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Articles de revues sur le sujet « Diagramme Causaux »

Auteur : Grafiati
Publié le 7 juillet 2024
Mis à jour le 7 juillet 2024

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1

Nicholls, Jim, and J. Kelly Russell. "Igneous Rock Associations 20. Pearce Element Ratio Diagrams: Linking Geochemical Data to Magmatic Processes." Geoscience Canada 43, no. 2 (2016): 133. http://dx.doi.org/10.12789/geocanj.2016.43.095.

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It has been nearly fifty years since Tom Pearce devised a type of element ratio diagram that isolates the effects of crystal fractionation and accumulation (sorting) hidden in the chemistry of a suite of igneous rocks. Here, we review the guiding principles and methods supporting the Pearce element ratio paradigm and provide worked examples with data from the Mauna Ulu lava flows (erupted 1970–1971, Kilauea Volcano, Hawaii). Construction of Pearce element ratio diagrams requires minimum data; a single rock analysis can suffice. The remaining data test the model. If the data fit the model, then the model is accepted as a plausible or likely explanation for the observed chemical variations. If the data do not fit, the model is rejected. Successful applications of Pearce element ratios require the presence and identification of conserved elements; elements that remain in the melt during the processes causing the chemical diversity. Conserved elements are identified through a priori knowledge of the physical-chemical behaviour of the elements in rock-forming processes, plots of weight percentages of pairs of oxides against each other, or by constant ratios of two elements. Three kinds of Pearce element ratio diagrams comprise a model: conserved element, assemblage test, and phase discrimination diagrams. The axial ratios for Pearce ratio diagrams are combinations of elements chosen on the basis of the chemical stoichiometry embedded in the model. Matrix algebra, operating on mineral formulae and analyses, is used to calculate the axis ratios. Models are verified by substituting element numbers from mineral formulae into the ratios. Different intercepts of trends on Pearce element ratio diagrams distinguish different magma batches and, by inference, different melting events. We show that the Mauna Ulu magmas derive from two distinct batches, modified by sorting of olivine, clinopyroxene, plagioclase and, possibly, orthopyroxene (unobserved).RÉSUMÉIl y a près de cinquante ans Tom Pearce a conçu un genre de diagramme de ratio d’éléments qui permet d’isoler les effets de la cristallisation fractionnée et de l'accumulation cristalline (tri) au sein de la chimie d'une suite de roches ignées. Dans le présent article, nous passons en revue les principes et les méthodes étayant le paradigme de ratio d’éléments de Pearce, et présentons des exemples pratiques à partir de données provenant de coulées de lave du Mauna Ulu (éruption 1970–1971 du volcan Kilauea, Hawaii). La confection des diagrammes de ratio d’éléments de Pearce requière un minimum de données; une seule analyse de roche peut suffire. Les données restantes servent à tester le modèle. Si les données sont conformes au modèle, alors le modèle est accepté comme explication plausible ou probable des variations chimiques observées. Si les données ne correspondent pas, le modèle est rejeté. Les applications réussies des ratios d’éléments de Pearce requièrent la présence et l'identification d’éléments conservés; éléments qui demeurent dans la masse fondue au cours des processus causant la diversité chimique. Les éléments conservés sont identifiés par la connaissance a priori du comportement physico-chimique des éléments dans les processus de formation des roches, le positionnement sur la courbe des pourcentages pondérés de pairs d'oxydes les uns contre les autres, ou par des ratios constants de deux éléments. Trois types de diagrammes de Pearce de ratio d’éléments constituent un modèle: élément conservé, test d'assemblage, et diagrammes de phase discriminant. Les ratios axiaux pour les diagrammes de ratio d’éléments de Pearce sont des combinaisons d'éléments choisis sur la base de la stœchiométrie inhérente au modèle. L’algèbre matricielle, appliquée à des formules minérales et à des analyses, est utilisée pour calculer les ratios axiaux. Les modèles sont vérifiés en utilisant les nombres d’élément des formules minérales dans les ratios. Différentes intersections dans les diagrammes de ratios d’éléments de Pearce distinguent différents lots de magma et, par inférence, différentes coulées. Nous montrons que les magmas de Mauna Ulu proviennent de deux lots distincts, modifiés par l’extraction de l'olivine, de clinopyroxène, de plagioclase et, éventuellement, orthopyroxène (non observé).
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Tipán, Luis, and Juan Carlos Muela. "Simulación causal para el consumo eléctrico residencial." Revista Técnica "energía" 17, no. 1 (2020): 60–70. http://dx.doi.org/10.37116/revistaenergia.v17.n1.2020.384.

