Structure analysis of titanate nanotube/organic molecule hybrid and self-healing polymer

In this dissertation I report the structure and property characterization of two kinds of materials on the micro- and nano-scale level, the self-healing polymer Surlyn and a titanate nanotube/organic molecule hybrid. Multiple techniques have been utilized to study the structural, dynamic, thermal, and optical properties of the materials. In the first study, the thermal, structural, and dynamic properties of the self-healing polymer Surlyn (poly(ethylene-co-methacrylic acid) polymer neutralized with Na+) were investigated. By introducing a suitable cation, Na+ e.g., Surlyn possesses unique properties, such as the intriguing property of self-healing. Understanding the role of the cations in the material, the chemical structure and the physical properties of the polymer is crucial for potential applications. The thermal property of Surlyn is characterized by differential scanning calorimetry (DSC) and microscopic structures are studied by NMR. It is found that although thermal properties change significantly, the structure and dynamics of ionic aggregates (consisting of Na+-O- pairs) remain unchanged under aging and mechanical deformation. The distance between Na+ ions was also estimated. In the second study titanate nanotubes were successfully synthesized. Titanate nanotubes have great potential for applications in photocatalysis due to their unique structural and photocatalytic properties. However, their wide band gap, 3.7 eV, and the Ti defect sites present problems for the photovoltaic applications Surface modification, e.g. attachment of charge-transfer ligands, is one of the most effective approaches to modify the optical absorption spectrum and restore the sixfold coordination of Ti sites. In order to study the mechanisms of bonding between titanate nanotubes and the charge transfer ligands, I chose three different molecules, hydroquinone, 4-methoxypenol(MEHQ) and catechol. Each of these three molecules is expected to form different bonding configuration. The optical and structural properties of titanate nanotubes and the three hybrid structures (titanate nanotube/hydroquinone, titanate nanotube/MEHQ and titanate nanotube/catechol) are characterized by multiple techniques, such as UV-vis, Raman spectroscopy, X-ray diffraction and NMR spectroscopy, etc. It is found that by forming a bidentate structure, organic molecules (hydroquinone and catechol) and titanate nanotubes can form hybrid structures which are relatively stable in the aqueous environment. Also, it was demonstrated that there are significant differences in local structures between water-washed and acid-washed titanate nanotubes. For acid-washed nanotube, the local structure can be changed reversibly into an anatase-like structure by the incorporation of HQ, MEHQ, or CAT. This provides important clues for understanding the structure of titanate nanotubes and the interaction between ligands and nanotube surfaces. The hybrid system of titanate nanotubes/organic molecules has optical absorption significantly beyond 700 nm. This system could have very important applications in photocatalysis and photovoltaic devices

Value-oriented Renewable Energy Forecasting for Coordinated Energy Dispatch Problems at Two Stages

Energy forecasting is deemed an essential task in power system operations. Operators usually issue forecasts and leverage them to schedule energy dispatch ahead of time (referred to as the 'predict, then optimize' paradigm). However, forecast models are often developed via optimizing statistical scores while overlooking the value of the forecasts in operation. In this paper, we design a value-oriented point forecasting approach for energy dispatch problems with renewable energy sources (RESs). At the training phase, this approach incorporates forecasting with day-ahead/real-time operations for power systems, thereby achieving reduced operation costs of the two stages. To this end, we formulate the forecast model parameter estimation as a bilevel program at the training phase, where the lower level solves the day-ahead and real-time energy dispatch problems, with the forecasts as parameters; the optimal solutions of the lower level are then returned to the upper level, which optimizes the model parameters given the contextual information and minimizes the expected operation cost of the two stages. Under mild assumptions, we propose a novel iterative solution strategy for this bilevel program. Under such an iterative scheme, we show that the upper level objective is locally linear regarding the forecast model output, and can act as the loss function. Numerical experiments demonstrate that, compared to commonly used point forecasting methods, the forecasts obtained by the proposed approach result in lower operation costs in the subsequent energy dispatch problems. Meanwhile, the proposed approach is more computationally efficient than traditional two-stage stochastic program.Comment: submitted to European Journal of Operational Researc

