Human surrogates are representations of living humans, commonly adopted to better understand human response to impacts. Though surrogates have been widely used in automotive, defence and medical industries with varying levels of biofidelity, their primary application in the sporting goods industry has been through primitive rigid anvils used in assessing personal protective equipment (PPE) effectiveness. In sports, absence from competition is an important severity measure and soft tissue injuries such as contusions and lacerations are serious concerns. Consequently, impact surrogates for the sporting goods industry need a more subtle description of the relevant soft tissues to assess impact severity and mitigation accurately to indicate the likelihood of injury. The fundamental aim for this research study was to establish a method to enable the development of superior, complementary, increasingly complex synthetic and computational impact surrogates for improved assessment of sports personal protective equipment. With a particular focus on the thigh segment, research was conducted to evaluate incremental increases in surrogate complexity. Throughout this study, empirical assessment of synthetic surrogates and computational evaluation using finite element (FE) models were employed to further knowledge on design features influencing soft tissue surrogates in a cost and time efficient manner. To develop a more representative human impact surrogate, the tissue structures considered, geometries and materials were identified as key components influencing the mechanical response of surrogates. As a design tool, FE models were used to evaluate the changes in impact response elicited with different soft tissue layer configurations. The study showed the importance of skin, adipose, muscle and bone tissue structures and indicated up to 15.4% difference in maximum soft tissue displacement caused by failure to represent the skin layer. FE models were further used in this capacity in a shape evaluation study from which it was determined that a full-scale anatomically contoured thigh was necessary to show the full diversity of impact response phenomena exhibited. This was particularly pertinent in PPE evaluations where simple surrogate shapes significantly underestimated the magnitudes of displacements exhibited (up to 155% difference) when rigid shell PPE was simulated under impact conditions. Synthetic PDMS silicone simulants were then fabricated for each of the organic soft tissues to match their dynamic responses. The developed simulants exhibited a superior representation of the tissues when compared to previous single material soft tissue simulant, Silastic 3483, which showed 324%, 11,140% and -15.8% greater differences than the PDMS when compared to previously reported target organic tissue datasets for relaxed muscle, skin and adipose tissues respectively. The impact response of these PDMS surrogates were compared in FE models with previously used single material simulants in representative knee and cricket ball sports impact events. The models were each validated through experimental tests and the PDMS simulants were shown to exhibit significantly closer responses to organic tissue predictions across all impact conditions and evaluation metrics considered. An anatomically contoured synthetic thigh surrogate was fabricated using the PDMS soft tissue simulants through a novel multi-stage moulding process. The surrogate was experimentally tested under representative sports impact conditions and showed a good comparison with FE model predictions with a maximum difference in impactor displacements and peak accelerations of +6.86% and +12.5% respectively at velocities between 2 - 4 m.s-1. The value of increased biofidelity in the anatomical synthetic and virtual surrogate thighs has been proven through the incremental adoption of important surrogate elements (tissue structures, material and geometries). The predictive capabilities of each surrogate have been demonstrated through their parallel developments and staged comparisons with idealised organic tissue responses. This increase in biofidelity is introduced at modestly higher cost compared to Silastic 3483, but, given the benefits of a more representative human impact response for PPE evaluations, this is shown to be worthwhile.

People with disabilities are often desexualized and excluded from many aspects of society. In America, historical and societal conditions contributed to the development of Surrogate Partner Therapy, designed to build client self-awareness and skills in the areas of physical and emotional intimacy. Surrogate Partner Therapists are certified through the International Professional Surrogates Association (IPSA). IPSA and other organizations work in a highly stigmatized field providing sexual education and experiences for marginalized people, including people with disabilities.

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The objective of this study is to develop a new modified Bayesian Kriging (MBKG) surrogate modeling method that can be used to carry out confidence-based reliability-based design optimization (RBDO) for problems in which simulation analyses are inherently noisy and standard Kriging approaches fail. The formulation of the MBKG surrogate modeling method is presented, and the full conditional distributions of the unknown MBKG parameters are derived and coded into a Gibbs sampling algorithm. Using the coded Gibbs sampling algorithm, Markov chain Monte Carlo is used to fit the MBKG surrogate model. A sequential sampling method that uses the posterior credible sets for inserting new design of experiment (DoE) sample points is proposed. The sequential sampling method is developed in such a way that the new DoE sample points added will provide the maximum amount of information possible to the MBKG surrogate model, making it an efficient and effective way to reduce the number of DoE sample points needed. Therefore, it improves the posterior distribution of the probability of failure efficiently. Finally, a confidence-based RBDO method using the posterior distribution of the probability of failure is developed. The confidence-based RBDO method is developed so that the uncertainty of the MBKG surrogate model is included in the optimization process. A 2-D mathematical example was used to demonstrate fitting the MBKG surrogate model and the developed sequential sampling method that uses the posterior credible sets for inserting new DoE. A detailed study on how the posterior distribution of the probability of failure changes as new DoE are added using the developed sequential sampling method is presented. Confidence-based RBDO is carried out using the same 2-D mathematical example. Three different noise levels are used for the example to compare how the MBKG surrogate modeling method, the sequential sampling method, and the confidence-based RBDO method behave for different amounts of noise in the response. A comparison of the optimization results for the three different noise levels for the same 2-D mathematical example is presented. A 3-D multibody dynamics (MBD) engineering block-car example is presented. The example is used to demonstrate using the developed methods to carry out confidence-based RBDO for an engineering problem that contains noise in the response. The MBD simulations for this example were done using the commercially available MBD software package RecurDyn. Deterministic design optimization (DDO) was first done using the MBKG surrogate model to obtain the mean response values, which then were used with standard Kriging methods to obtain the sensitivity of the responses. Confidence-based RBDO was then carried out using the DDO solution as the initial design point.

