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Dental anxiety is a major public health problem that leads to underutilization of dental care and poor oral health. Much research has demonstrated an association between the expectation of pain during dental treatment and dental anxiety; however, not all patients with high pain expectancy develop dental anxiety, suggesting that other factors may impact the degree to which pain expectancy increases dental anxiety. The present study examined whether anxiety sensitivity (AS; the fear of negative consequences of anxiety-related symptoms and sensations) increases the strength of the relationship between pain expectancy and dental anxiety. Participants were 104 adult patients of Temple University Kornberg School of Dentistry clinics. Baseline levels of AS and pain expectancy were assessed using self-report questionnaires. Baseline dental anxiety was assessed using self-report questionnaires and measures of psychological/physiological stress reactivity to films of dental procedures. Participants also underwent a pain expectancy induction, and all indices of dental anxiety were re-assessed following the pain expectancy induction. Linear regression analyses revealed that, in contrast to expectations, AS did not strengthen the relationship between self-reported or laboratory-induced pain expectancy and any indicators of dental anxiety. On the contrary, there was limited evidence that AS may weaken the pain expectancy-dental anxiety relationship. Consistent with previous studies, there was a strong pattern of findings supporting a direct association between pain expectancy and dental anxiety, but limited evidence of a direct association between AS and dental anxiety. AS may not be a strong risk candidate for dental anxiety, and future studies examining other theoretically-relevant vulnerability factors are needed to elucidate pathways through which pain expectancy leads to greater dental anxiety. / Psychology

<p> The thesis proposes a novel method for the computation of the design sensitivity of microwave network parameters. The approach is based on the finite-element method. When combined with the iterative update method (the Broyden method) during the gradient-based optimization process, the approach requires practically no overhead for the computation of the response Jacobian, thus accelerating the optimization. </p> <p> The efficiency and accuracy of the gradient-based optimization and the tolerance analysis greatly depend on the computation of the design sensitivity. However, common commercial full-wave electromagnetic solvers do not provide sensitivity information. With them, the design sensitivities are computed from the response themselves using finite-difference or higher-order approximations at the response level. Consequently, for each design parameter of interest, at least one additional full-wave analysis is performed. </p> <p> The proposed self-adjoint sensitivity analysis (SASA) is so far the most efficient way to extract the sensitivity information for the network parameters with the finite-element method. As an improvement of the adjoint-variable method (AVM), it eliminates the additional system analyses. With one single full-wave analysis, the sensitivities with respect to all design parameters are computed. This significantly improves the efficiency of the sensitivity computations. </p> <p> When employed in gradient-based optimization, the computational overhead of the SASA can be further reduced. Instead of the finite-difference approximation, the system matrix derivatives are updated iteratively using the Broyden update. This reduces the computational overhead of the sensitivity analysis to practically zero. Further, several switching criteria between the Broyden update and the finite-difference approximation of the system matrix derivatives is proposed to guarantee the robust convergence of the optimization algorithm. This leads to our Broyden/finite-difference SASA (B/FD-SASA). </p> <p> The efficiency in terms of CPU time as well as the accuracy of the SASA is verified by several numerical examples, where the reference results are provided through the traditional finite-difference approximations. Also, the efficiency of the B/FD-SASA is validated by a filter design example and a microwave imaging example, with implementations exploiting different gradientbased optimization algorithms. </p> / Thesis / Master of Applied Science (MASc)

