An Exploration of the Black Female Cosplay Experience

Cosplay is the practice of dressing up as a fictional character whether it\u27s from a television show, video games, or even a book series. Many people participate in cosplay to show their dedication or admiration for their favorite character and sometimes cosplay a character that helps reflect their own character or personality. This thesis will mainly focus on the experience of being a Black female cosplayer in pop culture, social media, conventions, and other places where people can share and communicate. In this thesis’s research, participants\u27 answers about their experiences were centered around their inspiration to start cosplaying, their ways of alternating their cosplays to their liking, participation experience, memorable experiences, and what they would like others to know about being a Black female cosplayer. Overall, the participants express that they have a bond with their cosplays and others that enjoy it alongside despite the issues of exclusion

Net versus combinatory effects of firm and industry antecedents of sales growth

This study examines antecedents of sales growth using a two-step mixed-method approach including analyses of net effects and combinatory effects. Based on a sample of 453 respondents from manufacturing and service firms, this article shows how the combination of structural equation modeling (SEM) and fuzzy set Qualitative Comparative Analysis (fsQCA) provides more detailed insights into the causal patterns of factors to explain sales growth. This article contributes to the extant literature by highlighting fsQCA as a useful method to analyze complex causality (specifically combinatory effects of antecedent conditions) and by discussing options regarding how this approach can be used to complement findings from conventional causal data analysis procedures that analyze net effects

The Role of Social Location in Coping with Caregiving

A salient concern stemming from population aging is the expected rise in demand for informal family caregivers for diseases impacting the elderly, including dementia (MMMI 2010; NIA and WHO 2011). Studies of caregiver well-being often problematize the sociodemographic caregivers (e.g. gender, marital status) while caregiver intervention studies typically focus on the program itself (Gallagher-Thompson et al., 2008; Rabinowitz et al., 2006; Shulz et al., 2003). In this dissertation I unite these two bodies of caregiver research and examine how the sociodemographic characteristics of participants in a caregiver intervention program relate to the program’s effectiveness. I use secondary data from the Stress Management Project Dataset (Spiegel, 2001) and evaluate how participants’ social location (specifically, race and education) impacts the effectiveness of each program on caregivers’ depressive symptoms and stress. This study employed a sociological perspective to examine how social location (specifically race and education) impacts the benefits of a dementia caregiver intervention program. Using secondary data, I performed OLS regression analyses and found support for the initial hypothesis that suggested that the Coping with Caregiving (CWC) intervention would be more effective than the Telephone Support Control (TSC). There was no support for the remaining hypotheses that proposed that White caregivers with more education would benefit more from the program, or that the effects of race and education on caregiver outcomes would be contingent upon each other

Synthesis and Cheracterization of Novel Poly (Sulfone- Sulfonates)

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A Hermeneutic Phenomenological Study: Perspectives on the Parental Engagement Strategies of Rural African American Parents of Middle School Students

The purpose of this hermeneutic phenomenological study was to explore and ascribe meaning to African American parents\u27 lived experiences in the education of their middle school students in rural east-central South Carolina. Two theoretical frameworks guided this study as they related to the levels and the effect of parental self-efficacy on parental engagement: Hoover-Dempsey and Sandler\u27s revised model of parental involvement and Bandura\u27s self-efficacy theory. The central research question for this study was What are the perceptions and lived experiences of rural African American middle-school parents and their involvement in parental engagement activities? Three subsequent sub-questions on parental self-efficacy, role construction, and invitations for engagements were: (a) How does African American parents\u27 self-efficacy influence their decisions to become involved with the school? (b) How do African American parents describe their parental role construction in their children\u27s education? (c) How do African American parents describe their response to the school\u27s invitations to become involved? Data on the phenomenon was collected through semi-structured interviews, document analysis, and a focus group. This study concluded that rural African American parents\u27 perspectives on parental engagement are influenced by their parental self-efficacy, role construction, communications with and from the school, and influences on community members. The findings on the influence of the community on the parental engagement of rural African American parents and child-specific non-academic related invitations to parents are the basis for future investigation as there is a scarcity of research literature addressing this issue

