This study adopted the traffic conflict technique, investigated and applied it for evaluation of freeway weaving section safety performance. Conflicts between vehicles were identified based on the state of interactions between vehicles in the traffic stream at microscopic level. The VISSIM microscopic simulation model was employed to simulate traffic operation. Surrogate safety measures were formulated based on deceleration rate required to avoid crash and these simulation-based measures were statistically compared and validated using crash data collected from the same study site. Three study sites located in Houston and Dallas areas were selected. Geometric and traffic data were collected using various technique including the use of traffic surveillance cameras and pneumatic tubes. The study revealed the existence of links between actually observed crashes and the surrogate safety measures. The study findings support the possible the use of microscopic simulation to evaluate safety performance of weaving areas and other transportation facilities.

This study hypothesized that fathers and surrogates (male role models) contribute a unique set of factors that help guide African American male youths (N=496) during their normal developmental stages. This study hypothesized that surrogate caregivers would have an impact on the overall level of delinquent behavior of this population. A path analysis tested direct and mediated effects of exposure to violence on delinquent behavior, with anger/aggression level as a potential mediator for all three levels of caregiver presence or absence as a moderator. In the analysis of archival data from 496 African American male youths, the findings did not support these hypotheses consistently. Exposure to family violence as a mediator consistently predicted level of anger, and level of anger negatively predicted delinquent behavior for the fatherless sample. However, exposure did not have a direct positive effect on delinquent behavior in any of the three samples. Implications of these findings as well as other unpredicted findings with these three groups are explored.

<p>The existence of trihalomethanes (THMs) in a drinking water plant of Nicaragua has been investigated in order to see whether the concentration exceeded the maximum contaminant level recommended by the environmental protection agency of the United States (USEPA) and the Nicaragua guidelines. The influence of pH, temperature, chlorine dose and contact time on the formation of THMs were studied. The contents of organic matter measured by surrogate parameters such as total organic carbon, dissolved organic carbon, ultraviolet absorbance and specific ultraviolet absorbance were also determined in order to show which type of organic matter is most reactive with chlorine to form THMs. Models developed by other researchers to predict the formation of trihalomethanes were tested to see whether they can be used to estimate the trihalomethane concentration. In addition, empirical models were development to predict the THM concentration of the drinking water plant analysed. The raw water was treated by conventional and enhanced coagulation and these processes were compared with regard to the removal of natural organic matter (NOM). The significance of the results was assessed using statistic procedures.</p><p>The average concentration of THMs found at the facility is below the USEPA and Nicaragua guideline values. Nevertheless the maximum contaminant level set by USEPA is sometimes exceeded in the rainy season when the raw water is rich in humic substances. Comparison between the water treated by conventional and enhanced coagulation shows that enhanced coagulation considerably diminished the trihalomethane formation and the value after enhanced coagulation never exceeded the guidelines. This is because enhanced coagulation considerably decreases the organic matter due to the high coagulant dose applied. The study of the trihalomethane formation when varying pH, time, temperature and chlorine dose using water treated by conventional and enhanced coagulation showed that higher doses of chlorine, higher pH, higher temperature and a longer time increases the formation of THMs. However, combinations of two and three factors are the opposite. The predicted THM formation equations cannot be used for the water at this facility, since the results shown that the measured THM differs significantly from the THM concentration predicted. Two empirical models were developed from the data for enhanced coagulation, using linear and non-linear regression. These models were tested using the database obtained with conventional coagulation. The non-linear model was shown to be able to predict the formation of THMs in the Boaco drinking water plant.</p>

Computational design has become a predominant element of various engineering tasks. However, the ever increasing complexity of numerical models creates the need for efficient methodologies. Specifically, computational design under uncertainties remains sparsely used in engineering settings due to its computational cost. This dissertation proposes a coherent framework for various branches of computational design under uncertainties, including model update, reliability assessment and reliability-based design optimization. Through the use of machine learning techniques, computationally inexpensive approximations of the constraints, limit states, and objective functions are constructed. Specifically, a novel adaptive sampling strategy allowing for the refinement of any approximation only in relevant regions has been developed, referred to as generalized max-min. This technique presents various computational advantages such as ease of parallelization and applicability to any metamodel. Three approaches tailored for computational design under uncertainties are derived from the previous approximation technique. An algorithm for reliability assessment is proposed and its efficiency is demonstrated for different probabilistic settings including dependent variables using copulas. Additionally, the notion of fidelity map is introduced for model update settings with large number of dependent responses to be matched. Finally, a new reliability-based design optimization method with local refinement has been developed. A derivation of sampling-based probability of failure derivatives is also provided along with a discussion on numerical estimates. This derivation brings additional flexibility to the field of computational design. The knowledge acquired and techniques developed during this Ph.D. have been synthesized in an object-oriented MATLAB toolbox. The help and ergonomics of the toolbox have been designed so as to be accessible by a large audience.

