Готові списки джерел за темами / Diagramme Causaux

Добірка наукової літератури з теми "Diagramme Causaux"

Автор: Grafiati

Опубліковано: 7 липня 2024

Оновлено: 7 липня 2024

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Статті в журналах з теми "Diagramme Causaux":

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 (May 18, 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).

Tipán, Luis, and Juan Carlos Muela. "Simulación causal para el consumo eléctrico residencial." Revista Técnica "energía" 17, no. 1 (July 30, 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.

Schisterman, Enrique F., Neil J. Perkins, Sunni L. Mumford, Katherine A. Ahrens, and Emily M. Mitchell. "Collinearity and Causal Diagrams." Epidemiology 28, no. 1 (January 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 (November 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 (April 5, 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 (October 20, 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 (June 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 (January 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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Дисертації з теми "Diagramme Causaux":

Pressat-Laffouilhère, Thibaut. "Modèle ontologique formel, un appui à la sélection des variables pour la construction des modèles multivariés." Electronic Thesis or Diss., Normandie, 2023. http://www.theses.fr/2023NORMR104.

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Répondre à une question de recherche causale dans un contexte d’étude observationnelle nécessite desélectionner des variables de confusion. Leur intégration dans un modèle multivarié en tant que co-variablespermet de diminuer le biais dans l’estimation de l'effet causal de l'exposition sur le critère de jugement. Leuridentification est réalisée grâce à des diagrammes causaux (DCs) ou des graphes orientés acycliques. Cesreprésentations, composées de noeuds et d'arcs orientés, permettent d’éviter la sélection de variables quiaugmenteraient le biais, comme les variables de médiation et de collision. Les méthodes existantes deconstruction de DCs manquent cependant de systématisme et leur représentation de formalisme, d’expressivité etde complétude. Afin de proposer un cadre de construction formel et complet capable de représenter toutes lesinformations nécessaires à la sélection des variables sur un DC enrichi, d’analyser ce DC et surtout d’expliquerles résultats de cette analyse, nous avons proposé d'utiliser un modèle ontologique enrichi de règles d'inférences.Un modèle ontologique permet notamment de représenter les connaissances sous la forme de graphe expressif etformel composé de classes et de relations similaires aux noeuds et arcs des DCs. Nous avons développél’ontologie OntoBioStat (OBS) à partir d’une liste de questions de compétence liée à la sélection des variables etde l'analyse de la littérature scientifique relative aux DCs et aux ontologies. Le cadre de construction d’OBS estplus riche que celui d’un DC, intégrant des éléments implicites tels que les causes nécessaires, contextuels d’uneétude, sur l’incertitude de la connaissance et sur la qualité du jeu de données correspondant. Afin d’évaluerl’apport d’OBS, nous l’avons utilisée pour représenter les variables d’une étude observationnelle publiée etavons confronté ses conclusions à celle d’un DC. OBS a permis d'identifier de nouvelles variables de confusiongrâce au cadre de construction différent des DCs et aux axiomes et règles d'inférence. OBS a également étéutilisée pour représenter une étude rétrospective en cours d’analyse : le modèle a permis d’expliquer dans unpremier temps les corrélations statistiques retrouvées entre les variables de l’étude puis de mettre en évidence lespotentielles variables de confusion et leurs éventuels substituts ("proxys"). Les informations sur la qualité desdonnées et l’incertitude des relations causales ont quant à elles facilité la proposition des analyses de sensibilité,augmentant la robustesse de la conclusion de l’étude. Enfin, les inférences ont été expliquées grâce aux capacitésde raisonnement offertes par le formalisme de représentation d'OBS. À terme OBS sera intégrée dans des outilsd’analyse statistique afin de bénéficier des bibliothèques existantes pour la sélection des variables et de permettreson utilisation par les épidémiologistes et les biostatisticiens
Responding to a causal research question in the context of observational studies requires the selection ofconfounding variables. Integrating them into a multivariate model as co-variables helps reduce bias in estimatingthe true causal effect of exposure on the outcome. Identification is achieved through causal diagrams (CDs) ordirected acyclic graphs (DAGs). These representations, composed of nodes and directed arcs, prevent theselection of variables that would introduce bias, such as mediating and colliding variables. However, existingmethods for constructing CDs lack systematic approaches and exhibit limitations in terms of formalism,expressiveness, and completeness. To offer a formal and comprehensive framework capable of representing allnecessary information for variable selection on an enriched CD, analyzing this CD, and, most importantly,explaining the analysis results, we propose utilizing an ontological model enriched with inference rules. Anontological model allows for representing knowledge in the form of an expressive and formal graph consisting ofclasses and relations similar to the nodes and arcs of Cds. We developed the OntoBioStat (OBS) ontology basedon a list of competency questions about variable selection and an analysis of scientific literature on CDs andontologies. The construction framework of OBS is richer than that of a CD, incorporating implicit elements likenecessary causes, study context, uncertainty in knowledge, and data quality. To evaluate the contribution of OBS,we used it to represent variables from a published observational study and compared its conclusions with thoseof a CD. OBS identified new confounding variables due to its different construction framework and the axiomsand inference rules. OBS was also used to represent an ongoing retrospective study analysis. The modelexplained statistical correlations found between study variables and highlighted potential confounding variablesand their possible substitutes (proxies). Information on data quality and causal relation uncertainty facilitatedproposing sensitivity analyses, enhancing the study's conclusion robustness. Finally, inferences were explainedthrough the reasoning capabilities provided by OBS's formal representation. Ultimately, OBS will be integratedinto statistical analysis tools to leverage existing libraries for variable selection, making it accessible toepidemiologists and biostatisticians

