Undergraduate Research and Creative Activities Journal

The zine is curated for people who have experienced a world made unlivable for them. It stands strong for those who have faced trauma and felt shattered beyond repair. This is my message to you: Even though some parts of us are lost forever, we can still become someone even greater. We can still cultivate a life that is worth living, having a radically different outlook and appreciation for life.

A documented phenomenon is that people want to quit social media but do not. A leading explanation is that the content shown on social media is addictive. However, I use a survey to show that the content is not the main reason people say they cannot leave social media. Instead, it is losing the ability to communicate through direct messaging.

This study aimed to look at the correlations between depersonalization, mindfulness, and specifically the decentering aspect of mindfulness. This study is a correlational design, where 144 participants completed the Mindfulness Attention Awareness Scale (MAAS), Cambridge Depersonalization Scale (CDS), the Clinician Administered Dissociative State Scale (CADSS), and the Toronto Mindfulness Scale’s subscale for state decentering (TMS-D) on an online Qualtrics survey. It was predicted those higher in depersonalization would also be higher in state decentering. A Pearson’s r was conducted. In line with the hypothesis, both trait, and state depersonalization positively correlated with state decentering. Results also replicate the overall negative relationship between mindfulness and depersonalization. This implies mindfulness is multi-faceted, with many positives for those that experience depersonalization; however, a focus on decentering may not be the best course of treatment. Future studies should continue to examine the effects of mindfulness-based interventions (MBIs) on depersonalization and the effects excessive decentering could have.

Cotton discs with a blend of sodium alginate and germination compounds were blended into a hydrogel and planted with green onion seeds in order to determine a resulting increase of germination rate. After its product viability was determined, apparatus design was reevaluated to minimize seed suffocation effects with varied results dependent on disc thickness. Though the germination rates were lower than planting regularly, it is believed that if the gel’s physical construction could more resemble soil, the suffocation factor would be negligible and drought tolerance can be more accurately measured.

The effects of motivation and the depletion of cognitive resources on performance in a sustained attention task were investigated. 17 participants completed a modified version of the continuous temporal expectancy task (CTET; O’Connell et al., 2009). Performance on the CTET is a measure of sustained attention. Monetary reward was introduced as a between-subjects manipulation. Overall performance and performance across time served as behavioural measures of general sustained attention and the vigilance decrement, respectively. An electroencephalogram (EEG) was used to measure the neural correlates of behaviour, in particular, the alpha band oscillation. EEG analysis revealed higher alpha power for pre-target misses compared to hits, indicating a phase-dependent influence on sustained attention influenced by motivation levels. The vigilance decrement occurred in both reward and no reward groups, unaffected by rewards alone. Ceiling effects may have weakened the impact of rewards. Task performance variability suggests the need for a larger sample size to enhance reliability. In conclusion, alpha power differences between misses and hits suggest its role in sustained attention. However, reward did not mitigate the vigilance decrement which persisted across groups. Further research with a larger sample size is necessary to gain a deeper understanding of these effects.

Machine learning techniques have revolutionized the field of financial engineering by providing accurate and efficient methods for pricing American options. This research project aims to explore the effectiveness of deep learning algorithms in accurately pricing American options. The project is divided into two schemes: Scheme I employs a sequence of neural networks, while Scheme II utilizes a single aggregate neural network to eliminate time discretization. By testing various combinations of neural network hyperparameters in both schemes, we seek to optimize the accuracy and computational speed for pricing nine different Put and Call options. Our results are compared against existing efficient algorithms, such as polynomial regression and random forest, as documented in [5]. Based on the analysis of optimal hyperparameters that enhance the accuracy of machine learning-based American option pricing, we identify the top five solvers (hyperparameter sets) in Scheme I and the top two solvers in Scheme II. These solvers are benchmarked and reproducible, serving as reference points for future comparisons with prior studies.

Past literature examining dissociation beyond white populations is sparse. The few studies that have diversified their sampling report higher rates of dissociative symptomatology amongst black participants as compared to other racial groups. However, much of this research has failed to extend their findings beyond the general conclusion of an observable racial discrepancy in experienced dissociation. The present study incorporates theoretical models of racial traumatic stress and maladaptive dissociative coping in its investigation of perceived exposure to racial abuse as a potential factor for racial differences in dissociation—specifically of the nonpathological variety. Findings reveal a significantly higher rate of nonpathological dissociation amongst black individuals as compared to white individuals. Less statistically conclusive results were obtained regarding the degree of exposure to racial abuse and related dissociative tendencies. Yet, intrinsic limitations within the study were largely due to the difficulty in quantifying the vast complexities of experienced racism, suggesting the tentative importance of the findings. Discussions on racial trauma, dissociative phenomena, and hierarchical networks—as well as suggested directions for future research—are included.

Students from low-socioeconomic status (SES) who are just starting college are more likely to experience discrimination. Frequent discrimination is associated with harmful effects on students’ well-being, including increased negative mental health symptoms (Hwang & Goto, 2008) and decreased sense of belonging in their college environment (Hussain and Jones, 2021). Discrimination is also associated with greater perceptions of cultural mismatch (Feasel et al., 2023), which occurs when a student’s home culture does not match their university culture. Cultural mismatch also negatively impacts college students’ sense of belonging (Phillips et al., 2020). Given that discrimination and cultural mismatch have similar effects on belonging, the current study tests our hypothesis that cultural mismatch mediates the relationship between SES discrimination and the perceived sense of belonging in low-SES college students. We found support for this mediational model in data analyses from a pre-existing longitudinal study. Students who experienced constant SES discrimination have frequently experienced more cultural mismatch and,as a result, felt less like they belonged during their first three years of college. In a follow-up study, these results were replicated in a cross-sectional study with low-SES college students using a more robust measure of SES discrimination.

California’s education system is in a unique position. As Covid-19 restrictions are being lifted across the country, schools and childcare facilities are losing the pandemic emergency funding that kept them barely hanging on as attendance waned and students fell through the cracks. However, in California, Governor Gavin Newsom recently signed off on a $307.9 billion state budget that features a record $128 billion toward reviving TK-12 public schools and community colleges. Understanding what to do with this significant budget allocation in order to serve California’s children best will be vital for educators and politicians in the coming years. This piece addresses how the state may use the influx in funding to address inequity. This is particularly pertinent in California, a highly diverse state with vast differences in socioeconomic status, ethnicity, identity, and need in the children served from district to district. Focusing on the Los Angeles Unified School District as a case study, the article delves into how the community school model has transformed suffering schools, and child outcomes, with pupils who faced high rates of dropout now regularly attending secondary education. Looking beyond the urban public school landscape, the article then presents how a rural school district has managed to defy the odds, focusing on the importance of teacher retention and programming. This article argues for an enormous need for change in the state– California has consistently performed poorly in public education quality rankings, and, unfortunately, the effects of this are distributed unequally, depending upon child socioeconomic status, ethnicity, and postcode.