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This research addresses the problem of creating surrogate models of high-level operations and sustainment (O&S) simulations with time sequential (TS) outputs. O&S is a continuous process of using and maintaining assets such as a fleet of aircraft, and the infrastructure to support this process is the O&S system. To track the performance of the O&S system, metrics such as operational availability are recorded and reported as a time history. Modeling and simulation (M&S) is often used as a preliminary tool to study the impact of implementing changes to O&S systems such as investing in new technologies and changing the inventory policies. A visual analytics (VA) interface is useful to navigate the data from the M&S process so that these options can be compared, and surrogate modeling enables some key features of the VA interface such as interpolation and interactivity. Fitting a surrogate model is difficult to TS data because of its size and nonlinear behavior. The Surrogate Modeling and Regression of Time Sequences (SMARTS) methodology was proposed to address this problem. An intermediate domain Z was calculated from the simulation output data in a way that a point in Z corresponds to a unique TS shape or pattern. A regression was then fit to capture the entire range of possible TS shapes using Z as the inputs, and a separate regression was fit to transform the inputs into the Z. The method was tested on output data from an O&S simulation model and compared against other regression methods for statistical accuracy and visual consistency. The proposed methodology was shown to be conditionally better than the other methodologies.

This thesis introduces a new optimization algorithm for simulation-based design of systems with multi-modal, nonlinear, black box objective functions. The algorithm extends the recently introduced adaptive multi-modal optimization by incorporating surrogate modeling features similar to response surface methods (RSM). The resulting optimization algorithm has reduced computational intensity and is therefore well-suited for optimization of expensive black box objective functions. The algorithm relies on an adaptive and multi-resolution mesh to obtain an initial estimation of the objective function surface. Local surrogate models are then constructed to represent the objective function and to generate additional trial points in the vicinity of local minima discovered. The steps of mesh refinement and surrogate modeling continue until convergence criteria are met. An important property of this algorithm is that it produces progressively accurate surrogate models around the local minima; these models can be used for post-optimization studies such as sensitivity and tolerance analyses with minimal computational effort. This algorithm is suitable for optimal design of complex engineering systems and enhances the design cycle by enabling computationally affordable uncertainty analysis. The mathematical basis of the algorithm is explained in detail. The thesis also demonstrates the effectiveness of the algorithm using comparative optimization of several multi-modal objective functions. It also shows several practical applications of the algorithm in the design of complex power and power-electronic systems.

This thesis introduces a new optimization algorithm for simulation-based design of systems with multi-modal, nonlinear, black box objective functions. The algorithm extends the recently introduced adaptive multi-modal optimization by incorporating surrogate modeling features similar to response surface methods (RSM). The resulting optimization algorithm has reduced computational intensity and is therefore well-suited for optimization of expensive black box objective functions. The algorithm relies on an adaptive and multi-resolution mesh to obtain an initial estimation of the objective function surface. Local surrogate models are then constructed to represent the objective function and to generate additional trial points in the vicinity of local minima discovered. The steps of mesh refinement and surrogate modeling continue until convergence criteria are met. An important property of this algorithm is that it produces progressively accurate surrogate models around the local minima; these models can be used for post-optimization studies such as sensitivity and tolerance analyses with minimal computational effort. This algorithm is suitable for optimal design of complex engineering systems and enhances the design cycle by enabling computationally affordable uncertainty analysis. The mathematical basis of the algorithm is explained in detail. The thesis also demonstrates the effectiveness of the algorithm using comparative optimization of several multi-modal objective functions. It also shows several practical applications of the algorithm in the design of complex power and power-electronic systems.

