Relevant bibliographies by topics / Structural causal models

Academic literature on the topic 'Structural causal models'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Structural causal models"

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Beckers, Sander. "Equivalent Causal Models." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 7 (May 18, 2021): 6202–9. http://dx.doi.org/10.1609/aaai.v35i7.16771.

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The aim of this paper is to offer the first systematic exploration and definition of equivalent causal models in the context where both models are not made up of the same variables. The idea is that two models are equivalent when they agree on all "essential" causal information that can be expressed using their common variables. I do so by focussing on the two main features of causal models, namely their structural relations and their functional relations. In particular, I define several relations of causal ancestry and several relations of causal sufficiency, and require that the most general of these relations are preserved across equivalent models.
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Vansteelandt, S., and E. Goetghebeur. "Causal inference with generalized structural mean models." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 65, no. 4 (October 28, 2003): 817–35. http://dx.doi.org/10.1046/j.1369-7412.2003.00417.x.

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HUBER, FRANZ. "STRUCTURAL EQUATIONS AND BEYOND." Review of Symbolic Logic 6, no. 4 (July 8, 2013): 709–32. http://dx.doi.org/10.1017/s175502031300018x.

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AbstractRecent accounts of actual causation are stated in terms of extended causal models. These extended causal models contain two elements representing two seemingly distinct modalities. The first element are structural equations which represent the “(causal) laws” or mechanisms of the model, just as ordinary causal models do. The second element are ranking functions which represent normality or typicality. The aim of this paper is to show that these two modalities can be unified. I do so by formulating two constraints under which extended causal models with their two modalities can be subsumed under so called “counterfactual models” which contain just one modality. These two constraints will be formally precise versions of Lewis’ (1979) familiar “system of weights or priorities” governing overall similarity between possible worlds.
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Robins, James M., Miguel Ángel Hernán, and Babette Brumback. "Marginal Structural Models and Causal Inference in Epidemiology." Epidemiology 11, no. 5 (September 2000): 550–60. http://dx.doi.org/10.1097/00001648-200009000-00011.

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Rothenhäusler, Dominik, Jan Ernest, and Peter Bühlmann. "Causal inference in partially linear structural equation models." Annals of Statistics 46, no. 6A (December 2018): 2904–38. http://dx.doi.org/10.1214/17-aos1643.

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Neugebauer, Romain, and Mark van der Laan. "Nonparametric causal effects based on marginal structural models." Journal of Statistical Planning and Inference 137, no. 2 (February 2007): 419–34. http://dx.doi.org/10.1016/j.jspi.2005.12.008.

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Zheng, Cheng, David C. Atkins, Xiao-Hua Zhou, and Isaac C. Rhew. "Causal Models for Mediation Analysis: An Introduction to Structural Mean Models." Multivariate Behavioral Research 50, no. 6 (November 2, 2015): 614–31. http://dx.doi.org/10.1080/00273171.2015.1070707.

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Talbot, Denis, Amanda M. Rossi, Simon L. Bacon, Juli Atherton, and Geneviève Lefebvre. "A graphical perspective of marginal structural models: An application for the estimation of the effect of physical activity on blood pressure." Statistical Methods in Medical Research 27, no. 8 (December 29, 2016): 2428–36. http://dx.doi.org/10.1177/0962280216680834.

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Estimating causal effects requires important prior subject-matter knowledge and, sometimes, sophisticated statistical tools. The latter is especially true when targeting the causal effect of a time-varying exposure in a longitudinal study. Marginal structural models are a relatively new class of causal models that effectively deal with the estimation of the effects of time-varying exposures. Marginal structural models have traditionally been embedded in the counterfactual framework to causal inference. In this paper, we use the causal graph framework to enhance the implementation of marginal structural models. We illustrate our approach using data from a prospective cohort study, the Honolulu Heart Program. These data consist of 8006 men at baseline. To illustrate our approach, we focused on the estimation of the causal effect of physical activity on blood pressure, which were measured at three time points. First, a causal graph is built to encompass prior knowledge. This graph is then validated and improved utilizing structural equation models. We estimated the aforementioned causal effect using marginal structural models for repeated measures and guided the implementation of the models with the causal graph. By employing the causal graph framework, we also show the validity of fitting conditional marginal structural models for repeated measures in the context implied by our data.
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Bazinas, Vassilios, and Bent Nielsen. "Causal Transmission in Reduced-Form Models." Econometrics 10, no. 2 (March 24, 2022): 14. http://dx.doi.org/10.3390/econometrics10020014.

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We propose a method to explore the causal transmission of an intervention through two endogenous variables of interest. We refer to the intervention as a catalyst variable. The method is based on the reduced-form system formed from the conditional distribution of the two endogenous variables given the catalyst. The method combines elements from instrumental variable analysis and Cholesky decomposition of structural vector autoregressions. We give conditions for uniqueness of the causal transmission.
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Steyer, Rolf. "Analyzing Individual and Average Causal Effects via Structural Equation Models." Methodology 1, no. 1 (January 2005): 39–54. http://dx.doi.org/10.1027/1614-1881.1.1.39.

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Abstract. Although both individual and average causal effects are defined in Rubin's approach to causality, in this tradition almost all papers center around learning about the average causal effects. Almost no efforts deal with developing designs and models to learn about individual effects. This paper takes a first step in this direction. In the first and general part, Rubin's concepts of individual and average causal effects are extended replacing Rubin's deterministic potential-outcome variables by the stochastic expected-outcome variables. Based on this extension, in the second and main part specific designs, assumptions and models are introduced which allow identification of (1) the variance of the individual causal effects, (2) the regression of the individual causal effects on the true scores of the pretests, (3) the regression of the individual causal effects on other explanatory variables, and (4) the individual causal effects themselves. Although random assignment of the observational unit to one of the treatment conditions is useful and yields stronger results, much can be achieved with a nonequivalent control group. The simplest design requires two pretests measuring a pretest latent trait that can be interpreted as the expected outcome under control, and two posttests measuring a posttest latent trait: The expected outcome under treatment. The difference between these two latent trait variables is the individual-causal-effect variable, provided some assumptions can be made. These assumptions - which rule out alternative explanations in the Campbellian tradition - imply a single-trait model (a one-factor model) for the untreated control condition in which no treatment takes place, except for change due to measurement error. These assumptions define a testable model. More complex designs and models require four occasions of measurement, two pretest occasions and two posttest occasions. The no-change model for the untreated control condition is then a single-trait-multistate model allowing for measurement error and occasion-specific effects.
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Dissertations / Theses on the topic "Structural causal models"

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Oberst, Michael Karl. "Counterfactual policy introspection using structural causal models." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/124128.