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Un modelo dinámico basado en diagramas causales pretende identificar todos los factores involucrados en el comportamiento de un fenómeno especifico. El presente artículo implementa un modelo dinámico basado en diagramas causales que busca identificar el comportamiento y respectivo consumo eléctrico residencial. La metodología aplicada involucra variables aleatorias que buscan replicar el comportamiento estocástico al interior de una vivienda durante el día, la intención de aplicar un modelo causal radica en la interacción y dependencia existente entre variables, es decir el condicionamiento que debe existir entre la ejecución de una actividad y su consecuente respuesta, tales criterios se ven reflejados en el uso de variables binarias, que simulan estados de encendido/apagado así como estados booleanos verdadero/ falso. Para la simulación, se consideran valores de consumo típicos en electrodomésticos para una residencia promedio en la ciudad de Quito, temporalidad de uso y su probabilidad de encendido bajo determinadas condiciones. La simulación se ejecuta en VENSIM, al tratarse de un software diseñado para trabajar con modelos dinámicos. Los resultados obtenidos establecen que la metodología propuesta presenta 24.95% de error con respecto a mediciones reales.
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Schisterman, Enrique F., Neil J. Perkins, Sunni L. Mumford, Katherine A. Ahrens, and Emily M. Mitchell. "Collinearity and Causal Diagrams." Epidemiology 28, no. 1 (2017): 47–53. http://dx.doi.org/10.1097/ede.0000000000000554.

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Ogburn, Elizabeth L., and Tyler J. VanderWeele. "Causal Diagrams for Interference." Statistical Science 29, no. 4 (2014): 559–78. http://dx.doi.org/10.1214/14-sts501.

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Suzuki, Etsuji, Tomohiro Shinozaki, and Eiji Yamamoto. "Causal Diagrams: Pitfalls and Tips." Journal of Epidemiology 30, no. 4 (2020): 153–62. http://dx.doi.org/10.2188/jea.je20190192.

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Picciotto*, Sally. "Causal Diagrams and Their Uses." ISEE Conference Abstracts 2014, no. 1 (2014): 2901. http://dx.doi.org/10.1289/isee.2014.s-063.

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Mansournia, Mohammad A., Miguel A. Hernán, and Sander Greenland. "Matched designs and causal diagrams." International Journal of Epidemiology 42, no. 3 (2013): 860–69. http://dx.doi.org/10.1093/ije/dyt083.

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Greenland, Sander, Judea Pearl, and James M. Robins. "Causal Diagrams for Epidemiologic Research." Epidemiology 10, no. 1 (1999): 37–48. http://dx.doi.org/10.1097/00001648-199901000-00008.

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PEARL, JUDEA. "Causal diagrams for empirical research." Biometrika 82, no. 4 (1995): 669–88. http://dx.doi.org/10.1093/biomet/82.4.669.

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COX, D. R., and NANNY WERMUTH. "Causal diagrams for empirical research." Biometrika 82, no. 4 (1995): 688–89. http://dx.doi.org/10.1093/biomet/82.4.688.

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DAWID, A. P. "Causal diagrams for empirical research." Biometrika 82, no. 4 (1995): 689–90. http://dx.doi.org/10.1093/biomet/82.4.689.

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FIENBERG, STEPHEN E., CLARK GLYMOUR, and PETER SPIRTES. "Causal diagrams for empirical research." Biometrika 82, no. 4 (1995): 690–92. http://dx.doi.org/10.1093/biomet/82.4.690.

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FREEDMAN, DAVID. "Causal diagrams for empirical research." Biometrika 82, no. 4 (1995): 692–93. http://dx.doi.org/10.1093/biomet/82.4.692.

