Relying on the perspectives of critical disability studies and mad studies, this graduate thesis seeks to uncover the experiences of youth with undiagnosed mental health issues who have been streamed into alternative education. Guided by methodological principles of interpretive phenomenological analysis and arts-informed inquiry, the 5 participants in this study were invited to a focus group where they could engage in an arts-based activity, meant to provide the opportunity to reflect on their experience, build rapport with the researcher, express themselves through alternative means, and connect with peers who have shared experience. Participants were then invited to discuss their experiences with the topic in a one-on-one, semi-structured interview. This study reveals the ways in which the system of education, school communities, teachers, and social workers can support youth who are not diagnosed with a mental illness but still experience mental health challenges that impede on their school experience. Supported by mad studies, this study reveals how peer support has become the method of mental health response and treatment through which students feel is most effective. This study also challenges medical hegemony and the ways in which access to services is dependent on medical diagnoses. Finally, this study reminds stakeholders of the value of building trusting and empathic relationships between school staff and students. School communities and school boards are challenged to think about the structuring of their systems, and the ways in which they may present barriers to the success of all students regardless of ability and/or need. / Thesis / Master of Social Work (MSW)

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This dissertation consists of two essays. The first essay develops a new methodology for estimating the probability of informed trading from the observed quotes and depths, by extending the Copeland and Galai (1983) model. This measure (PROBINF) can be computed for each quote and it represents the specialist's ex-ante estimate of the probability of informed trading. I show that PROBINF exhibits a strong and robust relationship with the observed level of insider trading and with measures of the price impact of trades (ë) estimated based on the models of Glosten and Harris (1988), Madhavan and Smidt (1991) and Foster and Viswanathan (1993). In contrast, the alternative measure of the probability of informed trading (PIN) developed by Easley, Kiefer, O'Hara and Paperman (1996) exhibits a weaker and less robust relationship with insider trading and price impact of trades. The time series pattern of PROBINF in an intra-day analysis around earnings announcement is consistent with previous findings regarding informed trading. An important advantage of PROBINF over PIN and other measures of information asymmetry such as price impact of trades and adverse selection component of the spread is that, unlike these measures, it can be estimated for each quote, and thus can also be used to measure intra-day changes in informed trading and information asymmetry. In the second essay, I examine whether the underwriting syndicate composition influences the secondary market liquidity for initial public offerings (IPOs). Specifically, I argue that co-managers improve the liquidity of IPOs through the other services they provide, besides market making. Using a comprehensive sample of initial public offerings completed between January 1993 and December 2005, I find that IPOs with a high number of co-managers in their syndicates have lower spreads and a lower level of information asymmetry in the aftermarket. I argue that the information produced during the premarket and the analyst coverage in the aftermarket are the main channels through which co-managers mitigate the information asymmetry risk in the secondary market. / Ph. D.

Smart manufacturing is a revolutionary domain integrating advanced sensing technology, machine learning methods, and the industrial internet of things (IIoT). The development of sensing technology provides large amounts and various types of data (e.g., profile, image, point cloud, etc.) to describe each stage of a manufacturing process. The machine learning methods have the advantages of efficiently and effectively processing and fusing large-scale datasets and demonstrating outstanding performance in different tasks (e.g., diagnosis, monitoring, etc.). Despite the advantages of incorporating machine learning methods into smart manufacturing, there are some widely existing concerns in practice: (1) Most of the edge devices in the manufacturing system only have limited memory space and computational capacity; (2) Both the performance and interpretability of the data analytics method are desired; (3) The connection to the internet exposes the manufacturing system to cyberattacks, which decays the trustiness of data, models, and results. To address these limitations, this dissertation proposed systematic engineering-driven machine learning methods to improve the system intelligence for smart manufacturing. The contributions of this dissertation can be summarized in three aspects. First, tensor decomposition is incorporated to approximately compress the convolutional (Conv) layer in Deep Neural Network (DNN), and a novel layer is proposed accordingly. Compared with the Conv layer, the proposed layer significantly reduces the number of parameters and computational costs without decaying the performance. Second, a physics-informed stochastic surrogate model is proposed by incorporating the idea of building and solving differential equations into designing the stochastic process. The proposed method outperforms pure data-driven stochastic surrogates in recovering system patterns from noised data points and exploiting limited training samples to make accurate predictions and conduct uncertainty quantification. Third, a Wasserstein-based out-of-distribution detection (WOOD) framework is proposed to strengthen the DNN-based classifier with the ability to detect adversarial samples. The properties of the proposed framework have been thoroughly discussed. The statistical learning bound of the proposed loss function is theoretically investigated. The proposed framework is generally applicable to DNN-based classifiers and outperforms state-of-the-art benchmarks in identifying out-of-distribution samples. / Doctor of Philosophy / The global industries are experiencing the fourth industrial revolution, which is characterized by the use of advanced sensing technology, big data analytics, and the industrial internet of things (IIoT) to build a smart manufacturing system. The massive amount of data collected in the engineering process provides rich information to describe the complex physical phenomena in the manufacturing system. The big data analytics methods (e.g., machine learning, deep learning, etc.) are developed to exploit the collected data to complete specific tasks, such as checking the quality of the product, diagnosing the root cause of defects, etc. Given the outstanding performances of the big data analytics methods in these tasks, there are some concerns arising from the engineering practice, such as the limited available computational resources, the model's lack of interpretability, and the threat of hacking attacks. In this dissertation, we propose systematic engineering-driven machine learning methods to address or mitigate these widely existing concerns. First, the model compression technique is developed to reduce the number of parameters and computational complexity of the deep learning model to fit the limited available computational resources. Second, physics principles are incorporated into designing the regression method to improve its interpretability and enable it better explore the properties of the data collected from the manufacturing system. Third, the cyberattack detection method is developed to strengthen the smart manufacturing system with the ability to detect potential hacking threats.

