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Thematische Bibliographien / Diagramme de causalité / Zeitschriftenartikel

Zeitschriftenartikel zum Thema „Diagramme de causalité“

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Veröffentlicht am 25. Mai 2024

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1

Junglas, Peter. „Causality of System Dynamics Diagrams“. SNE Simulation Notes Europe 26, Nr. 3 (September 2016): 147–54. http://dx.doi.org/10.11128/sne.26.tn.10343.

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2

BOUAZIZ, Yosra Jlaiel, und Daniel THIEL. „Enquête sur les mécanismes de pilotage des compétences dans le cadre de la mise en oeuvre de projets ERP en tunisie“. Revue Française de Gestion Industrielle 32, Nr. 2 (01.06.2013): 63–84. http://dx.doi.org/10.53102/2013.32.02.696.

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Cette recherche s'intéresse aux décisions de pilotage des compétences dans le cadre de mise en oeuvre de projets ERP. Après une revue de littérature et une pré-enquête sur le terrain, nous avons lancé une enquête auprès de 31 chefs de projets en Tunisie. Cet échantillon représente 30% de la population totale visée, à savoir les entreprises ayant récemment implanté un ERP composé d'au moins trois modules. Une analyse statistique des données a été réalisée à partir de 651 décisions observées. Ces décisions ont également été formalisées qualitativement à l'aide de diagrammes de causalité permettant de représenter les interrelations entre les variables de pilotage des compétences et les causes de leur défaillance.

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3

Zhang, Qin. „Probabilistic reasoning based on dynamic causality trees/diagrams“. Reliability Engineering & System Safety 46, Nr. 3 (Januar 1994): 209–20. http://dx.doi.org/10.1016/0951-8320(94)90114-7.

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4

Wu, Bing, Luyao Kou und Qi Ma. „Research on HFACS Based on Accident Causality Diagram“. Open Journal of Safety Science and Technology 07, Nr. 02 (2017): 77–85. http://dx.doi.org/10.4236/ojsst.2017.72007.

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5

Wang, Hongchun. „A Fuzzy Reasoning Algorithm in Hybrid Causality Diagram“. Journal of Algorithms & Computational Technology 6, Nr. 4 (Dezember 2012): 623–38. http://dx.doi.org/10.1260/1748-3018.6.4.623.

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6

Eichler, Michael. „Granger causality and path diagrams for multivariate time series“. Journal of Econometrics 137, Nr. 2 (April 2007): 334–53. http://dx.doi.org/10.1016/j.jeconom.2005.06.032.

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7

Jin, Feng, Jun Zhao, Chunyang Sheng und Wei Wang. „Causality diagram-based scheduling approach for blast furnace gas system“. IEEE/CAA Journal of Automatica Sinica 5, Nr. 2 (März 2018): 587–94. http://dx.doi.org/10.1109/jas.2017.7510715.

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8

Perdicoúlis, Anastássios, und Jake Piper. „Network and system diagrams revisited: Satisfying CEA requirements for causality analysis“. Environmental Impact Assessment Review 28, Nr. 7 (Oktober 2008): 455–68. http://dx.doi.org/10.1016/j.eiar.2007.08.004.

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9

Zhang, Chun You, und Xiao Qiang Wu. „Intrusion Scenario Dynamic Correlation Algorithm Based on Single Value Causality Diagram“. Advanced Materials Research 926-930 (Mai 2014): 3063–67. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.3063.

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In order to obtain the effective network intrusion alarm information, and reveal the intention of attackers, an intrusion scenario dynamic correlation algorithm is proposed based on single value causality diagram. According to the composition principle of single value causality diagram, the key factors of the cause and effect diagram are defined. By relating the alarm information of intrusion detection system, attack scenarios are constructed based on cause and effect diagram, and dynamic correction is conduct. Based on the MIT Lincoln laboratory data sets, the correlation test is done using the above attack scenario correlation algorithm. Test results show that the reconstruction of attack scenarios and actual condition have very good consistency, proving that the proposed correlation algorithm can correctly reflect the real hacker intrusion process. The research of this paper provides effective help for the security administrator to implement effective management measures.

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10

Zhou, Zu Xu. „Research on the Preventive Measures of Sports Injury Based on Causality Diagram and Analytic Hierarchy Process“. Applied Mechanics and Materials 380-384 (August 2013): 1838–42. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1838.