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IMBENS, GUIDO W., and DONALD B. RUBIN. "Causal diagrams for empirical research." Biometrika 82, no. 4 (1995): 694–95. http://dx.doi.org/10.1093/biomet/82.4.694.

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ROBINS, JAMES M. "Causal diagrams for empirical research." Biometrika 82, no. 4 (1995): 695–98. http://dx.doi.org/10.1093/biomet/82.4.695.

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ROSENBAUM, PAUL R. "Causal diagrams for empirical research." Biometrika 82, no. 4 (1995): 698–99. http://dx.doi.org/10.1093/biomet/82.4.698.

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SHAFER, GLENN. "Causal diagrams for empirical research." Biometrika 82, no. 4 (1995): 699–700. http://dx.doi.org/10.1093/biomet/82.4.699.

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SOBEL, MICHAEL E. "Causal diagrams for empirical research." Biometrika 82, no. 4 (1995): 700–702. http://dx.doi.org/10.1093/biomet/82.4.700.

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PEARL, JUDEA. "Causal diagrams for empirical research." Biometrika 82, no. 4 (1995): 702–10. http://dx.doi.org/10.1093/biomet/82.4.702.

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Shahar, Eyal, and Doron J. Shahar. "Causal diagrams and change variables." Journal of Evaluation in Clinical Practice 18, no. 1 (2010): 143–48. http://dx.doi.org/10.1111/j.1365-2753.2010.01540.x.

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Richardson, George P. "Problems with causal-loop diagrams." System Dynamics Review 2, no. 2 (1986): 158–70. http://dx.doi.org/10.1002/sdr.4260020207.

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Castello, Jonathan, Patrick Redmond, and Lindsey Kuper. "Inductive Diagrams for Causal Reasoning." Proceedings of the ACM on Programming Languages 8, OOPSLA1 (2024): 529–54. http://dx.doi.org/10.1145/3649830.

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The Lamport diagram is a pervasive and intuitive tool for informal reasoning about “happens-before” relationships in a concurrent system. However, traditional axiomatic formalizations of Lamport diagrams can be painful to work with in a mechanized setting like Agda. We propose an alternative, inductive formalization — the causal separation diagram (CSD) — that takes inspiration from string diagrams and concurrent separation logic, but enjoys a graphical syntax similar to Lamport diagrams. Critically, CSDs are based on the idea that causal relationships between events are witnessed by the paths that information follows between them. To that end, we model “happens-before” as a dependent type of paths between events. The inductive formulation of CSDs enables their interpretation into a variety of semantic domains. We demonstrate the interpretability of CSDs with a case study on properties of logical clocks , widely-used mechanisms for reifying causal relationships as data. We carry out this study by implementing a series of interpreters for CSDs, culminating in a generic proof of Lamport’s clock condition that is parametric in a choice of clock. We instantiate this proof on Lamport’s scalar clock, on Mattern’s vector clock, and on the matrix clocks of Raynal et al. and of Wuu and Bernstein, yielding verified implementations of each. The CSD formalism and our case study are mechanized in the Agda proof assistant.
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Sousa Rodrigues Duarte, Ingrid de, and Antony Marco Mota Polito. "Uma abordagem ausubeliana para o ensino do eletromagnetismo na interpretação causal." Revista de Enseñanza de la Física 35, no. 2 (2023): 243–57. http://dx.doi.org/10.55767/2451.6007.v35.n2.43737.

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Apresentamos as principais ideias associadas com uma proposta de abordagem ausubeliana para o ensino integrado da fenomenologia e das leis do eletromagnetismo, enfatizando sua interpretação causal. Desenvolve-se, progressivamente, os conceitos de eletrostática, de magnetostática, de eletrodinâmica, de campo eletromagnético e de ondas eletromagnéticas, assumindo o papel central do conceito de causalidade. Por hipótese, o princípio de causalidade deve funcionar como uma chave ausubeliana que, atuando como parte de (potenciais) estruturas subsunçoras, supomos ser, nesse contexto, efetiva para alcançar o que Ausubel entendia por aprendizagem significativa. Para tanto, propomos a utilização de um conjunto de instrumentos didáticos, elaborados em torno de quatro experimentos: o gerador de Van de Graaff/eletroscópio, o eletroímã/magnetoscópio, o experimento de Faraday e a bobina de Tesla. Esses experimentos devem estar representados pelos seus respectivos diagramas conceituais experimentais. Esses, por sua vez, devem ser construídos com base em diagramas conceituais teóricos, desenvolvidos para estruturar o eletromagnetismo de acordo com a interpretação causal. Partes específicas desses instrumentos didáticos podem também ser interpretadas como organizadores avançados, a depender dos objetivos e das circunstâncias em que forem utilizados.
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Li, Ang, and Judea Pearl. "Unit Selection with Causal Diagram." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 5 (2022): 5765–72. http://dx.doi.org/10.1609/aaai.v36i5.20519.

