Work-related muscle disorders are a main cause of missed work, globally, and arecostly for public health systems. However, development of musculoskeletal tissue diagnostics is lagging compared to other tissues and organs. Myofascial trigger points (MTP) are unique muscle tissue phenomenon that are challenging to address due to a lack of objective assessment methodology. This study seeks to meet this need by devising a non-invasive, objective methodology for evaluating musculoskeletal tissue following intervention or physical provocation, specific to the anterior forearm region. In Aim 1, current literature on MTP pathophysiology informs a multi-modal assessment approach, including: 1) pain pressure threshold (PPT), 2) power Doppler (PD) ultrasound, 3) strain elastography (SE), and 4) surface electromyography (sEMG). In Aim 2, controlled ultrasound image acquisition and standardization techniques are developed for imaging muscle tissue with PD (Aim 2a) and SE (Aim 2b) . These techniques improved differentiability of vascularity and compliance estimation after physical provocation or intervention. In Aim 3, the multi-modal approach is implemented in a human pilot study (n=34) investigating MTP response to osteopathic manipulative treatment, compared to rest and light exercise. Positive trends and significant changes are detected after OMT and rest. PPT significantly increased after OMT (p = 0.021). Tissue compliance significantly increase after rest (p ≪ 0.0001) and after OMT( p = 0.002). Principal component analysis finds 9 of 13 outcome measures to be salient features of MTP treatment effect. The data suggests high and low responders, yielding insights for improved patient screening and study design for future work. With further optimization and development, this method may be applied to a broad array of clinical scenarios for musculoskeletal tissue evaluation directed towards amelioration of neuromuscular symptoms.

In this work, the problem of multi-object tracking (MOT) is studied, particularly the challenges that arise from object occlusions. A solution based on a principled approximate dynamic programming approach called ADPTrack is presented. ADPTrack relies on existing MOT solutions and directly improves them. When matching tracks to objects at a particular frame, the proposed approach simulates executions of these existing solutions into future frames to obtain approximate track extensions, from which a comparison of past and future appearance feature information is leveraged to improve overall robustness to occlusion-based error. The proposed solution when applied to the renowned MOT17 dataset empirically demonstrates a 0.7% improvement in the association accuracy (IDF1 metric) over a state-of-the-art baseline that it builds upon while obtaining minor improvements with respect to all other metrics. Moreover, it is shown that this improvement is even more pronounced in scenarios where the camera maintains a fixed position. This implies that the proposed method is effective in addressing MOT issues pertaining to object occlusions.

The wide-spread use of insecticides has contributed to the rapid decline of insect diversity and abundance. In light of recent guidance from international and governmental organizations, other non-chemical control methods are necessary to control insect pest populations. In my study, I used occupancy modeling techniques and found that environmental variables could predict the presence of Rhaphidophoridae, in Hidalgo, Mexico. The results showed that variables associated with forested habitats increase the probability of Rhaphidophoridae detection, and higher elevation increases the probability of Rhaphidophoridae occupancy. Understanding the specific habitat variables associated with human detection and occupancy of Rhaphidophoridae give people the ability to utilize the Integrative Pest Management (IPM) strategy of cultural control to prevent Rhaphidophoridae pest populations in my study region.

