Abstract:Freshwaters are closely linked with adjacent terrestrial ecosystems through reciprocal resource subsidies, which are fluxes of nutrients, organisms, and materials between ecosystems. Terrestrial ecosystems provide many resource subsidies to freshwaters including leaf litter, one of the most prevalent terrestrial-derived subsidies. Inputs of leaf litter fuel detritivores food web, as food resources and refuges, and affect nutrients cycling in freshwaters. The decomposition of leaf litter is subjected to many biotic and abiotic factors, which makes it a good indicator of freshwater ecosystem functioning. Yet, this ecosystem process has been affected by anthropogenic disturbances that alter abiotic and biotic factors in the nature. Therefore, this thesis aimed to investigate some previously under-investigated or unclear but important factors that may affect the decomposition of leaf litter in streams. First, I reviewed the importance of resource subsidy fluxes between riparian zones and freshwaters and how these subsidies can influence recipient ecosystems. Then, I conducted a field experiment exploring the effects of anthropogenic carrion subsidy (chicken meat) and environmental-relevant concentration of glyphosate (the most widely applied herbicides worldwide) on leaf litter decomposition and invertebrate communities colonizing in the leaf-litter bags deploying in streams with different types of land use. Next, I conducted a mesocosm experiment nearby an urban stream to investigate the effects of water temperature (~ 8 oC above vs ambient), consumer - snails (presence vs absence), and leaf-litter quality (intact vs >40 % leaf area was consumed by terrestrial insects) on litter decomposition. Finally, I explored the global patterns of riparian leaf litter C, N, P, and their stoichiometric ratios to gradients of climatic (mean annual temperature and precipitation) and geographic (absolute latitude and altitude) factors, and the differences between biotic factors (phylogeny, leaf habit, N-fixing function, invasion status, and life form). The results of field experiment indicated that: in coarse mesh bags, glyphosate, carrion subsidy, and the addition of both decreased litter breakdown rates by 6.3 %, 22.6 %, and 24.3 % respectively; in fine mesh bags, glyphosate and the addition of both retarded litter breakdown rates by 8.3 % and 12.5 % respectively. Litter decomposition also differed among streams, with the highest breakdown rates in village streams and lowest in urban/suburban streams. Invertebrates were significantly different among streams, with biodiversity index and total taxon richness were highest in village streams and lowest in suburban stream. However, overall effects of carrion subsidy and glyphosate on macroinvertebrates were not significant. The results of mesocosm experiment indicated that warming and the presence of snails accelerated litter decomposition by 60.2 % and 34.9 % respectively, while litter breakdown rates of terrestrial insect damaged leaves were 5.1 % slower than intact leaves because of lower leaf litter quality. The results of meta-analysis study demonstrated that global riparian leaf litter had higher N and P, while lower C, C:N, and C:P ratios than terrestrial leaf litter in general. Riparian leaf litter quality changed with gradients of climatic and geographic predictors, and these patterns differed between leaf habits (evergreen or deciduous) and climate zones (tropical or non-tropical area). In general, my research provides important information on resource subsidy processes, which will benefit freshwater ecosystem management to support biodiversity and maintain ecosystem services.

Abstract:Mobility is an element that is highly related to the development of society and the quality of individual life. Through mass of automobile production and traffic infrastructure construction, advanced countries have reached a high degree of individual mobility. In order to increase the efficiency, convenience and safety of mobility, advanced traffic infrastructure construction, transportation systems and automobiles should be developed. Among all the systems for modern automobiles, cameras based assistance systems are one of the most important components. Recently, with the development of driver assistance systems and autonomous cars, detection and recognition of traffic scene objects based on computer vision become more and more indispensable. On the other hand, the deep learning methods, in particular convolutional neural networks have achieved excellent performance in a variety of computer vision tasks. This thesis mainly presents the contributions to the computer vision and deep learning methods for traffic scene objects detection and recognition. The first approach develops numbers of methods for traffic sign detection and recognition. For traffic sign detection, template matching is applied with new features extended from chain code. Moreover, the region based convolutional neural networks are applied for detecting traffic signs painted on road surface. For traffic sign recognition, convolutional neural networks with a variety of architectures are trained with different training algorithms. The second approach focuses on the detection related to traffic text. A novel license plate detection framework is developed that is able to improve detection performance by simultane- ously completing detection and segmentation. Due to the larger number and complex layout of Chinese characters, Chinese traffic text detection faces more challenges than English text detection. Therefore, Chinese traffic texts are detected by applying convolutional neural networks and directed acyclic graph. The final approach develops a method for pedestrian attribute classification. Generally, there are irrelevant elements included in features of convolutional neural networks. In order to improve classification performance, a novel feature selection algorithm is developed to refine features of convolutional neural networks.