Cortes, Taísa Rodrigues. "Utilização de diagramas causais em confundimento e viés de seleção." Universidade do Estado do Rio de Janeiro, 2014. http://www.bdtd.uerj.br/tde_busca/arquivo.php?codArquivo=8442.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico
Apesar do crescente reconhecimento do potencial dos diagramas causais por epidemiologistas, essa técnica ainda é pouco utilizada na investigação epidemiológica. Uma das possíveis razões é que muitos temas de investigação exigem modelos causais complexos. Neste trabalho, a relação entre estresse ocupacional e obesidade é utilizada como um exemplo de aplicação de diagramas causais em questões relacionadas a confundimento. São apresentadas etapas da utilização dos diagramas causais, incluindo a construção do gráfico acíclico direcionado, seleção de variáveis para ajuste estatístico e a derivação das implicações estatísticas de um diagrama causal. A principal vantagem dos diagramas causais é tornar explícitas as hipóteses adjacentes ao modelo considerado, permitindo que suas implicações possam ser analisadas criticamente, facilitando, desta forma, a identificação de possíveis fontes de viés e incerteza nos resultados de um estudo epidemiológico.
Despite the increasing recognition of the potential of causal diagrams by epidemiologists, this technique has not been widely used in epidemiological research. One possible reason is that many research topics require complex causal models. In this article, the relationship between occupational stress and obesity is used as an example of application of causal diagrams on confounding. Some steps are presented, including the construction of the directed acyclic graph, the selection of variables for statistical control and the derivation of the statistical implications of a causal diagram. The main advantage of causal diagrams is to make the assumptions explicit, thus facilitating critical evaluations and the identification of possible sources of bias and uncertainty in the results of an epidemiological study.

Madry, Martin. "Systémová dynamika: případ výkonnosti projektových týmů." Master's thesis, Vysoká škola ekonomická v Praze, 2014. http://www.nusl.cz/ntk/nusl-193285.

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This thesis deals with modelling of project teams and their behavior using the principles of system dynamics. Main goal of this thesis is to create a model using system dynamics, which will allow to simulate how projects are finished. Results from the model are going to be used to define the best possible strategy to finish a project in required time. Theoretical part of this work is composed of presentation of project management and further the thesis extensively describes system dynamics, principles of system dynamics, used diagrams and possible ways of application of system dynamics in the real world. Furthermore are described the principles and advantages of using models and specifically system dynamics models. In the practical part of this thesis is presented the created model, which allows for simulating of project team behavior based on the input from the user of the model. Model serves the purpose of finding the best possible strategy to finish the product successfully.

Arévalo, Mejía Julia Elvira, and Alania Macario charles Sobero. "“Incumplimiento con la calidad adecuada en los procesos constructivos de obras de edificación”, caso de estudio de centro comercial." Master's thesis, Universidad Peruana de Ciencias Aplicadas (UPC), 2020. http://hdl.handle.net/10757/653704.

