Academic literature on the topic 'Dynamic treatment regimes'
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Journal articles on the topic "Dynamic treatment regimes"
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Chakraborty, Bibhas, and Susan A. Murphy. "Dynamic Treatment Regimes." Annual Review of Statistics and Its Application 1, no. 1 (2014): 447–64. http://dx.doi.org/10.1146/annurev-statistics-022513-115553.
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Lavori, Philip W., and Ree Dawson. "Dynamic treatment regimes: practical design considerations." Clinical Trials 1, no. 1 (2004): 9–20. http://dx.doi.org/10.1191/1740774504cn002oa.
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Background Clinical management of chronic disease requires a dynamic treatment regime (DTR): rules for choosing the new treatment based on the history of response to past treatments. Estimating and comparing the effects of DTRs from a sample of observed trajectories of treatment and outcome depends on the untestable assumption that new treatments are assigned independently of potential future responses to treatment, conditional on the history of treatments and response to date (“sequential ignorability”). In longitudinal observational studies, sequential ignorability must be assumed, while randomization of dynamic regimes can guarantee it. Methods Using several clinical examples, we describe the simplest randomized experimental designs for comparing DTRs. We begin by considering an initial treatment A and a second treatment B, and discuss how a dynamic treatment regime that starts with A and leads (sometimes) to B, might be compared to either fixed treatment A or B. We also illustrate the problem of finding the optimal sequence of treatments in a DTR, when there are several choices. We describe and contrast two ways of incorporating randomization into studies to compare such regimes: baseline randomization among DTRs versus randomization at the decision points (sequentially randomized designs). Conclusions We discuss estimation and inference from both baseline randomized and sequentially randomized designs and conclude with a discussion of the differences between the experimental and observational approaches to optimizing and comparing dynamic treatment regimes.
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Murphy, S. A. "Optimal dynamic treatment regimes." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 65, no. 2 (2003): 331–55. http://dx.doi.org/10.1111/1467-9868.00389.
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Zhang, Yichi, Eric B. Laber, Marie Davidian, and Anastasios A. Tsiatis. "Interpretable Dynamic Treatment Regimes." Journal of the American Statistical Association 113, no. 524 (2018): 1541–49. http://dx.doi.org/10.1080/01621459.2017.1345743.
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Moodie, Erica E. M., Thomas S. Richardson, and David A. Stephens. "Demystifying Optimal Dynamic Treatment Regimes." Biometrics 63, no. 2 (2007): 447–55. http://dx.doi.org/10.1111/j.1541-0420.2006.00686.x.
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Zhao, Ying-Qi, and Eric B. Laber. "Estimation of optimal dynamic treatment regimes." Clinical Trials: Journal of the Society for Clinical Trials 11, no. 4 (2014): 400–407. http://dx.doi.org/10.1177/1740774514532570.
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Lavori, Philip W., and Ree Dawson. "Dynamic treatment regimes: practical design considerations." Clinical Trials 1, no. 1 (2004): 9–20. http://dx.doi.org/10.1191/1740774s04cn002oa.
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Johnson, Brent A. "Treatment-competing events in dynamic regimes." Lifetime Data Analysis 14, no. 2 (2007): 196–215. http://dx.doi.org/10.1007/s10985-007-9051-3.
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Lizotte, Daniel J., and Arezoo Tahmasebi. "Prediction and tolerance intervals for dynamic treatment regimes." Statistical Methods in Medical Research 26, no. 4 (2017): 1611–29. http://dx.doi.org/10.1177/0962280217708662.
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We develop and evaluate tolerance interval methods for dynamic treatment regimes (DTRs) that can provide more detailed prognostic information to patients who will follow an estimated optimal regime. Although the problem of constructing confidence intervals for DTRs has been extensively studied, prediction and tolerance intervals have received little attention. We begin by reviewing in detail different interval estimation and prediction methods and then adapting them to the DTR setting. We illustrate some of the challenges associated with tolerance interval estimation stemming from the fact that we do not typically have data that were generated from the estimated optimal regime. We give an extensive empirical evaluation of the methods and discussed several practical aspects of method choice, and we present an example application using data from a clinical trial. Finally, we discuss future directions within this important emerging area of DTR research.
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Murphy, S. A., and D. Bingham. "Screening Experiments for Developing Dynamic Treatment Regimes." Journal of the American Statistical Association 104, no. 485 (2009): 391–408. http://dx.doi.org/10.1198/jasa.2009.0119.
Full textDissertations / Theses on the topic "Dynamic treatment regimes"
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Moodie, Erica E. M. "Inference for optimal dynamic treatment regimes /." Thesis, Connect to this title online; UW restricted, 2006. http://hdl.handle.net/1773/9605.
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Mohamed, Nur Anisah. "Optimal dynamic treatment regimes : regret-regression method with myopic strategies." Thesis, University of Newcastle upon Tyne, 2013. http://hdl.handle.net/10443/2242.
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Optimal dynamic treatment strategies provide a set of decision rules that are based on a patient’s history. We assume there are a sequence of decision times j = 1,2,...,K. At each time a measurement of the state of the patient Sj is obtained and then some action Aj is decided. The aim is to provide rules for action choice so as to maximise some final value Y . In this thesis we will focus on the regret-regression method described by Henderson et al. (2009), and the regret approach to optimal dynamic treatment regimes proposed by Murphy (2003). The regret-regression method combines the regret function with regression modelling and it is suitable for both long term and myopic (short-term) strategies. We begin by describing and demonstrating the current theory using the Murphy and Robins G-estimation techniques. Comparison between the regret-regression method and these two methods is possible and it is found that the regret-regression method provides a better estimation method than Murphy’s and Robins G-estimation. The next approach is to investigate misspecification of the Murphy and regret-regression models. We consider the effect of misspecifying the model that is assumed for the actions, which is required for the Murphy method, and of the model for states, which is required for the regret-regression approach. We also consider robustness of the fitting algorithms to starting values of the parameters. Diagnostic tests are available for model adequacy. An application to anticoagulant data is presented in detail. Myopic one and twostep ahead strategies are studied. Further investigation involves the use of Generalised Estimating Equations (GEEs) and Quadratic Inference Functions (QIF) for estimation. We also assess the robustness of both methods. Finally we consider the influence of individual observations on the parameter estimates.
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Yazzourh, Sophia. "Apprentissage par renforcement et outcome-weighted learning bayésien pour la médecine de précision : Intégration de connaissances médicales dans les algorithmes de décision." Electronic Thesis or Diss., Université de Toulouse (2023-....), 2024. http://www.theses.fr/2024TLSES139.
