Inhalt Die Technologien des Rapid Prototypings erreichen eine immer größer werdende Marktdurchdringung und erweitern die Möglichkeiten von Ingenieuren und Spezialisten angrenzender Fachbereiche. Je nach Datenursprung werden Schnittstellen wie Step oder STL genutzt oder es sind Zwischenschritte erforderlich, um die Daten aus 3D Scannern oder bildgebenden Quellen (CT; MRT) weiter zu verarbeiten. Dabei gibt es Fehler und Ungenauigkeiten in der Datenkette, die analysiert hier werden sollen. Aus den Kenntnissen über die Fehler sollen Methoden aufgezeigt werden, um die Datenqualität zu erhöhen, die Datenmengen zu verringern und die Prozesse zu stabilisieren. / Data quality rapid prototyping processes The rapid prototyping technologies achieve a bigger market penetration and expand the capabilities of engineers and specialists fields. Depending on the data source interfaces such as Step or STL are used or there are intermediate steps required to process the data from 3D scanners or imaging sources such as CT, MRI or X-ray on. There are errors and inaccuracies in the data flow, which have to be analyzed. Methods are from the knowledge of the errors are pointed out, which will enable to increase the data quality and to make the process more stable.

This research paper is submitted to the Department of Geography in fulfillment of the requirements of Geography 4C6. / This study examined the trade area of a Hamilton restaurant in an attempt to determine the relevance of theoretical models in predicting trade areas based on delivery records. Through the use of four trade area models, a comparative study was devise for 'Chicago Style Pizza' restaurant. The findings were based on delivery records. Since delivery records were used, the distance factor that is used in most models is eliminated. The models that were used were a population demand, Market Penetration Model, Intervening Opportunity Model and a Spatial Interaction Model. The use of a Geographical Information System was used to predict surface demands for the Market Penetration Model and the Spatial Interaction Model. It was determined that classical models of trade area analysis had only a slight relevance in delimitating the trade areas of the store in question when compared to the actual trade area of 'Chicago Style' based on delivery records. / Thesis / Bachelor of Arts (BA)

Hybrid-electric technologies have recently been introduced into the market for heavy-duty vehicles (HDVs). However, challenging an established technology with a new and untried technology is difficult, also under the best conditions. Forecasting is a vital tool in product portfolio management, since it provides guidance on how much resources a firm should allocate on new innovative projects and products and when and where to enter the market. Therefore, this thesis forecasts the market penetration of hybrid HDVs in Europe by usage of innovation diffusion models – based on three different market scenarios assuming no, some and considerable incentives or legislative CO2 for HDVs. Hybrid-electric, hydraulic hybrid and flywheel hybrid vehicles are considered and an analogical approach is used based on sales data for radial tyres, disc brakes and anti-lock braking systems. The result from a non-linear regression analysis indicated that innovation diffusion models of mixed influence are capable of predicting future market demand, not only of hybrid HDVs, but also of other HDVs with new innovative technologies or solutions. Therefore, it was suggested that innovation diffusion modeling should be a standard tool in the strategic planning of a HDV firm’s all new innovative products. All market scenarios resulted in a rather low diffusion speed of hybrid HDVs during the first ten years, but the speed increased then rapidly during the next ten years such that 40-50 percent of the HDV market was penetrated in 2030. In the most hybrid-friendly scenario, the market was nearly fully penetrated after 50 years since the first introduction in 2010, while in the least hybrid-friendly scenario additional ten years was needed to fully penetrate the HDV market. The forecasts may be affected by possible pre-diffusion, the emergence of a dominant design or the diffusion acceleration effect. One of the major challenges of using innovation diffusion models for sales forecasting of hybrid HDVs, was to find appropriate and sufficient analogous sales data. Therefore, Thomas (1985) analogous approach was further developed to be more focused on finding analogous sales data from internal, external or public sources.

