Relevant bibliographies by topics / Causal relationship models

Academic literature on the topic 'Causal relationship models'

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Published: 6 September 2023

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Journal articles on the topic "Causal relationship models"

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Belevich, M. "Relationship between standard and causal fluid models." Acta Mechanica 180, no. 1-4 (September 8, 2005): 83–106. http://dx.doi.org/10.1007/s00707-005-0262-y.

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Ranstam, J., and J. A. Cook. "Causal relationship and confounding in statistical models." British Journal of Surgery 103, no. 11 (September 22, 2016): 1445–46. http://dx.doi.org/10.1002/bjs.10241.

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Baetu, Tudor M. "Do Biopsychosocial Causal Models Rule Out Physicalism?" Journal of Consciousness Studies 29, no. 1 (January 15, 2022): 6–29. http://dx.doi.org/10.53765/20512201.29.1.006.

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Donald Price and James Barrell, two eminent pain researchers, argue that every time an experiment demonstrates that a biological variable is causally relevant to a psychological outcome, we are entitled to further rule out a possible mind–brain identity or supervenience relationship. As experimental evidence for two-way causal links between biological and psychological variables accumulates, more and more identity and supervenience relationships are ruled out, suggesting that psychophysicalism is empirically better supported than physicalism. I raise an objection to this line of argumentation by pointing out that, in the studies in question, causation is established within an operationalized framework; that is, irrespective of whether one knows what exactly is being manipulated and measured and how the intervention and measurement techniques work. By itself, evidence for causal relevance doesn't demonstrate that to each manipulated variable corresponds an ontologically distinct cause. This opens the possibility that some variables share common referents, as postulated by physicalist accounts. Moreover, even if it is not clear how to test for identity or supervenience relationships, it is still possible to test for causal mediation, which can generate empirical evidence discriminating between reductive physicalism and non-reductive alternatives.
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Malina, Stephen, Daniel Cizin, and David A. Knowles. "Deep mendelian randomization: Investigating the causal knowledge of genomic deep learning models." PLOS Computational Biology 18, no. 10 (October 20, 2022): e1009880. http://dx.doi.org/10.1371/journal.pcbi.1009880.

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Multi-task deep learning (DL) models can accurately predict diverse genomic marks from sequence, but whether these models learn the causal relationships between genomic marks is unknown. Here, we describe Deep Mendelian Randomization (DeepMR), a method for estimating causal relationships between genomic marks learned by genomic DL models. By combining Mendelian randomization with in silico mutagenesis, DeepMR obtains local (locus specific) and global estimates of (an assumed) linear causal relationship between marks. In a simulation designed to test recovery of pairwise causal relations between transcription factors (TFs), DeepMR gives accurate and unbiased estimates of the ‘true’ global causal effect, but its coverage decays in the presence of sequence-dependent confounding. We then apply DeepMR to examine the global relationships learned by a state-of-the-art DL model, BPNet, between TFs involved in reprogramming. DeepMR’s causal effect estimates validate previously hypothesized relationships between TFs and suggest new relationships for future investigation.
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Chvykov, Pavel, and Erik Hoel. "Causal Geometry." Entropy 23, no. 1 (December 26, 2020): 24. http://dx.doi.org/10.3390/e23010024.

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Information geometry has offered a way to formally study the efficacy of scientific models by quantifying the impact of model parameters on the predicted effects. However, there has been little formal investigation of causation in this framework, despite causal models being a fundamental part of science and explanation. Here, we introduce causal geometry, which formalizes not only how outcomes are impacted by parameters, but also how the parameters of a model can be intervened upon. Therefore, we introduce a geometric version of “effective information”—a known measure of the informativeness of a causal relationship. We show that it is given by the matching between the space of effects and the space of interventions, in the form of their geometric congruence. Therefore, given a fixed intervention capability, an effective causal model is one that is well matched to those interventions. This is a consequence of “causal emergence,” wherein macroscopic causal relationships may carry more information than “fundamental” microscopic ones. We thus argue that a coarse-grained model may, paradoxically, be more informative than the microscopic one, especially when it better matches the scale of accessible interventions—as we illustrate on toy examples.
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Haynes, Stephen N. "Causal models and the assessment-treatment relationship in behavior therapy." Journal of Psychopathology and Behavioral Assessment 10, no. 2 (June 1988): 171–83. http://dx.doi.org/10.1007/bf00962642.

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HALPERN, JOSEPH Y. "FROM CAUSAL MODELS TO COUNTERFACTUAL STRUCTURES." Review of Symbolic Logic 6, no. 2 (November 12, 2012): 305–22. http://dx.doi.org/10.1017/s1755020312000305.

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AbstractGalles & Pearl (l998) claimed that “for recursive models, the causal model framework does not add any restrictions to counterfactuals, beyond those imposed by Lewis’s [possible-worlds] framework.” This claim is examined carefully, with the goal of clarifying the exact relationship between causal models and Lewis’s framework. Recursive models are shown to correspond precisely to a subclass of (possible-world) counterfactual structures. On the other hand, a slight generalization of recursive models, models where all equations have unique solutions, is shown to be incomparable in expressive power to counterfactual structures, despite the fact that the Galles and Pearl arguments should apply to them as well. The problem with the Galles and Pearl argument is identified: an axiom that they viewed as irrelevant, because it involved disjunction (which was not in their language), is not irrelevant at all.
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Guo, Guang, and Leah K. VanWey. "Sibship Size and Intellectual Development: Is the Relationship Causal?" American Sociological Review 64, no. 2 (April 1999): 169–87. http://dx.doi.org/10.1177/000312249906400202.

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Previous research has consistently found a negative statistical relationship between sibship size and children's intellectual development. Two explanations have been offered for this finding. The prevailing explanation is that the relationship is causal, suggesting that limiting family size would lead to more intelligent children. A second explanation maintains that the relationship is spurious—that one or more undetermined factors correlated with family size are causally related to intellectual development. Using data on children from the National Longitudinal Survey of Youth, we reexamine the issue using change models. These change models allow us to control for such unmeasured effects as family intellectual climate, family value system, and family genetic heritage. We begin by replicating in these data the negative statistical relationship between three cognitive measures and sibship size. We then apply the change models to siblings measured at two points in time and to repeated measures of the same individuals. By considering sibship size as an individual trait that changes over time, we control for effects that are shared across siblings and over time. When these shared effects are controlled, the negative relationship between sibship size and intellectual development disappears, casting doubt on the causal interpretation of the negative relationship conventionally found.
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Yen, Chi-Hsien (Eric), Haocong Cheng, Grace Yu-Chun Yen, Brian P. Bailey, and Yun Huang. "Narratives + Diagrams: An Integrated Approach for Externalizing and Sharing People's Causal Beliefs." Proceedings of the ACM on Human-Computer Interaction 5, CSCW2 (October 13, 2021): 1–27. http://dx.doi.org/10.1145/3479588.