A stand-alone genetic algorithm (GA) and an surrogate-based optimization (SBO) combined with a GA were compared for accuracy and performance. Comparisons took place using the Ackley Function and Rastrigin's Function, two functions with multiple local maxima and minima that could cause problems for more traditional optimization methods, such as a gradient-based method. The GA and SBO with GA were applied to the functions through a fortran interface and it was found that the SBO could use the same number of function evaluations as the GA and achieve at least 5 orders of magnitude greater accuracy through the use of surrogate evaluations. The two optimization methods were used in conjunction with computational fluid dy- namics (CFD) analysis to optimize the shape of a bumpy airfoil section. Results of opti- mization showed that the use of an SBO can save up to 553 hours of CPU time on 196 cores when compared to the GA through the use of surrogate evaluations. Results also show the SBO can achieve greater accuracy than the GA in a shorter amount of time, and the SBO can reduce the negative effects of noise in the simulation data while the GA cannot.

B lymphopoiesis in aged mice is characterized by reduced B cell precursors and an altered antibody repertoire. Aged mice maintain an ordinarily minor pool of early c-kit+ pre-B cells, indicative of poor preBCR expression, even as preBCR competent early pre-B cells are significantly reduced. Therefore, in aged mice, preBCR-mediated B2 B lymphopoiesis is significantly diminished; likely as a consequence of poor surrogate light chain expression. Notably, the remnant B1 B cell lineage present in adult bone marrow is retained in aged mice as evidenced by normal numbers (~0.3%) of Lin-CD19+B220low/- B1 B cell precursors. Of interest, B1 progenitors express substantially less lambda 5 surrogate light chain protein than do B2 pro-B cells and the surrogate light chain levels are further reduced in aged mice. B cells derived from putatively preBCR-deficient precursors, either B2 c-kit+ B cell precursors or B1 B cell progenitors, from either young or aged mice, generate new B cells in vitro that are biased to larger size, higher levels of CD43/S7, and decreased kappa light chain expression. Notably, immature B cells in aged bone marrow exhibit a similar phenotype in vivo, consistent with the changes seen in B cell precursor subpopulations. In aged mice, the B2 pathway is partially blocked with limited preBCR expression and signaling; however, continued B cell development via preBCR-deficient pathways, including B1 pathways, is observed. Increased generation of new B cells by these alternative pathways may contribute to altered phenotype, repertoire, and function in senescence.