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La médecine de précision vise à adapter les traitements aux caractéristiques de chaque patient en s'appuyant sur les formalismes des "Individualized Treatment Regimes" (ITR) et des "Dynamic Treatment Regimes" (DTR). Les ITR concernent une seule décision thérapeutique, tandis que les DTR permettent l'adaptation des traitements au fil du temps via une séquence de décisions. Pour être pertinentes, ces approches doivent être en mesure de traiter des données complexes et d'intégrer les connaissances médicales, essentielles pour permettre une utilisation clinique réaliste et sans risques. Cette thèse présente trois projets de recherche. Premièrement, un état de l'art des méthodes d'intégration des connaissances médicales dans les modèles de "Reinforcement Learning" (RL) a été réalisé, en tenant compte du contexte des DTR et de leurs contraintes spécifiques pour une application sur des données observationnelles. Deuxièmement, une méthode probabiliste de construction des récompenses a été développée pour les modèles de RL, s'appuyant sur les préférences des experts médicaux. Illustrée par des études de cas sur le diabète et le cancer, cette méthode génère des récompenses de manière à exploiter les données, le savoir de l'expert médical et les relations entre les patients, évitant les biais de construction "à la main" et garantissant une cohérence avec les objectifs médicaux. Troisièmement, un cadre bayésien pour la méthode "Outcome-Weighted Learning" (OWL) a été proposé afin de quantifier l'incertitude dans les recommandations de traitement, renforçant ainsi la robustesse des décisions thérapeutiques, et a été illustré à travers de simulations de données. Les contributions de cette thèse visent à améliorer la fiabilité des outils de prise de décision en médecine de précision, d'une part en intégrant les connaissances médicales dans les modèles de RL, et d'autre part en proposant un cadre bayésien pour quantifier l'incertitude dans le modèle OWL. Ces travaux s'inscrivent dans une perspective globale de collaboration interdisciplinaire en particulier entre les domaines de l'apprentissage automatique, des sciences médicales et des statistiques<br>Precision medicine aims to tailor treatments to the characteristics of each patient by relying on the frameworks of Individualized Treatment Regimes (ITR) and Dynamic Treatment Regimes (DTR). ITRs involve a single therapeutic decision, while DTRs allow for the adaptation of treatments over time through a sequence of decisions. For these approaches to be effective, they must be capable of handling complex data and integrating medical knowledge, which is essential for enabling realistic and safe clinical use. This work presents three research projects. First, a state-of-the-art review of methods for integrating medical knowledge into Reinforcement Learning (RL) models was conducted, considering the context of DTR and their specific constraints for application to observational data. Second, a probabilistic method for constructing rewards was developed for RL models, based on the preferences of medical experts. Illustrated by case studies on diabetes and cancer, this method generates data-driven rewards, avoiding the biases of "manual" construction and ensuring consistency with medical objectives in learning treatment recommendation strategies. Third, a Bayesian framework for the Outcome-Weighted Learning (OWL) method was proposed to quantify uncertainty in treatment recommendations, thereby enhancing the robustness of therapeutic decisions, and was illustrated through simulations studies. This contributions aim to improve the reliability of decision-making tools in precision medicine, by integrating medical knowledge into RL models on one hand, and proposing a Bayesian framework to quantify uncertainty in the OWL model on the other. This work is part of a global perspective of interdisciplinary collaboration, particularly among the fields of machine learning, medical sciences, and statistics
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Young, Katherine W. "Dynamic treatment regimens for congestive heart failure." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/129847.
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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2020<br>Cataloged from student-submitted PDF of thesis.<br>Includes bibliographical references (pages 59-60).<br>Each year, millions of patients are hospitalized with a diagnosis of congestive heart failure (CHF). This condition is characterized by inadequate tissue perfusion resulting from an inability of the heart to provide enough blood to meet the body's metabolic demands. Patients with CHF remain at a greater risk for mortality and other adverse events. A primary symptom of CHF is fluid overload ("congestion"), which is routinely treated with diuretic therapy. However, choosing a diuretic therapy that maximizes the therapeutic effects while minimizing harmful side effects remains a challenge. In order to assess a patient's response to a particular therapy and guide future treatment decisions, clinicians monitor a number of variables including a patients' vital signs, glomerular filtration rate (GFR, a measure of renal function), and fluctuations in volume status. Nevertheless, these variables are typically insufficient by themselves to ensure that a given therapy is optimal for a given patient. Current guidelines for heart failure management were developed, in part, from large clinical trials. However, it is not always clear how to apply these observations to a given patient, whose clinical characteristics may differ significantly from those of the patients in the original studies. Therefore, there is a need for methods that identify patient-specific treatments that would allow physicians to construct therapies that are truly personalized. This work describes an approach for building dynamic treatment regimens (DTRs) - a set of patient-specific treatment rules that optimize an outcome of interest. The method uses artificial neural networks to suggest diuretic doses that will improve a patient's volume status while simultaneously minimizing harmful side effects on renal function. This body of work suggests the potential that DTRs have in developing personalized diuretic regimens to improve the clinical outcomes of CHF patients.<br>by Katherine W. Young.<br>M. Eng.<br>M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
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Zhao, Pan. "Topics in causal inférence and policy learning with applications to precision medicine." Electronic Thesis or Diss., Université de Montpellier (2022-....), 2024. http://www.theses.fr/2024UMONS029.
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La causalité est un concept fondamental en science et en philosophie. Dans un contexte où la collecte massive de données de grande complexité s’impose dans tous les domaines, les statistiques jouent un rôle crucial dans l'inférence des causes et des effets. Cette thèse explore des méthodes avancées d'inférence causale. Elle met l'accent sur l'apprentissage de politiques d’action (“politiques” dans la suite), les variables instrumentales (IV), et les approches de différences en différences (DiD).Les méthodes IV et DiD sont utilisées par les chercheurs en épidémiologie, médecine, biostatistique, économétrie et sciences sociales quantitatives. Elles reposent sur des hypothèses restrictives, telles que, d’une part, l'exigence que l'IV n’ait aucun effet direct sur le résultat autre qu’à travers le traitement et, d’autre part, l'hypothèse de tendances parallèles en DiD, qui peut être violée en présence de confusion non mesurée.Dans ce contexte, cette thèse propose une approche innovante de DiD instrumentalisée pour l'apprentissage de politiques. Cette combinaison permet de relâcher certaines des hypothèses clés des méthodes IV et DiD conventionnelles. Des résultats d'identification novateurs pour les politiques optimales en présence de confusion non mesurée sont établis, et une gamme d'estimateurs (de Wald ; par pondération inverse des probabilités ; semi-paramétriques efficaces et multiplement robustes) sont introduits. Des garanties théoriques multiplement robustes sont fournies, incluant le taux cubique de convergence pour les politiques paramétriques et une inférence statistique valide avec des algorithmes de machine learning (ML) flexibles pour l'estimation des paramètres de nuisance. Ces méthodes sont en outre étendues à la configuration de données de panel.La majorité des méthodes d'inférence causale dans la littérature dépendent fortement de trois hypothèses causales standard pour identifier les effets causaux et les politiques optimales. Bien que des progrès aient été réalisés pour relâcher les hypothèses de consistance et de non-confusion, les avancées pour traiter les violations de l'hypothèse de positivité sont restées limitées.Dans ce contexte, cette thèse présente un cadre novateur d'apprentissage des politiques qui ne repose pas sur l'hypothèse de positivité, se concentrant plutôt sur des politiques dynamiques et stochastiques pratiques pour des applications réelles. Des politiques de score de propension incrémentale, ajustant les scores de propension par des paramètres individualisés, sont proposées. Leur analyse ne met en jeu que les hypothèses de consistance et de non-confusion. Ce cadre améliore le concept d'effets d'intervention incrémentale, l'adaptant aux contextes de politique de traitement individualisée, et utilise la théorie semi-paramétrique pour développer des fonctions d'influence efficaces et des estimateurs ML dédiés. Des méthodes pour optimiser les politiques en maximisant la fonction de valeur sous des contraintes spécifiques sont également introduites.De plus, le régime de traitement individualisé optimal (ITR) appris d'une population source peut ne pas se généraliser bien à une population cible en raison des décalages de covariables. Un cadre d'apprentissage