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Causal knowledge is of interest in many areas, such as statistics and machine learning, as it allows people and algorithms to predict outcomes and make data-driven decisions. Researchers in CSCW have proposed tools and workflows to externalize causal knowledge or beliefs from a group of people; however, most of the generated causal diagrams lack a deeper understanding of the causal mechanisms or could not capture diverse beliefs. By integrating narratives with causal diagrams, we implemented an interactive system that allows users to 1) write narratives to rationalize their perceived causal relationships, 2) visualize their causal models using directed diagrams, and 3) review and utilize others' causal diagrams and narratives. We conducted a user study (N=20) to learn how participants leveraged this integrated approach to externalize their perceived causal models for a given application context. Our results showed that the approach implemented in our tool enabled the externalization of users' causal beliefs (e.g., how and why a causal relationship might occur), allowed blind spots of individuals' causal reasoning to be revealed (e.g., learning new ideas from peers), and inspired their causal reasoning (e.g., revising or adding new causal relationships). We also identified the individual differences in people's causal beliefs and observed the impacts of showing others' causal models when one is building his/her causal diagram and narratives. This work provides practical design implications for developing collaborative tools that facilitate capturing and sharing causal beliefs.
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CHU, BEI-TSENG B. "DIAGNOSIS WITH CONTINUOUS AND DISCRETE CAUSAL RELATIONSHIPS: KNOWLEDGE REPRESENTATION." International Journal of Pattern Recognition and Artificial Intelligence 06, no. 04 (October 1992): 731–51. http://dx.doi.org/10.1142/s0218001492000370.

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Most present research on diagnostic reasoning has dealt with discrete causal relationships. However, continuous causal relationships are frequently encountered in many applications, particularly in equipment/instrument diagnostic applications. This paper offers an integrated model of discrete and continuous causal relationships based on probability theory. Four types of continuous and discrete causal relationships are identified. For each type of causal relationship the following issues are addressed: its semantics and causation strength based on probability theory, and how causation strength can be acquired. It is also demonstrated that causation probabilities for continuous causal relationships can be derived based on classical statistical theory and Bayesian probability theory. It will be shown in a companion paper that the knowledge representation framework presented in this paper can be used in causal network-based diagnostic inference models.
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Dissertations / Theses on the topic "Causal relationship models"

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Hagerty, Nicholas L. "Bayesian Network Modeling of Causal Relationships in Polymer Models." Miami University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=miami1619009432971036.

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Tringides, Constantinos A. "Alternative formulations of joint model systems of departure time choice and mode choice for non-work trips." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000240.

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Kasperskaya, Yulia. "Essays on causal performance measurement models." Doctoral thesis, Universitat Pompeu Fabra, 2007. http://hdl.handle.net/10803/7348.

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La tesis trata de los modelos de gestión modernos de naturaleza causal, entre los que destaca el Cuadro de Mando Integral (CMI). En primer lugar, se presenta un análisis conceptual su supuesto central, las relaciones causa-efecto. En segundo lugar, se ofrece un análisis estadístico de los supuestos del CMI. En tercer lugar, a partir dos casos de estudio de ayuntamientos, se compara y analiza la implementación del CMI en las organizaciones. El análisis conceptual indica que la complejidad, la incertidumbre y el carácter dinámico del entorno pueden obstaculizar la elaboración de modelos válidos de causa-efecto. Los resultados del análisis estadístico no permiten confirmar la hipótesis de existencia de relaciones estables en el CMI. Finalmente, el análisis de los casos sugiere que las organizaciones pueden recurrir a diversas tácticas y escenarios para implementar el CMI y enfatiza la influencia que las rutinas y normas establecidas en las organizaciones ejercen en ello.
This dissertation address a number of research questions related to causal performance measurement models, such as the Balanced Scorecard (BSC). First, we provide an analytical discussion on the topic of the cause-and-effect relationships in these models. Second, we test of the BSC assumptions on empirical data coming from the dynamic enterprises. Third, we compare and analyze the BSC adoption by two city councils. The conceptual analysis indicates that dynamic, complex and uncertain environment and the cognitive limitations of managers may greatly challenge the elaboration of valid causal models. Our statistical results give little support to the existence of stable BSC links. Our case studies findings show diverse organizational rationalities behind the BSC adoption in organizations and the importance of the existing rules and routines.
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Ye, Xin. "Development of models for understanding causal relationships among activity and travel variables." [Tampa, Fla] : University of South Florida, 2006. http://purl.fcla.edu/usf/dc/et/SFE0001842.

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Dobransky, Nicole Denise. "OPTIMIZING LEARNING THROUGH TEACHER-STUDENT RELATIONSHIPS: A TEST OF THE CAUSAL PROCESS STUDENT UNDERSTANDING MODEL." Lexington, Ky. : [University of Kentucky Libraries], 2008. http://hdl.handle.net/10225/959.

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Thesis (Ph. D.)--University of Kentucky, 2008.
Title from document title page (viewed on January 26, 2009). Document formatted into pages; contains: vii, 99 p. : ill. Includes abstract and vita. Includes bibliographical references (p. 87-96).
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Campbell, Lucy Jane. "Statistical models for time to event outcomes and determination of causal relationships in an observational HIV/TB study." Thesis, University of Cape Town, 2012. http://hdl.handle.net/11427/6570.

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Lamberts, Monique. "The relationship between stress levels and job satisfaction amongst high school educators in De Aar (Northern Cape)." Thesis, University of the Western Cape, 2011. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_2086_1363786447.

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A large number of studies show that educators are exposed to workloads that result particularly in stress. According to Olivier and Venter (2003), stress is considered to be the main factor contributing to job-related illnesses, early retirement, job dissatisfaction and absenteeism. Teaching, has without a doubt become a more demanding and stressful job. The lack of discipline in schools, abolishment of corporal punishment, large pupilteacher ratios and a new curriculum approach all contribute to raising the stress levels of teachers (Ngidi &
Sibaya, 2002). Stanton, Bachiochi, Robie, Perez and Smith (2002 cited in Brewer &
McMahan-Landers, 2003) is of the view that job stress is an antecedent of job satisfaction but the two constructs have been treated as related, yet they are different. The aim of this study is to determine the relationship between stress levels and job satisfaction amongst high school educators in De Aar as well as to 
determine whether biographical variables (namely, gender, age, tenure, marital status and number of children) have an impact on job satisfaction and stress levels. The researcher hopes that 
the findings would give the Department of Education in the Northern Cape an idea of the stress levels and job satisfaction experienced by high school educators in De Aar so that interventions can be implemented to issues proactively. The sample consisted of 86 educators employed at four high schools in De Aar. A biographical questionnaire, the Job Satisfaction Survey (JSS) and Stress Diagnostic Survey (SDS) were administered to assess respondents&rsquo
stress and job satisfaction levels. Informed consent was obtained from the various participants and anonymity of participation was ensured. Statistical analyses included inferential (Pearson&rsquo
s Product Moment Correlation Coefficient, Analysis of Variance &
T-Tests) as well as descriptive statistics. The results of the study 
indicate that there is a statistically significant, inverse relationship between stress levels and job satisfaction amongst high school educators in De Aar. Furthermore, the results indicate that there 
are statistically significant differences in stress levels as well as job satisfaction of high school educators on the basis of their biographical characteristics. The chapter concludes with presenting limitations of the study and putting forth recommendations for future research.

