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[pt] A sísmica é uma importante ferramenta utilizada no processo de exploração de petróleo e gás natural. A partir dos estudos sísmicos é possível obter informações referentes a probabilidade de encontrar situações favoráveis a acumulação de hidrocarbonetos. O presente trabalho visa integrar os dados adquiridos através deste método geofísico a um modelo de simulação de canais baseados em esqueletos em um ambiente deposicional turbidítico, e também apresentar a modelagem de tais canais condicionados a localização de um poço. / [en] The use of seismic data is an important tool in oil and gas research. It can show us the probability of having a high concentration of hydrocarbon in a possible reservoir. This work intends to condition skeleton-based modeling of channels reservoir in a turbidite system to seismic data. We also present such modeling process constraint by a well previously defined.

This research investigates the environment and development issues of three stakeholders at multiple scales—global, national, regional, and local. Through the analysis of financial, social, and environmental metrics, the potential benefits and risks of each case study are estimated, and their implications are considered. In the first case study, the relationship of manufacturing and environmental performance is investigated. Over 700 facilities of a global manufacturer that produce 11 products on six continents were investigated to understand global variations and determinants of environmental performance. Water, energy, carbon dioxide emissions, and production data from these facilities were analyzed to assess environmental performance; the relationship of production composition at the individual firm and environmental performance were investigated. Location-independent environmental performance metrics were combined to provide both global and local measures of environmental performance. These models were extended to estimate future water use, energy use, and greenhouse gas emissions considering potential demand shifts. Natural resource depletion risks were investigated, and mitigation strategies related to vulnerabilities and exposure were discussed. The case study demonstrated how data from multiple facilities can be used to characterize the variability amongst facilities and to preview how changes in production may affect overall corporate environmental metrics. The developed framework adds a new approach to account for environmental performance and degradation as well as assess potential risk in locations where climate change may affect the availability of production resources (i.e., water and energy) and thus, is a tool for understanding risk and maintaining competitive advantage. The second case study was designed to address the issue of delivering affordable and sustainable energy. Energy pricing was evaluated by modeling individual energy consumption behaviors. This analysis simulated a heterogeneous set of residential households in both the urban and rural environments in order to understand demand shifts in the residential energy end-use sector due to the effects of electricity pricing. An agent-based model (ABM) was created to investigate the interactions of energy policy and individual household behaviors; the model incorporated empirical data on beliefs and perceptions of energy. The environmental beliefs, energy pricing grievances, and social networking dynamics were integrated into the ABM model structure. This model projected the aggregate residential sector electricity demand throughout the 30-year time period as well as distinguished the respective number of households who only use electricity, that use solely rely on indigenous fuels, and that incorporate both indigenous fuels and electricity. The model is one of the first characterizations of household electricity demand response and fuel transitions related to energy pricing at the individual household level, and is one of the first approaches to evaluating consumer grievance and rioting response to energy service delivery. The model framework is suggested as an innovative tool for energy policy analysis and can easily be revised to assist policy makers in other developing countries. In the final case study, a framework was developed for a broad cost-benefit and greenhouse gas evaluation of transit systems and their associated developments. A case study was developed of the Atlanta BeltLine. The net greenhouse gas emissions from the BeltLine light rail system will depend on the energy efficiency of the streetcars themselves, the greenhouse gas emissions from the electricity used to power the streetcars, the extent to which people use the BeltLine instead of driving personal vehicles, and the efficiency of their vehicles. The effects of ridership, residential densities, and housing mix on environmental performance were investigated and were used to estimate the overall system efficacy. The range of the net present value of this system was estimated considering health, congestion, per capita greenhouse gas emissions, and societal costs and benefits on a time-varying scale as well as considering the construction and operational costs. The 95% confidence interval was found with a range bounded by a potential loss of $860 million and a benefit of $2.3 billion; the mean net present value was $610 million. It is estimated that the system will generate a savings of $220 per ton of emitted CO2 with a 95% confidence interval bounded by a potential social cost of $86 cost per ton CO2 and a savings of $595 per ton CO2.

