The past decades have seen rapid development of numerous high-throughput technologies to observe biomolecular phenomena. High-throughput biological data are inherently heterogeneous, providing information at the various levels at which organisms integrate inputs to arrive at an observable phenotype. Approaches are needed to not only analyze heterogeneous biological data, but also model the complex experimental observation procedures. We first present an algorithm for learning dynamic cell cycle transcriptional regulatory networks from gene expression and transcription factor binding data. We learn regulatory networks using dynamic Bayesian network inference algorithms that combine evidence from gene expression data through the likelihood and evidence from binding data through an informative structure prior. We next demonstrate how analysis of cell cycle measurements like gene expression data are obstructed by sychrony loss in synchronized cell populations. Due to synchrony loss, population-level cell cycle measurements are convolutions of the true measurements that would have been observed when monitoring individual cells. We introduce a fully parametric, probabilistic model, CLOCCS, capable of characterizing multiple sources of asynchrony in synchronized cell populations. Using CLOCCS, we formulate a constrained convex optimization deconvolution algorithm that recovers single cell estimates from observed population-level measurements. Our algorithm offers a solution for monitoring individual cells rather than a population of cells that lose synchrony over time. Using our deconvolution algorithm, we provide a global high resolution view of cell cycle gene expression in budding yeast, right from an initial cell progressing through its cell cycle, to across the newly created mother and daughter cell. Proteins, and not gene expression, are responsible for all cellular functions, and we need to understand how proteins and protein complexes operate. We introduce PROCTOR, a statistical approach capable of learning the hidden interaction topology of protein complexes from direct protein-protein interaction data and indirect co-complexed protein interaction data. We provide a global view of the budding yeast interactome depicting how proteins interact with each other via their interfaces to form macromolecular complexes. We conclude by demonstrating how our algorithms, utilizing information from heterogeneous biological data, can provide a dynamic view of regulatory control in the budding yeast cell cycle. / Dissertation

Optimization of the performance of absorption systems during transient operations such as start-up and shut-down is particularly important for small-capacity chillers and heat pumps to minimize lifecycle costs. Dynamic models in the literature have been used to study responses to step changes in a single parameter, but more complex processes such as system start-up have not been studied in detail. A robust system-level model for simulating the transient behavior of an absorption chiller is developed here. Individual heat and mass exchangers are modeled using detailed segmental models. The UA-values and thermal masses of heat exchangers used in the model are representative of a practical operational chiller. Thermal masses of the heat exchangers and energy storage in the heat exchanging fluids are accounted for to achieve realistic transient simulation of the heat transfer processes in the chiller. The pressure drop due to fluid flow across the heat exchangers is considered negligible in comparison to the pressure difference between the high- and low-side components (~ 1.5 MPa). In components with significant mass transfer effects, reduced-order models are employed to decrease computational costs while also maintaining accurate system response. Mass and species storage in the cycle are modeled using storage devices. The storage devices account for expansion and contraction of the refrigerant and solution in the cycle as the system goes through start-up, shut-down, and other transient events. A counterflow falling film desorber model is employed to account for the heat and mass transfer interactions between the liquid and vapor phases, inside the desorber. The liquid film flows down counter to the rising vapor, thereby exchanging heat with the counterflowing heated coupling fluid. A segmented model is used to account for these processes, and a solver is developed for performing rapid iteration and quick estimation of unknown vapor and liquid states at the outlet of each segment of the desorber. Other components such as the rectifier, expansion valves and solution pump are modeled as quasi-steady devices. System start-up is simulated from ambient conditions, and the coupling fluid temperatures are assumed to start up to their steady-state values within the first 90 s of simulation. It is observed that the system attains steady-state in approximately 550 s. The evaporator cooling duty and COP of the chiller during steady-state are observed to be 3.41 kW and 0.60, respectively. Steady-state parameters such as flow rates, heat transfer rates and concentrations are found to match closely with results from simulations using corresponding steady-state models. Several control responses are investigated using this dynamic simulation model. System responses to step changes in the desorber coupling fluid temperature and flow rate, solution pumping rate, and valve setting are used to study the effects of several control strategies on system behavior. Results from this analysis can be used to optimize start-up and steady state performances. The model can also be used for devising and testing control strategies in commercial applications.