The interaction between neuronal cells can be identified as the computing mechanism of the brain. Neurons are complex cells that do not operate in isolation, but they are organized in a highly connected network structure. There is experimental evidence that groups of neurons dynamically synchronize their activity and process brain functions at all levels of complexity. A fundamental step to prove this hypothesis is to analyze large sets of single neurons recorded in parallel. Techniques to obtain these data are meanwhile available, but advancements are needed in the pre-processing of the large volumes of acquired data and in data analysis techniques. Major issues include extracting the signal of single neurons from the noisy recordings (referred to as spike sorting) and assessing the significance of the synchrony. This dissertation addresses these issues with two complementary strategies, both founded on the manipulation of point processes under rigorous analytical control. On the one hand I modeled the effect of spike sorting errors on correlated spike trains by corrupting them with realistic failures, and studied the corresponding impact on correlation analysis. The results show that correlations between multiple parallel spike trains are severely affected by spike sorting, especially by erroneously missing spikes. When this happens sorting strategies characterized by classifying only good'' spikes (conservative strategies) lead to less accurate results than tolerant'' strategies. On the other hand, I investigated the effectiveness of methods for assessing significance that create surrogate data by displacing spikes around their original position (referred to as dithering). I provide analytical expressions of the probability of coincidence detection after dithering. The effectiveness of spike dithering in creating surrogate data strongly depends on the dithering method and on the method of counting coincidences. Closed-form expressions and bounds are derived for the case where the dither equals the allowed coincidence interval. This work provides new insights into the methodologies of identifying synchrony in large-scale neuronal recordings, and of assessing its significance. / Die Informationsverarbeitung im Gehirn erfolgt maßgeblich durch interaktive Prozesse von Nervenzellen, sogenannten Neuronen. Diese zeigen eine komplexe Dynamik ihrer chemischen und elektrischen Eigenschaften. Es gibt deutliche Hinweise darauf, dass Gruppen synchronisierter Neurone letztlich die Funktionsweise des Gehirns auf allen Ebenen erklären können. Um die schwierige Frage nach der genauen Funktionsweise des Gehirns zu beantworten, ist es daher notwendig, die Aktivität vieler Neuronen gleichzeitig zu messen. Die technischen Voraussetzungen hierfür sind in den letzten Jahrzehnten durch Multielektrodensyteme geschaffen worden, die heute eine breite Anwendung finden. Sie ermöglichen die simultane extrazelluläre Ableitung von bis zu mehreren hunderten Kanälen. Die Voraussetzung für die Korrelationsanalyse von vielen parallelen Messungen ist zunächst die korrekte Erkennung und Zuordnung der Aktionspotentiale einzelner Neurone, ein Verfahren, das als Spikesortierung bezeichnet wird. Eine weitere Herausforderung ist die statistisch korrekte Bewertung von empirisch beobachteten Korrelationen. Mit dieser Dissertationsschrift lege ich eine theoretische Arbeit vor, die sich der Vorverarbeitung der Daten durch Spikesortierung und ihrem Einfluss auf die Genauigkeit der statistischen Auswertungsverfahren, sowie der Effektivität zur Erstellung von Surrogatdaten für die statistische Signifikanzabschätzung auf Korrelationen widmet. Ich verwende zwei komplementäre Strategien, die beide auf der analytischen Berechnung von Punktprozessmanipulationen basieren. In einer ausführlichen Studie habe ich den Effekt von Spikesortierung in mit realistischen Fehlern behafteten korrelierten Spikefolgen modeliert. Zum Vergleich der Ergebnisse zweier unterschiedlicher Methoden zur Korrelationsanalyse auf den gestörten, sowie auf den ungestörten Prozessen, leite ich die entsprechenden analytischen Formeln her. Meine Ergebnisse zeigen, dass koinzidente Aktivitätsmuster multipler Spikefolgen durch Spikeklassifikation erheblich beeinflusst werden. Das ist der Fall, wenn Neuronen nur fälschlicherweise Spikes zugeordnet werden, obwohl diese anderen Neuronen zugehörig sind oder Rauschartefakte sind (falsch positive Fehler). Jedoch haben falsch-negative Fehler (fälschlicherweise nicht-klassifizierte oder missklassifizierte Spikes) einen weitaus grösseren Einfluss auf die Signifikanz der Korrelationen. In einer weiteren Studie untersuche ich die Effektivität einer Klasse von Surrogatmethoden, sogenannte Ditheringverfahren, welche paarweise Korrelationen zerstören, in dem sie koinzidente Spikes von ihrer ursprünglichen Position in einem kleinen Zeitfenster verrücken. Es zeigt sich, dass die Effektivität von Spike-Dithering zur Erzeugung von Surrogatdaten sowohl von der Dithermethode als auch von der Methode zur Koinzidenzzählung abhängt. Für die Wahrscheinlichkeit der Koinzidenzerkennung nach dem Dithern stelle ich analytische Formeln zur Verfügung. Die vorliegende Arbeit bietet neue Einblicke in die Methoden zur Korrelationsanalyse auf multi-variaten Punktprozessen mit einer genauen Untersuchung von unterschiedlichen statistischen Einflüssen auf die Signifikanzabschätzung. Für die praktische Anwendung ergeben sich Leitlinien für den Umgang mit Daten zur Synchronizitätsanalyse.

This thesis presents a new approach to calibration of hydrologic models using distributed computing framework. Distributed hydrologic models are known to be very computationally intensive and difficult to calibrate. To cope with the high computational cost of the process a Surrogate Model Optimization (SMO) technique that is built for distributed computing facilities is proposed. The proposed method along with two analogous SMO methods are employed to calibrate WATCLASS hydrologic model. This model has been developed in University of Waterloo and is now a part of Environment Canada MESH (Environment Canada community environmental modeling system called Modèlisation Environmentale Communautaire (MEC) for Surface Hydrology (SH)) systems. SMO has the advantage of being less sensitive to "curse of dimensionality" and very efficient for large scale and computationally expensive models. In this technique, a mathematical model is constructed based on a small set of simulated data from the original expensive model. SMO technique follows an iterative strategy which in each iteration the surrogate model map the region of optimum more precisely. A new comprehensive method based on a smooth regression model is proposed for calibration of WATCLASS. This method has at least two advantages over the previously proposed methods: a)it does not require a large number of training data, b) it does not have many model parameters and therefore its construction and validation process is not demanding. To evaluate the performance of the proposed SMO method, it has been applied to five well-known test functions and the results are compared to two other analogous SMO methods. Since the performance of all SMOs are promising, two instances of WATCLASS modeling Smoky River watershed are calibrated using these three adopted SMOs and the resultant Nash-Sutcliffe numbers are reported.

