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Nifty 50 Stock Market Data Analysis
Get PDFAbstract of Nifty 50 Stock Market Data Analysis By: Margaret Marie Coulter Instructor: Dr. Monireh Rahmati Bongisouei Data Analytics 3260
Analysis of the Nifty-50 Stock Market Dataset (2000 – 2021) and Stock Price Data of the Fifty Stocks in NIFTY-50 Index from National Stock Exchange (NSE) India The dataset analyzed contained the price history and trading volumes of the fifty stocks in the NIFTY-50 index from the time period January 1, 2000, to April 30, 2021. The analysis focused on determining whether the volume-weighted average, or the simple moving average better predicted the stock’s price. The effect of the stock’s trading volume on predictions based on the two methods was also evaluated. From 2000 to 2024, there was not much correlation between the volume of the stocks traded and the volume weighted moving average. However, from 2020 to 2024, there appeared to be some potential positive correlation between the average volume and the volume weighted average price. However, the simple moving average price correlated better with the actual stock prices over the entire data range. Obviously, since this was a limited time frame data set on only 50 stocks, much more research is needed
Using a Layered Ensemble of Physics-Guided Graph Attention Networks to Predict COVID-19 Trends
No full textThe COVID-19 pandemic has rapidly disseminated worldwide, profoundly impacting numerous nations. Accurately forecasting the trajectory of COVID-19 across various scales is crucial for informing public health decisions. However, existing forecasting models primarily operate at the state or country level. Conventional mathematical models face limitations due to oversimplified assumptions, while machine learning algorithms struggle to extrapolate novel trends. This underscores the necessity for hybrid machine learning models that amalgamate domain expertise to enable precise long-term predictions. In response, we propose a sophisticated three-layered ensemble, tailored to geographical insights and fostering extensive peer learning. Our framework aims to forecast COVID-19 trends at country, continent, and global levels. At its foundational tier, we introduce a country-level predictor utilizing a hybrid Graph Attention Network (GAT) fused with a modified SIR model, adaptive loss functions, and mobility-informed edge weights. By amalgamating 163 country GATs, we train the subsequent continent and world layers of the ensemble using Multilayer Perceptron (MLP) models. Our findings substantiate that integrating precise equations and empirical data to model inter-community interactions significantly enhances the performance of spatio-temporal machine learning algorithms. Moreover, our study demonstrates the efficacy of incorporating geographic information, such as continent composition, in enhancing the predictive accuracy of the world predictor within our layered architecture. This approach not only refines the forecasting capabilities but also underscores the critical role of geographic insights in augmenting predictive models for global health challenges like COVID-19
Integrated sustainability: unlocking environmental potential in electric vehicles and solar panels
Get PDFThis thesis investigates the environmental, economic, and individual level factors influencing consumer adoption of electric vehicles and solar panels. Through a comprehensive scoping review and case-based analysis, it identifies key incentives and barriers related to purchase intentions for each alternative energy technology. Central factors influencing adoption include cost-related concerns, individual purchasing power, demographic effects, and policy impact. These findings suggest that targeted interventions – specifically those used to amplify incentives and mitigate barriers – can positively impact individual consumer purchase intention, including through bundling options. Highlighting the importance of integrated sustainability, this study explored how electric vehicles and solar panels can function in tandem to maximize both individual and collective environmental and economic benefits in the short- and long-term. It concludes with a conceptual framework for understanding the interrelationship of these technologies while providing actionable insights for policy makers, industry stakeholders, and individual consumers alike
Safety Reporting, Production Rates, and their effects on the Severity of Occupational Injuries
Get PDFIntroduction Despite significant advancements in occupational safety and health, there will likely always be individuals who sustain injuries (Hassan & Khalifa, 2025). Prior work has examined observation and feedback as potential means to reduce injury rates over time (Ludwig & Laske, 2023). However, there is limited research on how to reduce the severity of injuries. If we can understand factors that relate to injury severity, we can reduce injury severity, which would lead to a safer work environment. In this study, we examine how production rates and safety variables influence the severity of injuries in the workplace. Methods This research will utilize three years of archival safety data from a large oil refinery. An observational study will be conducted examining the relationship between production rate, safety variables, and incident severity. Production rate is composed of actual production values, how much oil is being extracted, and flaring rates, how much excess gas is being burnt off. Safety variables are composed of safety audits, corporate-led job site inspections, behavioral observations, employee-led job site inspections, and safety moments, moments where safe employee behavior is recognized. A multinomial regression model will be used to determine the base rate for incident severity, and then use the previous variables to determine if there is a relationship between them. Expected Results It is anticipated that results will show a relationship between operational practices and the severity of workplace incidents. Specifically, increases in safety audits, behavioral observations, and safety moments during the average production periods are expected to predict lower incident severity. Conversely, higher flare rates and production levels are expected to be correlated with higher severity incidents. The effect sizes are expected to be moderate, consistent with prior evidence on the impact of proactive safety interventions, and may reveal diminishing returns at very high levels of auditing and observation (Yang et al., 2023). Practical Implications The findings of this study have the potential to expand and refine the occupational safety literature by shifting the focus from injury frequency to injury severity, a dimension that remains comparatively underexplored. By using a multinomial regression on a large archival data set, the research also contributes a methodological example of how operational and safety metrics can be integrated to predict severity outcomes in high-risk industrial settings. Beyond theory, these findings could help HR and safety professionals target the most effective interventions, such as audits, observations, and safety moments, where they matter most