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El presente trabajo se enfoca en la mejora de la calidad concerniente a los elementos estructurales de un centro comercial, a efectos de reducir y minimizar las No Conformidades más relevantes que se presentaron en obra. El proyecto se basó en la construcción y ampliación de locatarios de un centro comercial que tendrá como fin su alquiler. Mediante la aplicación del Análisis Causa Raíz y con las herramientas de diagrama de Ishikawa y diagrama de Pareto, se pudo encontrar las posibles causas del incumplimiento de la calidad en los elementos estructurales, las que subsecuente se validaron a fin de determinar acciones correctivas. En el primer capítulo se señala el planteamiento del problema, problemas principales, secundarios, justificación del estudio, limitación y los objetivos generales y específicos. En el Segundo Capitulo se señala el marco teórico, donde menciona la calidad en el Perú, la gestión de la calidad total, los costos de la calidad en la construcción, ingeniería de la calidad y definiciones. En el tercer capítulo se indica la utilización del Análisis Causa Raíz, las herramientas Diagrama Causa Efecto y Diagrama de Pareto. En el cuarto capítulo, se presenta el desarrollo del análisis de causa raíz mediante una secuencia de pasos. En el quinto capítulo, La Evaluación Económica, Presupuesto de obra, Costo de Reparación y Análisis del Gasto Incurrido. Finalmente, en el capítulo seis, se presentará las conclusiones y recomendaciones del presente trabajo.
This work focuses on quality improvement concerning the structural elements of a shopping center, in order to reduce and minimize the most relevant Non-Conformities that occurred on site. The project was based on the construction and expansion of tenants of a shopping center that will be rented as its purpose. By applying the Root Cause Analysis and using the Ishikawa diagram and Pareto diagram tools, it was possible to find the possible causes of quality noncompliance in the structural elements, which were subsequently validated in order to determine corrective actions. In the first chapter the problem statement, main and secondary problems, justification for the study, limitation and general and specific objectives are indicated. In the Second Chapter the theoretical framework is pointed out, where it mentions the quality in Peru, the total quality management, the costs of quality in construction, quality engineering and definitions. The third chapter indicates the use of Root Cause Analysis, the Cause Effect Diagram and Pareto Diagram tools. In the fourth chapter, the development of root cause analysis is presented using a sequence of steps. In the fifth chapter, The Economic Evaluation, Construction Budget, Repair Cost and Incurred Expense Analysis. Finally, in chapter six, the conclusions and recommendations of this work will be presented.
Trabajo de investigación

Laurenti, Rafael. "The Karma of Products : Exploring the Causality of Environmental Pressure with Causal Loop Diagram and Environmental Footprint." Doctoral thesis, KTH, Industriell ekologi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-184223.

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Environmental pressures from consumer products and mechanisms of predetermination were examined in this thesis using causal loop diagram (CLD) and life cycle assessment (LCA) footprinting to respectively illustrate and provide some indicators about these mechanisms. Theoretical arguments and their practical implications were subjected to qualitative and quantitative analysis, using secondary and primary data. A study integrating theories from various research fields indicated that combining product-service system offerings and environmental policy instruments can be a salient aspect of the system change required for decoupling economic growth from consumption and environmental impacts. In a related study, modes of system behaviour identified were related to some pervasive sustainability challenges to the design of electronic products. This showed that because of consumption and investment dynamics, directing consumers to buy more expensive products in order to restrict their availability of money and avoid increased consumption will not necessarily decrease the total negative burden of consumption. In a study examining product systems, those of washing machines and passenger cars were modelled to identify variables causing environmental impacts through feedback loops, but left outside the scope of LCA studies. These variables can be considered in LCAs through scenario and sensitivity analysis. The carbon, water and energy footprint of leather processing technologies was measured in a study on 12 tanneries in seven countries, for which collection of primary data (even with narrow systems boundaries) proved to be very challenging. Moreover, there were wide variations in the primary data from different tanneries, demonstrating that secondary data should be used with caution in LCA of leather products. A study examining pre-consumer waste developed a footprint metric capable of improving knowledge and awareness among producers and consumers about the total waste generated in the course of producing products. The metric was tested on 10 generic consumer goods and showed that quantities, types and sources of waste generation can differ quite radically between product groups. This revealed a need for standardised ways to convey the environmental and scale of significance of waste types and for an international standard procedure for quantification and communication of product waste footprint. Finally, a planning framework was developed to facilitate inclusion of unintended environmental consequences when devising improvement actions. The results as a whole illustrate the quality and relevance of CLD; the problems with using secondary data in LCA studies; difficulties in acquiring primary data; a need for improved waste declaration in LCA and a standardised procedure for calculation and communication of the waste footprint of products; and systems change opportunities for product engineers, designers and policy makers.

Jury committee

Henrikke Baumann, Associate Professor

Chalmers University of Technology

Department of Energy and Environment

Division of Environmental System Analysis

Joakim Krook, Associate Professor

Linköpings Universitet

Department of Management and Engineering (IEI) / Environmental Technology and Management (MILJÖ)

Karl Johan Bonnedal, Associate Professor

Umeå University

Umeå School of Business and Economics (USBE)

Sofia Ritzén, Professor

KTH Royal Institute of Technology

School of Industrial Engineering and Management

Department of Machine Design

Integrated Product Development

QC 20160405

Ziebart, Brian D. "Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy." Research Showcase @ CMU, 2010. http://repository.cmu.edu/dissertations/17.