Alzheimer’s disease (AD), a chronic terminal disease, progressively impairs cognitive function resulting in deterioration of intellect, memory, and personality. With disease progression, the surrogate decision-maker becomes more involved in intervention choices and end-of-life (EOL) care, which may or may not be based on patients’ wishes or best practice guidelines. Yet surrogate decision outcomes involve important issues of medical futility, quality of life and death. The purpose of this study was to examine factors that influence surrogate health care decision-making for a family member during the terminal stage of AD. A descriptive, predictive design was used to address the research questions: 1.What is the relationship between surrogate gender and decision motives?; 2. Do structure (surrogate age and gender, attachment, interpersonal conflict), interactional context (elder image, caregiving beliefs), situational context (dementia level), and perception (burden) variables predict the type of decision motive (reward seeking, altruistic, distress reduction, punishment avoidance) used by surrogates’ when making healthcare decisions for their family member with AD?; 3. What healthcare decision choices do surrogate decision-makers make for a family member with AD? A convenience sample of 58 women (67.2%) and men surrogates between the ages of 43 to 84 years of age (M = 62.22, SD = 9.67) living in one urban and several rural cities in a southeastern state were recruited. Participants were recruited during facility meetings for families at 15 long-term care facilities and 1 dementia care assisted living facility. The majority of participants were Caucasian (84.5%). Questionnaires were distributed to participants at a facility meeting. After the study was explained, written informed consent was obtained. Each participant was asked to complete the questionnaire booklet and return via mail in a stamped self-addressed envelope to the researcher. Data were analyzed with descriptive and inferential statistics including frequencies, percentages, means, standard deviations, t-tests, and multiple linear regressions. Types of decision motives did not differ by gender. For the regression models, the independent variables included gender, feelings of attachment, interpersonal conflict with the elder, beliefs about caregiving, dementia level and caregiver burden. For the model predicting punishment avoidance decision motive, simultaneous multiple linear regression results indicated that the overall model significantly predicted the dependent variable. The regression model predicting reward seeking decision motive results indicated that the overall model significantly predicted the dependent variable. Two of the variables, dementia level and surrogate burden, significantly contributed to the variance in the reward seeking decision motive. When asked about the decisions they have been asked to make in the past 12 months, surrogates were asked to make life supportive interventions (pain management and nutritional supplements) more frequently than life extending interventions. The most frequent life extending interventions chosen in descending order of frequency include surgery, central line placement, and feeding tube placement. This study supports the importance of providing surrogate and family information on AD and end-of-life healthcare interventions in a therapeutic and supportive environment. Nursing implications address pain management of the cognitively impaired patient, advocacy for advance directive completion and non-futile care, and patient and family AD education. Health care implications include process for completion of an advance directive and the burden of medical futility.

This thesis presents a new approach to calibration of hydrologic models using distributed computing framework. Distributed hydrologic models are known to be very computationally intensive and difficult to calibrate. To cope with the high computational cost of the process a Surrogate Model Optimization (SMO) technique that is built for distributed computing facilities is proposed. The proposed method along with two analogous SMO methods are employed to calibrate WATCLASS hydrologic model. This model has been developed in University of Waterloo and is now a part of Environment Canada MESH (Environment Canada community environmental modeling system called Modèlisation Environmentale Communautaire (MEC) for Surface Hydrology (SH)) systems. SMO has the advantage of being less sensitive to "curse of dimensionality" and very efficient for large scale and computationally expensive models. In this technique, a mathematical model is constructed based on a small set of simulated data from the original expensive model. SMO technique follows an iterative strategy which in each iteration the surrogate model map the region of optimum more precisely. A new comprehensive method based on a smooth regression model is proposed for calibration of WATCLASS. This method has at least two advantages over the previously proposed methods: a)it does not require a large number of training data, b) it does not have many model parameters and therefore its construction and validation process is not demanding. To evaluate the performance of the proposed SMO method, it has been applied to five well-known test functions and the results are compared to two other analogous SMO methods. Since the performance of all SMOs are promising, two instances of WATCLASS modeling Smoky River watershed are calibrated using these three adopted SMOs and the resultant Nash-Sutcliffe numbers are reported.