THE IMPACT OF PRIOR LEARNING ASSESSMENT CORRELATIONAL STUDY ON ACADEMIC OUTCOMES AMONG ADULT COMMUNITY COLLEGE STUDENTS

The purpose of the ex post facto study was to examine the extent to which the impact of awarding credit for Prior Learning Assessment (PLA) to adult learners increases community college enrollment and graduation rates, at an ethnically diverse community college in the northeastern United States. The study explored the relationship between adult learners who earn PLA credits or do not earn PLA credits, and student success. Data were collected from student archival records, September 2012 – May 2018 from 1,307 adult learners who ranged in age from 25 to 67, attended both full- and part-time, had a declared major, and had no previous credits from the study setting before September 2012. The main research question was to determine what, if any, impact awarding PLA had on a set of academic success indicators (GPA, persistence, and graduation). Results from a binary logistic regression analysis showed that community college adult students who earned PLA credit were significantly more likely to graduate than non-PLA adult learners who did not earn PLA credit. None of the demographic factors (age, gender, and ethnicity) added to the prediction of graduation attainment for adult learners beyond PLA status. The One-way Multivariate Analysis of Covariance analysis for the outcome measure GPA revealed that the PLA and the non-PLA group achieved statistically equivalent overall GPA, and persistence (length of time to degree attainment) was significant. The PLA group required less time to graduate than the non-PLA group. The relevant findings demonstrate to the community college policy and decision makers the unique needs of adult learners, and the potential contribution PLA status makes to student success. With the shift to a more diverse demographic, the community college needs to use the collected information to allocate resources to strategically develop a rigorous PLA program, and market the value and significant benefits gained from PLA to foster more enrollment, retention, and completion. During a critical period, when institutions continue to face the challenge of declining enrollment, this study will add to the literature within the context of the community college associated with the PLA process, as it relates to adult learners

Cholelithiasis in mice: Effects of different chemicals upon formation and prevention of gallstones

Prevention of gallstones induced in mice by 1% cholesterol and 0.5% cholic acid (lithogenic diet) for 8 weeks was obtained by simultaneously feeding sulfaguanidine (1.5%) and dodecyl sodium sulfate (0.5-1.0%) along with the lithogenic diet. Citrus pectin (3%), egg lecithin (1%), n-octyl alcohol (1%), neomycin sulfate (0.2-0.5%) and -ascorbic acid (5%) added to the lithogenic diet did not prevent gallstone formation. The condition of the liver, fatty or normal, in the experiment could not be correlated with the stone formation. Lower serum and liver cholesterol levels and an elevation of lecithin concentrations in serum was noticed in mice fed the sulfaguanidine and dodecyl sodium sulfate diet.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/22418/1/0000868.pd

Machine Learning Applications For Weather and Climate Modeling

This study investigates the applications of machine learning (ML) to weather and climate modeling. We first show the potential for data-driven weather prediction by creating a low resolution, ML-based global atmospheric model that predicts the 3-dimensional atmosphere in the same for-mat as a physic-based numerical model. The ML-only atmospheric model is stable during 21-day forecasts and can reproduce large-scale atmospheric dynamics (e.g. Rossby waves). The ML-only model is able to outperform persistence and climatology for the first three forecast days in the midlatitudes. When compared to a simplified atmospheric general circulation model (AGCM), the ML-only model performs best for variables most heavily influenced by parameterizations in the AGCM (e.g. low level specific humidity). Next, we combine a parallel, machine learning algorithm with a coarse resolution AGCM (SPEEDY) to create a hybrid atmospheric model. The hybrid model produces more accurate forecasts for all variables for at least the first 7 forecast days when compared to the host AGCM. Applications of the hybrid model for climate research are explored with a 11-year free run. The hybrid model is free of instability and can simulate the past climate with substantially smaller systematic errors and more realistic variability than the host AGCM. Lastly, we show potential of ML for Earth System modeling by dynamically coupling a hybrid atmospheric model and a ML-based ocean model trained to predict the sea surface temperature (SST). The ML-only ocean model is able to reproduce SST dynamics with minimal biases for the past and present climate. The coupled model can simulate long-term variability in both the atmosphere and ocean (e.g. El Niño–Southern Oscillation). During a 70-year free run, we find that the coupled model does not exhibit climate drift and able to conserve total atmospheric mass and water vapor mass

CAMPAGNA DI MISURE A LIPARI PER LA CALIBRAZIONE DI DATI IPERSPETTRALI DA SENSORE AEREO

INGV, ASIPublished5V. Sorveglianza vulcanica ed emergenze6A. Monitoraggio ambientale, sicurezza e territorioope