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AN EXPERIMENTAL STUDY ON LARGE ACTION MODELS IN AUTOMATED STOCK MARKET PREDICTION
No full textStock market prediction remains a complex and dynamic challenge due to its vast dimensionality and intricate nature. This study focuses on the development of a predictive large action model using historical data for stock market analysis. Publicly accessible platforms such as Yahoo Finance were utilized to collect baseline historical data, while the python library, pandas-ta, was leveraged for computation of various technical indicators including variants of momentum oscillators, bollinger bands, and moving averages. The processed data was then used to train and evaluate the proposed model, with the goal of identifying patterns and trends within the stock price movements. Various machine learning techniques were explored to find the optimal solution for the highest predictive accuracy. The results highlight the potential of the model in providing accurate insights of a stock price\u27s future directional movement. This study contributes to the ongoing efforts made towards financial prediction by taking advantage of publicly accessible data and advanced computational methods
THE INFLUENCE OF INDIVIDUAL NEEDS ON DISTINCT ENTREPRENEURIAL MOTIVES
No full textIndividual needs and the desire to fulfill such needs are key drivers of decision-making. In entrepreneurial ventures, the individual needs of founders shape the mission and direction of their firms. The motives for starting a business and the preference for social or commercial entrepreneurship are hypothesized to reflect individual, psychological needs of aspiring entrepreneurs. According to McClelland’s Human Motivation Theory, individuals are motivated to work when their need for achievement, need for affiliation, and need for power are activated. These needs may be activated as individuals recognize, either consciously or subconsciously, entrepreneurship as a means to fulfill such needs. This study examines the role of need-based motivation in entrepreneurial decision-making, focusing on how specific psychological needs influence both entrepreneurial motives and venture preferences. The current research contributes to literature on person-entrepreneurship fit by investigating how personal psychological needs shape entrepreneurial behavior. Hypotheses regarding gender and preferences for social and commercial entrepreneurship are also examined using Role Congruity Theory. Specifically, it was hypothesized that women will prefer social entrepreneurship, while men will be more oriented towards commercial entrepreneurship. Previous research has primarily focused on established entrepreneurs, often providing retrospective insights and leaving the aspiring entrepreneur less explored (Asante and Affum-Osei, 2019). Aspiring entrepreneurs were recruited via Prolific to take two self-administered, computer-based surveys. Previously validated Likert-type scales were used, measuring the needs for achievement, affiliation, and power, as well as entrepreneurial motives based on the Panel Study of Entrepreneurial Dynamics (PSED) and the work of Carter and colleagues (2003). The initial survey assessed participants’ levels of general psychological needs. A follow-up survey measured their entrepreneurial motives and their preferences towards social and commercial entrepreneurship. Multiple hierarchical regression analyses, correlations, and t-tests were used to test the hypotheses. Results indicated that individual needs significantly influence entrepreneurial motives. Specifically, need for achievement predicted self-realization, innovation, and independence motives; need for affiliation predicted recognition and role-based motives; need for power and personalized need for power predicted financial success and recognition motives; and socialized power exhibited significant relationships with self-realization motives. Socialized and personalized power further diverged in their relationships with motives, highlighting distinct patterns of association for each power type. Notably, although most psychological needs did not predict preferences for commercial or social entrepreneurship, socialized power did positively predict a preference for social entrepreneurship. Additionally, gender differences were observed, such that women favored social entrepreneurship while men preferred commercial entrepreneurship. These findings underscore the nuanced role of psychological needs in shaping entrepreneurial motives and the importance of distinguishing between types of power motivation. By understanding the role of individual needs in entrepreneurship, this study sheds light on the motivational forces that drive individuals to pursue specific entrepreneurial ventures. It also emphasizes the need to differentiate between subtypes of power motivation in entrepreneurial contexts and highlights implications of gendered expectations on entrepreneurial decision-making.The research contributes to both theory and practice by integrating motivational theory with entrepreneurship literature, offering insights into person-entrepreneurship fit and suggesting potential pathways for personalized entrepreneurial development and support programs. By advancing a more psychologically grounded understanding of entrepreneurial decision-making, the study provides a foundation for interventions aimed at improving entrepreneurial fit and additional outcomes. Future research can build on these findings to further disentangle the complexities of the entrepreneurial journey