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Predicting human behavior from a small amount of training examples is a challenging machine learning problem. In this thesis, we introduce the principle of maximum causal entropy, a general technique for applying information theory to decision-theoretic, game-theoretic, and control settings where relevant information is sequentially revealed over time. This approach guarantees decision-theoretic performance by matching purposeful measures of behavior (Abbeel & Ng, 2004), and/or enforces game-theoretic rationality constraints (Aumann, 1974), while otherwise being as uncertain as possible, which minimizes worst-case predictive log-loss (Gr¨unwald & Dawid, 2003). We derive probabilistic models for decision, control, and multi-player game settings using this approach. We then develop corresponding algorithms for efficient inference that include relaxations of the Bellman equation (Bellman, 1957), and simple learning algorithms based on convex optimization. We apply the models and algorithms to a number of behavior prediction tasks. Specifically, we present empirical evaluations of the approach in the domains of vehicle route preference modeling using over 100,000 miles of collected taxi driving data, pedestrian motion modeling from weeks of indoor movement data, and robust prediction of game play in stochastic multi-player games.

Ström, Simon. "Samrådsunderlag för Lysekilsprojektet : Forskning och utveckling av vågkraft." Thesis, Stockholms universitet, Institutionen för naturgeografi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-114303.

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The aim of this research is to find out what environmental impact a wave power park has on the Swedish west coast by creating a consultation paper (”Samrådsunderlag”) for the Lysekilproject at Uppsala University. To highlight the complexity of the problem a system analytic approach was used and illustrated by a Causal Loop Diagram. The overall assessment of the Lysekilprojects wave power park at the Swedish west coast is that it will have a low impact on the environment. This is due to the relative small size of the wave power park and some technical solutions made with the environmental aspect in mind. Artificial reefs and a sanctuary for marine species are effects created by the wave power park and in the longer term the impact will give access to an untapped source of renewable energy, wave energy. Thus reducing the need of fossil fuels and making it possible to reach the Swedish national environmental goals.
Lysekilsprojektet

OLIVEIRA, Felipe Andrade Gama de. "Avaliação probabilística de risco via modelo causal híbrido em cirurgia: o caso da histerectomia vaginal." Universidade Federal de Pernambuco, 2006. https://repositorio.ufpe.br/handle/123456789/5851.

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Made available in DSpace on 2014-06-12T17:42:09Z (GMT). No. of bitstreams: 2 arquivo7393_1.pdf: 6657569 bytes, checksum: 3ca8f1d5810f2745659af9b2fe042065 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2006
A análise probabilística de risco é uma metodologia que identifica, avalia e quantifica os riscos nos mais diversos procedimentos, desde de sistemas de alta complexidade tecnológica a sistemas onde só existe o homem executando tarefas. Esta análise tem como objetivo melhorar a segurança e o desempenho destes processos. A área de saúde ainda encontra-se bastante carente de estudos que analisem e quantifiquem os riscos envolvidos nos seus procedimentos. E é com este intuito, que este trabalho propõe uma metodologia de avaliação probabilística de risco para cirurgias, sendo apresentado o caso da histerectomia vaginal. Esta análise aborda tanto os aspectos da confiabilidade humana como a confiabilidade dos equipamentos utilizados. No modelo híbrido proposto, a análise de riscos é baseada na integração dos diagramas de seqüências de eventos, árvore de falhas e redes Bayesianas. Na modelagem os eventos pivotais dos diagramas de seqüência de eventos relacionados a erros humanos, ou seja, resultantes diretamente de ações humanas, são modelados via redes Bayesianas, proporcionando uma representação mais realista da natureza dinâmica destas ações, enquanto que os eventos pivotais relacionados à falha de equipamentos são modelados via árvores de falhas. Assim esta metodologia contribui para a melhoria do processo de gerenciamento dos riscos envolvidos durante a execução da atividade cirúrgica

Arias, Trujillo Milagros. "Aplicación del diagrama causa-efecto para identificar los principales riesgos ante un posible siniestro en el planeamiento de una auditoría de procesos." Bachelor's thesis, Universidad Nacional Mayor de San Marcos, 2008. https://hdl.handle.net/20.500.12672/12652.

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Muestra cómo el diagrama causa efecto puede resultar eficiente al momento de identificar los riesgos críticos de un proceso durante la etapa de planeamiento de una auditoría de Procesos, para que así el programa de auditoría se pueda focalizar en aquellos riesgos que se considerarían los más críticos. Tenemos que existen muchos marcos de referencia, normas, modelos y metodologías para el análisis e identificación de riesgos, así como también existen diversas herramientas que nos facilitan tales tareas. El uso de tales herramientas para la identificación de riesgos depende de la realidad de cada organización, utilizando muchas veces más de una herramienta. Los temas presentados en la tesina se alinean a los nuevos enfoques de procesos y riesgos, que hoy en día están tomando muchas organizaciones, como consecuencia de la globalización, que exige que las empresas sean más eficientes.
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Rawlins, Jonathan Mark. "Exploring the suitability of causal loop diagrams to assess the value chains of aquatic ecosystem services: a case study of the Baviaanskloof, South Africa." Thesis, Rhodes University, 2017. http://hdl.handle.net/10962/4909.