par transfert est proposé pour l'estimation de l'ITR dans des populations hétérogènes avec des données de survie censurées à droite, que l’on rencontre fréquemment dans les études cliniques. Un estimateur doublement robuste pour la fonction de valeur ciblée est proposé, qui accommode une large classe de fonctionnelles de distributions de survie. Pour une classe pré-spécifiée d'ITRs, un taux cubique de convergence pour le paramètre estimé indexant l'ITR optimal est établi. L'utilisation de procédures de cross-fitting (ajustement croisé) assure la consistance et la normalité asymptotique de l'estimateur de valeur optimal proposé, y compris lorsque l’on a recours à des méthodes ML flexibles pour estimer des paramètres de nuisance<br>Causality is a fundamental concept in science and philosophy, and with the increasing complexity of data collection and structure, statistics plays a pivotal role in inferring causes and effects. This thesis delves into advanced causal inference methods, with a focus on policy learning, instrumental variables (IV), and difference-in-differences (DiD) approaches.The IV and DiD methods are critical tools widely used by researchers in fields like epidemiology, medicine, biostatistics, econometrics, and quantitative social sciences. However, these methods often face challenges due to restrictive assumptions, such as the IV's requirement to have no direct effect on the outcome other than through the treatment, and the parallel trends assumption in DiD, which may be violated in the presence of unmeasured confounding.In that context, this thesis introduces an innovative instrumented DiD approach to policy learning, which combines these two natural experiments to relax some of the key assumptions of conventional IV and DiD methods. To the best of our knowledge, the thesis presents the first comprehensive study of policy learning under the DiD setting. The direct policy search approach is proposed to learn optimal policies, based on the conditional average treatment effect estimators using instrumented DiD. Novel identification results for optimal policies under unmeasured confounding are established. Moreover, a range of estimators, including a Wald estimator, inverse probability weighting (IPW) estimators, and semiparametric efficient and multiply robust estimators, are introduced. Theoretical guarantees for these multiply robust policy learning approaches are provided, including the cubic rate of convergence for parametric policies and valid statistical inference with flexible machine learning algorithms for nuisance parameter estimation. These methods are further extended to the panel data setup.The majority of causal inference methods in the literature heavily depend on three standard causal assumptions to identify causal effects and optimal policies. While there has been progress in relaxing the consistency and unconfoundedness assumptions, addressing the violations of the positivity assumption has seen limited advancements.In that context, this thesis presents a novel policy learning framework that does not rely on the positivity assumption, instead focusing on dynamic and stochastic policies that are practical for real-world applications. Incremental propensity score policies, which adjust propensity scores by individualized parameters, are proposed, requiring only the consistency and unconfoundedness assumptions. This approach enhances the concept of incremental intervention effects, adapting it to individualized treatment policy contexts, and employs semiparametric theory to develop efficient influence functions and debiased machine learning estimators. Methods to optimize policy by maximizing the value function under specific constraints are also introduced.Additionally, the optimal individualized treatment regime (ITR) learned from a source population may not generalize well to a target population due to covariate shifts. A transfer learning framework is proposed for ITR estimation in heterogeneous populations with right-censored survival data, which is common in clinical studies and motivated by medical applications. This framework characterizes the efficient influence function (EIF) and proposes a doubly robust estimator for the targeted value function, accommodating a broad class of survival distribution functionals. For a pre-specified class of ITRs, a cubic rate of convergence for the estimated parameter indexing the optimal ITR is established. The use of cross-fitting procedures ensures the consistency and asymptotic normality of the proposed optimal value estimator, even with flexible machine learning methods for nuisance parameter estimation
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Rich, Benjamin. "Optimal dynamic treatment regime structural nested mean models: improving efficiency through diagnostics and re-weighting and application to adaptive individual dosing." Thesis, McGill University, 2013. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=114179.
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Dynamic treatment regimes are common in medicine, for example in the treatment of chronic diseases. As information about a patient is gathered over time, it is desirable to make use of this accumulating information to make treatment decisions that are specifically tailored to the individual patient, or to base decisions on dynamically evolving observations. Dynamic treatment regimes have been the topic of much recent work in the area of causal inference. In particular, semi-parametric methods for estimating a "best" or "optimal" treatment rule or strategy from observational data have been developed. One such method proposed by Robins is the optimal dynamic treatment regime structural nested mean model (ODTR-SNMM) and associated g-estimation procedure. Of significant concern when applying this methodology are the modelling assumptions involved. In this work, checking of modelling assumptions using residual and influence diagnostics as is typically done in a traditional regression setting is extended to the ODTR-SNMM. The methodology is evaluated on simulated data under different model specification settings. These ideas are also applied to real data from a breastfeeding cessation study. Subsequently, partially misspecified models, which give rise to consistent though inefficient estimation of the parameter of interest due to misspecification of a nuisance model, are considered. In addition to the possibility of addressing partial misspecification through the proposed diagnostic techniques, re-weighting is considered as a means of improving the efficiency of estimators under these modeling assumptions. A re-weighting approach based on sample influence is proposed and studied with simulations. Finally, the application of optimal dynamic treatment regimes estimation to adaptive dosing strategies for drugs with narrow therapeutic windows and highly variable dosing is considered. Using oral anticoagulation therapy as a motivating example, a simulation is designed using realistic pharmacokinetic (PK) and pharmacodynamic (PD) models to generate the data. A modelling approach for ODTR-SNMM with continuous dosing is proposed and applied to the PK/PD simulated data. The performance of various models under different settings is compared.<br>Les régimes de traitement dynamiques sont utilisés fréquemment en médecine. Nous les retrouvons, par exemple, dans le traitement des maladies chroniques. Alors que l'information obtenue chez un patient est récupérée dans le temps, il est souhaitable d'utiliser cette information afin de pouvoir faire des décisions de traitement qui sont adaptées à chaque patient, ou de pouvoir baser des décisions de traitements sur des observations qui évoluent. Les régimes de traitement dynamiques ont fait le sujet de travaux récents dans le domaine de l'inférence causale. Plus particulièrement, des méthodes semi-paramétriques ont été développées pour estimer, à partir de données non expérimentales, la règle de traitement ou la stratégie la meilleure ou optimale. Une de ces méthodes, proposée par Robins, est le modèle moyen structurel emboîté pour régime de traitement optimal dynamique (Optimal Dynamic Treatment Regime Structural Nested Mean Model : ODTR-SNMM) et la procédure g-estimation associée. Les suppostitions impliquées dans la modèlisation sont une préoccupation importante lors de l'application de cette méthodologie. Dans cette thèse, la vérification des suppositions de modélisation en utilisant les diagnostics résiduels et d'influence, normalement réalisée dans une analyse de régression traditionelle, est étendue à l'approche ODTR-SNMM. La méthodogie est évaluée en utilisant des données simulées, obtenues à partir de différents réglages de simulation. L'approche est aussi mise en application dans une étude d'arrêt d'allaitement. Par la suite, nous considérons des modèles partiellement mal spécifiés qui engendrent une estimation cohérente mais inefficace du paramètre d'intérêt en raison de la mal spécification du modèle de nuisance. En plus de la possibilité de traiter les mal spécifications partielles par les méthodes de diagnostic proposées, la repondération est considérée comme façon d'améliorer l'efficacité des estimateurs sous ces suppositions de modélisation. Une méthode de repondération basée sur l'influence des échantillons est proposée et étudiée par simulations. Finalement, nous considérons l'application de l'estimation des régimes de traitement dynamiques optimaux sur les stratégies de dosage adaptatifs pour les médicaments ayant une marge thérapeutique étroite et un dosage hautement variable. Utilisant l'anticoagulothérapie orale en exemple, nous concevons une simulation dans laquelle les données sont réalisées à partir de modèles pharmacocinétique (PK) et pharmacodynamique (PD) réalistes. Une technique de modélisation pour l'ODTR-SNMM avec dosage continu est proposée et appliquée aux données PK et PD simulées. Nous comparons la performance de plusieurs modèles utilisant différent réglages.
