Stanford University Student Journals
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Analyzing Barriers to Small Modular Reactors Acceptance: Factors Behind Support and Opposition in South Korea
Full text linkAs the Korean government announced the construction of four more nuclear power reactors, including small modular reactors, there is a growing apprehension about the adoption of this new technology. In this regard, this study aims to investigate the factors contributing to people’s support or opposition to this novel technology in light of demographic, social, and psychological dynamics. A total of 315 people from South Korea participated in the research. The t-test and multiple regression analysis revealed that supporters had greater trust in authorities while opponents had greater environmental awareness. There was no difference in cognitive dissonance when compared to opponents and supporters. The multiple regression analysis revealed that marital status, risk tolerance, trust in authorities, cognitive dissonance, temporal discount, cognitive closure, and environmental awareness were significant predictors of support for SMRs. Policymakers and authorities must be mindful of these characteristics when strategizing their approach to promoting SMRs in society
Constructing a Super Artificial Intelligence Model For The Luna Land On The Moon Game
Full text linkThe development of large language models has brought a new era of hope for the advancement of artificial intelligence, particularly in the realm of agent systems (Xi et al. 2023). Recent progress in the field of large language models has significantly impacted various industries, from healthcare to entertainment (Hayawi, Shahriar, and Mathew 2023). These powerful AI systems, often referred to as LLMs, have demonstrated remarkable capabilities in language generation and processing, paving the way for more sophisticated agent-based applications. (Xi et al. 2023). Constructing a super AI model for the Luna Land on the Moon game would involve leveraging the latest advancements in LLMs to create a comprehensive and intelligent system capable of navigating the complexities of the lunar simulation environment
Are students academically weakening from online learning?
Full text linkOnline learning is defined as the instruction delivered electronically through various multimedia channels, Internet platforms and applications (Swerdloff, 2016). For several years, online learning has been the key force multiplier for traditional educational methods. The COVID-19 pandemic also emphasized online learning’s necessity which most schools relied upon amidst the crisis. The advent of online learning today has brought unprecedented challenges and opportunities for all stakeholders.
Taking cognizance of all viewpoints, this report examines arguments both in proposition and opposition of online learning and the ways it has impacted education. By examining perspectives across global, national, and local contexts, it aims to provide actionable insights to optimize digital education
Analyzing Economic Feasibility in Green Energy Projects: A Case Study of a Green Streetlight Project
Full text linkGreen energy project proposals are often criticized for their high upfront cost and face concerns that anticipated cost savings will not materialize. An analysis showing that an initiative will benefit a given area economically will be key to securing funding and support for any city-wide infrastructure project. This paper offers a method to identify whether the benefits of green infrastructure projects justify the costs of implementing them. The Philadelphia Streetlight Improvement Project (PSIP) is used as a case study. This study conducts a thorough economic analysis of the implementation of light-emitting diode (LED) streetlight replacement projects through the lens of cost-benefit analysis.
The methods presented in this paper can be used as a framework for city agencies around the world considering similar sustainable projects. This study concludes that certain sustainable infrastructure projects are economically feasible and can provide a range of positives beyond cost savings. The key conclusions are: (i) the PSIP is economically profitable, with the project’s net present value (NPV) around $60 million; (ii) funding strategies such as serial green bonds can help spread out initial costs and decrease investor risk; and (iii) spillover benefits external to NPV calculations, including benefits to the environment and safety, boost the overall value of sustainable infrastructure.
Clicking on Danger: Ranking Cyber Threat Factors and the Protective Role of Awareness
Full text linkSMS Phishing (SMShing) attack is the act of sending messages containing malicious links that cause malware or breach data. Cyber criminals conduct these attacks by leveraging psychological factors, including Urgency, Fear, Curiosity, and Trust. These attacks have become prevalent in Pakistan, and research into responsible factors can help strengthen security infrastructure. In this study, we order the four psychological factors in order of their potency, analyze the effect of SMiShing literacy, and draw safety measures for security. We surveyed 200 college students and concluded that Fear and Urgency are potential factors behind engaging in malicious messages
Navigating the AI Hype: Why AI Snake Oil is Crucial for Interpreting Advancements and Inherent Limitations
Full text linkIn AI Snake Oil, authors Arvind Narayanan and Sayash Kapoor debunk AI hype with a cautious, informed perspective, equipping readers to do the same. As renowned computer scientists and TIME’s 100 Most Influential People in AI, they bring both expertise and clarity to a wide audience—including general readers, technical professionals, and policymakers. Using examples from spell-check to self-driving cars, they demonstrate how the definition of AI constantly shifts with new advancements. The authors address widespread misconceptions by providing tools to identify inflated claims and understand the limitations of both predictive and generative AI. By demystifying the technology, they offer a grounded and accessible guide to what AI can—and cannot—do
Advantages and Limitations of NHANES-Dataset Driven Childhood Obesity Guidelines: an Interdisciplinary Case Study
Full text linkSignificant time, energy, and money is spent towards addressing childhood obesity, yet there is hardly scientific consensus surrounding both species-level causes of obesity, and individual factors that lead to the development of obesity. In the face of the controversial new American Academy of Pediatrics (AAP) guidelines from early 2023 that now recommend surgery and medication for certain groups of obese children, I propose that an interdisciplinary synthesis of current obesity research and assessment of the AAP’s research practices can offer a meaningful critique on whether or not such drastic, individualistic measures are warranted based on controversial measures of excess adiposity like BMI. An analysis of the history of obesity shows that policy often does not follow scientific consensus. I then present a synthesis of various schools of thought on obesity, showing how the issue largely lies outside the control of the individual. Finally, I propose investigating the role of the NHANES database in the AAP clinical guideline formulation, and find that applying an interdisciplinary scope of analysis demonstrates how such epidemiological databases are being used to bolster clinical guideline formulation that still focuses on poorly understood correlations between BMI and cardiometabolic outcomes, despite the proclaimed focus on the “whole child” and community in the guidelines–suggesting a need for updated research practices to truly incorporate a more holistic, community based approach to childhood obesity guidelines.