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Healthy, functioning aquatic ecosystems are fundamental to the survival and development of any nation, particularly so for water-stressed countries like South Africa. Aquatic ecosystem services (AESs) are becoming increasingly recognised for their importance to society with regards to the ecological goods and services they provide in terms of health, social, cultural and economic benefits. The development of markets for AESs begins with a clear understanding of the nature and extent of the goods and services provided by aquatic ecosystems. However, an inclusive understanding of AESs and their associated values is currently lacking in South Africa. Although flows of ecosystem services provide a nearly limitless set of valuable properties, a large proportion of their services remain unpriced or inaccurately priced through traditional neo-classical markets. This often results in market failure, as these markets do not reflect the full social costs and/or benefits of ecosystem services. This provides incentive to identify and develop a tool to bridge the gap between ecosystem service valuation and practical, sustainable management recommendations for improving the provision of ecosystem services and their associated markets. This study explores the suitability of causal loop diagrams (CLDs) to assess the value chains of AESs in South Africa within the context of a case study. AESs do not usually have finite market values nor are they traded in formal markets, thus, a traditional approach to value chain analysis is unsuitable. A professional workshop environment was utilised to facilitate a transdisciplinary approach towards identifying relevant AESs and their complex inputs, interactions and trade-offs. Numerous CLDs were developed in an effort to map the complex relationships between these AESs and their associated inputs, which formed the basis to attempt subsequent scenario analyses and 'alternative' value chain analyses. The findings of this study show that CLDs have the potential to qualitatively identify challenges and opportunities within the value chains of AESs. Thus, the use of such 'alternative' value chain analyses can directly contribute towards the development of recommendations for improving sustainable management of aquatic ecosystems.

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Книги з теми "Diagramme Causaux":

Kern, Johannes. Utilizar con éxito Los Diagramas de Causa-Efecto: El Diagrama de Ishikawa en la Teoría y la Práctica. Independently Published, 2021.

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Coecke, Bob, and Aleks Kissinger. Categorical Quantum Mechanics I: Causal Quantum Processes. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198748991.003.0012.

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We derive the category-theoretic backbone of quantum theory from a process ontology. More specifically, we treat quantum theory as a theory of systems, processes, and their interactions. We first present a general theory of diagrams, and in particular, of string diagrams, and discuss why diagrams are a very natural starting point for developing scientific theories. Then we define process theories, and define a very general notion of quantum type. We show how our process ontology enables us to assert causality, that is, compatibility of quantum theory and relativity theory, prove the no-signalling theorem, provide a new elegant derivation of the no-broadcasting theorem, unitarity of evolution, and Stinespring dilation, all for any `quantum' type in a general class of process theories.

Wittman, David M. Time Skew. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199658633.003.0006.

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This chapter explores one major consequence of the invariance of c: events that are simultaneous in one frame are not necessarily simultaneous in other frames.We will find that the time coordinates of events are just as frame‐dependent as their positions. This is no accident, but a symmetry between space and time. Viewed in a spacetime diagram, a frame change rotates the grid lines marking time just as much as it rotates the grid lines marking position; this preserves c as the same speed in all frames. Along the way, we practice using skewed grids in spacetime diagrams: identifying the time coordinates of events, identifying events that are simultaneous in a given frame, and adding velocities. Although the skewed grids change the time coordinates of events and even their order in time, we show that they do not change causal relationships between events.

Hoyle, Rick H. Applications of structural equation modelling in clinical and health psychology research. Oxford University Press, 2015. http://dx.doi.org/10.1093/med:psych/9780198527565.003.0020.

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This chapter discusses applications of structural equation modelling (SEM, or causal modelling) in clinical and health psychology research. It outlines path diagrams, measurement models, structural models, the inclusion of latent variables, validity (factorial and construct), and measurement invariance. Structural hypotheses are also explored, along with caveats for the use of SEM.

Garcia, Juan Martin. Feedbacks. from Causal Diagrams to System Thinking: Manage Dynamical Systems in Business, Econony, Biology and Social Sciences, Using Balancing and Reinforcing Loops. Independently Published, 2018.

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Zaanen, Jan. On Time. Oxford University PressOxford, 2024. http://dx.doi.org/10.1093/9780198920793.001.0001.

