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The purpose of this study was to develop and test an optimization model that will provide a deployment plan of emission reduction technologies to reduce emissions from non-road equipment. The focus of the study was on the counties of Texas that have nonattainment (NA) and near-nonattainment (NNA) status. The objective of this research was to develop methodologies that will help to deploy emission reduction technologies for non-road equipment of TxDOT to reduce emissions in a cost effective and optimal manner. Three technologies were considered for deployment in this research, (1) hydrogen enrichment (HE), (2) selective catalytic reduction (SCR) and (3) fuel additive (FA). Combinations of technologies were also considered in the study, i.e. HE with FA, and SCR with FA. Two approaches were investigated in this research. The first approach was "Method 1" in which all the technologies, i.e. FA, HE and SCR were deployed in the NA counties at the first stage. In the second stage the same technologies were deployed in the NNA counties with the remaining budget, if any. The second approach was called "Method 2" in which all the technologies, i.e. FA, HE and SCR were deployed in the NA counties along with deploying only FA in the NNA counties at the first stage. Then with the remaining budget, SCR and HE were deployed in the NNA counties in the second stage. In each of these methods, 2 options were considered, i.e. maximizing NOx reduction with and without fuel economy consideration in the objective function. Thus, the four options investigated each having different mixes of emission reduction technologies include Case 1A: Method 1 with fuel economy consideration; Case 1B: Method 1 without fuel economy consideration; Case 2A: Method 2 with fuel economy consideration; and Case 2B: Method 2 without fuel economy consideration and were programmed with Visual C++ and ILOG CPLEX. These four options were tested for budget amounts ranging from $500 to $1,183,000 and the results obtained show that for a given budget one option representing a mix of technologies often performed better than others. This is conceivable because for a given budget the optimization model selects an affordable option considering the cost of technologies involved while at the same time maximum emission reduction, with and without fuel economy consideration, is achieved. Thus the alternative options described in this study will assist the decision makers to decide about the deployment preference of technologies. For a given budget, the decision maker can obtain the results for total NOx reduction, combined diesel economy and total combined benefit using the four models mentioned above. Based on their requirements and priorities, they can select the desired model and subsequently obtain the required deployment plan for deploying the emission reduction technologies in the NA and NNA counties.

Thesis (Ph. D.)--University of Alabama at Birmingham, 2009. / Additional advisors: Barrett Bryant, Aniruddha Gokhale, Marjan Mernik, Chengcui Zhang. Description based on contents viewed June 3, 2009; title from PDF t.p. Includes bibliographical references (p. 161-177).

This research examines certain modifications of the mathematical programming models to improve their classificatory performance. These modifications involve the inclusion of second-order terms and secondary goals in mathematical programming models. A Monte Carlo simulation study is conducted to investigate the performance of two standard parametric models and various mathematical programming models, including the MSD (minimize sum of deviations) model, the MIP (mixed integer programming) model and the hybrid linear programming model.

The diploma thesis deals with the teaching of programming at elementary schools in the Czech Republic. The theoretical part of the thesis focuses on mapping the current state of the teaching of programming at elementary schools. In addition, the currently used resources to support the teaching of algorithmization and programming for children are mapped. These resources are evaluated by selected evaluation criteria. On the theoretical basis of thesis, a teaching model focused on programming is designed as a practical output, including learning materials and a methodological manual for teachers. The design of the model of teaching is verified in practice.

O problema de alocação de aeronaves a voos, ou tail assignment problem (TAP), consiste em determinar qual aeronave realizará cada voo da malha de uma empresa aérea, visando a minimizar o custo total da operação e respeitando diversas restrições de conectividade de voos, permanência de aeronaves no solo, serviços obrigatórios de manutenção, limitações técnicas e desempenho de aeronaves, conexões de passageiros e tripulantes e famílias com diversos modelos de aeronaves. Este trabalho apresenta um modelo matemático exato e um método heurístico para a solução do TAP considerando todas as restrições citadas, o que não ocorre com os modelos encontrados na literatura. Os modelos desenvolvidos, baseados em programação linear inteira e na meta-heurística Busca Tabu, foram aplicados a problemas reais, extraídos da malha de uma empresa aérea brasileira, operadora de 35 aeronaves e cerca de 210 voos diários. Os resultados obtidos são compatíveis com a operação da empresa e apresentam ganhos em relação ao método de alocação de aeronaves utilizado na operação diária. Os tempos de processamento para solução pelo método exato são excessivamente longos, indicando que o método heurístico é mais adequado para a utilização em empresas aéreas, com resultados adequados obtidos em tempos de processamento satisfatórios. / The problem known as Aircraft Assignment or Tail Assignment Problem (TAP) is the problem of assigning flights to each aircraft of an airline\'s fleet, aiming at minimizing the total operating cost while complying with several constraints, such as network connectivity, aircraft time on ground, mandatory maintenance services, aircraft technical restrictions, passengers and crew connections, aircraft performance and aircraft families with more than one type. This work presents a deterministic mathematical model and a heuristic method to solve the TAP considering all constraints listed above, what does not happen with the models found in the literature. The proposed methods, based on mathematical integer programming and on the Tabu Search metaheuristic, were applied to problems obtained from the network of a Brazilian airline, operating 35 aircraft and around 210 daily flights. The results show the models are suitable to solve the problem and savings are observed when compared to the current assignment method. The long processing times intrinsic to the deterministic method show the heuristic method is more suitable for use in airlines, with suitable results obtained at acceptable computational times.