With the rapid development of information technology and the rise of the Internet+ era, the sharing economy has fundamentally changed people's lives and consumption patterns, and has also reshaped the process of value co-creation between enterprises and customers. In the sharing economy, enterprises no longer play the dominant role; instead, they have evolved into platforms that provide support and services for users. In this economy, users not only determine the profitability and reputation of enterprises but also create substantial value through the exchange of usage rights to idle resources and interactions both online and offline. Therefore, studying the value co-creation behavior of bilateral users on sharing service platforms is of significant necessity for enterprise development. This research aims to explore the impact mechanism of bilateral user value co-creation on customer value in sharing service platforms. Through the analysis and summarization of literature, a theoretical model of bilateral user value co-creation and customer value in the context of the sharing economy has been established. The research subjects include users of the homestay/inn platform on Ctrip, as well as users of other types of sharing platforms, such as Lazy Housekeeping, LoveChef, Didi and Airbnb. In the empirical part, questionnaires were designed and sample data was collected through the Questionnaire Star platform. SPSS 25 and AMOS 23 data analysis software were used to perform reliability and validity analysis and correlation analysis of the scales. Multivariate regression analysis was employed to investigate the impact of bilateral user value co-creation on various aspects of customer value. Additionally, the Bootstrap method was used to test the mediating roles of relevant factors. Through the above research methods, we will be able to have an in-depth understanding of the impact mechanism of two-sided user value co-creation on customer value of the shared service platform and obtain more comprehensive and concrete research findings. I believe that the findings of this study will have important implications for the theory and practice in the domain of sharing economy. Key words: sharing economy, sharing service platform, two-sided users, value co-creation, customer value

The Mainland-Hong Kong Stock Connect program is a globally unique institutional innovation. This partially open financial system is unparalleled worldwide. As the influence of the Mainland-Hong Kong Stock Connect on A-shares has grown, the volume of research literature has gradually increased, and studies on the policy impact from various sectors have become prevalent. Prior to the introduction of the Mainland-Hong Kong Stock Connect, studies indicated that A-share stock prices did not significantly react to stock information, indicating low informational content in stock prices. The Mainland-Hong Kong Stock Connect, through its moderate openness, has effectively introduced mature overseas investment philosophies and international capital, altering the investor structure of A-shares and impacting trading behavior. This paper aims to explore whether the initiation of the Mainland-Hong Kong Stock Connect policy positively affects the informational content of A-share stock prices under the aforementioned premises. To minimize the interference of short-term market fluctuations on the research, this paper uses the relatively long-term future earnings response as the entry point for studying the informational content of stock prices. Specifically, it first selects a full sample of Mainland-Hong Kong Stock Connect stocks to conduct annual cross-sectional regression and multi-year linear regression to examine changes in the informational content of stock prices before and after policy implementation. It then includes a control group of stocks not selected for the Mainland-Hong Kong Stock Connect, conducting multi-year linear regression analysis with the experimental group samples to investigate whether the policy initiation has improved the informational content of stock prices for Mainland-Hong Kong Stock Connect stocks compared to those not selected. The results show that after the initiation of the Mainland-Hong Kong Stock Connect policy, the informational content of stock prices increased for Shanghai Stock Connect but decreased for Shenzhen Stock Connect. Compared to stocks not selected for the Mainland-Hong Kong Stock Connect, the informational content of stock prices also increased for Shanghai Stock Connect and decreased for Shenzhen Stock Connect. Overall, the results of this study indicate that the Mainland-Hong Kong Stock Connect policy has indeed achieved its initial policy design goals, warranting further exploration into deepening openness to optimize the structure of the capital market.

This study delves into the reversal effects in the U.S. stock market using American stock data listed on the New York Stock Exchange, American Stock Exchange, and NASDAQ from 1970 to 2022. The aim is to answer two key questions: What characteristics make certain groups of stocks exhibit stronger reversal effects? And what market conditions contribute to stronger reversal effects?To begin with, the paper examines whether growth stocks exhibit stronger reversal effects compared to value stocks from the perspective of growth stocks. The study uses the price-to-earnings ratio (P/E ratio) to measure stock growth, with high P/E ratio stocks classified as growth stocks and low P/E ratio stocks classified as value stocks. The findings reveal: 1) The reversal effects of growth stocks are significantly stronger than those of value stocks; 2) After a substantial market decline in the previous year, the reversal effects of stocks are significantly stronger; 3) Across different market environments, the reversal effects of growth stocks are consistently stronger than those of value stocks, and growth stocks exhibit the most pronounced reversal effects in markets following significant declines. Furthermore, the paper explains why the reversal effects of growth stocks are stronger from three perspectives: market risk exposure, interest rate sensitivity, and profit volatility. The study discovers that the market BETA, duration, interest rate sensitivity, earnings per share volatility, and positive correlation with the Purchasing Managers' Index (PMI) for growth stocks are all significantly higher than those for value stocks. This helps explain why, during stock market rallies/interest rate declines/economic expansions, the rebound strength of growth stocks' prices/profits is higher than that of value stocks, leading to stronger reversal effects. Finally, the study finds that the phenomenon of "stronger reversal effects in growth stocks" also holds true in the A-share market, which serves as an emerging market.

