This paper analyses the role of the spatial structure of migrant networks in the location decision of migrants to the European Union at the regional level. Using a random parameters logit specification, a significant positive effect of migrant networks in neighbouring regions on migrants' location decisions is found. Although this spatial spillover effect is smaller than the effect of networks in the host regions, omitting to control for this spatial dependence results in a 40% overestimation of the effect of regional migrant networks on the location decision of newly arriving migrants.

Concerns regarding energy security, resource sustainability, and environmental protection have heightened interests in renewable fuels and sparked the research and development of ethanol as a transportation fuel. This study examines consumers’ willingness to pay for ethanol from various potential feedstocks; corn, switchgrass and wood wastes. Data was collected via a survey of fuel consumers across the United States in 2009. Results show that consumers have a preference for E85 (a fuel blend with 85 percent ethanol and 15 percent gasoline) from corn, switchgrass and wood wastes compared to E0 (gasoline) and a preference for E85 from switchgrass and wood wastes, but not corn when compared to E10 (10 percent ethanol and 90 percent gasoline). Also, consumers have a preference for E85 compared to E10 but not compared to E0. Mean WTP for E85 was insignificant across all models, but significant for all other product attributes; percentage of fuel imported, percentage of greenhouse gas emissions reduced, and the proximity of fuel in driving distance. This suggests a WTP for a combination of fuel attributes associated with ethanol rather than just for E85. Results suggest that price and proximity of the fuel have a greater impact on fuel selection than percentage of the fuel imported and reductions in greenhouse gas emissions. Republicans had a positive WTP for E85 compared to E10 and a negative WTP for E85 compared to E0 regardless of feedstock, which may suggest that Republicans actually have no preference for E85; however, these findings may also suggest that Republicans view E85 as a voluntary “policy” whereas E10 is an example of government intrusion in the free market. Thus, they may ultimately have preferences over the manner in which the blend is being introduced to the market. Across all models, those undecided in political affiliation, those previously familiar with ethanol, and those who prefer to devote U.S. farmland to food instead of fuel generally exhibited a lower WTP for E85 while Westerners, those worried about the environment, and those believe that reducing dependence on foreign oil is more important than environmental protection generally had a greater WTP for E85.

Concerns regarding energy security, resource sustainability, and environmental protection have heightened interests in renewable fuels and sparked the research and development of ethanol as a transportation fuel. This study examines consumers’ willingness to pay for ethanol from various potential feedstocks; corn, switchgrass and wood wastes. Data was collected via a survey of fuel consumers across the United States in 2009. Results show that consumers have a preference for E85 (a fuel blend with 85 percent ethanol and 15 percent gasoline) from corn, switchgrass and wood wastes compared to E0 (gasoline) and a preference for E85 from switchgrass and wood wastes, but not corn when compared to E10 (10 percent ethanol and 90 percent gasoline). Also, consumers have a preference for E85 compared to E10 but not compared to E0. Mean WTP for E85 was insignificant across all models, but significant for all other product attributes; percentage of fuel imported, percentage of greenhouse gas emissions reduced, and the proximity of fuel in driving distance. This suggests a WTP for a combination of fuel attributes associated with ethanol rather than just for E85. Results suggest that price and proximity of the fuel have a greater impact on fuel selection than percentage of the fuel imported and reductions in greenhouse gas emissions. Republicans had a positive WTP for E85 compared to E10 and a negative WTP for E85 compared to E0 regardless of feedstock, which may suggest that Republicans actually have no preference for E85; however, these findings may also suggest that Republicans view E85 as a voluntary “policy” whereas E10 is an example of government intrusion in the free market. Thus, they may ultimately have preferences over the manner in which the blend is being introduced to the market. Across all models, those undecided in political affiliation, those previously familiar with ethanol, and those who prefer to devote U.S. farmland to food instead of fuel generally exhibited a lower WTP for E85 while Westerners, those worried about the environment, and those believe that reducing dependence on foreign oil is more important than environmental protection generally had a greater WTP for E85.

