Compulsory vaccination is one of the fundamental pillars of public health protection that has helped eradicate many previously lethal and widespread diseases from our geographical area. Although the vaccination rates remain high in the Czech Republic, the number of parents who refuse to vaccinate their children is growing. Reasons for this restraint are various. This thesis looks at the issue from the perspective of social constructivism. By implementing in-depth interviews, it tries to contribute to understanding how parents perceive the influence of the media on their decisions on the issue of compulsory vaccination. Other theoretical starting points are the media construction of reality, the theory of representation or biopower, and biopolitics in Michel Foucault's conception. A significant part of the theory also describes the current period, which is characterized by terms such as post-truth or post-factual era, and addresses which role plays new media such as social networks and the internet in the growing mistrust in expert knowledge and institutions. The analytical part is devoted to the analysis of collected data in the grounded theory approach. The identified categories then describe how parents evaluate the role of the media in the debate on compulsory vaccination, how they evaluate...

Probabilistic inference, especially in the Bayesian framework, is a foundation for quantifying uncertainties in geophysical inversion applications. However, due to the presence of high-dimensional datasets and the large-scale nature of geophysical inverse problems, the applicability and scalability of probabilistic inference face significant challenges for such applications. This thesis is dedicated to improving the probabilistic inference algorithms' scalability and demonstrating their applicability for large-scale geophysical inversion applications. In this thesis, I delve into three leading applied approaches in computing the Bayesian posterior distribution in geophysical inversion applications: Laplace's approximation, Markov chain Monte Carlo (MCMC), and variational Bayesian inference. The first approach, Laplace's approximation, is the simplest form of approximation for intractable Bayesian posteriors. However, its accuracy relies on the estimation of the posterior covariance matrix. I study the visualization of the misfit landscape in low-dimensional subspace and the low-rank approximations of the covariance for full waveform inversion (FWI). I demonstrate that a non-optimal Hessian's eigenvalues truncation for the low-rank approximation will affect the approximation accuracy of the standard deviation, leading to a biased statistical conclusion. Furthermore, I also demonstrate the propagation of uncertainties within the Bayesian physics-informed neural networks for hypocenter localization applications through this approach. For the MCMC approach, I develop approximate Langevin MCMC algorithms that provide fast sampling at efficient computational costs for large-scale Bayesian FWI; however, this inflates the variance due to asymptotic bias. To account for this asymptotic bias and assess their sample quality, I introduce the kernelized Stein discrepancy (KSD) as a diagnostic tool. When larger computational resources are available, exact MCMC algorithms (i.e., with a Metropolis-Hastings criterion) should be favored for an accurate posterior distribution statistical analysis. For the variational Bayesian inference, I propose a regularized variational inference framework that performs posterior inference by implicitly regularizing the Kullback-Leibler divergence loss with a deep denoiser through a Plug-and-Play method. I also developed Plug-and-Play Stein Variational Gradient Descent (PnP-SVGD), a novel algorithm to sample the regularized posterior distribution. The PnP-SVGD demonstrates its ability to produce high-resolution, trustworthy samples representative of the subsurface structures for a post-stack seismic inversion application.

