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As the circuitry of literature grows increasingly international, translation studies of women writers have in recent years attracted great critical attention. This thesis will explore the Chinese translation and the power of feminism expressed in the translated text. This thesis will mainly focus on the reception of Charlotte Brontë's Jane Eyre, which is one of the most popular English novels in China. The 20th century is an important period for China when Chinese society developed from a feudalist to modern society. And the 21st century is a new period for China when achievements came into being following two decades of reform and opening-up policies. The translations of Western writers' works bringing feminism, a new trend of thought in China, provided a new figure and image of women and prompted Chinese women's ideas of liberation and independence. In this thesis, I enquire after the connection between the development of feminism and translations. I research how women's agency and feminist thought are presented through the comparison between translations by both feminist-leaning and non-feminist translators. The ideology of translators is evaluated and classified with an analysis of their prefaces, academic study, and life experience. I build the corpora of translated texts and collect data to study the language features of texts. Comparative analysis is directed towards the actual language used consciously or unconsciously in texts by translators with different feminist thoughts. The findings address three issues: firstly, feminism tends to be presented through strong-woman models in the Chinese context; secondly, feminist translations have contributed to the reconstruction of Chinese femininity; and finally, feminist strategies are used as special tendencies in the Chinese context.

Driven by the abundant availability of spectrum resources and the high data rate demands, millimetre wave (mmWave) technologies have emerged as one of the strongest contenders for the delivery of future wireless applications. Nonetheless, due to their shorter wavelengths when compared to traditional microwave transmissions, the wireless signals at these frequencies are extremely sensitive to the blocking and shadowing caused by obstacles within the local environment. This challenge is especially pronounced for indoor dense small cell deployments. One potential approach to mitigate signal deteriorations is the application of distributed antenna systems (DASs) upon which the research in this thesis is focused. In this work, a distributed mmWave measurement system operating at 60 GHz is designed in order to take the snapshots of the signal received simultaneously at multiple candidate access point locations. This experimental mmWave DAS setup enables the time series data to be collected and analysed in this thesis. In more detail, this thesis first investigates the influence of elevation angle on near-body path gain at 60 GHz and then presents a fading characterisation for indoor mmWave DASs. This is followed by a study of the cross correlation coefficient (CCC) and channel power imbalance (CPI) using statistical time series tools. The potential improvement in signal reliability has also been empirically studied by considering different access point selection and signal combining approaches. With the aim of assisting radio resource management, using small-scale fading features extracted from the raw received signal time series, a machine learning (ML)-based system which combines supervised and unsupervised learning is proposed to recognize common UE use cases in indoor mmWave DASs. This thesis also presents an empirical analysis of the handover performance in indoor mmWave DASs. This is achieved by considering some conventional handover algorithms and then determining the optimal ranges for the associated handover parameters.

This research uses secondary data from the Improving Teacher Development and Educational Quality in China (ITDEQC) project to conduct new school and teacher effectiveness research for senior high schools in one Local Education Authority (LEA) in western China. The significant substantive findings include: 1) There are remarkable differences in raw estimates of school and teacher effects on student's achievement in Entrance Examination for Higher Education (EEHE) English as well as adjusted estimates when controlling Entrance Examination for senior high schools (EESHS) English score regarding the different teachers and different senior high schools in one LEA of western China. 2)The raw, unadjusted estimates without controlling the prior attainment and five student background characteristics (gender, citizenship status, place of household registration , the number of siblings and tuition fee status) may underestimate the school and teacher effects on student's EEHE English score. 3) Some student background characteristics (e.g. place of household registration and tuition fee status) also have significant effects on student's achievements and progress in English. Moreover, after aggregating these five student background characteristics to the teacher level and school level, the additional contextual effects of some background variables also tend to influence the individual, teacher and school performance in EEHE English. 4) The results also suggest there is marked differential school effectiveness by prior attainment in terms of student's achievement in English. Schools and teachers are not equally effective in promoting the progress of all initial achievers. When compared with top and bottom initial achievers, the average students seem to make the least progress. 5) The effects of prior attainment on student's achievement also differ for students with different citizenship status or their number of siblings. All these findings can make contributions to current education policies in China. For example, due to the lack of the database in China, it is crucial to create a larger electronic database which contains more of student's background characteristics and practical family background information for relatively valid and fair evaluation of teachers and schools' performances in EEHE in western China's context. Also, more policies with respect to equal opportunity for senior secondary education, the optimization of students' allocations across different schools and teachers are needed for the improvement of education quality. Likewise, from the methodological perspective, this research also revealed that the three-level fixed and random effects model can give different estimates and standard error for school and teacher effects because of the different estimation methods used. The bias of the effects of the individual-level covariates in three-level random intercept model can be adjusted by including its cluster-level means. And regarding using weighting to compensate the possible bias for unit non-response in variables from the questionnaire, the standard error of the coefficients of individual-level covariates tend to be overestimated in three-level random intercept and random slope model in our case. Also, the three approaches (deleting all observations with missing teacher ID, the missing category method and multiple membership models method) tend to perform differently when dealing with the missing identifiers in our ITDEQC dataset.

