Left-turning maneuvers are considered to be the highest risk movements at intersections and two-thirds of the crashes associated with left-turns are reported at signalized intersections. Left-turning vehicles typically encounter conflicts from opposing through traffic. To separate conflicting movements, transportation agencies use a protected-only phase at signalized intersections where each movement is allowed to move alone. However, this could create delays and thus the concept of a protected-permissive phase has been introduced to balance safety and delays. However, the permissive part of this phasing scheme retains the safety concerns and could increase the possibility of conflicts resulting in crashes. This research developed a model that can predict the number of crashes for protected-permissive left-turn phasing, based on traffic volumes and calculated conflicts. A total of 103 intersections with permissive-protected left-turn phasing in Kentucky were simulated and their left-turn related conflicts were obtained from post processing vehicle trajectories through the Surrogate Safety Assessment Model (SSAM). Factors that could affect crash propensity were identified through the Principal Component Analysis in Negative Binomial Regression. Nomographs were developed from the models which can be used by traffic engineers in left-turn phasing decisions with enhanced safety considerations.

In this research, I carefully and coherently examine Chapter 19 of the Children's Act 38 of 2005 as the first legislation to afford surrogate motherhood agreements legal recognition in South Africa. I argue that the application of Chapter 19 imposes a number of unwarranted limitations on several of the constitutional rights of the parties to a surrogacy agreement. In addition, I propose that Chapter 19 is not in accordance with the principal of the best interests of the child. I examine the history of surrogate motherhood in South Africa and establish that, prior to the enactment of Chapter 19, no legislation expressly afforded surrogate motherhood agreements legal recognition. Hence, prior to the enactment of Chapter 19, parties who entered surrogacy agreements could, first, not rely on the agreement to enforce contractual obligations, and secondly, the legal positions of the parties to the agreement were uncertain. Thirdly, a child born of a surrogacy agreement was seen as the child of the surrogate mother and not of the commissioning parents.

The purpose of this research is to understand what role surrogate boycotts can play on companies targeted by a surrogate boycott and how companies can work with their marketing strategies to reduce the effects of a surrogate boycotts. We have used a qualitative approach in this thesis, with multiple cases studies as a method. Our empirical data was collected through personal interviews with people from the Danish companies: Mette Munk A/S, Nordex Foods, Quantum and Arla Foods. We conclude that the role that surrogate boycotts play on companies involved is that it effect the company’s financial situation and gives the problems with the image. We also conclude that companies can work with promotion, altering their products and choosing other ways to distribute their products in order to reduce the effects of a surrogate boycott. Also different marketing tools and strategies can be used during different stages of the boycott in order to be most effective.

This thesis presents a method for streamlining the process of obtaining and interpreting quantitative data for the purpose of creating a low-fidelity modeling and simulation environment. By providing a more efficient means for obtaining such information, quantitative analyses become much more practical for decision-making in the very early stages of design, where traditionally, quants are viewed as too expensive and cumbersome for concept evaluation. The method developed to address this need uses a Systematic Process for Adaptive Concept Exploration (SPACE). In the SPACE method, design space exploration occurs in a sequential fashion; as data is acquired, the sampling scheme adapts to the specific problem at hand. Previously gathered data is used to make inferences about the nature of the problem so that future samples can be taken from the more interesting portions of the design space. Furthermore, the SPACE method identifies those analyses that have significant impacts on the relationships being modeled, so that effort can be focused on acquiring only the most pertinent information. The results show that the combination of a tailored data set, and an informed model structure work together to provide a meaningful quantitative representation of the system while relying on only a small amount of resources to generate that information. In comparison to more traditional modeling and simulation approaches, the SPACE method provides a more accurate representation of the system using fewer resources to generate that representation. For this reason, the SPACE method acts as an enabler for decision making in the very early design stages, where the desire is to base design decisions on quantitative information while not wasting valuable resources obtaining unnecessary high fidelity information about all the candidate solutions. Thus, the approach enables concept selection to be based on parametric, quantitative data so that informed, unbiased decisions can be made.