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Daddi, Hammou Aoumeur. "Improved theoretical treatment of the dynamics of quarkonia in the quark gluon plasma : from semiclassical approximation to unified quantum master equations between the quantum Brownian and the quantum optical regimes." Electronic Thesis or Diss., Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2024. http://www.theses.fr/2024IMTA0425.
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Les quarkonia lourds sont l'une des sondes du plasma de quarks et de gluons (QGP) formé dans les collisions ultrarelativistes d'ions lourds. Au cours de la dernière décennie, l'utilisation du formalisme des systèmes quantiques ouverts (SQO) dans l'étude de la dynamique des quarkonia dans le QGP a suscité un intérêt croissant. En particulier, il a été montré que l'équation de Lindblad peut donner lieu à des équations semi-classiques, qui ont été précédemment utilisées par plusieurs modèles phénoménologiques. Dans une première partie de cette thèse, nous étudions la validité de l'approximation semi-classique, et sa capacité à décrire certains effets quantiques non triviaux et à atteindre la limite thermique appropriée. La deuxième partie de la thèse a pour but de relever certains défis théoriques associés à l'utilisation du formalisme SQO dans l'étude de la dynamique des quarkonia dans le QGP. En particulier, en raison de l'aspect dynamique du QGP et de sa température dépendant du temps, la dynamique des quarkonia dans le QGP couvre deux régimes différents du formalisme SQO qui sont décrits par deux équations maîtresses différentes, à savoir le régime brownien quantique et le régime optique quantique. Nous explorons tout d'abord la transition entre les deux régimes et élucidons le lien entre leurs équations maîtresses respectives. Ensuite, afin de décrire l'évolution temporelle complète du quarkonia dans le QGP avec une seule équation maîtresse, nous appliquons l'équation universelle de Lindblad au système QGPquarkonia et dérivons un ensemble d'équations universelles couplées des états singlet et octet<br>Heavy quarkonia are one of the probes of Quark-Gluon Plasma (QGP) formed in Ultra-Relativistic Heavy-Ion collisions (URHIC). Over the past decade, there has been an increasing interest in the use of open quantum systems (OQS) formalism in the study of the in- QGP quarkonia dynamics. In particular, it has been shown that the Lindblad equation can result in semiclassical equations, which have been previously employed by several phenomenological models. In the first part of this thesis, we investigate the validity of the semiclassical approximation, and its ability to describe certain non-trivial quantum effects and reach the appropriate thermal limit. The second part of the thesis aims to address some theoretical challenges associated with the use of the OQS formalism in the study of in-QGP quarkonia dynamics. In particular, due to the dynamical aspect of the QGP and its time-dependent temperature, the dynamics of in- QGP quarkonia covers two different regimes of the OQS formalism, which are described by two different master equations, namely, the quantum Brownian and optical regimes. We first explore the transition between the two regimes and elucidate the link between their respective master equations. Secondly, in order to describe the complete in-QGP quarkonia time evolution with a single master equation, we apply the universal Lindblad equation (ULE) to the QGP-quarkonia system and derive a set of coupled singlet-octet universal equations
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Mohammadighavam, S. (Shahram). "Hydrological and hydraulic design of peatland drainage and water treatment systems for optimal control of diffuse pollution." Doctoral thesis, Oulun yliopisto, 2017. http://urn.fi/urn:isbn:9789526214511.
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Abstract Peatland drainage for forestry, agriculture and peat extraction results in runoff rich in organic matter, sediments and nutrients. This has a significant effect on downstream ecosystems. Therefore, water purification using sedimentation basins and wetlands is required in environmental permits for peat extraction in Finland, to reduce downstream impacts. Due to increasingly strict environmental regulations, more advanced water purification methods need to be developed. Using field measurements, laboratory experiments and hydrological/hydraulic modelling, this thesis sought to develop new methods based on: i) more refined hydrological information related to runoff and pollutant load control and ii) hydraulic design of sedimentation basins used in chemical purification. The hydrology of three peatland forestry and two drained peat extraction areas in northern Finland was studied and simulated using the DRAINMOD 6.1 hydrological model. Watertable depth (WTD) and drainage outflow were recorded continuously during several years and the data were used for model calibration and validation. Despite some under- and over-estimation of certain events, WTD fluctuations were simulated quite accurately for all peatland areas. The results demonstrated that DRAINMOD 6.1 can satisfactorily simulate WTD fluctuations in a cold climate such as northern Finland, but the model did not simulate drainage outflow adequately. Chemical treatment facilities were optimised using 3D computational fluid dynamic (CFD) models. COMSOL Multiphysics 5.1 was employed to evaluate the influence of inlet design on treatment efficiency in commonly used treatment basins without any barrier, and for optimization of barrier design through gravity-driven hydraulic flocculators. The results showed that inlet design had a significant effect on treatment efficiency. Several barrier designs were simulated and the best combination was tested for different distances between barriers, to find a geometry ratio and flow depth producing optimal mixing conditions for the treatment process<br>Tiivistelmä Turvemaiden ojitus metsätaloutta, maataloutta ja turvetuotantoa varten lisää orgaanisen aineen, kiintoaineineen ja ravinteiden huuhtoutumista alapuolisiin vesistöihin. Lisääntyneellä kuormituksella voi olla merkittäviä vaikutuksia vesiekosysteemeihin, minkä vuoksi turvetuotannon ympäristöluvissa vaaditaan valumavesien puhdistamista mm. laskeutusaltaiden ja pintavalutuskenttien avulla. Tiukentuneiden vesiensuojelumääräysten vuoksi tarvitaan uusia vesiensuojelumenetelmiä sekä tulee tehostaa jo käytössä olevien menetelmien toimintaa. Tämän työn tavoitteena on suositella uusia menetelmiä perustuen I) entistä tarkempaan hydrologiseen tietoon valunnasta ja vesistökuormituksesta ja II) kemiallisen vesienpuhdistuksen yhteydessä käytettävien laskeutusaltaiden hydrauliseen suunnitteluun. Tämä väitöstyö rakentuu maastossa ja laboratoriossa tehtyjen tutkimusten sekä hydrologisen/hydraulisen mallinnuksen varaan. Valuma-alueiden hydrologiaa tutkittiin ja mallinnettiin kolmella turvemetsäalueella ja kahdella turvetuotantoalueella Pohjois-Suomessa. Ojituksen hydrologisten vaikutusten arviointiin käytettiin DRAINMOD 6.1 ohjelmaa, jonka kalibrointia ja validointia varten kerättiin jatkuvatoimisilla antureilla aineistoa pohjaveden pinnankorkeuksista ja virtaamasta useiden vuosien ajalta. Mallin avulla voitiin pohjaveden pinnan vaihtelut kuvata yleisesti melko hyvin kaikilla tutkimusalueilla yksittäisistä sadanta-valuntatapahtuminen yli- tai aliarvioinneista huolimatta. Saadut tulokset osoittavat, että DRAINMOD 6.1 ohjelmalla voidaan riittävällä tarkkuudella simuloida pohjaveden pinnan vaihteluita kylmässä ilmastossa, kuten Pohjois-Suomessa, mutta malli ei soveltunut hyvin ojitusalueelta lähtevän valunnan tarkkaan määrittämiseen. Kemiallisen vesienpuhdistusrakenteiden optimointiin käytettiin COMSOL Multiphysics 5.1 ohjelmaa, jolla voidaan toteuttaa ja laskea veden virtauksia kolmessa dimensiossa (computational fluid dynamic, CFD, model). Mallilla arvioitiin kemikalointialtaan tuloaukon rakenteen vaikutuksia tyypillisesti kemikaloinnissa käytetyn allasrakenteen puhdistustehokkuuteen. Lisäksi mallilla mitoitettiin virtausesteitä optimaalisen sekoittumisolosuhteiden saamiseksi ja puhdistustehokkuuden parantamiseksi painovoimaisesti toimivissa flokkausaltaissa (hidas sekoitus). Saadut tulokset osoittavat, että laskeutusaltaiden tuloaukon rakenteella on merkittävä vaikutus kemikaloinnissa saavutettuun puhdistustehokkuuteen. Lisäksi työssä esitettiin optimaalisia virtausesteiden mitoituksia (geometria, esteiden välinen etäisyys, virtaussyvyys yms.) puhdistuksen kannalta parhaiden mahdollisten sekoitusolosuhteiden saavuttamiseksi
