This thesis is about the development and testing of a novel two-dimensional numerical model (the GIAMT2D model) able to address the hydro-morphodynamic evolution of gravel-bed rivers. The model solves the two-dimensional hyperbolic system of partial differential equations (PDEs) arising from the shallow water-Exner model, describing free surface shallow flows over erodible bed, with suitable closure relations for bedload transport. A coupled formulation of the mathematical problem, which is needed in order to correctly handle sediment transport in Froude trans-critical flow conditions, is implemented, resulting in a non-conservative hyperbolic problem, which requires the adoption of a path-conservative scheme. A drawback of the fully-coupled shallow water-Exner model is that in general the solution of the Riemann problem is not easily available, at least if complex empirical sediment transport formulae are applied, which makes the upwind approach inadequate for designing numerical approximations to the solutions. Adoption of the more general, Riemann solver-free centred approach is thus required, the drawback being that centred schemes are significantly less accurate than upwind schemes in some specific cases, namely for intermediate waves and computations at low CFL number. In GIAMT2D an original centred upwind-biased scheme (UPRICE2-C delta) is applied, recovering accuracy typical of upwind methods, still being able to include any bedload transport formula. The proposed scheme results from original studies in applied mathematics, presented in the first part of the thesis, concerning the development of upwind-biased variations of the centred FORCE scheme for the solution of hyperbolic systems of PDEs, in conservative and non-conservative form. The performance of these schemes is thoroughly assessed in a suite of tests for the shallow water equations. The GIAMT2D model embeds the UPRICE2-Cd scheme extended to second-order accuracy in the ADER framework, inserted in a robust second-order preserving splitting technique for the treatment of frictional source terms, and includes an original wetting-and-drying procedure. The model performance is checked in well-established classical test cases with fixed and movable bed. These applications highlight the capability of the model in correctly and accurately solving the equations in various cases, e.g. in computations at low local CFL number, in the solution of wet-dry fronts with fixed and movable bed and in the prediction of sediment transport in Froude trans-critical conditions. The concept of "morphodynamic benchmark" is introduced for the purpose of assessing the model performance in reproducing basic river morphodynamic processes for which established theoretical and experimental knowledge is available. Unit processes with utmost importance for gravel-bed river morphodynamics, like free and forced bar instability and the stability of channel bifurcations, are chosen for this aim. In this novel approach for assessing the model capabilities, the numerical solutions satisfactorily compare with approximate analytical morphodynamic solution and laboratory data. Having proved that the model is able to reproduce the salient features of these classical morphodynamic solutions, an original morphodynamic study is finally carried out, concerning the non-linear interaction of free and forced bars in straight channels, for which a mature analytical theory is not available at present. The numerical runs of GIAMT2D are used to validate the research hypotheses developed on the basis of existing analytical theories and satisfactorily compare with field observations.

The underlying idea of the Semantic Web is that web content should be expressed not only in natural language but also in a language that can be unambiguously understood, interpreted and used by software agents, thus permitting them to find, share and integrate information more easily. The central notion of the Semantic Web's syntax are ontologies, shared vocabularies providing taxonomies of concepts, objects and relationships between them, which describe particular domains of knowledge. A vocabulary stores words, synonyms, word sense definitions (i.e. glosses), relations between word senses and concepts; such a vocabulary is generally referred to as the Controlled Vocabulary (CV) if choice or selection of terms are done by domain specialists. A facet is a distinct and dimensional feature of a concept or a term that allows a taxonomy, ontology or CV to be viewed or ordered in multiple ways, rather than in a single way. The facet is clearly defined, mutually exclusive, and composed of collectively exhaustive properties or characteristics of a domain. For example, a collection of rice might be represented using a name facet, place facet etc. This thesis presents a methodology for producing mappings between Controlled Vocabularies, based on a technique called \Hidden Semantic Matching". The \Hidden" word stands for it not relying on any sort of externally provided background knowledge. The sole exploited knowledge comes from the \semantic context" of the same CVs which are being matched. We build a facet for each concept of these CVs, considering more general concepts (broader terms), less general concepts (narrow terms) or related concepts (related terms).Together these form a concept facet (CF) which is then used to boost the matching process.