INTEGRATED APPROACH TO EXPLAINING DIGITAL VIOLENCE VICTIMIZATION EXPERIENCES IN SCHOOL MILIEUS
No full textThis study investigates digital violence in schools by examining cyberbullying victimization among students in the St. Louis Public Schools system across three distinct periods: pre-pandemic, during the pandemic, and post-pandemic. Grounded in Flag Theory as a key integrative framework, the research incorporates elements from well-established criminological perspectives, including the General Theory of Crime and Routine Activities Theory. This theoretical integration enables a multidimensional analysis of both the stability and transformation of cyberbullying dynamics amid changing social and digital environments. Findings reveal that cyberbullying victimization remained largely stable across all three waves. However, the influence of key predictors shifted over time. Parental supervision consistently emerged as a protective factor, significantly reducing victimization across all periods, although it declined notably in the post-pandemic phase. Time spent online increased sharply during the pandemic and remained elevated afterward, yet its impact on victimization diminished over time, challenging traditional assumptions and suggesting a need for reinterpretation in light of post-pandemic digital behavior. Engagement with delinquent peers decreased across the waves but showed a modest positive effect on victimization in the post-pandemic period. Although the study did not reveal significant indirect effects, the findings underscore the enduring influence of direct social and familial factors on online victimization within school environments. Notably, the research identifies a potential post-pandemic increase in self-control, which aligns with the emerging theoretical notion of new global turning points, where critical events, such as terrorist attacks or public health crises, can reshape stable social behaviors, routines, and individual self-regulation. By mapping these changing patterns, the study deepens our understanding of cyberbullying in an increasingly digital world. It also provides valuable insights for shaping targeted policies and intervention strategies within educational settings in the post-pandemic era
POSE TO PROTECT: FEDERATED SKELETON-BASED ANOMALY DETECTION FOR PRIVACY-CONSCIOUS VIDEO SURVEILLANCE
No full textIn an era of increasing concern over data privacy, especially in surveillance applications, this thesis explores a privacy-preserving approach to crime detection using pose-based features. Rather than analyzing raw video footage, which often raises ethical and legal issues, our method uses OpenPose to extract 2D skeletal keypoints from surveillance videos. These pose sequences serve as an abstract yet informative representation of human activity.To classify pose sequences as either normal or criminal behavior, we employ a two-layer LSTM model capable of capturing both short and long-range temporal patterns. The study also compares two training paradigms: centralized learning, where all data is collected and trained on a single server, and federated learning, where multiple clients train locally and share only model updates. This federated setup helps preserve user privacy by keeping raw data decentralized. Experimental results show that skeletal features are highly effective for recognizing anomaly behavior, and the LSTM model performs well across both setups. Notably, the federated learning approach achieves performance comparable to the centralized model while significantly improving data privacy. This research demonstrates the viability of combining pose-based representations with federated learning for secure and effective crime detection, offering a practical solution for real-world surveillance systems operating under privacy constraints
UNDERSTANDING THE REGULATION OF TBX2 IN RHABDOMYOSARCOMA
No full textRhabdomyosarcoma (RMS) is the most common soft tissue sarcoma in children and is characterized by disrupted muscle cell differentiation and unchecked proliferation. Among its subtypes, alveolar RMS (ARMS) is especially aggressive and is classically associated with the PAX3-FOXO1 fusion gene, leading to the designation of ARMS as the fusion positive subtype of RMS. The PAX3-FOXO1 fusion activates a host of oncogenic signaling pathways such as PI3K/AKT and the FGF pathway through the direct regulations of Fibroblast Growth Receptor (FGFR4). Our lab has shown that TBX2 is a potent oncogene in RMS which contributes to tumor development by repressing several growth-regulating genes, such as CDKN1A, PTEN, and TP53. In other systems, FGF and the PI3K/AKT pathway have been shown to drive high expression of TBX2, so we sought to investigate how TBX2 is regulated in RMS. This project will explore how upstream signaling events regulate TBX2 expression and the extent to which this affects downstream TP53 activity. We also sought to determine the effect of restoring p53 expression in RMS cells. To do this, a doxycycline-inducible PiggyBac system will be used to reintroduce wild-type TP53 in ARMS cells to test whether restoring its function can reduce proliferation and improve response to therapy. Together, these studies aim to clarify the signaling mechanisms that support RMS progression and identify molecular points of intervention for future treatment strategies