The institution of sport has an extensive history of heterosexism, and homophobia, making sport a problematic and unsafe space for gay-identified males. The lack of representation of gay athletes in professional sports highlights the risks associated with openly identifying one’s sexual identity within athletic settings, as gay-identified players often are faced with discrimination and harassment. As a result, gay-identified athletes may choose not to be open about their sexual identity or leave sports altogether as a way to avoid being subjected to discrimination and marginalization within athletics. Grounded in queer theory, and engaging in phenomenology and arts-informed inquiry, this study seeks to explore the ways in which gay-identified males involved in sport negotiate their identies and masculinities. After each participant was interviewed, he wrote a letter to his past self as a way to share what he had come to learn about his process of coming into his own identity and negotiating masculinity. The study interrogates how gay males experience team-based competitive sports differently than individually-based sports and personal fitness activities. Additionally, it explores the personal process of defining and embracing masculinity. Deconstructing the definition of hegemonic masculinity, this study explores how masculinity can be understood in multiple ways. Changing the heterosexist and homophobic discourse that informs the organization of sport on multiple levels creates more opportunities for gay-identified athletes to be welcomed into the arena of sport and safely access the benefits associated with competitive sports and healthy active living activities. This study brings light to the emotional and psychosocial consequences that stem from homophobia and heterosexism’s dominance in our society. The perpetual discrimination and marginalization faced by those who identify as gay males highlights the need for social work’s involvement in justice-oriented research and practice as a way to bring greater equality and equity into our communities. / Thesis / Master of Social Work (MSW)

<p>Extrusion Deposition Additive Manufacturing is a process by which short fiber-reinforced polymers are extruded in a screw and deposited onto a build platform using a set of instructions specified in the form of a machine code. The highly non-isothermal process can lead to undesired effects in the form of residual deformation and part delamination. Process simulations that can predict residual deformation and part delamination have been a thrust area of research to prevent the repeated trial and error process before a useful part has been produced. However, populating the material properties required for the process simulations require extensive characterization efforts. Tackling this experimental bottleneck is the focus of the first half of this research.</p><p>The first contribution is a method to infer the fiber orientation state from only tensile tests. While measuring fiber orientation state using computed tomography and optical microscopy is possible, they are often time-consuming, and limited to measuring fibers with circular cross-sections. The knowledge of the fiber orientation is extremely useful in populating material properties using micromechanics models. To that end, two methods to infer the fiber orientation state are proposed. The first is Bayesian methodology which accounts for aleatoric and epistemic uncertainty. The second method is a deterministic method that returns an average value of the fiber orientation state and polymer properties. The inferred orientation state is validated by performing process simulations using material properties populated using the inferred orientation state. A different challenge arises when dealing with multiple extrusion systems. Considering even the same material printed on different extrusion systems requires an engineer to redo the material characterization efforts (due to changes in microstructure). This, in turn, makes characterization efforts expensive and time-consuming. Therefore, the objective of the second contribution is to address this experimental bottleneck and use prior information about the material manufactured in one extrusion system to predict its properties when manufactured in another system. A framework that can transfer thermal conductivity data while accounting for uncertainties arising from different sources is presented. The predicted properties are compared to experimental measurements and are found to be in good agreement.</p><p>While the process simulations using finite element methods provide a reliable framework for the prediction of residual deformation and part delamination, they are often computationally expensive. Tackling the fundamental challenges regarding this computational bottleneck is the focus of the second half of this dissertation. To that end, as the third contribution, a neural network based solver is developed that can solve parametric partial differential equations. This is attained by deriving the weak form of the governing partial differential equation. Using this variational form, a novel loss function is proposed that does not require the evaluation of the integrals arising out of the weak form using Gauss quadrature methods. Rather, the integrals are identified to be expectation values for which an unbiased estimator is developed. The method is tested for parabolic and elliptical partial differential equations and the results compare well with conventional solvers. Finally, the fourth contribution of this dissertation involves using the new solver to solve heat transfer problems in additive manufacturing, without the need for discretizing the time domain. A neural network is used to solve the governing equations in the evolving geometry. The weak form based loss is altered to account for the evolving geometry by using a novel sequential collocation sampling method. This work forms the foundational work to solve parametric problems in additive manufacturing.</p>

Female survivors of intimate partner violence (IPV) in Nigeria endure harsh and traumatic experiences that affect their rights as women and their well-being. As the phenomenon of IPV persists in Nigeria, it is not only a family problem but a critical social and psychological problem. This study examined Nigerian female survivors’ hidden strength, agency, and resilience, rather than their powerlessness and vulnerability. Analysis of survey questionnaires, interviews, and secondary scholarship reveals that some Nigerian female survivors of IPV are able to cope whilst navigating stressful and traumatic experiences. The results also show that survivors’ ability to thrive and cope under stress not only results from individual traits and use of agency, but also from external support. This study infers sociocultural change and female empowerment. The results propose a need for interventions and further research on the development of the concept of resilience in female Nigerian survivors of IPV.