Mining Brain Networks using Multiple Side Views for Neurological Disorder Identification

Mining discriminative subgraph patterns from graph data has attracted great interest in recent years. It has a wide variety of applications in disease diagnosis, neuroimaging, etc. Most research on subgraph mining focuses on the graph representation alone. However, in many real-world applications, the side information is available along with the graph data. For example, for neurological disorder identification, in addition to the brain networks derived from neuroimaging data, hundreds of clinical, immunologic, serologic and cognitive measures may also be documented for each subject. These measures compose multiple side views encoding a tremendous amount of supplemental information for diagnostic purposes, yet are often ignored. In this paper, we study the problem of discriminative subgraph selection using multiple side views and propose a novel solution to find an optimal set of subgraph features for graph classification by exploring a plurality of side views. We derive a feature evaluation criterion, named gSide, to estimate the usefulness of subgraph patterns based upon side views. Then we develop a branch-and-bound algorithm, called gMSV, to efficiently search for optimal subgraph features by integrating the subgraph mining process and the procedure of discriminative feature selection. Empirical studies on graph classification tasks for neurological disorders using brain networks demonstrate that subgraph patterns selected by the multi-side-view guided subgraph selection approach can effectively boost graph classification performances and are relevant to disease diagnosis.Comment: in Proceedings of IEEE International Conference on Data Mining (ICDM) 201

Multi-view Graph Embedding with Hub Detection for Brain Network Analysis

Multi-view graph embedding has become a widely studied problem in the area of graph learning. Most of the existing works on multi-view graph embedding aim to find a shared common node embedding across all the views of the graph by combining the different views in a specific way. Hub detection, as another essential topic in graph mining has also drawn extensive attentions in recent years, especially in the context of brain network analysis. Both the graph embedding and hub detection relate to the node clustering structure of graphs. The multi-view graph embedding usually implies the node clustering structure of the graph based on the multiple views, while the hubs are the boundary-spanning nodes across different node clusters in the graph and thus may potentially influence the clustering structure of the graph. However, none of the existing works in multi-view graph embedding considered the hubs when learning the multi-view embeddings. In this paper, we propose to incorporate the hub detection task into the multi-view graph embedding framework so that the two tasks could benefit each other. Specifically, we propose an auto-weighted framework of Multi-view Graph Embedding with Hub Detection (MVGE-HD) for brain network analysis. The MVGE-HD framework learns a unified graph embedding across all the views while reducing the potential influence of the hubs on blurring the boundaries between node clusters in the graph, thus leading to a clear and discriminative node clustering structure for the graph. We apply MVGE-HD on two real multi-view brain network datasets (i.e., HIV and Bipolar). The experimental results demonstrate the superior performance of the proposed framework in brain network analysis for clinical investigation and application

Revisiting Problem Gamblers’ Harsh Gaze on Casino Services: Applying Complexity Theory to Identify Exceptional Customers

This study revisits the theory, data, and analysis in Prentice and Woodside (2013). The study here applies fuzzy-set qualitative comparative analysis (fsQCA) to customer service evaluation data from seven mega casinos in the world gambling capital—Macau. The study includes contrarian case analysis and offers complex algorithms of highly favourable customer outcomes—an alternative stance to theory and data analysis in comparison to the dominant logic of statistical analyses that Prentice and Woodside (2013) report. The findings here include more complex, nuanced views on the antecedent conditions relating to high problem-gambling, immediate service evaluations and desired customer behavior measures in casinos. Contrary to the findings using symmetric testing via multiple regression analysis in Prentice and Woodside (2013), this study, using asymmetric testing via fuzzy-set qualitative comparative analysis (fsQCA), recognizes the occurrence of causal asymmetry, and draws conclusions on different algorithms leading to high scores in favorable and unfavorable outcome conditions. The findings indicate that not all problem gamblers gaze on casino services harshly; the minority of problem gamblers who view casinos positively versus harshly may be the most valuable customers for the casinos—the casinos’ exceptional customers