Search

This thesis investigates the way in which humour in Chinese comedies is transferred forEnglish-speaking audiences. It employs multimodality as a concept to underline that to privilege only linguistic approaches to subtitling is to ignore the way in which verbal and non-verbal modes interplay in the generation of humour. The corpus under examination contains four Chinese comedies from different genres: the romantic comedyIf You Are the One(2008), the chick flick Finding Mr. Right(2013), the black comedy Let the Bullets Fly(2011), and the martial arts comedy Monster Hunt(2015). These are used as case studies to investigate the linguistic and cultural barriers inherent in the subtitling of Chinese humour into English. This thesis analyses different types of humour in the original Chinese and their transposition into English subtitles through the prism of multimodality to underline the complex interaction of different semiotic modes in the generation of laughter. This thesis demonstrates that verbally expressed humour and cultural humour tend to travel badly by comparison with non-verbal modes. Non-verbal modes, to a certain degree, however, can compensate for the loss of linguistic or cultural humour. Moreover, on the basis of the case studies of these four Chinese comedies, this thesis works to offer a more precise classification of audiovisual humour. This work highlights that the analysis of humour in this context cannot be limited to linguistic evaluations of subtitles like those in the case studies. Films are multimodal texts, and subtitling humour is a multimodal activity in which verbal and non-verbal modes combine to trigger comic effects. Multimodality is key to understanding the translation strategies which enable the transposition of humour from Chinese into English. This study unpicks the complex ways in which Chinese humour and culture are rendered for English-speaking audiences and argues powerfully that multimodality is a key framework for further studies of subtitling.

Seismically vulnerable, especially collapse-prone, buildings often represent the greatest life-safety hazard worldwide. Identifying these buildings is the first step in seismic risk mitigation efforts for a given urban region’s resilience. This thesis aims to devise a workflow for the application of state-of-the-art deep neural networks (DNNs) for detecting and classifying seismically vulnerable buildings using three-dimensional point clouds. A number of prior studies have focused on using 2D image data in the field of structural health monitoring for damage recognition. The performance of those approaches for building classification at regional scales is highly dependent on well-controlled imagery data and may not be guaranteed when applied to real-world data. The present study, therefore, differs from prior studies in that it uses 3D point clouds, which implicitly contain depth information that can become a highly useful feature for training DNNs. Here, a specific DNN, namely, PointNet, is used for detecting soft-story buildings, which are ubiquitous in west-coast cities of the US. A workflow is devised for binary classification of point clouds into soft-story and non-soft-story points as well as the segmentation and association of classified point clouds with specific addresses. Over 10 billion point clouds obtained from the city of Santa Monica in California are manually labeled and split into training, validation, and testing sets, and the sensitive ranges of DNN hyperparameters are investigated to obtain the good performance.

The urban region's seismic resilience is being actively studied in recent years as a measure for risk mitigation, where the identification of seismically vulnerable buildings and the assessment of their performance play indispensable roles. However, it is a labor-intensive and computationally expensive task to evaluate tens of thousands of buildings in a region because the identification requires professional judgment at a site and the seismic assessment demands comprehensive modeling depending on structure-specific data. Nevertheless, it is feasible with the aid of advanced development of the Internet of Things (IoT) and computer technology. In this study, a data-driven framework including two pipelines that focus on soft-story buildings and non-ductile reinforced concrete frames is proposed.The first pipeline focuses on identifying soft-story buildings in the city of Santa Monica (California) through 3D point clouds and convolutional neural networks (CNNs). Although prior studies showed promising results in detecting soft-story buildings based on well-selected street-view images, false predictions are common when it is applied to real-world data. To address this issue, the pipeline implements point-cloud data where spatial information is available to segment building points and extract density features for training deep learning models and identifying soft-story buildings. The transfer learning (TL) technique is adopted to avoid overfitting in deep neural networks, and the parameters within the pipeline are investigated for optimal performance. The results illustrate the potential applicability of the pipeline for developing pre-and post-event countermeasures. The second pipeline focuses on another seismically vulnerable building, namely, the non-ductile reinforced concrete building (NDRCB). Prior studies indicated around 1,500 NDRCBs in Los Angeles that are urgently waiting for detailed assessment and mandatory retrofit or demolition if necessary. Because the fulfillment of these ordinances will last for decades, the potential risk of major losses will persist. To this end, an automatic method that harvests building information from IoT and imagery data generates archetypal models, conducts probabilistic seismic assessment, and estimates the losses for NDRCB frames is hence developed. The accuracy of the data harvesting module using deep CNNs is validated with the existing inventory data. The archetypal frames are developed based on the era-specific representative code and are validated through nonlinear static and nonlinear dynamic analyses of previously investigated NDRCBs. State-of-the-practice loss estimation methodologies including HAZUS and FEMA P-58 are adopted in the pipeline for constructing damage fragility functions and corresponding losses. The regional application focuses on intensity-based assessment for thousands of individual buildings instead of a scenario-based assessment. The outcomes of expected losses and repair/reconstruction time emphasize the vulnerability of NDRCBs in Los Angeles, and the presented pipeline is believed to bridge the gaps between property owners, engineers, and decision-makers.This research demonstrates how advanced data mining techniques and data-driven approaches can aid to solve civil engineering problems. While the framework currently focuses on soft-story and non-ductile frame buildings, it is expected to be extended in-depth and breadth in the future. That is, more detailed models and other seismically vulnerable infrastructures can be included.