Abstract:Rapid urbanisation brings severe challenges on sustainable development and living quality of urban residents. Smart cities develop holistic solutions in the field of urban ecosystems using collected data from different types of Internet of Things (IoT) sources. Today, smart city research and applications have significantly surged as consequences of IoT and machine learning technological enhancement. As advanced machine learning methods, deep learning techniques provide an effective framework which facilitates data mining and knowledge discovery tasks especially in the area of computer vision and natural language processing. In recent years, researchers from various research fields attempted to apply deep learning technologies into smart city applications in order to establish a new smart city era. Much of the research effort on smart city has been made, for example, intelligent transportation, smart healthcare, public safety, etc. Meanwhile, we still face a lot of challenges as the deep learning techniques are still premature for smart city. In this thesis, we first provide a review of the latest research on the convergence of deep learning and smart city for data processing. The review is conducted from two perspectives: while the technique-oriented view presents the popular and extended deep learning models, the application-oriented view focuses on the representative application domains in smart cities. We then focus on two areas, which are intelligence transportation and social media analysis, to demonstrate how deep learning could be used in real-world applications by addressing some prominent issues, e.g., external knowledge integration, multi-modal knowledge fusion, semi-supervised or unsupervised learning, etc. In intelligent transportation area, an attention-based recurrent neural network is proposed to learn from traffic flow readings and external factors for multi-step prediction. More specifically, the attention mechanism is used to model the dynamic temporal dependencies of traffic flow data and a general fusion component is designed to incorporate the external factors. For the traffic event detection task, a multi-modal Generative Adversarial Network (mmGAN) is designed. The proposed model contains a sensor encoder and a social encoder to learn from both traffic flow sensor data and social media data. Meanwhile, the mmGAN model is extended to a semi-supervised architecture by leveraging generative adversarial training to further learn from unlabelled data. In social media analysis area, three deep neural models are proposed for crisis-related data classification and COVID-19 tweet analysis. We designed an adversarial training method to generate adversarial examples for image and textual social data to improve the robustness of multi-modal learning. As most social media data related to crisis or COVID-19 is not labelled, we then proposed two unsupervised text classification models on the basis of the state-of-the-art BERT model. We used the adversarial domain adaptation technique and the zero-shot learning framework to extract knowledge from a large amount of unlabeled social media data. To demonstrate the effectiveness of our proposed solutions for smart city applications, we have collected a large amount of real-time publicly available traffic sensor data from the California department of transportation and social media data (i.e., traffic, crisis and COVID-19) from Twitter, and built a few datasets for examining prediction or classification performances. The proposed methods successfully addressed the limitations of existing approaches and outperformed the popular baseline methods on these real-world datasets. We hope the work would move the relevant research one step further in creating truly intelligence for smart cities.

