Stanford University Student Journals
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Evaluating US CO2 Emissions Targets: Statistical Models and Strategic Solutions
Full text linkClimate change caused by the increasing greenhouse emissions is one of the biggest threats impacting the world. One of the primary contributors to CO2 emissions is energy production. The energy demand is expected to rise in the developing countries too because of the rising living standards and growing population. In developed countries such as the US, this growth is driven by technological advancements in AI (Artificial Intelligence) leading to expansion in data centers. The United States has set intermediate targets to meet its long-term net-zero target, i.e., no net CO2 emissions by 2050. Our hypothesis is that the US is not on track to meet intermediate targets such as about 50% reduction in greenhouse gases compared to 2005 and carbon-free electricity by 2035. We assessed the progress against these targets using statistical models and public data for energy related CO2 emissions since they cover about 80% of total emissions. Although the US is making progress on decarbonization through production and application of renewable energy, we found that they are not on track to meet either of these intermediate targets. CO2 emissions are reducing by about 30% by 2030 compared to 2005 and electricity generation still has 30% carbon emissions compared to 2022. To bridge these gaps, the US needs to reduce dependence on fossil fuel energy and move towards electrification through electric vehicles and improving the fuel economy which bridges about 3% of the decarbonization gap. If solar and wind generation is increased by 20% on top of projected increase, the decarbonization gap is reduced further by about 4%. Even with these initiatives, the US is unable to meet its 2030 or 2035 targets. Thus, the US needs to explore and implement other decarbonization options such as sustainable fuels for aviation and maritime applications, hydrogen for heavy duty transportation, or carbon capture for industrial use as a couple of other options to get closer to these targets. Reaching this target will involve researchers, business leaders, policy makers and the public to work collectively to solve this impending challenge
The Role of Dance in Cognition: A Narrative Review
Full text linkThere are several aspects of cognition, physiology, and brain structure that are uniquely impacted by dance. These effects occur across the lifespan, from development to aging. This review explores current research efforts in dance neuroscience, with a focus on cognition. Specific topics highlighted in this review include general cognition, memory, predictive processing, and more. Results show that dance can greatly strengthen your brain, ease the symptoms of mental illnesses, and have protective effects against cognitive decline. Evidence-based recommendations to integrate dance into school systems are presented
Comparative Analysis of Deep Learning and Traditional Machine Learning Models for Arrhythmia Classification using ECG Signals
Full text linkArrhythmias, a form of cardiovascular disease, are a major contributor to the high global mortality rate. Early detection of arrhythmias through electrocardiogram (ECG) analysis can significantly improve patient outcomes. This study investigates the application of var- ious machine learning (ML) and deep learning models for the classification of arrhythmias using ECG signals from the MIT-BIH Arrhythmia Dataset. The models evaluated include Random Forest, Support Vector Machines (SVM), Logistic Regression, Multilayer Perceptron (MLP), and Convolutional Neural Networks (CNN). Additionally, feature selection techniques, such as the Fourier Transform, were applied to enhance the performance of the ML models. Among the models tested, the CNN achieved the highest accuracy (89.29%), F1 score (85.69%), and AUC (87.98%), demonstrating its superior ability in accurately detecting arrhythmias. In contrast, traditional ML models, including Random Forest and SVM, showed moderate performance with lower accuracy and discriminatory power. The study highlights the potential of CNN-based architectures for automated ECG analysis and emphasizes the importance of integrating explainable AI techniques to increase the transparency and clinical adoption of deep learning models. Future research could focus on larger, more diverse datasets and the use of Recurrent Neural Networks (RNNs) for longer ECG recordings to improve classification performance further.
Artificial Intelligence in Healthcare: Early Pancreatic Cancer Detection Using Urinary Biomarkers
Full text linkPancreatic cancer is one of the deadliest malignancies due to its late-stage diagnosis and lack of effective early detection tools. Existing detection and screening methods currently fail to identify the tumor at its early, more treatable stages, contributing to persistently low survival rates and necessitating alternative approaches. However, in recent times, machine learning (ML), which is a branch of artificial intelligence (AI), has shown immense promise in the field, potentially enhancing early cancer detection by identifying minute and subtle patterns in clinical data. This study explores the application of machine learning and deep learning in the prediction of pancreatic cancer, using notably as input a set of patient urinary and blood biomarkers identified in previous studies as potentially promising for early detection of pancreatic cancer. The goal, after all, of this study is to predict the presence of the disease before it is diagnosed. Four classification models (Neural Network, Decision Tree, Random Forest, and K-Nearest Neighbors) were implemented to analyze the data features, classifying individuals as healthy, having benign hepatobiliary disease, or having pancreatic cancer. To further improve prediction reliability, a Multiplicative Weight Update (MWU) method was applied to dynamically adjust the influence of each model based on their testing performance, finally forming an overall more robust and accurate program. The integration of four distinct classification models, in tandem with the MWU method, distinguishes this research from previous studies and enhances its predictive performance. Given the varying concentrations of biomarkers associated with different pancreatic conditions, the use of multiple diverse models to capture both linear and complex non-linear patterns in the biomarker data was particularly important, something prior studies relying on individual models rarely achieved. As a result, the final prediction accuracy was significantly improved. The results demonstrate high accuracies for most models, with the Decision Tree achieving the highest predictive accuracy of 98.7%. These results highlight the potential of AI-driven diagnostic tools in improving early pancreatic cancer detection.
