As the world’s population exponentially grows, more food production is required. This increasing food production currently has led to the un-sustainable production of chemical fertilizers and resultant overuse. A more sustainable option to enhance food production could be the use of fertilizer derived from food waste. To address this, we investigated the possibility of utilizing a fertilizer derived from food waste to grow hydroponic vegetables. Arugula (Eruca sativa) ‘Slow Bolt’ and lettuce (Lactuca sativa) ‘Cherokee’ and ‘Rex’ were cultivated using indoor deep-flow hydroponic systems at 23 ºC under a photosynthetic photon flux density of 170 µmol∙m−2∙s−1 with an 18-hour photoperiod. Plant nutrient solutions were provided by food waste fertilizer or commercial 15:5:20 NPK fertilizer at the identical electrical conductivity (EC) of 2.3 mS·cm–1. At the EC of 2.3 mS·cm–1, chemical fertilizer contained 150 ppm N, 50 ppm P, and 200 ppm K, while food waste fertilizer had 60 ppm N, 26 ppm P, and 119 ppm K. Four weeks after the nutrient treatments were implemented, compared to plants grown with chemical fertilizer, lettuce ‘Rex’ grown with food waste fertilizer had four less leaves, 27.1% shorter leaves, 68.2% and 23.1% less shoot and root fresh weight, respectively. Lettuce ‘Cherokee’ and arugula grown with food waste fertilizer followed a similar trend with fresh shoot weights that were 80.1% and 95.6% less compared to the chemical fertilizer, respectively. In general, the magnitude of reduction in the plant growth was greatest in arugula. These results suggest that both fertilizers were able to successfully grow lettuce and arugula, although the reduced plant growth with the food waste fertilizer in our study is likely from a lower concentration of nutrients when we considered EC as an indicator of nutrient concentration equivalency of the two fertilizer types.

During the global COVID-19 pandemic in 2020, many universities shifted their focus to hosting classes and events online for their student population in order to keep them engaged. The present study investigated whether an association exists between student engagement (an individual’s engagement with class and campus) and resilience. A single-shot survey was administered to 200 participants currently enrolled as undergraduate students at Arizona State University. A multiple regression analysis and Pearson correlations were calculated. A moderate, significant correlation was found between student engagement (total score) and resilience. A significant correlation was found between cognitive engagement (student’s approach and understanding of his learning) and resilience and between valuing and resilience. Contrary to expectations, participation was not associated with resilience. Potential explanations for these results were explored and practical applications for the university were discussed.

We attempted to apply a novel approach to stock market predictions. The Logistic Regression machine learning algorithm (Joseph Berkson) was applied to analyze news article headlines as represented by a bag-of-words (tri-gram and single-gram) representation in an attempt to predict the trends of stock prices based on the Dow Jones Industrial Average. The results showed that a tri-gram bag led to a 49% trend accuracy, a 1% increase when compared to the single-gram representation’s accuracy of 48%.