Analyzing Emerging Adulthood Narratives and the Role of Anxiety in Developmental Functioning

Emerging adulthood is a developmental period characterized by the themes of identity exploration, instability, feeling in-between, being self-focused, and exploring possibilities (Arnett, 2006). Emerging adults are at higher risk for anxiety as they navigate novel developmental experiences and responsibilities (Kranszler et al., 2019). This study explores whether these themes map on to the experiences of modern-day emerging adults, and whether developmental functioning corresponds to anxiety. College students completed standard questionnaires and provided free-text responses about adjusting to adulthood. Identity exploration and instability were perceived as the most positive and negative aspects of aging, respectively, though response-type varied by participant demographics. Several responses were not captured by these themes. Participants’ ability to engage in self-care and their living situation were negatively associated with current anxiety. These findings provide important directions for expanding theoretical models and measurement of emerging adulthood

Exploring the Experiences of Emerging Adulthood Among Rural Appalachian Students

Emerging adulthood is a developmental stage that has risen out of a need to recontextualize the lives of individuals aged 18-29 years old in modern times. Sometimes described as a prolonged period of adolescence, emerging adulthood presents unique challenges and opportunities as individuals launch from adolescence and begin to assume more adult roles. Early research on this developmental period details five themes that commonly prevail the lives of emerging adults: identity exploration, feeling in-between, being self-focused, exploring possibilities, andinstability (Arnett, 2000). Though previous studies suggest that these themes remain fairly consistent despite the heterogeneity of the emerging adult population (Arnett et al., 2014; Baggio et al., 2017), there is a call for greater exploration of these themes and how they present based upon different demographic variables. To date, the experiences of rural Appalachian emerging adults have not been examined through the context of these themes. This gap presents an opportunity to explore the rich nuances of Appalachian culture, including its focus on social support and its impact on student success (Hand & Payne, 2008) and emotional well-being (Gottron, 2020). Participants of this study were college students (N = 296, mean age = 20.13) in northeast Tennessee. Participants answered a series of standard questionnaires and several open-ended questions about transitioning to adulthood. The present study examines the written responses to two open-ended questions: What have you most enjoyed about becoming an adult? and What is most stressful about becoming an adult? Two raters coded these responses based on their applicability to the aforementioned themes of emerging adulthood. Chi square analyses examined the presence of these themes in relation to geographic region (rural, urban, suburban). Positive endorsement of being self-focused differed by the area in which one grew up, X2(2, 277) = 13.34, p = .001. Post-hoc analyses with a Bonferonni-corrected alpha (p = .008) examined group differences. Individuals from rural areas reported being self-focused more positively compared to students from suburban or urban areas (p = .002). Due to the strong family commitments often associated with rural culture (Hand & Payne, 2008), rural emerging adults may perceive the ability to focus on their own priorities and goals, rather than that of their family members, as the most salient benefit of growing older. Additionally, these findings suggest that future discussions surrounding Arnett’s theory of emerging adulthood requires further nuance when considering region-specific cultural differences. Understanding where and why the experiences of individuals from rural areas may be different is key as educators and faculty continue to find ways to support the success of their students

SMO-based system for identifying common lung conditions using histogram

A radiograph is a visualization aid that physicians use in identifying lung abnormalities. Although digitized x-ray images are available, diagnosis by a medical expert through pattern recognition is done manually. Thus, this paper presents a system that utilizes machine learning for pattern recognition and classification of three lung conditions, namely Normal, Pleural Effusion and Pneumothorax cases. Using two histogram equalization techniques, the designed system achieves an accuracy rate of 76.19% and 78.10% by using Sequential Minimal Optimization (SMO). © 2013 IEEE