A simulação de propagação de onda é uma ferramenta crucial na pesquisa de geofísica (para análise eficiente dos terremotos, mitigação de riscos e a exploração de petróleo e gáz). Devido à sua simplicidade e sua eficiência numérica, o método de diferenças finitas é uma das técnicas implementadas para resolver as equações da propagação das ondas. Estas aplicações são conhecidas como estênceis porque consistem num padrão que replica a mesma computação num domínio multidimensional de dados. A Computação de Alto Desempenho é requerida para solucionar este tipo de problemas, como consequência do grande número de pontos envolvidos nas simulações tridimensionais do subsolo. A optimização do desempenho dos estênceis é um desafio e depende do arquitetura usada. Neste contexto, focamos nosso trabalho em duas partes. Primeiro, desenvolvemos nossa pesquisa nas arquiteturas multicore; analisamos a implementação padrão em OpenMP dos modelos numéricos da transferência de calor (um estêncil Jacobi de 7 pontos), e o aplicativo Ondes3D (um simulador sísmico desenvolvido pela Bureau de Recherches Géologiques et Minières); usamos dois algoritmos conhecidos (nativo, e bloqueio espacial) para encontrar correlações entre os parâmetros da configuração de entrada, na execução, e o desempenho computacional; depois, propusemos um modelo baseado no Aprendizado de Máquina para avaliar, predizer e melhorar o desempenho dos modelos estênceis na arquitetura usada; também usamos um modelo de propagação da onda acústica fornecido pela empresa Petrobras; e predizemos o desempenho com uma alta precisão (até 99%) nas arquiteturas multicore. Segundo, orientamos nossa pesquisa nas arquiteturas heterogêneas, analisamos uma implementação padrão do modelo de propagação de ondas em CUDA, para encontrar os fatores que afetam o desempenho quando o número de aceleradores é aumentado; então, propusemos uma implementação baseada em tarefas para amelhorar o desempenho, de acordo com um conjunto de configuração no tempo de execução (algoritmo de escalonamento, tamanho e número de tarefas), e comparamos o desempenho obtido com as versões de só CPU ou só GPU e o impacto no desempenho das arquiteturas heterogêneas; nossos resultados demostram um speedup significativo (até 25) em comparação com a melhor implementação disponível para arquiteturas multicore. / Wave modeling is a crucial tool in geophysics, for efficient strong motion analysis, risk mitigation and oil & gas exploration. Due to its simplicity and numerical efficiency, the finite-difference method is one of the standard techniques implemented to solve the wave propagation equations. This kind of applications is known as stencils because they consist in a pattern that replicates the same computation on a multi-dimensional domain. High Performance Computing is required to solve this class of problems, as a consequence of a large number of grid points involved in three-dimensional simulations of the underground. The performance optimization of stencil computations is a challenge and strongly depends on the underlying architecture. In this context, this work was directed toward a twofold aim. Firstly, we have led our research on multicore architectures and we have analyzed the standard OpenMP implementation of numerical kernels from the 3D heat transfer model (a 7-point Jacobi stencil) and the Ondes3D code (a full-fledged application developed by the French Geological Survey). We have considered two well-known implementations (naïve, and space blocking) to find correlations between parameters from the input configuration at runtime and the computing performance; thus, we have proposed a Machine Learning-based approach to evaluate, to predict, and to improve the performance of these stencil models on the underlying architecture. We have also used an acoustic wave propagation model provided by the Petrobras company and we have predicted the performance with high accuracy on multicore architectures. Secondly, we have oriented our research on heterogeneous architectures, we have analyzed the standard implementation for seismic wave propagation model in CUDA, to find which factors affect the performance; then, we have proposed a task-based implementation to improve the performance, according to the runtime configuration set (scheduling algorithm, size, and number of tasks), and we have compared the performance obtained with the classical CPU or GPU only versions with the results obtained on heterogeneous architectures.