The Role of microRNAs in Bladder Urothelium Development and Tumorigenesis

There are two morphologically distinct cell types in the normal urothelium: umbrella cells and basal/intermediate cells. Immunohistochemical studies from our group suggest that there may be more than one urothelial progenitor. Bladder cancer is the fifth most common cancer in the United States and the second most prevalent genitourinary malignancy. Urothelial carcinoma accounts for 90% of bladder cancers. Based on clinical and histological studies, urothelial carcinomas are thought to develop through two independent pathways and are classified into two main phenotypic variants: low-grade tumors (usually papillary and "superficial" with high recurrence), and high-grade tumors (usually flat carcinoma in situ lesions that are often associated with and progress to muscle invasion). MicroRNAs (miRNAs) are single-stranded non-coding RNA molecules, approximately 21-23 nucleotides in length, that regulate gene expression. Since their discovery in 1993, they have emerged as major mediators of cellular functions and tissue homeostasis. Importantly, distortion of their normal function is commonly observed in human malignancies, suggesting that they act as a new class of tumor suppressors and oncogenes. Despite the strong links reported between miRNAs and the pathogenesis of numerous human cancers, there are few studies centering on their characterization in normal urothelium and there is little consensus on which miRNAs contribute to urothelial tumor initiation and progression. Through a series of studies, we profiled the expression of miRNAs in distinct compartments of the normal bladder, including umbrella and basal-intermediate urothelial cells, as well as the muscularis propria; and bladder carcinoma in situ (CIS) lesions. We discovered and validated the expression of miR-133a and miR-139-3p in umbrella cells, and miR-142-3p in basal-intermediate cells. This study represents the first molecular characterization of miRNA expression in the normal urothelium. Strikingly, we found that miRNA expression levels of CIS most closely resembled the miRNA profile of umbrella cells. Finally, we examined well-established umbrella and basal-intermediate cell immunohistochemical biomarkers in an independent series of CIS samples. Once more, this analysis revealed that CIS lesions shared a common phenotype with umbrella cells through the expression of umbrella-specific markers. Mechanistic studies were performed in parallel to further delineate the potential role of two critical miRNAs involved in cell invasion that were previously unassociated with urothelial carcinomas: miR-198 and miR-126. Overexpression of miR-198 increased cell invasion in non-invasive bladder cancer cells, an effect that was magnified with concurrent down-regulation of the miR-200 family. In contrast, elevated levels of miR-126 suppressed cell invasion in invasive bladder cancer cells, possibly through regulation of gene expression of the matrix metalloproteinase ADAM9. Correspondingly, knock-down studies of ADAM9 in invasive bladder cancer cells also inhibited cell invasion. We further demonstrated preferential expression of ADAM9 in muscle-invasive bladder tumors compared to non-muscle invasive tumors, and that ADAM9 expression significantly correlated with a poor prognosis in patients with urothelial carcinoma. Our studies represent a comprehensive and accurate description of the different miRNAs expressed in distinct urothelial cellular compartments and CIS tumors. This study is also the first to provide evidence of the possible origin of CIS lesions from umbrella cells. Additionally, important translational results of our studies support the use of miR-198, miR-126, and ADAM9 as clinical biomarkers of disease progression, and provide a rationale for the therapeutic inhibition of ADAM9 in aggressive urothelial carcinomas. Overall, the findings reported here indicate that several miRNAs are differentially regulated in urothelium development and tumorigenesis, and may form a basis for clinical development of new biomarkers for urothelial carcinoma

Multi-Agent Robust Control Synthesis from Global Temporal Logic Tasks

This paper focuses on the heterogeneous multi-agent control problem under global temporal logic tasks. We define a specification language, called extended capacity temporal logic (ECaTL), to describe the required global tasks, including the number of times that a local or coupled signal temporal logic (STL) task needs to be satisfied and the synchronous requirements on task satisfaction. The robustness measure for ECaTL is formally designed. In particular, the robustness for synchronous tasks is evaluated from both the temporal and spatial perspectives. Mixed-integer linear constraints are designed to encode ECaTL specifications, and a two-step optimization framework is further proposed to realize task-satisfied motion planning with high spatial robustness and synchronicity. Simulations are conducted to demonstrate the expressivity of ECaTL and the efficiency of the proposed control synthesis approach.Comment: 7 pages, 3 figure

Achievements, Open Problems and Challenges for Search Based Software Testing

testing as an optimisation problem, which can be attacked using computational search techniques from the field of Search Based Software Engineering (SBSE). We present an analysis of the SBST research agenda1, focusing on the open problems and chal-lenges of testing non-functional properties, in particular a topic we call ‘Search Based Energy Testing ’ (SBET), Multi-objective SBST and SBST for Test Strategy Identification. We conclude with a vision of FIFIVERIFY tools, which would automatically find faults, fix them and verify the fixes. We explain why we think such FIFIVERIFY tools constitute an exciting challenge for the SBSE community that already could be within its reach. I

MS-DCANet: A Novel Segmentation Network For Multi-Modality COVID-19 Medical Images

The Coronavirus Disease 2019 (COVID-19) pandemic has increased the public health burden and brought profound disaster to humans. For the particularity of the COVID-19 medical images with blurred boundaries, low contrast and different sizes of infection sites, some researchers have improved the segmentation accuracy by adding model complexity. However, this approach has severe limitations. Increasing the computational complexity and the number of parameters is unfavorable for model transfer from laboratory to clinic. Meanwhile, the current COVID-19 infections segmentation DCNN-based methods only apply to a single modality. To solve the above issues, this paper proposes a symmetric Encoder-Decoder segmentation framework named MS-DCANet. We introduce Tokenized MLP block, a novel attention scheme that uses a shift-window mechanism similar to the Transformer to acquire self-attention and achieve local-to-global semantic dependency. MS-DCANet also uses several Dual Channel blocks and a Res-ASPP block to expand the receptive field and extract multi-scale features. On multi-modality COVID-19 tasks, MS-DCANet achieved state-of-the-art performance compared with other U-shape models. It can well trade off the accuracy and complexity. To prove the strong generalization ability of our proposed model, we apply it to other tasks (ISIC 2018 and BAA) and achieve satisfactory results