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War Camp Community Recreational Center, Savannah, Georgia
No full textPhotograph: Group at the War Camp Community Recreational Center in Savannah, Georgia. Copy has Eartha White\u27s photo in upper right corner (for newspaper). Photographer: E. L. Weems. Undated.https://digitalcommons.unf.edu/eartha_images/1885/thumbnail.jp
Henry Harrison
No full textPhotograph: Henry Harrison. Handwritten on front: Born 1810 Feb 7 in Nassau County, Fla. 117 years. Preacher 89 years. Undated.https://digitalcommons.unf.edu/eartha_images/1877/thumbnail.jp
Incorporating Student OER Advocacy into an Online Library Research Course
Get PDFObjectives: 1. Describe How the OER Advocacy Project Aligns with Course Objectives & Philosophy 2. Showcase Student Work 3. Share Student Feedbac
Predicting Florida\u27s extreme wind events: A deep learning approach
Get PDFExtreme wind events, such as hurricanes and tropical storms, have devastating impacts on infrastructure, economy, and public safety in Florida. Traditional predictive methods, linear regression, logistic regression, and autoregressive integrated moving-average (ARIMA) models have limitations in accounting for non-stationarity and nonlinearity in meteorological data, leading to less accurate predictions. The primary objective of this study is to leverage the capabilities of deep learning algorithms, specifically, Feedforward Neural Networks (FFNNs), to develop a predictive model that outperforms traditional predictive methods in forecasting extreme wind events in Florida. The deep learning model was trained, validated, and tested using historical wind speed data obtained from 34 stations located throughout Florida. The performance of the model was evaluated and compared to the Support Vector Regression model (SVR) using measures such as Mean Square Error (MSE), Root Mean Square Error (RMSE), and Coefficient of Determination (R2). Results indicate that the feedforward neural network (FFNN) model achieved higher prediction accuracy than the Support Vector Regression (SVR) model. Deep learning offers a promising avenue for more accurate, adaptable, and comprehensive models for predicting extreme wind events in Florida
The relationship between social capital and juvenile recidivism risk in Bexar County, Texas: Critical Psychology Theory and Latent Class Analysis in archival research
Get PDFWithin the field of psychology, many theories exist about the origins and correlates of criminal/antisocial behavior at the individual or micro level. However, there exists a gap in psychological literature regarding the role of broader environmental factors, including the impact of systems and institutions on individual behavior and experience. This gap includes the role of sociohistorical factors and institutionalized biases regarding race/ethnicity, gender identity/sexual orientation, class, geographical location, and differing ability. The current study attempts to address this gap by conducting a Latent Class Analysis (LCA) using measures of social capital and other intake measures associated with juvenile crime to highlight the micro-, meso-, and macro-level factors that influence juvenile recidivism. Results of the LCA will allow the comparison of the relative strength in predicting the adverse outcome of re-offense for juveniles with a prior history of justice system involvement. A Critical Psychology lens informs the methodology, analysis, and interpretation of results in the current study