This thesis presents research aimed at examining rejection sensitivity in adolescent girls with borderline personality disorder (BPD) features. Although rejection sensitivity has been discussed more generally in the literature, few studies have identified how this construct may contribute to psychopathology in adolescence. There is also limited research regarding outcome behaviours that may be associated with high rejection sensitivity as well as factors that contribute to the manifestation of this construct. Here, this thesis aims to further the understanding of rejection sensitivity in adolescence and provide evidence to support the clinical utility of examining and offering treatment for this factor in youth presenting with BPD features. Although research has shown that BPD and high rejection sensitivity are strongly correlated, few studies have investigated the outcomes that may result from having this comorbidity. In the first paper of this thesis, disordered eating was examined as an outcome behaviour in a clinical sample of girls with BPD features. The results showed that girls who met diagnostic criteria for BPD had significantly higher disordered eating behaviour and that rejection sensitivity, operationalized as fears of abandonment, mediated this relationship. In the second paper of this thesis, the relationship between self-esteem, BPD features and perceived peer rejection was investigated in a longitudinal community sample of adolescent girls. We tested the sociometer hypothesis (Leary, 2005) that self-esteem served as a metric to detect the degree of belongingness in a group context. The results indicated that the relationship between BPD features and perceived peer rejection depended on self-esteem over time. Overall, the two studies presented in this thesis contribute to the knowledge regarding rejection sensitivity in adolescents with BPD features and explores correlates and outcomes of this relationship to aid in the identification of novel treatments to target and ameliorate rejection sensitivity in this population. / Thesis / Master of Science (MSc)

Objective: To evaluate whether abnormal concentrations in cardiac and inflammatory biomarkers could predict reductions in left ventricular ejection fraction (LVEF) for cancer patients undergoing curative treatment. Materials and Methods: Longitudinal testing was performed for high-sensitivity cardiac troponin I (hs-cTnI), N-terminal pro-B-type natriuretic peptide (NT-proBNP), heart-type fatty acid binding protein (H-FABP) and C-reactive protein (CRP) in HER2+ breast cancer (BC) patients receiving adjuvant trastuzumab treatment (n=22) and in lymphoma patients treated with radiotherapy (n=4). Sex-specific and overall upper limit of normal (ULN) cutoffs were used to identify abnormal results with a reduction in LVEF (<50% and decrease of ≥10% from baseline) indicative of cardiotoxicity. A secondary analysis was performed on the BC patients with normal LVEFs (n=12 with baseline prior to chemotherapy through to 6-months on trastuzumab) with 15 blood collections spaced between 6- and 254-days post-baseline LVEF measurement. Results: A majority of the BC patients had evidence of myocardial injury (hs-cTnI >female ULN=90%) or myocardial dysfunction (NT-proBNP >overall ULN=91%) at any timepoint with fewer patients having abnormal CRP or H-FABP concentrations (H-FABP >ULN=14%; CRP >ULN=45%). Myocardial injury and dysfunction were most evident during the first two cycles of trastuzumab treatment, with myocardial injury also evident during this early timeframe in the female lymphoma patients (3 with hs-cTnI >ULN). In the 12 patients who completed trastuzumab with normal LVEFs (median=60% at 6-months), myocardial injury (hs-cTnI >ULN) and dysfunction (NT-proBNP >ULN) was evident in >50% of patients. Four of the 22 patients did develop cardiotoxicity, but there was no difference in biomarker concentrations between patients with or without cardiotoxicity. Conclusion: The use of the recommended ULN cutoffs identified myocardial injury and dysfunction in a majority of cancer patients in this setting. Biomarker assessments did not relate to cardiac functional imaging studies. Future studies are warranted to assess different cutoffs or biomarker combinations for predicting cardiotoxicity. / Thesis / Master of Science (MSc)