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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 97-102).
Inspired by a growing interest in applying reinforcement learning (RL) to healthcare, we introduce a procedure for performing qualitative introspection and `debugging' of models and policies. In particular, we make use of counterfactual trajectories, which describe the implicit belief (of a model) of 'what would have happened' if a policy had been applied. These serve to decompose model-based estimates of reward into specific claims about specific trajectories, a useful tool for 'debugging' of models and policies, especially when side information is available for domain experts to review alongside the counterfactual claims. More specically, we give a general procedure (using structural causal models) to generate counterfactuals based on an existing model of the environment, including common models used in model-based RL. We apply our procedure to a pair of synthetic applications to build intuition, and conclude with an application on real healthcare data, introspecting a policy for sepsis management learned in the recently published work of Komorowski et al. (2018).
by Michael Karl Oberst.
S.M.
S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
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Odondi, Lang'O. "Causal modelling of survival data with informative noncompliance." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/causal-modelling-of-survival-data-with-informative-noncompliance(74f40dc0-e5d1-46c0-ab2f-ac42a3425ac7).html.

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Noncompliance to treatment allocation is likely to complicate estimation of causal effects in clinical trials. The ubiquitous nonrandom phenomenon of noncompliance renders per-protocol and as- treated analyses or even simple regression adjustments for noncompliance inadequate for causal inference. For survival data, several specialist methods have been developed when noncompliance is related to risk. The Causal Accelerated Life Model (CALM) allows time-dependent departures from randomized treatment in either arm and relates each observed event time to a potential event time that would have been observed if the control treatment had been given throughout the trial. Alternatively, the structural Proportional Hazards (C-Prophet) model accounts for all-or-nothing noncompliance in the treatment arm only while the CHARM estimator allows time-dependent departures from randomized treatment by considering survival outcome as a sequence of binary outcomes to provide an 'approximate' overall hazard ratio estimate which is adjusted for compliance. The problem of efficacy estimation is compounded for two-active treatment trials (additional noncompliance) where the ITT estimate provides a biased estimator for the true hazard ratio even under homogeneous treatment effects assumption. Using plausible arm-specific predictors of compliance, principal stratification methods can be applied to obtain principal effects for each stratum. The present work applies the above methods to data from the Esprit trials study which was conducted to ascertain whether or not unopposed oestrogen (hormone replacement therapy - HRT) reduced the risk of further cardiac events in postmenopausal women who survive a first myocardial infarction. We use statistically designed simulation studies to evaluate the performance of these methods in terms of bias and 95% confidence interval coverage. We also apply a principal stratification method to adjust for noncompliance in two treatment arms trial originally developed for binary data for survival analysis in terms of causal risk ratio. In a Bayesian framework, we apply the method to Esprit data to account for noncompliance in both treatment arms and estimate principal effects. We apply statistically designed simulation studies to evaluate the performance of the method in terms of bias in the causal effect estimates for each stratum. ITT analysis of the Esprit data showed the effects of taking HRT tablets was not statistically significantly different from placebo for both all cause mortality and myocardial reinfarction outcomes. Average compliance rate for HRT treatment was 43% and compliance rate decreased as the study progressed. CHARM and C-Prophet methods produced similar results but CALM performed best for Esprit: suggesting HRT would reduce risk of death by 50%. Simulation studies comparing the methods suggested that while both C-Prophet and CHARM methods performed equally well in terms of bias, the CALM method performed best in terms of both bias and 95% confidence interval coverage albeit with the largest RMSE. The principal stratification method failed for the Esprit study possibly due to the strong distribution assumption implicit in the method and lack of adequate compliance information in the data which produced large 95% credible intervals for the principal effect estimates. For moderate value of sensitivity parameter, principal stratification results suggested compliance with HRT tablets relative to placebo would reduce risk of mortality by 43% among the most compliant. Simulation studies on performance of this method showed narrower corresponding mean 95% credible intervals corresponding to the the causal risk ratio estimates for this subgroup compared to other strata. However, the results were sensitive to the unknown sensitivity parameter.
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Aten, Jason Erik. "Causal not confounded gene networks inferring acyclic and non-acyclic gene bayesian networks in mRNA expression studies using recursive v-structures, genetic variation, and orthogonal causal anchor structural equation models /." Diss., Restricted to subscribing institutions, 2007. http://proquest.umi.com/pqdweb?did=1563274791&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.

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Ewings, F. M. "Practical and theoretical considerations of the application of marginal structural models to estimate causal effects of treatment in HIV infection." Thesis, University College London (University of London), 2012. http://discovery.ucl.ac.uk/1346448/.

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Standard marginal structural models (MSMs) are commonly applied to estimate causal effects in the presence of time-dependent confounding; these may be extended to history-adjusted MSMs to estimate effects conditional on time-updated covariates, and dynamic MSMs to estimate e¤ects of pre-speci…ed dynamic regimes (Cain et al., 2010). We address methods to assess the optimal time for treatment initiation with respect to CD4 count in HIV-infected persons, and apply these to CASCADE cohort data. We advocate the application of all three types of MSM to address such causal questions and investigate gaps in the literature concerning their application. Of importance is the construction of suitable inverse probability weights. We have structured this process as four key decisions, de…fining a range of strategies; all demonstrated a bene…ficial effect of ART in CASCADE. We found a trend towards greater treatment bene…fit at lower CD4 across a range of models. Via large simulated randomised trials based on CASCADE data, longer grace periods (permitted delay in treatment initiation) and in particular less-frequently observed CD4 indicated higher optimal regimes (earlier treatment initiation at higher CD4), although similar AIDS-free survival rates may be achieved at these higher optimal regimes. In realistically-sized observational simulations, the optimal regime estimates lacked precision, mainly due to broadly constant AIDS-free survival rates at higher CD4. Optimal regimes estimated from dynamic MSMs should be interpreted with regard to the shape of the outcome-by-regime curve and the precision. In our clinical setting, we found that allowing a 3-month grace period may increase precision with little bias under the interpretation of no grace period; under longer grace periods, the bias outweighed the efficiency gain. In our CASCADE population, immediate treatment was preferable to delay, although estimation was limited by relatively short follow-up. Comparison across the MSM approaches offers additional insights into the methodology and clinical results.
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Rosich, Oliva Albert. "Sensor placement for fault diagnosis based on structural models: application to a fuel cell stak system." Doctoral thesis, Universitat Politècnica de Catalunya, 2011. http://hdl.handle.net/10803/53635.