Surrogacy is an arrangement in which a woman carries, delivers, and then relinquishes a baby to commissioning parents. The arrangement challenges traditional norms and definitions of reproduction, pregnancy and motherhood, and at the same time raises difficult ethical, philosophical and social questions. There is currently an ongoing debate in Sweden, where all forms of surrogacy is illegal. The debate is polemical and harsh, with advocates arguing that surrogacy is defensible on the basis of individual rights and women's right to choose over their own bodies, and those in opposition are convinced on an intersectional basis that women are being used and exposed on the market of surrogacy.The purpose of this study is to investigate the debate on surrogacy in the Swedish context. Using qualitative text analysis, the study aims to increase understanding about the debate over surrogacy by analyzing texts published in Swedish press during 2010-2013. I have analyzed the advocating arguments as well as the oppositional arguments constructing the ongoing debate of surrogacy with a focus on the transnational relations and the discrepancy between the views on altruistic and commercial forms of surrogacy.The findings indicate that surrogacy is in general seen as either a win-win situation or exploitation and confirms earlier intersectional postcolonial research that centers on surrogacy, which highlights a general tendency to not acknowledge the dimensions and intersections of gender, class, race, and ethnicity. This is crucial especially concerning surrogacy is such a transnational phenomenon, where for example (white) Swedes longing for babies go to India and through an Indian surrogate mother become parents. The study shows that the debate is somewhat a minefield, and while being polemical the debate is also very complex.

A systematic approach to planning, decision-making and management has become best-practice in conservation over the past two of decades. The field of ‘systematic conservation planning’ is concerned with identifying cost-effective places and actions to protect biological diversity. Past research has focused on static assessments. However, given the fact that biological diversity and processes that threaten its persistence vary in space and time, conservation assessments might need to be made in a dynamic context. In addition, we must explicitly account for the trade-offs associated with implementing conservation actions and investing in improved knowledge and learning to reduce uncertainty on where, how and when to act. The aim of this thesis was to develop novel approaches for accounting for both ecosystem dynamics and uncertainty in conservation planning. Ecosystems are generally treated as static in conservation planning despite many being spatially and temporally dynamic. For example, pelagic marine ecosystems are quite dynamic because ecological processes, such as eddies, that produce resources that many species depend on can be erratic. In chapter two we explored the issue of developing a system of fixed protected areas that consider the physical and biological dynamics typical of the pelagic realm. The approach was to maximize the representation of key fisheries species and species of conservation concern due to significant declines in their abundance, within a network of protected areas. We also ensured that protected area design reflected system dynamics and this was achieved by representing key oceanographic process (such as upwellings and eddies), and biological processes (such as the abundance of small pelagic fish) in protected areas. To account for the variability where these processes occur, we used time series data to find both predictable areas and anomalies, assuming that their past location was somewhat reflective of their future locations. Implementing conservation actions that are fixed in space and time are probably not the most effective strategy in ecosystems that are dynamic. This is because of the movements of particular species. For example, many species have distributions and abundances that change seasonally and might only require temporary management in particular areas. In chapter three, we tested the utility of three approaches to implementing fisheries closures to reduce bycatch in the South African Longline Fishery; 1) time closures, 2) permanent spatial closures and 3) episodic spatial closures. In chapter three, we identified these closures using an existing database containing catch and bycatch data from 1998 to 2005. There was variation where and when different species were caught as bycatch, and it was determined seasonal area closures were the best strategy. This was because it achieved the same conservation objectives for bycatch species as the other types of closures, but impacted less on the long-lining industry. While this result is intuitive, it demonstrated quantitatively, how much more effective moveable management can be. Decisions on where conservation actions are implemented are always based on incomplete knowledge about biological diversity. It is generally assumed that gathering more data is a good investment for conservation planning. However, data can take time and incur costs to collect and given habitat loss, there are both costs and benefits associated with different levels of investments in knowledge versus conservation implementation. In chapter four, the aim was to determine the return on investment from spending different amounts on survey data before undertaking a program of implementing new protected areas. We found that, after an investment of only US$100,000, there was little increase in the effectiveness of conservation actions, despite the full species dataset costing at least 25 times that amount. Surveying can take time because of expertise limitations, logistics and funding shortfalls. Biological diversity may be lost while data collection occurs conversely, not collecting enough data can lead to erroneous decisions. Additionally, resources spent on learning may be better spent on other actions. In chapter five, in a series of retrospective simulations, we compared the impact of spending different amounts of time collecting biological data prior to the implementation of new protected areas. The aim was to find the optimal survey period given the trade-off between gaining knowledge to improve conservation decisions while there is concurrent loss of habitat. We discovered that surveying beyond two years rarely increased the effectiveness of conservation decisions, despite a substantial increase in the knowledge of species distributions. Often there are choices between different actions and uncertainty as to which are the most effective. In chapter six, we discuss how the principles of adaptive management might be applied to conservation planning. Improving future management decisions through learning should be viewed as essential in all conservation plans but such learning is often included as a minor step, or is completely ignored. In this chapter we provide a brief overview of an adaptive framework for conservation planning and ideas for future research.