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Lance, Charles E. "Evaluation of competing models of the causal relationships among job satisfaction and organizational commitment as precursors to voluntary employee turnover." Diss., Georgia Institute of Technology, 1985. http://hdl.handle.net/1853/29210.

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Brunelli, Renata Trevisan. "Análise do impacto de perturbações sobre medidas de qualidade de ajuste para modelos de equações estruturais." Universidade de São Paulo, 2012. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-24032013-123415/.

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A Modelagem de Equações Estruturais (SEM, do inglês Structural Equation Modeling) é uma metodologia multivariada que permite estudar relações de causa/efeito e correlação entre um conjunto de variáveis (podendo ser elas observadas ou latentes), simultaneamente. A técnica vem se difundindo cada vez mais nos últimos anos, em diferentes áreas do conhecimento. Uma de suas principais aplicações é na conrmação de modelos teóricos propostos pelo pesquisador (Análise Fatorial Conrmatória). Existem diversas medidas sugeridas pela literatura que servem para avaliar o quão bom está o ajuste de um modelo de SEM. Entretanto, é escassa a quantidade de trabalhos na literatura que listem relações entre os valores de diferentes medidas com possíveis problemas na amostra e na especicação do modelo, isto é, informações a respeito de que possíveis problemas desta natureza impactam quais medidas (e quais não), e de que maneira. Tal informação é importante porque permite entender os motivos pelos quais um modelo pode estar sendo considerado mal-ajustado. O objetivo deste trabalho é investigar como diferentes perturbações na amostragem, especicação e estimação de um modelo de SEM podem impactar as medidas de qualidade de ajuste; e, além disso, entender se o tamanho da amostra influencia esta resposta. Simultaneamente, também se avalia como tais perturbações afetam as estimativas, dado que há casos de perturbações em que os parâmetros continuam sendo bem ajustados, mesmo com algumas medidas indicando um mau ajuste; ao mesmo tempo, há ocasiões em que se indica um bom ajuste, enquanto que os parâmetros são estimados de forma distorcida. Tais investigações serão realizadas a partir de simulações de exemplos de amostras de diferentes tamanhos para cada tipo de perturbação. Então, diferentes especicações de modelos de SEM serão aplicados a estas amostras, e seus parâmetros serão estimados por dois métodos diferentes: Mínimos Quadrados Generalizados e Máxima Verossimilhança. Conhecendo tais resultados, um pesquisador que queira aplicar a técnica de SEM poderá se precaver e, dentre as medidas de qualidade de ajuste disponíveis, optar pelas que mais se adequem às características de seu estudo.
The Structural Equation Modeling (SEM) is a multivariate methodology that allows the study of cause-and-efect relationships and correlation of a set of variables (that may be observed or latent ones), simultaneously. The technique has become more diuse in the last years, in different fields of knowledge. One of its main applications is on the confirmation of theoretical models proposed by the researcher (Confirmatory Factorial Analysis). There are several measures suggested by literature to measure the goodness of t of a SEM model. However, there is a scarce number of texts that list relationships between the values of different of those measures with possible problems that may occur on the sample or the specication of the SEM model, like information concerning what problems of this nature impact which measures (and which not), and how does the impact occur. This information is important because it allows the understanding of the reasons why a model could be considered bad fitted. The objective of this work is to investigate how different disturbances of the sample, the model specification and the estimation of a SEM model are able to impact the measures of goodness of fit; additionally, to understand if the sample size has influence over this impact. It will also be investigated if those disturbances affect the estimates of the parameters, given the fact that there are disturbances for which occurrence some of the measures indicate badness of fit but the parameters are not affected; at the same time, that are occasions on which the measures indicate a good fit and there are disturbances on the estimates of the parameters. Those investigations will be made simulating examples of different size samples for which type of disturbance. Then, SEM models with different specifications will be fitted to each sample, and their parameters will be estimated by two dierent methods: Generalized Least Squares and Maximum Likelihood. Given those answers, a researcher that wants to apply the SEM methodology to his work will be able to be more careful and, among the available measures of goodness of fit, to chose those that are more adequate to the characteristics of his study.
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Islam, Muhammad Saiful. "Modelling cost overrun risks in power plant projects." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/125508/1/Muhammad%20Saiful_Islam_Thesis.pdf.

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Cost overruns in power plant projects frequently occur, and this very alarming phenomenon requires proper risk assessment and management in the early phase of power plant project development. A modified fuzzy group decision-making approach (FGDMA) was developed and the critical risks in different phases of thermal power plant project were identified. Further, a novel fuzzy canonical model (FCM) was developed and the complex causal risk-networks were modelled to understand the root causes of cost overruns. The benefits of this research to practitioners are such that it provides greater understanding of the risks involved in power plant projects and sound analytical methods to asses the risks.
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Books on the topic "Causal relationship models"

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Fell, John P. C. In search of a causal relationship between industrial output and employment in Ireland. Dublin: Central Bank of Ireland, 1989.

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Varlamov, Oleg. Mivar databases and rules. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1508665.

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The multidimensional open epistemological active network MOGAN is the basis for the transition to a qualitatively new level of creating logical artificial intelligence. Mivar databases and rules became the foundation for the creation of MOGAN. The results of the analysis and generalization of data representation structures of various data models are presented: from relational to "Entity — Relationship" (ER-model). On the basis of this generalization, a new model of data and rules is created: the mivar information space "Thing-Property-Relation". The logic-computational processing of data in this new model of data and rules is shown, which has linear computational complexity relative to the number of rules. MOGAN is a development of Rule - Based Systems and allows you to quickly and easily design algorithms and work with logical reasoning in the "If..., Then..." format. An example of creating a mivar expert system for solving problems in the model area "Geometry"is given. Mivar databases and rules can be used to model cause-and-effect relationships in different subject areas and to create knowledge bases of new-generation applied artificial intelligence systems and real-time mivar expert systems with the transition to"Big Knowledge". The textbook in the field of training "Computer Science and Computer Engineering" is intended for students, bachelors, undergraduates, postgraduates studying artificial intelligence methods used in information processing and management systems, as well as for users and specialists who create mivar knowledge models, expert systems, automated control systems and decision support systems. Keywords: cybernetics, artificial intelligence, mivar, mivar networks, databases, data models, expert system, intelligent systems, multidimensional open epistemological active network, MOGAN, MIPRA, KESMI, Wi!Mi, Razumator, knowledge bases, knowledge graphs, knowledge networks, Big knowledge, products, logical inference, decision support systems, decision-making systems, autonomous robots, recommendation systems, universal knowledge tools, expert system designers, logical artificial intelligence.
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Varlamov, Oleg. 18 examples of mivar expert systems. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1248446.