Unobtrusive Sleep Tracking and Occupational Well-being in Emergency Physicians
No full textEmergency physicians (EP) require sufficient sleep to provide optimal patient care. Sufficient sleep may lead to improved cognition and other psychological factors that are especially vital in high stress situations, such as those encountered in the emergency department; however, EP frequently work long and irregular hours under great stress, which can lead to a decline in sleep health. Most studies in the occupational health sphere track sleep with self-reported data, which is subject to limitations and biases, and there is little research using objective sleep data. By examining sleep longitudinally with objective measures, researchers are better able to understand the effects sleep has on fatigue, burnout, and overall well-being. A deepened understanding can enable better management of sleep in the healthcare sector, ensuring physicians are able to protect their health and well-being and patient safety. This study examines whether objective sleep measures predict self-reported health and well-being outcomes in EP. 39 EP participated in a longitudinal study over five months as part of a larger six-month study. They completed monthly surveys to self-report their sleep health (Buysse, 2014), fatigue (Frone & Tidwell, 2015), emotional exhaustion (Maslach et al., 1997), burnout (Muir et al., 2023), and professional fulfillment (Trockel et al., 2015). Sleep duration and sleep efficiency were continuously measured using Oura rings. Linear mixed models were implemented to examine how the objective sleep variables predicted subjective outcomes, controlling for individual differences between physicians. Correlational analyses between the study variables revealed multiple associations – specifically, sleep duration was correlated with subjective sleep health (ρ = .26, p \u3c .001) and sleep efficiency was correlated with professional fulfillment (ρ = .23, p \u3c .01) and sleep health (ρ = .37, p \u3c .001). Linear mixed models found two significant relationships after accounting for individual differences. Average sleep duration significantly predicted professional fulfillment (β = .11, SE = .05, p = .04) and sleep efficiency significantly predicted sleep health (β = .09, SE = .04, p = .03). These findings support sleep efficiency as a predictor of overall sleep health. While objective sleep measures did not predict other self-reported well-being outcomes in this study, a significant relationship was found for Professional Fulfillment, suggesting that sleep may play a role in a physician\u27s overall feelings towards their career. Future studies could aim to explore this area of impact further with more assessments of Professional Fulfillment, objective measures of sleep, and perhaps larger sample sizes. This project is partially supported by the Clemson University Creative Inquiry program
Arguments with my mother
No full textMy thesis consists of a 13-page craft paper and a series of linked narratives forming a braided memoir. This multi-perspective memoir is based on a combination of my grandmother\u27s written reminiscences, stories my mother has told me, my own experiences, and a draft of a personal memoir. My grandmother\u27s experiences function as the foundation of the work; the additional braided elements explore the places where our experiences converge and diverge, address the question of what happened, and acknowledge that we might never reach agreement on that question, even where these experiences were shared. My title for this project, Arguments With My Mother, is a reference to many mothers (mine, and hers, and hers, as well). It refers to the arguments we have always had about our shared history, and points a bit more obliquely to an acknowledgement that our personal and familial stories may never be a settled matter
The Influence of Media Exposure on Perceptions of Police
Get PDFThe current literature suggests that People of Color (POC) often hold more negative perceptions of law enforcement compared to White individuals. These perceptions are influenced by various factors, including environment, exposure to media depictions of police brutality, and personal experiences. Since flawed portrayals of police procedures in television programs have been found to erode public trust in police legitimacy, the study analyzes how perceptions change through media portrayals of law enforcement interactions. We investigate how the media influences perceptions of law enforcement by presenting participants (n = 245) with mock news articles depicting various law enforcement interactions. Additionally, we consider the impact of past encounters and perceptions of law enforcement, race and ethnicity, and experiences as a victim of crime. Although the mock news articles successfully influenced participant ratings of the portrayed officer and victim, the mock news articles did not significantly influence participant ratings for any measure of police perceptions. The results of the present study revealed that being victimized, consuming crime television, and being a POC were associated with significantly less favorable perceptions of police. The results suggest a need to promote interventions aimed at fostering better police and community relations, alongside recognizing the potential influence of media portrayals of law enforcement, which may serve as a barrier to positive relations
Stress fasting under pressure: The role of demands and control on healthy eating
