Tato práce popisuje a aplikuje parametrické a neparametrické modely spolehlivosti na cenzorovaná data. Ukazuje implementaci spolehlivosti v metodologii Six Sigma. Metody jsou využity pro přežití/spolehlivost reálných technických dat.

Formal language theory, originally developed to model and study our natural spoken languages, is nowadays also put to use in many other fields. These include, but are not limited to, the definition and visualization of programming languages and the examination and verification of algorithms and systems. Formal languages are instrumental in proving the correct behavior of automated systems, e.g., to avoid that a flight guidance system navigates two airplanes too close to each other. This vast field of applications is built upon a very well investigated and coherent theoretical basis. It is the goal of this dissertation to add to this theoretical foundation and to explore ways to make formal languages and their models more expressive. More specifically, we are interested in models that are able to model quantitative features of the behavior of systems. To this end, we define and characterize weighted automata over structures with hierarchical information and over graphs. In particular, we study infinite nested words, operator precedence languages, and finite and infinite graphs. We show Büchi-like results connecting weighted automata and weighted monadic second order (MSO) logic for the respective classes of weighted languages over these structures. As special cases, we obtain Büchi-type equivalence results known from the recent literature for weighted automata and weighted logics on words, trees, pictures, and nested words. Establishing such a general result for graphs has been an open problem for weighted logics for some time. We conjecture that our techniques can be applied to derive similar equivalence results in other contexts like traces, texts, and distributed systems.

No description available.

A key component to conservation is an accurate understanding of genetic subdivision within a species. Despite their ecological and economic importance, relatively little is understood about the genetic structuring of Yellowstone cutthroat trout in Yellowstone National Park. Here, we use traditional (Fst, Rst, Nm, and AMOVA) and modern (Bayesian assignment tests, coalescent theory, and nested clade analysis) analytical approaches to describe the population genetic subdivision of cutthroat trout spawning populations in Yellowstone Lake and to identify genetically distinct population segments throughout Yellowstone National Park. Evidence for restricted gene flow between spawning populations within Yellowstone Lake was detected using nested clade analysis. This is the first molecular evidence for restricted gene flow between spawning populations in Yellowstone Lake. In contrast, traditional methods such as Fst and Rst as well as the Bayesian clustering program STRUCTURE v2.0 failed to detect evidence for restricted gene flow. Across our sampling range within Yellowstone National Park, eleven genetically distinct cutthroat trout population segments were detected. These showed a general pattern of small, isolated populations with low genetic diversity in headwater streams and wide-spread, genetically diverse populations in higher-order rivers. We recommend populations be managed to maintain current levels of genetic diversity and gene flow. Based on the recent decline of and distinct morphological, behavioral, and genetic nature of cutthroat trout in Yellowstone Lake, we recommend the Yellowstone Lake spawning populations collectively be recognized as an evolutionarily significant unit.

Marine turtle conservation is most successful when it is based on sound data incorporating life history, historical population stability, and gene flow among populations. This research attempts to provide that information through two studies. In chapter I, I identify historical patterns of gene flow, population sizes, and contraction/expansion during major climatic shifts. In chapter II, I reveal a life history characteristic of loggerhead turtles previously undocumented. I identify a pattern of juvenile recruitment to foraging grounds proximal to their natal nesting beach. This pattern results in a predictable recruitment pattern from juvenile foraging ground aggregations to local rookeries. This research will provide crucial information to conservation managers by demonstrating how sensitive marine turtles are to global climate change. In the second component of my research, I demonstrate how threats posed to juvenile foraging grounds will have measurable effects on rookeries proximal to those foraging grounds. The addition of this basic life history information will have dramatic effects on marine turtle conservation in the future, and will serve as the basis for more thorough, forward-looking recovery plans.

