Adaptation to climate change is a core sustainability challenge across the Global South. Development and government organizations conceptualize and govern climate adaptation by creating national and sub-national action plans and implementing projects. This dissertation confronts the inherent tensions that arise when formal planned adaptation interventions encounter the complex, often messy realities of the implementation context. In doing so, this research examines how planned adaptation—with its incentives, provisioned resources, prescribed behaviors, and expectations of commitment from target beneficiaries —interacts with individuals and communities already balancing diverse risks while pursuing their livelihood aspirations. Two broad questions guide this dissertation: 1) how is adaptation envisioned by planners and practitioners? and, 2) how do project beneficiaries engage with, and experience planned adaptation interventions? The research employs an exploratory and inductive qualitative research design. Using Foucault’s lens of governmentality, this research utilises document analysis to examine how the first wave of Indian adaptation projects envision goals, conceptualize problems, delineate roles, and frame expectations of intended beneficiaries. Next, using a case study of an adaptation project implemented in Uttarakhand, India, the study examines the motivations and associated trade-offs behind the engagement and disengagement of the intended beneficiaries: smallholder farmers. Insights from gender-differentiated focus group discussions guide this analysis. Both inquiries are supplemented with findings from semi-structured interviews with Indian adaptation experts and project implementers. The analysis finds that: 1) project reports construct identities of the climate vulnerable beneficiary, implicitly assigning roles and transferring responsibilities for sustaining adaptation efforts beyond project timelines, 2) project participants are not default beneficiaries, but instead exercise agency in decision-making by either opting-in or opting-out of planned initiatives, and 3) the implicit and explicit costs of engaging in planned adaptation interventions are substantial, encompassing significant contributions of time, physical labor, and active participation during and post the project period. This dissertation challenges existing notions of whom planned adaptation serves, and to what end, offering new insights into its design and effectiveness. Furthermore, this research suggests that for planned adaptation to be sustainable, a concerted effort to align with evolving needs, aspirations and livelihood shifts of those on the frontlines of climate change is essential.

Recent advancements in computer vision models have largely been driven by supervised training on labeled data. However, the process of labeling datasets remains both costly and time-intensive. This dissertation delves into enhancing the performance of deep neural networks when faced with limited or no labeling information. I address this challenge through four primary methodologies: domain adaptation, self-supervision, input regularization, and label regularization. In situations where labeled data is unavailable but a similar dataset exists, domain adaptation emerges as a valuable strategy for transferring knowledge from the labeled dataset to the target dataset. This dissertation introduces three innovative domain adaptation methods that operate at pixel, feature, and output levels.Another approach to tackle the absence of labels involves a novel self-supervision technique tailored to train Vision Transformers in extracting rich features. The third and fourth approaches focus on scenarios where only a limited amount of labeled data is available. In such cases, I present novel regularization techniques designed to mitigate overfitting by modifying the input data and the target labels, respectively.

This dissertation presents a comprehensive study on the advancement of astrophysical radio, microwave, and terahertz instrumentation/simulations with three pivotal components.First, theoretical simulations of high metallicity galaxies are conducted using the supercomputing resources of Purdue University and NASA. These simulations model the evolution of a gaseous cloud akin to a nascent galaxy, incorporating variables such as kinetic energy, mass, radiation fields, magnetic fields, and turbulence. The objective is to scrutinize the spatial distribution of various isotopic elements in galaxies with unusually high metallicities and measure the effects of magnetic fields on their structural distribution. Next, I proceed with an investigation of the technology used for reading out Microwave Kinetic Inductance Detectors (MKIDs) and their dynamic range limitations tied to the current method of FPGA-based readout firmware. In response, I introduce an innovative algorithm that employs PID controllers and phase-locked loops for tracking the natural frequencies of resonator pixels, thereby eliminating the need for costly mid-observation frequency recalibrations which currently hinder the widespread use of MKID arrays. Finally, I unveil the novel Spectroscopic Lock-in Firmware (SpLiF) algorithm designed to address the pernicious low-frequency noise plaguing emergent quantum-limited detection technologies. The SpLiF algorithm harmonizes the mathematical principles of lock-in amplification with the capabilities of a Fast Fourier Transform to protect spectral information from pink noise and other low-frequency noise contributors inherent to most detection systems. The efficacy of the SpLiF algorithm is substantiated through rigorous mathematical formulation, software simulations, firmware simulations, and benchtop lab results.

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.

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.

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.

Virtual reality (VR) provides significant opportunities for students to experience immersive education. In VR, students can travel to the international space station, or go through a science experiment at home. However, the current tactile feedback provided by these systems do not feel real. Controllers do not provide the same tactile feedback experienced in the physical world. This dissertation aims to bridge the gap between the virtual and physical learning environments through the development of novel haptic devices capable of emulating tactile sensations found in physical science labs. My research explores haptic devices that can emulate the sensations of fluids in vessels within the virtual environment. Fluid handling is a cornerstone experience of science labs. I also explore how to emulate the handling of other science equipment. I describe and research on four novel devices. These are 1) SWISH: A shifting-weight interface of simulated hydrodynamics for haptic perception of virtual fluid vessels, 2) Geppetteau, 3) Vibr-eau, and 4) Pneutouch. SWISH simulates the sensation of virtual fluids in vessels using a rack and pinion mechanism, while Geppetteau employs a string-driven mechanism to provide haptic feedback for a variety of vessel shapes. Vibr-eau utilizes vibrotactile actuators in the vessel’s interior to emulate the behavior of virtual liquids. Finally, Pneutouch enables users to interact with virtual objects through pneumatic inflatables. Through systematic evaluations and comparisons with baseline comparisons, the usability and effectiveness of these haptic devices in enhancing virtual experiences is demonstrated. The development of these haptic mechanisms and interfaces represents a significant step towards creating transformative educational tools that provide customizable, hands-on learning environments in both Mixed (MR) and Virtual Reality (VR) - now called XR. This dissertation contributes to advancing the field of haptics for virtual education and lays the foundation for future research in immersive learning technologies.