JURSW Volume 7

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12588/230

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Now showing 1 - 20 of 23

Front Matter
(UTSA Office of Undergraduate Research, 2020-12)
Note from the Director
(UTSA Office of Undergraduate Research, 2020-12) Balderrama, Darrell C.
Book Review: T. Jackie Cuevas Shatters the Hegemony
(UTSA Office of Undergraduate Research, 2020-12) MacManus, Azia
Application of Neuroevolution in Blackjack
(UTSA Office of Undergraduate Research, 2020-12) Tejani, Iraz K.
Blackjack is one of the few casino games with an extremely low house edge. In the past, many brute force simulations have been done to derive basic strategy. Classical brute force methods are tedious, time consuming, and often require hundreds of millions of games played to achieve results. In this project, I use reinforcement learning, specifically neuroevolution (NE), which is an attempt to simulate biological evolution, to see if an artificial neural net (ANN) can evolve to learn basic strategy and achieve the theoretical maxima provided by a basic strategy simulation. Two main simulations are run in this project, one using basic strategy charts and the other using the evolved ANN. These are then compared to see how effective the ANN was in learning strategy as well as how quickly it was able to learn.
Predicting the Next Big Impact: Modelling the Rate of Massive Meteorite Strikes
(UTSA Office of Undergraduate Research, 2020-12) Woods, Ethan; Han, David
Meteorites are solid pieces of debris from an astronomical object such as a comet, asteroid, or meteoroid that originates in outer space and survives its passage through the atmosphere to reach the surface of a planet. Although rare, a collision between massive astronomical objects, known as an impact event, can have measurable effects, and physical and biospheric consequences. In this work, we investigate the distributional trend of heavy meteorites that strike the earth and determine if any probability distributions can serve as effective predictive models. NASA meteorite data from 1980 to 2012 were imported into R after pre-processing. Pre-processing activities involved the following: removal of missing data, irrelevant features to meteorite mass or the year of meteorite impact. Statistical analysis was then restricted to meteorites at or above the 98th percentile of mass. It was found that while the distribution of mass for all meteorites is lognormal, the distribution for the top 2% is severely right-skewed, indicating that an extreme-value distribution could be used to model them. Furthermore, the rate of impact for these massive meteorites can be modelled with a zero-inflated negative binomial distribution.
Stochastic SIR-based Examination of the Policy Effects on the COVID-19 Spread in the U.S. States
(UTSA Office of Undergraduate Research, 2020-12) Song, Mina; Belle, Macy K.; Medlovitz, Aaron; Han, David
Since the global outbreak of the novel COVID-19, many research groups have studied the epidemiology of the virus for short-term forecasts and to formulate the effective disease containment and mitigation strategies. The major challenge lies in the proper assessment of epidemiological parameters over time and of how they are modulated by the effect of any publicly announced interventions. Here we attempt to examine and quantify the effects of various (legal) policies/orders in place to mandate social distancing and to flatten the curve in each of the U.S. states. Through Bayesian inference on the stochastic SIR models of the virus spread, the effectiveness of each policy on reducing the magnitude of the growth rate of new infections is investigated statistically. This will inform the public and policymakers, and help them understand the most effective actions to fight against the current and future pandemics. It will aid the policy-makers to respond more rapidly (select, tighten, and/or loosen appropriate measures) to stop/mitigate the pandemic early on.
Quantum Computation, Quantum Algorithms and Implications on Data Science
(UTSA Office of Undergraduate Research, 2020-12) Kim, Nathan; Garcia, Jeremy; Han, David
Quantum computing is a new revolutionary computing paradigm, first theorized in 1981. It is based on quantum physics and quantum mechanics, which are fundamentally stochastic in nature with inherent randomness and uncertainty. The power of quantum computing relies on three properties of a quantum bit: superposition, entanglement, and interference. Quantum algorithms are described by the quantum circuits, and they are expected to solve decision problems, functional problems, oracular problems, sampling tasks and optimization problems so much faster than the classical silicon-based computers. They are expected to have a tremendous impact on the current Big Data technology, machine learning and artificial intelligence. Despite the theoretical and physical advancements, there are still several technological barriers for successful applications of quantum computation. In this work, we review the current state of quantum computation and quantum algorithms, and discuss their implications on the practice of Data Science in the near future. There is no doubt that quantum computing will accelerate the process of scientific discoveries and industrial advancements, having a transformative impact on our society.