Model-based control and process optimization technologies are becoming more commonly used by chemical engineers. These algorithms rely on fundamental or empirical models that are frequently described by systems of differential equations with unknown parameters. It is, therefore, very important for modellers of chemical engineering processes to have access to reliable and efficient tools for parameter estimation in dynamic models. The purpose of this thesis is to develop an efficient and easy-to-use parameter estimation algorithm that can address difficulties that frequently arise when estimating parameters in nonlinear continuous-time dynamic models of industrial processes. The proposed algorithm has desirable numerical stability properties that stem from using piece-wise polynomial discretization schemes to transform the model differential equations into a set of algebraic equations. Consequently, parameters can be estimated by solving a nonlinear programming problem without requiring repeated numerical integration of the differential equations. Possible modelling discrepancies and process disturbances are accounted for in the proposed algorithm, and estimates of the process disturbance intensities can be obtained along with estimates of model parameters and states. Theoretical approximate confidence interval expressions for the parameters are developed. Through a practical two-phase nylon reactor example, as well as several simulation studies using stirred tank reactors, it is shown that the proposed parameter estimation algorithm can address difficulties such as: different types of measured responses with different levels of measurement noise, measurements taken at irregularly-spaced sampling times, unknown initial conditions for some state variables, unmeasured state variables, and unknown disturbances that enter the process and influence its future behaviour. / Thesis (Ph.D, Chemical Engineering) -- Queen's University, 2008-06-20 16:34:44.586

This study investigates the relevance of dynamic traffic assignment models under uncertainty. In the last years researchers have dealt with advanced traffic control systems since road provision is not regarded as a proper solution to relieve congestion. Dynamic assignment which is an essential component of investment planning is regarded as a new research area in the field of urban transportation. In this study the performance of dynamic traffic assignment method, which incorporates time dependent flow, is compared with that of static model. Research outcomes showed that dynamic assignment method provides more reliable outcomes in predicting traffic flow / therefore its solution algorithm is integrated to conventional four staged model. Literature survey showed that researches have hot provided an appropriate framework for transforming networks. This study investigates travel demand variations in a dynamic city and discuses possible strategies to respond dynamic and uncertain properties of individuals&rsquo / travel behavior. Research findings showed that both external and internal uncertainties have significant influences on reliability of the model. Recommended procedure aims reducing uncertainty in order to improve reliability of model. Finally, the relevancy of the problem and the applicability of recently developed methods are discussed in Ankara case.

Diss. (sammanfattning) Linköping : Univ., 2004. / Härtill 4 uppsatser.