Abstract:Solid oxide fuel cell (SOFC) offers clean, renewable power generation with high efficiency, but it is susceptible to chromium poisoning that leads to considerable degradation of electrochemical performance. Volatile chromium species are released from Cr-containing interconnect materials, diffused, and deposited on electrodes in new phases by the interaction with electrode materials. The mechanisms of Cr poisoning are not clear completely yet, it requires studying how to alleviate Cr poisoning in SOFC.  This thesis presents several possible mechanisms and corresponding degradation phenomena on cathodes. While there have been several studies on chromium deposition on conventional La0.6Sr0.4Co0.2Fe0.8O3 (LSCF) cathode, to date little research has focused on understanding the microstructure, especially in porosity, the effect on the chromium deposition process. We analysed the microstructure of the initial ceramic at four porosities by various physisorption methods. We found that macropores dominate at porosity 50% (LSCF-50), while mesopores dominate at porosity 20% (LSCF-20). Porosity also changes the initial surface composition: Co-rich at LSCF-50 and Sr-rich at LSCF-20.  Upon Cr exposure, the phase and chemical state changes were identified by XRD, Raman spectroscopy, ICP- OES, and XPS, with respect to different porosities and ageing times. Among the Cr deposits, it appeared a novel phase, correlated with Cr substitution into LSCF lattice. The Cr deposition had three valence states of Cr, as a result of the atomic interactions and interfusion between Cr source and LSCF.  At the porous ceramic, Sr on the surface is correlated with the formation of SrCrO4, whereas the dense ceramic showed the lower concentration of SrCrO4 and favourably formation of Cr substitution into the LSCF lattice. The Cr adsorption also causes the redistribution of other cations at the surface and in bulk.  At LSCF-50, La, Fe and Co cations preferably dissolved into the bulk with ageing time, meanwhile, CoOx was formed and segregated at specific sites, associated with macropore distribution. The Cr penetration could be detected at depth up to 17 mm by EDX.  Depth profile showed the Cr concentration non-linearly decreased with depth. The Cr adsorption increased the concentration of Fe and Co in the near-surface region; moreover, the Sr enrichment was at the near-surface area and bulk. At LSCF-20, the surface concentration of La, Fe, and Co fluctuated with ageing time. Chromium deposition on LSCF at porosities at 20 and 50 had distinct kinetic mechanisms that may be caused by different gas transport preferences. In terms of porosity, the possible mechanism was proposed in the thesis. The density functional theory plus U (DFT+U) calculation method investigated the electronic structure and stability of LSCF and Cr-substituted perovskites at different spin states. Moreover, theoretical calculation predicted the decomposition products of LSCF and Cr-substituted perovskites and may explain the cation diffusion at the surface. LSCF and Cr doped LSF was suggested to decompose into bi/trinary oxides or/and simper perovskites. The decomposition pathway of Cr doped LSF greatly depends on temperature and environment. Computational approaches suggest the potential oxide electrolyte material LaSiAl5/6Ge1/6O5.083, LaSi5/6AlP1/6O5.083 and LaSi7/6Al5/6O5.083. The simulated interstitial positions suggest the possible pathway and predict the flexibility.  These insights can guide further composition optimization. The relatively high energy above the convex hull for other doping schemes of Ca2Al2SiO7, BaSi2O5 and K2Ba7Si16O40, indicates high phase instability, which suggests those are not good candidates for electrolyte material. 

Abstract:Although Virtual Reality (VR) has been developed for a while, the last decade has seen a surge in its popularity with the advent of commercial VR Head-Mounted Displays (HMDs), making the technology more accessible. One field that significantly benefits from VR is the entertainment industry, for example, games. Games can be challenging to design as they involve several components that are found in other types of applications as well, such as presentation, navigation, interaction with virtual agents, and in-game measurements. Despite recent advances, the optimal configurations for game applications in VR are still widely unexplored. In this thesis, we propose to fill this gap by a series of studies that analyse different components involved in making VR applications more enjoyable. We propose studying three characteristics that are heavily influential in game enjoyment (1) the aesthetical realism and emotions of virtual agents; (2) viewing perspective (First-Person Perspective and Third-Person Perspective), its influence on subjective feelings and how to measure those feelings; and (3) how to reduce or eliminate VR Sickness without affecting the experience (or affecting it positively). Our results showed that Virtual Agents' facial expressions are one of the most important aspects to be considered. On the second topic, we have observed that viewing perspective is influential on VR Sickness; however, other subjective feelings were challenging to measure in this context. On the last topic, we analysed existing tendencies in Simulator Sickness mitigation techniques that do not affect in-game mechanics and present a novel solution that has a good trade-off between mitigating VR Sickness and maintaining or enhancing immersion and performance. Finally, we propose some guidelines based on our results.