An unmistakable trend in embedded systems is the growth of soft real-time computing. A soft real-time application is one for which deadlines can occasionally be missed, but the probability of this event has to be controllable and predictable. This work is aimed to close the gap in the research of stochastic real-time analysis related to resource reservation scheduling algorithms. This dissertation attempts to: 1. give a quick overview of classic real-time analysis 2. analyze the problems related to use the well-known techniques in the context of soft real-time applications: • overvalue the assignation of parameters as in hard real- time systems based on worst case execution times • time and memory complexity using the known theoretical stochastic analysis 3. propose solutions able to overcome the limitation showed in point 2 4. show some specific examples (theoretical and practical) in which resource reservation lead to advantages. The novel contributions of this thesis are: • a new bound to predict the probability of a deadline misses in a resource reservation systems • a very efficient numeric solution for matrix generated with well-know abstraction models of reservation based on Quasi Birth Death Markov Process • an analytical solution, with some conservative approximations, for the same models. • a new model for specific applications, like interrupts. • experiments using resource reservation in different contexts The thesis is evolved following two different approaches: 1. the first based on the exact model of reservation, and the contributions is: • define a new pessimistic bound, efficient in term of computation, able to overcome the problem of complete knowledge of the computation time. The solution is an approximation of the real solution of the model. 2. the second based on an approximation model in which the novel contributions are: • presents an exact and numeric efficient solution for the model based on Quasi Birth and Death Markov Process • introduces an approximate analytical solution which can be computed with no complexity and which is reversible These techniques are applicable since the minimum interarrival of a request is greater than a server period. Unfortunately exists situations in which this assumption is not feasible. An important example is using resource reservation to scheduling interrupts. In order to consider also this situation, another important novel result of this thesis is: • to introduce a new model for scheduling interrupts In addition, some practical examples of using resource reservation are presented.

We present some upper bounds on the size of nonlinear codes and their restriction to systematic codes and linear codes. These bounds, which are an improvement of a bound by Zinoviev, Litsyn and Laihonen, are independent of other classical known theoretical bounds. Among these, we mention the Griesmer bound for linear codes, of which we provide a partial generalization for the systematic case. Our experiments show that in some cases (the majority of cases for some q) our bounds provide the best value, compared to all other closed-formula upper-bounds. We also present an algebraic method for computing the minimum weight of any nonlinear code. We show that for some particular code, using a non-standard representation of the code, our method is faster than brute force. An application of this method allows to compute the nonlinearity of a Boolean function, improving existing algebraic methods and reaching the same complexity of algorithms based on the fast Fourier transform.

Internet is evolving in both its structure and usage patterns; this work addresses two trends: i) the increasing popularity and the related generated traffic of media streaming applications and ii) the emerging of network portions following different philosophies from the rest of the internet and being characterized by a mesh topology, such as Community Networks. This thesis presents a modeling for decentralized live streaming for mesh networks based on graph theory, considering the different inter-dependent network abstractions involved. It proposes optimization strategies based on popular centrality metrics, such as betweenness and PageRank. Results on real-world datasets validate the theoretical work and the derived optimizing strategies are implemented in open-source streaming platforms.

Many real applications consume data that is intrinsically uncertain and error-prone. An uncertain data series is a series whose point values are uncertain. An uncertain data stream is a data stream whose tuples are existentially uncertain and/or have an uncertain value. Typical sources of uncertainty in data series and data streams include sensor data, data synopses, privacy-preserving transformations and forecasting models. In this thesis, we focus on the following three problems: (1) the formulation and the evaluation of similarity search queries in uncertain data series; (2) the evaluation of nearest neighbor search queries in uncertain data series; (3) the adaptation of sliding windows in uncertain data stream processing to accommodate existential and value uncertainty. We demonstrate experimentally that the correlation among neighboring time-stamps in data series can be leveraged to increase the accuracy of the results. We further show that the "possible world" semantics can be used as underlying uncertainty model to formulate nearest neighbor queries that can be evaluated efficiently. Finally, we discuss the relation between existential and value uncertainty in data stream applications, and verify experimentally our proposal of uncertain sliding windows.

Several states introduced alternative certification (AC) programs in the mid-1980s to alleviate the teacher shortage situation, and the Master of Arts in Teaching (MAT) program is an example of just such a curriculum. Three research questions for this study were: 1) what were the challenges beginning MAT teachers faced? 2) what were beginning MAT teachers? perceptions of their training program?, and 3) what were the beginning MAT teachers? attitudes toward their teaching career? Case methodology was conducted to highlight the research questions. Six beginning MAT teachers that graduated from Mississippi State University participated in this study. Data were drawn from interviews, observations, and a concomitant related document analysis. Results revealed three themes as challenges for the majority of MAT teachers: (a) students? low motivation, (b) No Child Left Behind (NCLB)ountability, and (c) lack of parental support. In terms of perception regarding their training program, all six MAT teachers exhibited gratefulness for their training programs availability, especially for the opportunity to gain teacher licensure. In addition, they reported they were confident about their teaching preparation. However, some teachers agreed that there was room for improvement. All participants suggested the following as potential enhancements for their MAT program: (a) exhibit more specificity concerning program requirements, (b) contain more content knowledge courses, (c) assign a mentor, (d) make the certification process easier and less complicated, and (e) add any training applicable to their future teaching career. Again, these findings could be perceived as guidelines for the improvement of the MAT program and as methods for ensuring effective AC teachers graduate from the training program. Among the six participants, five wanted to quit teaching in the near future, with only one, an older second career seeker teacher, deciding to remain in the profession. Most importantly, she was the only one whose previous educational background and job experience were the same as the subject she taught. The above findings filled several research gaps when compared with previous studies. For example, there is an inconsistency between MAT teachers? licensure area and content knowledge background. Further studies are needed to investigate samples of graduates from other AC programs.