Since the transportation sector still relies mostly on fossil fuels, the emissions and overall environmental impacts of the transportation sector are particularly relevant to the mitigation of the adverse effects of climate change. Sustainable transportation therefore plays a vital role in the ongoing discussion on how to promote energy insecurity and address future energy requirements. One of the most promising ways to increase energy security and reduce emissions from the transportation sector is to support alternative fuel technologies, including electric vehicles (EVs). As vehicles become electrified, the transportation fleet will rely on the electric grid as well as traditional transportation fuels for energy. The life cycle cost and environmental impacts of EVs are still very uncertain, but are nonetheless extremely important for making policy decisions. Moreover, the use of EVs will help to diversify the fuel mix and thereby reduce dependence on petroleum. In this respect, the United States has set a goal of a 20% share of EVs on U.S. roadways by 2030. However, there is also a considerable amount of uncertainty in the market share of EVs that must be taken into account. This dissertation aims to address these inherent uncertainties by presenting two new models: the Electric Vehicles Regional Optimizer (EVRO), and Electric Vehicle Regional Market Penetration (EVReMP). Using these two models, decision makers can predict the optimal combination of drivetrains and the market penetration of the EVs in different regions of the United States for the year 2030. First, the life cycle cost and life cycle environmental emissions of internal combustion engine vehicles, gasoline hybrid electric vehicles, and three different EV types (gasoline plug-in hybrid EVs, gasoline extended-range EVs, and all-electric EVs) are evaluated with their inherent uncertainties duly considered. Then, the environmental damage costs and water footprints of the studied drivetrains are estimated. Additionally, using an Exploratory Modeling and Analysis method, the uncertainties related to the life cycle costs, environmental damage costs, and water footprints of the studied vehicle types are modeled for different U.S. electricity grid regions. Next, an optimization model is used in conjunction with this Exploratory Modeling and Analysis method to find the ideal combination of different vehicle types in each U.S. region for the year 2030. Finally, an agent-based model is developed to identify the optimal market shares of the studied vehicles in each of 22 electric regions in the United States. The findings of this research will help policy makers and transportation planners to prepare our nation*s transportation system for the future influx of EVs. The findings of this research indicate that the decision maker*s point of view plays a vital role in selecting the optimal fleet array. While internal combustion engine vehicles have the lowest life cycle cost, the highest environmental damage cost, and a relatively low water footprint, they will not be a good choice in the future. On the other hand, although all-electric vehicles have a relatively low life cycle cost and the lowest environmental damage cost of the evaluated vehicle options, they also have the highest water footprint, so relying solely on all-electric vehicles is not an ideal choice either. Rather, the best fleet mix in 2030 will be an electrified fleet that relies on both electricity and gasoline. From the agent-based model results, a deviation is evident between the ideal fleet mix and that resulting from consumer behavior, in which EV shares increase dramatically by the year 2030 but only dominate 30 percent of the market. Therefore, government subsidies and the word-of-mouth effect will play a vital role in the future adoption of EVs.

The macroscopic measure of traffic stream density is crucial in advanced traffic management systems. However, measuring the traffic stream density in the field is difficult since it is a spatial measurement. In this dissertation, several estimation approaches are developed to estimate the traffic stream density on signalized approaches using connected vehicle (CV) data. First, the dissertation introduces a novel variable estimation interval that allows for higher estimation precision, as the updating time interval always contains a fixed number of CVs. After that, the dissertation develops model-driven approaches, such as a linear Kalman filter (KF), a linear adaptive KF (AKF), and a nonlinear Particle filter (PF), to estimate the traffic stream density using CV data only. The proposed model-driven approaches are evaluated using empirical and simulated data, the former of which were collected along a signalized approach in downtown Blacksburg, VA. Results indicate that density estimates produced by the linear KF approach are the most accurate. A sensitivity of the estimation approaches to various factors including the level of market penetration (LMP) of CVs, the initial conditions, the number of particles in the PF approach, traffic demand levels, traffic signal control methods, and vehicle length is presented. Results show that the accuracy of the density estimate increases as the LMP increases. The KF is the least sensitive to the initial traffic density estimate, while the PF is the most sensitive to the initial traffic density estimate. The results also demonstrate that the proposed estimation approaches work better at higher demand levels given that more CVs exist for the same LMP scenario. For traffic signal control methods, the results demonstrate a higher estimation accuracy for fixed traffic signal timings at low traffic demand levels, while the estimation accuracy is better when the adaptive phase split optimizer is activated for high traffic demand levels. The dissertation also investigates the sensitivity of the KF estimation approach to vehicle length, demonstrating that the presence of longer vehicles (e.g. trucks) in the traffic link reduces the estimation accuracy. Data-driven approaches are also developed to estimate the traffic stream density, such as an artificial neural network (ANN), a k-nearest neighbor (k-NN), and a random forest (RF). The data-driven approaches also utilize solely CV data. Results demonstrate that the ANN approach outperforms the k-NN and RF approaches. Lastly, the dissertation compares the performance of the model-driven and the data-driven approaches, showing that the ANN approach produces the most accurate estimates. However, taking into consideration the computational time needed to train the ANN approach, the large amount of data needed, and the uncertainty in the performance when new traffic behaviors are observed (e.g., incidents), the use of the linear KF approach is highly recommended in the application of traffic density estimation due to its simplicity and applicability in the field. / Doctor of Philosophy / Estimating the number of vehicles (vehicle counts) on a road segment is crucial in advanced traffic management systems. However, measuring the number of vehicles on a road segment in the field is difficult because of the need for installing multiple detection sensors in that road segment. In this dissertation, several estimation approaches are developed to estimate the number of vehicles on signalized roadways using connected vehicle (CV) data. The CV is defined as the vehicle that can share its instantaneous location every time t. The dissertation develops model-driven approaches, such as a linear Kalman filter (KF), a linear adaptive KF (AKF), and a nonlinear Particle filter (PF), to estimate the number of vehicles using CV data only. The proposed model-driven approaches are evaluated using real and simulated data, the former of which were collected along a signalized roadway in downtown Blacksburg, VA. Results indicate that the number of vehicles produced by the linear KF approach is the most accurate. The results also show that the KF approach is the least sensitive approach to the initial conditions. Machine learning approaches are also developed to estimate the number of vehicles, such as an artificial neural network (ANN), a k-nearest neighbor (k-NN), and a random forest (RF). The machine learning approaches also use CV data only. Results demonstrate that the ANN approach outperforms the k-NN and RF approaches. Finally, the dissertation compares the performance of the model-driven and the machine learning approaches, showing that the ANN approach produces the most accurate estimates. However, taking into consideration the computational time needed to train the ANN approach, the huge amount of data needed, and the uncertainty in the performance when new traffic behaviors are observed (e.g., incidents), the use of the KF approach is highly recommended in the application of vehicle count estimation due to its simplicity and applicability in the field.