Improving the role the nurse plays in health care delivery should be embodied in the performance improvement initiatives to successfully improve the quality of care that is delivered. The purpose of this evidence-based practice project was to collect performance improvement data and present it to staff who, in turn, used the information to improve practice and influence patient safety outcomes. The practice-focused question addressed what would occur if a tool that allowed frequent data trending was used to measure effectiveness of care and thereby influence key outcome measures. Duffy's quality caring model provided a framework for the study to support the need for the development of a dashboard for staff and to ensure that staff were informed as they developed interventions to improve patient outcomes. Publicly available data published by the Centers for Medicare/ Medicaid (CMS) for the Quality Star Report were explored to inform the project. Workgroups, comprised of volunteers from leadership and staff providing care at the bedside, were formed to implement practice changes based on the dashboard reports. By bringing the data to the attention of nurses within the organization, improvements were made in the overall score for safety of care from below national average (25th percentile of the reported 3,647 hospitals across the nation) to the same as national average (47th percentile) as reported by CMS. Through staff involvement, social change occurred as strategies were hardwired to improve categories of the Quality Star Report and ultimately patient care. The project showed that quality improvement tools can assist in empowering staff to understand the data needed to implement process improvement strategies.

U.S. Coast Guard leaders have received feedback concerning gaps in performance management of the Marine Inspection Program (MIP) from maritime industry stakeholders, Department of Homeland Security representatives, and internal agents over the past decade. The purpose of this case study was to explore strategies to improve performance in the U.S. Coast Guard MIP. Data were gathered through a review of documentation pertinent to marine inspection (i.e., policy, requirements, analyses, reports, and job aids) and 13 semistructured interviews with personnel from 3 distinct organizational levels. Study participants represented civilian and active duty personnel from all geographical U.S. Coast Guard districts, as well as tactical, strategic, and policy levels of the MIP. The conceptual framework of the study was Fusch and Gillespie's human competence model. Data analysis was based on coding of words, phrases, and sentences from multiple sources of data to identify recurring themes through methodological triangulation. The thematic analysis of the study data revealed themes that included lack of mission clarity, limited information management resources, differences in skills and knowledge management among inspectors, and unclear requirements for selecting a marine inspector. The study framework provided a basis for additional performance management research in government entities. The recommendations from this study may lead to social change through improved U.S. Coast Guard marine inspection services, which could result in greater safety, reduced pollution, and fewer security risks in the navigable waterways of the United States.

For a longer period of time, manufacturing was the core business activity and hence service has gained lesser attention. However, a time came when manufacturers faced a huge challenge to stay profitable which apparently gave service to get more attention. The reason to this can be classified in to three categories: economic reasons, customer satisfaction and competitive advantage (Oliva et al., 2003). Understanding this, Volvo Group has set high target for revenues from soft products including service. In order to achieve this it is necessary to expand existing service offerings and explore more new service offerings. As part of this goal, Volvo Technology has been working on several projects. One of the projects which are closely related to this thesis is the Hauler Development Service (HDS) which started in 2008 for the trucking (Hauling) industry. HDS has two versions; HDS Green Field and HDS Efficiency and Effectiveness. HDS Green field focuses on starting up new road transport operations and establish business processes including system support on emerging markets. HDS Efficiency and Effectiveness focuses on performing assessments and improvement programs on already established firms on mature markets. These business offerings started to get their orders from customers and thus proving their functionality. However, unlike the trucking industry, the construction equipment business area within Volvo Group is lacking such business offerings today. Volvo Construction Equipment is the second largest business area within the Volvo group generating about 16 % of the total sales. Volvo CE is mostly offering hard products and wants to expand its service offerings and assume a better position as a professional service solution provider. The development of HDS for the trucking industry and the need for Volvo CE to expand its service offerings laid the background for this thesis. One of the market segments Volvo CE provides equipments is to the quarry and aggregate business segment. Customers with in this business area were successfully contacted and collaborated in this study. This master thesis investigated what the customers in the quarry and aggregate business area needs and problems are and developed a service concept which Volvo CE can offer while at the same time solving customer problems. This service concept was found out to be Energy Efficiency Improvement Service intended to help lower production costs of the customer by eliminating or lowering energy wastes and improve environmental impacts by lowering carbon emissions. Moreover, customer energy performance measures were studied and analysed if they are robust enough to be used as measures to the improvements inevitable by the new service offer, the Energy Efficiency Improvement Service. Results show that the energy performance measures currently in use are not robust enough, and suggest further development of energy performance measure system. In order to realize the service offer in a practical manner, the five lean principles (define value, identify value stream, floe, pull and continuous improvement) were tested if they can be used as tools to identify and measure energy wastes at the customers operation site and proved to be useful.

