Introduction: In the United States, standardized tests have risen in prevalence, extending their importance from education placement to employment. Attention is crucial to improving testing performance. Past studies have established that acute, coordinative, aerobic exercise improves attention, which is measured by the D2 Attention Test, emotional analysis, reading time, and eye movement tracking. No studies have drawn connections between physical exercise's quantifiable improvement in attention to improvements in standardized tests; therefore, this study would attempt to do so.Methods: This study defines attention to be positively related to reading speed and negatively related to the number of eye drifts. High school students were selected to read and answer two reading passages from an SAT (Scholastic Assessment Test) exam, before and after a short 80% intensity run. Their reading times, facial video, and test scores were recorded. Dlib plots the facial landmark and OpenCV tracks movement of the pupil.Results: Through paired-samples t-tests, this study found out that after exercise, subjects displayed increased reading speed and fewer eye drifts, coupled with increased mean scores. Conclusion:Thus, this study demonstrated that running, as an acute, coordinative, aerobic exercise, helps increase the testing performance of the SAT reading section by measuring attention. Future research could focus on including head movement as an attention index, replicate the experiment on different standardized tests or exercises, and conduct natural experiments to better simulate real-life conditions to increase applicability.
Machine learning constructs computer systems that develop through experience. Applications surround disciplines in daily life ranging from malware filtering to image recognition. Recent research has shifted towards maximizing efficiency in decision-making, creating algorithms that quickly and accurately process patterns to generate insight. This research focuses on reinforcement learning, a paradigm of machine learning that makes decisions through maximizing reward. Specifically, we use Q-learning – a model-free reinforcement learning algorithm – to assign scores for different decisions given the unique states of the problem. Widyantoro et al. (2009) have studied the effect of Q-learning on learning to play Tic-Tac-Toe. However, the study yielded a win/tie rate of less than 50 percent. We believe that does not represent an effective algorithm to exploit the benefits of Q-learning fully. In the same environment, this research aims to close the gaps in the effectiveness of Q-learning while minimizing human input. Data were processed by setting the epsilon value as 0.9 to ensure randomness, then consecutively decrease with a constant rate as possible states increase. The program played 300,000 games against its previous version, eventually securing a win/tie rate of approximately 90 percent. Future directions include improving the efficiency of Q-learning algorithms and applying the research in practical fields.
Improper disposal of pharmaceutical wastes, coupled with low pollutant removal efficiency at wastewater treatment plants (WWTPs), has created a disastrous ecological issue. Ecotoxicity reviews have consolidated that low concentrations of pharmaceutical pollutants, specifically non-steroidal anti-inflammatory drugs, impose significant toxicological risks on the aquatic ecosystem. Research solutions have shifted towards electrochemical oxidation for its environmental-friendly, precise, and flexible reduction characteristics over non-electrochemical technologies. However, there isn't significant literature dedicated to finding the conditions for small-scale pretreatment, leaving the environment between the pollutant source and WWTPs at risk. This research explores the optimal conditions to pretreat pharmaceutical wastewater using electrochemical oxidation on a small-scale. A conventional approach, utilizing accessible materials and simple procedures, was selected to ease the implementation of pretreatment outside of WWTPs. Spectrophotometric analysis was performed to identify the concentration changes through absorbance for reagents. Manipulated variables of temperature, pH level, scale, and electrode metal type were analyzed individually per solution and in combination to produce the overall effect. F-Test and Tukey-Kramer post-hoc tests were employed to derive the maximum electrochemical oxidation ability for different variables. Results indicate that at the 95% confidence level, temperatures below 25˚C, pH levels below 4, larger scale, and higher reactivity metal plates produce the highest electrochemical oxidation magnitude. Overall analysis comparing the combined optimal conditions with the control group yielded an approximately 50% greater concentration reduction magnitude. Future directions include the implementation of electrochemical oxidation as a pretreatment appliance in the household using our optimal conditions and exploring other manipulative variables to increase the flexibility and efficiency of such devices.
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