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Because of the frequent occurrence of injury accident in the sports curriculum, the security of physical education cannot be ignored. So, this paper analyzes the factors of injury accident in physical education teaching process and proposes prevention measures. The establishment of security system in physical education is an urgent problem. This paper studies the causes and countermeasures of sports injury accidents based on two methods: causality diagram and analytic hierarchy process. The first section introduces the principles of causality diagram and hierarchical analysis in detail. The second part of the article establishes mathematical model of the analysis method. The part three of the text investigates and analyzes the accident status and prevention measures of 100 primary schools and universities in Heilongjiang province in the form of literature researches and questionnaires. It also proposes weight coefficients and causality diagrams of Sports Injury Accidents using causal analysis theory. It converts causal diagram into a hierarchical model using the analytic hierarchy process AHP.

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11

Zhang, Boyi, Pengjian Shang und Jinzhao Liu. „Transition-based complexity-entropy causality diagram: A novel method to characterize complex systems“. Communications in Nonlinear Science and Numerical Simulation 95 (April 2021): 105660. http://dx.doi.org/10.1016/j.cnsns.2020.105660.

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12

Jia, Lulu, Yi Ren, Dezhen Yang, Qiang Feng, Bo Sun und Cheng Qian. „Reliability analysis of dynamic reliability block diagram based on dynamic uncertain causality graph“. Journal of Loss Prevention in the Process Industries 62 (November 2019): 103947. http://dx.doi.org/10.1016/j.jlp.2019.103947.

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13

Zhang, Qin. „A frequency and knowledge tree/causality diagram based expert system approach for fault diagnosis“. Reliability Engineering & System Safety 43, Nr. 1 (Januar 1994): 17–28. http://dx.doi.org/10.1016/0951-8320(94)90092-2.

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14

Gaskell, Amy L., und Jamie W. Sleigh. „An Introduction to Causal Diagrams for Anesthesiology Research“. Anesthesiology 132, Nr. 5 (01.05.2020): 951–67. http://dx.doi.org/10.1097/aln.0000000000003193.

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Abstract Making good decisions in the era of Big Data requires a sophisticated approach to causality. We are acutely aware that association ≠ causation, yet untangling the two remains one of our greatest challenges. This realization has stimulated a Causal Revolution in epidemiology, and the lessons learned are highly relevant to anesthesia research. This article introduces readers to directed acyclic graphs; a cornerstone of modern causal inference techniques. These diagrams provide a robust framework to address sources of bias and discover causal effects. We use the topical question of whether anesthetic technique (total intravenous anesthesia vs. volatile) affects outcome after cancer surgery as a basis for a series of example directed acyclic graphs, which demonstrate how variables can be chosen to statistically control confounding and other sources of bias. We also illustrate how controlling for the wrong variables can introduce, rather than eliminate, bias; and how directed acyclic graphs can help us diagnose this problem. This is a rapidly evolving field, and we cover only the most basic elements. The true promise of these techniques is that it may become possible to make robust statements about causation from observational studies—without the expense and artificiality of randomized controlled trials.

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15

Kocabas, Verda, und Suzana Dragicevic. „Enhancing a GIS Cellular Automata Model of Land Use Change: Bayesian Networks, Influence Diagrams and Causality“. Transactions in GIS 11, Nr. 5 (Oktober 2007): 681–702. http://dx.doi.org/10.1111/j.1467-9671.2007.01066.x.

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16

Scripcaru, Răzvan, Angela Digulescu, Leontin Tuță und Alexandru Șerbănescu. „Discussion upon the Stationarity and the Causality of a Time Series Using the Phase Diagram Representation“. Journal of Military Technology 2, Nr. 2 (18.12.2019): 11–16. http://dx.doi.org/10.32754/jmt.2019.2.02.

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17

Zhang, Mengyao, Deming Yu, Tianyu Wang und Can Xu. „Coupling Analysis of Tunnel Construction Safety Risks Based on N-K Model and SD Causality Diagram“. Buildings 13, Nr. 4 (20.04.2023): 1081. http://dx.doi.org/10.3390/buildings13041081.