Optimal Dynamic Treatment Regime by Reinforcement Learning in Clinical Medicine
(UTSA Office of Undergraduate Research, 2020-12) Song, Mina; Han, David
Precision medicine allows personalized treatment regime for patients with distinct clinical history and characteristics. Dynamic treatment regime implements a reinforcement learning algorithm to produce the optimal personalized treatment regime in clinical medicine. The reinforcement learning method is applicable when an agent takes action in response to the changing environment over time. Q-learning is one of the popular methods to develop the optimal dynamic treatment regime by fitting linear outcome models in a recursive fashion. Despite its ease of implementation and interpretation for domain experts, Q-learning has a certain limitation due to the risk of misspecification of the linear outcome model. Recently, more robust algorithms to the model misspecification have been developed. For example, the inverse probability weighted estimator overcomes the aforementioned problem by using a nonparametric model with different weights assigned to the observed outcomes for estimating the mean outcome. On the other hand, the augmented inverse probability weighted estimator combines information from both the propensity model and the mean outcome model. The current statistical methods for producing the optimal dynamic treatment regime however allow only a binary action space. In clinical practice, some combinations of treatment regime are required, giving rise to a multi-dimensional action space. This study develops and demonstrates a practical way to accommodate a multi-level action space, utilizing currently available computational methods for the practice of precision medicine.
Mycobacterium tuberculosis H37Rv Induces Gene Expression of PDE4A and PDE7A in Human Macrophages
(UTSA Office of Undergraduate Research, 2020-12) Naoun, Adrian; Wager, Chrissy M. Leopold; Arnett, Eusondia; Schlesinger, Larry S.
The World Health Organization reported that one-fourth of the global population is infected with Mycobacterium tuberculosis (M.tb), the causative agent of an airborne infectious disease known as tuberculosis (TB). In 2017, TB alone caused 1.6 million deaths. M.tb is an intracellular pathogen equipped with specialized evolutionary traits to evade immune mechanisms. Upon inhalation, macrophages phagocytose M.tb and become a niche due to their inability to resolve the infection. The intracellular growth of M.tb is influenced, in part, by host transcription factors and immunosuppressive second messengers like cyclic adenosine monophosphate (cAMP). The importance of cAMP as an inflammatory response mediator derives from its ability to suppress innate immunity functions in macrophages, monocytes, and neutrophils by limiting pro-inflammatory cytokine release. Despite its known effects, the mechanisms underlying cAMP activation in response to M.tb are incompletely understood, particularly in human macrophages. Preliminary data indicate that cAMP levels are increased in human monocyte-derived macrophages (hMDMs) following infection with virulent M.tb H37Rv and attenuated M.tb H37Ra. Phosphodiesterases (PDEs) comprise a group of enzymes that degrade cAMP to regulate signal transduction. We hypothesize that elevated cAMP levels induce gene expression of certain PDEs as a host response mechanism to degrade M.tb-induced cAMP. Gene expression studies demonstrated that transcription of PDE4A and PDE7A increased 48 and 72 h after infection, whereas PDE3A and PDE5A remained unaltered. These data suggest that human macrophages up-regulate PDE expression to limit M.tb from dampening the immune response via high cAMP levels. Further studies will demonstrate the clinical feasibility of cAMP degradation as a novel host-directed therapy to reduce M.tb pathogenesis.