Pseudomonas aeruginosa can grow efficiently on spermine and other biogenic polyamines via the γ-glutamylpolyamine synthetase (GPS) pathway. Not only subjected to growth inhibition by spermine, the pauA2 mutant without a functional γ-glutamylpolyamine synthetase PauA2 became more sensitive to β-lactam antibiotics in human serum. To explore PauA2 as a potential target of drug development, the native form of PauA2 protein overexpressed in E. coli was purified to homogeneity for biochemical characterization. The specific activity of PauA2 was monitored by spectrophotometric measurements (660 nm) of the releasing phosphate from ATP in the presence of ammonium molybdate and malachite green. PauA2 displayed a sigmoid curve on Velocity-Concentration plot, indicating an allosteric modulation in the catalytic reaction. The apparent Km values were 0.2mM, 2.1mM, 6.1mM, and 1.1mM for ATP, L-glutamate, spermidine, and spermine, respectively. The obtained values of Hill coefficient were 3.4 and 5.4 for spermidine and spermine, respectively. Although P. aeruginosa can degrade the spermine by PauA2, it seems likely that other mechanisms may alleviate the spermine toxicity in the absence of pauA2. All the pauA2 suppressors were isolated from spermine selection plates and shared common changes in various pathways including delayed growth rate, retarded swarming motility, and pyocyanin overproduction. Genome resequencing of a representative suppressor revealed a unique C599T mutation at the phoU gene that results in Ser200Leu substitution and a constitutive expression of the Pho regulon as evidenced by measurements of promotor activities and transcriptome analysis. All of the observed phenotypes could be complemented by a recombinant plasmid carrying the wild-type phoU gene. Also, accumulation of polyphosphate granules and spermine resistance in the suppressor mutant were reversed concomitantly when exopolyphosphatase PPX was overexpressed from a recombinant plasmid. Identical phenotypes were also observed in a ΔpauA2ΔphoU double knockout mutant. In conclusion, we characterized the γ-glutamylspermine synthetase PauA2 as the essential enzyme and provide the foundations for PauA2 inhibitors screening as a potential antibacterial. Furthermore, we identified polyphosphate accumulation as a potential protection mechanism against spermine toxicity in P. aeruginosa.

In recent years, the Global Positioning System (GPS) and the Inertial Navigation System (INS) integrated systems or, Inertial Measurement Unit (IMU), have become a core positioning technique due to their complementary characteristics. In the past, GPS was considered the primary sensor that contributed to the GPS/INS integrated system's solution, while INS only played an aiding role in bridging GPS outages. The evolution of the navigation philosophy, due to a broad availability of inexpensive inertial sensors, has made the IMU a primary navigation sensor. While GPS is still the key to generate a reliable integrated solution, its role is to focus on proper estimation of the IMU errors. Though GPS has been outperforming itself in recent years, the system is highly dependent on environment, and infrastructure, which is outside the users' control. Hence, in order to enable the GPS/INS integrated system to operate in GPS challenged environments, it is crucial for INS to perform properly when GPS is not available. Since the drifts and biases of the IMU sensors caused by the random noise are the major components that affect the performance of the inertial navigation solution, this thesis is aimed at assessing the effect of customized stochastic error model on free inertial navigation solutions. In essence, if the stochastic processes assumed by the customized model can represent the behavior of the IMU sensor error better than a generic model formulated by the parameters obtained from the manufacturer's specification sheet, there is an opportunity to improve the performances of the free inertial navigation solution.In this thesis, the Allan variance is applied to analyze the noise in the IMU measurements. Two methods: (1) slope reading and (2) model matching methods are used to estimate the coefficients of the customized stochastic error model. The results show that in most of the tested trajectories, a customized model improves the accuracy of the free inertial navigation between 40 \% and 80\% after a 30-second GPS gap. Despite the improvements with respect to the case of using the manufacturer's specifications, the accuracy of the solution after 30-second GPS gap obtained using customized model is insufficient for many land vehicle navigation applications. Considering 5 m as the loosely defined maximum position error for some land vehicle navigation applications, using the low-cost sensors tested here (micro-electro-mechanical, MEMS IMU). under such restriction, the solution estimated from the customized model was able to generate usable solutions for 5-11 seconds of the GPS outage, 2-3 seconds longer than using the default stochastic error model. As far as the attitude angles go, they were not improved in any of the tested trajectories even in situations when the position performance was improved. The model matching method performs slightly better than the slope reading approach; however, in terms of the overall magnitude of the drifts in MEMS IMUs, this small difference is insignificant.