Les clauses de rendement constituent une incitation au dépassement, à l’action, à la performance. Dès lors, comment peuvent-elles être conciliées avec les différents pans du droit dans lesquelles elles interviennent ? On les rencontre en droit social et elles prennent une dimension quasiment envahissante en droit de la distribution. Ainsi intégrée au domaine contractuel, la clause de rendement oblige le débiteur et nourrit les attentes du créancier. Mais, pas seulement. Car, si le débiteur de la clause de rendement doit s’astreindre à réaliser le rendement consenti, le créancier, dans l’optique de favoriser l’atteinte de l’objectif, ne doit pas adopter un simple comportement passif. En somme, l’obligation de rendement renvoie au « rapport tout entier » qui existe entre le créancier et le débiteur de la clause. L’on pressent leur difficile insertion au regard de la théorie générale du droit des contrats. Leur maniement délicat réclame l’étude de nombreux facteurs, paramètres, à prendre en considération pour assurer leur efficacité. A l’issue de cette étude, ces clauses apparaissent comme un outil de performance au service des contractants mais aussi de l’intérêt économique. Car, les clauses de rendement, bien maniées et encadrées strictement sont sources d’efficacité concurrentielle.En somme, la contrainte apparue initialement s’efface et révèle leur utilité dans l’intérêt général. Les clauses de rendement dynamisent ainsi le contrat, avivent la concurrence et par là-même sauvegardent les intérêts des consommateurs. Il semble alors que les comportements consistant à la réalisation des objectifs, participent, non seulement à une quête d’efficacité contractuelle, mais également à une efficience concurrentielle dans l’intérêt de la collectivité toute entière. / Performance clauses are an incentive to overshoot, to act, and to perform.Therefore, how can they be reconciled with the different parts of the law in which they operate? They are found in labor law and take an almost overwhelming size in distribution law. Well integrated into the contractual sphere, the return clause obliges the debtor and feeds the creditor’s expectations. But not only that, for if the debtor of the return clause must discipline himself to achieve the agreed performance, the creditor, with a view to help achieve the goal, should not simply adopt a passive attitude. In sum, the obligation to return back to the “whole report” exists between the creditor and the debtor.It is difficult to present their inclusion under the general theory of contract law. Their delicate handling calls for the consideration of many factors and parameters that must be taken into consideration to ensure their effectiveness. At the end of this study, these terms appear as a performance tool in the service of contractors but also of economic interest. Well-handled and strictly supervised clauses of performance are competitive sources of efficiency. All in all, the initial constraint is cleared and reveals the usefulness of performance clauses in the public interest. Thus, in terms of the contract and performance boost, performance clauses sharpen competition and thereby safeguard the interests of consumers. It seems then that the performance in achieving the objectives, participate not only in a quest of contractual effectiveness, but also for competitive efficiency for the whole community.

The aim of this thesis is to design a marketing strategy. The first part presents theoretical knowledge of marketing, marketing strategy, competitive analysis and methods of penetration for new markets. In second part is introduced the company and made the analysis of the current situation. Furthermore, this work deals with analysis of the situation and competition on the Romanian markets (searching for potential distributors and their evaluation). On the basis of analysis is proposed appropriate marketing strategies, options of the penetration to the markets and costing. These options are then evaluated and selected the most appropriate.