This dissertation develops mathematical models to efficiently manage the inland waterway port operations while minimizing the overall supply chain cost. In the first part, a capacitated, multi-commodity, multi-period mixed-integer linear programming model is proposed capturing diversified inland waterway transportation network related properties. We developed an accelerated Benders decomposition algorithm to solve this challenging NP-hard problem. The next study develops a two-stage stochastic mixed-integer nonlinear programming model to manage congestion in an inland waterway transportation network under stochastic commodity supply and water-level fluctuation scenarios. The model also jointly optimizes trip-wise towboat and barge assignment decisions and different supply chain decisions (e.g., inventory management, transportation decisions) in such a way that the overall system cost can be minimized. We develop a parallelized hybrid decomposition algorithm, combining Constraint Generation algorithm, Sample Average Approximation (SAA), and an enhanced variant of the L-shaped algorithm, to effectively solve our proposed optimization model in a timely fashion. While the first two parts develop models from the supply chain network design viewpoint, the next two parts propose mathematical models to emphasize the port and waterway transportation related operations. Two two-stage, stochastic, mixed-integer linear programming (MILP) models are proposed under stochastic commodity supply and water level fluctuations scenarios. The last one puts the specific focus in modeling perishable inventories. To solve the third model we propose to develop a highly customized parallelized hybrid decomposition algorithm that combines SAA with an enhanced Progressive Hedging and Nested Decomposition algorithm. Similarly, to solve the last mathematical model we propose a hybrid decomposition algorithm combining the enhanced Benders decomposition algorithm and SAA to solve the large size of test instances of this complex, NP-hard problem. Both proposed approaches are highly efficient in solving the real-life test instances of the model to desired quality within a reasonable time frame. All the four developed models are validated a real-life case study focusing on the inland waterway transportation network along the Mississippi River. A number of managerial insights are drawn for different key input parameters that impact port operations. These insights will essentially help decisions makers to effectively and efficiently manage an inland waterway-based transportation network.

No description available.

No description available.

<p>This thesis concerns itself with drawing out some interesting connections between the fields of group theory and formal language theory. Given a group with a finite set of generators, it is natural to consider the set of generators and their inverses as an alphabet. We can then consider formal languages such that every group element has at least one representative in the language. We examine what the structure of the language can tell us about group theoretic properties, focusing on the word problem, automatic structures on groups, and generalizations of automatic structures. Finally we prove new results concerning applications of languages of nested words for studying the word problem.</p> / Master of Science (MSc)

Rather than being randomly wired together, the components of complex network systems are recently reported to represent a scale-free architecture, in which the node degree distribution follows power-law. While social networks are scale-free, it is natural to utilize their structural properties in some social network applications. As a result, this dissertation explores social network architectures, and in turn, leverages these architectures to facilitate some influence and information propagation applications. Social network architectures are analyzed in two different aspects. The first aspect focuses on the node degree snowballing effects (i.e., degree growth effects) in social networks, which is based on an age-sensitive preferential attachment model. The impact of the initial links is explored, in terms of accelerating the node degree snowballing effects. The second aspect focuses on Nested Scale-Free Architectures (NSFAs) for social networks. The scale-free architecture is a classic concept, which means that the node degree distribution follows the power-law distribution. `Nested' indicates that the scale-free architecture is preserved when low-degree nodes and their associated connections are iteratively removed. NSFA has a bounded hierarchy. Based on the social network structure, this dissertation explores two influence propagation applications for the Social Influence Maximization Problem (SIMP). The first application is a friend recommendation strategy with the perspective of social influence maximization. For the system provider, the objective is to recommend a fixed number of new friends to a given user, such that the given user can maximize his/her social influence through making new friends. This problem is proved to be NP-hard by reduction from the SIMP. A greedy friend recommendation algorithm with an approximation ratio of $1-e^{-1}$ is proposed. The second application studies the SIMP with the crowd influence, which is NP-hard, monotone, non-submodular, and inapproximable in general graphs. However, since user connections in Online Social Networks (OSNs) are not random, approximations can be obtained by leveraging the structural properties of OSNs. The modularity, denoted by $\Delta$, is proposed to measure to what degree this problem violates the submodularity. Two approximation algorithms are proposed with ratios of $\frac{1}{\Delta+2}$ and $1-e^{-1/(\Delta+1)}$, respectively. Beside the influence propagation applications, this dissertation further explores three different information propagation applications. The first application is a social network quarantine strategy, which can eliminate epidemic outbreaks with minimal isolation costs. This problem is NP-hard. An approximation algorithm with a ratio of 2 is proposed through utilizing the problem properties of feasibility and minimality. The second application is a rating prediction scheme, called DynFluid, based on the fluid dynamics. DynFluid analogizes the rating reference among the users in OSNs to the fluid flow among containers. The third application is an information cascade prediction framework: given the social current cascade and social topology, the number of propagated users at a future time slot is predicted. To reduce prediction time complexities, the spatiotemporal cascade information (a larger size of data) is decomposed to user characteristics (a smaller size of data) for subsequent predictions. All these three applications are based on the social network structure. / Computer and Information Science