No description available.

This thesis aims to contribute to ethical discussions on the production of photography in different humanitarian contexts, and in circumstances where it is facilitated by non-governmental and international non-governmental organisations (I/NGOs). Humanitarian photography is often reproduced and circulated in various forms by different actors, highlighting the need for research on the actions, decisions, and interactions that influence how these images are produced. An extensive literature review captures the many ethical challenges surrounding humanitarian photography and provides an overview of related standards. A conceptual framework is then built around informed consent as an accountability mechanism, with consideration for certain relational and situational factors that influence the quality and effectiveness of the process of obtaining consent. Emphasising photographer and organisational accountability, an analysis of how photographers apply the concept of informed consent and its potential as an accountability mechanism is explored through the case study of embedded photographers in search and rescue (SAR) I/NGO operations in the Mediterranean Sea.     Four semi-structured in-depth interviews were completed with photographers involved in SAR I/NGO missions in the Mediterranean from 2015 to 2021. The interviews suggest that a continuous and deliberate process of individual, organisational, and collaborative self-regulation unfolds throughout a mission, largely through verbal communication and body language, in an effort to obtain consent to take or use images of people who have been rescued. Use of formal means such as written consent forms are only rarely used. Photographs during the rescues were generally taken without prior consent, and photographers’ ability to obtain meaningful subsequent informed consent was easily compromised due to the unpredictable conditions during SAR operations and the variation across I/NGO-photographer partnerships. These findings support the need for further dialogue in this context to ensure that practices and processes related to the production of humanitarian photography, such as obtaining informed consent, are compatible with humanitarian principles, respect the rights and dignity of people affected by crisis, and foster greater accountability.

This study presents a procedure for the estimation of stopping behavior of industrial robots with a trained neural network. This trained network is presented as a single channel in a redundant architecture for safety control applications, where its potential for future integration with an analytical model of robot stopping is discussed. Basic physical relations for simplified articulated manipulators are derived, which motivate a choice of quantities to predict robot stopping behavior and inform the training and testing of a network for prediction of stopping distances and times. Robot stopping behavior is considered in the context of relevant standards ISO 10218-1, ISO/TS 15066 and IS0 13849-1, which inform the definitions for safety related stopping distances and times used in this study. Prior work on the estimation of robot stopping behavior is discussed alongside applications of machine learning to the broader field of industrial robotics, and particularly to the cases of prediction of forward and inverse kinematics with trained networks. A state-driven data collection program is developed to perform repeated stopping experiments for a controlled stop on path within a specified sampling domain. This program is used to collect data for a simulated and real robot system. Special attention is given to the identification of meaningful stopping times, which includes the separation of stopping into pre-deceleration and post-deceleration phases. A definition is provided for stopping of a robot in a safety context, based on the observation that residual motion over short distances (less than 1 mm) and at very low velocities (less than 1 mm/s) is not relevant to robot safety. A network architecture and hyperparameters are developed for the prediction of stopping distances and times for the first three joints of the manipulator without the inclusion of payloads. The result is a dual-network structure, where stopping distance predictions from the distance prediction network serve as inputs to the stopping time prediction network. The networks are validated on their capacity to interpolate and extrapolate predictions of robot stopping behavior in the presence of initial conditions not included in the training and testing data. A method is devised for the calculation of prediction errors for training training, testing and validation data. This method is applied both to interpolation and extrapolation to new initial velocity and positional conditions of the manipulator. In prediction of stopping distances and times, the network is highly successful at interpolation, resulting in comparable or nominally higher errors for the validation data set when compared to the errors for training and testing data. In extrapolation to new initial velocity and positional conditions, notably higher errors in the validation data predictions are observed for the networks considered. Future work in the areas of predictions of stopping behavior with payloads and tooling, further applications to collaborative robotics, analytical models of stopping behavior, inclusion of additional stopping