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Many years of research on mivar technologies of logical artificial intelligence have allowed us to create a new powerful, versatile and fast tool, which is called "multidimensional open gnoseological active net" — "multidimensional open gnoseological active net: MOGAN". This tool allows you to quickly and easily design algorithms and work with logical reasoning in the "If..., Then..." format, and it can be used to model cause-and-effect relationships in different subject areas and create knowledge bases of new-generation applied artificial intelligence systems and real-time mivar expert systems with "Big Knowledge". The reader, after studying this tutorial, you will be able to create mivar expert system with the help of CASMI Wi!Mi. Designed for students, bachelors, masters and postgraduate students studying artificial intelligence methods, as well as for users, experts and specialists, creating a system of information processing and management, mivar models, expert systems, automated control systems, systems of decision support and Recommender systems.
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Goul, Pauline, and Usher, eds. Early Modern Écologies. NL Amsterdam: Amsterdam University Press, 2020. http://dx.doi.org/10.5117/9789462985971.

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Early Modern Écologies is the first collective volume to offer perspectives on the relationship between contemporary ecological thought and early modern French literature. If Descartes spoke of humans as being ‘masters and possessors of Nature’ in the seventeenth century, the writers taken up in this volume arguably demonstrated a more complex and urgent understanding of the human relationship to our shared planet. Opening up a rich archive of literary and non-literary texts produced by Montaigne and his contemporaries, this volume foregrounds not how ecocriticism renews our understanding of a literary corpus, but rather how that corpus causes us to re-think or to nuance contemporary eco-theory. The sparsely bilingual title (an acute accent on écologies) denotes the primary task at hand: to pluralize (i.e. de-Anglophone-ize) the Environmental Humanities. Featuring established and emerging scholars from Europe and the United States, Early Modern Écologies opens up new dialogues between ecotheorists such as Timothy Morton, Gilles Deleuze, and Bruno Latour and Montaigne, Ronsard, Du Bartas, and Olivier de Serres.
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Paolella, Christopher. Human Trafficking in Medieval Europe. NL Amsterdam: Amsterdam University Press, 2020. http://dx.doi.org/10.5117/9789463723336.

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Human trafficking has become a global concern over the last twenty years, but its violence has terrorized and traumatized its victims and survivors for millennia. This study examines the deep history of human trafficking from Late Antiquity to the Early Modern Period. It traces the evolution of trafficking patterns: the growth and decline of trafficking routes, the everchanging relationships between traffickers and authorities, and it examines the underlying causes that lead to vulnerability and thus to exploitation. As the reader will discover, the conditions that lead to human trafficking in the modern world, such as poverty, attitudes of entitlement, corruption, and violence, have a long and storied past. When we understand that past, we can better anticipate human trafficking’s future, and then we are better able to fight it.
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Isakov, Vladimir. Speak the language of schemes. ru: INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1860649.

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Schematization and visualization are the necessary means to ensure the activity of a modern specialist. Schematization allows you to highlight the main thing in an object, to discover its constituent elements, to show their relationship, gives impetus to the construction of conceptual approaches. Visualization "dresses" schematic concepts in a bright, expressive artistic and graphic form. The handbook provides descriptions of the most popular means of analytical graphics - maps, graphs, tables, graphs, diagrams, flowcharts (algorithms), chronolents, maps, methodological schemes, etc. The ways of using schemes for analyzing goals, causes, problems, versions are considered. A thematic dictionary of terms and definitions, a "hot twenty" useful schematization are given. For students, undergraduates, postgraduates, teachers of law schools and faculties, as well as for representatives of other specialties - everyone who draws diagrams and works with them.
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Le Pelley, Mike E., Oren Griffiths, and Tom Beesley. Associative Accounts of Causal Cognition. Edited by Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.2.

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Humans are clearly sensitive to causal structures—we can describe and understand causal mechanisms and make predictions based on them. But this chapter asks: Is causal learning always causal? Or might seemingly causal behavior sometimes be based on associations that merely encode the information that two events “go together,” not that one causes the other? This associative view supposes that people often (mis)interpret associations as supporting the existence of a causal relationship between events; they make the everyday mistake of confusing correlation with causation. To assess the validity of this view, one must move away from considering specific implementations of associative models and instead focus on the general principle embodied by the associative approach—that the rules governing learning are general-purpose, and so do not differentiate between situations involving cause–effect relationships and those involving signaling relationships that are non-causal.
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Sutton, Margaret Rose. THE RELATIONSHIP AND PREDICTABILITY OF FIVE PSYCHOSOCIAL FACTORS IN CANCER PATIENTS' COPING PROCESSES: A TEST OF TWO PROPOSED CAUSAL MODELS. 1990.

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NANDE-VÁZQUEZ, Edgard Alfredo, Teodoro REYES-FONG, and Omar Alejandro PÉREZ-CRUZ. The Generalized Least Squares Method (GMM) as a tool for causal analysis of spending, budget management and electoral results. ECORFAN, 2021. http://dx.doi.org/10.35429/b.2021.8.1.130.

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In the different fields of science, many times, there is a need to estimate the associations between variables, as an approach to understanding the interaction of one as a function of the others. It is usually done by applying restrictive models, such as analysis of variance and linear regression. This type of analysis requires that the dependent variable be continuous, have a normal and constant distribution of the mean and variance. However, when the dependent variable is discrete or categorical, the linear model is not viable. Faced with this impediment, the theory of linear models arises and is expanded to broader categories, which have been called Generalized Linear Models. This category assumes that all distribution functions are exponential, in which the normal distribution is located. In this sense, in this research, Generalized Least Squares methods were applied in their various variants: of moments, ordinary and feasible. These models allow calculating the parameters of models in which the dependent variable has a Poisson or multinomial distribution. In such a way that the Generalized Least Squares serve as a tool to analyze the effect of the elections on public spending and its relationship with the electoral results, analyzing the variables of a budgetary nature, derived from the possibility that the government in power continues or is re-elected. For this, data related to the states and municipalities of México in the period 2007 to 2019 are used.
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Holyoak, Keith J., and Hee Seung Lee. Inferring Causal Relations by Analogy. Edited by Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.25.

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When two situations share a common pattern of relationships among their constituent elements, people often draw an analogy between a familiar source analog and a novel target analog. This chapter reviews major subprocesses of analogical reasoning and discusses how analogical inference is guided by causal relations. Psychological evidence suggests that analogical inference often involves constructing and then running a causal model. It also provides some examples of analogies and models that have been used as tools in science education to foster understanding of critical causal relations. A Bayesian theory of causal inference by analogy illuminates how causal knowledge, represented as causal models, can be integrated with analogical reasoning to yield inductive inferences.
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Book chapters on the topic "Causal relationship models"

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von Eckardstein, Arnold. "High Density Lipoproteins: Is There a Comeback as a Therapeutic Target?" In Prevention and Treatment of Atherosclerosis, 157–200. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/164_2021_536.