Segmenting vessels from retinal images, like segmentation in many other medical image domains, is a challenging task, as there is no unified way that can be adopted to extract the vessels accurately. However, it is the most critical stage in automatic assessment of various forms of diseases (e.g. Glaucoma, Age-related macular degeneration, diabetic retinopathy and cardiovascular diseases etc.). Our research aims to investigate retinal image segmentation approaches based on textons as they provide a compact description of texture that can be learnt from a training set. This thesis presents a brief review of those diseases and also includes their current situations, future trends and techniques used for their automatic diagnosis in routine clinical applications. The importance of retinal vessel segmentation is particularly emphasized in such applications. An extensive review of previous work on retinal vessel segmentation and salient texture analysis methods is presented. Five automatic retinal vessel segmentation methods are proposed in this thesis. The first method focuses on addressing the problem of removing pathological anomalies (Drusen, exudates) for retinal vessel segmentation, which have been identified by other researchers as a problem and a common source of error. The results show that the modified method shows some improvement compared to a previously published method. The second novel supervised segmentation method employs textons. We propose a new filter bank (MR11) that includes bar detectors for vascular feature extraction and other kernels to detect edges and photometric variations in the image. The k-means clustering algorithm is adopted for texton generation based on the vessel and non-vessel elements which are identified by ground truth. The third improved supervised method is developed based on the second one, in which textons are generated by k-means clustering and texton maps representing vessels are derived by back projecting pixel clusters onto hand labelled ground truth. A further step is implemented to ensure that the best combinations of textons are represented in the map and subsequently used to identify vessels in the test set. The experimental results on two benchmark datasets show that our proposed method performs well compared to other published work and the results of human experts. A further test of our system on an independent set of optical fundus images verified its consistent performance. The statistical analysis on experimental results also reveals that it is possible to train unified textons for retinal vessel segmentation. In the fourth method a novel scheme using Gabor filter bank for vessel feature extraction is proposed. The ii method is inspired by the human visual system. Machine learning is used to optimize the Gabor filter parameters. The experimental results demonstrate that our method significantly enhances the true positive rate while maintaining a level of specificity that is comparable with other approaches. Finally, we proposed a new unsupervised texton based retinal vessel segmentation method using derivative of SIFT and multi-scale Gabor filers. The lack of sufficient quantities of hand labelled ground truth and the high level of variability in ground truth labels amongst experts provides the motivation for this approach. The evaluation results reveal that our unsupervised segmentation method is comparable with the best other supervised methods and other best state of the art methods.

This thesis aims to bring China into comparative welfare research by using a historical institutionalist framework to study Chinese pension reforms (1980s-2010s). The study takes as its starting point the literature on welfare state development and change in the West and thus traces the development of Chinese pensions in comparative perspective. The discussion of the determinants of Chinese pension development concentrates on the history (path dependence, timing and sequences) as well as meso level institutions (veto points, veto players) and their interplay with macro context and micro actors in the policy making process. In order to explore the key explanatory and outcome variables in the research framework, this study includes a detailed discussion of the conceptualization, operationalization, and measurement of the dependent variable, Chinese pension reform, as well as, by way of synthesizing key contributions in the existing historical institutionalist pension reform literature, a detailed theoretical attempt to disentangle the explanatory power of mainstream determinants of pension reform. ‘Bringing China into comparative welfare research’ thereby necessitates the proposition of a revised framework of analysis based on China’s context and new developments within historical institutionalism. By using this alternative framework, this study attempts to improve the theoretical understanding of China as well as to discuss practical policy issues facing China.