functions, use of explainable AI methods and physics-informed networks are discussed. / Master of Science / As the uses for industrial robots continue to grow and expand, so do the need for robust safety measures to avoid, control, or limit the risks posed to human operators and collaborators. This is exemplified by Isaac Asimov's famous first law of robotics - "A robot may not injure a human being, or, through inaction, allow a human being to come to harm." As applications for industrial robots continue to expand, it is beneficial for robots and human operators to collaborate in work environments without fences. In order to ethically implement such increasingly complex and collaborative industrial robotic systems, the ability to limit robot motion with safety functions in a predictable and reliable way (as outlined by international standards) is paramount. In the event of either a technical failure (due to malfunction of sensors or mechanical hardware) or change in environmental conditions, it is important to be able to stop an industrial robot from any position in a safe and controlled manner. This requires real-time knowledge of the stopping distance and time for the manipulator. To understand stopping distances and times reliability, multiple independent methods can be used and compared to predict stopping behavior. The use of machine learning methods is of particular interest in this context due to their speed of processing and the potential for basis on real recorded data. In this study, we will attempt to evaluate the efficacy of machine learning algorithms to predict stopping behavior and assess their potential for implementation alongside analytical models. A reliable, multi-method approach for estimating stopping distances and times could also enable further methods for safety in collaborative robotics such as Speed and Separation Monitoring (SSM), which monitors both human and robot positions to ensure that a safe stop is always possible. A program for testing and recording the stopping distances and times for the robot is developed. As stopping behavior varies based on the positions and speeds of the robot at the time of stopping, a variety of these criteria are tested with the robot stopping program. This data is then used to train an artificial neural network, a machine learning method that mimics the structure of human and animal brains to learn relationships between data inputs and outputs. This network is used to predict both the stopping distance and time of the robot. The network is shown to produce reasonable predictions, especially for positions and speeds that are intermediate to those used to train the network. Future improvements are suggested and a method is suggested for use of stopping distance and time quantities in robot safety applications.

<p>  </p> <p>A <em>digital twin</em> (DT), which keeps track of nuclear reactor history to provide real-time predictions, has been recently proposed for nuclear reactor monitoring. A digital twin can be implemented using either a differential equations-based physics model, or a data-driven machine learning model<strong>. </strong>The principal challenge in physics model-based DT consists of achieving sufficient model fidelity to represent a complex experimental system, while the main challenge in data-driven DT appears in the extensive training requirements and potential lack of predictive ability. </p> <p>In this thesis, we investigate the performance of a hybrid approach, which is based on physics-informed neural networks (PINNs) that encode fundamental physical laws into the loss function of the neural network. In this way, PINNs establish theoretical constraints and biases to supplement measurement data and provide solution to several limitations of purely data-driven machine learning (ML) models. We develop a PINN model to solve the point kinetic equations (PKEs), which are time dependent stiff nonlinear ordinary differential equations that constitute a nuclear reactor reduced-order model under the approximation of ignoring the spatial dependence of the neutron flux. PKEs portray the kinetic behavior of the system, and this kind of approach is the basis for most analyses of reactor systems, except in cases where flux shapes are known to vary with time. This system describes the nuclear parameters such as neutron density concentration, the delayed neutron precursor density concentration and reactivity. Both neutron density and delayed neutron precursor density concentrations are the vital parameters for safety and the transient behavior of the reactor power. </p> <p>The PINN model solution of PKEs is developed to monitor a start-up transient of the Purdue University Reactor Number One (PUR-1) using experimental parameters for the reactivity feedback schedule and the neutron source. The facility under modeling, PUR-1, is a pool type small research reactor located in West Lafayette Indiana. It is an all-digital light water reactor (LWR) submerged into a deep-water pool and has a power output of 10kW. The results demonstrate strong agreement between the PINN solution and finite difference numerical solution of PKEs. We investigate PINNs performance in both data interpolation and extrapolation. </p> <p>The findings of this thesis research indicate that the PINN model achieved highest performance and lowest errors in data interpolation. In the case of extrapolation data, three different test cases were considered, the first where the extrapolation is performed in a five-seconds interval, the second where the extrapolation is performed in a 10-seconds interval, and the third where the extrapolation is performed in a 15-seconds interval. The extrapolation errors are comparable to those of interpolation predictions. Extrapolation accuracy decreases with increasing time interval.</p>