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The present work aims to increase the diagnosis systems capabilities by choosing the location of sensors in the process. Therefore, appropriate sensor location will lead to better diagnosis performance and implementation easiness. The work is based on structural models ands some simplifications are considered in order to only focus on the sensor placement analysis. Several approaches are studied to solve the sensor placement problem. All of them find the optimal sensor configuration. The sensor placement techniques are applied to a fuel cell stack system. The model used to describe the behaviour of this system consists of non-linear equations. Furthermore, there are 30 candidate sensors to improve the diagnosis specifications. The results obtained from this case study are used to strength the applicability of the proposed approaches.
El present treball té per objectiu incrementar les prestacions dels diagnosticadors mitjançant la localització de sensors en el procés. D'aquesta manera, instal·lant els sensors apropiats s'obtenen millors diagnosticador i més facilitats d'implementació. El treball està basat en models estructurals i contempla una sèrie de simplificacions per tal de entrar-se només en la problemàtica de la localització de sensors. S'utilitzen diversos enfocs per tal de resoldre la localització de sensors, tot ells tenen com objectiu trobar la configuració òptima de sensors. Les tècniques de localització de sensors són aplicades a un sistema basat en una pila de combustible. El model d'aquest sistema està format per equacions no lineals. A més, hi ha la possibilitat d'instal·lar fins a 30 sensors per tal de millorar la diagnosis del sistema. Degut a aquestes característiques del sistema i del model, els resultats obtinguts mitjançant aquest cas d'estudi reafirmen l'aplicabilitat dels mètodes proposats.
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Dubois, Florent. "Dynamic models of segregation." Thesis, Aix-Marseille, 2017. http://www.theses.fr/2017AIXM0313.

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Cette thèse étudie les causes et conséquences du processus de ségrégation résidentielle dans l’Afrique du Sud (AFS) post-Apartheid. Nous nous intéressons à plusieurs aspects encore débattus dans la littérature. Le premier concerne l’impact des préférences des individus pour la composition raciale de leur voisinage sur la ségrégation. Le second a trait à l’impact de la ségrégation résidentielle sur les niveaux de revenus des différents groupes raciaux. Le dernier quantifie les différentes causes de la ségrégation. Dans le premier chapitre, nous réconcilions la littérature théorique sur l’impact des préférences pour la composition raciale du voisinage avec les observations empiriques de niveaux décroissants de ségrégation aux US et en AFS. Nous soutenons l’idée que si les individus internalisent les apports économiques et sociaux de chaque nouvel arrivant dans leur voisinage alors des voisinages intégrés peuvent émerger. Cet effet est empiriquement plus fort que l’homophilie et le racisme. Dans le second chapitre, nous étudions l’impact de la ségrégation sur l’ensemble de la distribution des revenus. Nous montrons que la ségrégation a un effet positif sur les hauts revenus pour les Blancs tandis qu’elle a un effet négatif pour les Noirs au bas de la distribution. L’effet de la ségrégation est souvent plus important que l’effet de l’éducation. Enfin, dans le troisième chapitre, nous quantifions l’impact de chaque déterminant de la ségrégation. Nous trouvons que le manque d’accès aux services publics de base est le déterminant principal, alors que les différences de caractéristiques sociodémographiques ne comptent que pour une faible part pour les quartiers les plus ségrégués
This thesis studies the causes and consequences of the residential segregation process in the post-Apartheid South Africa.Inside this general issue, we are interested in several aspects still debated in the literature on residential segregation. Thefirst concerns the impact of individuals’ preferences for the racial composition of their neighborhood on the segregationlevels. The second question deals with the impact of residential segregation on the income levels of each racial group. Thelast issue is related to quantifying the different causes of segregation.Three chapters constitute this thesis. In the first chapter, we reconcile the theoretical literature on the impact of preferencesfor the racial composition of the neighborhood with the empirical evidences of declining levels of segregation in theUnited-States and South Africa. We argue that if individuals internalize the economic and social life that a new entrantbrings with him, then integrated neighborhoods can emerge. This effect is empirically stronger than homophilly andracism. In the second chapter, we study the impact of residential segregation on the whole income distribution. We showthat residential segregation has a positif effect on top incomes for Whites, whereas it has a negatif effect for Blacks at thebottom of the distribution. The effect of residential segregation is even more important than the effect of education inmost cases. In the third chapter, we quantify the impact of each determinant of segregation. We find that the lackof access to basic public services is the main determinant, whereas differences in sociodemographics only account for asmall part in the most segregated areas
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Oba, Koji. "How to use marginal structural models in randomized trials to estimate the natural direct and indirect effects of therapies mediated by causal intermediates." 京都大学 (Kyoto University), 2011. http://hdl.handle.net/2433/152045.

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Bailly, Sébastien. "Utilisation des antifongiques chez le patient non neutropénique en réanimation." Thesis, Université Grenoble Alpes (ComUE), 2015. http://www.theses.fr/2015GREAS013/document.