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DELIU, NINA. "Reinforcement learning in modern biostatistics: benefits, challenges and new proposals." Doctoral thesis, 2021. http://hdl.handle.net/11573/1581572.
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Applications of reinforcement learning (RL) for supporting, managing and improving decision-making are becoming increasingly popular in a variety of medicine and healthcare domains where the problem has a sequential nature. By continuously interacting with the underlying environment, RL techniques are able to learn by trial-and-error on how to take better actions in order to maximize an outcome of interest over time. However, if on one hand RL offers a new powerful framework, on the other hand it poses some unique challenges for data analysis and interpretability, which call for new statistical techniques in both predictive and descriptive learning.
Notably, several methodological challenges, for which the contribution of the biostatistical community may play a crucial role, limit the use of RL in real life. In an aim to bridge the statistics and RL communities, we start by assimilating the different existing RL terminologies, notations and approaches into a coherent body of work, and by translating them from a machine learning (ML) to a statistical perspective. Then, through a comprehensive methodological review, we report and discuss the state-of-the-art RL-based research in healthcare. Two main applied domains emerged: 1) adaptive interventions (AIs), encompassing both dynamic treatment regimes and just-in-time adaptive interventions in mobile health (mHealth); and 2) adaptive designs of clinical trials, specifically dose-finding designs and adaptive randomization. We illustrate existing RL-based methods in these areas, discussing their benefits and existing open problems that may impact their application in real life. A major barrier to adopting RL in real-world experiments is the lack of clarity on how statistical analyses and inference are impacted. In clinical trials for example, if on one side, to achieve the practical (and more ethical) goal of improving patients’ benefits, RL may have better abilities in terms of maximising clinical outcomes by adaptively randomizing participants to the best evidence-based treatment; on the other side, to achieve the scientific goal of e.g., discovering whether one treatment is more effective compared to a control treatment, less is known about their inferential properties. Through a simulation study, we investigate the challenges of conducting hypothesis testing from data collected through a class of RL, i.e., multi-armed bandits (MABs), outlining the harms MAB algorithms can cause to traditional statistical tests’ type-I error and power. This empirical evaluation provides guidance to two alternative ways of pursuing improved statistical hypothesis testing: 1) to explore ways of modifying the test statistic using knowledge of the adaptive data collection nature; 2) to modify the algorithm or framework for a more sensitive problem to both statistical inference as well as reward maximization. Focusing on the Thompson Sampling (a randomized MAB strategy), we show how a modified version of it results in an optimal intermediate between these two objectives. These findings can provide insights into how challenges can be surmounted by bridging machine learning, statistics, and applied sciences, to conduct adaptive experiments in the real-world, aiming to simultaneously help individuals and advance
scientific research. We finally combine our methodological knowledge with a motivating mHealth study for improving physical activity, to illustrate the tremendous collaboration opportunities between statistics and RL researchers in the space of developing adaptive interventions into the increasingly growing area of mHealth.
Books on the topic "Dynamic treatment regimes"
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Tsiatis, Anastasios A., Marie Davidian, Shannon T. Holloway, and Eric B. Laber. Dynamic Treatment Regimes. Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429192692.
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Chakraborty, Bibhas, and Erica E. M. Moodie. Statistical Methods for Dynamic Treatment Regimes. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7428-9.
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Chakraborty, Bibhas. Statistical methods for dynamic treatment regimes: Reinforcement learning, causal inference, and personalized medicine. Springer, 2013.
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Davidian, Marie, Anastasios A. Tsiatis, Shannon T. Holloway, and Eric Laber. Introduction to Dynamic Treatment Regimes. Taylor & Francis Group, 2019.
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Davidian, Marie, Anastasios A. Tsiatis, Shannon T. Holloway, and Eric B. Laber. Dynamic Treatment Regimes: Statistical Methods for Precision Medicine. Taylor & Francis Group, 2019.
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Davidian, Marie, Anastasios A. Tsiatis, Shannon T. Holloway, and Eric B. Laber. Dynamic Treatment Regimes: Statistical Methods for Precision Medicine. Taylor & Francis Group, 2019.
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Davidian, Marie, Anastasios A. Tsiatis, Shannon T. Holloway, and Eric B. Laber. Dynamic Treatment Regimes: Statistical Methods for Precision Medicine. Taylor & Francis Group, 2019.
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Moodie, Erica E. M., and Bibhas Chakraborty. Statistical Methods for Dynamic Treatment Regimes: Reinforcement Learning, Causal Inference, and Personalized Medicine. Springer New York, 2015.
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Orellana, Liliana del Carmen. Methodological challenges for the estimation of optimal dynamic treatment regimes from observational studies. 2007.
Find full textBook chapters on the topic "Dynamic treatment regimes"
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Qian, Min, Inbal Nahum-Shani, and Susan A. Murphy. "Dynamic Treatment Regimes." In Modern Clinical Trial Analysis. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-4322-3_5.
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Tsiatis, Anastasios A., Marie Davidian, Shannon T. Holloway, and Eric B. Laber. "Introduction." In Dynamic Treatment Regimes. Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429192692-1.
