Doctor of Philosophy

dissertationIn the era of big data, many applications generate continuous online data from distributed locations, scattering devices, etc. Examples include data from social media, financial services, and sensor networks, etc. Meanwhile, large volumes of data can be archived or stored offline in distributed locations for further data analysis. Challenges from data uncertainty, large-scale data size, and distributed data sources motivate us to revisit several classic problems for both online and offline data explorations. The problem of continuous threshold monitoring for distributed data is commonly encountered in many real-world applications. We study this problem for distributed probabilistic data. We show how to prune expensive threshold queries using various tail bounds and combine tail-bound techniques with adaptive algorithms for monitoring distributed deterministic data. We also show how to approximate threshold queries based on sampling techniques. Threshold monitoring problems can only tell a monitoring function is above or below a threshold constraint but not how far away from it. This motivates us to study the problem of continuous tracking functions over distributed data. We first investigate the tracking problem on a chain topology. Then we show how to solve tracking problems on a distributed setting using solutions for the chain model. We studied online tracking of the max function on ""broom"" tree and general tree topologies in this work. Finally, we examine building scalable histograms for distributed probabilistic data. We show how to build approximate histograms based on a partition-and-merge principle on a centralized machine. Then, we show how to extend our solutions to distributed and parallel settings to further mitigate scalability bottlenecks and deal with distributed data

Ranking Large Temporal Data

Ranking temporal data has not been studied until recently, even though ranking is an important operator (being promoted as a firstclass citizen) in database systems. However, only the instant top-k queries on temporal data were studied in, where objects with the k highest scores at a query time instance t are to be retrieved. The instant top-k definition clearly comes with limitations (sensitive to outliers, difficult to choose a meaningful query time t). A more flexible and general ranking operation is to rank objects based on the aggregation of their scores in a query interval, which we dub the aggregate top-k query on temporal data. For example, return the top-10 weather stations having the highest average temperature from 10/01/2010 to 10/07/2010; find the top-20 stocks having the largest total transaction volumes from 02/05/2011 to 02/07/2011. This work presents a comprehensive study to this problem by designing both exact and approximate methods (with approximation quality guarantees). We also provide theoretical analysis on the construction cost, the index size, the update and the query costs of each approach. Extensive experiments on large real datasets clearly demonstrate the efficiency, the effectiveness, and the scalability of our methods compared to the baseline methods.Comment: VLDB201

Aggregation-Induced Emission (AIE), Life and Health

Light has profoundly impacted modern medicine and healthcare, with numerous luminescent agents and imaging techniques currently being used to assess health and treat diseases. As an emerging concept in luminescence, aggregation-induced emission (AIE) has shown great potential in biological applications due to its advantages in terms of brightness, biocompatibility, photostability, and positive correlation with concentration. This review provides a comprehensive summary of AIE luminogens applied in imaging of biological structure and dynamic physiological processes, disease diagnosis and treatment, and detection and monitoring of specific analytes, followed by representative works. Discussions on critical issues and perspectives on future directions are also included. This review aims to stimulate the interest of researchers from different fields, including chemistry, biology, materials science, medicine, etc., thus promoting the development of AIE in the fields of life and health

A rare sequelae of esophageal perforation: Fibrosing mediastinitis

Abstract Fibrosing mediastinitis (FM) is a rare disease caused by different causes. If left untreated, the prognosis is poor. The common causes of FM are Tuberculosis and Histoplasma capsulatum infection. Esophageal perforation is also a rare condition that is often easily under‐ and mis‐diagnosed due to the lack of specificity of symptoms. Here we report a case of FM caused by esophageal perforation

Modeling and Optimization of the Air-Supported Membrane Coal Shed Structure in Ports

The air-supported membrane coal shed is widely used in bulk cargo terminals. It not only effectively protects goods from adverse weather conditions but also helps reduce coal dust and harmful gas emissions, promoting the green and sustainable development of ports. However, in practical engineering, the design parameters of the coal shed are often based on experience, making it difficult to accurately assess the quality of the structural design. The flexibility of the membrane material also makes the structure susceptible to deformation or tearing. This paper mainly focuses on modeling and solving the optimization design issues of air-supported membrane coal shed structures. According to the evaluation criteria for the form of air-supported membrane coal sheds, a multi-objective structural optimization model is established to minimize the maximum stress on the membrane surface, ensure uniform stress distribution, maximize structural stiffness, and minimize costs. The study utilizes a combined optimization approach using ANSYS 19.0 and MATLAB 2016a, incorporating an improved NSGA-II algorithm program developed in MATLAB into ANSYS for structural form analysis and load calculation. The research results indicate that the optimal solution reduces the maximum stress on the loaded membrane surface by 5.36%, shortens the maximum displacement by 30.3%, and saves on economic costs by 9.85%. Compared to traditional empirical design methods, the joint use of MATLAB and ANSYS for optimization design can provide more superior solutions, helping ports to achieve environmental protection and economic efficiency goals

State-of-the-art self-luminescence: a win-win situation

Self-luminescence, which eliminates the real-time external optical excitation, can effectively avoid background autofluorescence in photoluminescence, endowing with ultrahigh signal-to-noise ratio and sensitivity in bioassay. Furthermore, in situ generated and emitted photons have been applied to develop excitation-free diagnostics and therapeutic agents against deeply seated diseases. "Enhanced" self-luminescence, referring to the aggregation-induced emission (AIE)-integrated self-luminescence systems, is endowed with not only the above merits but also other superiorities including stronger luminous brightness and longer half-life compared with "traditional" self-luminescence platforms. As an emerging and booming hotspot, the "enhanced" self-luminescence facilitated by the win-win cooperation of the aggregation-induced emission and self-luminescent techniques has become a powerful tool for interdisciplinary research. This tutorial review summarizes the advancements of AIE-assisted self-luminescence including chemiluminescence and afterglow imaging, starting from the discussion on the design and working principles, luminescent mechanisms of self-luminescence fuels, versatile integrated approaches and advantages, and a broad range of representative examples in biosensors and oncotherapy. Finally, the current challenges and perspectives are discussed to further actuate the development of "enhanced" self-luminescence agents for biomedical diagnosis and treatment.Agency for Science, Technology and Research (A*STAR)Ministry of Education (MOE)This work was partially supported by the National Natural Science Foundation of China Grant (21788102), the Research Grants Council of Hong Kong (16305518, 16307020, C6014- 20W, C6009-17G and 16305618), the Innovation and Technology Commission (ITC-CNERC14SC01), and the Material Science Foundation of Guangdong Province (2019B121205012); J. F. thanks National Science Foundation of China 21925802, 21878039; K. P. thanks Singapore Ministry of Education, Academic Research Fund Tier 1 (2019-T1-002-045, RG125/19, RT05/ 20), Academic Research Fund Tier 2 (MOE2018-T2-2-042), and A*STAR SERC AME Programmatic Fund (SERC A18A8b0059) for the financial support