Binding of chemical functionalities onto silicon surfaces

Ph.DDOCTOR OF PHILOSOPH

Optimal Sequential Detection by Sparsity Likelihood

Consider the problem on sequential change-point detection on multiple data streams. We provide the asymptotic lower bounds of the detection delays at all levels of change-point sparsity and we derive a smaller asymptotic lower bound of the detection delays for the case of extreme sparsity. A sparsity likelihood stopping rule based on sparsity likelihood scores is designed to achieve the optimal detections. A numerical study is also performed to show that the sparsity likelihood stopping rule performs well at all levels of sparsity. We also illustrate its applications on non-normal models

Effect of substrate (ZnO) morphology on enzyme immobilization and its catalytic activity

In this study, zinc oxide (ZnO) nanocrystals with different morphologies were synthesized and used as substrates for enzyme immobilization. The effects of morphology of ZnO nanocrystals on enzyme immobilization and their catalytic activities were investigated. The ZnO nanocrystals were prepared through a hydrothermal procedure using tetramethylammonium hydroxide as a mineralizing agent. The control on the morphology of ZnO nanocrystals was achieved by varying the ratio of CH3OH to H2O, which were used as solvents in the hydrothermal reaction system. The surface of as-prepared ZnO nanoparticles was functionalized with amino groups using 3-aminopropyltriethoxysilane and tetraethyl orthosilicate, and the amino groups on the surface were identified and calculated by FT-IR and the Kaiser assay. Horseradish peroxidase was immobilized on as-modified ZnO nanostructures with glutaraldehyde as a crosslinker. The results showed that three-dimensional nanomultipod is more appropriate for the immobilization of enzyme used further in catalytic reaction

A Study of the Impact of Tourism Economic and Non-economic Benefits on Residents\u27 Pro-Environmental Behaviors in Community-based Ecotourism

A survey was conducted on 362 residents of a classical ecotourism destination in China to explore the impacts of both tourism economic and non-economic benefits on residents’ pro-environmental behaviors. The results of Structural Equation Modeling (SEM) indicated that tourism economic and non-economic benefits impacted on residents’ pro-environmental behaviors through perceived positive tourism impact as a mediator. These findings enriched literatures in ecotourism and had managerial value for the practitioners in domestic ecotourism community

A histopathological image classification method for cholangiocarcinoma based on spatial-channel feature fusion convolution neural network

Histopathological image analysis plays an important role in the diagnosis and treatment of cholangiocarcinoma. This time-consuming and complex process is currently performed manually by pathologists. To reduce the burden on pathologists, this paper proposes a histopathological image classification method for cholangiocarcinoma based on spatial-channel feature fusion convolutional neural networks. Specifically, the proposed model consists of a spatial branch and a channel branch. In the spatial branch, residual structural blocks are used to extract deep spatial features. In the channel branch, a multi-scale feature extraction module and some multi-level feature extraction modules are designed to extract channel features in order to increase the representational ability of the model. The experimental results of the Multidimensional Choledoch Database show that the proposed method performs better than other classical CNN classification methods

Comparison of outcomes between immediate implant-based and autologous reconstruction: 15-year, single-center experience in a propensity score-matched Chinese cohort

Objective: The number of immediate breast reconstruction (IBR) procedures has been increasing in China. This study aimed to investigate the oncological safety of IBR, and to compare the survival and surgical outcomes between implant-based and autologous reconstruction. Methods: Data from patients diagnosed with invasive breast cancer who underwent immediate total breast reconstruction between 2001 and 2016 were retrospectively reviewed. Long-term breast cancer-specific survival (BCSS), disease-free survival (DFS), and locoregional recurrence-free survival (LRFS) were evaluated. Patient satisfaction with the breast was compared between the implant-based and autologous groups. BCSS, DFS, and LRFS were compared between groups after propensity score matching (PSM). Results: A total of 784 IBR procedures were identified, of which 584 were performed on patients with invasive breast cancer (implant-based, n = 288; autologous, n = 296). With a median follow-up of 71.3 months, the 10-year estimates of BCSS, DFS, and LRFS were 88.9% [95% confidence interval (CI) (85.1%–93.0%)], 79.6% [95% CI (74.7%–84.8%)], and 94.0% [95% CI (90.3%–97.8%)], respectively. A total of 124 patients completed the Breast-Q questionnaire, and no statistically significant differences were noted between groups (P = 0.823). After PSM with 27 variables, no statistically significant differences in BCSS, DFS, and LRFS were found between the implant-based (n = 177) and autologous (n = 177) groups. Further stratification according to staging, histological grade, lymph node status, and lymph-venous invasion status revealed no significant survival differences between groups. Conclusions: Both immediate implant-based and autologous reconstruction were reasonable choices with similar long-term oncological outcomes and patient-reported satisfaction among patients with invasive breast cancer in China

Towards Identifying Social Bias in Dialog Systems: Frame, Datasets, and Benchmarks

The research of open-domain dialog systems has been greatly prospered by neural models trained on large-scale corpora, however, such corpora often introduce various safety problems (e.g., offensive languages, biases, and toxic behaviors) that significantly hinder the deployment of dialog systems in practice. Among all these unsafe issues, addressing social bias is more complex as its negative impact on marginalized populations is usually expressed implicitly, thus requiring normative reasoning and rigorous analysis. In this paper, we focus our investigation on social bias detection of dialog safety problems. We first propose a novel Dial-Bias Frame for analyzing the social bias in conversations pragmatically, which considers more comprehensive bias-related analyses rather than simple dichotomy annotations. Based on the proposed framework, we further introduce CDail-Bias Dataset that, to our knowledge, is the first well-annotated Chinese social bias dialog dataset. In addition, we establish several dialog bias detection benchmarks at different label granularities and input types (utterance-level and context-level). We show that the proposed in-depth analyses together with these benchmarks in our Dial-Bias Frame are necessary and essential to bias detection tasks and can benefit building safe dialog systems in practice