Across Cultures: Pakistani All-Female Speaking Rituals
(UTSA Office of Undergraduate Research, 2020-12) Virani, Zuwena
A common finding in Language and Gender studies is that women aim for a united conversational dynamic, while men tend towards the opposite. However, I argue that native culture plays a more significant role in Language and Gender studies than has previously been considered. To do so, I compared previous conclusions—from Jennifer Coates’s Gossip Revisited (2011)—to my own drawn from data collected during a gathering of Pakistani Muslim women and analyzed that data, considering culture as well as gender. The following hypotheses were made prior to collection of data: Culture, religion, and ethnicity will heavily influence the frequency and overall use of certain, typically female, linguistic rituals generally observed in Western contexts, and certain rituals will be used in an exaggerated or minimized capacity in comparison to Coates’ findings. Over five days, I observed three conversations among a group of five to eight Pakistani women, aged between 50 and 60. The following rituals were observed: interruptions, floor sharing, tag questions, code-switching, minimal responses, “butterfinger buts,” and razzing. Certain rituals were just as consistent among my participants as they were in Coates’. However, use of razzing, “butterfinger buts,” floor sharing, and tag questions differed greatly—all were used in a different context and capacity than expected. These rituals were significantly affected by culture, religion, and ethnicity; further analysis revealed that additional factors such as age and familiarity between speakers also play a role in motivating ritualistic behavior.
Statistical Perspectives in Teaching Deep Learning from Fundamentals to Applications
(UTSA Office of Undergraduate Research, 2020-12) Kim, Nathan; Han, David
The use of Artificial Intelligence, machine learning and deep learning have gained a lot of attention and become increasingly popular in many areas of application. Historically machine learning and theory had strong connections to statistics; however, the current deep learning context is mostly in computer science perspectives and lacks statistical perspectives. In this work, we address this research gap and discuss how to teach deep learning to the next generation of statisticians. We first describe some backgrounds and how to get motivated. We discuss different terminologies in computer science and statistics, and how deep learning procedures work without getting into mathematics. In response to a question regarding what to teach, we address organizing deep learning contents and focus on the statistician’s view; form basic statistical understandings of the neural networks to the latest hot topics on uncertainty quantifications for prediction of deep learning, which has been studied in the Bayesian frameworks. Further, we discuss how to choose computational environments and help develop programming skills for the students. We also discuss how to develop homework incorporating the idea of experimental design. Finally, we discuss how to expose students to the domain knowledge and help to build multi-discipline collaborations.
Nonverbal Immediacies: The Benefits of Nonverbal Immediacy Behaviors to Intimate Relationships
(UTSA Office of Undergraduate Research, 2020-12) Gonzalez, Gabrielle
The purpose of this study is to determine if couples that are in longer relationships express nonverbal immediacy behaviors just as often as couples that are in shorter relationships do. Evidence of prior research regarding communication in romantic relationships discusses topics such as types of nonverbal immediacy behaviors, their effect on a relationship, and why they are detrimental in order for a relationship to blossom. A survey was given to test the hypothesis that couples who have been in a relationship longer are less likely to practice nonverbal immediacies to maintain a relationship compared to couples who have not been together as long. Respondents were divided into two categories, long-term and short-term relationships, and asked about the frequency of their usage of nonverbal immediacy behaviors with their partners. The major finding from this study was that couples in longer relationships do not express nonverbal immediacy behaviors as often as couples in shorter relationships do.
The New Black Wall Street
(UTSA Office of Undergraduate Research, 2020-12) Harris, Devall
Current studies on the racial wealth gap explores the origins of the problem and addresses it through the law. The paper provides an avenue to address the issue of racial wealth with the goal of providing insights for the establishment and identification of a Black Community for systematic success for Black community growth. The study builds on the social/ contextual knowledge founded upon the teachings of Marcus Garvey, history of Prairie View, history of black business and Greenwood District. This paper aims to inspire current and aspiring young Black professionals with ways we can help foster economic growth within the Black community.