Underserved youth (e.g., from families facing inadequate housing, food insecurity, financial instability) are disproportionately exposed to traumatic experiences (e.g., family discord or violence, neglect, poverty, racism), which can lead to several negative life-long consequences (e.g., affective and somatic disturbances, anxiety, depression, suicidal ideation). Community organizations that target underserved youth may be ideally situated to offset the negative consequences of trauma through leveraging a trauma-sensitive sport model for youth programming. A trauma-sensitive sport model involves a blend of positive youth development approaches (i.e., creating safe environments for youth to experience positive relationships, autonomy, and opportunities to build skills), trauma-sensitive approaches (e.g., considering the potential effects of trauma on youth’s participation and development, and prioritising their needs for safety, voice, empowerment, choice, and collaboration), and program designs that leverage and re-design sporting activities to help youth navigate trauma symptoms, build a social support system, and develop various resilience-related skills (e.g., emotional regulation, decision-making). Little research has examined the implementation and effectiveness of a trauma-sensitive sport model for youth programming in a community setting. Use of this model can contribute to greater capacities of youth sport leaders to promote underserved youth’s healthy participation and development. This dissertation was conducted in partnership with BGC Canada, a national non-profit community organization that serves disadvantaged communities. From 2016-2021, a trauma-sensitive sport model for youth programming was implemented in this organization through the Bounce Back League (BBL) initiative. The purpose of this dissertation was to explore the knowledge-to-action processes involved in translating this model in a community program setting. The knowledge-to-action cycle (KTAC; Graham et al., 2006) was used to conceptually guide the studies carried out in this dissertation, in outlining key phases for consideration in translating a trauma-sensitive sport model. While most of these phases are described in this dissertation, the empirical articles focused on assessing four phases of this cycle, which included: (a) assess barriers and facilitators to knowledge use, (b) select, tailor, and implement interventions, (c) monitor knowledge use, and (d) evaluate outcomes. Intrinsic case study methodologies were used to understand community-based knowledge translation processes and outcomes within the case of the BBL initiative. A community-based participatory research approach was used to engage in equitable collaboration between researchers and community members for the development of this initiative. Utilisation-focused evaluation principles were used to work with community members to determine what to evaluate, how to evaluate, and how results would be used. Grounded in a pragmatic paradigm, a mixed methods research design was used to collect data through the initiative, which included individual and group interviews with leaders, leader-reported logbooks, leaders self-reported questionnaires, leaders' assessments of youth's participation, communications on an online messaging platform (Slack), and researchers' observations of training opportunities and leaders' practices. The purpose of Article 1 was to outline the overarching process of integrating a trauma-sensitive sport model within the BBL program, from 2016-2021. Several stages of program development were described, including: (a) collaboratively planning the program; (b) piloting the program to three clubs; (c) adapting the program using pilot insights; (d) expanding the adapted program to ten clubs; and (e) creating opportunities to maintain, sustain, and scale-out practices throughout grant duration and beyond. Lessons learned regarding the leadership team’s experiences in terms of developing, adapting, and integrating a trauma-sensitive sport model for youth programming in this community context were shared. The purpose of Article 2 was to explore factors involved in the implementation of a trauma-sensitive sport model for youth programming in BBL. This article paralleled the KTAC phase of assess barriers and facilitators to knowledge use. A mixed-methods evaluation of the pilot phase of BBL was conducted. Three clubs participated in training, implementation, and evaluation of BBL. The data were collected through interviews, logbooks, and assessments. The quantitative data were interpreted using descriptive statistics and comparative t-tests; the qualitative data were interpreted using thematic and content analyses. The RE-AIM framework was used to categorise the various processes and outcomes involved in program implementation. The results showed that programs reached a large number of youth but struggled to retain youth from season to season. The leaders perceived that the intentional structure of the program, opportunities to practice self-regulation, relationship