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Les levures du genre Candida figurent parmi les pathogènes majeurs isolés chez les patients en soins intensifs et sont responsables d'infections systémiques : les candidoses invasives. Le retard et le manque de fiabilité du diagnostic sont susceptibles d'aggraver l'état du patient et d'augmenter le risque de décès à court terme. Pour respecter les objectifs de traitement, les experts recommandent de traiter le plus précocement possible les patients à haut risque de candidose invasive. Cette attitude permet de proposer un traitement précoce aux malades atteints, mais peut entraîner un traitement inutile et coûteux et favoriser l'émergence de souches de moindre sensibilité aux antifongiques utilisés.Ce travail applique des méthodes statistiques modernes à des données observationnelles longitudinales. Il étudie l'impact des traitements antifongiques systémiques sur la répartition des quatre principales espèces de Candida dans les différents prélèvements de patients en réanimation médicale, sur leur sensibilité à ces antifongiques, sur le diagnostic des candidémies ainsi que sur le pronostic des patients. Les analyses de séries de données temporelles à l'aide de modèles ARIMA (moyenne mobile autorégressive intégrée) ont confirmé l'impact négatif de l'utilisation des antifongiques sur la sensibilité des principales espèces de Candida ainsi que la modification de leur répartition sur une période de dix ans. L'utilisation de modèles hiérarchiques sur données répétées a montré que le traitement influence négativement la détection des levures et augmente le délai de positivité des hémocultures dans le diagnostic des candidémies. Enfin, l'utilisation des méthodes d'inférence causale a montré qu'un traitement antifongique préventif n'a pas d'impact sur le pronostic des patients non neutropéniques, non transplantés et qu'il est possible de commencer une désescalade précoce du traitement antifongique entre le premier et le cinquième jour après son initiation sans aggraver le pronostic
Candida species are among the main pathogens isolated from patients in intensive care units (ICUs) and are responsible for a serious systemic infection: invasive candidiasis. A late and unreliable diagnosis of invasive candidiasis aggravates the patient's status and increases the risk of short-term death. The current guidelines recommend an early treatment of patients with high risks of invasive candidiasis, even in absence of documented fungal infection. However, increased antifungal drug consumption is correlated with increased costs and the emergence of drug resistance whereas there is yet no consensus about the benefits of the probabilistic antifungal treatment.The present work used modern statistical methods on longitudinal observational data. It investigated the impact of systemic antifungal treatment (SAT) on the distribution of the four Candida species most frequently isolated from ICU patients', their susceptibilities to SATs, the diagnosis of candidemia, and the prognosis of ICU patients. The use of autoregressive integrated moving average (ARIMA) models for time series confirmed the negative impact of SAT use on the susceptibilities of the four Candida species and on their relative distribution over a ten-year period. Hierarchical models for repeated measures showed that SAT has a negative impact on the diagnosis of candidemia: it decreases the rate of positive blood cultures and increases the time to positivity of these cultures. Finally, the use of causal inference models showed that early SAT has no impact on non-neutropenic, non-transplanted patient prognosis and that SAT de-escalation within 5 days after its initiation in critically ill patients is safe and does not influence the prognosis
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Bergman, Ruth. "Learning models of environments with manifest causal structure." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/36559.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.
Includes bibliographical references (leaves 188-192).
by Ruth Bergman.
Ph.D.
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Baltar, Valéria Troncoso. "Equações estruturais aplicadas a modelos causais de câncer de pulmão." Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/6/6132/tde-01032011-150337/.

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Introdução: O câncer de pulmão (CP) é o tipo de câncer que mais mata no mundo e o cigarro ainda é sua causa mais importante. Além disso, a alimentação tem sido associada ao CP, por ser fonte de vitaminas e aminoácidos que fazem parte do metabolismo do carbono (MC). O MC é considerado mecanismo chave na manutenção da integridade do DNA e na regulação da expressão gênica, que, dessa forma, deve estar relacionado à carcinogênese. A ativação da imunidade está associada ao envelhecimento em indivíduos saudáveis, assim como a uma série de patologias, incluindo o câncer. Objetivo: Estudar como o MC, a ativação da imunidade e o tabaco estão relacionados ao risco de CP em um estudo caso-controle aninhado à coorte do EPIC (European Prospective Investigation into Cancer and Nutrition). Métodos: Para avaliar se os níveis plasmáticos de cotinina são um bom biomarcador da exposição ao tabaco, foram utilizados modelos lineares generalizados. Para avaliar os efeitos do tabaco, do MC e da ativação da imunidade no risco de CP, foram aplicados modelos de equações estruturais (MEE) de duas maneiras diferentes (com e sem variáveis latentes). Resultados: Com base nas respostas aos questionários de qualidade de vida, com relação às questões sobre fumo ativo e passivo, a cotinina se mostrou um bom biomarcador de exposição recente ao tabaco (tanto o aumento da exposição passiva quanto ativa foram significativas, P<0,001 e P<0,001 respectivamente). Em um MEE com variáveis observadas, incluindo o MC e a via de ativação da imunidade, a metionina e o folato como causas proximais apresentaram uma forte e inversa associação com o risco de CP. O aumento em um desvio-padrão nos níveis séricos de metionina e de folato significou uma redução no risco de 19 por cento (P<0,01) e 12 por cento (P=0,03) respectivamente. Em um MEE com variáveis latentes (cada uma representando o conjunto de vitaminas e aminoácidos importantes para promover: metilação de DNA, síntese de núcletídeos e imune ativação), foram encontrados efeitos protetores diretos da metilação do DNA (P=0,018) e da imune ativação (P=0,037); por outro lado, a síntese de nucletídeos não apresentou efeito no risco do câncer (P=0,098). Nas duas abordagens de MEE o cigarro permaneceu como a causa de maior impacto. Conclusões: A cotinina mostrou-se um bom biomarcador da exposição ao tabaco (ativa e passivamente). Confirmou-se que a via de metilação é um fator de proteção contra o CP. A ativação da imunidade apresentou um efeito direto de proteção contra o CP no modelo com variáveis latentes, equanto que, a síntese de nucletídeos não apresentou relação com o CP. O tabaco continua sendo o fator de maior impacto no risco de CP
Background: Lung cancer (LC) continues to be the most common cancer death in the world. Tobacco exposure continues to be the most important cause. In addition, micronutrient intake has been linked to LC, because they are the main source of vitamins and amino acids involved in the one-carbon metabolism (OCM) which is considered key in maintaining DNA integrity, regulating gene expression, and may thus affect carcinogenesis. Immune activation is involved in the aging process in normal healthy individuals as well as in a number of pathologies, including cancer. Objectives: To investigate how OCM, immune activation and tobacco are related to LC incidence in a nested case-control study from the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Methods: To validate plasma cotinine levels as a good biomarker for tobacco exposure, a generalized linear model was applied. To evaluate the effects of tobacco, OCM and immune activation in LC, structural equation models (SEM) were applied in two different ways. Results: Based on questions about smoking, passive smoking and number of cigarettes smoked, it was shown that cotinine is a good biomarker for tobacco exposure (passive and active exposure with significant relation, p<0.001 and P<0.001, respectively). In a SEM model with only observed variables, including OCM and immune activation, methionine and folate as proximal causes presented a strong and inverse relation with LC risk. An increase in one standard deviation of serum levels of methionine and folate meant a 19 per cent (P<0.01) and 12 per cent (P<0.01) reduction in LC risk, respectively. In a SEM including latent variables (each one including vitamins and amino acids important to promote DNA methylation, nucleotide synthesis and immune activity), a direct and protective effect for DNA methylation (p=0.018) and immune activation was found (p=0.037), whereas nucleotide synthesis did not present a significant total effect. In both approaches of SEM, tobacco exposure remains with the highest impact on LC risk. Conclusions: It was found that cotinine is a good biomarker of tobacco exposure (active and passive). It was confirmed that methylation protects against LC. Immune activation presented a direct protective effect in the latent model, while nucleotide synthesis was not confirmed to be related to LC risk. Tobacco effect remains as the factor with highest impact in lung cancer
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Books on the topic "Structural causal models"

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Linear causal modeling with structural equations. Boca Raton, FL: Chapman & Hall/CRC, 2009.

