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Impact of Racial and Sexual Identity on the Development of Black Sexual Minorities
No full textMany studies exploring the impact of the college environment on identity development among Black lesbian, gay, and bisexual students have primarily focused on Black people who attend predominately white institutions (Goode-Cross & Good, 2008; Gossett et al., 1998; Harris, 2003; Lett & Wright, 2003; Washington & Wall, 2006). Furthermore, a smaller number of studies have examined how the intersection of race and sexuality may affect the development of individuals depending on their cultural experiences. The present quantitative study investigated the impact of college campus perception, sexual identity development, and racial identity development affect the sexual and psychological health of Black students who identify as lesbian, gay, or bisexual. The final sample included 144 undergraduate and graduate students who self-identified as Black. In addition to a demographic questionnaire, five measures were included in this research study to include: Cross Racial Identity Scale, Lesbian, Gay, and Bisexual Identity Scale, Flourishing Scale, Safe Sex Behavior Questionnaire, University Environment Scale. Findings support a positive relationship between racial and sexual identity development and some significant impact on campus perception, health, and institutional choice (i.e., PWI vs. HBCU). This research may contribute to the intersectionality literature by providing additional insight regarding the health of racial and sexual minorities
Effect of Peer Mentoring on STEM Students’ Academic Success in Gatekeeper Courses
No full textThis study employed a mixed-methods explanatory sequential design to provide a richer understanding of the relationship between peer mentoring and the academic success of students in STEM gatekeeper courses. The purpose of this study was to identify the factors contributing to the students’ retention and persistence in a STEM program at an HBCU by obtaining quantitative survey data from senior level STEM students and then following up with purposefully selected individuals to explore those results in a qualitative interview. This study also determined if there were any differences in the reporting of gatekeeper course challenges of underrepresented STEM participants in a peer mentoring program in contrast to non-participants. This study also explored if participating in a peer mentoring program promoted academic and social integration at the university. The years examined were 2019 to 2022. The peer mentoring program was a component of a statewide STEM minority initiative. The peer mentoring program was a freshman intervention at a public historically black four-year institution, located in Tennessee
A Theological Extension of Self-Efficacy: Academic Implications
Get PDFResearch indicates a positive correlation between self-efficacy and increased student achievement. Self-efficacy is an individual’s belief that they can exercise control over their functioning. Mastery Experiences, Vicarious Experiences, Social Persuasion and the Physiological well-being of an individual, are components of self-efficacy. This paper extends the traditional view of self-efficacy by introducing another component that effects the motivating factors within an individual. Through theological experiences, individuals in a Christian context utilize faith-based principles to access mastery, vicarious experiences, social support and physiological well-being. Through theological experiences, mastery experiences are acquired with God’s help; vicarious experiences are extended to a global Christian context, Social persuasion occurs through scriptures and testimonies, and physiological well-being is taught and supported via God and His Word (scripture). This theological extension has implications in academia, mental health, leadership and policy articulation
Reducing Tillage Affects Long-Term Yields but Not Grain Quality of Maize, Soybeans, Oats, and Wheat Produced in Three Contrasting Farming Systems
Get PDFReducing tillage has been widely promoted to reduce soil erosion, maintain soil health, and sustain long-term food production. The effects of reducing tillage on crop nutritional quality in organic and conventional systems, however, has not been widely explored. One possible driver of crop nutritional quality might be the changing soil nitrogen (N) availability associated with reduced tillage in various management systems. To test how reducing tillage affects crop nutritional quality under contrasting conventional and organic farming systems with varied N inputs, we measured nutritional quality (protein, fat, starch, ash, net energy, total digestible nutrients, and concentrations of Ca, K, Mg, P, and S) of maize, wheat, oats, and soybeans harvested from a long-term trial comprised of three farming systems under two tillage regimes: a conventional grain system (CNV); a low-input organic grain system (LEG); and an organic, manure-based grain + forage system (MNR) under conventional full-tillage (FT) and reduced-till (RT) management. Although maize and wheat yields were 10–13% lower under RT management, grain quality metrics including protein, fat, starch, energy, and mineral concentrations were not significantly affected by reducing tillage. Differences in nutrient quality were more marked between farming systems: protein levels in maize were highest in the MNR system (8.1%); protein levels in soybeans were highest in the LEG system (40.4%); levels of protein (12.9%), ash (2.0%), and sulfur (1430 ppm) in wheat were highest in the CNV system, and oat quality was largely consistent between the LEG and MNR systems. As grain quality did not significantly respond to reducing tillage, other management decisions that affect nutrient availability appear to have a greater effect on nutrient quality
A Novel Process of Pd-Catalyzed Cross-Coupling of Acyl Halides and Aryltrifluoroborates
No full textAcylation of acyl chloride with aryl trifluoroborates builds a novel synthetic cross-coupling reaction. This new cross-coupling reaction, simply called direct acylation is accomplished in the presence of PdCl2(Ph3P)4 complex as a palladium catalyst in a microwave irradiated system. By using microwave irradiated system, our developed minute reactions are a green chemistry focused eco-friendly system. We were able to produce and purify many aromatic ketones compounds with variable tolerance groups
Missing value estimation using clustering and deep learning within multiple imputation framework
Get PDFMissing values in tabular data restrict the use and performance of machine learning, requiring the imputation of missing values. Arguably the most popular imputation algorithm is multiple imputation by chained equations (MICE), which estimates missing values from linear conditioning on observed values. This paper proposes methods to improve both the imputation accuracy of MICE and the classification accuracy of imputed data by replacing MICE’s linear regressors with ensemble learning and deep neural networks (DNN). The imputation accuracy is further improved by characterizing individual samples with cluster labels (CISCL) obtained from the training data. Our extensive analyses of six tabular data sets with up to 80% missing values and three missing types (missing completely at random, missing at random, missing not at random) reveal that ensemble or deep learning within MICE is superior to the baseline MICE (b-MICE), both of which are consistently outperformed by CISCL. Results show that CISCL + b-MICE outperforms b-MICE for all percentages and types of missing values. In most experimental cases, our proposed DNN-based MICE and gradient boosting MICE plus CISCL (GB-MICE-CISCL) outperform seven state-of-the-art imputation algorithms. The classification accuracy of GB-MICE-imputed data is further improved by our proposed GB-MICE-CISCL imputation method across all percentages of missing values. Results also reveal a shortcoming of the MICE framework at high percentages of missing values (50%) and when the missing type is not random. This paper provides a generalized approach to identifying the best imputation model for a tabular data set based on the percentage and type of missing values