The Sahelian Niger farming systems spatial expansion over the last century is about to reach its end. Meanwhile, rural societies organizations & managements of economic activities have evolved. This research objective is to develop an integrative approach to evaluate the impact of social factors on farming system transitions. The study focuses on three contrasted sites of Sahelian Niger. Regional, village & individual level interviewing tools are used to define differentiated individual behavior rules to be translated into an Agent-based model simulating the populations & their related "terroirs" along two or three generations. The model is based on reactive individual agents acting empirically, i.e. without optimisation processes. The model is realistic concerning the individual behaviors & realistically simulates their impacts on village populations & natural resources. Simulation results show that once dominant unitary families have shifted towards non-cooperative ones around the 70's. Simulations with no transition processes of inheritance system & family organization show that villages specialize themselves: more a "terroir" is well endowed, more its population involves itself in local activities. Introducing such processes, differentiation occurs within the population level, subdividing it into specializing groups according to their village anteriority & manpower & land availability. Introducing development proposals (inorganic fertilizer availability & yield-based inventory credit) reinforce this social differentiation: only well-endowed sites & among them, only favored groups have the saving capacity to get involved. The securizing inventory credit proposal has more success than the intensification-oriented inorganic fertilizer use. Combining different individual-level tools in a multidisciplinary approach is efficient in underlining the impact of micro level constraints on long-term population evolutions in such constrained environments. Such approach may be used in development diagnosis to identify the constraint hierarchy affecting differentially the population. Simulating population behaviors keep open epistemological debates that have strong implications for rural populations.

With increasing computational power and more elaborate software comes greater opportunities to complement traditional research methods with alternative methods. In this paper we argue for why the area of two-sided markets could benefit from this alternative approach and attempt to implement a theoretical model in an agent-based framework. By first replicating the theoretical findings in this framework we expand the model in increments in different directions through introducing different set of heterogeneity and behavioral limitations on our actors to see how the theoretical model develops. Only changing the model in increments found the analytical outcome to be robust for many of our changes, in this regard we have not managed to successfully take advantage of the full potential of the agent-based framework.

Aus der Einführung "Durch die Vernetzung von Produkten im Internet der Dinge / Internet of Things (IoT) und die damit einhergehende Verfügbarkeit von Daten, können nicht nur Produkte selbstständig agieren, reagieren und Aktionen auslösen, sondern auch externe Empfänger die von ihnen gelieferten Daten auswerten und für zusätzliche Services nutzen (Eisenhauer 2007, Abramovici et al. 2014). Dies birgt unter Anderem enorme Potentiale bei der Instandhaltung von technischen Anlagen (Wohlgemut 2007). Diese Anlagen oder Produktionsstätten sind in aller Regel komplexe Systeme, die aus einer heterogenen Landschaft von Subsystemen bestehen. Ohne vorhergehende Kenntnisse einer Maschine ist die Analyse oder Überprüfung solcher Systeme schwierig bis unmöglich. Weiterhin stehen die technischen Dokumentationen und Wartungshistorien bei solchen Tätigkeiten häufig nicht vollständig oder nur in Papierform vor Ort zur Verfügung, während der aktuelle Status der Anlage nicht mit den vorhandenen Informationen überlagert werden kann. ..."

Widespread unexplained variations in clinical practices and patient outcomes, together with rapidly growing availability of data, suggest major opportunities for improving the quality of medical care. One way that healthcare practitioners try to do that is by participating in organized healthcare quality improvement collaboratives (QICs). In QICs, teams of practitioners from different hospitals exchange information on clinical practices, with the aim of improving health outcomes at their own institutions. However, what works in one hospital may not work in others with different local contexts, due to non-linear interactions among various demographics, treatments, and practices. I.e., the clinical landscape is a complex socio-technical system that is difficult to search. In this dissertation we develop methods for analysis and modeling of complex systems, and apply them to the problem of healthcare improvement. Searching clinical landscapes is a multi-objective dynamic problem, as hospitals simultaneously optimize for multiple patient outcomes. We first discuss a general method we developed for finding which changes in features may be associated with various changes in outcomes at different points in time with different delays in affect. This method correctly inferred interactions on synthetic data, however the complexity and incompleteness of the real hospital dataset available to us limited the usefulness of this approach. We then discuss an agent-based model (ABM) of QICs to show that teams comprising individuals from similar institutions outperform those from more diverse institutions, under nearly all conditions, and that this advantage increases with the complexity of the landscape and the level of noise in assessing performance. We present data from a network of real hospitals that provides encouraging evidence of a high degree of similarity in clinical practices among hospitals working together in QIC teams. Based on model outcomes, we propose a secure virtual collaboration system that would allow hospitals to efficiently identify potentially better practices in use at other institutions similar to theirs, without any institutions having to sacrifice the privacy of their own data. To model the search for quality improvement in clinical fitness landscapes, we need benchmark landscapes with tunable feature interactions. NK landscapes have been the classic benchmarks for modeling landscapes with epistatic interactions, but the ruggedness is only tunable in discrete jumps. Walsh polynomials are more finely tunable than NK landscapes, but are only defined on binary alphabets and, in general, have unknown global maximum and minimum. We define a different subset of interaction models that we dub as NM landscapes. NM landscapes are shown to have smoothly tunable ruggedness and difficulty and known location and value of global maxima. With additional constraints, we can also determine the location and value of the global minima. The proposed NM landscapes can be used with alphabets of any arity, from binary to real-valued, without changing the complexity of the landscape. NM landscapes are thus useful models for simulating clinical landscapes with binary or real decision variables and varying number of interactions. NM landscapes permit proper normalization of fitnesses so that search results can be fairly averaged over different random landscapes with the same parameters, and fairly compared between landscapes with different parameters. In future work we plan to use NM landscapes as benchmarks for testing various algorithms that can discover epistatic interactions in real world datasets.