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J, Thomas J. The links between structural adjustment and poverty: Causal or remedial? [Santiago, Chile]: PREALC, 1993.

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Leonovich, Sergey, Evgeniy Shalyy, Elena Polonina, Elena Sadovskaya, Lev Kim, and Valentin Dorkin. Durability of port reinforced concrete structures (Far East and Sakhalin). ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1816638.

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Section I of the monograph is devoted to an urgent problem - forecasting the durability of port reinforced concrete structures, the destruction of which is associated with corrosion of steel reinforcement caused by chloride aggression and carbonation of concrete. The analysis of models for calculating the service life of structures and experimental data is carried out, the life cycles for the main degradation processes in concrete and reinforcement, the periods of initiation and propagation of corrosion are considered, the influence of environmental factors (temperature, humidity) and the quality of concrete (In/C, cement consumption, diffusion coefficient) on the kinetics of chloride penetration and the movement of the carbonation front is taken into account. Probabilistic models of basic variables are considered, the limiting states of port reinforced concrete structures for the durability of reinforced concrete structures based on the reliability coefficient for service life are formulated. Sections II and III describe modern methods of restoration and restoration of reinforced concrete port structures subjected to corrosion destruction using nanofibrobeton. The concept of multilevel reinforcement has been implemented. Methods of experimental fracture mechanics were used to evaluate the joint work of exploited concrete and reinforcement nanofibre concrete. It is intended for scientific and engineering staff of universities, research and design organizations.
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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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Kravchenko, Igor', Maksim Glinskiy, Sergey Karcev, Viktor Korneev, and Diana Abdumuminova. Resource-saving plasma technology in the repair of processing equipment. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1083289.

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In the monograph methodological bases of selection of method of coating, design of technological processes of hardening and recovery of the wearing surfaces of parts using a systems engineering analysis and information support technologist. The mathematical model of plasma spraying of materials with different thermal conductivity and methods criteria for evaluation of technical and technological opportunities of a plasma coating method. Describes the methods and results of experimental studies, the analysis of the conditions and causes of loss of efficiency of processing equipment APK. The proposed scientific and methodical approach to the justification of expediency of the recovery and strengthening of the working bodies and parts expensive imported technological equipment. The proposed mathematical model describing the physical processes in plasma coating for various applications. The structure of the algorithm for solving the task of hardening and recovery of worn parts plasma methods on the basis of the integrated CAE system. This monograph is intended for employees of scientific research institutions, specialists of machine-building production and enterprises of technical service, as well as teachers, postgraduates and students of agricultural engineering areas of training.
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Sang-in, Chŏn, ed. Hanʼguk hyŏndaesa: Chinsil kwa haesŏk. Kyŏnggi-do Pʻaju-si: Nanam Chʻulpʻan, 2005.

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Shimizu, Shohei. Semiparametric Structural Equation Models for Causal Discovery. Springer, 2021.

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Shimizu, Shohei. Semiparametric Structural Equation Models for Causal Discovery. Springer London, Limited, 2017.

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Bollen, Kenneth A., Sophia Rabe‐Hesketh, and Anders Skrondal. Structural Equation Models. Edited by Janet M. Box-Steffensmeier, Henry E. Brady, and David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0018.

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This article explains the use of factor analysis types of models to develop measures of latent concepts which were then combined with causal models of the underlying latent concepts. In particular, it offers an overview of the classic structural equation models (SEMs) when the latent and observed variables are continuous. Then it looks at more recent developments that include categorical, count, and other noncontinuous variables as well as multilevel structural equation models. The model specification, assumptions, and notation are covered. This is followed by addressing implied moments, identification, estimation, model fit, and respecification. The penetration of SEMs has been high in disciplines such as sociology, psychology, educational testing, and marketing, but lower in economics and political science despite the large potential number of applications. Today, SEMs have begun to enter the statistical literature and to re-enter biostatistics, though often under the name ‘latent variable models’ or ‘graphical models’.
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Coseru, Christian. Consciousness and Causal Emergence. Edited by Jonardon Ganeri. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199314621.013.24.

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In challenging the physicalist conception of consciousness advanced by Cārvāka materialists such as Bṛhaspati, the Buddhist philosopher Śāntarakṣita addresses a series of key issues about the nature of causality and the basis of cognition. This chapter considers whether causal accounts of generation for material bodies are adequate in explaining how conscious awareness comes to have the structural features and phenomenal properties that it does. Arguments against reductive physicalism, it is claimed, can benefit from an understanding of the structure of phenomenal consciousness that does not eschew causal-explanatory reasoning. Against causal models that rely on the concept of potentiality, the Buddhist principle of “dependent arising” underscores a dynamic conception of efficient causality, which allows for elements defined primarily in terms of their capacity for sentience and agency to be causally efficacious.
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Book chapters on the topic "Structural causal models"

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Bergsma, Wicher, Marcel Croon, and Jacques A. Hagenaars. "Causal Analyses: Structural Equation Models and (Quasi-)Experimental Designs." In Marginal Models, 155–90. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/b12532_5.

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Robins, James M. "Marginal Structural Models versus Structural nested Models as Tools for Causal inference." In Statistical Models in Epidemiology, the Environment, and Clinical Trials, 95–133. New York, NY: Springer New York, 2000. http://dx.doi.org/10.1007/978-1-4612-1284-3_2.

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Shimizu, Shohei. "Non-Gaussian Structural Equation Models for Causal Discovery." In Statistics and Causality, 153–84. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781118947074.ch7.

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Helian, Shanjun, Babette A. Brumback, Matthew C. Freeman, and Richard Rheingans. "Structural Nested Models for Cluster-Randomized Trials." In Statistical Causal Inferences and Their Applications in Public Health Research, 169–86. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41259-7_9.

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Stern, Hal S., and Yoonsook Jeon. "Applying Structural Equation Models with Incomplete Data." In Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives, 331–42. Chichester, UK: John Wiley & Sons, Ltd, 2005. http://dx.doi.org/10.1002/0470090456.ch30.

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Narendra, Tanmayee, Prerna Agarwal, Monika Gupta, and Sampath Dechu. "Counterfactual Reasoning for Process Optimization Using Structural Causal Models." In Lecture Notes in Business Information Processing, 91–106. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26643-1_6.