The prevalence of distressing content on social media raises concerns about users' mental well-being, prompting the use of trigger warnings (TW) and content warnings (CW). However, varying practices across platforms indicate a lack of clarity among users regarding these warnings. To gain insight into how users experience and use these warnings, we conducted interviews with 15 regular social media users. Our findings show that users generally have a positive view of warnings, but there are differences in how they understand and use them. Challenges related to using TW/CW on social media emerged, making it a complex decision when dealing with such content. These challenges include determining which topics require warnings, navigating logistical complexities related to usage norms, and considering the impact of warnings on social media engagement. We also found that external factors, such as how the warning and content are presented, and internal factors, such as the viewer's mindset, tolerance, and level of interest, play a significant role in the user's decision-making process when interacting with content that has TW/CW. Participants emphasized the need for better education on warnings and triggers in social media and offered suggestions for improving warning systems. They also recommended post-trigger support measures. The implications and future directions include promoting author accountability, introducing nudges and interventions, and improving post-trigger support to create a more trauma-informed social media environment. / Master of Science / In today's world of social media, you often come across distressing content that can affect your mental well-being. To address this concern, platforms and content authors use something called trigger warnings (TW) and content warnings (CW) to alert users about potentially upsetting content. However, different platforms have different ways of using these warnings, which can be confusing for users. To better understand how people like you experience and use these warnings, we conducted interviews with 15 regular social media users. What we found is that, in general, users have a positive view of these warnings, but there are variations in how they understand and use them. Using TW/CW on social media can be challenging because it involves deciding which topics should have warnings, dealing with the different rules on each platform, and thinking about how warnings affect people's engagement with content. We also discovered that various factors influence how people decide whether to engage with warned content. These factors include how the warning and content are presented and the person's own mindset, tolerance for certain topics, and level of interest. Our study participants highlighted the need for better education about warnings and triggers on social media. They also had suggestions for improving how these warnings are used and recommended providing support to users after they encounter distressing content. Looking ahead, our findings suggest the importance of holding content creators accountable, introducing helpful tools and strategies, and providing better support to make social media a more empathetic and supportive place for all users.

BACKGROUND: For years, the trauma of acute inpatient psychiatric treatment has been studied. Trauma-informed models have been created to reduce the trauma of receiving care. These models primarily focus on patient-provider relationships and not the interpersonal dynamics between patients on acute psychiatric units. METHODS: A literature review via Temple University Library and Google Scholar databases as well as interviews with mental health professionals were conducted on the current trauma prevention initiatives in mental healthcare and on strategies to strengthen interpersonal relationships between patients in acute psych units and to quell patients’ perceived risk of harm from one another. RESULTS: A set of five principles is proposed for fostering community and safety in acute adult inpatient psychiatric units as it pertains to the interpersonal relationships between patients. These principles include: 1) Fostering a sense of community within the patient population 2) Rethinking the physical space to reduce patient stress and therefore reduce patient aggression 3) Providing a mentorship program led by peer mentors from the community 4) Providing better mental health education and awareness within society 5) Bridging the gap between the community and inpatient psychiatry. CONCLUSION: The five principles of this thesis can aid in positively transforming patients’ experiences in acute psychiatric units. This transformation requires a significant amount of activism and collaboration in order to stop repeating the cycles of trauma seen within the psychiatric field. / Urban Bioethics

