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Innovative Machine Learning, Isotopic, and Hydrogeochemical Techniques for Groundwater Analysis in Arid Landscapes in Egypt’s Eastern Desert
Full text linkGroundwater serves as a lifeline in Egypt’s hyper-arid Eastern Desert, particularly for agricultural and domestic uses. However, a comprehensive understanding of groundwater origin, quality, and recharge dynamics in the region remains limited due to geological complexity, data scarcity, and the high cost of isotopic analysis. This study addresses these challenges by integrating stable isotopes (δ¹⁸O and δ²H), hydrogeochemical parameters, remote sensing, and explainable artificial intelligence (AI) to investigate groundwater dynamics and support sustainable water management strategies. A total of 34 groundwater samples were collected from three key aquifers: the Quaternary alluvium, Nubian Sandstone, and fractured Basement aquifers. Hydrochemical analyses and isotopic signatures distinguish meteoric water from paleowater sources, revealing significant mixing and recharge processes. The findings indicate that the Quaternary aquifer is increasingly influenced by upward leakage from the Nubian aquifer, facilitated by deep-seated faults. Between 2014 and 2021, water levels in the Quaternary aquifer declined by up to 14 m due to over-extraction, particularly in agricultural zones. To enhance predictive capabilities, a Support Vector Machine (SVM) model was developed to estimate δ¹⁸O values using multiple hydrochemical indicators, achieving strong performance (R² = 0.92, MSE = 2.89). SHapley Additive exPlanations (SHAP) analysis identified Mg²⁺, HCO₃⁻, and SO₄²⁻ as dominant factors influencing isotope variation. This integrated approach represents a novel application of explainable machine learning in hydrogeology and offers a scalable, cost-effective tool for assessing groundwater systems in arid regions. The study contributes directly to national water security goals and supports the global Sustainable Development Goal 6 (SDG 6) for clean water and sanitation
From Seasonality to Causality: Understanding Urban Water Usage Using Statistical and Machine Learning Models
Full text linkThis study examines the relationship between climate conditions and residential water usage, focusing on how seasonal and environmental changes influence water consumption. Utilizing data from over 100,000 households across three micro-climate zones for over a five-year period, we apply statistical analysis and machine learning techniques to assess the impact of temperature, precipitation, evapotranspiration, and location on water usage. By integrating climate and billing data, this research provides a data-driven approach on water usage behaviors in Irvine, CA, in collaboration with Irvine Ranch Water District (IRWD).
Our analysis utilizes time series modeling, including a Seasonal Autoregressive Integrated Moving Average (SARIMA) and Long Short-Term Memory (LSTM) model to identify seasonal trends and assess predictive power in water usage. Results indicate a steady decline in overall water usage since 2020. Geographic location also plays a role in determining water usage, ET Zone 2 on average has the highest water usage across the study period. The LSTM significantly outperforms the SARIMA model in capturing seasonal patterns of water usage. Utilizing a causal forest to assess direct causality between individual climate factors and water usage showed that precipitation and evapotranspiration have a strong causal effect on water usage. Confirming that increased rainfall leads to lower water usage, while hotter and drier weather conditions drive up water usage. Other climate factors such as humidity and wind speed show negligible direct effects once evapotranspiration is accounted for.
These findings emphasize the importance of addressing seasonal cycles in water usage. By identifying key drivers of water usage and demonstrating the performance of machine learning models and causal inference, this research provides valuable insights for water resource managers to improve upon conservation efforts. Future research can expand on these findings by exploring policy interventions to further optimize water usage in response to climate variability
Queer x Trans Memoir: In Sight of an Embodied History
Full text linkAre queer and trans identities innate or constructed? What constitutes a queer or trans identity? What prompts a person to identify with a particular queer or trans identity category? Prominent queer theorist Judith Butler contends that “There is no gender identity behind the expressions of gender; that identity is performatively constituted” (Butler, Gender Trouble 34). However, many scholars have claimed that Butler’s theory is “not compatible with lived experience” (McCann & Monaghan 134). This project utilizes the genre of memoir to gain insight into lived experiences to put this theory to the test. Through a mini historical archive of twelve queer and trans memoirs by authors born in the United States of America, this project uses theories of assemblage as its framework to identify characteristics of queer and trans identities in this context. Stivale defines an assemblage as follows: “A collection of things and their relations expresses something, a particular character…the elements that make up an assemblage also include the qualities present (large, poisonous, find, blinding, etc.)” (78). Through exploring what a queer or trans identity is by identifying characteristics present across twelve memoirs, this project simultaneously analyzes what a queer or trans identity does by examining the role that identity categories play in the lives of memoirists. How does the existence of identity labels shape reality in itself? How do these labels function as a mode of self-understanding and relationship building? What intersectional differences are present across the LGBTQIA+ community? The cross-analysis of this sample of twelve queer and trans memoirs provides insight into the lives of queer and trans individuals in the United States of America
At the Intersection of Foreign and Domestic Policy: The United States and Central America During the Cold War and the Rise of the Gang Wars
No full textToday\u27s intense debates surrounding immigration at the U.S. southern border, frequently framed as an invasion, often overshadow the complex historical forces driving migration. Much of the conversation oversimplifies the reasons for migration and fails to acknowledge the United States’ role in creating some of the reasons for outward migration. This paper argues that the influx of migrants from Central America, particularly El Salvador, is inextricably linked to past U.S. foreign and domestic policies. Existing scholarship often examines these policy spheres in isolation, either focusing on U.S. interventionism in Central America or on domestic immigration reforms. This thesis, however, posits that these seemingly distinct policies are deeply intertwined, and a failure to consider their combined impact leads to unintended consequences, perpetuating cycles of migration.