Neurological Abnormalities’ Impact on Crime and Behavior
(UTSA Office of Undergraduate Research, 2020-12) Marr, Caleb
This study analyzes and discusses various types of neurological abnormalities and the ways in which they affect antisocial behavior and criminal propensity. It also explains how many of these abnormalities are caused and why they can lead to antisocial behavior. Further, the article discusses gaps in the extant literature, the various legal impacts related to neurological abnormalities, and policy implications. Throughout the study, a series of real life examples and cases that are used to put things into perspective are analyzed in order to demonstrate how serious this subject is and the potential it has to be an even more serious problem if not addressed properly and promptly. The findings of this article suggest that neurological abnormalities play a vital role in determining if an individual is subject to increased criminal propensity, and in some cases, psychopathy, while questioning if those affected are right to be considered fully responsible for their actions due to the abnormalities affecting mental ability and reasoning.
The impacts of leaf damage on the isotopic composition of leaf transpiration
(UTSA Office of Undergraduate Research, 2020-12) Weddle, John; Lanning, Matthew; Ewing, Remi; Wang, Lixin
Stable isotopes of water are useful tracers to study water cycling between the subsurface, plants, and the atmosphere. The development of water vapor based isotope methods has facilitated the direct and continuous measurements of plant transpiration isotopic composition, which is often used to determine plant water sources and partition evapotranspiration. The assumption that isotopic composition of transpiration is equal to the source water is fundamental in such applications. However, it is possible that leaf damage may obscure the transpiration isotopic signature violating this key assumption. We hypothesized that leaf damage would expose isotopically enriched lamina water to the chamber environment resulting in more enriched measurements. We compared the isotopic compositions of transpiration for un-damaged, artificially damaged, and recovered leaves using a leaf chamber and laser isotope analyzer. Our results showed significant enrichment in the transpiration isotope signature after leaf damage and the signature returned to pre-damage values after a few days. This study is the first to evaluate the consequences of damaged leaves on the isotopic composition of transpiration using chamber based methods and has direct applications for source water
Optimizing the Separation of an Antiparasitic Medication using High-Pressure Liquid Chromatography (HPLC)
(UTSA Office of Undergraduate Research, 2020-12) Barnett, Karis R.; LaCourse, William R.
Excess pharmaceutical waste in water is an emerging concern that can increase parasitic drug resistance, interrupt animal food chains, and threaten drinking water sources. In this work, a high-pressure liquid chromatography (HPLC) method with ultraviolet detection (210 nm) was optimized for sensitively detecting and separating antiparasitic compounds praziquantel (PZQ) and metronidazole (MET). This method has the potential to commercially monitor antiparasitic treatments administered to aquatic species, which can ultimately prevent pharmaceutical waste in water. The latest HPLC method was altered over seven experiment trials to improve resolution and Gaussian shape of chromatogram peaks. The most efficient separation of PZQ and MET was achieved on a Phenomenex™ Luna C18 analytical column (150 x 4.60mm, 5μm, 100A) using acetonitrile:water at alternating ratios of 20:80 v/v and 80:20 v/v as a mobile phase. This separation resulted in the shortest acquisition time with satisfactory peak shape. Aquarium facilities may ultimately use this method to understand how to safely treat parasitic fish diseases
Viktor Frankl and COVID-19: Finding Hope Amidst a Pandemic
(UTSA Office of Undergraduate Research, 2020-12) Driskill, Landri
This essay applies Viktor Frankl’s logotherapy on the anxiety produced by the coronavirus outbreak (COVID-19). During the Holocaust, survivor and psychologist Viktor Frankl utilized his study of logotherapy to discover how hope can be found in the midst of suffering and death. The coronavirus pandemic has heightened fears and anxieties as businesses, schools, and countries have shut down worldwide. This essay offers insight into the situations of those affected firsthand by the coronavirus outcomes and applies Frankl’s logotherapy as the pivot of hope during this pandemic.