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The unit selection problem aims to identify a set of individuals who are most likely to exhibit a desired mode of behavior, for example, selecting individuals who would respond one way if encouraged and a different way if not encouraged. Using a combination of experimental and observational data, Li and Pearl derived tight bounds on the "benefit function" - the payoff/cost associated with selecting an individual with given characteristics. This paper shows that these bounds can be narrowed significantly (enough to change decisions) when structural information is available in the form of a causal model. We address the problem of estimating the benefit function using observational and experimental data when specific graphical criteria are assumed to hold.
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Sánchez-Niubò, Albert, Carlos G. Forero, and Fernando G. Benavides. "The application of causal diagrams to conceptualize mechanisms in occupational epidemiology." Archivos de Prevención de Riesgos Laborales 19, no. 2 (2016): 103–6. http://dx.doi.org/10.12961/aprl.2016.19.02.4.

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HU Xiaoxuan, JIANG Fan, and XIA Wei. "Causal Influence Diagrams for Decision-making." Journal of Convergence Information Technology 8, no. 5 (2013): 397–407. http://dx.doi.org/10.4156/jcit.vol8.issue5.46.

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Oates, Chris J., Jessica Kasza, Julie A. Simpson, and Andrew B. Forbes. "Repair of Partly Misspecified Causal Diagrams." Epidemiology 28, no. 4 (2017): 548–52. http://dx.doi.org/10.1097/ede.0000000000000659.

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Richardson, George P. "Problems in causal loop diagrams revisited." System Dynamics Review 13, no. 3 (1997): 247–52. http://dx.doi.org/10.1002/(sici)1099-1727(199723)13:3<247::aid-sdr128>3.0.co;2-9.

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Lutomski, Jennifer E., A. Rogier T. Donders, and René J. F. Melis. "Causal Diagrams to Better Understand Missingness." JAMA Pediatrics 168, no. 2 (2014): 187. http://dx.doi.org/10.1001/jamapediatrics.2013.3650.

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Roatta, Analía, and Reinaldo Welti. "Efecto Doppler para pulsos y su representación en el plano (x, t)." Revista Brasileira de Ensino de Física 31, no. 1 (2009): 1304.1–1304.7. http://dx.doi.org/10.1590/s1806-11172009000100004.

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Los textos de física de nivel universitario generalmente discuten el efecto Doppler como un tema vinculado sólo a las ondas armónicas, sin embargo, en términos más amplios el efecto está vinculado a compresiones y expansiones, en el dominio del espacio y del tiempo, de pulsos de cualquier tipo. En este trabajo, se estudia analíticamente el efecto que introduce la fuente, o el observador, en movimiento sobre la extensión espacial y temporal del pulso. Se utiliza también diagramas en el plano (x, t), para analizar las compresiones (o expansiones) tanto temporales como espaciales del pulso causado por el movimiento de la fuente, el detector y el refector.
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Jacobs, Bart, Aleks Kissinger, and Fabio Zanasi. "Causal inference via string diagram surgery." Mathematical Structures in Computer Science 31, no. 5 (2021): 553–74. http://dx.doi.org/10.1017/s096012952100027x.