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Tsiatis, Anastasios A., Marie Davidian, Shannon T. Holloway, and Eric B. Laber. "Statistical Inference." In Dynamic Treatment Regimes. Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429192692-10.
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Tsiatis, Anastasios A., Marie Davidian, Shannon T. Holloway, and Eric B. Laber. "Additional Topics." In Dynamic Treatment Regimes. Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429192692-11.
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Tsiatis, Anastasios A., Marie Davidian, Shannon T. Holloway, and Eric B. Laber. "Preliminaries." In Dynamic Treatment Regimes. Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429192692-2.
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Tsiatis, Anastasios A., Marie Davidian, Shannon T. Holloway, and Eric B. Laber. "Single Decision Treatment Regimes: Fundamentals." In Dynamic Treatment Regimes. Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429192692-3.
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Tsiatis, Anastasios A., Marie Davidian, Shannon T. Holloway, and Eric B. Laber. "Single Decision Treatment Regimes: Additional Methods." In Dynamic Treatment Regimes. Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429192692-4.
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Tsiatis, Anastasios A., Marie Davidian, Shannon T. Holloway, and Eric B. Laber. "Multiple Decision Treatment Regimes: Overview." In Dynamic Treatment Regimes. Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429192692-5.
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Tsiatis, Anastasios A., Marie Davidian, Shannon T. Holloway, and Eric B. Laber. "Multiple Decision Treatment Regimes: Formal Framework." In Dynamic Treatment Regimes. Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429192692-6.
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Tsiatis, Anastasios A., Marie Davidian, Shannon T. Holloway, and Eric B. Laber. "Optimal Multiple Decision Treatment Regimes." In Dynamic Treatment Regimes. Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429192692-7.
Full textConference papers on the topic "Dynamic treatment regimes"
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Jain, Nishan, and James Baeder. "Assessment of Turbulence Model Length Scales based on Hybrid RANS-LES Modeling of Unsteady Flow Over Airfoil." In Vertical Flight Society 72nd Annual Forum & Technology Display. The Vertical Flight Society, 2016. http://dx.doi.org/10.4050/f-0072-2016-11393.
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Current work investigates the sensitivity of DES type hybrid methods toward turbulence length scales and anisotropy of grid. Different length scales available from literature are implemented into a compressible, finite volume GPU accelerated Navier-Stokes solver. A novel length scale is proposed based on the properties of available length scales and the grid requirements in mildly separated flows. Predictive capabilities of RANS and DDES method with the conventional and proposed length scales is assessed by conducting static and dynamic stall simulations on SC1095 and modified VR12 airfoils. Two different grids are used to examine the boundary layer resolving capabilities of the turbulence length scales. RANS based Spalart-Allmaras model and DDES method with conventional length scale predicted excessive eddy viscosity in the boundary layer of flows operating in near-stall regime. Proposed length scale demonstrated good predictive capabilities in mildly separated flows by reducing eddy viscosity levels at the outer region boundary layer. Three dimensional dynamic stall simulations are also conducted on flows over modified VR12 airfoil at moderate reduced frequency. When using proposed length scale, DDES method captured multiple lift peaks before stall and enabled flow to enter deep stall. The predictions agreed well with experimental data and captured cycle-to-cycle variation of integrated aerodynamic quantities. This work further employs specialized treatment to model laminar-turbulent transition and very low Mach number flows.
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Wang, Lu, Wenchao Yu, Xiaofeng He, et al. "Adversarial Cooperative Imitation Learning for Dynamic Treatment Regimes✱." In WWW '20: The Web Conference 2020. ACM, 2020. http://dx.doi.org/10.1145/3366423.3380248.
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Jiang, Yushan, Wenchao Yu, Dongjin Song, Wei Cheng, and Haifeng Chen. "Interpretable Skill Learning for Dynamic Treatment Regimes through Imitation." In 2023 57th Annual Conference on Information Sciences and Systems (CISS). IEEE, 2023. http://dx.doi.org/10.1109/ciss56502.2023.10089648.
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Liu, Ying, Brent Logan, Ning Liu, Zhiyuan Xu, Jian Tang, and Yangzhi Wang. "Deep Reinforcement Learning for Dynamic Treatment Regimes on Medical Registry Data." In 2017 IEEE International Conference on Healthcare Informatics (ICHI). IEEE, 2017. http://dx.doi.org/10.1109/ichi.2017.45.
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Yin, Changchang, Ruoqi Liu, Jeffrey Caterino, and Ping Zhang. "Deconfounding Actor-Critic Network with Policy Adaptation for Dynamic Treatment Regimes." In KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 2022. http://dx.doi.org/10.1145/3534678.3539413.
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Li, Mingyi, Xiao Zhang, Haochao Ying, Yang Li, Xu Han, and Dongxiao Yu. "Data Quality Aware Hierarchical Federated Reinforcement Learning Framework for Dynamic Treatment Regimes." In 2023 IEEE International Conference on Data Mining (ICDM). IEEE, 2023. http://dx.doi.org/10.1109/icdm58522.2023.00131.
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Andryushchenko, Aleksei Dmitrievitch. "Halite Precipitation in Brine Reservoirs: Prediction and Control by Numerical Model, Optimization of the Fresh Water Treatments and Well Production Regimes." In SPE Russian Petroleum Technology Conference. SPE, 2021. http://dx.doi.org/10.2118/206645-ms.
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Abstract The aim of the work is to optimize the fresh water treatment design, frequency and production regimes (maximize NPV of the well operation) for wells with high NaCl content formation water (brines) production, which are very common for the Eastern Siberia, and forecast productivity index (PI) decline rates and production profiles for the wells by means of halite deposition model for brine flow in porous media united with fresh water treatment model and economic model. New numerical halite deposition model for brine flow in porous media is developed based on Darcy's law and equation of halite precipitation dynamics from formation water taking into account the fresh water treatments, solubility of descipitated halite in the fresh water and permeability profile. It enables to predict deposited halite saturation (Shalite), dynamic porosity and permeability radially and versus time. Thus, we can forecast PI versustime and unite production and economic models,vary fresh water treatment design, frequency andproduction regimes for the given geological conditions and to determine treatment design, frequency and production regimes that brings the maximum NPV.PI decline rates and exploitation factor are calculated and analyzed for different scenarios of the fresh water treatment design, frequency and production regimes. These main conclusions are made from the results of the work:
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McDonald, Dale B., and Joseph O. Falade. "Parameter Identification in Ecological Systems via Discontinuous and Singular Control Regimes." In ASME 2012 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/imece2012-86063.
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Policy decisions regarding commercial harvesting of aquatic species by (typically governmental) regulatory agencies are often based in part upon field data, simulation results, and mathematical models. Regulatory agencies may limit or expand seasons, determine total harvest allowed, increase or decrease licensure fees, and raise or lower taxation rates in response to the state of the ecological system. Ultimately, the regulatory agency uses such measures to ensure viable populations in an attempt to balance ecosystem health and benefits for society. Such decisions impact commercial fishing ventures affecting the nature of harvesting efforts and their intensity. Conclusions drawn from mathematical models of ecological systems, and derived simulation results which affect this reality are highly dependent upon the validity of information available. Knowledge or estimates of critical parameters such as intrinsic growth rate, carrying capacity, etc. and dynamic variables such as biomass levels dictate the usefulness of analytical and numerical analyses. The purpose of this treatment is to illustrate that control laws applied to mathematical models of species dynamics may be used to discern estimates of parameters that inherently exist in such models in an effort to provide more valuable information upon which to base policy decisions. Dynamic models of both single-species evolution and predator-prey interactions are examined.