This essay will analyze how the confluence of U.S. foreign policy decisions in El Salvador, under Reagan during the Cold War, and subsequent domestic immigration policies, most notably the Clinton-era 1996 Illegal Immigration Reform and Immigrant Responsibility Act (IIRIRA), have significantly contributed to the ongoing migration from the country. By examining U.S. support for the Salvadoran government during its civil war and the later enforcement-centric immigration policies in the United States, this research demonstrates how actions taken by previous administrations created an environment that compelled Salvadorans to flee, a trend that continues to this day. El Salvador serves as a critical case study, illustrative of broader U.S. policies towards North Central America (Guatemala, Honduras, and El Salvador) and Latin America as a whole.
The failure of both the Reagan and Clinton administrations to consider the interconnectedness of their foreign and domestic agendas exacerbated the issue of irregular immigration from Central America. Reagan’s Cold War strategy prioritized geopolitical concerns over the potential for displacement, while Clinton’s domestic immigration reforms overlooked the impact on a fragile, post-conflict society and government. This analysis, based on prior research of a variety of scholars, journalists, and first-hand accounts, offers crucial insights for contemporary policymaking, highlighting the need for a more integrated approach that considers the multifaceted impacts of U.S. actions abroad on domestic immigration patterns
A Narrative-Focused Machine Learning Approach to Predicting Feature Film Success
No full textFor decades, the field of film production has been driven by marketability, and it has relied on gut feelings and subjectivity to produce feature films. The analysis of the relationship between a screenplay’s narrative and a film’s success has been widely overlooked due to the challenges involved in data acquisition and complexity. This study investigates the predictive power of narrative structure on film success and aims to build evidence for hypothesized narrative principles. The results suggest that narrative structural elements exhibit moderate predictive power, with strong support for the alignment of the 2nd act crucial moments and the 2nd act main tension moments. Additionally, the data supports empathy moments clustering towards the beginning of the first act. The predictive analysis was conducted using a logistic regression model fitted on augmented data split into training and testing sets. Inferential analysis was performed through Bayesian logistic regression, calculating causal estimates and 80% credible intervals derived from four chains with 10,000 iterations, including a 5,000 iteration burn-in period to ensure convergence. Overall, the findings reveal that narrative principles are worth further investigation, with the potential to develop practical tools for enhancing narrative effectiveness
Analyzing the Impact that Partisan Media had on Vote Choice in the 2020 U.S. Presidential Election
Full text linkThis study examines whether the partisan media that voters consume significantly influenced their vote in the 2020 U.S. Presidential Election. The study tests the hypothesis that individuals who frequently engaged with left-leaning media such as CNN were more likely to vote for Joe Biden, while voters consuming right-leaning media like FOX were more likely to vote for Donald Trump. This research contributes to a broader discussion of how different political, social, and economic factors influence voters\u27 choices. Understanding the intersections between these ideas can provide deeper insights into some influencers in the outcomes of major elections as well as broader political dynamics in the United States. While partisan media may play a significant role in shaping political preferences, additional factors such as personal ideology, life experiences, economic conditions, and social environments are likely to be crucial factors in the decision as well. Using data from the 2020 ANES (American National Election Studies) Time Series, this study analyzes the strength of the relationship between media consumption and vote choice. A logistic regression analysis assesses the likelihood of voting for Joe Biden versus Donald Trump based on respondents\u27 self-reported frequency of visits to popular news websites. Results revealed that respondents who regularly visited CNN but not FOX were over 900% more likely to vote for Joe Biden, and those who recorded visiting FOX but not CNN were approximately 91.4% more likely to vote for Trump over Biden. Interestingly, those who recorded visiting both CNN and FOX regularly showed no statistical significance, indicating that cross-partisan media exposure may lead to more moderate voting behavior. Results highlight the influence of partisan media in voter decision making and the need for further research on media consumption, voter choices, and other variables to continue developing an understanding of broader political ideas
Connection Beyond Coupledom: Do Single Men with Experience in Consensual Non-Monogamy Report Less Loneliness than Those Without Experience?
Full text linkBackground: While loneliness is a common experience, research suggests that men may be uniquely affected because of masculine ideals (Berke, Reidy, & Zeichner, 2018). People in consensually non-monogamous (CNM) relationships commonly highlight relationship benefits like shared experiences and social connection (Moors et al., 2017). To better understand how relationship structure shapes men’s connection and loneliness, we examined the experiences of men with and without prior engagement in CNM.
