This paper examines the role of historical preservation in spatial restructurings of postindustrial cities, through a detailed case study of Xintiandi, a preservation-based redevelopment project in inner city Shanghai. At Xintiandi, two blocks of Shikumen houses, a Shanghainese tenement built by Western landlords for Chinese tenants in the colonial period, are turned into a chic entertainment quarter by international developers and architectural firms, with support from local governments. The history of Shikumen as dwellings of lower-middle-class tenants for most part of the twentieth century is carefully erased. Instead, by emphasizing Shikumen’s international linkages, such as its Western-influenced architectural features, the private-public coalition has repackaged Shikumen into a symbol of Shanghai’s cosmopolitan colonial past, and used it to project an even brighter global future. The paper argues that historical preservation in post-industrial Shanghai serves the same development goal of urban growth, and in the process, historical elements in the built environment are selectively recycled and inserted in the newly created transnational space.

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This dissertation studies two topics related to the problem of estimation in networked systems, namely, the problem of secure state estimation against possible stealthy false data injection attacks in electric power network, and the problem of distributed dynamic state estimation using networked local multi-agents. The two topics are, respectively, presented in Part I and Part II.

The study in Part I is motivated by the importance of the state estimation in electric power systems as many applications such as Optimal Power Flow, Automatic Generation Control and Contingency Analysis are highly dependent on the state estimation. Therefore, it is critical to have an accurate and reliable estimate of the states as bad estimates will offer the system operator with inaccurate information about the system, which may cause wrong decisions and finally, cause malfunction or even power blackout in the network. The stealthy attack, as a strategically designed false data injection attack against the power system state estimation mechanism, is able to let the corrupted measurements bypass residue-based bad data detectors with the same probability as that of uncorrupted measurements, and to fool the system operator with the deviated estimates. While most of the existing articles assume the network topology to be fixed, the effects of switching topologies on such an attack is shown in this work. In Part I, a new mechanism is proposed to eliminate the possibility of such an attack via strategically shutting down some preselected transmission lines by turns and therefore switching the network topologies. The necessary and sufficient condition to achieve such elimination, as well as the general form of possible attacks when the elimination is impossible, are both formulated. The case where the attack is only stealthy in a subset of the preselected is also studied. The general form of the possible estimate deviations caused by this "partially" stealthy attack is derived. Simulations and case studies are provided using different IEEE bus systems to show the efficiency of the proposed strategy, to discuss the countermeasures in the case when there always exist possible stealthy attacks, and to show how the possible deviations introduced by a "partially" stealthy attack could be affected by the decisions made by both the attacker and the system operator.

Part II studies the problem of distributed dynamic state estimation using networked local agents with sensing and communication abilities. This problem has become a popular research area in recent years due to its wide range of applications such as target tracking, region monitoring and area surveillance. Specifically, Part II considers the scenario where the local agents take local measurements and communicate with only their nearby neighbors to estimate the state of interest in a cooperative and fully distributed manner. A distributed hybrid information fusion (DHIF) algorithm is proposed in the scenario where the process model of the target and the sensing models of the local agents are linear and time varying. The proposed DHIF algorithm is shown to be fully distributed and hence scalable, to be run in an automated manner and hence adaptive to locally unknown changes in the network, to have agents communicate for only once during each sampling time interval and hence inexpensive in communication, and to be able to track the interested state with uniformly upper bounded estimate error covariance. It is also explored the very mild conditions on general directed time-varying graphs and joint network observability/detectability to guarantee the stochastic stability of the proposed algorithm. Then the DHIF algorithm is extended to two more general scenarios, namely, the scenario with nonlinearities involved in both the process and the sensing models, and the scenario with uncertain process models. In the former scenario, a nonlinear DHIF algorithm is proposed by adopting the unscented transformation approach. In the latter one, two algorithms are proposed by following the two well-known multiple model (MM) paradigms, namely, the first order generalized pseudo Bayesian and the interacting MM approaches. The extended algorithms in both scenarios inherit the aforementioned advantages of the original DHIF algorithm. The stability is also rigorously analyzed in each extended case with sufficient conditions formulated.

Traditional genetics, which includes forward and reverse genetics, has been employed extensively to study influenza virus. Although traditional genetics is powerful, it has a limited throughput which only focuses on the linkage of one mutation with one phenotype at a time. In my thesis research, a high-throughput genetics platform is being developed to examine the phenotypic outcomes of all point mutations in a viral gene or genome in parallel. The underlying concept is to randomly mutagenize every nucleotide of an entire genome, monitor enrichment or diminishment of all point mutations under specified growth conditions, and perform massive deep-sequencing to determine which mutations contribute to negative, neutral, or positive outcomes under the given conditions. Using this high-throughput genetics platform, the fitness effects of individual point mutations was profiled across influenza A virus hemagglutinin gene. This technique was further applied to identify novel functional residues and interferon-sensitive mutation. The high-throughput genetics platform can potentially be adapted to study any microbes that can be genetically manipulated. My thesis also describes a novel experimental approach, tag linkage sequencing, to monitor viral quasispecies. Tag linkage sequencing utilizes a molecular tag to identify short sequencing reads that are from the same original DNA template. This allows the reconstruction of individual viral genomes within a viral quasispecies from deep sequencing data. This approach was employed to investigate the genetic content of a clinical sample from a patient infected with human immunodeficiency virus (HIV).