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Valente, Bruno Dourado, and Guilherme Jordão de Magalhães Rosa. "Mixed Effects Structural Equation Models and Phenotypic Causal Networks." In Methods in Molecular Biology, 449–64. Totowa, NJ: Humana Press, 2013. http://dx.doi.org/10.1007/978-1-62703-447-0_21.

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Wu, Pan, and Xin M. Tu. "Structural Functional Response Models for Complex Intervention Trials." In Statistical Causal Inferences and Their Applications in Public Health Research, 217–38. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41259-7_12.

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Carter, Christopher L., and Thad Dunning. "Instrumental Variables: From Structural Equation Models to Design-Based Causal Inference." In The SAGE Handbook of Research Methods in Political Science and International Relations, 748–68. 1 Oliver's Yard, 55 City Road London EC1Y 1SP: SAGE Publications Ltd, 2020. http://dx.doi.org/10.4135/9781526486387.n43.

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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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Conference papers on the topic "Structural causal models"

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Yang, Mengyue, Furui Liu, Zhitang Chen, Xinwei Shen, Jianye Hao, and Jun Wang. "CausalVAE: Disentangled Representation Learning via Neural Structural Causal Models." In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2021. http://dx.doi.org/10.1109/cvpr46437.2021.00947.

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Heyn, Hans-Martin, and Eric Knauss. "Structural causal models as boundary objects in AI system development." In CAIN '22: 1st Conference on AI Engineering - Software Engineering for AI. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3522664.3528615.

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Lee, Burton Hoyt. "Design FMEA for Mechatronic Systems Using Bayesian Network Causal Models." In ASME 1999 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/detc99/dac-8605.

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Abstract This paper presents the use of Bayesian networks as a methodology for eliciting and encoding Failure Modes and Effects Analysis (BN-FMEA) models of mechatronic systems. The method uses probabilistic directed acyclic graphs to construct causal dependency structures between functional, behavioral and structural random variables of the physical system. Default apriori probabilities and conditional probability tables are generated and attached to the graph structure for post-design evaluation by diagnostic engineers. BN-FMEA provides a language for design teams to articulate — with less ambiguity and greater precision — component failure cause-effect relationships across sub-systems. An example of an inkjet printer illustrates how BN-FMEA can be applied. The approach supports traditional FMEA objectives — identification of system failure modes, provides improved knowledge representation and inferencing power over spreadsheets, and is generally applicable to the class of industry- and government-standard FMEA spreadsheets and tables in use today. Finally, BN-FMEA is presented as a basis for improved integration of design and diagnostic modeling of mechatronic systems.
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Laurent, Jonathan, Jean Yang, and Walter Fontana. "Counterfactual Resimulation for Causal Analysis of Rule-Based Models." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/260.

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Models based on rules that express local and heterogeneous mechanisms of stochastic interactions between structured agents are an important tool for investigating the dynamical behavior of complex systems, especially in molecular biology. Given a simulated trace of events, the challenge is to construct a causal diagram that explains how a phenomenon of interest occurred. Counterfactual analysis can provide distinctive insights, but its standard definition is not applicable in rule-based models because they are not readily expressible in terms of structural equations. We provide a semantics of counterfactual statements that addresses this challenge by sampling counterfactual trajectories that are probabilistically as close to the factual trace as a given intervention permits them to be. We then show how counterfactual dependencies give rise to explanations in terms of relations of enablement and prevention between events.
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Ibeling, Duligur, and Thomas Icard. "On the Conditional Logic of Simulation Models." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/258.

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We propose analyzing conditional reasoning by appeal to a notion of intervention on a simulation program, formalizing and subsuming a number of approaches to conditional thinking in the recent AI literature. Our main results include a series of axiomatizations, allowing comparison between this framework and existing frameworks (normality-ordering models, causal structural equation models), and a complexity result establishing NP-completeness of the satisfiability problem. Perhaps surprisingly, some of the basic logical principles common to all existing approaches are invalidated in our causal simulation approach. We suggest that this additional flexibility is important in modeling some intuitive examples.
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Lübke, Karsten, Bianca Krol, and Sandra Sülzenbrück. "Draw (Causal) Conclusions from Data – Some Evidence." In IASE 2021 Satellite Conference: Statistics Education in the Era of Data Science. International Association for Statistical Education, 2022. http://dx.doi.org/10.52041/iase.ujhqs.

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To be data literate, one should be able to draw conclusions from multivariable observational data. But this is tricky. E.g., to investigate the gender pay gap, it must be decided whether the effect should be calculated adjusted or unadjusted for job. The correct conclusion depends on the qualitative assumptions about the data generating process. To investigate the conclusions drawn by students, a randomized experiment is conducted. The same data is presented in two different contexts with (possible) different structural causal models so once the adjusted and once the unadjusted effect might be appropriate. Also it is varied whether a directed acyclic graph is presented before or after the data table with the estimated effect. Results indicates that conclusions drawn from the same data differ by context but may also be inconsistent to the assumed data generating process.
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Cai, Ruichu, Jie Qiao, Kun Zhang, Zhenjie Zhang, and Zhifeng Hao. "Causal Discovery with Cascade Nonlinear Additive Noise Model." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/223.

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Identification of causal direction between a causal-effect pair from observed data has recently attracted much attention. Various methods based on functional causal models have been proposed to solve this problem, by assuming the causal process satisfies some (structural) constraints and showing that the reverse direction violates such constraints. The nonlinear additive noise model has been demonstrated to be effective for this purpose, but the model class is not transitive--even if each direct causal relation follows this model, indirect causal influences, which result from omitted intermediate causal variables and are frequently encountered in practice, do not necessarily follow the model constraints; as a consequence, the nonlinear additive noise model may fail to correctly discover causal direction. In this work, we propose a cascade nonlinear additive noise model to represent such causal influences--each direct causal relation follows the nonlinear additive noise model but we observe only the initial cause and final effect. We further propose a method to estimate the model, including the unmeasured intermediate variables, from data, under the variational auto-encoder framework. Our theoretical results show that with our model, causal direction is identifiable under suitable technical conditions on the data generation process. Simulation results illustrate the power of the proposed method in identifying indirect causal relations across various settings, and experimental results on real data suggest that the proposed model and method greatly extend the applicability of causal discovery based on functional causal models in nonlinear cases.
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Huegle, Johannes, Christopher Hagedorn, and Matthias Uflacker. "How Causal Structural Knowledge Adds Decision-Support in Monitoring of Automotive Body Shop Assembly Lines." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/758.

