efficiency and effectiveness—based on expert opinions and the actual operating conditions of the China Railway *** from a sensitivity analysis and evidence inference of the bayesian network model indicated that the sensitivity coefficients ofnodes N01, N04, N07, N08 and N17 of the network were high. Moreover, the risk-occurrence probabilities for nodes N01, N04, N06,N07 and N09 were higher in the case of reverse inference. Our results revealed the crucial factors influencing the risk. The identifiedrisk factors included the stability of the political environment in countries along the route, conditions of station infrastructures andthe complexity of the process of changing rails and reloading. Further, risk-management suggestions were provided. By establishinga sound risk-management framework, reliable assessment and management could be realized in accordance with changes in theoperating conditions of the China Railway Express.

关键词： Risk evaluation bayesian network China railway express risk-management suggestion

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MapReduce Based Parallel bayesian network for Manufacturing Quality Control

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Chinese Journal of Mechanical Engineering 2017年第5期30卷 1216-1226页

作者： Mao-Kuan Zheng Xin-Guo Ming Xian-Yu Zhang Guo-Ming Li School of Mechanical Engineering Shanghai Jiao Tong University

Increasing complexity of industrial products and manufacturing processes have challenged conventional statistics based quality management approaches in the cir- cumstances of dynamic production. A bayesian network and big data analytics integrated approach for manufacturing process quality analysis and control is proposed. Based on Hadoop distributed architecture and MapReduce parallel computing model, big volume and variety quality related data generated during the manufacturing process could be dealt with. Artificial intelligent algorithms, including bayesian network learning, classification and reasoning, are embedded into the Reduce process. Relying on the ability of the bayesian network in dealing with dynamic and uncertain problem and the parallel computing power of MapReduce, bayesian net- work of impact factors on quality are built based on prior probability distribution and modified with posterior probability distribution. A case study on hull segment manufacturing precision management for ship and offshore platform building shows that computing speed accelerates almost directly pro- portionally to the increase of computing nodes. It is also proved that the proposed model is feasible for locating and reasoning of root causes, forecasting of manufacturing outcome, and intelligent decision for precision problem solving. The inte- gration ofbigdata analytics and BN method offers a whole new perspective in manufacturing quality control.

关键词： bayesian network Big data analytics MapReduce Quality control

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A Structure Learning Algorithm for bayesian network Using Prior Knowledge

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Journal of Computer Science & Technology 2015年第4期30卷 713-724页

作者：徐俊刚赵越陈健韩超 School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing 101408 China School of Software Engineering South China University of Technology Guangzhou 510006 China

Learning structure from data is one of the most important fundamental tasks of bayesian network research. Particularly, learning optional structure of bayesian network is a non-deterministic polynomial-time （NP） hard problem. To solve this problem, many heuristic algorithms have been proposed, and some of them learn bayesian network structure with the help of different types of prior knowledge. However, the existing algorithms have some restrictions on the prior knowledge, such as quality restriction and use restriction. This makes it di？cult to use the prior knowledge well in these algorithms. In this paper, we introduce the prior knowledge into the Markov chain Monte Carlo （MCMC） algorithm and propose an algorithm called Constrained MCMC （C-MCMC） algorithm to learn the structure of the bayesian network. Three types of prior knowledge are defined： existence of parent node, absence of parent node, and distribution knowledge including the conditional probability distribution （CPD） of edges and the probability distribution （PD） of nodes. All of these types of prior knowledge are easily used in this algorithm. We conduct extensive experiments to demonstrate the feasibility and effectiveness of the proposed method C-MCMC.

关键词： bayesian network structure learning Markov chain Monte Carlo prior knowledge

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Reconstruction of Gene Regulatory networks Based on Two-Stage bayesian network Structure Learning Algorithm

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Journal of Bionic Engineering 2009年第1期6卷 86-92页

作者： Gui-xia Liu, Wei Feng, Han Wang, Lei Liu, Chun-guang ZhouCollege of Computer Science and Technology, Jilin University, Changchun 130012,P.R. China [a]College of Computer Science and Technology Jilin University Changchun 130012 P. R. China

In the post-genomic biology era,the reconstruction of gene regulatory networks from microarray gene expression data is very important to understand the underlying biological system,and it has been a challenging task in *** bayesian network model has been used in reconstructing the gene regulatory network for its advantages,but how to determine the network structure and parameters is still important to be *** paper proposes a two-stage structure learning algorithm which integrates immune evolution algorithm to build a bayesian *** new algorithm is evaluated with the use of both simulated and yeast cell cycle *** experimental results indicate that the proposed algorithm can find many of the known real regulatory relationships from literature and predict the others unknown with high validity and accuracy.

关键词： gene regulatory networks two-stage learning algorithm bayesian network immune evolutionary algorithm

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Study of testability measurement method for equipment based on bayesian network model

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Journal of Systems Engineering and Electronics 2009年第5期20卷 1017-1023页

作者： Lian Guangyao Huang Kaoli Chen Jianhui Wei Zhonglin Ordnance Engineering Coll. Shijiazhuang 050003 P. R. China

To analyze and evaluate the testability design of equipment, a testability analysis method based on bayesian network inference model is proposed in the paper. The model can adequately apply testability information and many uncertainty information of design and maintenance process, so it can analyze testability by and large from bayesian inference. The detailed procedure to analyze and evaluate testability for equipments by bayesian network is given in the paper. Its modeling process is simple, its formulation is visual, and the analysis results are more reliable than others. Examples prove that the analysis method based on bayesian network inference can be applied to testability analysis and evaluation for complex equipments.

关键词： design for testability testability analysis and evaluation uncertainty information bayesian network

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bayesian network on labour dissonance:a social sector development challenge to India

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Journal of Management Analytics 2016年第1期3卷 80-111页

作者： Udayan Chanda Remica Aggarwal Department of Management BITS PilaniPilani CampusPilaniIndia

India’s unorganized labour force contributes about one third of the total labour *** scenario is even worse in the Indian automotive industry which employs a little over 7%on a permanent *** get exaggerated due to the outdated labour laws,ironically established to support and protect *** disappointing areas in the labour contract act and labour laws have led to unfair wage practices and a hostile work environment,giving way to labour *** research paper discusses the key issues of labour dissonance in the Indian automobile industry using a bayesian network ***-life casestudy examples from the Indian automobile industry were considered to identify the rationale behind labour *** analysis of a set of 250 responses helped us to understand the associations among key attributes of labour dissatisfaction.

关键词： labour unrest unorganized labour force unfair wage practices bayesian network

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