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Abstract This text revolves around a new and unusual view on the most fundamental puzzle of physics. It focusses on the key aspect that makes the role of the time dimension fundamentally different, dealing on the one hand with general relativity and quantum theory, and on the other hand: causality. The implicit and intuitive way by which causality is usually taken for granted is just made explicit and less self-evident, shedding new light on the gravity–quantum conflict. The case is made that gravity is a necessary condition for a causal universe. But upon turning to the ‘pure’ unitary quantum physics explaining the nature of matter, one is dealing with the strictly acausal time expressed through the thermal quantum field theory machinery. When this acausal microscopic and causal macroscopic world meet, one encounters the wavefunction collapse, that itself may be rooted in the quantum–gravity conflict. Modern ideas are discussed resting on eigenstate thermalization, showing how this may lie eventually at the origin of the irreversible thermodynamics, with its famous second law setting also a direction of time. The case is anchored in the sophisticated modern mathematical machinery of both general relativity and quantum physics, which is typically barely disseminated beyond the theoretical physics floors. The book is unique in the regard that the consequences of this machinery—Riemannian geometry and Penrose diagrams, thermal quantum fields, quantum non-equilibrium, and so forth—are explained in an original, descriptive language, conveying the conceptual consequences while avoiding mathematical technicalities.

Частини книг з теми "Diagramme Causaux":

Turner, J. Rick. "Causal Diagrams." In Encyclopedia of Behavioral Medicine, 360–61. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1005-9_993.

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Turner, J. Rick. "Causal Diagrams." In Encyclopedia of Behavioral Medicine, 401–2. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39903-0_993.

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Greenland, Sander, and Judea Pearl. "Causal Diagrams." In International Encyclopedia of Statistical Science, 208–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_162.

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Huntington-Klein, Nick. "Causal Diagrams." In The Effect, 87–100. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003226055-7.

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Gudlaugsson, Bjarnhedinn, Huda Dawood, Gobind Pillai, and Michael Short. "First Step Towards a System Dynamic Sustainability Assessment Model for Urban Energy Transition." In Springer Proceedings in Energy, 225–32. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63916-7_28.

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AbstractThis paper presents a conceptual model that describes the correlation between an urban energy system and sustainability. The model captures the complexity of the urban energy transition, and the task of achieving sustainable development needs to embrace all aspects of sustainability. This paper portrays the aspects of sustainability as four-dimensional—Environment, Economic, Society, and Technology. The relationship between these four dimensions and the urban energy system is presented in a simplified and aggregated-qualitative based causal-loop diagram. The causal-loop diagram illustrates the causal and interconnective relationships between the four dimensions and their different variables. The causal-loop diagram describes the complex dynamic relationships within a simple urban energy system. The paper also provides a brief description of balancing and reinforcing loops, with the causal-loop diagram present. The conceptual model along with the causal-loop diagrams visually illustrate the dynamic relationship between the four dimensions as well as highlights the complexity and challenging problems that decision-makers are facing today when it comes energy planning and energy system development.

Inghels, Dirk. "Causal Loop Diagrams." In Introduction to Modeling Sustainable Development in Business Processes, 149–53. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58422-1_8.

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Bala, Bilash Kanti, Fatimah Mohamed Arshad, and Kusairi Mohd Noh. "Causal Loop Diagrams." In Springer Texts in Business and Economics, 37–51. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2045-2_3.

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Barbrook-Johnson, Pete, and Alexandra S. Penn. "Causal Loop Diagrams." In Systems Mapping, 47–59. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-01919-7_4.

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AbstractThis chapter introduces Causal Loop Diagrams. We explore what exactly Causal Loop Diagrams are, describe how you can use them, take a step back to consider common issues and ‘tricks of the trade’, as well as present a brief history of the development of the method. This chapter can be viewed as a companion to Chap. 10.1007/978-3-031-01919-7_8 on System Dynamics; these two methods are closely related. Causal Loop Diagrams emerged from Systems Dynamics practice, and though it is a systems mapping method in its own right now, it is still often used as a stepping-stone to the development of System Dynamics models.

Sherwood, Dennis. "Causal Loop Diagrams." In Strategic Thinking Illustrated, 23–36. New York: Productivity Press, 2022. http://dx.doi.org/10.4324/9781003304050-4.

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Huntington-Klein, Nick. "Drawing Causal Diagrams." In The Effect, 101–14. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003226055-8.

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Тези доповідей конференцій з теми "Diagramme Causaux":

Kalinowski, Marcos, and Guilherme Horta Travassos. "Uma Abordagem Probabilística para Análise Causal de Defeitos de Software." In Simpósio Brasileiro de Qualidade de Software. Sociedade Brasileira de Computação - SBC, 2012. http://dx.doi.org/10.5753/sbqs.2012.15335.