No description available.

Primary productivity is a fundamental ecological process and an important measure of ecosystem response to environmental change. Currently, there is a considerable lapse in our understanding of primary productivity in hot and cold deserts, due to the difficulty of measuring production in cryptogam vegetation. However, remote sensing can provide long-term, spatially-extensive estimates of primary production and are particularly well suited to remote environments, such as in the McMurdo Dry Valleys (MDV) of Antarctica, where cyanobacterial communities are the main drivers of primary production. These microbial communities form multi-layered sheets (i.e., microbial mats) on top of desert pavement. The cryptic nature of these communities, their often patchy spatial distribution, and their ability to survive desiccation make assessments of productivity challenging. I used field-based surveys of microbial mat biomass and pigment chemistry in conjunction with analyses of multispectral satellite data to examine the distribution and activity of microbial mats. This is the first satellite-derived estimate of microbial mat biomass for Antarctic microbial mat communities. I show strong correlations between multispectral satellite data (i.e., NDVI) and ground based measurements of microbial mats, including ground cover, biomass, and pigment chemistry. Elemental (C, N) and isotopic composition (15N, 13C) of microbial mats show that they have significant effects on biogeochemical cycling in the soil and sediment of this region where they occur. Using these relationships, I developed a statistical model that estimates biomass (kg of C) in selected wetlands in the Lake Fryxell Basin, Antarctica. Overall, this research demonstrates the importance of terrestrial microbial mats on C and N cycling in the McMurdo Dry Valleys, Antarctica. / Master of Science / Primary productivity is an essential ecological process and a useful measure of how ecosystems respond to climate change. Primary production is more difficult to measure in polar desert ecosystems where there is little to no vascular vegetation. Polar regions are also ecosystems where we expect to see significant responses to a changing climate. Remote sensing and image analysis can provide estimates of primary production and are particularly useful in remote environments. For example, in the McMurdo Dry Valleys (MDV) of Antarctica, cyanobacterial communities are the main primary producers. These microbial communities form multi-layered sheets (i.e., microbial mats) on top of rocks and soil. These communities are cryptic, do not cover large areas of ground continuously, and are able to survive desiccation and freezing. All of these characteristics make assessments of productivity especially challenging. For my master’s research, I collected microbial mat samples in conjunction with the acquisition of a satellite image of my study area in the MDV, and I determined biological parameters (e.g., percent ground cover, organic matter, and chlorophyll-a content) through laboratory analyses using these samples. I used this satellite image to extract spectral data and perform a vegetation analysis using the normalized difference vegetation index (i.e., NDVI), which determines areas in the image that contain vegetation (i.e., microbial mats). By linking the spectral data to the biological parameters, I developed a statistical model that estimates biomass (i.e., carbon content) of my study areas. These are the first microbial mat biomass estimates using satellite imagery for this region of Antarctica. Additionally, I researched the importance of microbial mats on nitrogen cycling in Taylor Valley. Using elemental and isotopic analyses, I determined microbial mats have significant effects on the underlying soil and nutrient cycling. Overall, this research demonstrates the importance of terrestrial microbial mats on C and N fixation in Antarctic soil environments.

We consider edge-colorings and flows problems in Graph Theory that are hard to solve for Class 2 graphs. Most of them are strongly related to some outstanding open conjectures, such as the Cycle Double Cover Conjecture, the Berge-Fulkerson Conjecture, the Petersen Coloring Conjecture and the Tutte's 5-flow Conjecture. We obtain some new restrictions on the structure of a possible minimum counterexample to the former two conjectures. We prove that the Petersen graph is, in a specific sense, the only graph that could appear in the Petersen Coloring Conjecture, and we provide evidence that led to propose an analogous of the Tutte's 5-flow conjecture in higher dimensions. We prove a characterization result and a sufficient condition for general graphs in relation to another edge-coloring problem, which is the determination of the palette index of a graph.