focus, and life skill focus, were all linked to positive participation in youth members. Components of leaders' training and program delivery were noted as successful, but the sustained benefits of these successes were challenged by leader turnover and funding limitations. In line with the KTAC phases, insights were generated on what works and what does not in facilitating this type of programming in a community setting for underserved youth and helped inform future adaptations to the program as it was rolled out (discussed in Article 1). The purpose of Article 3 was to explore leaders' learning experiences from participating in an initial training workshop and prior to their implementation of programming. This article paralleled the select, tailor, and implement interventions phase and the evaluate outcomes phase of the KTAC model. The value-creation framework was used to explore learning experiences based on the interactions and values that leaders discussed. Participants were leaders who attended initial training workshops. A mixed-methods approach was used to collect data through observations, interviews, and self-reported questionnaires. The quantitative data were interpreted using descriptive statistics and Wilcoxon Signed-Ranks Tests; the qualitative data were interpreted using thematic analysis. The results showed that the leaders: (a) valued having a variety of learning opportunities that were relevant to their roles and contexts, (b) appreciated the diverse focus on foundational and practical content, and (c) shared an interest to learn how to support trauma-exposed youth and facilitate better programming. Implications were discussed for the improvement of training opportunities to better meet leaders' needs within a community organization and support leaders' intentions to apply knowledge into action. Article 4 builds on the previous study, where the purpose was to explore leaders' learning experiences as they implemented programming and while they participated in continuing training and development activities. This article also paralleled the select, tailor, and implement interventions phase and the evaluate outcomes phase of the KTAC model. The participants were leaders who were involved in implementing BBL at their clubs. A mixed-methods approach was used to collect data through interviews, observations, surveys, and communications on an online messaging platform (Slack). The quantitative data were analysed using descriptive statistics, data charting, and non-parametric analyses; the qualitative data were interpreted using thematic analysis. The results indicated that the leaders learned through various interactions throughout their practice (e.g., implementing programs at their homes sites, receiving mentoring, conversations with peers), and discussed gains in applied value (e.g., program facilitation strategies, youth-support skills), realised value (e.g., youth's receptivity and behaviour change), and transformative value (e.g., transfer of leaders' skills, influence on club culture). Implications were discussed for the improvement of training opportunities to promote ongoing social learning and maintenance of program practices. The purpose of Article 5 was to explore the fidelity and quality of leaders' application of a trauma-sensitive sport model to programming. This article paralleled the monitor knowledge use phase of the KTAC model. The promising practices criteria were used as an evaluation framework to categorise dimensions of quality relevant to program effectiveness. Leaders from 11 BBL programs participated. A mixed-methods approach was used to collect data through observations, interviews, and logbooks. The quantitative data were interpreted using descriptive statistics; the qualitative data were interpreted using thematic analysis. The results showed that: (a) all programs showed evidence of supportive adult and youth relationships, (b) programs led by trained leaders maintained program fidelity and implemented more features to a stronger extent than untrained leaders, (c) trained leaders may have compromised mastery orientation opportunities in favour of other program components. Implications were discussed related to what may facilitate or constrain program fidelity and quality in this setting, and how training and development opportunities can mitigate challenges in leaders' capacities. This dissertation offered an evaluation of the knowledge-to-action processes involved in integrating a trauma-sensitive sport model into BGC Canada. The results of this dissertation provided insights of how BGC Canada leaders learned and facilitated a trauma-sensitive sport model for youth programming, the differences training and development may contribute to the quality of their program practices, and how involvement in this initiative resulted in changes in leaders' behaviours, skills, and identities, as well as positive youth developmental outcomes. Practical implications were shared on how BGC Canada and similar community organizations can enhance their partnership and facilitate these interventions. As well, the value of taking a systems-based approach to planning future interventions with a trauma-sensitive sport model was also discussed to maximise multi-level impacts. Academic implications were shared on how future research can also take a systems-based approach to evaluating knowledge translation processes in youth sport interventions.