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AbstractLow plasma levels of High Density Lipoprotein (HDL) cholesterol (HDL-C) are associated with increased risks of atherosclerotic cardiovascular disease (ASCVD). In cell culture and animal models, HDL particles exert multiple potentially anti-atherogenic effects. However, drugs increasing HDL-C have failed to prevent cardiovascular endpoints. Mendelian Randomization studies neither found any genetic causality for the associations of HDL-C levels with differences in cardiovascular risk. Therefore, the causal role and, hence, utility as a therapeutic target of HDL has been questioned. However, the biomarker “HDL-C” as well as the interpretation of previous data has several important limitations: First, the inverse relationship of HDL-C with risk of ASCVD is neither linear nor continuous. Hence, neither the-higher-the-better strategies of previous drug developments nor previous linear cause-effect relationships assuming Mendelian randomization approaches appear appropriate. Second, most of the drugs previously tested do not target HDL metabolism specifically so that the futile trials question the clinical utility of the investigated drugs rather than the causal role of HDL in ASCVD. Third, the cholesterol of HDL measured as HDL-C neither exerts nor reports any HDL function. Comprehensive knowledge of structure-function-disease relationships of HDL particles and associated molecules will be a pre-requisite, to test them for their physiological and pathogenic relevance and exploit them for the diagnostic and therapeutic management of individuals at HDL-associated risk of ASCVD but also other diseases, for example diabetes, chronic kidney disease, infections, autoimmune and neurodegenerative diseases.
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Cleophas, Ton J., and Aeilko H. Zwinderman. "Bayes and Causal Relationships." In Modern Bayesian Statistics in Clinical Research, 131–42. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92747-3_13.

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de Jong, Gjalt, and Bart Nooteboom. "The Theoretical Model." In The Causal Structure of Long-Term Supply Relationships, 25–42. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4525-5_3.

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Esposito, Floriana, Donato Malerba, and Giovanni Semeraro. "Discovering Probabilistic Causal Relationships: A Comparison Between Two Methods." In Selecting Models from Data, 233–42. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-2660-4_24.

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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.
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Entner, Doris, and Patrik O. Hoyer. "Discovering Unconfounded Causal Relationships Using Linear Non-Gaussian Models." In New Frontiers in Artificial Intelligence, 181–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25655-4_17.

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Bakker, Bram, and Paul den Dulk. "Causal Relationships and Relationships between Levels: The Modes of Description Perspective." In Proceedings of the Twenty First Annual Conference of the Cognitive Science Society, 43–48. New York: Psychology Press, 2020. http://dx.doi.org/10.4324/9781410603494-13.

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Wake, Akira, and Herbert R. Morgan. "Human Disease Caused by Yersinia." In Host-Parasite Relationships and the Yersinia Model, 181–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 1986. http://dx.doi.org/10.1007/978-3-642-71344-6_20.

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Hair, Joseph F., G. Tomas M. Hult, Christian M. Ringle, Marko Sarstedt, Nicholas P. Danks, and Soumya Ray. "An Introduction to Structural Equation Modeling." In Classroom Companion: Business, 1–29. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80519-7_1.

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AbstractStructural equation modeling is a multivariate data analysis method for analyzing complex relationships among constructs and indicators. To estimate structural equation models, researchers generally draw on two methods: covariance-based SEM (CB-SEM) and partial least squares SEM (PLS-SEM). Whereas CB-SEM is primarily used to confirm theories, PLS represents a causal–predictive approach to SEM that emphasizes prediction in estimating models, whose structures are designed to provide causal explanations. PLS-SEM is also useful for confirming measurement models. This chapter offers a concise overview of PLS-SEM’s key characteristics and discusses the main differences compared to CB-SEM. The chapter also describes considerations when using PLS-SEM and highlights situations that favor its use compared to CB-SEM.
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Gao, Ya, Shaoxiong Ji, Tongxuan Zhang, Prayag Tiwari, and Pekka Marttinen. "Contextualized Graph Embeddings for Adverse Drug Event Detection." In Machine Learning and Knowledge Discovery in Databases, 605–20. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26390-3_35.

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AbstractAn adverse drug event (ADE) is defined as an adverse reaction resulting from improper drug use, reported in various documents such as biomedical literature, drug reviews, and user posts on social media. The recent advances in natural language processing techniques have facilitated automated ADE detection from documents. However, the contextualized information and relations among text pieces are less explored. This paper investigates contextualized language models and heterogeneous graph representations. It builds a contextualized graph embedding model for adverse drug event detection. We employ different convolutional graph neural networks and pre-trained contextualized embeddings as the building blocks. Experimental results show that our methods can improve the performance by comparing recent ADE detection models, suggesting that a text graph can capture causal relationships and dependency between different entities in a document.
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Conference papers on the topic "Causal relationship models"

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Perçin, Selçuk, Aykut Karakaya, and Seymur Ağazade. "The Relationship between Export and Innovation in Turkish Manufacturing Industry." In International Conference on Eurasian Economies. Eurasian Economists Association, 2015. http://dx.doi.org/10.36880/c06.01408.

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Posner’s (1961) “technological gap theory” and Vernon’s (1966) “product life cycle theory” predicts that innovation causes market power and facilitates export. In other side export encourages firms and provides more affirmative environment for innovative processes. These theoretical approaches have different predictions regarding the direction of causality between export and innovation. Using Two-Step System Generalized Moments of Method this study investigates causality relationship between export intensity and R&D intensity for the period 2008-2013 in Turkish Manufacturing Industry. Causality relationship was modeled within two different equations and analyzed by Wald Test. First equation models export intensity as function of R&D intensity and real exchange rate. In the second equation R&D intensity estimated as a function of export intensity and Herfindahl-Hirschman Index as competition variable. Causality test results show that there is unidirectional causal relationship from R&D intensity to export intensity. This finding supports the predictions of Posner’s (1961) “technological gap theory” and Vernon’s (1966) “product life cycle theory” related to the innovation and export relationship in Turkish Manufacturing Industry.
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Sun, Schyler C., Bailu Jin, Zhuangkun Wei, and Weisi Guo. "Revealing the Excitation Causality between Climate and Political Violence via a Neural Forward-Intensity Poisson Process." 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.2022/718.

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The causal mechanism between climate and political violence is fraught with complex mechanisms. Current quantitative causal models rely on one or more assumptions: (1) the climate drivers persistently generate conflict, (2) the causal mechanisms have a linear relationship with the conflict generation parameter, and/or (3) there is sufficient data to inform the prior distribution. Yet, we know conflict drivers often excite a social transformation process which leads to violence (e.g., drought forces agricultural producers to join urban militia), but further climate effects do not necessarily contribute to further violence. Therefore, not only is this bifurcation relationship highly non-linear, there is also often a lack of data to support prior assumptions for high resolution modeling. Here, we aim to overcome the aforementioned causal modeling challenges by proposing a neural forward-intensity Poisson process (NFIPP) model. The NFIPP is designed to capture the potential non-linear causal mechanism in climate induced political violence, whilst being robust to sparse and timing-uncertain data. Our results span 20 recent years and reveal an excitation-based causal link between extreme climate events and political violence across diverse countries. Our climate-induced conflict model results are cross-validated against qualitative climate vulnerability indices. Furthermore, we label historical events that either improve or reduce our predictability gain, demonstrating the importance of domain expertise in informing interpretation.
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Purmalis, Ritvars. "Cēloņsakarības konstatēšana mākslīgā intelekta darbībā." In Latvijas Universitātes 80. starptautiskā zinātniskā konference. LU Akadēmiskais apgāds, 2022. http://dx.doi.org/10.22364/juzk.80.14.