We present Roko, a system that allows parallelization of sequential C codes with a modest user intervention. The user exposes parallelism at the function level by annotating the code with pragmas. Roko defines only two pragmas: the parallel pragma is used to denote function calls that will be executed asynchronously, and the exposed pragma is used to describe data usage of the marked function calls. Architecturally, Roko consists of three components: a compiler that analyzes pragmas, a software environment that spreads the execution over multiple processors, and a hardware support that implements a novel synchronization scheme, versioning. We have designed, implemented and evaluated an FPGA-based prototype of Roko. Our experimental evaluation shows: (i) that few simple pragmas are all that is needed to expose parallelism in benchmark applications and (ii) that Roko can deliver good performance in terms of application speedup.

We present Roko, a system that allows parallelization of sequential C codes with a modest user intervention. The user exposes parallelism at the function level by annotating the code with pragmas. Roko defines only two pragmas: the parallel pragma is used to denote function calls that will be executed asynchronously, and the exposed pragma is used to describe data usage of the marked function calls. Architecturally, Roko consists of three components: a compiler that analyzes pragmas, a software environment that spreads the execution over multiple processors, and a hardware support that implements a novel synchronization scheme, versioning. We have designed, implemented and evaluated an FPGA-based prototype of Roko. Our experimental evaluation shows: (i) that few simple pragmas are all that is needed to expose parallelism in benchmark applications and (ii) that Roko can deliver good performance in terms of application speedup.

Technology improvements and power constrains have taken multicore architectures to dominate microprocessor designs over uniprocessors. At the same time, accelerator based architectures have shown that heterogeneous multicores are very efficient and can provide high throughput for parallel applications, but with a high-programming effort. We propose Castell a scalable chip multiprocessor architecture that can be programmed as uniprocessors, and provides the high throughput of accelerator-based architectures. Castell relies on task-based programming models that simplify software development. These models use a runtime system that dynamically finds, schedules, and adds hardware-specific features to parallel tasks. One of these features is DMA transfers to overlap computation and data movement, which is known as double buffering. This feature allows applications on Castell to tolerate large memory latencies and lets us design the memory system focusing on memory bandwidth. In addition to provide programmability and the design of the memory system, we have used a hierarchical NoC and added a synchronization module. The NoC design distributes memory traffic efficiently to allow the architecture to scale. The synchronization module is a consequence of the large performance degradation of application for large synchronization latencies. Castell is mainly an architecture framework that enables the definition of domain-specific implementations, fine-tuned to a particular problem or application. So far, Castell has been successfully used to propose heterogeneous multicore architectures for scientific kernels, video decoding (using H.264), and protein sequence alignment (using Smith-Waterman and clustalW). It has also been used to explore a number of architecture optimizations such as enhanced DMA controllers, and architecture support for task-based programming models. iii

With the plateauing of processor frequencies and increase in energy consumption in computing, application developers are seeking new sources of performance acceleration. Heterogeneous platforms with multiple processor architectures offer one possible avenue to address these challenges. However, modern heterogeneous programming models tend to be either so low-level as to severely hinder programmer productivity, or so high-level as to limit optimization opportunities. The novel systems presented in this thesis strike a better balance between abstraction and transparency, enabling programmers to be productive and produce high-performance applications on heterogeneous platforms. This thesis starts by summarizing the strengths, weaknesses, and features of existing heterogeneous programming models. It then introduces and evaluates four novel heterogeneous programming models and runtime systems: JCUDA, CnC-CUDA, DyGR, and HadoopCL. We'll conclude by positioning the key contributions of each piece in this thesis relative to the state-of-the-art, and outline possible directions for future work.