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Aguiar, Janaina I. S., Antonio A. Pontifes, Jonathan Rogers, and Amir Mahmoudkhani. "Selecting a Product for Wax Remediation: From Characterization of Field Wax Deposits to Improvement of Treatment Sustainability." In Offshore Technology Conference. OTC, 2021. http://dx.doi.org/10.4043/30951-ms.
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Abstract Wax deposition is one of the known challenges of flow assurance management in upstream oil production and operations demanding continuous improvements and the search for more effective prevention and remediation methods. At the same time, there are no universally agreed upon test methods to evaluate the efficiency and mechanisms related to the chemical treatments. The objective of this paper is to present and debate different methods to evaluate the effectiveness of batch treatments for remediation of wax deposits and compare commonly applied solvents with fluids containing biosurfactants. One of the presented methods is a new test methodology that simulates dynamic and quasi-static flow regimes in production tubing and pipelines, as benchmarked methods, showed that the chemical treatments with biosurfactants, besides being a greener, sustainable option, were more efficient at dispersing wax deposits than the traditional solvent treatments.
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Pacaldo, Renato S., Miraç Aydın, and Randell Keith Amarille. "Soil CO2 Effluxes in Post-fire and Undisturbed Pinus nigra Forests: A Soil Moisture Manipulation Study." In 3rd International Congress on Engineering and Life Science. Prensip Publishing, 2023. http://dx.doi.org/10.61326/icelis.2023.41.
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Climate change impacts are driving hydrological extremes and frequent occurrences of forest fires. Whether these impacts result in dramatic changes in the soil CO2 efflux (FCO2) remains poorly understood. This study seeks to understand the changes in the soil FCO2 in recently burned forest (post-fire) and an undisturbed black pine (Pinus nigra, Arnold) forest in Türkiye. A field experiment in a three-way factorial randomized complete block design experiment was established with four replications and three factors; shaded (west) and exposed (east), types of forest fires (surface, crown, and control) and soil moisture regimes (dry, wet, and control). A dynamic survey chamber soil respiration machinery (LI-8100A) was employed to measure simultaneously the soil Fco2, the soil temperature, and the soil moisture for a total duration of one-year. The soil FCO2 showed significant differences among treatments (p<0.0001), time (p<0.0001), and moisture regimes (p<0.0001), but not with the interaction effects between treatment and time (p = 0.0058), aspects (p = 0.95410), and types of forest fires (p = 0.0059). A dry soil in the crown fire site situated in the exposed aspect exhibited a significantly different and lowest soil FCO2 compared to other treatments. No statistically significant differences in the FCO2 in the wet soil were detected among treatments. The soil and air temperatures showed a strongly positive correlation (r = 0.78), suggesting that a near-surface air temperature provides a good approximation of the soil temperature. This piece of information is a vital input for the projection of future trajectory of soil CO2 emissions and conservation of C stocks in the forest fire and undisturbed forests.
Reports on the topic "Dynamic treatment regimes"
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Neugebauer, Romain, Julie Schmittdiel, Oleg Sofrygin, Alyce Adams, Richard Grant, and Mark van der Laan. Methods to Assess the Effect of Dynamic Treatment Regimens Using Electronic Health Records. Patient-Centered Outcomes Research Institute (PCORI), 2020. http://dx.doi.org/10.25302/06.2020.me.140312506.
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Banin, Amos, Joseph Stucki, and Joel Kostka. Redox Processes in Soils Irrigated with Reclaimed Sewage Effluents: Field Cycles and Basic Mechanism. United States Department of Agriculture, 2004. http://dx.doi.org/10.32747/2004.7695870.bard.
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The overall objectives of the project were: (a) To measure and study in situ the effect of irrigation with reclaimed sewage effluents on redox processes and related chemical dynamics in soil profiles of agricultural fields. (b) To study under controlled conditions the kinetics and equilibrium states of selected processes that affect redox conditions in field soils or that are effected by them. Specifically, these include the effects on heavy metals sorption and desorption, and the effect on pesticide degradation. On the basis of the initial results from the field study, increased effort was devoted to clarifying and quantifying the effects of plants and water regime on the soil's redox potential while the study of heavy metals sorption was limited. The use of reclaimed sewage effluents as agricultural irrigation water is increasing at a significant rate. The relatively high levels of suspended and, especially, dissolved organic matter and nitrogen in effluents may affect the redox regime in field soils irrigated with them. In turn, the changes in redox regime may affect, among other parameters, the organic matter and nitrogen dynamics of the root zone and trace organic decomposition processes. Detailed data of the redox potential regime in field plots is lacking, and the detailed mechanisms of its control are obscure and not quantified. The study established the feasibility of long-term, non-disturbing monitoring of redox potential regime in field soils. This may enable to manage soil redox under conditions of continued inputs of wastewater. The importance of controlling the degree of wastewater treatment, particularly of adding ultrafiltration steps and/or tertiary treatment, may be assessed based on these and similar results. Low redox potential was measured in a field site (Site A, KibutzGivat Brenner), that has been irrigated with effluents for 30 years and was used for 15 years for continuous commercial sod production. A permanently reduced horizon (Time weighted averaged pe= 0.33±3.0) was found in this site at the 15 cm depth throughout the measurement period of 10 months. A drastic cultivation intervention, involving prolonged drying and deep plowing operations may be required to reclaim such soils. Site B, characterized by a loamy texture, irrigated with tap water for about 20 years was oxidized (Time weighted average pe=8.1±1.0) throughout the measurement period. Iron in the solid phases of the Givat Brenner soils is chemically-reduced by irrigation. Reduced Fe in these soils causes a change in reactivity toward the pesticide oxamyl, which has been determined to be both cytotoxic and genotoxic to mammalian cells. Reaction of oxamyl with reduced-Fe clay minerals dramatically decreases its cytotoxicity and genotoxicity to mammalian cells. Some other pesticides are affected in the same manner, whereas others are affected in the opposite direction (become more cyto- and genotoxic). Iron-reducing bacteria (FeRB) are abundant in the Givat Brenner soils. FeRB are capable of coupling the oxidation of small molecular weight carbon compounds (fermentation products) to the respiration of iron under anoxic conditions, such as those that occur under flooded soil conditions. FeRB from these soils utilize a variety of Fe forms, including Fe-containing clay minerals, as the sole electron acceptor. Daily cycles of the soil redox potential were discovered and documented in controlled-conditions lysimeter experiments. In the oxic range (pe=12-8) soil redox potential cycling is attributed to the effect of the daily temperature cycle on the equilibrium constant of the oxygenation reaction of H⁺ to form H₂O, and is observed under both effluent and freshwater irrigation. The presence of plants affects considerably the redox potential regime of soils. Redox potential cycling coupled to the irrigation cycles is observed when the soil becomes anoxic and the redox potential is controlled by the Fe(III)/Fe(II) redox couple. This is particularly seen when plants are grown. Re-oxidation of the soil after soil drying at the end of an irrigation cycle is affected to some degree by the water quality. Surprisingly, the results suggest that under certain conditions recovery is less pronounced in the freshwater irrigated soils.