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Contemporary research indicates that the era of autonomous vehicles (AVs) is not only inevitable but may be reached sooner than expected; however, not enough research has been done to address road infrastructure readiness for supporting AV operations. Highway agencies at all levels of governments seek to identify the needed infrastructure changes to facilitate the successful integration of AVs into the existing roadway system. Given multiple sources of uncertainty particularly the market penetration of AVs, agencies find it difficult to justify the substantial investments needed to make these infrastructure changes using traditional value engineering approaches. It is needed to account for these uncertainties by doing a phased retrofitting of road infrastructure to keep up with the AV market penetration. This way, the agency can expand, defer, or scale back the investments at a future time. This dissertation develops a real options analysis (ROA) framework to address these issues while capturing the monetary value of investment timing flexibility. Using key stakeholder feedback, an extensive literature review, and discussions with experts, the needed AV-motivated changes in road infrastructure were identified across two stages of AV operations; the transition phase and the fully-autonomous phase. For a project-level case study of a 66-mile stretch of Indiana’s four-six lane Interstate corridor, two potential scenarios of infrastructure retrofitting were established and evaluated using the net present value (NPV) and ROA approaches. The results show that the NPV approach can lead to decisions at the start of the evaluation period but does not address the uncertainty associated with AV market penetration. In contrast, ROA was found to address uncertainty by incorporating investment timing flexibility and capturing its monetary value. Using the dissertation’s framework, agencies can identify and analyze a wide range of possible scenarios of AV-oriented infrastructure retrofitting to enhance readiness, at both the project and network levels.

Submitted by Luanna Matias (lua_matias@yahoo.com.br) on 2015-02-04T20:56:59Z No. of bitstreams: 2 Dissertação - Augusta Finotti Brazão - 2014.pdf: 4325407 bytes, checksum: ed015d93a79ebdcbed577af5e0f9a797 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2015-02-05T09:48:34Z (GMT) No. of bitstreams: 2 Dissertação - Augusta Finotti Brazão - 2014.pdf: 4325407 bytes, checksum: ed015d93a79ebdcbed577af5e0f9a797 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Made available in DSpace on 2015-02-05T09:48:34Z (GMT). No. of bitstreams: 2 Dissertação - Augusta Finotti Brazão - 2014.pdf: 4325407 bytes, checksum: ed015d93a79ebdcbed577af5e0f9a797 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2014-04-04 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / The present study aims to investigate the influence of uncertainties in physical and geometric parameters to obtain the load parametric instability of cylindrical shell, using the Galerkin method with the stochastic polynomial Hermite-Caos. The nonlinear equations of motion of the cylindrical shell are deduced from their functional power considering the strain field proposed by Donnell´s nonlinear shallow shell theory. The uncertainties are considered as random parameters with probability density function known in the partial differential equation of motion of the cylindrical shell, which it becomes a stochastic partial differential equation due to the presence of randomness. First, the discretization of the stochastic problem is performed using the stochastic Galerkin method together with polynomial Hermite-Chaos, to transform the stochastic partial differential equation into a set of equivalent deterministic partial differential equations, which take into account the randomness of the system. Then, the discretization of the lateral field displacement is made by a perturbation procedure, indicating the nonlinear vibration modes which couple to the linear vibration mode. The set of partial differential equations is transformed into a deterministic system of equations deterministic ordinary second order in time. Uncertainty is considered in one of its parameters: the Young modulus, thickness and amplitude of initial geometric imperfection. Then we analyze the influence of randomness in two parameters simultaneously: the thickness and the Young modulus. Once obtained the system of ordinary differential equations deterministic containing the randomness of the parameters, the integration over discrete time system is made from the Runge- Kutta fourth order to obtain results as the time response, bifurcation diagrams and boundaries of instability which are compared with deterministic analysis, indicating that polynomial Hermite-Chaos is a good numerical tool for predicting the load parametric instability without the need to perform a process of sampling. / O presente trabalho tem como objetivo investigar a influência de incertezas nos parâmetros físicos e geométricos para a determinação da carga de instabilidade paramétrica da casca cilíndrica, utilizando o método de Galerkin Estocástico juntamente com o polinômio de Hermite-Caos. As equações não-lineares de movimento da casca cilíndrica são deduzidas a partir de seus funcionais de energia considerando o campo de deformações proposto pela teoria não linear de Donnell para cascas esbeltas. As incertezas são consideradas como parâmetros aleatórios com função de densidade de probabilidade conhecida na equação diferencial parcial de movimento da casca cilíndrica, que passa a ser uma equação diferencial parcial estocástica devido à presença da aleatoriedade. Primeiramente, faz-se a discretização do problema estocástico utilizando o método de Galerkin Estocástico juntamente com o polinômio de Hermite-Caos, para transformar a equação diferencial parcial estocástica em um conjunto de equações diferenciais parciais determinísticas equivalentes, que levem em consideração a aleatoriedade do sistema. Em seguida, apresenta-se a discretização do campo de deslocamentos laterais através do Método da Perturbação, indicando os modos não-lineares de vibração que se acoplam ao modo linear de vibração, para que o conjunto de equações diferenciais parciais determinísticas seja transformado em um sistema de equações ordinárias determinísticas de segunda ordem no tempo. A incerteza é considerada inicialmente em apenas um de seus parâmetros: no módulo de elasticidade, na espessura e na amplitude da imperfeição geométrica inicial. Em seguida, analisa-se a influência de aleatoriedades em dois parâmetros simultaneamente, sendo eles: a espessura e o módulo de elasticidade. Uma vez obtido o sistema de equações diferenciais ordinárias determinísticas que contêm as aleatoriedades dos parâmetros, a integração ao longo do tempo do sistema discretizado é feita a partir do método de Runge-Kutta de quarta ordem, obtendo-se resultados como resposta no tempo, diagramas de bifurcação e fronteiras de instabilidade, que são comparados com análises determinísticas, indicando que o polinômio de Hermite-Caos é uma boa ferramenta numérica para prever a carga de instabilidade paramétrica sem a necessidade de se realizar um processo de amostragens.