Long Range Ultrasonic Testing (LRUT) is an emerging ultrasound Non-destructive Testing (NDT) method. The LRUT is a variant of the conventional NDT approach. By using ultrasound guided waves (UGWs) , it is efficient in quick long range defect scanning, which is impossible with other traditional NDT techniques. Increasing numbers of requirements for quick long range testing have led to urgent need for the improvement of testing methods and the development of new testing equipment to help researchers in laboratory and help technicians in field inspection. The market for multi-channel ultrasonic instruments is characterized by small volumes of sales and a high ratio of development costs to sale price. The consequence is that the investment required to develop an instrument is significant and upgrades and other changes to instrument specification or configuration are difficult for many manufacturers to justify. These factors are particularly relevant for long range guided wave technology, where the technology is still relatively new in the market place and the instrumentation has different characteristics from other type of ultrasonic systems. In order to support the design and manufacture of the Teletest®MK4 system, Plant Integrity Ltd has supported this study into the use of a field-programmable gates array (FPGA) based control circuits for the electronics, which allows the component count to be decreased and also permits the system to be modified via flexible reconfiguration of the FPGA. This has benefits of reduction of size, weight and power consumption of the electronics and provides a means of upgrading the product without expensive re-design of the hardware. FPGA device has been the enabling technology and main thrust behind the evolution of many technologies, unleashing new opportunities for improving the performance of the LRUT equipment and providing more functions for industrial and academic applications. A novel system has been developed to provide multichannel waveform generation, data capture and signal processing for advanced lab-based research and faster- field testing. This system includes an FPGA to process multichannel data in parallel and to provide the flexibility of reconfiguration for various testing applications proposed by new research. The design was tested in pre-prototype hardware. Subsequent construction of the commercial prototype unit enabled successful operation of the design to be demonstrated. Implementation of the FPGA design reduced component count, PCB dimensions and power consumption. Though the SpartanTM 3A DSP FPGA and Xilinx ISE software have been used in this project, the concepts of the design can be applied to other FPGA devices from other FPGA vendors using other design softwares.

The work in this thesis mainly focuses on the preparation and optimization of materials for intermediate temperature fuel cells (ITFCs) with the aim of achieving high fuel cell performance as well as good stability. The fuel cell fabrication was also studied in order to develop a cost-effective fabrication process. Methods such as solid state reaction, combustion and carbonate co-precipitation were adopted for the synthesis of the materials. The densification temperature of Ce0.8Gd0.05Y0.15O1.9 (GYDC) electrolyte was greatly reduced by the carbonate co-precipitation synthesis and subsequently a simple one-step co-press-sintering fabrication process was developed. LiNO3 as sintering additive further reduced the densification temperature of GYDC and up to 96% relative density was achieved at 800 °C. Lithiated NiO was employed as cathode for IT-SOFCs and demonstrated good electrocatalytic activity. In addition, lithiated NiO was also investigated as both anode and cathode for IT-SOFCs and its stability was studied. Oxide-carbonate composites have demonstrated very high ionic conductivity as the melting of carbonates greatly enhanced the mobility of ions in materials. High power densities up to 670 mW cm-2 at 550 °C were achieved for the composite electrolytebased ITFCs. However, the traditional lithiated NiO cathode can gradually dissolve into the carbonate melt and scanning electron microscopy studies found obvious morphology change nearby the cathode/electrolyte interface which may be due to the dissolution of nickel ions. Perovskite oxide Sm0.5Sr0.5Fe0.8Cu0.2O3-δ (SSFCu) has been demonstrated to be a compatible and stable cathode for the composite electrolyte based ITFCs, as a stable current output of about 0.4 A cm-2 was observed under a constant voltage of 0.7 V during a cell test lasting 100 h. Instead of GYDC, BaCe0.5Zr0.3Y0.16Zn0.04O3-δ (BCZYZn) was also employed as substrate material for the carbonate composite electrolyte and SrFe0.7Mn0.2Mo0.1O3-δ (SFMMo) was developed and used as cathode.