Isosteric Heats of Adsorption of Carbon Dioxide on Commercially Available Biochar Samples
No full textThis thesis explores the surface properties of commercially-available, highly-porous biochar materials. More specifically, our goal was to investigate how biochar, a material with a growing market, high scalability, and low cost, could help mitigate the release of carbon dioxide (CO2) into the atmosphere. To achieve this goal, we first tested the effective specific surface areas (ESSA) available for the commercially-available biochar sample using volumetric gas adsorption measurements, using Nitrogen as the adsorbate gas and the biochar as the substrate at liquid nitrogen temperature (77K). The nitrogen adsorption data provide a good baseline for what the material can achieve under ideal conditions while the carbon dioxide is the adsorbate that we want to focus on. Broadly speaking, we looked at the efficacy of the biochar surfaces by utilizing the principles of physical adsorption (physisorption) measurements using an Extended Pressure Adsorption Analyzer (ASAP 2050) from Micromeritics. Two different biochar samples were tested, which we classified as flake biochar and granular biochar by examining the physical texture of the biochar samples acquired. The specific surface area (ESSA) for the flake biochar was ~446 m2/g and for the granular biochar materials was ~645 m2/g.The adsorption behavior of carbon dioxide was also investigated on both these samples. Volumetric adsorption isotherms using CO2 as the gas adsorbate on the flake biochar were measured at various temperatures between 273 and 315 K. The isosteric heat of adsorption for carbon dioxide on the flake biochar was determined from these isotherms. We found that the isosteric heat of adsorption for carbon dioxide on the flake biochar is of the order of 260 meV. Similarly, we measured the CO2 adsorption on the granular biochar at 273, 293, 298, 303, 308, and 318 K. Using these isotherms, the isosteric heat of adsorption for carbon dioxide on the granular biochar was found to be on the order of 260 meV. In conclusion, the flake biochar has a lower ESSA than the granular, 446 ± 12 m2/g to 645 ± 14 m2/g, while having a higher heat of adsorption 300 meV to 260 meV. As the climate continues to change, the use of Carbon Capture and Utilization (CCU) to reduce the amount of carbon dioxide emitted each year seems to be a viable way to slow the emissions. These values for biochar suggest that it could be a strong candidate for such technologies. Biochar itself offers a unique dual role: not only serving as a functional capture medium but also sequesters the carbon in a stable solid form
Instructors’ Needs for Teaching and Mentoring Undergraduate Students in the Water Sciences
No full textUndergraduate education in water resources is crucial, involving faculty from diverse disciplines and providing meaningful learning and mentoring experiences. In 2022, the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) launched a national survey and organized a virtual collaborative session with respondents to better understand: 1) What are the primary needs faced by faculty members who teach and mentor water science to undergraduate students? and 2) How can academic institutions and external organizations effectively support faculty members in meeting the needs associated with teaching and mentoring water science to undergraduate students? We collected data from N = 95 survey respondents, and n = 13 collaborative session participants. We employed both quantitative and qualitative analyses of instructors’ perceived needs. Quantitative analyses focused on comparative assessment of reported needs and differences between respondent subgroups. Qualitative analysis involved open coding of open-ended survey responses and transcribed collaborative sessions. Results indicate that instructors require support to identify and implement student-centered instructional strategies, help students use evidence to formulate claims about water-related phenomena, connect classroom learning to real-life experiences, design summative assessments, use computer-based tools, and improve students’ computational and quantitative skills. Additionally, the need for summer stipends, access to external funding, and support for early-career instructors and graduate students to enhance teaching and mentoring skills in water research is highlighted. Improving students’ research capabilities, technical proficiency, and soft skills are also crucial. These findings provide insights for enhancing undergraduate water education to support student success
The Supernatural Shadow of the Philosophic Fallacy: A Critical Examination of John Dew-ey’s Experience and Nature