<p dir="ltr">The generalization error of a classifier is related to the complexity of the set of functions among which the classifier is chosen. We study a family of low-complexity classifiers consisting of thresholding a random one-dimensional feature. The feature is obtained by projecting the data on a random line after embedding it into a higher-dimensional space parametrized by monomials of order up to k. More specifically, the extended data is projected n-times and the best classifier among those n, based on its performance on training data, is chosen. </p><p dir="ltr">We show that this type of classifier is extremely flexible, as it is likely to approximate, to an arbitrary precision, any continuous function on a compact set as well as any Boolean function on a compact set that splits the support into measurable subsets. In particular, given full knowledge of the class conditional densities, the error of these low-complexity classifiers would converge to the optimal (Bayes) error as k and n go to infinity. On the other hand, if only a training dataset is given, we show that the classifiers will perfectly classify all the training points as k and n go to infinity. </p><p dir="ltr">We also bound the generalization error of our random classifiers. In general, our bounds are better than those for any classifier with VC dimension greater than O(ln(n)). In particular, our bounds imply that, unless the number of projections n is extremely large, there is a significant advantageous gap between the generalization error of the random projection approach and that of a linear classifier in the extended space. Asymptotically, as the number of samples approaches infinity, the gap persists for any such n. Thus, there is a potentially large gain in generalization properties by selecting parameters at random, rather than optimization. </p><p dir="ltr">Given a classification problem and a family of classifiers, the Rashomon ratio measures the proportion of classifiers that yield less than a given loss. Previous work has explored the advantage of a large Rashomon ratio in the case of a finite family of classifiers. Here we consider the more general case of an infinite family. We show that a large Rashomon ratio guarantees that choosing the classifier with the best empirical accuracy among a random subset of the family, which is likely to improve generalizability, will not increase the empirical loss too much. </p><p dir="ltr">We quantify the Rashomon ratio in two examples involving infinite classifier families in order to illustrate situations in which it is large. In the first example, we estimate the Rashomon ratio of the classification of normally distributed classes using an affine classifier. In the second, we obtain a lower bound for the Rashomon ratio of a classification problem with a modified Gram matrix when the classifier family consists of two-layer ReLU neural networks. In general, we show that the Rashomon ratio can be estimated using a training dataset along with random samples from the classifier family and we provide guarantees that such an estimation is close to the true value of the Rashomon ratio.</p>