The concept of More Electric Aircraft, where is to utilize the electrical power to drive more or all aircraft subsystem instead of conventional combination of pneumatic, hydraulic, mechanical and electrical power, can be recalled to World War II. It has been proven to have more advantages for decades in terms of energy efficiency, environmental issues, logistics and operational maintenance. It can also enhance aircraft weight, volume and battle damage reconfigurability.Thanks to the new electronics technologies and the emergence of new materials, It becomes feasible for high power density electrical power components to drive the majority of aircraft subsystem. However, sustaining the transmission of hundreds of kilowatts of electrical power at low voltages is not feasible owing to the penalties incurred due to high cable weights and voltage drop may become critical. It is very easy to come up with the solution of the increase of voltage. However, higher voltage will introduce other problems such as the reliability of insulation coordination on the aircraft due to the increased probability of electrical discharge. For aircraft designers, it is very important to understand the rules of insulation coordination on the aircraft including determining clearance and creepage distances, and also have a clear investigation of the phenomena and mechanism of electrical discharges. Past research has identified a number of the concerns of operating electrical systems at higher voltages in an aerospace environment, especially for dimensioning of clearances. However, there is little study on dimensioning of creepage distances and relevantly flashover and electrical tracking on solid insulating material surfaces. This thesis firstly discusses the rules for determining clearances and creepage distances. The experimental validation work was done for breakdown in air gap and on the solid insulating material surfaces under dry condition so that some standard recommended values can be evaluated not only with theoretical values such Paschen's law. Suggestions of application of those standards were provided. Secondly, the complex electrical discharge under wet condition on solid insulating material surfaces was discussed. A mathematical model to predict this type of electrical failure -electrical tracking (the electrical discharges on solid insulation materials which will lead to physical damage in the materials) with the consideration of different environmental conditions including air pressure, ambient temperature, and pollution degrees was developed. A series of electrical tracking tests were carried out on organic materials to find out the mechanism of electrical tracking and validate the finding by the mathematic model. Finite element analysis simulations were also conducted to find out the background thermal transfer mechanism to support our explanation of those phenomena of electrical tracking. Different test techniques have ben developed for specific impact factors. Finally, the suggestions for utilization of the standards and feasible test techniques for electrical tracking under wet conditions were provided.

The main objective of this thesis is to identify the reasons why firms choose to issue unit IPOs instead of share-only IPOs. Evidence is found that unit firms are smaller, riskier, with higher level of agency costs and higher levels of information asymmetry than share-only firms and unit IPOs are underwritten by less reputable underwriters. The initial return results provide strong support to the Agency Cost hypothesis that unit IPOs is significantly more underpriced than share-only IPOs. Unit firms have lower survival rate than that of share-only IPO firms; however, unit firms that do survive are more likely to issue seasoned equity offerings (SEOs) for further funding. A clear pattern of price run-up is observed before SEO announcements by unit firms and a significant negative price adjustment is found when the SEOs are announced. In the long-term, this thesis provides evidence that unit IPOs present significantly worse underperformance comparing to both the matching share-only IPOs and various market indices. Such results contradict both the Agency Cost and the Signalling hypotheses and imply that unit firms cannot significantly improve performance by simply attaching warrants, regardless as whether they are used to reduce agency costs or to signal firm value.