Deep Learning for Neuroimaging: Explore the Use of Deep Learning Algorithms in Analyzing Neuroimaging Data
Full text linkNeuroimaging techniques, such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), have provided significant insights into the complex workings of the human brain. However, the analysis of neuroimaging data poses considerable challenges due to the vast amount of information generated and the inherent complexity of brain processes. Deep learning algorithms have emerged as powerful tools capable of automatically extracting meaningful patterns and representations from high-dimensional and complex data. In this research paper, we explore the application of deep learning algorithms in analyzing neuroimaging data to enhance our understanding of brain function, map intricate brain networks, and detect abnormalities. By leveraging the potential of deep learning, we aim to improve the accuracy, efficiency, and interpretability of neuroimaging analysis, ultimately advancing our knowledge of the human brain and its disorders
Systematic Review of Emerging Technologies in Cystic Fibrosis Treatment: Gene Therapy and CRISPR Strategies for the Future: Cystic Fibrosis Treatment
No full textThe current systematic review aimed to evaluate research on cystic fibrosis (CF), a genetic disease that affects multiple organs, particularly the lungs, and is associated with high morbidity and mortality. A total of 7,831 relevant studies were identified from search databases, and 27 studies were ultimately considered appropriate for review after applying eligibility criteria, including three longitudinal studies and the remainder being cross-sectional. All studies included a healthy control group, with a combined total of 1,839 individuals with CF and 2,178 controls. The age range varied across studies; however, the majority were conducted in adults.The studies had different aims, including evaluating and comparing different techniques for gene therapy and CRISPR, and assessing changes in body nutrition status. Other studies focused on the evaluation of lung function, inflammation, and clinical parameters. Animal models have played a crucial role in advancing CF gene therapy. Various animal models have been developed, including pigs, ferrets, rats, zebrafish, and sheep, each with its advantages and limitations. The CF pig model has facilitated the measurement of CFTR correction in vivo and has helped define the relationship between CFTR expression and Cl– and HCO3– transport, with important implications for CF gene therapies. Gene editing technologies, such as CRISPR/Cas9, have emerged as promising approaches to modifying nucleic acid sequences in CF research. These tools hold the potential to repair the endogenous CFTR gene and restore its function, but efficient in vivo gene delivery remains a significant challenge. Assessing changes in body composition can provide valuable information on the effects of gene therapy or CRISPR on the overall health of CF patients. The assessment of body composition changes in CF treatment is essential, as current therapies such as CFTR modulators primarily target the respiratory system and may not fully address the systemic effects of the disease. Gene therapy and CRISPR have the potential to provide more comprehensive and long lasting treatments for CF, and assessment of body composition changes can serve as a clinical endpoint in future clinical trials
Most Compatible Reinforcement Learning Algorithm for Deep Brain Stimulation
Full text linkTremors are a symptom of Parkinson’s disease that causes involuntary shaking movements in the hands and other parts of the body, which can disturb one’s quality of life. Tremors happen when malfunctioning neurons synchronize. Therefore, suppression and control of this collective synchronous activity are of great importance. Deep brain stimulation is where surgeons decide the amplitude and frequency of the stimulation to nullify collected signals from synchronous neurons in the brain according to their observation and expertise. A virtual Reinforcement Learning environment Krylov et al. created in 2020 can simulate this collective behavior of neurons and allows us to find suppression parameters for the environment of synthetic degenerate models of neurons. Although the newest generation of Deep Brain Stimulation technology does provide feedback functionality (they can be controlled using both traditional, physical controllers and machine algorithms), it is still challenging to decide which algorithm is most suitable for the task; the study by Krylov et al. applies Proximal Policy Optimization to their environment and successfully suppresses the synchronization in neuron activity. However, they do not test other types of algorithms. This paper expands upon their findings by systematically evaluating six reinforcement learning algorithms (A2C, DDPG, PPO, SAC, TD3, and TRPO). Our results indicate that Trust Region Policy Optimization (TRPO) is the most effective under conditions of low learning rate, moderate divergence between updates, and prioritizing long-term rewards