In practical multibody systems, various factors such as friction, joint clearances, and external events play a significant role and can greatly influence the optimal design of the system and its controller. This research focuses on the use of gradient-based optimization methods for multibody dynamic systems with the incorporation of joint friction. The dynamic formulation has been derived in using two distinct techniques: Index-1 DAE and the tangent-space formulation in minimal coordinates. It employs a two different approaches for gradient computation: direct sensitivity approach and the adjoint sensitivity approach. After a comprehensive review of different friction models developed over time, the Brown McPhee model is selected as the most suitable due to its accuracy in dynamic simulations and its compatibility with sensitivity analysis. The proposed methodology supports the simultaneous optimization of both the system and its controller. Moreover, the sensitivities obtained using these formulations have been thoroughly validated for numerical accuracy and benchmarked against other friction models that are based on dynamic events for stiction to friction transition. The approach presented is particularly valuable in applications like robotics and servo-mechanical systems where the design and actuation are closely interconnected. To obtain numerical results, a new implementation of the MBSVT (Multi-Body Systems at Virginia Tech) software package, known as MBSVT 2.0, is reprogrammed in Julia and MATLAB to ensure ease of implementation while maintaining high computational efficiency. The research includes multiple case studies that illustrate the advantages of the concurrent optimization of design and control for specific applications. Efficient techniques for control signal parameterization are presented using linear basis functions. A special focus has been made on the computational efficiency of the formulation and various techniques like sparse-matrix algebra and Jacobian-free products have been employed in the implementation. The dissertation concludes with a summary of key results and contributions and the future scope for this research. / Doctor of Philosophy / In simpler terms, this research focuses on improving the design and control of complex mechanical systems, like robots and automotive systems, by considering factors such as friction in the joints. Friction in a system can greatly affect how it performs for the desired task. The research uses a method called gradient-based optimization, which essentially means finding the most optimal parameters of the system and its controller such that they achieve a desired goal in the most optimal way. Before a model for such a system can be developed, various techniques need to be researched for incorporation of friction mathematically. A model known as Brown McPhee friction is one such model suitable for such an analysis. When optimizing any system on a computer, an iterative process needs to be performed which may prove to be very expensive in terms of computational resources required and the time taken to achieve a solution. Hence, proper mathematical and computational techniques need to be employed to ensure that the resources of a computer are utilized in the most efficient way to get the solution is the quickest way possible. Among the various novelties of this research, it is worth noting that this method that allows for simultaneous design and control optimization, which is particularly useful for applications such as robotics and servo-mechanical systems. Considering the design and control together, leads to more efficient and effective systems. The approach is tested using a software package called MBSVT 2.0, which was specifically developed as part of this research. The software is available in 3 languages: Julia, MATLAB and Fortran for universal access to people from various communities. The results from various case studies are presented that demonstrate this simultaneous design and control approach and highlights its effectiveness making the systems more robust and better performing.

Most studies of the investment-cash flow sensitivity hypothesis in the literature compare estimates of the sensitivity coefficients from cross sectional regressions across groups of firms classified into more or less financially constrained groups based on some measure of perceived financial constraint. These studies report conflicting results depending on the classification scheme used to stratify the sample. They have been criticized on conceptual and methodological grounds. In this study we mitigate some of these problems reported in the literature by using the insights from Cleary, Povel and Raith (2007) in a new research design. We test for the significances of the changes in the investment-cash flow sensitivity, in a time-series rather than cross sectional framework, for the same set of firms surrounding an exogenous shock to the firms' information asymmetry. The CPR (2007) model predicts an unambiguous increase (decrease) in investment-cash flow sensitivity when information asymmetry of the firm increases (decreases). Further, by examining the differences in the sensitivity coefficients we expect some of the biases in the coefficient from measurement errors in Q to cancel out. The two events we study are (i) the implementation of SOX which is expected to decrease information asymmetry from improved and increased disclosure and (ii) the deregulation of industries which is expected to increase information asymmetry largely from the lifting of price controls and entry barriers. We report that information asymmetry decreases following SOX and that there is a commensurate decrease in the investment-cash flow sensitivity, pre- to post SOX. The hypothesis that a greater change in investment cash flow sensitivity is associated with a greater change in information asymmetry is only weakly supported by the data. We also report that information asymmetry increases following deregulation with a commensurate increase in investment cash flow sensitivity, pre to post deregulation. The hypothesis of a greater increase in the sensitivity for subsamples with a greater increase in information asymmetry is not supported by the data. Overall, however, the study supports the investment-cash flow sensitivity hypothesis using a research design that corrects for some of the problems identified in the existing literature on the hypothesis. / Ph. D.

Organizational citizenship behavior (OCB) is essential to the smooth functioning of organizations. A vast amount of research examining OCB has established the benefits of such behavior to businesses. In addition, individual- and organizational-level antecedents of citizenship behavior have been widely studied and well established. However, a sound assessment of OCB, which acknowledges the true social cognitive nature of the phenomenon, is yet to be developed. The purpose of this study is two-fold: First, this study seeks to develop a reliable, accurate measure of OCB. Second, this study utilizes the newly developed measure to determine how personal characteristics and situational influences interact to produce helping behavior. More specifically, this study explores how equity sensitivity, locus of control, self-esteem, and affectivity determine whether an employee engages in helping behavior. Further, the current study examines whether situation cue strength moderates the relationship between the aforementioned personality characteristics and an employee's decision to engage in helping behavior. / Master of Science