According to some commentary, employees are empowered by simple, flexible and lightweight social software such as blogs and wikis. Using such software, it is claimed they will be able to connect and collaborate beyond boundaries and to benefit organisations in innovation and growth. Extending such claims, commentators also argue that social software challenges existing ways of networking, communicating and collaborating and is likely to cause disruption to some organisations. However, a literature search reveals that it is still unclear for both researchers and practitioners how social software can be exploited for work purposes, and what are the grounds for its adoption. Systematic investigation of the adoption of social software for work collaboration is found to be absent in academic writing. Organisations continue experimenting on social software, seeking ways to encourage their employees to adopt the tools, and expect to benefit from self-organised, distributed collaboration. This technology adoption is a contingent process. The way members use social software is embedded within the social interaction process, group characteristics, tasks and environments. Adaptive structuration theory (AST) is adopted for social software-supported collaboration in a way that emphasised its value as a contextual analysis approach. A conceptual framework was developed from the researcher's experiential knowledge, AST and empirical evidence, and then revised with the findings from exploratory studies. The main theoretical insights induced from empirical evidence are: influence from 'meta-users', e.g., supervisors and managers, affects both users' use intention and behaviour. Social structures embedded in social capital can enable or inhibit the adoption of social software. Nevertheless, new social structures do emerge from online conversations. Additionally, the results show that task-technology fit has little relevance to social software adoption. A case study approach with mixed methods is adopted in this study. Social network analysis and statistical analysis provide complementary support to qualitative analysis. The UK public sector was chosen as the research context. Individuals are knowledge workers in distributed and cross-boundary groups. The asynchronous social software applications studied are blogs and wikis.

This PhD thesis demonstrates a reliability analysis methodology to solve the safety issues existing in the current structural design of fabric structures: The safety coefficients proposed by different countries and academic institutes are not consistent, and the leading structural safeties are obscure and require a justification. A reliability tool specific for fabric structures is developed to estimate the structural safety and justify the safety coefficients for structural design based on the variations of the design variables like loads, material strength. The research work includes three main parts: the first one aims at a finite element formulation proposed for a highly accurate and efficient deterministic analysis. Four element types have been compared and discussed, and the linear strain triangle coupled with Dynamic Relaxation algorithm are shown to be most efficient with the satisfactory accuracy, The second part is focus on a probabilistic methodology to identify and analyze the material uncertainties based on the experiment data. The probabilistic models to qualify the variation in the fabric strength and Young’s modulus under uniaxial tension are demonstrated, and a practical algorithm to determine best data-fit distributions is also presented. The third part is the reliability formulation which consists of first order reliability method(FORM) and the finite element method based on the six node linear strain triangles. The analytical method with the principle of chain rule is applied in deriving the gradients of the limit state functions. The sensitivities of the structural safety corresponding to different uncertainties are compared and analyzed through numerical examples. Finally a case study based a realistic design example is undertaken, and safety factors for loads, materials, and other design elements are justified and discussed. This thesis demonstrates that the safety coefficients currently used in the fabric structure design may not be either economic or safe. Based on the uncertainty information of the design elements, the appropriate safety factors required for the structural safety standards can be evaluated using the reliability tool, and then an optimized design decision in consideration of safety and cost can be subsequently determined.

The functional mariner element Mos1 is 1286 bp long with 28 bp imperfect terminal repeats and contains a single open reading frame encoding a 345 amino acid transposase. In this thesis, the N-terminal domain of mariner transposase was found to bind specificity to inverted repeats at both ends. The minimal DNA-binding domain was identified to be located between amino acid 1 and 120. This contains a helix-turn-helix (HTH) motif. Site-directed mutagenesis indicates that the HTH is required for sequence-specific recognition of the terminal inverted repeats by Mos1 transposase. The transposase has 5 to 6 times higher affinity for the right end sequence of Mos1 than for the left end. Yeast two-hybrid assays were performed to identify protein-protein interactions of Mos1 transposase. Deletion mutants suggest that residues required for the interaction of transposase monomers are distributed along the length of the protein. Twelve single point mutations which reduce subunit interactions have been isolated. The L124S mutation is located in neither the DNA-binding nor the catalytic domain. The purified Mos1(L124S) mutant reduces transposase activity in both excision and transposition assay, indicating that subunit interactions are involved in the transposition reaction. A change in target site selection other than TA dinucleotide observed with this mutant may reflect a change in the conformation of the transpososome. The promoter activity of Mos1 was detected in transgenic flies by P element-mediated germline transformation. The promoter of Mos1 lies within nucleotides 1-171 which shows low activity. There is an enhancer-like element in the region of nucleotides 172-516 that stimulates reporter gene expression. No autoregulatory inhibition of Mos1 transposase on its own promoter was observed. This suggests that "overproduction inhibition" may not act at the level of transcription through autorepression of the Mos1 promoter.