The primary goal of this research is to provide quantitative information used for the implementation of project management attributes. Nineteen project management attributes and five project performance outcomes were investigated to examine their relationships. A series of statistical analyses were conducted to quantify the relationships between the implementation levels of the attributes and the probabilities for better project performance. The analyses utilized data from 212 projects collected by the Construction Industry Institute (CII) Benchmarking and Metric database from 2007 through 2010. The results showed that the beneficial effects of project management attributes varied depending on the implementation levels of project management attributes, the performance outcome type, and whether the project was conducted by the owner or contractor organization. Quantified analysis results were provided by this research. Project management attributes were identified as critical if they were found to be significantly related to a particular performance outcome. Minimum implementation levels were provided for these critical attributes, as were results covering the combined effects of the critical attributes on performance outcomes. Based on the analysis results, the research generated specific and quantitative information used in implementing project management attributes for better performance. This information will help practitioners achieve improved project performance in the most effective and efficient way, and thus it is the practical contribution of this research. From an academic point of view, this study designed a data analysis process by a series of statistical analysis methods which makes it possible to examine the relationships between project management attributes and various project performance outcomes in a quantitative manner. / text

Desalination has been increasingly adopted over the last decades as an option, and sometimes as a necessity to overcome water shortages in many areas around the world. Today, several thermal and physical separation technologies are well established in large scale production for domestic and industrial purposes.  Membrane distillation is a novel thermally-driven process that can be adapted effectively for water desalination or water treatment in industrial applications, due to its potential lower energy consumption and simplicity. The general objective of this thesis is to contribute to the technical understanding of membrane distillation as a new technology in water treatment for both industrial and drinking water purposes, as a starting point for further improvement. The thesis includes experimental and numerical investigations that highlight some aspects of the technology application and fundamental aspects. In the field of industrial application, an experimental and numerical assessment has been carried out on an Air Gap Membrane Distillation (AGMD) prototype to assess the utilization of the technology in thermal cogeneration plants; in particular, demineralization of water boiler feed water and treating flue gas condensate. The main assessment parameters were water quality and energy consumption. The results from full-scale simulations of a system of 10 m3/hr production capacity,  connected to the district heating network were as follows: 5 to 12 kWh/m3 specific thermal energy consumption, and  0,6 to 1,5 kWh/m3 specific electricity consumption, depending upon the heat source (district heat supply line or low-grade steam). For desalination applications, experimental and simulation work was conducted on an AGMD semi-commercial system as part of the EU MEDESOL project. The aim was to evaluate AGMD performance with saline water of 35 g/l NaCl in order to establish an operation data base for simulation of a three-stage AGMD desalination system. Specific thermal energy consumption was calculated as 950 kWht/m3 for a layout without heat recovery, and 850 kWht/m3 for a layout with one stage heat recovery.  The lack of internal heat recovery in the current MD module means that most of the heat supplied to MD system was not utilized efficiently, so the thermal energy consumption is high. This would mean that a large solar field is needed. In order to analyze the flow conditions in feed flow and cooling channels, CFD was used as tool to analyze a spacer-obstructed flow channel for different types of spacer geometrical characteristics: flow of attack angle, spacer to channel thickness ratio, and void ratio. Velocity profiles, shear stress, and pressure drop were the main assessment criteria. Results show the flow of attack angle has a very minimum effect on the performance of spacers. The effect of spacer to channel thickness ratio was significant in all assessment parameters. Higher void ratios were found advantageous in promoting flow mixing, but resulted in lower sheer stress and hence reduced heat transfer. Physical modifications were implemented on a semi-commercial AGMD prototype to assess experimentally any improvement in its performance. These modifications were mainly focused on reducing the conductive heat transfer losses by modifying the physical support in the air gap that separates the membrane from the condensation surface. In addition, several feed channel spacers were tested and assessed based on their effect in increasing the mass transfer while maintaining or reducing pressure drop. The modifications yielded a two-fold augmentation: slight increase in the distillate mass flow rate (9-11%), and increased thermal efficiency (6%). The pressure drop in the module was reduced by 50% through selecting the appropriate spacer that would achieve the above mass flow rate increase. / QC 20111021