Post-Cold War military conflicts have highlighted the need for a flexible, agile joint force responsive to emerging crises around the globe. The 2005 Joint Capabilities Integration and Development System (JCIDS) acquisition policy document mandates a shift away from stove-piped threat-based acquisition to a capability-based model focused on the multiple ways and means of achieving an effect. This shift requires a greater emphasis on scenarios, tactics, and operational concepts during the conceptual phase of design and structured processes for technology evaluation to support this transition are lacking. In this work, a methodology for quantitative technology evaluation for systems-of-systems is defined. Physics-based models of an aircraft system are exercised within a hierarchical, object-oriented constructive simulation to quantify technology potential in the context of a relevant scenario. A major technical challenge to this approach is the lack of resources to support real-time human-in-the-loop tactical decision making and technology analysis. An approach that uses intelligent agents to create a "Meta-General" capable of forecasting strategic and tactical decisions based on technology inputs is used. To demonstrate the synergy between new technologies and tactics, surrogate models are utilized to provide intelligence to individual agents within the framework and develop a set of tactics that appropriately exploit new technologies. To address the long run-times associated with constructive military simulations, neural network surrogate models are implemented around the forecasting environment to enable rapid trade studies. Probabilistic techniques are used to quantify uncertainty and richly populate the design space with technology-infused alternatives. Since a large amount of data is produced in the analysis of systems-of-systems, dynamic, interactive visualization techniques are used to enable "what-if" games on assumptions, systems, technologies, tactics, and evolving threats. The methodology developed in this dissertation is applied to a notional Long Range Strike air vehicle and system architecture in the context of quantitative technology evaluation for the United States Air Force.

Modern reservoir management typically involves simulations of geological models to predict future recovery estimates, providing the economic assessment of different field development strategies. Integrating reservoir data is a vital step in developing reliable reservoir performance models. Currently, most effective strategies for traditional manual history matching commonly follow a structured approach with a sequence of adjustments from global to regional parameters, followed by local changes in model properties. In contrast, many of the recent automatic history matching methods utilize parameter sensitivities or gradients to directly update the fine-scale reservoir properties, often ignoring geological inconsistency. Therefore, there is need for combining elements of all of these scales in a seamless manner. We present a hierarchical streamline-assisted history matching, with a framework of global-local updates. A probabilistic approach, consisting of design of experiments, response surface methodology and the genetic algorithm, is used to understand the uncertainty in the large-scale static and dynamic parameters. This global update step is followed by a streamline-based model calibration for high resolution reservoir heterogeneity. This local update step assimilates dynamic production data. We apply the genetic global calibration to unconventional shale gas reservoir specifically we include stimulated reservoir volume as a constraint term in the data integration to improve history matching and reduce prediction uncertainty. We introduce a novel approach for efficiently computing well drainage volumes for shale gas wells with multistage fractures and fracture clusters, and we will filter stochastic shale gas reservoir models by comparing the computed drainage volume with the measured SRV within specified confidence limits. Finally, we demonstrate the value of integrating downhole temperature measurements as coarse-scale constraint during streamline-based history matching of dynamic production data. We first derive coarse-scale permeability trends in the reservoir from temperature data. The coarse information are then downscaled into fine scale permeability by sequential Gaussian simulation with block kriging, and updated by local-scale streamline-based history matching. he power and utility of our approaches have been demonstrated using both synthetic and field examples.

Protected area site selection is generally carried out using biodiversity data as surrogates. However, reliable and complete biodiversity data is rarely available due to limited resources, time and equipment. Instead of drawing on inadequate biodiversity data, an alternative is to use environmental diversity (ED) as a surrogate in conservation planning. However, there are few studies that use environmental diversity for site selection or that evaluates its efficiency / unfortunately, no such example exists for Turkey, where biodiversity is high but our knowledge about it is unsatisfactory. Hence, this study was carried out to investigate the efficiency of environmental surrogates and the utility of different biological taxa in conservation planning. The objective was to find out the most efficient surrogates, either environmental or biological, for conservation planning, so that limited resources can be used more efficiently to establish an effective protected areas network. The study was carried out in northeastern Turkey, within the Lesser Caucasus ecoregion. The taxonomic groups considered include large mammals, breeding birds, globally threatened reptiles and amphibians, butterflies, highly threatened plants, and ecological communities. The distribution data was taken from a previous study, while climate and topographical data were obtained from various sources and produced through spatio-statistical techniques. Complementarity-based site selection was carried out with Marxan software, where the planning unit was the 100 sq.km. UTM grid square. Various statistical methods, including geographically weighted regression, principal components analysis, and p-median algorithm, were used to determine ED across the units. Performance of different approaches and different sets of surrogates were tested by comparing them to a random null model as well as representation success. Results indicate that endemic or non-endemic highly threatened plant species, butterfly species and ecological communities represent biodiversity better than other taxa in the study area. As such, they can be used on their own as efficient biodiversity surrogates in conservation area planning. Another finding is that highly threatened plant species are required to be used in the site selection process if they need to be represented well / in other words, they are their own surrogates. It was demonstrated that while ED alone can be used as a surrogate to represent biodiversity of an area, they are not as good as biodiversity surrogates themselves. It is also suggested that using species taxa with smaller distributional ranges or taxa that complement each other due to ecological differences as surrogates provide better results. On the other hand, ED might be a more suitable surrogate if resources are very limited or field work is impossible. In such cases, using ED in conjunction with one of the better biodiversity surrogates is probably the best solution.