Economic decisions under uncertainty generally involve a change of stochastic regime. This thesis examines the formal conditions for optimizing such decisions and looks at applications to exchange rate intervention, physical investment and consumption behaviour. Many of these economic regime switchings can be mathematically formulated as stopping problems. Global optimality is achieved by applying Hamilton-Jacobi-Bellman equations in each regime, together with the joining conditions at the switching boundaries. Chapter 1 establishes the framework for optimisation and provides various boundary conditions for different switching cases. Chapter 2 applies optimal stopping techniques to derive optimal “time-consistent” exchange rate target zones in the presence of proportional/lump sum intervention costs. It further shows that such discretionary equilibria can be improved upon by a credible commitment to an exchange rate mechanism (such as ERM). Chapter 3 characterises the irreversible oil investment decision in the North Sea as an optimal regime switching problem. In the absence of Petroleum Revenue Tax (PRT), it shows how the optimal development decision will be deferred when real oil prices follow a geometric Brownian motion. In chapter 4, an intertemporal partial equilibrium model of investment is used to assess the effects of stochastic capital depreciation on optimal investment behaviour, in a context where a sales constraint effectively decomposes the problem into two distinct regimes. The presence of the uncertainty about depreciation reduces firm’s demand for investment; and increasing the variability of capital depreciation further reduces investment. The uncertainty also makes investment “smoother” than that under certainty. Finally, chapter 5 and 6 deal with optimal consumption/portfolio decisions in a two-asset model with shortselling and borrowing restrictions imposed. Chapter 5 formulates a regime switching problem due to the presence of the borrowing constraint and specifies the corresponding boundary conditions. Chapter 6 characterises optimal solutions to various combinations of parameters for constant relative and constant absolute risk aversion utility functions. In many cases, if labour income is fully diversifiable, the borrowing constraint only binds when the wealth level falls below a threshold, and risk taking behaviour at the low level of wealth is associated with a convex portion of the indirect utility function (value function). In such regime-switch cases, the introduction of the borrowing constraint makes consumption more volatile relative to income. It also generates the precautionary motive for saving.

Neural networks, coupled with their associated optimization algorithms, have demonstrated remarkable efficacy and versatility across an extensive array of tasks, encompassing image recognition, speech recognition, object detection, sentiment analysis, and more. The inherent strength of neural networks lies in their capability to autonomously learn intricate representations that map input data to corresponding output labels seamlessly. Nevertheless, not all tasks can be neatly encapsulated within the confines of an end-to-end learning paradigm. The complexity and diversity of real-world challenges necessitate innovative approaches that extend beyond conventional formulations. This calls for the exploration of specialized architectures and optimization strategies tailored to the unique intricacies of specific tasks, ensuring a more nuanced and effective solution to the myriad demands of diverse applications. The bi-level optimization problem stands out as a distinctive form of optimization, characterized by the embedding or nesting of one problem within another. Its relevance persists significantly in the current era dominated by deep learning. A notable instance of its application in the realm of deep learning is observed in hyperparameter optimization. In the context of neural networks, the automatic training of weights through backpropagation represents a crucial aspect. However, certain hyperparameters, such as the learning rate (lr) and the number of layers, must be predetermined and cannot be optimized through the conventional chain rule employed in backpropagation. This underscores the importance of bi-level optimization in addressing the intricate task of fine-tuning these hyperparameters to enhance the overall performance of deep learning models. The domain of deep learning presents a fertile ground for further exploration and discoveries in optimization. The untapped potential for refining hyperparameters and optimizing various aspects of neural network architectures highlights the ongoing opportunities for advancements and breakthroughs in this dynamic field. Within this thesis, we delve into significant bi-level optimization challenges, applying these techniques to pertinent real-world tasks. Given that bi-level optimization entails dual layers of optimization, we explore scenarios where neural networks are present in the upper-level, the inner-level, or both. To be more specific, we systematically investigate four distinct tasks: optimizing neural networks towards optimizing neural networks, optimizing attractors towards optimizing neural networks, optimizing graph structures towards optimizing neural network performance, and optimizing architecture towards optimizing neural networks. For each of these tasks, we formulate the problems using the bi-level optimization approach mathematically, introducing more efficient optimization strategies. Furthermore, we meticulously evaluate the performance and efficiency of our proposed techniques. Importantly, our methodologies and insights transcend the realm of bi-level optimization, extending their applicability broadly to various deep learning models. The contributions made in this thesis offer valuable perspectives and tools for advancing optimization techniques in the broader landscape of deep learning.