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Abstract Extracting causal relationships from observed correlations is a growing area in probabilistic reasoning, originating with the seminal work of Pearl and others from the early 1990s. This paper develops a new, categorically oriented view based on a clear distinction between syntax (string diagrams) and semantics (stochastic matrices), connected via interpretations as structure-preserving functors. A key notion in the identification of causal effects is that of an intervention, whereby a variable is forcefully set to a particular value independent of any prior propensities. We represent the effect of such an intervention as an endo-functor which performs ‘string diagram surgery’ within the syntactic category of string diagrams. This diagram surgery in turn yields a new, interventional distribution via the interpretation functor. While in general there is no way to compute interventional distributions purely from observed data, we show that this is possible in certain special cases using a calculational tool called comb disintegration. We demonstrate the use of this technique on two well-known toy examples: one where we predict the causal effect of smoking on cancer in the presence of a confounding common cause and where we show that this technique provides simple sufficient conditions for computing interventions which apply to a wide variety of situations considered in the causal inference literature; the other one is an illustration of counterfactual reasoning where the same interventional techniques are used, but now in a ‘twinned’ set-up, with two version of the world – one factual and one counterfactual – joined together via exogenous variables that capture the uncertainties at hand.
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Silva, Samuel Bloch, Marcelo Dias Ferreira, Mischel Carmen Neyra Belderrain, and Anderson Ribeiro Correia. "Modelagem da Cadeia Logística de Reposição de Componentes Aeronáuticos no contexto do System Dynamics." Aplicações Operacionais em Áreas de Defesa 22 (September 30, 2021): 19–24. http://dx.doi.org/10.55972/spectrum.v22i1.322.

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Desde a década de 60, a metodologia conhecida como Dinâmica de Sistemas (System Dynamics - SD) vem se destacando como ferramenta para analisar a dinâmica dos sistemas produtivos e logísticos. Através de diversos tipos de diagramas (causais, estoque e fluxo) é possível expressar graficamente um sistema, possibilitando representar a complexidade dinâmica das relações entre as partes ao longo do tempo. Isto envolve desde a análise estrutural até a definição de políticas de tomada de decisão um determinado ambiente produtivo. O presente trabalho apresenta resultados relativos ao apoio à tomada de decisão de uma cadeia genérica de reposição de componentes aeronáuticos MRO, tendo como objetivo a melhoria no planejamento através da simulação do seu comportamento dinâmico
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Dawid, A. P. "Influence Diagrams for Causal Modelling and Inference." International Statistical Review / Revue Internationale de Statistique 70, no. 2 (2002): 161. http://dx.doi.org/10.2307/1403901.

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Witmer, Jeff. "Simpson’s Paradox, Visual Displays, and Causal Diagrams." American Mathematical Monthly 128, no. 7 (2021): 598–610. http://dx.doi.org/10.1080/00029890.2021.1932237.

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Williamson, Elizabeth J., Zoe Aitken, Jock Lawrie, Shyamali C. Dharmage, John A. Burgess, and Andrew B. Forbes. "Introduction to causal diagrams for confounder selection." Respirology 19, no. 3 (2014): 303–11. http://dx.doi.org/10.1111/resp.12238.

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Hernan, M. A., and S. R. Cole. "Invited Commentary: Causal Diagrams and Measurement Bias." American Journal of Epidemiology 170, no. 8 (2009): 959–62. http://dx.doi.org/10.1093/aje/kwp293.

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Dawid, A. P. "Influence Diagrams for Causal Modelling and Inference." International Statistical Review 70, no. 2 (2002): 161–89. http://dx.doi.org/10.1111/j.1751-5823.2002.tb00354.x.

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Aste, Andreas. "Two-Loop Diagrams in Causal Perturbation Theory." Annals of Physics 257, no. 2 (1997): 158–204. http://dx.doi.org/10.1006/aphy.1997.5686.

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Crabolu, Gloria, Xavier Font, and Sibel Eker. "Evaluating policy complexity with Causal Loop Diagrams." Annals of Tourism Research 100 (May 2023): 103572. http://dx.doi.org/10.1016/j.annals.2023.103572.

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Naser, M. Z. "Causal diagrams for civil and structural engineers." Structures 63 (May 2024): 106398. http://dx.doi.org/10.1016/j.istruc.2024.106398.

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Aste, Andreas. "Causal Construction of the Massless Vertex Diagram." Letters in Mathematical Physics 78, no. 2 (2006): 157–72. http://dx.doi.org/10.1007/s11005-006-0113-3.

