Eastern Arizona College (EAC), is a community college in Graham County, Arizona. The main campus is in Thatcher, with satellite locations in Gila County, and Greenlee County. It is the oldest community college in Arizona and the only community college in Arizona with a marching band.
This study explores multidimensional wellness among 15 white queer and trans Latter-day Saints using found poetry. Drawn from interview data, and guided by a queer theory critique of white cisheteronormativity, the six poems illuminate how participants navigate harm, seek healing, and imagine hope within a faith tradition rooted in white Christian cultural norms. By centering the coresearchers' experiences, we call for justice-oriented, affirming spaces that support the wellness of queer and trans Latter-day Saints.
In this essay, we rhetorically analyze X Gonz & aacute;lez's speech at the 2018 March for Our Lives rally, a response to the Parkland school shooting. Focusing on the role of silence in Gonz & aacute;lez's speech, we introduce and develop the concept of "unsettling silence," a rhetorical mode of silence that disrupts expectations, provokes discomfort, exposes inaction, and manipulates time. For members of marginalized communities, we argue, unsettling silence can function as a proactive rhetorical tactic, one that challenges dominant power structures and has the potential to catalyze social and political transformation.
This study explores how asexual people of color experience minority stress due to encounters with allonormativity. Drawing on word association interviews with 33 asexual Black, Asian, Latine, Indigenous, and multiracial North Americans, ages 18-45 and varied genders, this study explores the unconscious and affective dimensions of racialized rhetorics of allonormativity as they are internalized and reproduced through communication. Word association was employed to access spontaneous and embodied meanings that other interview prompts may overlook, illuminating the power of words in reifying and resisting allonormativity. These responses map onto both distal and proximal stressors, impacting wellness and identity. This article contributes to health communication by using word association as a methodological approach and analyzing how allonormative rhetorics shape minority stress outcomes for asexual people of color.
This research paper examines the differences between accelerated eight-week college courses and full-semester college courses at Eastern Arizona College (EAC), focusing on student success measured by course grade (GPA). It analyzes research studies and the literature to provide a comprehensive background of other studies that have examined the differences between accelerated and full-semester course formats mainly in majors other than business. Overall success improved significantly statistically for all students regardless of gender, race/ethnicity, or age when they took accelerated EAC business and business administration courses. The paper concludes by discussing the implications for students and institutions in choosing between these two course structures in the context of business courses.
As noncommunicable diseases (NCDs) pose a significant global health burden, identifying effective diagnostic and predictive markers for these diseases is of paramount importance. Epigenetic modifications, such as DNA methylation, have emerged as potential indicators for NCDs. These have previously been exploited in other contexts within the framework of neural network models that capture complex relationships within the data. Applications of neural networks have led to significant breakthroughs in various biological or biomedical fields but these have not yet been effectively applied to NCD modeling. This is, in part, due to limited datasets that are not amenable to building of robust neural network models. In this work, we leveraged a neural network trained on one class of NCDs, cancer, as the basis for a transfer learning approach to non-cancer NCD modeling. Our results demonstrate promising performance of the model in predicting three NCDs, namely, arthritis, asthma, and schizophrenia, for the respective blood samples, with an overall accuracy (f-measure) of 94.5%. Furthermore, a concept based explanation method called Testing with Concept Activation Vectors (TCAV) was used to investigate the importance of the sample sources and understand how future training datasets for multiple NCD models may be improved. Our findings highlight the effectiveness of transfer learning in developing accurate diagnostic and predictive models for NCDs.