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The efficiency of modern automotive body shop assembly lines is highly related to the reduction of downtimes due to failures and quality deviations within the manufacturing process. Consequently, the need for implementing tools into the assembly lines for on-line monitoring, and failure diagnosis, also under the prism of improving the troubleshooting, is of great importance. While the identification of root causes and elimination of failures is usually built upon individual on-site expert knowledge, causal graphical models (CGMs) have opened the possibility to make a purely data-driven assessment. In this demo, we showcase how a CGM of the production process is incorporated into a monitoring tool to function as a decision-support system for an operator of a modern automotive body shop assembly line and enables fast and effective handling of failures and quality deviations.
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Bochman, Alexander. "Actual Causality in a Logical Setting." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/239.

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We provide a definition of actual causation in the logical framework of the causal calculus, which is based on a causal version of the well-known NESS (or INUS) condition. We compare our definition with other, mainly counterfactual, approaches on standard examples. On the way, we explore general capabilities of the logical representation for structural equation models of causation and beyond.
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Kavicka, Frantisek, Karel Stransky, Bohumil Sekanina, Jana Dobrovska, and Josef Stetina. "Cooling of a Massive Casting of Ductile Cast-Iron and Its Numerical Optimization." In ASME 2009 Pressure Vessels and Piping Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/pvp2009-77914.

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The numerical models of the temperature field of solidifying castings often observe two main goals: directed solidification and optimization of the technology. These goals can be achieved only if the deciding factors which either characterize the process or accompany it are analysed and their influence controlled. An original application of ANSYS, based on the numerical finite-element method, is applied. The numerical model simulated the forming of the temperature field of a two-ton 500×500×1000 mm casting from ductile cast-iron during the application of various methods of its cooling using steel chills. This model managed to optimize more than one method of cooling but, in addition to that, provided results for the successive model of structural and chemical heterogeneity, and so it also contributes to influencing the pouring structure. The file containing the acquired results from both models, as well as from their organic unification, brings new and, simultaneously, remarkable findings of causal relationships between the structural and chemical heterogeneity and the local solidification time in any point of the casting. This has established a tool for the optimization of the structure with an even distribution of the spheroids of graphite in such a way so as to minimize the occurrence of degenerated shapes of graphite, which is one of the conditions for achieving good mechanical properties of castings of ductile cast-iron.
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Reports on the topic "Structural causal models"

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Clarke, Paul S., and Frank Windmeijer. Identification of causal effects on binary outcomes using structural mean models. Institute for Fiscal Studies, March 2010. http://dx.doi.org/10.1920/wp.cem.2010.0210.

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Naugle, Asmeret, Laura Swiler, Kiran Lakkaraju, Stephen Verzi, Christina Warrender, and Vicente Romero. Graph-Based Similarity Metrics for Comparing Simulation Model Causal Structures. Office of Scientific and Technical Information (OSTI), August 2022. http://dx.doi.org/10.2172/1884926.

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Triplett, Josh. Relativistic Causal Ordering A Memory Model for Scalable Concurrent Data Structures. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.497.

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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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Bao, Jieyi, Xiaoqiang Hu, Cheng Peng, Yi Jiang, Shuo Li, and Tommy Nantung. Truck Traffic and Load Spectra of Indiana Roadways for the Mechanistic-Empirical Pavement Design Guide. Purdue University, 2020. http://dx.doi.org/10.5703/1288284317227.

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The Mechanistic-Empirical Pavement Design Guide (MEPDG) has been employed for pavement design by the Indiana Department of Transportation (INDOT) since 2009 and has generated efficient pavement designs with a lower cost. It has been demonstrated that the success of MEPDG implementation depends largely on a high level of accuracy associated with the information supplied as design inputs. Vehicular traffic loading is one of the key factors that may cause not only pavement structural failures, such as fatigue cracking and rutting, but also functional surface distresses, including friction and smoothness. In particular, truck load spectra play a critical role in all aspects of the pavement structure design. Inaccurate traffic information will yield an incorrect estimate of pavement thickness, which can either make the pavement fail prematurely in the case of under-designed thickness or increase construction cost in the case of over-designed thickness. The primary objective of this study was to update the traffic design input module, and thus to improve the current INDOT pavement design procedures. Efforts were made to reclassify truck traffic categories to accurately account for the specific axle load spectra on two-lane roads with low truck traffic and interstate routes with very high truck traffic. The traffic input module was updated with the most recent data to better reflect the axle load spectra for pavement design. Vehicle platoons were analyzed to better understand the truck traffic characteristics. The unclassified vehicles by traffic recording devices were examined and analyzed to identify possible causes of the inaccurate data collection. Bus traffic in the Indiana urban areas was investigated to provide additional information for highway engineers with respect to city streets as well as highway sections passing through urban areas. New equivalent single axle load (ESAL) values were determined based on the updated traffic data. In addition, a truck traffic data repository and visualization model and a TABLEAU interactive visualization dashboard model were developed for easy access, view, storage, and analysis of MEPDG related traffic data.
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Nantung, Tommy E., Jusang Lee, John E. Haddock, M. Reza Pouranian, Dario Batioja Alvarez, Jongmyung Jeon, Boonam Shin, and Peter J. Becker. Structural Evaluation of Full-Depth Flexible Pavement Using APT. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317319.

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The fundamentals of rutting behavior for thin full-depth flexible pavements (i.e., asphalt thickness less than 12 inches) are investigated in this study. The scope incorporates an experimental study using full-scale Accelerated Pavement Tests (APTs) to monitor the evolution of each pavement structural layer's transverse profiles. The findings were then employed to verify the local rutting model coefficients used in the current pavement design method, the Mechanistic-Empirical Pavement Design Guide (MEPDG). Four APT sections were constructed using two thin typical pavement structures (seven-and ten-inches thick) and two types of surface course material (dense-graded and SMA). A mid-depth rut monitoring and automated laser profile systems were designed to reconstruct the transverse profiles at each pavement layer interface throughout the process of accelerated pavement deterioration that is produced during the APT. The contributions of each pavement structural layer to rutting and the evolution of layer deformation were derived. This study found that the permanent deformation within full-depth asphalt concrete significantly depends upon the pavement thickness. However, once the pavement reaches sufficient thickness (more than 12.5 inches), increasing the thickness does not significantly affect the permanent deformation. Additionally, for thin full-depth asphalt pavements with a dense-graded Hot Mix Asphalt (HMA) surface course, most pavement rutting is caused by the deformation of the asphalt concrete, with about half the rutting amount observed within the top four inches of the pavement layers. However, for thin full-depth asphalt pavements with an SMA surface course, most pavement rutting comes from the closet sublayer to the surface, i.e., the intermediate layer. The accuracy of the MEPDG’s prediction models for thin full-depth asphalt pavement was evaluated using some statistical parameters, including bias, the sum of squared error, and the standard error of estimates between the predicted and actual measurements. Based on the statistical analysis (at the 95% confidence level), no significant difference was found between the version 2.3-predicted and measured rutting of total asphalt concrete layer and subgrade for thick and thin pavements.
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Gutnick, David, and David L. Coplin. Role of Exopolysaccharides in the Survival and Pathogenesis of the Fire Blight Bacterium, Erwinia amylovora. United States Department of Agriculture, September 1994. http://dx.doi.org/10.32747/1994.7568788.bard.