Method: Survey data were drawn from a U.S. Census-based quota sample of 747 men with experience in polyamorous, swinging, or open relationships, and 1,431 men with no such experience (total N = 5,035). Participants answered four items measuring meaningful social interactions, using a 5-point Likert scale (1 = Never, 5 = Very Often), including in-person interactions, phone/internet interactions, conversations with dating partners, and in-person dates. Participants also reported on how loneliness positively, negatively, or does not impact their friendships, dating/romantic life, and sex life.
Results: Men with CNM experience reported significantly more frequent meaningful in-person social interactions, phone or internet interactions, conversations with dating partners, and in-person dates compared to men without CNM experience (t-range = 2.88–15.67, all p values \u3c .01). Additionally, men with CNM experience were more likely than monogamous men to report that feeling lonely positively impacted their friendships, dating/romantic lives, and sex lives (χ² range = 135–161, all p values \u3c .001).
Conclusion: Research suggests when men challenge restrictive masculine norms, they may become better equipped to cultivate emotional connection and reduce loneliness, an experience potentially fostered through participation in CNM (Nordin et al., 2024). These findings may highlight the importance of community and alternative relationship structures as an additional benefit for men who practice CNM
Clinical Value of ChatGPT for Epilepsy Presurgical Decision-Making: Systematic Evaluation of Seizure Semiology Interpretation
Full text linkBackground: For patients with drug-resistant focal epilepsy, surgical resection of the epileptogenic zone (EZ) is an effective treatment to control seizures. Accurate localization of the EZ is crucial and is typically achieved through comprehensive presurgical approaches such as seizure semiology interpretation, electroencephalography (EEG), magnetic resonance imaging (MRI), and intracranial EEG (iEEG). However, interpreting seizure semiology is challenging because it heavily relies on expert knowledge. The semiologies are often inconsistent and incoherent, leading to variability and potential limitations in presurgical evaluation. To overcome these challenges, advanced technologies like large language models (LLMs)—with ChatGPT being a notable example—offer valuable tools for analyzing complex textual information, making them well-suited to interpret detailed seizure semiology descriptions and accurately localize the EZ.
Objective: This study evaluates the clinical value of ChatGPT for interpreting seizure semiology to localize EZs in presurgical assessments for patients with focal epilepsy and compares its performance with that of epileptologists.
Methods: We compiled 2 data cohorts: a publicly sourced cohort of 852 semiology-EZ pairs from 193 peer-reviewed journal publications and a private cohort of 184 semiology-EZ pairs collected from Far Eastern Memorial Hospital (FEMH) in Taiwan. ChatGPT was evaluated to predict the most likely EZ locations using 2 prompt methods: zero-shot prompting (ZSP) and few-shot prompting (FSP). To compare the performance of ChatGPT, 8 epileptologists were recruited to participate in an online survey to interpret 100 randomly selected semiology records. The responses from ChatGPT and epileptologists were compared using 3 metrics: regional sensitivity (RSens), weighted sensitivity (WSens), and net positive inference rate (NPIR).
Results: In the publicly sourced cohort, ChatGPT demonstrated high RSens reliability, achieving 80% to 90% for the frontal and temporal lobes; 20% to 40% for the parietal lobe, occipital lobe, and insular cortex; and only 3% for the cingulate cortex. The WSens, which accounts for biased data distribution, consistently exceeded 67%, while the mean NPIR remained around 0. These evaluation results based on the private FEMH cohort are consistent with those from the publicly sourced cohort. A group t test with 1000 bootstrap samples revealed that ChatGPT-4 significantly outperformed epileptologists in RSens for the most frequently implicated EZs, such as the frontal and temporal lobes (P\u3c .001). Additionally, ChatGPT-4 demonstrated superior overall performance in WSens (P\u3c .001). However, no significant differences were observed between ChatGPT and the epileptologists in NPIR, highlighting comparable performance in this metric.
Conclusions: ChatGPT demonstrated clinical value as a tool to assist decision-making during epilepsy preoperative workups. With ongoing advancements in LLMs, their reliability and accuracy are anticipated to improve
Armenian Genocide Looted Art and the Story of the Armenian Genocide Restitution Movement: A Tribute
Full text linkThis Article explores the emergence and development of the Armenian Genocide Restitution Movement and its founder, Vartkes Yeghiayan, with particular focus on legal efforts to recover looted cultural and private property. Drawing on case law, historical research, and interdisciplinary analysis, the authors examine how U.S. courts have been used to pursue redress for mass atrocity crimes, including lawsuits against insurance companies, corporations, and sovereign entities. Central to the discussion is the Zeytun Gospels case, the first major attempt to reclaim art looted during the Armenian Genocide, which highlights the complex interplay of law, history, and cultural identity in restitution efforts. The Article also introduces the Armenian Genocide Looted Art (AGLA) project, a collaborative initiative aimed at documenting and recovering cultural heritage displaced by genocide. Through this lens, this Article addresses the legal, ethical, and historical challenges of pursuing justice long after mass atrocity
En Banc
No full textEN BANC Refreshments and hors d’oeuvres will be served immediately following the last panel in the Kennedy Hall Lobby