Multiple research problems related to the lung involve a need to take into account the spatiotemporal dynamics of the underlying component cells. Two such problems involve better understanding the nature of the allergic inflammatory response to explore what might cause chronic inflammatory diseases such as asthma, and determining the rules underlying stem cells used to engraft decellularized lung scaffolds in the hopes of growing new lungs for transplantation. For both problems, we model the systems computationally using agent-based modeling, a tool that enables us to capture these spatiotemporal dynamics by modeling any biological system as a collection of agents (cells) interacting with each other and within their environment. This allows to test the most important pieces of biological systems together rather than in isolation, and thus rapidly derive biological insights from resulting complex behavior that could not have been predicted beforehand, which we can then use to guide wet lab experimentation. For the allergic response, we hypothesized that stimulation of the allergic response with antigen results in a response with formal similarity to a muscle twitch or an action potential, with an inflammatory phase followed by a resolution phase that returns the system to baseline. We prepared an agent-based model (ABM) of the allergic inflammatory response and determined that antigen stimulation indeed results in a twitch-like response. To determine what might cause chronic inflammatory diseases where the twitch presumably cannot resolve back to baseline, we then tested multiple potential defects to the model. We observed that while most of these potential changes lessen the magnitude of the response but do not affect its overall behavior, extending the lifespan of activated pro-inflammatory cells such as neutrophils and eosinophil results in a prolonged inflammatory response that does not resolve to baseline. Finally, we performed a series of experiments involving continual antigen stimulation in mice, determining that there is evidence in the cytokine, cellular and physiologic (mechanical) response consistent with our hypothesis of a finite twitch and an associated refractory period. For stem cells, we made a 3-D ABM of a decellularized scaffold section seeded with a generic stem cell type. We then programmed in different sets of rules that could conceivably underlie the cell's behavior, and observed the change in engraftment patterns in the scaffold over selected timepoints. We compared the change in those patterns against the change in experimental scaffold images seeded with C10 epithelial cells and mesenchymal stem cells, two cell types whose behaviors are not well understood, in order to determine which rulesets more closely match each cell type. Our model indicates that C10s are more likely to survive on regions of higher substrate while MSCs are more likely to proliferate on regions of higher substrate.

Metabolic engineering of microorganisms for chemical production involves the coordination of regulatory, kinetic, and thermodynamic parameters within the context of the entire network, as well as the careful allocation of energetic and structural resources such as ATP, redox potential, and amino acids. The exponential progression of “omics” technologies over the past few decades has transformed our ability to understand these network interactions by generating enormous amounts of data about cell behavior. The great challenge of the new biological era is in processing, integrating, and rationally interpreting all of this information, leading to testable hypotheses. In silico metabolic reconstructions are versatile computational tools for integrating multiple levels of bioinformatics data, facilitating interpretation of that data, and making functional predictions related to the metabolic behavior of the cell. To explore the use of this modeling paradigm as a tool for enabling metabolic engineering in a poorly understood microorganism, an in silico constraint-based metabolic reconstruction for the anaerobic, cellulolytic bacterium Clostridium thermocellum was constructed based on available genome annotations, published phenotypic information, and specific biochemical assays. This dissertation describes the analysis and experimental validation of this model, the integration of transcriptomic data from an RNAseq experiment, and the use of the resulting model for generating novel strain designs for significantly improved production of ethanol from cellulosic biomass. The genome-scale metabolic reconstruction is shown to be a powerful framework for understanding and predicting various metabolic phenotypes, and contributions described here enhance the utility of these models for interpretation of experimental datasets for successful metabolic engineering.

Student Number : 9801145J - MSc dissertation - School of Electrical and Information Engineering - Faculty of Engineering / In this document, a paper is presented that reports on the optimisation of a system that assists in time series prediction. Daily closing prices of a stock are used as the time series under which the system is being optimised. Concepts of machine learning, Artificial Neural Networks, Genetic Algorithms, and Agent-Based Modeling are used as tools for this task. Neural networks serve as the prediction engine and genetic algorithms are used for optimisation tasks as well as the simulation of a multi-agent based trading environment. The simulated trading environment is used to ascertain and optimise the best data, in terms of quality, to use as inputs to the neural network. The results achieved were positive and a large portion of this work concentrates on the refinement of the predictive capability. From this study it is concluded that AI methods bring a sound scientific approach to time series prediction, regardless of the phenomena that is being predicted.