Bad Bunny: A Contemporary Latinx Activist
(UTSA Office of Undergraduate Research, 2020-12) Sulaica, Analisa
Oppressive and hostile values of sexism, racism, and homophobia remain active within the Latinx community: values resulting from a continued history of U.S. imperialistic practices. This research explores the resistance against these values within the Latinx community through Reggaetón. I focus this work on Bad Bunny (also known as Benito Antonio Martinez Ocasio), an increasingly influential and popular Reggaetonero from Puerto Rico. Using a feminist approach to textual and media analysis, I demonstrate how Bad Bunny is engaging in politicization, gender-play, and cultural resistance to challenge and dismantle the oppressive themes of citizenship, white privilege, and gender/sexuality hostility that maintain the prejudiced perceptions of race, gender, and sexuality within the immediate Latinx community as well as society as a whole. With the increasing academic attention to Reggaetón and its unique potential to serve as a global means of resistance, this investigation is an important contribution to the growing body of scholarly work seeking to outline the significance of Reggaetón to Latinx activism, praxis, and community.
How do caregivers affect children’s academic performance? Evidence from Primary Caregivers’ Educational Attainment and Children’s Performance on Standardized Assessments
(UTSA Office of Undergraduate Research, 2020-12) Wilson, Sofia Santillan; Grenier, Amandine E.; Wicha, Nicole, Y.Y.
Children's performance on standardized testing are affected by a variety of external factors, such as access to resources, school environment, or primary caregiver’s education. Educational inequalities, likewise, have a negative impact on the quality of education and access to resources, and impact student performance. Student outcomes and performance, hence, are multidimensional in that there are many factors that play a role in student success. For instance, previous research has shown that mother’s educational attainment has an impact on their children’s academic performance. By understanding the effects of primary caregivers’ educational attainment on student achievement, policies can be created to promote equity in the education system. The goal of the present study is to understand the impacts of primary caregivers’ educational attainment and language history on children’s standardized assessment performance. We analyzed data from a large-scale study that collected demographic information (age, language background, socioeconomic status, primary caregivers’ education), standardized assessment scores (math fluency, oral comprehension, working memory, phonological awareness, and vocabulary size), and performance (accuracy, response times) on a simple multiplication task. The study included a total of 176 children, and we hypothesized that children with highly educated primary caregivers were more likely to perform higher on academic assessments and the math task. Results showed that children with fathers as primary caregivers performed better on our measure of math fluency compared to children with mothers as primary caregivers. Additionally, the primary caregiver's educational attainment showed significance in performance on math fluency, oral comprehension, and math task accuracy in the “some college” and “graduate degree” category. Together, these findings suggest that primary caregivers’ educational attainment can affect children’s performance on standardized assessments, though future research should explore a broader population sample.
Going Natural: Reading Black Women’s Hair in Americanah as a Sociopolitical Narrative to Battle American Misogynoir
(UTSA Office of Undergraduate Research, 2020-12) Green, Erin
America is founded on several different forms of oppression--two most notably being white supremacy and patriarchy. As white supremacy affects people of color and patriarchy affects women, Black women can suffer both forms of oppression, which many Black feminist scholars have deemed as “misogynoir.” Coined by Black feminist Moya Bailey, misogynoir is defined as “anti-Black racist misogyny that Black women experience.” One specific form of misogynoir is hair discrimination, which affects Black women personally and professionally. Americanah, written by Chimamanda Ngozi Adichie, is a novel that uses the trope of Black women’s hair not only to bring awareness to anti-Black, sexist society, but also to fight against the actual sociopolitical hierarchy seeking to disenfranchise Black women. This paper explores Adichie’s illustration of Black women’s hair about perming, workplace discrimination, “the big chop,” going natural, and finding a community. Additionally, this paper argues about the different kinds of spaces Black women and their hair must exist in society, from anti-Black workplaces to Africana womanist hair salons. The paper, also, argues that Adichie uses the novel itself as an act of resistance against America’s system of anti-Black misogyny, misogynoir.

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