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Análise causal de defeitos tem se mostrado uma forma eficiente para melhoria de processos com base no produto. Nesta tese uma abordagem probabilística para análise causal, chamada DPPI (Defect Prevention-Based Process Improvement) foi elaborada com base em evidências obtidas a partir de quatro rodadas de revisão sistemática da literatura e feedback obtido de especialistas da área. DPPI representa uma abordagem inovadora que integra mecanismos de aprendizado de causa e efeito (redes Bayesianas) nos procedimentos de análise causal de defeitos. Adicionalmente, para facilitar o uso destes mecanismos em reuniões de análise causal, o tradicional diagrama de causa e efeito foi estendido para um diagrama de causa e efeito probabilístico. DPPI foi aplicada a um projeto real e avaliada através de três rodadas de um estudo experimental. A aplicação ao projeto real indicou sua viabilidade e permitiu refinar requisitos para a construção de apoio ferramental. As rodadas do estudo experimental forneceram indícios de que o uso dos diagramas de causa e efeito probabilísticos de DPPI aumenta a eficácia e reduz o esforço na identificação de causas de defeitos, quando comparado à identificação de causas de defeitos sem o uso dos diagramas.

Erwig, Martin, and Eric Walkingshaw. "Causal Reasoning with Neuron Diagrams." In 2010 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC). IEEE, 2010. http://dx.doi.org/10.1109/vlhcc.2010.23.

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Lübke, Karsten, and Matthias Gehrke. "Causal Diagrams for Descriptive Statistics." In Bridging the Gap: Empowering and Educating Today’s Learners in Statistics. International Association for Statistical Education, 2022. http://dx.doi.org/10.52041/iase.icots11.t3b1.

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Without random sampling and/or random allocation, even descriptive statistics such as simple means or proportions can be quite misleading. Therefore, causal diagrams were added to existing course materials to address this topic and to illustrate the differences between random and convenience samples and between observational and experimental studies. We assessed student understanding in different courses with a pre-/post-survey. Additionally, we asked students to evaluate the helpfulness of the diagrams for their understanding. There is a statistically discernible positive effect with 280 students from more than seven different courses on pre- to post-knowledge. Also, most of the students agreed with the statement that the causal diagrams helped in their understanding.

Margetts, Rebecca, and Roger F. Ngwompo. "Comparison of Modeling Techniques for a Landing Gear." In ASME 2010 International Mechanical Engineering Congress and Exposition. ASMEDC, 2010. http://dx.doi.org/10.1115/imece2010-39722.

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A wide range of modeling techniques is available to the engineer. The objective of this paper is to compare some typical modeling techniques for the simulation of a multi-domain mechatronic system. Usual dynamic modeling methods, such as block diagrams and iconic diagrams, can cause problems for the engineer. Differential algebraic equations (DAEs) and algebraic loops can significantly increase simulation times and cause numeric errors. Bond graphs are less common in industry, and are presented here as a method which allows the engineer to easily identify causal loops and elements in differential causality. These can indicate DAEs in the underlying equations. An aircraft landing gear is given as an example of a multi-domain system, and is modeled as a block diagram, an iconic diagram and as a bond graph. The time to construct the model, time to solve and problems faced by the analyst are presented. Bond graphs offer distinct advantages in terms of the ease of implementing algebraic equations and visibility of causality. The time taken to model a system can be significantly reduced and the results appear free from computational errors. Bond graphs are therefore recommended for this type of multi-domain systems analysis.

Rios, Nicolli, Rodrigo Spínola, and Manoel Mendonça. "Organização de um Conjunto de Descobertas Experimentais sobre Causas e Efeitos da Dívida Técnica através de uma Família de Surveys Globalmente Distribuída." In Anais Estendidos do Congresso Brasileiro de Software: Teoria e Prática. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/cbsoft_estendido.2021.17296.

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Conhecer as causas da dívida técnica (DT) pode auxiliar equipes de desenvolvimento a definir ações que podem ser tomadas para evitar a ocorrência de itens de dívida. Identificar os efeitos da DT auxilia análises de impacto e a definir ações corretivas para minimizar possível consequências negativas para o projeto. Nesse contexto, foi criado o projeto InsighTD, uma família de surveys globalmente distribuída. Seu objetivo é investigar o estado da prática sobre DT, incluindo causas que levam à sua ocorrência, efeitos de sua existência e como esses problemas se manifestam no processo de desenvolvimento de software. Respostas de 206 profissionais da indústria de software do Brasil e Estados Unidos foram analisadas. Identificou-se as principais causas da DT, os efeitos de sua presença, e a relação entre modelos de processo e os efeitos da DT. Diagramas probabilísticos de causa e efeito e um mapa conceitual focado em dívida de documentação foram propostos para apoiar a gestão da DT. InsighTD, o primeiro estudo em larga escala da área de DT, também permitiu organizar uma rede de cooperação envolvendo pesquisadores e instituições de 12 países.