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Tunnel construction is characterized by its large scale, long periods and vulnerability to environmental impact, which pose great challenges to tunnel construction safety. In order to analyze the coupling mechanism of tunnel construction safety risks and assess these risks, we conducted a study on the coupling evaluation of these risks in order to improve tunnel construction safety risk management. By analyzing 150 accident cases related to tunnel construction safety, an N-K model (natural killing model) was constructed to quantify the risk level of each coupling form from four aspects—personnel risk factors, equipment risk factors, environmental risk factors and management risk factors—and the SD (system dynamics) causality diagram was used to construct risk element conduction paths and identify the key influencing factors of different coupling forms. The research results show that with the increase in risk coupling factors, the risk of tunnel construction safety accidents also increases; weak personnel safety awareness, aging and wear of equipment, poor operating environment and construction site management chaos are the key risk factors whose prevention needs to be focused on. The related research results can provide a new method for decision makers to assess tunnel construction safety risks and enrich the research on tunnel construction safety risk management.

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18

Mutisari, Rini, Syafrial, Nuhfil Hanani und Sujarwo. „Enhancing household rice farmers’ welfare under climate change conditions using loop diagrams of causality: A system thinking approach“. IOP Conference Series: Earth and Environmental Science 1323, Nr. 1 (01.04.2024): 012009. http://dx.doi.org/10.1088/1755-1315/1323/1/012009.

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Abstract Climate change has hurt several sectors, but agriculture has been a tough hit. Small farmers are the people most affected by climate change because they have less access to resources and information. Their household welfare is lowered as a result, and they are unable to avoid the effects of climate change. To lessen the negative effects of climate change, farmers have to figure out how to adapt to it. This study uses a household economic model to examine the relationship between livelihood assets and strategic adaptation using the framework of systems thinking. This research has created causal loop diagrams (CLDs) based on a literature review to capture crucial variables in farmer households’ economic behavior under climate change conditions. By using these CLDs, more studies can be conducted to enhance the welfare of rice farmers through multiple policy simulations.

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19

Gholipour, Amir, Rita Fragoso, Ana Galvão und David C. Finger. „Mapping the causality of interacting perceptions for nature-based solution and sludge treatment reed bed: A causal loop diagram in Iceland“. Nature-Based Solutions 3 (Dezember 2023): 100049. http://dx.doi.org/10.1016/j.nbsj.2023.100049.

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20

ZAINAL ABIDIN, NORHASLINDA, SHRI DEWI APPLANAIDU, MOHAMMED BABA ABDULLAHI und SURIANIE BAKAR. „UNDERSTANDING PADDY PRODUCTIVITY AT MADA ESTATE FROM A SYSTEM DYNAMICS PERSPECTIVE: A MAPPING TOOL OF CAUSAL LOOP DIAGRAM“. JOURNAL OF SUSTAINABILITY SCIENCE AND MANAGEMENT 18, Nr. 2 (28.02.2023): 81–1012. http://dx.doi.org/10.46754/jssm.2023.02.007.

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The Muda Agricultural Development Authority (MADA) paddy estate project represents Malaysia’s national effort to enhance food security. However, the Economic Transformation Programme (ETP) by Malaysia falls short of attaining the 10 MT/Ha paddy productivity potential. Amidst rising food insecurity, national efforts have focused on improving the efficiency of domestic productivity, this study specifically assessed factors influencing paddy productivity under the MADA estate project. Against the econometric technique, the causal loop diagram (CLD) in the system dynamics (SD) approach was used to analyse the systemic character of productivity and its drivers. The systemic relationship was explored using expert interviews and an extensive literature reviews. Findings established four major sub-systems of the rice value chain. These include paddy production, rice production, government subsidy and self-sufficiency level. The loop polarity feature was used to establish the causal relationships and directions of causality between the sub-systems. This study contributes directly to EPP10 and EPP11 of the ETP. As a result, it supports Malaysia’s effort towards strengthening food security and self-sufficiency.

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21

Apertet, Y. „Causality in thermoelectric systems: Insights from block diagrams“. European Physical Journal Plus 131, Nr. 12 (Dezember 2016). http://dx.doi.org/10.1140/epjp/i2016-16434-6.

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22

Sborlini, German Fabricio Roberto. „Geometry and causal flux in multi-loop Feynman diagrams“. Suplemento de la Revista Mexicana de Física 3, Nr. 2 (31.03.2022). http://dx.doi.org/10.31349/suplrevmexfis.3.020703.