Detta examensarbete har utförts på Vattenfall Eldistribution AB i Trollhättan och syftar till att beräkna förlustförändringarna i lågspänningsnätet då mikroproduktion i form av solceller ansluts. Genom att ansluta mikroproduktion i lågspänningsnätet kan nätets energiförluster reduceras. Denna reducering samt den energi som inte behöver överföras från överliggande nät anses vara den nätnytta solceller ger upphov till. Samtliga elproducenter som är anslutna till elnätet har rätt till ersättning för nätnytta uppkommen av produktionen. Ersättningen skall baseras på inmatad energi samt vid vilken tidpunkt inmatningen sker. För mikroproducenter är det svårt att motivera att i varje enskilt fall beräkna nätnyttan, varför ett generellt schablonvärde beräknats för Vattenfalls lågspänningsnät. Beräkningen har utförts utifrån uppmätta sol- och lastdata på sex olika lågspänningsnät vilka tillsammans anses representera ett generellt nät. Resultaten visar att mikroproduktion med solceller generellt ger upphov till förlustminskningar motsvarande 2,51 % av varje inmatad kWh under låglasttid och 4,82 % under höglasttid. / This bachelor’s thesis aims to investigate how photovoltaic micro production alters energy losses in the Swedish low voltage grid. The thesis has been composed in collaboration with Vattenfall Eldistribution AB. By connecting micro production to the low voltage distribution network, the grid energy losses may be reduced. Additionally, less energy needs to be transferred from the overlying network. These two features are considered the network performance improvements that apply to photovoltaics. All producers connected to the grid are entitled to economic compensation due to network performance improvements caused by the production. The compensation shall be based on the amount of energy fed in as well as on the time when this happens. Regarding micro producers, it is hard to motivate separate calculations for each producer. Therefore, a general value of loss alteration applicable to the low voltage grid has been calculated. The calculations have been based on actual sun radiation and load data and have been carried out for six different low voltage grids. Combined, these six networks are regarded representative for the Swedish low voltage grid. The result of the calculations state that photovoltaic micro production will reduce the grid energy losses by 2.51 % of each fed in kWh during low load time and by 4.82 % during high load time.