No full textIn Experience and Nature, John Dewey asserts the existence of the philosophic fallacy. He describes the philosophic fallacy as understanding reality through abstractions rather than material causes that are situated in experience. However, Dewey’s writings on moral and religious development, particularly his criticisms of the supernatural, puts his philosophic fallacy into question. I argue that Dewey’s rejection of the supernatural is a mistake that limits his ability to clearly articulate his metaphysical project, demonstrating that Dewey’s rejection of supernatural experience commits the philosophic fallacy and is based in a Eurocentric tendency that can be escaped by challenging Eurocentric metaphysical conceptions
LEADERSHIP STYLES IN PUBLIC OR PRIVATE ORGANIZATIONS AND INTERGROUP RELATIONS: PERCEIVED LEADER EFFECTIVENESS AND JOB SATISFACTION
No full textLeadership research has been a search for the most effective leader personality traits and behavior. Leader behavior, organizations, and situations can be sources of information that shape follower perception of leader effectiveness. Follower preferences for leadership style can be contingent on environmental demands and a leader candidate’s dominant or communal traits. This study was a 3 x 2 x 2 between-subjects factorial design of leadership style (Transformational, Daoist water-like, or agentic) and shifting situational requirements (competition or cooperation) and organizational type (public or private). Twelve vignettes were created to be stimulus materials for each condition in the study. Respondents (N = 296) were randomly assigned to one of 12 vignettes and asked to evaluate the extent they perceive leader effectiveness and perception of their own job satisfaction. Data were collected via Prolific and were analyzed using MANOVA and one-way ANOVAs with linear contrasts. Results indicated a significant main effect of leadership style, significant pairwise comparisons, and significant linear contrasts. Findings indicated that Transformational leadership or Daoist water-like leadership, compared to Agentic leadership, were rated higher on perceived leader effectiveness, and perceived job satisfaction
FLOOD SUSCEPTIBILITY ASSESSMENT THROUGH GIS INTEGRATED ANALYTICAL HIERARCHY PROCESS AND MACHINE LEARNING MODELS
No full textFlooding is among the most destructive natural disasters globally, and it inflicts severe damage on both natural environments and human-made structures. The frequency of floods has been increasing due to unplanned urbanization, climate change, and changes in land use. Flood susceptibility maps help identify at-risk areas, supporting informed decisions in disaster preparedness, risk management, and mitigation. This study aims to generate a flood susceptibility map for two regions: Davidson County of Tennessee using an integrated geographic information system (GIS) and analytical hierarchical process (AHP), and the Briar Creek watershed of Mecklenburg County, North Carolina using an integrated GIS and machine learning (ML) algorithms. For GIS integrated AHP approach, ten flood causative factors are employed to generate flood-prone zones. AHP, a form of weighted multi-criteria decision analysis, is applied to assess the relative impact weights of these flood causative factors. Subsequently, these factors are integrated into ArcGIS Pro (Version 3.3) to create a flood susceptibility map for the study area using a weighted overlay approach. The resulting flood susceptibility map classified the county into five susceptibility zones: very low (17.48%), low (41.89%), moderate (37.53%), high (2.93%), and very high (0.17%). The FEMA flood hazard map of Davidson County is used to validate the flood susceptibility map created from this approach. For GIS integrated ML approach, three machine learning algorithms —bagging (random forest), extreme gradient boosting (XGBoost), and logistic regression—were used to develop a flood susceptibility model that incorporates topographical, hydrological, and meteorological variables. Key predictors included slope, aspect, curvature, flow velocity, flow concentration, discharge, and rainfall. A flood inventory of 750 data points was compiled from historic flood records. The dataset was divided into training (70%) and testing (30%) subsets, and model performance was evaluated using accuracy metrics, confusion matrices, and classification reports. The results indicate that logistic regression outperformed both XGBoost and bagging in terms of predictive accuracy. According to the logistic regression model, the study area was classified into five flood risk zones: 5.55% as very high risk, 8.66% as high risk, 12.04% as moderate risk, 21.56% as low risk, and 52.20% as very low risk. The FEMA flood hazard map is used to validate the flood susceptibility map created from this approach The resulting flood susceptibility maps constitute a valuable tool for emergency preparedness, infrastructure planning, and sustainable land-use management. By identifying and categorizing areas based on varying levels of flood vulnerability, these maps enable local authorities, planners, and policymakers to prioritize mitigation measures, optimize resource allocation, and enhance community resilience against future flood events