In this thesis, several Reliability-based Design Optimization (RBDO) methods and algorithms for airplane floor grid layout optimization are proposed. A general RBDO process is proposed and validated by an example. Copula as a mathematical method to model random variable correlations is introduced to discover the correlations between random variables and to be applied in producing correlated data samples for Monte Carlo simulations. Based on Hasofer-Lind (HL) method, a correlated HL method is proposed to evaluate a reliability index under correlation. As an alternative method for computing a reliability index, the reliability index is interpreted as an optimization problem and two nonlinear programming algorithms are introduced to evaluate reliability index. To evaluate the reliability index by Monte Carlo simulation in a time efficient way, a kriging-based surrogate model is proposed and compared to the original model in terms of computing time. Since in RBDO optimization models the reliability constraint obtained by MCS does not have an analytical form, a kriging-based response surface is built. Kriging-based response surface models are usually segment functions that do not have a uniform expression over the design space; however, most optimization algorithms require a uniform expression for constraints. To solve this problem, a heuristic gradient-based direct searching algorithm is proposed. These methods and algorithms, together with the RBDO general process, are applied to the layout optimization of aircraft floor grid structural design.

The developmental processes of lymphopoiesis generate mature B lymphocytes from hematopoietic stem cells through increasingly restricted intermediates. Networks of transcription factors regulate these cell fate choices and are composed of both ubiquitously expressed and B lineage-specific factors. E-protein transcription factors are encoded by the three genes E2A, E2-2 (SEF2-1), and HEB. The E2A gene is required for B cell development and encodes the alternatively spliced proteins E12 and E47. During B lymphocyte development, the cells have to pass several checkpoints verifying the functionality of their antigen receptors. Early in the development, the expression of a pre-B cell receptor (pre-BCR) with membrane-bound immunoglobulin (Ig) heavy chain protein associated with surrogate light chain (SLC) proteins is a critical checkpoint that monitors for functional Ig heavy chain rearrangement. Signaling from the pre-BCR induces survival and a limited clonal expansion. Here it is shown that pre-BCR signaling rapidly down-regulates the SLCs l5 and VpreB and also the co-receptor CD19. Ca2+ signaling and E2A were shown to be essential for this regulation. E2A mutated in its binding site for the Ca2+ sensor protein calmodulin (CaM), and thus with CaM-resistant DNA binding, makes l5, VpreB and CD19 expression resistant to the inhibition following pre-BCR stimulation. Thus, Ca2+ down-regulates SLC and CD19 gene expression upon pre-BCR stimulation through inhibition of E2A by Ca2+/CaM. A general negative feedback regulation of the pre-BCR proteins as well as many co-receptors and proteins in signal pathways from the receptor was also shown. After the ordered recombination of Ig heavy chain gene segments, also Ig light chain gene segments are recombined together to create antibody diversity. The recombinations are orchestrated by the recombination activating gene (RAG) enzymes, other enzymes that cleave/mutate/assemble DNA of the Ig loci, and the transcription factor Pax5. A key feature of the immune system is the concept that one lymphocyte has only one antigen specificity that can be selected for or against. This requires that only one of the alleles of genes for Ig chains is made functional. The mechanism of this allelic exclusion has however been an enigma. Here pre-BCR signaling was shown to down-regulate several components of the recombination machinery including RAG1 and RAG2 through CaM inhibition of E2A. Furthermore, E2A, Pax5 and the RAGs were shown to be in a complex bound to key sequences on the IgH gene before pre-BCR stimulation and instead bound to CaM after this stimulation. Thus, the recombination complex is directly released through CaM inhibition of E2A. Upon encountering antigens, B cells must adapt to produce a highly specific and potent antibody response. Somatic hypermutation (SH), which introduces point mutations in the variable regions of Ig genes, can increase the affinity for antigen, and antibody effector functions can be altered by class switch recombination (CSR), which changes the expressed constant region exons. Activation-induced cytidine deaminase (AID) is the mutagenic antibody diversification enzyme that is essential for both SH and CSR. The AID enzyme has to be tightly controlled as it is a powerful mutagen. BCR signaling, which signals that good antibody affinity has been reached, was shown to inhibit AID gene expression through CaM inhibition of E2A.  SH increases the antigen binding strength by many orders of magnitude. Each round of SH leads to one or a few mutations, followed by selection for increased affinity. Thus, BCR signaling has to enable selection for successive improvements in antibodies (Ab) over an extremely broad range of affinities. Here the BCR is shown to be subject to general negative feedback regulation of the receptor proteins as well as many co-receptors and proteins in signal pathways from the receptor. Thus, the BCR can down-regulate itself to enable sensitive detection of successive improvements in antigen affinity. Furthermore, the feedback inhibition of the BCR signalosome and most of its protein, and most other gene regulations by BCR stimulation, is through inhibition of E2A by Ca2+/CaM. Differentiation to Ab-secreting plasmablasts and plasma cells is antigen-driven. The interaction of antigen with the membrane-bound Ab of the BCR is critical in determining which clones enter the plasma cell response. Genome-wide analysis showed that differentiation of B cells to Ab-secreting cell is induced by BCR stimulation through very fast regulatory events, and induction of IRF-4 and down-regulation of Pax5, Bcl-6, MITF, Ets-1, Fli-1 and Spi-B gene expressions were identified as immediate early events. Ca2+ signaling through CaM inhibition of E2A was essential for these rapid down-regulations of immediate early genes after BCR stimulation in initiation of plasma cell differentiation.

