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Barriers to Charitable Nonprofit Access and Advocacy amid a Pandemic: A Case Study of the Louisiana State Legislature
Get PDFResearch has long established nonprofit organizations’ vital role advocating for the needs of vulnerable populations before legislative policymakers. In the best of times, it is difficult for 501(c)(3) charitable nonprofits employing grassroots advocacy to mobilize vulnerable constituencies to compete with 501(c)(4) and 501(c)(6) advocacy and special interest groups. The latter organizations inherently have greater flexibility and resources to lobby lawmakers directly, permitting greater access to influencing the policy agenda. Through a multi-method case study of the 2020 regular session of the Louisiana State Legislature, this article demonstrates how the COVID-19 pandemic’s unique contextual conditions made legislative advocacy more difficult than usual for charitable nonprofits promoting a progressive policy response to the pandemic within a politically conservative state. Conducted through interviews with nonprofit leaders and an analysis of legislative records and committee hearings, the case study reveals specific barriers that hampered charitable nonprofits’ access to the legislative process, including physical capacity restrictions and health concerns, as well as issues with virtual legislative protocols and conservative committee chairs’ discretion to ignore remote testimony. The article analyzes how these barriers negatively impacted charitable nonprofits’ ability to advocate for vulnerable populations and explores potential implications for equitable political participation and response to the pandemic
Lessons from Perceived Rejection: An Argument for Process Oriented Thinking in Theatre
Get PDFRejection is a part of human existence. This is especially true in the world of art. The following is a case-study that documents the trial-and-error process by which I accepted the challenge of portraying a character 30 years my senior and in my opinion the least substantial and meaningful role within the ensemble of the play “Stupid Fucking Bird”; the result of this study led to the discovery of the truth that serious growth as an actor comes not from the external acknowledgement of peers but from the internal effort produced by the actor. This work is examined through the physical, psychological, historical, and philosophical theories amalgamated throughout the course of two semesters that culminated in what I believe to be my successful attempt to breathe important life into a what I believe to be a one-note character. As an additional note, this document demonstrates my process in real time. Many of the early sections may come across as bitter and while I have kept those sections largely intact, they only serve to reflect my change in attitude over time as a way of reflecting the result of peace and understanding I was able to reach by the end
The effect of conditional volatility, skewness, and excess kurtosis on interest rates
Get PDFThis paper examines the lognormality assumption of per capita, real consumption growth, which is a common assumption in asset pricing models. We found that shocks to household consumption growth are persistent, negatively skewed, and have excess kurtosis. Therefore, we revisited the fundamental relation between expected growth and the real risk-free rate, assuming a non-Gaussian distribution of consumption growth, and found a robust positive association between real consumption growth and real risk-free interest rate, and a negative relationship between macroeconomic uncertainty and real rates, although less in magnitude, which is consistent with both intertemporal smoothing and precautionary savings. This paper offers an answer to the puzzle of why real rates and macroeconomy appear to be empirically unrelated. Adding higher moments lowers the level of persistence of consumption growth and adds more dimensions of risk that are not captured by conditional variance. Specifically, conditional skewness amplifies the effect of intertemporal smoothing while excess kurtosis amplifies the effect of precautionary savings. The results imply that using variance as a description of uncertainty is incomplete
Enchanting Music: How English Playwrights Use Music in Renaissance Witchcraft Plays
Get PDFMusic is an integral aspect of the Early Modern theater, but because most of this music is lost, scholars and students typically only analyze these works using literary theories. This approach does not allow for a full understanding these plays, which is especially true of witchcraft plays because witches typically utilize music for their spells. In this thesis, I am exploring the interdisciplinary connection of music and literature in the Jacobean witchcraft plays The Witch (c. 1616) by Thomas Middleton and The Tragedy of Sophonisba or The Wonder of Women (1604-1606). From my analysis of the existing music from The Witch and the music’s function in characterizing the witch, I recreated the infernal music from Sophonisba to gain new insights into this play’s witch figure. From this project, I intend to emphasize the importance of musical analysis in discourses on Early Modern witchcraft plays and inspire interdisciplinary research into these works
Somewhere in Between and Other Stories
Get PDFThis thesis is a short story collection which explores themes of queer identity and trauma through the lens of magical realism, written in fulfillment of the Master of Fine Arts in Creative Writing at the University of New Orleans
Solitary Pulse
Get PDFThis collection of short stories explores themes of disillusionment, love, and isolation
Effects of Organic Matter on Settling Velocity of Coastal Sediments Used in Coastal Restoration and Marsh Creation Projects
Get PDFCoastal wetland loss is a critical environmental problem across the United States. These ecosystems provide vital services to people and the environment including erosion control, flood protection, carbon sequestration, and maintenance of water quality. The natural flood protection has already been greatly weakened by coastal land loss in Louisiana and similar deltaic areas throughout the world. If this trend continues, many communities are at severe risk of physical and infrastructural damage. One of the proposed methods to reverse the erosion of the coastal wetlands is marsh creation. Comprehensive characterization of the dredged sediments is crucial in successfully constructing a marsh creation project. The settling characteristics of the dredged material are affected by organic content, solids concentration, and geotechnical properties of the sediments. The effects of organic content on settling characteristics are evaluated and discussed. The zone settling velocity was lower for dredged slurry with higher percentages of organic matter
Analyzing the Robustness of Prevalent Social Engineering Defense Mechanisms
Get PDFMost cybersecurity attacks begin with a social engineering attack component that exploits human fallibilities. Hence, it is very important to study the prevailing defense mechanisms against such attacks. Unfortunately, not much is known about the effectiveness of these defense mechanisms. This dissertation attempts to fill this knowledge gap by adopting a two-fold approach that conducts a holistic analysis of social engineering attacks.