Σε μια εποχή που οι αλλαγές του οικονομικού περιβάλλοντος συμβαίνουν όλο και πιο συχνά, κάθε επιχειρηματικός οργανισμός πρέπει να αποκτήσει ικανότητα να παίρνει γρήγορα τις σωστές αποφάσεις και να τις υλοποιεί. Η λήψη αποφάσεων αποτελεί σημαντικότατο στοιχείο της καθημερινής μας ζωής και καθορίζει τη μετέπειτα πορεία μας καθώς τα αποτελέσματά της φαίνονται σε διάφορους τομείς. Το πλήθος των αποφάσεων που καλούμαστε να λάβουμε είναι τέτοιο που αρκετές φορές αποφασίζουμε ασυνείδητα και μηχανικά. Ωστόσο, το ίδιο δεν μπορεί να συμβεί και στο επιχειρηματικό περιβάλλον όπου οι αποφάσεις έχουν συνήθως στρατηγική σημασία και επηρεάζουν τόσο τη σωστή λειτουργία όσο τη καλή πορεία και τη βιωσιμότητα των επιχειρήσεων. Ο σκοπός της συγκεκριμένης διπλωματικής εργασίας είναι η ανάδειξη του ρόλου της εφοδιαστικής αλυσίδας στη λήψη αποφάσεων. Μέσα από τη δημιουργία κατάλληλων μοντέλων γραμμικού προγραμματισμού, τα οποία αντιπροσωπεύουν τη λειτουργία της εφοδιαστικής αλυσίδας, είναι εφικτή η πρόβλεψη πιθανών μελλοντικών σεναρίων. Συνεπώς, η διοίκηση μιας εταιρείας θα μπορεί να είναι σε θέση να λαμβάνει γρήγορα και αντικειμενικά τις απαραίτητες αποφάσεις στρατηγικής σημασίας για την αντιμετώπιση οποιασδήποτε κατάστασης. Η παρούσα διπλωματική εργασία στηρίχτηκε στην εργασία των M.T. Melo, S. Nickela,b, F. Saldanha da Gama «Dynamic multi-commodity capacitated facility location: a mathematical modeling framework for strategic supply chain planning». Το συγκεκριμένο paper βραβεύτηκε ως ένα από τα καλύτερα το 2012 στο «Euro Award for the Best Ejor Paper 2012». Η δομή της εργασίας είναι η εξής: Στο 1ο κεφάλαιο γίνεται μια εισαγωγή σε βασικές έννοιες όπως αυτή της χωροθέτησης εγκαταστάσεων και της εφοδιαστικής αλυσίδας. Επίσης παρουσιάζεται και η μεθοδολογική προσέγγιση για την παρουσίαση των προβλημάτων. Στο 2ο κεφάλαιο παρουσιάζονται τα μοντέλα χωροθέτησης. Αυτά κατηγοριοποιούνται στα Βασικά Μοντέλα Χωροθέτησης όπου παρατίθενται αναλυτικά οχτώ από αυτά, στα Δυναμικά Μοντέλα Χωροθέτησης και στα Στοχαστικά Μοντέλα Χωροθέτησης. Το 3ο κεφάλαιο ασχολείται με την εφαρμογή. Συγκεκριμένα, παρουσιάζεται η περιγραφή και η διατύπωση του προβλήματος όπου παρατίθενται εκτενώς τα σύνολα, οι παράμετροι, οι μεταβλητές απόφασης και οι περιορισμοί που απαιτούνται για την επίλυση του μοντέλου μας. Το 4ο κεφάλαιο ασχολείται με την ανάλυση των αποτελεσμάτων που προκύπτουν ύστερα από την επίλυση του μοντέλου στο περιβάλλον του AIMMS. Συγκεκριμένα παρατίθενται αναλυτικά τα σενάρια που έχουμε θεωρήσει ως πραγματικά καθώς και οι βέλτιστες λύσεις για κάθε ένα ξεχωριστά από αυτά. Εν κατακλείδι, στο 5ο κεφάλαιο παρουσιάζονται τα συμπεράσματα της συγκεκριμένης εργασίας και η μελλοντική έρευνα. / We are living in a time where the economic environment changes are happening more and more frequently. For that reason, every business organization must acquire capacity to make and implement the right decisions quickly. Decision making is an important part of our daily lives and determines our future since the results are shown in different areas. The number of decisions we take are so many that we decide unconsciously and mechanically. However, the same cannot happen in the business environment where the decisions are usually of strategic importance, while they affect both the proper functioning and the good performance and sustainability of the business. The purpose of this thesis is to highlight the role of supply chain decision making. Through the creation of suitable linear programming models, which represent the operation of the supply chain, it is possible to predict potential future scenarios. Therefore, the management of a company will be able to quickly and objectively make the necessary strategic decisions to deal with any situation. This thesis was based on the work of MT Melo, S. Nickel, F. Saldanha da Gama «Dynamic multi-commodity capacitated facility location: a mathematical modeling framework for strategic supply chain planning». This paper was awarded as one of the best in 2012 in «Euro Award for the Best Ejor Paper 2012». The structure of the paper is as follows: The first chapter contains an introduction to basic concepts such as the facilities location and supply chain. Also, it is presented the methodological approach to the problems. In the second chapter, they are presented the facility location models. These are categorized into Basic Location Models, into Dynamic Location Models and into Stochastic Location Models. The third chapter deals with the application. Specifically, it is presented the description and the formulation of the problem, in detail, along with the sets, the parameters, the decision variables and the constraints that are required in order to solve our model. The fourth chapter deals with the analysis of the results obtained after solving the model in the environment of AIMMS. Specifically, they are presented in detail the scenarios that we consider as real along with the best solutions for each one of them. In conclusion, in the fifth chapter they are presented the conclusions of this study and the future research.