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While there is still a debate at European Union level as to which of the known models of civil liability should be used to determine a person’s civil liability for damage caused by an artificial intelligence-driven system, particular attention should be directed at the issues relating to the “transparency” and “explainability” of the operation of artificial intelligence-driven system. The purpose of this article is to provide a brief insight into the importance of establishing the causal relationship between the way in which the inputs (various datasets) available for the artificial intelligence-driven system are used as a basis to reach a particular conclusion within its autonomous operation, and the importance of this matter within the context of recent legal policy updates.
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Elmas, Bekir, and Ömer Esen. "Determining a Dynamic Relationship Between Stock Prices and Exchange Rates: An Empirical Study on Eurasia." In International Conference on Eurasian Economies. Eurasian Economists Association, 2010. http://dx.doi.org/10.36880/c01.00168.

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The stock price has a close relationship with some macroeconomic variables. As examples of the main macroeconomic variables can be shown that exchange rates, inflation, interest rate, growth rates. This paper empirically examined the relationship between the local stock market indexes and exchange rate (USD) in six Eurasian countries namely Turkey, Germany, France, Netherlands, Russia, France and India. The paper set out by testing existence of a long-term relationship between considered two variables using the Engle-Granger (1987), Johansen (1988, 1995) and Johansen-Juselius (1990) cointegration methods. Results of Engle- Granger cointegration test showed that there is no cointegration linkage between two variables under consideration. Furthermore, The Johansen cointegration test found that there is a long-term relationship between two variables (variables in the two countries). Under the VAR (Vector Autoregressive) and VEC (Vector Error Correction) models appllied the Granger causality test, revealed an unidirectional casual relationship between two variables in each of the six countries. In addition as regards the relationship While there is a unidirectional causal relationship running from exchange rate to stock market for four countries. However this relation is casual running from stock market to exchange rate for other two countries. According to the direction of the relationship these results that relationship between stock prices and exchange rate in four countries supports for the “Traditional Approach”. Furthermore, this relation also supports for the “Portfolio Approach” for other two countries.
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Vilcu, Adrian, Ion Verzea, Mihaela Cojan, and Adrian Vilcu. "EDUCATIONAL SOFTWARE FOR THE OPTIMIZATION OF SAFETY IN OPERATION IN THE TEXTILE PROCESSES." In eLSE 2017. Carol I National Defence University Publishing House, 2017. http://dx.doi.org/10.12753/2066-026x-17-243.

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One of the present challenges of the textile production management consists in correlating the production- quality policies corroborated with the need to control and optimize the manufacturing process from the standpoint of the times of good operation and maintenance respectively. The studies undertaken until now have proved the necessity of a new approach of the causal relationship between safety in operation and production strategies, based on mathematical and neuronal modelling. The objective of our research consists in the determination of reliability/ maintainability relationship for a manufacturing process (dependent output variables), and production/resulted quality (independent input variables). In this connection, we propose a unitary software instrument applied to this optimization problem, able to refine qualitatively the results and to combine the algorithmic character of this mathematical approach with the neuronal networks learning based one. In order to reach the proposed objective, one resorted to the application of quantitative learning techniques (numerical measurement of parameters), qualitative research (proposing the working hypotheses), mathematical modelling, and research in artificial intelligence field (neuronal modelling- generation of test patterns, definition of internal network structure). The two correlation models are comprised in a unitary software application that validates experimentally the hypotheses on causality relationships between variables, and can be proposed as a decision instrument for the operational management problem concerning the Safety in Operation, to support production-quality policies. The application has been tested in the concrete case of spinning process, being in process of validation. The obtained results are used at present in teaching activity (educational software used in practical classes for the disciplines "Maintenance Management" and "Optional Research"), meaning to be extended in the practice of textile production management
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Catania, Giuseppe, and Silvio Sorrentino. "Experimental Identification of a Fractional Derivative Linear Model for Viscoelastic Materials." In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-85725.

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Non integer, fractional order derivative rheological models are known to be very effective in describing the linear viscoelastic dynamic behaviour of mechanical structures made of polymers [1]. The application of fractional calculus to viscoelasticity can be physically consistent [2][3][4] and the resulting non integer order differential stress-strain constitutive relation provides good curve fitting properties, requires only a few parameters and leads to causal behaviour [5]. When using such models the solution of direct problems, i.e. the evaluation of time or frequency response from a known excitation can still be obtained from the equations of motion using standard tools such as modal analysis [6]. But regarding the inverse problem, i.e. the identification from measured input-output vibrations, no general technique has so far been established, since the current methods do not seem to easily work with differential operators of non integer order. In this paper a frequency domain method is proposed for the experimental identification of a linear viscoelastic model, namely the Fractional Zener also known as Fractional Standard Linear Solid [5], to compute the frequency dependent complex stress-strain relationship parameters related to the material. The procedure is first checked with respect to numerically generated frequency response functions for testing its accuracy, and then to experimental inertance data from a free-free homogeneous beam made of High Density Polyethylene (HDPE) in plane flexural and axial vibration.
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Dalte, Olha. "HYPER-HYPONYMIC RELATIONSHIPS BETWEEN PARONYMIC TERMS «CAUSAL» AND «CAUSATIVE»: CORPORA-BASED VERIFICATION." In Modern Science: Processes of Globalisation and Transformation. Publishing House “Baltija Publishing”, 2023. http://dx.doi.org/10.30525/978-9934-26-309-5-36.

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Meredith, David. "A CAUSAL RELATIONSHIP MODEL OF UNDERGRADUATE STUDENTS’ WILLINGNESS TO COMMUNICATE ENGLISH." In 35th International Academic Conference, Barcelona. International Institute of Social and Economic Sciences, 2018. http://dx.doi.org/10.20472/iac.2018.935.029.

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Li, Shuang, Si-Ze Hou, Yutong Yao, Yongqi Sun, and Bin Ding. "MMINN: Management-Model Informed Neural Network with Interpretability and Causal Relationship." In 2022 5th International Conference on Pattern Recognition and Artificial Intelligence (PRAI). IEEE, 2022. http://dx.doi.org/10.1109/prai55851.2022.9904239.

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Guo, Zhenghao, Verity M. McClelland, and Zoran Cvetkovic. "Unravelling Causal Relationships Between Cortex and Muscle with Errors-in-variables Models." In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2021. http://dx.doi.org/10.1109/embc46164.2021.9630485.

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Reports on the topic "Causal relationship models"

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Khvostina, Inesa, Serhiy Semerikov, Oleh Yatsiuk, Nadiia Daliak, Olha Romanko, and Ekaterina Shmeltser. Casual analysis of financial and operational risks of oil and gas companies in condition of emergent economy. [б. в.], October 2020. http://dx.doi.org/10.31812/123456789/4120.

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The need to control the risk that accompanies businesses in their day- to-day operations, and at the same time changing economic conditions make risk management an almost indispensable element of economic life. Selection of the main aspects of the selected phases of the risk management process: risk identification and risk assessment are related to their direct relationship with the subject matter (risk identification to be managed; risk analysis leading to the establishment of a risk hierarchy, and, consequently, the definition of risk control’ methods) and its purpose (bringing the risk to acceptable level). It is impossible to identify the basic patterns of development of the oil and gas industry without exploring the relationship between economic processes and enterprise risks. The latter are subject to simulation, and based on models it is possible to determine with certain probability whether there have been qualitative and quantitative changes in the processes, in their mutual influence on each other, etc. The work is devoted to exploring the possibilities of applying the Granger test to examine the causal relationship between the risks and obligations of oil and gas companies. The analysis is based on statistical tests and the use of linear regression models.
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Rizzo, Tesalia. Shaping political trust through participatory governance in Lat in America. Inter-American Development Bank, August 2021. http://dx.doi.org/10.18235/0003601.