In the first fold, we focused on phishing attacks, which remain a predominant class of social engineering attacks despite two decades of their existence. Entities such as Google and Microsoft deploy enormous Anti-Phishing Entity systems (APEs) to enable automatic and manual visits to billions of candidate phishing websites globally. We developed a novel, low-cost framework named PhishPrint to evaluate APEs. Our framework found several flaws in APEs of 22 companies which enable attackers to easily deploy evasive phishing sites that can blindside them. These flaws include a lack of network diversity as well as exposure to crawler artifacts. One significant flaw that affected every entity we analyzed was the lack of browser fingerprint diversity. We then continued our efforts in this direction by enhancing PhishPrint to enable it to differentiate between automated and human visits. Using this, we evaluated the weaknesses of the very expensive human-driven components of 5 APEs. Our analysis again revealed a significant lack of diversity in their infrastructure thus exposing them to practical evasive attacks. We revealed all these weaknesses as well as suitable remediation measures for affected entities prompting several bug reports as well as monetary rewards.
In the second fold, we focused our attention on emerging social engineering attacks and their defense mechanisms. We chose cryptocurrency scams that run rampant on social media networks such as Twitter as an example of such emerging attacks. In order to evaluate the effectiveness of Twitter’s defense mechanisms, we developed a novel system named HoneyTweets that periodically posts messages on Twitter as bait to attract social engineering attackers. We then deployed HoneyTweets over a 3-week period and conducted extensive analysis of the collected attacks to reveal several attack mechanisms that remain out of the scope of Twitter’s existing defenses. Our analysis also resulted in the collection of thousands of ensuing attack points such as e-mail accounts, Instagram handles, and externally hosted web pages built by attackers for the purpose of accomplishing the next stages of attacks.
Our work thus presents multiple evaluation frameworks which can be used for continuous evaluation of existing social engineering defenses in future
Protein-Protein Interaction Prediction from Language of Biological Coding
Get PDFProtein-protein interactions in a cell are essential to the characterization and performance of various fundamental biological processes. Due to the tedious, resource-expensive, and time-consuming experimental processes, computational techniques to solve protein pair interaction difficulties have emerged as an active research area in bioinformatics. This research seeks to develop an innovative machine learning-based technique that predicts the interaction of a protein pair based on carefully selected input features and exploits information-rich evolutionary information. We developed a protein-protein interaction predictor, PPILS, that leverages the evolutionary knowledge from the protein language model. We examined several distinct neural network architectures: CNN+LSTM, Transformer, Encoder-Decoder, and FNN and found that the encoder-decoder architecture with light attention performs the best. The method is straightforward; there are only four learnable weight matrices. The model will receive protein representations from the language model, perform one convolution on them to get attention coefficients, and then normalize them along the length dimension using the SoftMax function to generate attention. A second convolution is applied to input features to create values. Then, take the element-wise product of attention and values to construct a representation of the protein. After calculating the sum over the length dimension, a fixed-size protein representation is obtained. This is then concatenated with the maximum length dimension of the data and fed to the decoder. The decoder is our classification engine to predict protein interactions. We found that the PPILS outperformed other cutting-edge techniques for PPI prediction. We believe the proposed method could serve as an essential tool in protein-protein interaction prediction, further accelerating the protein drug discovery process