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De Julio, Marcelo, Eduardo Fausto de Almeida Neves, Julio Cesar Trofino, and Luiz Di Bernardo. "Emprego do reagente de fenton como agente coagulante na remoção de substâncias húmicas de água por meio da flotação por ar dissolvido e filtração." Engenharia Sanitaria e Ambiental 11, no. 3 (2006): 260–68. http://dx.doi.org/10.1590/s1413-41522006000300009.

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O reagente de Fenton foi empregado como agente coagulante no tratamento de água com cor verdadeira elevada (100 ± 5 uH) causada pela introdução de substâncias húmicas extraídas de turfa, empregando-se a flotação por ar dissolvido. Otimizou-se o par de valores dosagem de coagulante x pH de coagulação para posterior construção dos diagramas de coagulação, obtendo-se eficiências de remoção de cor aparente pouco superiores a 60%. Procurou-se simular um tratamento em ciclo completo, realizando-se ensaio de filtração em areia após flotação, obtendo-se efluente de excelente qualidade, apresentando cor aparente, turbidez e absorvância a 253,7 nm remanescentes menores ou iguais a 2 uH, 0,40 uT e 0,009 cm-1, respectivamente, e ferro total residual &lt; 0,005 mg/L e COD &lt; 0,001 mg/L.
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Tinoco, Maria Auxiliadora Cannarozzo, and José Luis Duarte Ribeiro. "Uma nova abordagem para a modelagem das relações entre os determinantes da satisfação dos clientes de serviços." Production 17, no. 3 (2007): 454–70. http://dx.doi.org/10.1590/s0103-65132007000300005.

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Este artigo apresenta uma nova abordagem para a modelagem das relações entre os determinantes da satisfação dos clientes de serviços. As principais vantagens da abordagem proposta em relação aos métodos tradicionais de modelagem, como, por exemplo, as equações estruturais, são: (i) possibilidade de identificar as relações entre múltiplos determinantes utilizando um tamanho de amostra relativamente pequeno, (ii) utilização de um procedimento estatístico mais simples, (iii) possibilidade de minimizar o erro de especificação no processo de modelagem, visto que podem ser consideradas todas as variáveis relevantes sem tornar o modelo muito complexo. A abordagem proposta é ilustrada através de um estudo aplicado a clientes de restaurantes à la carte. Esse estudo possibilitou construir diagramas de enlaces causais, validar a modelagem proposta e identificar aspectos que convergem e divergem com resultados previamente apresentados na literatura.
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Hamra, Ghassan, Jay Kaufman, and Anjel Vahratian. "Model Averaging for Improving Inference from Causal Diagrams." International Journal of Environmental Research and Public Health 12, no. 8 (2015): 9391–407. http://dx.doi.org/10.3390/ijerph120809391.

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Jupiter, Daniel C. "Causal Diagrams and Multivariate Analysis II: Precision Work." Journal of Foot and Ankle Surgery 53, no. 6 (2014): 829–31. http://dx.doi.org/10.1053/j.jfas.2014.08.023.

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Jupiter, Daniel C. "Causal Diagrams and Multivariate Analysis III: Confound It!" Journal of Foot and Ankle Surgery 54, no. 1 (2015): 145–47. http://dx.doi.org/10.1053/j.jfas.2014.11.003.

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Gaskell, Amy L., and Jamie W. Sleigh. "An Introduction to Causal Diagrams for Anesthesiology Research." Anesthesiology 132, no. 5 (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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Shahar, E. "Shahar Responds to "Causal Diagrams and Measurement Bias"." American Journal of Epidemiology 170, no. 8 (2009): 963–64. http://dx.doi.org/10.1093/aje/kwp289.

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Scholl, Raphael. "Spot the difference: Causal contrasts in scientific diagrams." Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 60 (December 2016): 77–87. http://dx.doi.org/10.1016/j.shpsc.2016.06.003.

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Pala, Özge, Dirk J. Vriens, and Jac AM Vennix. "Causal loop diagrams as a de-escalation technique." Journal of the Operational Research Society 66, no. 4 (2015): 593–601. http://dx.doi.org/10.1057/jors.2014.24.

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