Extending far beyond migration to a new home, the cultural, emotional, and mental plight of immigration plagues immigrants and refugees of all ages. Nonetheless, immigrant youth are commonly overlooked in acculturation studies. This mixed methods approach to arts-informed research examined the acculturation strategies adopted by immigrant and refugee youth attending community-based programs. Through the use of participant drawings, the think-aloud technique, and the Acculturation, Habits, and Interests Multicultural Scale for Adolescents (AHIMSA) instrument, this research also examines the ways in which immigrant and refugee participants communicate their cultural paradigms. The findings emerging from this study illustrate that immigrant and refugee youth cope with cultural transitions through varied approaches that integrate expressions of individuality and cultural behaviors. By exploring six key findings, this study contributes to literature examining acculturation in youth populations as it provides an analysis of cultural transition that expands beyond traditional examinations of cultural behaviors, and highlights the importance of expressing individuality, values, and interests, in the acculturation process of immigrant youth. / Ph. D. / Extending far beyond migration to a new home, the cultural, emotional, and mental plight of immigration plagues immigrants and refugees of all ages. Nonetheless, immigrant youth are commonly overlooked in acculturation studies. This mixed methods research study uses participant drawings, the think-aloud approach, and the Acculturation, Habits, and Interests Multicultural Scale for Adolescents (AHIMSA) instrument to examine the acculturation strategies adopted by immigrant and refugee youth attending community-based afterschool programs. The findings of this study suggest that immigrant and refugee youth cope with cultural transitions through varied approaches that integrate expressions of individuality and cultural behaviors. Exploring six key findings, this study contributes to the literature examining acculturation in youth populations as it provides analysis of cultural transitions that expands beyond traditional examinations of cultural behaviors and highlights the importance of expressing individuality, values, and interests.

京都大学 / 新制・課程博士 / 博士(人間・環境学) / 甲第25366号 / 人博第1108号 / 新制||人||259(附属図書館) / 京都大学大学院人間・環境学研究科共生人間学専攻 / (主査)教授 日置 尋久, 教授 立木 秀樹, 准教授 櫻川 貴司, 准教授 深沢 圭一郎, 教授 小山田 耕二 / 学位規則第4条第1項該当 / Doctor of Human and Environmental Studies / Kyoto University / DGAM

Early stressors like trauma can lead to developmental changes that have life-long negative health consequences (Merikangas et al., 2010; Anda et al., 2006). Approximately 1 in 4 youth experience substantial trauma during their developmental years (Merikangas et al., 2010; Duke, Pettingell, McMorris, and Borowsky, 2010). Such findings suggest the need for early intervention and treatment for adolescents exposed to traumatic events and adversity. Ideally, adolescents could be treated within primary care settings where parents overwhelmingly seek services for their children. Primary care settings are sought out at a 94% to 97% rate of services as compared to only a 4% to 33% rate of parents seeking out mental health services (Guevara et al., 2001). Unfortunately, no adolescent trauma-informed interventions have yet been adapted for use in primary care (Glowa, Olson, and Johnson, 2016). This study aimed to fill this critical gap between adolescent mental health issues associated with trauma and adverse childhood experiences and the lack of treatment in integrated primary care settings. The need for trauma-informed treatment for adolescents who have experienced trauma and adverse experiences is especially salient as evidence-based treatment for adolescents in this setting is limited. A modified Delphi approach was employed to address this gap in the research. Two rounds of questionnaires and focus groups were utilized with a panel of experts and youth stakeholders to gain consensus on treatment recommendations. Ultimately, expert panelists and youth stakeholders identified 59 recommendations for adolescent trauma treatment to be delivered in integrated primary care settings. / Doctor of Philosophy / Childhood trauma can have negative health, social, and educational outcomes that extend into adulthood and over one’s lifespan (Black, Woodworth, Tremblay, & Carpenter, 2012; Merikangas et al., 2010). Approximately 1 in 4 youth today experience trauma (Duke et al., 2010). Trauma can include a variety of things such as physical, sexual, or emotional abuse; being the victim of a crime; witnessing violence in the home; living through a natural disaster or experiencing an accident (Anda et al., 2006; APA, 2017). The frequency of trauma in adolescence suggests the need for early intervention and treatment. Ideally, adolescents could be treated within primary care settings where parents and adolescents frequently seek care services. Unfortunately, no adolescent trauma interventions have been created for this setting (Glowa, Olson, & Johnson, 2016). This study was designed to improve the treatment of adolescent trauma in primary care settings. For this research study a modified Delphi technique was used. Two rounds of questionnaires and focus groups were utilized with participants that consisted of a panel of experts from the field and youth aged 14-18 years old. Ultimately, the study participants made 59 recommendations for adolescent trauma treatment to be delivered in primary care settings.

