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Bachelor of Science in Nursing Course Learning Outcomes by Semester
No full textDocument is seven pages in length.This document provides a comprehensive list of the learning outcomes expected in each semester of the BSN program.Texas A&M University College of Nursing
Content contributor: College of Nursing (Re)Design Tea
Effects of Repeated Footshock Punishment on Punishment Resistance for Cocaine Seeking
Get PDFA defining characteristic of addiction is compulsive drug-seeking, despite aversive consequences. In a rodent model, rats continue to self-administer cocaine despite footshock, indicating punishment resistance. Preliminary data from the lab found that a history of exposure to noncontingent footshock did not affect responding towards subsequent contingent footshock in male rats. Here, we investigated whether the contingency of the footshock is crucial to how male rats respond to subsequent contingent footshock. We wanted to compare our previous data to rats that received repeated contingent footshock punishment. We trained 8 male Sprague Dawley rats to self-administer intravenous cocaine on a seeking-taking chained schedule of reinforcement for 2 hours daily. Once reaching the final schedule, rats were given contingent footshock punishment (delivered randomly ��� of trials after completion of seeking). All rats were given 4 days of contingent footshock punishment (0.4 mA, 0.3 sec), up to 5 days of ramped punishment (increasing the intensity and/or duration), and then 4 more days of the contingent footshock (0.4 mA, 0.3 sec). Rats were given at least 4 days for recovery between punishment tests. We observed that regardless of the history of footshock, rats increase punishment sensitivity after repeated exposure. Additionally, male rats with a history of noncontingent footshock were more sensitive to contingent footshock than those who received repeated contingent footshock. Parallel with previous findings, contingent footshock reduced cocaine seeking, but noncontingent footshock did not. Our results may have implications for how individuals struggling with drug addiction process control over stressful events or negative outcomes, as well as how these processes will affect future decision-making to subsequent stressful events
An Artificial Intelligence Approach to Yield Estimation and Prediction Using UAV-Based Imagery
Get PDFIn this study, the effect of Hyperparameter Tuning (HT) and Feature Selection (FS) were automated and evaluated for large datasets with application in agriculture. The automated Unmanned Aerial Vehicle (UAV) based prediction and estimation system incorporating both FS and HT was developed using big data extracted through image analysis, with the aim of showing the benefit of incorporating FS and HT in models used to accurately predict crop yield from high spatial resolution images; thus enabling the producer to practice precision farming. The need to incorporate precision agriculture to both maximize the yield and enable more efficient resource utilization has been recognized by researchers for many years. And while the methodologies have differed, many recent studies have been conducted with the aim of incorporating an embedded system of sensors, creating a network of Internet Of Things (IOT) devices which monitors the field in real time and aims to limit human interactions and efforts. The automated system developed in this work paves the way for high accuracy yield prediction with a sole reliance on UAV collected imaging data, eliminating the need for incorporating a network of sensors in the field, hence allowing farmers to incorporate precision agriculture into their practice without invasive monitoring.
For this work, high resolution pictures were captured using the deployed UAV, which yielded high spatial resolution pictures while also resulting in highly dimensional data. High dimensionality is one of the challenges that faces machine learning algorithms as it can result in lower model performance and an increase in computational requirements. A combination of FS and HT was hence developed and incorporated into an automated and streamlined process that optimized the prediction capabilities of the regression models