REVIEW: The Body in Flannery O\u27Connor\u27s Fiction: Computational Technique and Linguistic Voice

Review of the non-fiction book The Body in Flannery O\u27Connor\u27s Fiction: Computational Technique and Linguistic Voice, by Donald E. Hardy

REVIEW: Elephant on My Roof

Review of the children\u27s book Elephant on My Roof, by Erin Harris

REVIEW: Return to Good and Evil: Flannery O’Connor’s Response to Nihilism

Review of the non-fiction book Return to Good and Evil: Flannery O’Connor’s Response to Nihilism, by Henry T. Edmondson III; contributor Marion Montgomery

Enrollment Management Strategies at Rural Community Colleges Resulting from the Pandemic

Higher education institutions around the world were impacted by the COVID-19 pandemic that began in early 2020. Because U.S. community colleges focus on two-year degrees and workforce development, they were affected differently than their four-year counterparts. This study examined how academic administrators at different rural community colleges in Virginia, United States, perceived enrollment management practices that were implemented or changed due to the pandemic. This sequential explanatory study\u27s first phase was quantitative and measured mid- to senior-level administrators\u27 views of how the pandemic affected their colleges\u27 enrollment management practices. The second qualitative phase resulted in five themes surrounding COVID-related enrollment management practices: a) COVID-19 led to crisis management and operations in phases; b) managing student onboarding during COVID-19; c) COVID-19 created unique challenges for community college students; d) COVID-19 affected decision-making procedures; and e) COVID-19 resulted in work/life balance issues and COVID fatigue. Implications and future directions are provided to ensure that community college enrollment managers and policymakers understand how to continue to pivot to ensure student services are maintained or enhanced during a crisis

Refining real-time predictions of Vibrio vulnificus concentrations in a tropical urban estuary by incorporating dissolved organic matter dynamics

The south shore of Oʻahu, Hawaiʻi is one of the most visited coastal tourism areas in the United States with some of the highest instances of recreational waterborne disease. A population of the pathogenic bacterium Vibrio vulnificus lives in the estuarine Ala Wai Canal in Honolulu which surrounds the heavily populated tourism center of Waikīkī. We developed a statistical model to predict V. vulnificus dynamics in this system using environmental measurements from moored oceanographic and atmospheric sensors in real time. During a year-long investigation, we analyzed water from 9 sampling events at 3 depths and 8 sites along the canal (n = 213) for 36 biogeochemical variables and V. vulnificus concentration using quantitative polymerase chain reaction (qPCR) of the hemolysin A gene (vvhA). The best multiple linear regression model of V. vulnificus concentration, explaining 80% of variation, included only six predictors: 5-day average rainfall preceding water sampling, daily maximum air temperature, water temperature, nitrate plus nitrite, and two metrics of humic dissolved organic matter (DOM). We show how real-time predictions of V. vulnificus concentration can be made using these models applied to the time series of water quality measurements from the Pacific Islands Ocean Observing System (PacIOOS) as well as the PacIOOS plume model based on the Waikīkī Regional Ocean Modeling System (ROMS) products. These applications highlight the importance of including DOM variables in predictive modeling of V. vulnificus and the influence of rain events in elevating nearshore concentrations of V. vulnificus. Long-term climate model projections of locally downscaled monthly rainfall and air temperature were used to predict an overall increase in V. vulnificus concentration of approximately 2- to 3-fold by 2100. Improving these predictive models of microbial populations is critical for management of waterborne pathogen risk exposure, particularly in the wake of a changing global climate

Synapse at CAp 2017 NER challenge: Fasttext CRF

We present our system for the CAp 2017 NER challenge which is about named entity recognition on French tweets. Our system leverages unsupervised learning on a larger dataset of French tweets to learn features feeding a CRF model. It was ranked first without using any gazetteer or structured external data, with an F-measure of 58.89\%. To the best of our knowledge, it is the first system to use fasttext embeddings (which include subword representations) and an embedding-based sentence representation for NER

Transformational Leadership in Higher Education Programs

The current mixed-method study investigates transformational leadership qualities through higher education doctoral programs in the Commonwealth of Virginia. This study relies on three data points: interviews with graduate program directors of higher education doctoral programs (whether PhD or EdD), a program evaluation of programs, and Multifactor Leadership Questionnaire (MLQ-5x™) survey results from students within said programs. Data were collected from the five public universities that offer higher education doctoral programs within the Commonwealth of Virginia. Students completed a self-rating using the Multifactor Leadership Questionnaire (MLQ-5x™) and then were peer-rated by colleagues to strengthen the validity of the study. Additionally, themes surrounding the structures of these doctoral programs were collected. As researchers of higher education and leadership studies cite transformational leadership as a high competency for college and university presidents, coupled with a looming shortage of college and university presidents on the horizon, measuring the programs that train these potential future leaders is warranted