Abstract: This thesis focuses on the structural performance of mass timber panel-concrete composite floors with notches. Mass timber panels (MTPs) such as cross-laminated timber, glue-laminated timber, and nail-laminated timber, are emerging construction materials in the building industry due to their high strength, great dimensional stability, and prefabrication. The combination of MTPs and concrete in the floor system offers many structural, economic, and ecological benefits. The structural performance of MTP-concrete composite floors is governed by the shear connection system between timber and concrete. The notched connections made by cutting grooves on timber and filling them with concrete are considered as a structurally efficient and cost-saving connecting solution for resisting shear forces and restricting relative slips between timber and concrete. However, the notched connection design in the composite floors is not standardized and the existing design guidelines are inadequate for MTP-concrete composite floors. To study the structural performance of notched connections and notch-connected composite floors, this thesis presented experimental, numerical, and analytical investigations. Push-out tests were conducted on the notched connections first, and then bending tests and vibration tests were conducted on full-scale composite floors. Finite element models were built for the notched connections to derive the connection shear stiffness. Finally, analytical solutions were developed to predict the internal actions of the composite floors under external loads. This study shows that the structural performance of notched connections is affected by the geometry of the connections and material properties of timber and concrete. The notch-connected MTP-concrete composite floors showed high bending stiffness but were not fully composite. The floors with shallow notches tended to fail in a ductile manner but had lower bending stiffness than floors with deep notches. The composite floors with deep notches, however, often fail abruptly in the concrete notches. By reinforcing the notched connections with steel fasteners, the composite floor can achieve high bending stiffness, high load-carrying capacity, and controlled failure pattern. The proper number and locations of notched connections in the composite floors can be determined from the proposed composite beam model. This thesis presented promising results in terms of the static and dynamic structural performance of notch-connected MTP-concrete composite floors. The test investigations added additional data to the current research body and prompted further evolvement of timber-concrete composite floors. The proposed empirical equations for estimating the connection stiffness and strength and composite beam model for predicting the serviceability and ultimate structural performance of composite floors provide useful tools to analyze the notch-connected MTP-concrete composite floors. The design recommendations for MTP-concrete composite floors with notches are provided in the thesis.

Immersive and interactive storytelling in virtual reality (VR) is an emerging creative practice that has been thriving in recent years. Educational applications using immersive VR storytelling to explain complex science concepts have very promising pedagogical benefits because on the one hand, storytelling breaks down the complexity of science concepts by bridging them to people's everyday experiences and familiar cognitive models, and on the other hand, the learning process is further reinforced through rich interactivity afforded by the VR experiences. However, it is unclear how different amounts of storytelling and interactivity in an interactive VR storytelling experience may affect learning outcomes due to a paucity of literature on educational VR storytelling research. This dissertation aims to add to the literature through an exploration of interactivity and essential storytelling elements in educational VR storytelling experiences and their impact on learning. We designed a working prototype of interactive and immersive VR storytelling experience, Immunology VR, that focuses on the learning of specific immunology concepts: neutrophil transmigration and killing mechanisms. Based on the initial prototype, we further developed six variations that allowed us to conduct two major experiments below. Our first experiment explored designs of three different levels of interactivity, low, medium, and high, in the VR storytelling experiences and their effects on immunology learning. We found subjective evidence to support our research hypothesis that increased level of interactivity will lead to increased engagement in VR learning. Our finding suggests that interactivity is a key design element in VR learning design for effective learning and should be considered in all VR learning applications. Our second experiment focused on the designs of the level of storytelling richness and their effects on learning. Specifically, we designed three storytelling conditions, minimal storytelling, basic storytelling, and advanced storytelling, and investigated how each of them affected immunology learning. Subjective evidence from our user interview data suggested that participants from higher levels of storytelling conditions were more likely to perceive storytelling elements as the most useful features in the VR experience that helped with their learning. It is also suggested that higher levels of richness in essential storytelling elements may trigger certain emotions and empathy in more users and positively affect their learning.