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Fireblight, a disease of apples and pears, is caused by Erwinia amylovora. Mutants of E. amylovora that do not produce the extreacellular polysaccharide (EPS), amylovoran, are avirulent. A similar EPS, stewartan, is produced by E. stewartii, which caused Stewart's wilt of corn, and which has also been implicated in the virulence of this strain. Both stewartan and amylovoran are type 1 capsular polysaccharides, typified by the colanic acid slime produced by Escherichia coli. Extracellular polysaccharide slime and capsules are important for the virulence of bacterial pathogens of plants and animals and to enhance their survival and dissemination outside of the host. The goals of this project were to examine the importance of polysaccharide structure on the pathogenicity and survival properties of three pathogenic bacteria: Erwinia amylovora, Erwinia stewartii and Escherichia coli. The project was a collaboration between the laboratories of Dr. Gutnick (PI, E. coli genetics and biochemistry), Dr. Coplin (co-PI, E. stewartii genetics) and Dr. Geider (unfunded collaborator, E. amylovora genetics and EPS analysis). Structural analysis of the EPSs, sequence analysis of the biosynthetic gene clusters and site-directed mutagenesis of individual cps and ams genes revealed that the three gene clusters shared common features for polysaccharide polymerization, translocation, and precursor synthesis as well as in the modes of transcriptional regulation. Early EPS production resulted in decreased virulence, indicating that EPS, although required for pathogenicity, is anot always advantageous and pathogens must regulate its production carefully.
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Rahmani, Mehran, Xintong Ji, and Sovann Reach Kiet. Damage Detection and Damage Localization in Bridges with Low-Density Instrumentations Using the Wave-Method: Application to a Shake-Table Tested Bridge. Mineta Transportation Institute, September 2022. http://dx.doi.org/10.31979/mti.2022.2033.

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This study presents a major development to the wave method, a methodology used for structural identification and monitoring. The research team tested the method for use in structural damage detection and damage localization in bridges, the latter being a challenging task. The main goal was to assess capability of the improved method by applying it to a shake-table-tested prototype bridge with sparse instrumentation. The bridge was a 4-span reinforced concrete structure comprising two columns at each bent (6 columns total) and a flat slab. It was tested to failure using seven biaxial excitations at its base. Availability of a robust and verified method, which can work with sparse recording stations, can be valuable for detecting damage in bridges soon after an earthquake. The proposed method in this study includes estimating the shear (cS) and the longitudinal (cL) wave velocities by fitting an equivalent uniform Timoshenko beam model in impulse response functions of the recorded acceleration response. The identification algorithm is enhanced by adding the model’s damping ratio to the unknown parameters, as well as performing the identification for a range of initial values to avoid early convergence to a local minimum. Finally, the research team detect damage in the bridge columns by monitoring trends in the identified shear wave velocities from one damaging event to another. A comprehensive comparison between the reductions in shear wave velocities and the actual observed damages in the bridge columns is presented. The results revealed that the reduction of cS is generally consistent with the observed distribution and severity of damage during each biaxial motion. At bents 1 and 3, cS is consistently reduced with the progression of damage. The trends correctly detected the onset of damage at bent 1 during biaxial 3, and damage in bent 3 during biaxial 4. The most significant reduction was caused by the last two biaxial motions in bents 1 and 3, also consistent with the surveyed damage. In bent 2 (middle bent), the reduction trend in cS was relatively minor, correctly showing minor damage at this bent. Based on these findings, the team concluded that the enhanced wave method presented in this study was capable of detecting damage in the bridge and identifying the location of the most severe damage. The proposed methodology is a fast and inexpensive tool for real-time or near real-time damage detection and localization in similar bridges, especially those with sparsely deployed accelerometers.
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9

Zhang, Xingyu, Matteo Ciantia, Jonathan Knappett, and Anthony Leung. Micromechanical study of potential scale effects in small-scale modelling of sinker tree roots. University of Dundee, December 2021. http://dx.doi.org/10.20933/100001235.

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When testing an 1:N geotechnical structure in the centrifuge, it is desirable to choose a large scale factor (N) that can fit the small-scale model in a model container and avoid unwanted boundary effects, however, this in turn may cause scale effects when the structure is overscaled. This is more significant when it comes to small-scale modelling of sinker root-soil interaction, where root-particle size ratio is much lower. In this study the Distinct Element Method (DEM) is used to investigate this problem. The sinker root of a model root system under axial loading was analysed, with both upward and downward behaviour compared with the Finite Element Method (FEM), where the soil is modelled as a continuum in which case particle-size effects are not taken into consideration. Based on the scaling law, with the same prototype scale and particle size distribution, different scale factors/g-levels were applied to quantify effects of the ratio of root diameter (𝑑𝑟) to mean particle size (𝐷50) on the root rootsoil interaction.
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PARSHUTKINA, T., O. BERKU, and T. KALENTSOVA. FORMATION OF THE FOUNDATIONS OF THE CONTEXTUAL APPROACH IN HIGHER DOMESTIC FOREIGN LANGUAGE EDUCATION IN THE 1970-1980S OF THE XX CENTURY. Science and Innovation Center Publishing House, 2021. http://dx.doi.org/10.12731/2658-4034-2021-12-4-2-59-66.

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The article is devoted to the problem of the formation of the foundations of the contextual approach in foreign language education as the most important scientific foundation of modern pedagogy. In the historical path of development of this approach, the authors distinguish the 1970-1980s of the XX century, since its main structural characteristics were formed during this period. The article concludes that the structuring of the contextual approach in teaching foreign languages in higher education was caused by the need to create a professional context in the conditions of educational activity. To this end, researchers and methodologists used the pedagogical and methodological tools of the contextual approach.
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