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This paper critically assesses research that examines the link between participatory institutions and political trust in the context of developing countries, with a focus on Latin America. A significant limitation in the systematic accumulation of knowledge in this field is inattention to identifying a clear causal chain through which citizen participation shapes political, economic, and attitudinal outcomes such as political trust. This is particularly important in the Latin American case where constitutionally stated objectives of participatory governance include the improvement of citizen welfare as well as strengthening of political trust in public institutions. Future work should concentrate in providing clear and testable models of the complex relationship between participatory mechanisms, policy, governance, and trust, with particular attention to what mediates and moderates this relationship. Additionally, empirical work done of the Latin America case should move away from a predominantly case-study based and macro-level perspective in the study of participatory institutions to micro-level studies from the citizens point of view. A new frontier for the study of participatory governance in Latin America lies in understanding how citizens experiences with and expectations of participatory institutions as well as the policy outcomes delivered by these institutions shape political trust.
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Kneale, Dylan, James Thomas, Mukdarut Bangpan, Hugh Waddington, and David Gough. Causal chain analysis in systematic reviews of international development interventions. Centre for Excellence and Development Impact and Learning (CEDIL), 2018. http://dx.doi.org/10.51744/cip4.

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Understanding the extent to which an intervention ‘works’ can provide compelling evidence to decision-makers, although without an accompanying explanation of how an intervention works, this evidence can be difficult to apply in other settings, ultimately impeding its usefulness in making judicious and evidence-informed decisions. In this paper, we describe causal chain analysis as involving the development of a logic model, which outlines graphically a hypothesis of how an intervention leads to a change in an outcome. This logic model is then used to anchor subsequent decisions in the systematic review process, including decisions on synthesis. In this paper, we outline the steps taken in building a logic model, which usually consists of a series of boxes depicting intervention components and processes, outputs, and outcomes with arrows depicting connecting relationships. The nature of these connecting relationships and their basis in causality are considered, through a focus on complex causal relationships and the way in which contextual factors about the intervention setting or population may moderate these. We also explore the way in which specific combinations of intervention components may lead to successful interventions. Evidence synthesis techniques are discussed in the context of causal chain analysis, and their usefulness in exploring different parts of the causal chain or different types of relationship. The approaches outlined in this paper aim to assist systematic reviewers in producing findings that are useful to decision-makers and practitioners, and in turn, help to confirm existing theories or develop entirely new ways of understanding how interventions to effect change.
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Acosta, Karina. Locked up? The development and internal migration nexus in Colombia. Banco de la República, January 2022. http://dx.doi.org/10.32468/dtseru.304.

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Although a sizable number of studies have been exploring the migration development nexus in international settings, there is still a reduced number on internal contexts in recent years. This research aims to estimate the causal effect of origin economic conditions on internal population migration using a time series of the Colombian states between 2012 and 2019. This analysis provides a macro perspective of associations and causation between population dynamics and development in the current changes observed using spatial interaction models. Likewise, it analyses the current portray of internal migration in Colombia (defined by five-years and one-year flows). The evidence shows that the migration hump depends on the scale at which it is analyzed. At an aggregated scale, initial economic conditions are negatively associated with migration until a threshold where this relationship is reversed. The opposite is observed in the rural migrants subsample.
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Montville, Thomas J., and Roni Shapira. Molecular Engineering of Pediocin A to Establish Structure/Function Relationships for Mechanistic Control of Foodborne Pathogens. United States Department of Agriculture, August 1993. http://dx.doi.org/10.32747/1993.7568088.bard.

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This project relates the structure of the bacteriocin molecule (which is genetically determined) to its antimicrobial function. We have sequenced the 19,542 bp pediocin plasmid pMD136 and developed a genetic transfer system for pediococci. The pediocin A operon is complex, containing putative structural, immunity, processing, and transport genes. The deduced sequence of the pediocin A molecule contains 44 amino acids and has a predicted PI of 9.45. Mechanistic studies compared the interaction of pediocin PA-1 and nisin with Listeria monocytgenes cells and model lipid systems. While significant nisin-induced intracellular ATP depletion is caused by efflux, pediocin-induced depletion is caused exclusively by hydrolysis. Liposomes derived from L. monocytogenes phospholipids were used to study the physical chemistry of pediocin and nisin interactions with lipids. Their different pH optima are the results of different specific ionizable amino acids. We generated a predicted 3-D structural model for pediocin PA-1 and used a variety of mutant pediocins to demonstrate that the "positive patch" at residues 11 and 12 (and not the YGNGV consensus sequence) is responsible for the binding step of pediocin action. This structure/function understanding gained here provides necessary prerequisites to the more efficacious use of bacteriocins to control foodborne pathogens.
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Ruvinsky, Alicia, Maria Seale, R. Salter, and Natàlia Garcia-Reyero. An ontology for an epigenetics approach to prognostics and health management. Engineer Research and Development Center (U.S.), March 2023. http://dx.doi.org/10.21079/11681/46632.

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Techniques in prognostics and health management have advanced considerably in the last few decades, enabled by breakthroughs in computational methods and supporting technologies. These predictive models, whether data-driven or physics-based, target the modeling of a system’s aggregate performance. As such, they generalize assumptions about the modelled system’s components, and are thus limited in their ability to represent individual components and the dynamic environmental factors that affect composite system health. To address this deficiency, we have developed an epigenetics-inspired knowledge representation for engineered system state that encompasses components and environmental factors. Epigenetics is concerned with explaining how environmental factors affect the expression of an organism’s genetic material. The field has derived important in-sights into the development and progression of disease states based on how environmental factors impact genetic material, causing variations in how a gene is expressed. The health of an engineered system is similarly influenced by its environment. A foundation for a new approach to prognostics based on epigenetics must begin by representing the entities and relationships of an engineered system from the perspective of epigenetics. This paper presents an ontology for an epigenetics-inspired representation of an engineered system. An ontology describing the epigenetics of an engineered system will enable the composition of a formal model and the incremental development of a more robust, causal reasoning system.
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Foeken, van, and Gresnigt. L51809 Buckling and Collapse of UOE Manufactured Steel Pipes. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), November 1998. http://dx.doi.org/10.55274/r0010236.

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In the past 20 years, much research has been conducted into buckling and collapse of pipelines under external pressure, bending or tension and combinations. Also many finite element analyses have been performed into the behavior of pipelines under these loads. The available test results show considerable scatter, which is considered to be caused by variations in the stress-strain relationship, the anisotropy of the steel, the Bauschinger effect, the geometrical deviations, the residual stresses, the test conditions, etc. The manufacturing method (seamless, UO, UOE) has a considerable influence on these properties and on the collapse and local buckling resistance. In this project, design formulations for collapse and buckling with appropriate safety factors, calibrated against experimental and numerical models using probabilistic methods, have been selected for a practical range of design considerations. The project consisted of three parts: experiments, probabilistic calculations, and finite element calculations.
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Lawrence, David. UK trade and the war in Ukraine. Royal Institute of International Affairs, September 2022. http://dx.doi.org/10.55317/9781784135379.