Les Algorithmes Évolutionnaires (AEs) ont été très étudiés en raison de leur capacité à résoudre des problèmes d'optimisation complexes en utilisant des opérateurs de variation adaptés à des problèmes spécifiques. Une recherche dirigée par une population de solutions offre une bonne robustesse par rapport à un bruit modéré et la multi-modalité de la fonction optimisée, contrairement à d'autres méthodes d'optimisation classiques telles que les méthodes de quasi-Newton. La principale limitation de AEs, le grand nombre d'évaluations de la fonction objectif, pénalise toutefois l'usage des AEs pour l'optimisation de fonctions chères en temps calcul. La présente thèse se concentre sur un algorithme évolutionnaire, Covariance Matrix Adaptation Evolution Strategy (CMA-ES), connu comme un algorithme puissant pour l'optimisation continue boîte noire. Nous présentons l'état de l'art des algorithmes, dérivés de CMA-ES, pour résoudre les problèmes d'optimisation mono- et multi-objectifs dans le scénario boîte noire. Une première contribution, visant l'optimisation de fonctions coûteuses, concerne l'approximation scalaire de la fonction objectif. Le meta-modèle appris respecte l'ordre des solutions (induit par la valeur de la fonction objectif pour ces solutions) ; il est ainsi invariant par transformation monotone de la fonction objectif. L'algorithme ainsi défini, saACM-ES, intègre étroitement l'optimisation réalisée par CMA-ES et l'apprentissage statistique de meta-modèles adaptatifs ; en particulier les meta-modèles reposent sur la matrice de covariance adaptée par CMA-ES. saACM-ES préserve ainsi les deux propriété clé d'invariance de CMA-ES~: invariance i) par rapport aux transformations monotones de la fonction objectif; et ii) par rapport aux transformations orthogonales de l'espace de recherche. L'approche est étendue au cadre de l'optimisation multi-objectifs, en proposant deux types de meta-modèles (scalaires). La première repose sur la caractérisation du front de Pareto courant (utilisant une variante mixte de One Class Support Vector Machone (SVM) pour les points dominés et de Regression SVM pour les points non-dominés). La seconde repose sur l'apprentissage d'ordre des solutions (rang de Pareto) des solutions. Ces deux approches sont intégrées à CMA-ES pour l'optimisation multi-objectif (MO-CMA-ES) et nous discutons quelques aspects de l'exploitation de meta-modèles dans le contexte de l'optimisation multi-objectif. Une seconde contribution concerne la conception d'algorithmes nouveaux pour l'optimi\-sation mono-objectif, multi-objectifs et multi-modale, développés pour comprendre, explorer et élargir les frontières du domaine des algorithmes évolutionnaires et CMA-ES en particulier. Spécifiquement, l'adaptation du système de coordonnées proposée par CMA-ES est couplée à une méthode adaptative de descente coordonnée par coordonnée. Une stratégie adaptative de redémarrage de CMA-ES est proposée pour l'optimisation multi-modale. Enfin, des stratégies de sélection adaptées aux cas de l'optimisation multi-objectifs et remédiant aux difficultés rencontrées par MO-CMA-ES sont proposées.