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Agradecemos a compreensão. on 2016-05-13T13:14:09Z (GMT) / Submitted by FREDERICO MONFARDINI null (fred.monf@gmail.com) on 2016-05-16T04:01:38Z No. of bitstreams: 1 DISSSERTAÇÃO - FREDERICO.pdf: 1003174 bytes, checksum: 3449613d0bfa6567b122b1461608bc55 (MD5) / Approved for entry into archive by Felipe Augusto Arakaki (arakaki@reitoria.unesp.br) on 2016-05-16T14:41:59Z (GMT) No. of bitstreams: 1 monfardini_f_me_prud.pdf: 1003174 bytes, checksum: 3449613d0bfa6567b122b1461608bc55 (MD5) / Made available in DSpace on 2016-05-16T14:41:59Z (GMT). No. of bitstreams: 1 monfardini_f_me_prud.pdf: 1003174 bytes, checksum: 3449613d0bfa6567b122b1461608bc55 (MD5) Previous issue date: 2016-02-19 / Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) / Os Modelos Lineares Generalizados (GLM) foram introduzidos no início dos anos 70, tendo um grande impacto no desenvolvimento da teoria estatística. Do ponto de vista teórico, esta classe de modelos representa uma abordagem unificada de muitos modelos estatísticos, correntemente usados nas aplicações, podendo-se utilizar dos mesmos procedimentos de inferência. Com o avanço computacional das últimas décadas foi notável o desenvolvimento de extensões nesta classe de modelos e de métodos para os procedimentos de inferência. No contexto da abordagem Bayesiana, até a década de 80 utilizava-se de métodos aproximados de inferência, tais como aproximação de Laplace, quadratura Gaussiana e outros. No início da década de 90, foram popularizados os métodos de Monte Carlo via Cadeias de Markov (Monte Carlo Markov Chain - MCMC) que revolucionaram as aplicações no contexto Bayesiano. Apesar de serem métodos altamente eficientes, a convergência do algoritmo em modelos complexos pode ser extremamente lenta, o que gera alto custo computacional. Em 2009 surgiu o método de Aproximações de Laplace Aninhadas Integradas (Integrated Nested Laplace Aproximation - INLA) que busca eficiência tanto no custo computacional como na precisão das estimativas. Considerando a importância desta classe de modelos, neste trabalho propõem-se explorar extensões dos MLG para dados longitudinais e recentes propostas apresentadas na literatura para os procedimentos de inferência. Mais especificamente, explorar modelos para dados binários (binomiais) e para dados de contagem (Poisson), considerando a presença de variabilidade extra, incluindo superdispersão e presença de efeitos aleatórios através de modelos hierárquicos e modelos hierárquicos dinâmicos. Além disso, explorar diferentes procedimentos de inferência no contexto Bayesiano, incluindo MCMC e INLA. / Generalized Linear Models (GLM) were introduced in the early 70s, having a great impact on the development of statistical theory. From a theoretical point of view, this class of model is a unified approach to many statistical models commonly used in applications and can be used with the same inference procedures. With advances in the computer over subsequent decades has come a remarkable development of extensions in this class of design and method for inference procedures. In the context of Bayesian approach, until the 80s, it was used to approximate inference methods, such as approximation of Laplace, Gaussian quadrature, etc., The Monte Carlo Markov Chain methods (MCMC) were popularized in the early 90s and have revolutionized applications in a Bayesian context. Although they are highly efficient methods, the convergence of the algorithm in complex models can be extremely slow, which causes high computational cost. The Integrated Nested Laplace Approximations method (INLA), seeking efficiency in both computational cost and accuracy of estimates, appeared in 2009. This work proposes to explore extensions of GLM for longitudinal data considering the importance of this class of model, and recent proposals in the literature for inference procedures. More specifically, it explores models for binary data (binomial) and count data (Poisson), considering the presence of extra variability, including overdispersion and the presence of random effects through hierarchical models and hierarchical dynamic models. It also explores different Bayesian inference procedures in this context, including MCMC and INLA.