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Bobashev, Georgiy, John Holloway, Eric Solano, and Boris Gutkin. A Control Theory Model of Smoking. RTI Press, 2017. http://dx.doi.org/10.3768/rtipress.2017.op.0040.1706.
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We present a heuristic control theory model that describes smoking under restricted and unrestricted access to cigarettes. The model is based on the allostasis theory and uses a formal representation of a multiscale opponent process. The model simulates smoking behavior of an individual and produces both short-term (“loading up” after not smoking for a while) and long-term smoking patterns (e.g., gradual transition from a few cigarettes to one pack a day). By introducing a formal representation of withdrawal- and craving-like processes, the model produces gradual increases over time in withdrawal- and craving-like signals associated with abstinence and shows that after 3 months of abstinence, craving disappears. The model was programmed as a computer application allowing users to select simulation scenarios. The application links images of brain regions that are activated during the binge/intoxication, withdrawal, or craving with corresponding simulated states. The model was calibrated to represent smoking patterns described in peer-reviewed literature; however, it is generic enough to be adapted to other drugs, including cocaine and opioids. Although the model does not mechanistically describe specific neurobiological processes, it can be useful in prevention and treatment practices as an illustration of drug-using behaviors and expected dynamics of withdrawal and craving during abstinence.
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Anderson, Donald M., Lorraine C. Backer, Keith Bouma-Gregson, et al. Harmful Algal Research & Response: A National Environmental Science Strategy (HARRNESS), 2024-2034. Woods Hole Oceanographic Institution, 2024. http://dx.doi.org/10.1575/1912/69773.
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Harmful and toxic algal blooms (HABs) are a well-established and severe threat to human health, economies, and marine and freshwater ecosystems on all coasts of the United States and its inland waters. HABs can comprise microalgae, cyanobacteria, and macroalgae (seaweeds). Their impacts, intensity, and geographic range have increased over past decades due to both human-induced and natural changes. In this report, HABs refers to both marine algal and freshwater cyanobacterial events. This Harmful Algal Research and Response: A National Environmental Science Strategy (HARRNESS) 2024-2034 plan builds on major accomplishments from past efforts, provides a state of the science update since the previous decadal HARRNESS plan (2005-2015), identifies key information gaps, and presents forward-thinking solutions. Major achievements on many fronts since the last HARRNESS are detailed in this report. They include improved understanding of bloom dynamics of large-scale regional HABs such as those of Pseudo-nitzschia on the west coast, Alexandrium on the east coast, Karenia brevis on the west Florida shelf, and Microcystis in Lake Erie, and advances in HAB sensor technology, allowing deployment on fixed and mobile platforms for long-term, continuous, remote HAB cell and toxin observations. New HABs and impacts have emerged. Freshwater HABs now occur in many inland waterways and their public health impacts through drinking and recreational water contamination have been characterized and new monitoring efforts have been initiated. Freshwater HAB toxins are finding their way into marine environments and contaminating seafood with unknown consequences. Blooms of Dinophysis spp., which can cause diarrhetic shellfish poisoning, have appeared around the US coast, but the causes are not understood. Similarly, blooms of fish- and shellfish-killing HABs are occurring in many regions and are especially threatening to aquaculture. The science, management, and decision-making necessary to manage the threat of HABs continue to involve a multidisciplinary group of scientists, managers, and agencies at various levels. The initial HARRNESS framework and the resulting National HAB Committee (NHC) have proven effective means to coordinate the academic, management, and stakeholder communities interested in national HAB issues and provide these entities with a collective voice, in part through this updated HARRNESS report. Congress and the Executive Branch have supported most of the advances achieved under HARRNESS (2005-2015) and continue to make HABs a priority. Congress has reauthorized the Harmful Algal Bloom and Hypoxia Research and Control Act (HABHRCA) multiple times and continues to authorize the National Oceanic and Atmospheric Administration (NOAA) to fund and conduct HAB research and response, has given new roles to the US Environmental Protection Agency (EPA), and required an Interagency Working Group on HABHRCA (IWG HABHRCA). These efforts have been instrumental in coordinating HAB responses by federal and state agencies. Initial appropriations for NOAA HAB research and response decreased after 2005, but have increased substantially in the last few years, leading to many advances in HAB management in marine coastal and Great Lakes regions. With no specific funding for HABs, the US EPA has provided funding to states through existing laws, such as the Clean Water Act, Safe Drinking Water Act, and to members of the Great Lakes Interagency Task Force through the Great Lakes Restoration Initiative, to assist states and tribes in addressing issues related to HAB toxins and hypoxia. The US EPA has also worked towards fulfilling its mandate by providing tools and resources to states, territories, and local governments to help manage HABs and cyanotoxins, to effectively communicate the risks of cyanotoxins and to assist public water systems and water managers to manage HABs. These tools and resources include documents to assist with adopting recommended recreational criteria and/or swimming advisories, recommendations for public water systems to choose to apply health advisories for cyanotoxins, risk communication templates, videos and toolkits, monitoring guidance, and drinking water treatment optimization documents. Beginning in 2018, Congress has directed the U.S. Army Corps of Engineers (USACE) to develop a HAB research initiative to deliver scalable HAB prevention, detection, and management technologies intended to reduce the frequency and severity of HAB impacts to our Nation’s freshwater resources. Since the initial HARRNESS report, other federal agencies have become increasingly engaged in addressing HABs, a trend likely to continue given the evolution of regulations(e.g., US EPA drinking water health advisories and recreational water quality criteria for two cyanotoxins), and new understanding of risks associated with freshwater HABs. The NSF/NIEHS Oceans and Human Health Program has contributed substantially to our understanding of HABs. The US Geological Survey, Centers for Disease Control and Prevention, and the National Aeronautics Space Administration also contribute to HAB-related activities. In the preparation of this report, input was sought early on from a wide range of stakeholders, including participants from academia, industry, and government. The aim of this interdisciplinary effort is to provide summary information that will guide future research and management of HABs and inform policy development at the agency and congressional levels. As a result of this information gathering effort, four major HAB focus/programmatic areas were identified: 1) Observing systems, modeling, and forecasting; 2) Detection and ecological impacts, including genetics and bloom ecology; 3) HAB management including prevention, control, and mitigation, and 4) Human dimensions, including public health, socio-economics, outreach, and education. Focus groups were tasked with addressing a) our current understanding based on advances since HARRNESS 2005-2015, b) identification of critical information gaps and opportunities, and c) proposed recommendations for the future. The vision statement for HARRNESS 2024-2034 has been updated, as follows: “Over the next decade, in the context of global climate change projections, HARRNESS will define the magnitude, scope, and diversity of the HAB problem in US marine, brackish and freshwaters; strengthen coordination among agencies, stakeholders, and partners; advance the development of effective research and management solutions; and build resilience to address the broad range of US HAB problems impacting vulnerable communities and ecosystems.” This will guide federal, state, local and tribal agencies and nations, researchers, industry, and other organizations over the next decade to collectively work to address HAB problems in the United States.
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Optional dynamic treatment regimes and partial welfare ordering. Cemmap, 2020. http://dx.doi.org/10.47004/wp.cem.2020.5020.
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