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In this review, we discuss recent developments concerning efficient calculations of multi-loop multi-leg scattering amplitudes. Inspired by the remarkable properties of the Loop-Tree Duality (LTD), we explain how to reconstruct an integrand level representation of scattering amplitudes which only contains physical singularities. These so-called causal representations can be derived from connected binary partitions of Feynman diagrams, properly entangled according to specific rules. We will focus on the detection of flux orientations which are compatible with causality, describing the implementation of a quantum algorithm to identify such configurations.

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23

„Research on Fault Diagnosis Based on Dynamic causality diagram and Fuzzy Reasoning Fusion Method“. Tehnicki vjesnik - Technical Gazette 27, Nr. 2 (April 2020). http://dx.doi.org/10.17559/tv-20190804140256.

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24

Eichhorn, Astrid, Antonio D. Pereira und Andreas G. A. Pithis. „The phase diagram of the multi-matrix model with ABAB interaction from functional renormalization“. Journal of High Energy Physics 2020, Nr. 12 (Dezember 2020). http://dx.doi.org/10.1007/jhep12(2020)131.

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Abstract At criticality, discrete quantum-gravity models are expected to give rise to continuum spacetime. Recent progress has established the functional renormalization group method in the context of such models as a practical tool to study their critical properties and to chart their phase diagrams. Here, we apply these techniques to the multi-matrix model with ABAB interaction potentially relevant for Lorentzian quantum gravity in 3 dimensions. We characterize the fixed-point structure and phase diagram of this model, paving the way for functional RG studies of more general multi-matrix or tensor models encoding causality and subjecting the technique to another strong test of its performance in discrete quantum gravity by comparing to known results.

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25

Zhu, Yan, Ming Peng, Peng Zhang und Limin Zhang. „Warning Decision-Making for Landslide Dam Breaching Flood Using Influence Diagrams“. Frontiers in Earth Science 9 (06.08.2021). http://dx.doi.org/10.3389/feart.2021.679862.

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Warning and evacuation are among the most effective ways for saving human lives and properties from landslide dam hazards. A new warning decision model for landslide dam break is developed using Influence Diagrams to minimize the total losses. An Influence Diagram is a simple visual representation of a decision problem. It analyzes the qualitative (causal) relationships between the variables via a logic diagram and determines the quantitative relationships via conditional probability and Bayes’ theorem. The model is applied for the warning decision-making of the 2008 Tangjiashan landslide dam. The new model unifies the dam failure probability, evacuation, life loss, and flood damage in an Influence Diagram. Besides, a warning criterion is proposed for efficient decision-making. The model is more advanced than the decision tree since the inter-relationships of influence factors are qualitatively analyzed with causality connections and quantitatively analyzed with conditional probabilities. It is more efficient than a dynamic decision-making model (DYDEM) as it can directly calculate the three types of flood loss (i.e., evacuation cost, flood damage, and monetized life loss) and the expected total loss. Moreover, the probabilities of the influence factors leading to known results can be obtained through inversion analysis based on Bayesian theory. The new warning decision model offers an efficient way to save lives from landslide dam breaking and avoid unnecessary expenses from premature warning and evacuation.

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Dong, Wei, Shuqing Zhang, Shanshan Song, Xiaowen Zhang und Xiang Wu. „Refined composite multiscale dynamic causality diagram: a novel time-series analysis approach and its application in fault diagnosis of rotating machinery“. Structural Health Monitoring, 12.01.2024. http://dx.doi.org/10.1177/14759217231215351.

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Entropy-based feature extraction methods have been widely used in the fault diagnosis of rotating machinery, but the entropy-based methods still have the defects of poor noise robustness, weak feature extraction, and low computational efficiency. To solve this problem, this article proposes a fault diagnosis method based on refined composite multiscale dynamic causal diagram (RCMSDCD) and local receptive field extreme learning machine (LRFELM). First, a novel feature extraction method, named dynamic causal diagram (DCD), is proposed to comprehensively quantify static and dynamic complexity. DCD is obtained by combining generalized inverse fractional order entropy with complexity–entropy causal plane. Then, combined with the coarse-graining process, DCD is extended to a multiscale analysis called RCMSDCD to complement the feature description at cross-time scales. Third, RCMSDCD features are input into LRFELM classifier for fault recognition of rotating machinery. The effectiveness of the proposed RCMSDCD-LRFELM method is verified by the Paderborn University bearing test and real wind turbine gear signals. The results show that this method has the highest classification accuracy of 100% with high computational efficiency, good stability, and strong generalization ability.

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