Metal additive manufacturing (AM) is an emerging technology for producing parts with virtually no constraint on the geometry. AM builds a part by depositing materials in a layer-by-layer fashion. Despite the benefits in several critical applications, quality issues are one of the primary concerns for the widespread adoption of metal AM. Addressing these issues starts with a better understanding of the underlying physics and includes monitoring and controlling the process in a real-world manufacturing environment. Digital Twins (DTs) are virtual representations of physical systems that enable fast and accurate decision-making. DTs rely on Artificial Intelligence (AI) to process complex information from multiple sources in a manufacturing system at multiple levels. This information typically comes from partially known process physics, in-situ sensor data, and ex-situ quality measurements for a metal AM process. Most current AI models cannot handle ill-structured information from metal AM. Thus, this work proposes three novel machine-learning methods for improving the quality of metal AM processes. These methods enable DTs to control quality in several processes, including laser powder bed fusion (LPBF) and additive friction stir deposition (AFSD). The proposed three methods are as follows 1. Process improvement requires mapping the process parameters with ex-situ quality measurements. These mappings often tend to be non-stationary, with limited experimental data. This work utilizes a novel Deep Gaussian Process-based Bayesian optimization (DGP-SI-BO) method for sequential process design. DGP can model non-stationarity better than a traditional Gaussian Process (GP), but it is challenging for BO. The proposed DGP-SI-BO provides a bagging procedure for acquisition function with a DGP surrogate model inferred via Stochastic Imputation (SI). For a fixed time budget, the proposed method gives 10% better quality for the LPBF process than the widely used BO method while being three times faster than the state-of-the-art method. 2. For metal AM, the process physics information is usually in the form of Partial Differential Equations (PDEs). Though the PDEs, along with in-situ data, can be handled through Physics-informed Neural Networks (PINNs), the activation function in NNs is traditionally not designed to handle multi-scale PDEs. This work proposes a novel activation function Self-scalable tanh (Stan) function for PINNs. The proposed activation function modifies the traditional tanh function. Stan function is smooth, non-saturating, and has a trainable parameter. It can allow an easy flow of gradients and enable systematic scaling of the input-output mapping during training. Apart from solving the heat transfer equations for LPBF and AFSD, this work provides applications in areas including quantum physics and solid and fluid mechanics. Stan function also accelerates notoriously hard and ill-posed inverse discovery of process physics. 3. PDE-based simulations typically need to be much faster for in-situ process control. This work proposes to use a Fourier Neural Operator (FNO) for instantaneous predictions (1000 times speed up) of quality in metal AM. FNO is a data-driven method that maps the process parameters with a high dimensional quality tensor (like thermal distribution in LPBF). Training the FNO with simulated data from PINN ensures a quick response to alter the course of the manufacturing process. Once trained, a DT can readily deploy the model for real-time process monitoring. The proposed methods combine complex information to provide reliable machine-learning models and improve understanding of metal AM processes. Though these models can be independent, they complement each other to build DTs and achieve quality assurance in metal AM. / Doctor of Philosophy / Metal 3D printing, technically known as metal additive manufacturing (AM), is an emerging technology for making virtually any physical part with a click of a button. For instance, one of the most common AM processes, Laser Powder Bed Fusion (L-PBF), melts metal powder using a laser to build into any desired shape. Despite the attractiveness, the quality of the built part is often not satisfactory for its intended usage. For example, a metal plate built for a fractured bone may not adhere to the required dimensions. Improving the quality of metal AM parts starts with a better understanding the underlying mechanisms at a fine length scale (size of the powder or even smaller). Collecting data during the process and leveraging the known physics can help adjust the AM process to improve quality. Digital Twins (DTs) are exactly suited for the task, as they combine the process physics and the data obtained from sensors on metal AM machines to inform an AM machine on process settings and adjustments. This work develops three specific methods to utilize the known information from metal AM to improve the quality of the parts built from metal AM machines. These methods combine different types of known information to alter the process setting for metal AM machines that produce high-quality parts.