A simulação de propagação de onda é uma ferramenta crucial na pesquisa de geofísica (para análise eficiente dos terremotos, mitigação de riscos e a exploração de petróleo e gáz). Devido à sua simplicidade e sua eficiência numérica, o método de diferenças finitas é uma das técnicas implementadas para resolver as equações da propagação das ondas. Estas aplicações são conhecidas como estênceis porque consistem num padrão que replica a mesma computação num domínio multidimensional de dados. A Computação de Alto Desempenho é requerida para solucionar este tipo de problemas, como consequência do grande número de pontos envolvidos nas simulações tridimensionais do subsolo. A optimização do desempenho dos estênceis é um desafio e depende do arquitetura usada. Neste contexto, focamos nosso trabalho em duas partes. Primeiro, desenvolvemos nossa pesquisa nas arquiteturas multicore; analisamos a implementação padrão em OpenMP dos modelos numéricos da transferência de calor (um estêncil Jacobi de 7 pontos), e o aplicativo Ondes3D (um simulador sísmico desenvolvido pela Bureau de Recherches Géologiques et Minières); usamos dois algoritmos conhecidos (nativo, e bloqueio espacial) para encontrar correlações entre os parâmetros da configuração de entrada, na execução, e o desempenho computacional; depois, propusemos um modelo baseado no Aprendizado de Máquina para avaliar, predizer e melhorar o desempenho dos modelos estênceis na arquitetura usada; também usamos um modelo de propagação da onda acústica fornecido pela empresa Petrobras; e predizemos o desempenho com uma alta precisão (até 99%) nas arquiteturas multicore. Segundo, orientamos nossa pesquisa nas arquiteturas heterogêneas, analisamos uma implementação padrão do modelo de propagação de ondas em CUDA, para encontrar os fatores que afetam o desempenho quando o número de aceleradores é aumentado; então, propusemos uma implementação baseada em tarefas para amelhorar o desempenho, de acordo com um conjunto de configuração no tempo de execução (algoritmo de escalonamento, tamanho e número de tarefas), e comparamos o desempenho obtido com as versões de só CPU ou só GPU e o impacto no desempenho das arquiteturas heterogêneas; nossos resultados demostram um speedup significativo (até 25) em comparação com a melhor implementação disponível para arquiteturas multicore. / Wave modeling is a crucial tool in geophysics, for efficient strong motion analysis, risk mitigation and oil & gas exploration. Due to its simplicity and numerical efficiency, the finite-difference method is one of the standard techniques implemented to solve the wave propagation equations. This kind of applications is known as stencils because they consist in a pattern that replicates the same computation on a multi-dimensional domain. High Performance Computing is required to solve this class of problems, as a consequence of a large number of grid points involved in three-dimensional simulations of the underground. The performance optimization of stencil computations is a challenge and strongly depends on the underlying architecture. In this context, this work was directed toward a twofold aim. Firstly, we have led our research on multicore architectures and we have analyzed the standard OpenMP implementation of numerical kernels from the 3D heat transfer model (a 7-point Jacobi stencil) and the Ondes3D code (a full-fledged application developed by the French Geological Survey). We have considered two well-known implementations (naïve, and space blocking) to find correlations between parameters from the input configuration at runtime and the computing performance; thus, we have proposed a Machine Learning-based approach to evaluate, to predict, and to improve the performance of these stencil models on the underlying architecture. We have also used an acoustic wave propagation model provided by the Petrobras company and we have predicted the performance with high accuracy on multicore architectures. Secondly, we have oriented our research on heterogeneous architectures, we have analyzed the standard implementation for seismic wave propagation model in CUDA, to find which factors affect the performance; then, we have proposed a task-based implementation to improve the performance, according to the runtime configuration set (scheduling algorithm, size, and number of tasks), and we have compared the performance obtained with the classical CPU or GPU only versions with the results obtained on heterogeneous architectures.

With increasing trade, freight transport demand has grown tremendously and as sustainability has become an essential concern of our globe, interests in improving and achieving effective and efficient rail freight transport have become an essential needs and focus of the 21st century. However, achieving an improved rail freight transport service and an increased market share in a competitive environment is rather complex in several aspects as freight trains would need to operate with principles and characteristics resembling those of passenger’s traffic in order to attract new type of goods. In order to adopt the principles and characteristics used in passenger trains, airlines and hotel industries into intermodal line train systems, a simulation model has been developed and implemented. The principles for pricing which we have considered are base on the available train capacity along a travel sub-leg and our objective was to increase the performance of the intermodal line cargo train system. We adopted the yield management concept with rail freight customers given the possibility to change their start and/or end train stations (travel sub-leg) and/or to change their departure day in an intermodal line cargo train system. Using our developed simulation tool, we have examined the performance of an intermodal line cargo train system with respect to the dynamic and constant pricing strategy. Our prime objective was to investigate and answer the questions which pricing strategy leads to the best space utilization and performance of an intermodal line train cargo system? Our simulation results show that the dynamic pricing gives the best space utilization and rail freight performance. Dynamic pricing strategy appears good to both the train operators, in term of the revenue generated, and the freight transporters as they achieved reduced transport cost and freights accommodation at train stations different from their closest train stations. / JANNEBERGVÄGEN 37 374 50 ASARUM (+46)736 978 052

Behavior analysis procedures have been used to improve sports performance and enhance player safety across a wide variety of sports. The current study evaluated the effects of behavioral skills training on three common field hockey hits, a slap shot, drive, and sweep for young field hockey players. The procedures were evaluated in a multiple baseline across behaviors for three players. Results showed increases in each type of shot once intervention was implemented.