Today many companies are using influencers in the marketing strategy. However, there is an expressed need for more research on the topic influencer marketing and on how the marketing strategy should be formed in order to match and facilitate the growth of a company. Therefore, this thesis aims to fill these gaps in literature by forming an influencer marketing strategy in a growth perspective. The goal is to manage the risks with influencer marketing and contribute to the literature with more research in the topics growth and influencer marketing by answering the research questions: How can influencer marketing affect the growth of a company? And How can an influencer marketing strategy be formed to favour growth and manage the risks with influencer marketing? In order to answer the research questions both qualitative data from seven interviews with experts on the topic influencer marketing and quantitative data from a survey regarding the usage of social media. This thesis indicates that influencer marketing can promote growth but needs to be adapted according to which growth strategy the company chooses. The benefits of influencer marketing are intangible, while the risks can be handled through working more strategically and stepping away from the more common trial and error approach. The findings from this thesis are relevant for companies that operate in the demanding business landscape of today, striving for growth through various growth strategies and are invested in influencer marketing. Furthermore, the findings are relevant for anyone interested in how risks with influencer marketing can be managed and how the strategy can favour firm growth. / Idag använder många företag influencers i sin marknadsföringsstrategi. Det finns ett behov av mer forskning inom influencer marketing men också i hur en marknadsföringsstrategi ska utformas för att gynna tillväxt inom ett företag. Den här masteruppsatsen strävar därför efter att fylla gapet i litteraturen genom att forma en influencer marketing-strategi som gynnar tillväxt. Målet med uppsatsen är formulera en strategi i syfte att minska riskerna med användandet av influencer marketing och bidra till litteraturen inom influencer marketing och företagstillväxt genom att besvara de två forskningsfrågorna: Hur kan influencer marketing påverka ett företags tillväxt? samt Hur kan en influencer marketing-strategi utformas för att gynna tillväxt i ett företag och minska riskerna med användandet av influencer marketing? För att besvara dessa två frågor har både kvalitativa data i form av sju intervjuer med experter inom influencer marketing och kvantitativa data från en undersökning om användandet av sociala medier genomförts. Resultaten visar att influencer marketing kan gynna tillväxt i ett företag, men måste anpassas till vilken tillväxtstrategi företaget väljer. Influencer marketing bidrar med immateriella tillgångar som är svåra att replikera medan riskerna kopplade till influencer marketing kan hanteras genom att jobba mer strategiskt och som bygger mindre på “trial and error”. Resultaten är värdefulla för företag som verkar i dagens krävande affärslandskap, strävar efter tillväxt och har investerat i influencer marketing. Resultaten är dessutom värdefulla för alla som är intresserade av hur riskerna med influencer marketing kan hanteras och hur den kan kopplas till företags tillväxtstrategier.

Managed care plans purport to improve the health of their members with chronic diseases. How has the growing adoption of Medicare Advantage (MA), the managed care program for Medicare beneficiaries, affected the progression of chronic disease? The literature is rich with articles focusing on managed care organizations' impacts on quality of care, access, patient satisfaction, and costs. However, few studies have analyzed these impacts with respect to market penetration of Medicare managed care. The objective of this research has been to analyze the relationships between the market penetration of MA plans and the progression of chronic diseases among Medicare beneficiaries. The Chronic Disease Severity Index scale (CDSI) was constructed to represent beneficiaries' overall chronic disease states for survey or claims-based data, when more direct clinical measures of disease progression are not available. Using the CDSI on the MEPS survey dataset from AHRQ, we sought to assess the impacts of MA market penetration and other covariates on the overall chronic disease state of Medicare beneficiaries from 2004 through 2008. Though the model explains much of the variation in CDSI change, the author expected the multilevel model would show that MA penetration explains a significant level of variation in CDSI change. However, this hypothesis was not substantiated, and the findings suggest that unmeasured factors may be contributing to additional unexplained heterogeneity. Policymakers should explore opportunities to refine the current MA program. The MA program costs the federal government more than the Traditional Fee-for-Service Medicare program, and there is no definitive evidence that outcomes differ. Within both programs, there is opportunity to experiment with different models of payment, healthcare service delivery and care coordination. The Patient Protection and Affordable Care Act (ACA) contains provisions for innovative demonstration projects in delivery and payment. The effectiveness of these ACA initiatives must be monitored, both for impacts on health outcomes and for economic effects. This research can inform future approaches to outcomes assessment using the CDSI, and multilevel modeling methodologies similar to those employed here. Firms offering MA health plans would be prudent to proactively demonstrate their value to beneficiaries and taxpayers. They should explore means of better monitoring and reporting the longitudinal outcomes of their enrolled beneficiaries. Demonstrating that they can bring value in terms of improved health outcomes will help insure their long-term survival, both in the marketplace and in the political arena. / Graduation date: 2012