Cette thèse se compose de trois chapitres qui présentent trois articles indépendants portant sur l’application de modèles dynamiques dans les domaines du marketing et de la finance. Le premier article adopte une approche structurelle des modèles dynamiques pour analyser la relation entre les revenus et l’impact des taxes sur les produits de grignotage (fat taxes). Le deuxième et le troisième article utilisent des modèles dynamiques en forme réduite : nous y mobilisons des modèles hiérarchiques dynamiques qui intègrent un cadre bayésien hiérarchique à un Modèle Linéaire Dynamique. Le second article étudie la tarification dynamique des produits de saison à l’aide d’un modèle hiérarchique dynamique et flexible. Le troisième article analyse le coût du financement des opérations commerciales au cours de la crise financière de 2008-2009 au moyen d’un modèle hiérarchique dynamique. Dans le premier article, nous utilisons un modèle structurel dynamique pour analyser la corrélation entre les revenus et l’impact d’une taxe sur les produits de grignotage (ou fat tax). Les résultats montrent qu’une telle taxe a moins d’impact sur les individus à faibles revenus que sur ceux dont les revenus sont plus élevés, dans la mesure où le premier groupe a davantage tendance à consommer des snacks. Dans le second article, nous élaborons un modèle hiérarchique dynamique et flexible pour estimer la trajectoire des sensibilités-prix afin d’en déduire le tarif dynamique des produits de saison. Nous constatons que les prix optimaux dépendent de la composition de la clientèle du magasin, et que les vendeurs de produits de saisons peuvent en tirer profit lorsqu’ils fixent leurs tarifs. Dans le troisième article, nous utilisons un modèle hiérarchique dynamique pour étudier l’impact de quatre indicateurs macroéconomiques sur le coût du financement des opérations commerciales pendant la crise financière de 2008-2009, ainsi que pendant les périodes qui l’ont précédée et suivie. Nous constatons que l’impact de trois de ces facteurs macroéconomiques (croissance du PIB, échanges commerciaux et inflation) sur le financement commercial est conforme aux prédictions théoriques, tandis que l’impact du quatrième facteur (capitalisation boursière) semble assez surprenant. / This dissertation consists of three chapters that present three standalone essays on the application of dynamic models to marketing and finance. The first essay uses a structural approach to dynamic models to study the role of income on the impact of fat taxes. The second and third essays use a reduced form approach to dynamic models: we use dynamic hierarchical models which incorporate a Hierarchical Bayesian framework in a Dynamic Linear Model. The second essay studies the dynamic pricing of seasonal goods with the help of a flexible dynamic hierarchical model. The third essay studies the cost of trade finance during the financial crisis of 2008-2009 using a dynamic hierarchical model. In the first essay, we use a dynamic structural model to investigate how income interacts with the impact of a “fat tax” (a tax on snack food). We find that the low-income group is less impacted by a “fat tax” compared to the higher income group as they have a higher tendency to consume snack food. In the second essay, we develop a flexible dynamic hierarchical model to estimate the trajectory of price sensitivities which allows us to infer the dynamic prices of seasonal goods. We find that optimal prices depend on the customer composition of the store, and seasonal goods retailers can take advantage of this while setting prices. In the third essay, using a dynamic hierarchical model we examine the impact of four macroeconomic indicators on trade finance costs in and around the financial crisis of 2008-2009. We find the impact of three of these macroeconomic factors (GDP growth, trade and inflation) on trade finance to be in line with the theory, while the impact of the fourth factor (stock market capitalization) on trade finance appears somewhat surprising.

Orientador: Diogenes Cortijo Costa / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Civil, Arquitetura e Urbanismo / Made available in DSpace on 2018-08-10T20:25:50Z (GMT). No. of bitstreams: 1 Scarassatti_DaniellaFarias_M.pdf: 5257230 bytes, checksum: 1a12da8901dbdb89959b9daa81bb7784 (MD5) Previous issue date: 2007 / Resumo: As inovações tecnológicas presentes na sociedade contemporânea, como as redes de comunicações e transportes, têm proporcionado uma profunda transformação na produção dos espaços urbanos e colocam o desafio de formular modelos que traduzam a mecânica da organização espacial das cidades. É cada vez mais freqüente a integração de modelos dinâmicos no ambiente dos Sistemas de Informações Geográficas para promover simulações e gerar representações de processos espaçotemporais. Neste contexto, modelagens dinâmicas utilizadas para o planejamento urbano têm se mostrado um campo promissor na identificação de possíveis trajetórias de expansão urbana e de suas tendências, auxiliando o poder público local no direcionamento de ações na gestão da cidade. É a proposta deste trabalho obter avaliações integradas de fatores, como a rede viária de transportes, que procurem explicar as mudanças de uso do solo. Para este propósito foi utilizado o software Idrisi Kilimanjaro 6.02 que possui funções como modelagem de processos e simulação baseado em cadeia de Markov e autômatos celulares. Foram identificadas duas regiões do município de Campinas, SP, escolhidas por suas peculiaridades na dinâmica de uso e ocupação do solo. As modelagens prospectivas das áreas de estudo são comparadas permitindo-se avaliar a metodologia e precisar suas contribuições e limites / Abstract: Technological innovations, such as transport and communication networks, have deeply transformed urban space and have imposed model conceptions that reflect the real city organization in contemporary society. In order to perform simulations and to generate spatial-temporal process representations, the integration of dynamic models in Geographic Information Systems has been frequently applied. In this context, dynamic models represent a propitious field in urban planning to identify urban expansion trends, as well an appropriate tool that supports local public policies aimed at the urban management. The purpose of this work is to obtain integrated evaluations about factors, such as network transportation, that can explain land use transformations. In order to investigate the applicability of dynamic models on the land use projections, Markov Chain algorithms complemented with cellular automata were performed on Idrisi Kilimanjaro 6.02 software. Two areas in Campinas City, Brazil, were chosen based on their different land use and occupation. The comparison of the prospective modeling applied to these areas permitted the evaluation of the methodology, according to its contribution and limitation / Mestrado / Transportes / Mestre em Engenharia Civil

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