Murawiowa, Nelly, Elena Mudrova, and Viktoria Degtereva. "Smart Housing and Utilities: A Causal Diagram." In SPBPU IDE-2021: 3rd International Scientific Conference on Innovations in Digital Economy. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3527049.3527134.

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Fox, James, Tom Everitt, Ryan Carey, Eric Langlois, Alessandro Abate, and Michael Wooldridge. "PyCID: A Python Library for Causal Influence Diagrams." In Python in Science Conference. SciPy, 2021. http://dx.doi.org/10.25080/majora-1b6fd038-008.

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Niu, Xueyan, Xiaoyun Li, and Ping Li. "Learning Cluster Causal Diagrams: An Information-Theoretic Approach." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/675.

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Many real-world phenomena arise from causal relationships among a set of variables. As a powerful tool, Bayesian Network (BN) has been successful in describing high-dimensional distributions. However, the faithfulness condition, enforced in most BN learning algorithms, is violated in the settings where multiple variables synergistically affect the outcome (i.e., with polyadic dependencies). Building upon recent development in cluster causal diagrams (C-DAGs), we initiate the formal study of learning C-DAGs from observational data to relax the faithfulness condition. We propose a new scoring function, the Clustering Information Criterion (CIC), based on information-theoretic measures that represent various complex interactions among variables. The CIC score also contains a penalization of the model complexity under the minimum description length principle. We further provide a searching strategy to learn structures of high scores. Experiments on both synthetic and real data support the effectiveness of the proposed method.

Zhang, Pan, Raúl Leal Ascencio, and Giles Poulsom. "Exploring Mobile Banking Adoption through Causal-Loop Diagrams." In 4th European International Conference on Industrial Engineering and Operations Management. Michigan, USA: IEOM Society International, 2021. http://dx.doi.org/10.46254/eu04.20210174.

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Subramonyam, Hari, Eytan Adar, Priti Shah, and Colleen M. Seifert. "Causal Pattern Diagrams in Science Texts Support Explanation." In 18th International Conference of the Learning Sciences (ICLS) 2024. International Society of the Learning Sciences, 2024. http://dx.doi.org/10.22318/icls2024.106268.

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Звіти організацій з теми "Diagramme Causaux":

Blake, Carolyn, Benjamin P. Rigby, Roxanne Armstrong-Moore, Peter Barbrook-Johnson, Nigel Gilbert, Mohammad Hassannezhad, Petra Meier, et al. Participatory systems mapping for population health research, policy and practice: guidance on method choice and design. University of Glasgow, January 2024. http://dx.doi.org/10.36399/gla.pubs.316563.

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What is participatory systems mapping? Participatory systems mapping engages stakeholders with varied knowledge and perspectives in creating a visual representation of a complex system. Its purpose is to explore, and document perceived causal relations between elements in the system. This guidance focuses on six causal systems mapping methods: systems-based theory of change maps; causal loop diagrams; CECAN participatory systems mapping; fuzzy cognitive maps; systems dynamics models; and Bayesian belief networks. What is the purpose of this guidance? This guidance includes a Framework that aids the choice and design of participatory systems mapping approaches for population health research, policy and practice. It offers insights on different systems mapping approaches, by comparing them and highlighting their applications in the population health domain. This guidance also includes case studies, signposting to further reading and resources, and recommendations on enhancing stakeholder involvement in systems mapping. Who is this guidance for? This guidance is designed for anyone interested in using participatory systems mapping, regardless of prior knowledge or experience. It primarily responds to calls to support the growing demand for systems mapping (and systems-informed approaches more broadly) in population health research, policy and practice. This guidance can however also be applied to other disciplines. How was it developed? The guidance was created by an interdisciplinary research team through an iterative, rigorous fivestage process that included a scoping review, key informant interviews, and a consultation exercise with subject experts. What is the ‘Participatory Systems Design Framework’ included in this guidance? The Design Framework supports users to choose between different methods and enhance the design of participatory systems mapping projects. Specifically, it encourages users to consider: 1) the added value of adopting a participatory approach to systems mapping; 2) the differences between methods, including their relative advantages and disadvantages; and 3) the feasibility of using particular methods for a given purpose. An editable version of the Framework is available to download as a supplementary file. How will this guidance support future use of these methods? Participatory systems mapping is an exciting and evolving field. This guidance clarifies and defines the use of these methods in population health research, policy and practice, to encourage more thoughtful and purposeful project design, implementation, and reporting. The guidance also identifies several aspects for future research and development: methodological advancements; advocating for and strengthening participatory approaches; strengthening reporting; understanding and demonstrating the use of maps; and developing skills for the design and use of these methods.