Get PDFStress fasting under pressure: The role of demands and control on healthy eating Gulnur Ashyrnepesova Dr. Alexander T. Jackson Stress plays an essential role in daily life, influencing physical and mental health. Many people face recurring stress cycles, like exam periods requiring a balance of deadlines and responsibilities. Over time, these cycles can become chronic stress, affecting psychological well-being, eating, sleep, and overall health. Stress often leads to coping behaviors such as overeating, reliance on stimulants, or stress fasting, skipping meals when demands and strain overwhelm. Academic and professional environments with high demands often push individuals to prioritize performance over self-care, making eating a secondary choice. This leads to irregular meal patterns that harm both body and mind. While research has focused on stress eating, stress fasting remains underexplored. (Widaman, 2014; Weaver et al., 2021). This study aims to examine whether cognitive factors help explain why students engage in stress fasting. Specifically, executive functioning will be measured in high-demand academic environments, as it plays a central role in impulse control and decision-making. When stress levels rise, weakened executive functioning may impair self-regulation, making it more difficult for students to maintain consistent eating routines (Hormann et al., 2012; O’Neill, 2020). Similarly, academic pressures may reduce interoceptive awareness, the ability to recognize and respond to internal hunger cues, which can increase the likelihood of skipping meals or developing erratic eating habits (Herbert, 2020; Robinson et al., 2021). These predictions will be tested using mediation analyses. Hypothesis 1: Executive function in high-demand environments affects impulse control and decision-making. Hypothesis 2: Weakened executive function under stress impairs self-regulation, leading to irregular eating patterns.Hypothesis 3: Academic demands reduce interoceptive awareness, increasing erratic eating habits. Participants will be recruited from the psychology research pool at Middle Tennessee State University. The measures include: Perceived Stress Scale (Cohen et al., 1983) to measure perceived stress, Multidimensional Assessment of Interoceptive Awareness (Mehling et al., 2018) to measure body awareness and response to internal signals; Emotion and stress-related eating to measure emotional and stress-related eating patterns, and Fasting Behavior Questions to measure reasons behind meal skipping. The results of this study should provide insights into why stress fasting occurs and how to develop interventions that promote healthier eating habits among university students. Ultimately, this project will contribute to identifying strategies that reduce stress-related fasting and support healthier coping during periods of high academic demand
Psychological Safety in Project-Based Workgroups: The Effect of Individual Experiences on Performance Outcomes
Get PDFAbstract Psychological safety is a vital resource across all occupations because it enables employees to learn, contribute, and adapt without fear of negative consequences. Psychological safety empowers members of high stakes, project-based teams to raise concerns, share expertise, and act ethically before miscommunications escalate into costly failures. While prior research in healthcare and aviation has underscored benefits of psychological safety in team environments, less is known about how psychological safety operates across other project-based professions (civil engineering, construction, software development, disaster response) and job sectors that rely on temporary, interdependent project teams. Centering on individual perceptions rather than aggregated team averages, this study surveys professionals from multiple project-based fields (e.g., architecture, public infrastructure, emergency management, IT development) and examines how psychological safety shapes promotive and prohibitive voice behavior, task performance outcomes (accuracy, timeliness, innovation), and ethical accountability. Survey instruments will be used to capture self-reported perceptions of psychological safety, voice behavior, performance, and ethical accountability on the individual team member level. By linking individual psychological safety to communication, performance, and ethics across varied project-based professions, the current study extends existing literature and offers practical guidance for leaders charged with delivering complex projects safely, on time, and within budget. The findings will highlight actionable strategies for fostering safer and more effective collaboration in dynamic, high stakes work settings. Keywords: psychological safety, project-based teams, communication, voice behavior, performance, ethical accountability, high-stakes profession
A comparative analysis of statistical and machine learning models with application in AI-powered stroke risk prediction
Get PDFRapid detection of large vessel occlusion (LVO) is critical due to its high mortality and narrow treatment window. Although machine learning (ML) and deep learning tools show promise for LVO prediction, their clinical use is hindered by inconsistent pre-hospital data, variable LVO rates, limited interpretability, and high costs. This study introduces a hybrid neural network (HNN) that integrates classical statistical learning with neural networks to combine interpretability and structure with flexibility and regularization. The model was validated through simulations using NIHSS scores, demographics, and medical history across diverse sample sizes and LVO prevalence rates, and benchmarked against logistic regression, Naive Bayes, Decision Tree, Random Forest, and standard neural networks using sensitivity, specificity, accuracy, PPV, NPV, AUC, and ROC metrics. When applied to a large multi-center dataset from over 100 hospitals, the HNN maintained consistent performance, with sampling methods improving data balance and SHAP analysis revealing key predictors. Across simulated and real-world data, the HNN improved sensitivity by at least 20% while sustaining strong overall accuracy, demonstrating its potential as an interpretable, scalable tool for pre-hospital LVO detection to enhance clinical decision-making and improve stroke outcomes