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- The UK is less exposed to the direct effects of Russia’s invasion of Ukraine than its European neighbours. However, the indirect and longer-term effects of the war on UK trade could still be significant. - Inflationary pressure worldwide will contribute to price rises in the UK, particularly for food and energy. The ongoing effects of both Brexit – which has caused cross-border supply frictions and worker shortages – and the COVID-19 pandemic – which led to pent-up domestic demand and further reduced the labour force – will only increase this upward pressure. - The war has prompted increased scrutiny of London’s role as a centre for kleptocratic wealth from Russia and elsewhere, and has lead the UK government to impose sanctions against Russian-linked individuals and companies. This could impact inward investment directly, through legal restrictions on business activities, or indirectly, by indicating that the UK is no longer open to trade with certain countries. - In the longer term, post-war Ukraine’s future status and trading relationship with the EU could affect the UK, as the process of defining Ukraine’s position with regard to the EU may encourage Brussels to revisit its relations with the UK or even to explore alternative models for other non-member countries on the EU’s periphery. - A renewed focus on European security could also encourage the UK and EU to improve trade, security and political ties. The need to enhance security closer to home may also cause the UK to rethink the ‘Indo-Pacific tilt’ outlined in the 2021 Integrated Review.
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Watkins, Chris B., Susan Lurie, Amnon Lers, and Patricia L. Conklin. Involvement of Antioxidant Enzymes and Genes in the Resistance Mechanism to Postharvest Superficial Scald Development. United States Department of Agriculture, December 2004. http://dx.doi.org/10.32747/2004.7586539.bard.

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The objective of this research project was to evaluate the involvement of antioxidant enzymes and genes in the resistance mechanism to postharvest superficial scald development using two primary systems: 1. Resistant and susceptible progenies of an apple cross between a scald resistant crab apple, ‘White Angel’ and a scald susceptible cultivar, ‘Rome Beauty’; 2. Heat-treatment of ‘Granny Smith’, which is known to reduce scald development in this cultivar. In 2002 we asked for, and received (October 14), permission to revise our initial objectives. The US side decided to expand their results to include further work using commercial cultivars. Also, both sides wanted to include an emphasis on the interaction between these antioxidant enzymes and the á-farnesene pathway, with the cooperation of a third party, Dr. Bruce Whitaker, USDA-ARS, Beltsville. Background: Superficial scald is a physiological storage disorder that causes damage to the skin of apple and pear fruit. It is currently controlled by use of an antioxidant, diphenylamine (DPA), applied postharvest by drenching or dips, but concern exists about such chemical usage especially as it also involves application of fungicides. As a result, there has been increased emphasis on understanding of the underlying mechanisms involved in disorder development. Our approach was to focus on the oxidative processes that occur during scald development, and specifically on using the two model systems described above to determine if the levels of specific antioxidants and/or antioxidant enzyme activities correlated with the presence/absence of scald. It was hoped that information about the role of antioxidant-defense mechanisms would lead to identification of candidate genes for future transgenic manipulation. Major conclusions, solutions, achievements: Collectively, our results highlight the complexity of superficial scald developmental processes. Studies involving comparisons of antioxidant enzyme activities in different crab apple selection, commercial cultivars, and in response to postharvest heat and 1-methylcyclopropene (1-MCP) treatments, show no simple direct relationships with antioxidant contents and susceptibility of fruit to scald development. However, a correlative relationship was found between POX activity or isoenzyme number and scald resistance in most of the studies. This relationship, if confirmed, could be exploited in breeding for scald resistance. In addition, our investigations with key genes in the á-farnesenebiosynthetic pathway, together with antioxidant processes, are being followed up by analysis of exposed and shaded sides of fruit of cultivars that show different degrees of scald control by 1-MCP. These data may further reveal productive areas for future research that will lead to long term control of the disorder. However, given the complexity of scald development, the greatest research need is the production of transgenic fruit with down-regulated genes involved in á- farnesene biosynthesis in order to test the currently popular hypothesis for scald development.
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10

Zhang, Renduo, and David Russo. Scale-dependency and spatial variability of soil hydraulic properties. United States Department of Agriculture, November 2004. http://dx.doi.org/10.32747/2004.7587220.bard.

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Water resources assessment and protection requires quantitative descriptions of field-scale water flow and contaminant transport through the subsurface, which, in turn, require reliable information about soil hydraulic properties. However, much is still unknown concerning hydraulic properties and flow behavior in heterogeneous soils. Especially, relationships of hydraulic properties changing with measured scales are poorly understood. Soil hydraulic properties are usually measured at a small scale and used for quantifying flow and transport in large scales, which causes misleading results. Therefore, determination of scale-dependent and spatial variability of soil hydraulic properties provides the essential information for quantifying water flow and chemical transport through the subsurface, which are the key processes for detection of potential agricultural/industrial contaminants, reduction of agricultural chemical movement, improvement of soil and water quality, and increase of agricultural productivity. The original research objectives of this project were: 1. to measure soil hydraulic properties at different locations and different scales at large fields; 2. to develop scale-dependent relationships of soil hydraulic properties; and 3. to determine spatial variability and heterogeneity of soil hydraulic properties as a function of measurement scales. The US investigators conducted field and lab experiments to measure soil hydraulic properties at different locations and different scales. Based on the field and lab experiments, a well-structured database of soil physical and hydraulic properties was developed. The database was used to study scale-dependency, spatial variability, and heterogeneity of soil hydraulic properties. An improved method was developed for calculating hydraulic properties based on infiltration data from the disc infiltrometer. Compared with the other methods, the proposed method provided more accurate and stable estimations of the hydraulic conductivity and macroscopic capillary length, using infiltration data collected atshort experiment periods. We also developed scale-dependent relationships of soil hydraulic properties using the fractal and geostatistical characterization. The research effort of the Israeli research team concentrates on tasks along the second objective. The main accomplishment of this effort is that we succeed to derive first-order, upscaled (block effective) conductivity tensor, K'ᵢⱼ, and time-dependent dispersion tensor, D'ᵢⱼ, i,j=1,2,3, for steady-state flow in three-dimensional, partially saturated, heterogeneous formations, for length-scales comparable with those of the formation heterogeneity. Numerical simulations designed to test the applicability of the upscaling methodology to more general situations involving complex, transient flow regimes originating from periodic rain/irrigation events and water uptake by plant roots suggested that even in this complicated case, the upscaling methodology essentially compensated for the loss of sub-grid-scale variations of the velocity field caused by coarse discretization of the flow domain. These results have significant implications with respect to the development of field-scale solute transport models capable of simulating complex real-world scenarios in the subsurface, and, in turn, are essential for the assessment of the threat posed by contamination from agricultural and/or industrial sources.
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