This research project seeks to develop an innovative data visualization tool tailored for beginners to enhance their ability to interpret and present data effectively. Central to the approach is creating an intuitive, user-friendly interface that simplifies the data visualization process, making it accessible even to those with no prior background in the field. The tool will introduce users to standard visualization formats and expose them to various alternative chart types, fostering a deeper understanding and broader skill set in data representation. I plan to leverage innovative visualization techniques to ensure the tool is compelling and engaging. An essential aspect of my research will involve conducting comprehensive user studies and surveys to assess the tool's impact on enhancing data visualization competencies among the target audience. Through this, I aim to gather valuable insights into the tool's usability and effectiveness, enabling further refinements. The outcome of this project is a powerful and versatile tool that will be an invaluable asset for students, researchers, and professionals who regularly engage with data. By democratizing data visualization skills, I envisage empowering a broader audience to comprehend and creatively present complex data in a more meaningful and impactful manner.

Diffuse pleural mesothelioma (DPM) is a devastating lung cancer most commonly diagnosed at an advanced stage with a poor prognosis for patients. Therapies available to patients after diagnosis currently include surgical resection, radiotherapy, immunotherapy, and chemotherapy. However, these therapies only prolong life for about a year and a half on average. DPM patients desperately need effective therapies in the form of drugs, drug combinations, and miRNA-based therapies, that could lengthen overall survival and provide a better quality of life. I hypothesized that focusing on DPM tumor biology would streamline the process for discovering new therapies that will have a lasting impact for patients. I have applied systems biology methods to mine multiomic data from patient DPM tumors to discover new therapeutic options. I began by developing a somatic mutation integration pipeline, which created a comprehensive somatic mutational profile of DPM tumors from patient genomic and transcriptomic data. The somatic mutational profile was used in the generation of dpmSYGNAL, a disease-relevant gene regulatory network (GRN) trained on patient tumor multiomic data. I integrated this GRN with functional genomics screens performed on two low-passage primary DPM tumor cell lines and identified gene vulnerabilities that could be targeted by FDA-approved inhibitors and drug combinations. I also developed a pipeline to integrate miRNA target genes from biotinylated pulldowns with RNA-seq data from a study re-expressing the miRNA hsa-miR-497-5p in DPM cell lines. I determined that the re-expression of hsa-miR-497-5p had early pro-apoptotic effects and inhibited the cell cycle at later time points. The identification of inhibitors, combinations of inhibitors, and a therapeutic miRNA demonstrates that DPM biology can be used as a guide to discover new therapeutics for DPM.

While sexual assault is a crime that frequently occurs, public perceptions tend to greatly differ from the reality of the act. Sexual assault is the most underreported crime in the United States. This is perhaps due to the negative stigma that surrounds victims of sexual assault and the presence of rape myths in society today. Perceptions of sexual assault can vary depending on the relationship between the victim and the offender and the presence of physical evidence, if any, that was collected at the scene of the crime. This study uses data on sex crimes reported to the Los Angeles Police Department and the Los Angeles County Sheriff’s Department in 2008, to address the following research questions: How are sexual assault cases prosecuted when the offender is a stranger vs. non-stranger to the victim? Does physical evidence play a role in the charging decisions of stranger vs. non-stranger cases? Data are analyzed using logistic and multinomial regression. Findings show that there is little to no significance among victim-offender relationship and charging decision, but this varies among different evidence types. Implications of this study and areas of future research are discussed.