Variational inverse problems integrate computational simulations of physical phenomena with physical measurements in an informational feedback control system. Control parameters of the computational model are optimized such that the simulation results fit the physical measurements.The solution procedure is computationally expensive since it involves running the simulation computer model (the emph{forward model}) and the associated emph {adjoint model} multiple times. In practice, our knowledge of the underlying physics is incomplete and hence the associated computer model is laden with emph {model errors}. Similarly, it is not possible to measure the physical quantities exactly and hence the measurements are associated with emph {data errors}. The errors in data and model adversely affect the inference solutions. This work develops methods to address the challenges posed by the computational costs and by the impact of data and model errors in solving variational inverse problems. Variational inverse problems of interest here are formulated as optimization problems constrained by partial differential equations (PDEs). The solution process requires multiple evaluations of the constraints, therefore multiple solutions of the associated PDE. To alleviate the computational costs we develop a parallel in time discretization algorithm based on a nonlinear optimization approach. Like in the emph{parareal} approach, the time interval is partitioned into subintervals, and local time integrations are carried out in parallel. Solution continuity equations across interval boundaries are added as constraints. All the computational steps - forward solutions, gradients, and Hessian-vector products - involve only ideally parallel computations and therefore are highly scalable. This work develops a systematic mathematical framework to compute the impact of data and model errors on the solution to the variational inverse problems. The computational algorithm makes use of first and second order adjoints and provides an a-posteriori error estimate for a quantity of interest defined on the inverse solution (i.e., an aspect of the inverse solution). We illustrate the estimation algorithm on a shallow water model and on the Weather Research and Forecast model. Presence of outliers in measurement data is common, and this negatively impacts the solution to variational inverse problems. The traditional approach, where the inverse problem is formulated as a minimization problem in $L_2$ norm, is especially sensitive to large data errors. To alleviate the impact of data outliers we propose to use robust norms such as the $L_1$ and Huber norm in data assimilation. This work develops a systematic mathematical framework to perform three and four dimensional variational data assimilation using $L_1$ and Huber norms. The power of this approach is demonstrated by solving data assimilation problems where measurements contain outliers. / Ph. D.

Multibody dynamics simulations are currently widely accepted as valuable means for dynamic performance analysis of mechanical systems. The evolution of theoretical and computational aspects of the multibody dynamics discipline make it conducive these days for other types of applications, in addition to pure simulations. One very important such application is design optimization for multibody systems. Sensitivity analysis of multibody system dynamics, which is performed before optimization or in parallel, is essential for optimization. Current sensitivity approaches have limitations in terms of efficiently performing sensitivity analysis for complex systems with respect to multiple design parameters. Thus, we bring new contributions to the state-of-the-art in analytical sensitivity approaches in this study. A direct differentiation method is developed for multibody dynamic models that employ Maggi's formulation. An adjoint variable method is developed for explicit and implicit first order Maggi's formulations, second order Maggi's formulation, and first and second order penalty formulations. The resulting sensitivities are employed to perform optimization of different multibody systems case studies. The collection of benchmark problems includes a five-bar mechanism, a full vehicle model, and a passive dynamic robot. The five-bar mechanism is used to test and validate the sensitivity approaches derived in this paper by comparing them with other sensitivity approaches. The full vehicle system is used to demonstrate the capability of the adjoint variable method based on the penalty formulation to perform sensitivity analysis and optimization for large and complex multibody systems with respect to multiple design parameters with high efficiency. In addition, a new multibody dynamics software library MBSVT (Multibody Systems at Virginia Tech) is developed in Fortran 2003, with forward kinematics and dynamics, sensitivity analysis, and optimization capabilities. Several different contact and friction models, which can be used to model point contact and surface contact, are developed and included in MBSVT. Finally, this study employs reference point coordinates and the penalty formulation to perform dynamic analysis for the passive dynamic robot, simplifying the modeling stage and making the robotic system more stable. The passive dynamic robot is also used to test and validate all the point contact and surface contact models developed in MBSVT. / Ph. D.