Resisting homogenization and recognizing power dynamics: an intersectional and decolonial approach to discussing the marginalization of disabled women in ‘low-income’ countries
Full text linkWithin the related fields of international development, sociology and social policy, the contention that disabled women in low-income countries (LICs) are the most marginalised demographic is extremely common and widely accepted. This is with good reason, yet the inclusion of intersectional and decolonial approaches within the related literature is surprisingly sparse. Instead, discussions often overgeneralise disabled women as a homogenous group in LICs, failing to recognise other aspects of identity, differing impairments, and therefore different experiences and barriers. This paper discusses and analyses the nuances behind these discussions, challenging the generalisation of disabled women as a monolith in LICs within the related literature. It also subsequently confronts the questionable use of the term ‘low-income’ itself, as commonly used in the literature and related debates, due to the power dynamics and history behind it. This paper firstly introduces key context and relevant terminology, before discussing the importance of recognizing other aspects of identity, such as socioeconomic status, citizenship status, and sexuality in addressing the marginalization of disabled women. It also discusses the importance of intersectional and, towards the end of the paper, decolonial approaches when exploring disability, including the contextual history and emergence of countries as low-income. Lastly, considering the above, this paper provides recommendations on how to best address the marginalization of disabled women in low-income countries
But I’m a Cheerleader; a Camp Critique of Heteronormativity
Full text linkThe essay critically analyzes Jamie Babbit\u27s 1999 film But I\u27m a Cheerleader as a subversive critique of heteronormativity through the lens of Camp. The film, set in a conversion therapy camp, uses humor and exaggerated gender roles to challenge societal expectations of gender and sexual orientation. By employing Camp, the movie undermines the seriousness of the conversion therapy process and highlights the absurdity of gender norms when taken to their most literal sense. The analysis draws on theories of gender performativity by Judith Butler and concepts of heteronormative discourse by Gayle Rubin, alongside various scholarly interpretations of Camp. This essay argues that the film reclaims Camp as a queer mode of expression, using it to ridicule dominant culture while validating queer identities and relationships. The film’s excessive use of gendered symbolism, such as color-coding and gender-specific tasks, serves to expose the artificiality of these norms, ultimately positioning queer love as authentic and society’s rigid expectations as flawed
Unveiling L\u27objet petit a in Anaïs Nin’s “The Veiled Woman”: A Lacanian Reading of Femininity in Erotica
Full text linkThe similarities between the works of French-born Cuban writer Anaïs Nin and French psychoanalyst Jacques Lacan suggest a novel avenue to interpret Erotica as a literary genre. I hypothesize that by reading Nin through Lacan—more specifically by applying the Lacanian gaze as an instance of objet petit a to Nin’s short story “The Veiled Woman”—we subversively perceive Nin’s female characters as the more active and dominant agents, thus reconsidering themes such as power, femininity, and sexuality in Erotica. Analyzing the gaze as a thematic agent in Nin’s erotica can also provide new insight into psychoanalysis that encompasses a wider range of subjects. Possible gaps that this paper hopes to bridge include the overlooked and misunderstood value of psychoanalytic theories in literary criticism as well as Lacan’s own elusive seminars and repeatedly remodeled theories. I attempt to put forth possible explanations for and reconsiderations of Lacan’s gaze as exemplified in “The Veiled Woman.
Regulatory Measures and Consumer Protection in Electronic Gambling
Full text linkThis paper advances a novel regulatory framework to address the growing cognitive and behavioral harms associated with electronic gambling machines (EGMs) and iGaming (online gambling) platforms. As the gambling industry rapidly expands, it increasingly capitalizes on users’ psychological vulnerabilities and the persuasive design of digital interfaces. Identifying the limitations of AI-driven harm mitigation strategies, which often lack contextual sensitivity and regulatory accountability, this paper argues for an integrated approach that combines targeted AI interventions with enforceable regulatory measures. Drawing on the UK Gambling Commission’s model, the paper proposes a new, region-specific framework for the United States and North America that foregrounds fairness, transparency, and consumer protection. This framework makes a novel contribution by incorporating explainable design standards, structural game modifications, and cross-border regulatory cooperation into a cohesive policy blueprint. Key recommendations include rigorous licensing and audit processes, mandated user protections, and international collaboration to reduce gambling-related harms and promote ethical industry practices
Ethics of Care in the Era of Algorithms: Giovanni Rubeis’ Ethical Blueprint for Medical AI
Full text linkGiovanni Rubeis’ Ethics of Medical AI offers a comprehensive analysis of the ethical challenges posed by integrating artificial intelligence into healthcare. The book examines algorithmic bias, depersonalization, and technological reductionism, while advocating for empathy, trust, and equity in clinical practice. Framing Medical AI within broader social and institutional contexts, Rubeis challenges the belief that technology alone can resolve systemic healthcare disparities. Instead, he emphasizes the importance of humanistic values and narrative medicine. With seven actionable lessons for stakeholders, the book provides practical guidance for ensuring transparency, accountability, and fairness. Rubeis’ interdisciplinary approach makes this a vital resource for healthcare professionals, policymakers, and ethicists shaping the future of ethical healthcare
“Making it Out”: Uncovering and Recovering the Cambodian Identity at Stanford University
Full text linkIn efforts to seek refuge from the Khmer Rouge genocide, Cambodian communities began to form ethnic enclaves in the United States as more immigrants arrived in waves throughout the 1980s. Assimilation for these immigrants and their children was taken up through multiple forms; one prominent one identifying as language. Therefore, this begs the question of how one maintains a sense of identity and community away from home for college students at a predominantly white institution. This effort to recover the Cambodian identity can be observed through the lenses of the historical diaspora of refugees, socio-cultural impacts in the United States, and the intersection of education. Applying these to observed findings with interviewed college students at Stanford University leads us to conflicted results, where the institution can act to expand this gap but also help foster community resilience within the population. Thus, identity maintenance is still attainable with the assistance of academic courses relating to culture and language, as well as funding to student associations to promote community. However, there is still work to be done in order to fully aid in assimilating to the greater student population and into the community itself