Abstract:Increasing evidences suggest that post-transcriptional RNA modifications regulate essential biomolecular functions and are related to the pathogenesis of various diseases. To date, the study of epitranscriptome layer gene regulation is mostly focused on the function of mediator proteins of RNA methylation limited by laborious experimental procedures. However, there is limited investigation of the functional relevance of individual m6A RNA methylation sites. To address this, we annotated human m6A sites in large-scale based on the guilt-by-association principle from complex biological networks. In the first chapter, the network was constructed based on public human MeRIP-Seq datasets profiling the m6A epitranscriptome under independent experimental conditions. By systematically examining the network characteristics obtained from the RNA methylation profiles, a total of 339,158 putative gene ontology functions associated with 1446 human m6A sites were identified. These are biological functions that may be regulated at epitranscriptome layer via reversible m6A RNA methylation. The results were further validated on a soft benchmark by comparing to a random predictor. In the second chapter, another approach was applied to annotate the individual human m6A sites by integrating the methylation profile, gene expression profile and protein-protein interaction network with guilt-by-association principle. The consensus signals on sites were amplified by multiplying the co-methylation network and the methylation-expression network. The PPI network smoothed the correlation for a query site to gene expression for furthering GSEA functional annotation. In the third chapter, we functionally annotated 18,886 m6A sites that are conserved between human and mouse from a larger epitranscriptome datasets using method previously described. Besides, we also completed two side projects related to SARS-CoV-2 viral m6A site prediction and m6A site prediction from Nanopore sequencing technology.

Abstract:A main theme in sociolinguistics is register variation, a situation and use dependent variation of language. Numerous studies have provided evidence of linguistic variation across situations of use in English. However, very little attention has been paid to the language of corporate blogs (CBs), which is often seen as an emerging genre of computer-mediated communication (CMC). Previous studies on blogs and corporate blogs have provided important information about their linguistic features as well as functions; however, our understanding of the linguistic variation in corporate blogs remains limited in particular ways, because many of these previous studies have focused on individual linguistic features, rather than how features interact and what the possible relations between forms (linguistic features) and functions are. Given these limitations, it would be necessary to have a more systematic perspective on linguistic variation in corporate blogs. In order to study register variation in corporate blogs more systematically, a combined framework rooted in Systemic Functional Linguistics (SFL), and register theories (e.g., Biber, 1988, 1995; Halliday & Hasan, 1989) is adopted. This combination is based on some common grounds they share, which concern the functional view of language, co-occurrence patterns of linguistic features, and the importance of large corpora to linguistic research. Guided by this framework, this thesis aims to: 1) investigate the functional linguistic variations in corporate blogs, and identify the text types that are distinguished linguistically, as well as how the CB text types cut across CB industry-categories, and 2) to identify salient linguistic differences across text types in corporate blogs in the configuration of the three components of the context of situation - field, tenor, and mode of discourse. In order to achieve these goals, a 590,520-word corpus consisting of 1,020 textual posts from 41 top-ranked corporate blogs is created and mapped onto the combined framework which consists of Biber’s multi-dimensional (MD) approach and Halliday’s SFL. Accordingly, two sets of empirical analyses are conducted one after another in this research project. At first, by using a corpus-based MD approach which applies multivariate statistical techniques (including factor analysis and cluster analysis) to the investigation of register variation, CB text types are identified; and then, some linguistic features, including the most common verbs and their process types, personal pronouns, modals, lexical density, and grammatical complexity, are selected from language metafunctions of mode, tenor and field within the SFL framework, and their linguistic differences across different text types are analysed. The results of these analyses not only show that the corporate blog is a hybrid genre, representing a combination of various text types, which serve to achieve different communicative purposes and functional goals, but also exhibit a close relationship between certain text types and particular industries, which means the CB texts categorized into a certain text type are mainly from a particular industry. On this basis, the lexical and grammatical features (i.e., the most common verbs, pronouns, modal verbs, lexical density and grammatical complexity) associated with Halliday’s metafunctions are further explored and compared across six text types. It is found that language features which are related to field, tenor and mode in corporate blogs demonstrate a dynamic nature: centring on an interpersonal function, the online blogs in a business setting are basically used for the purposes of sales, customer relationship management and branding. This research project contributes to the existing field of knowledge in the following ways: Firstly, it develops the methodology used in corpus investigation of language variation, and paves the way for further research into corporate blogs and other forms of electronic communication and, more generally, for researchers engaging in corpus-based investigations of other language varieties. Secondly, it adds greatly to a description of corporate blog as a language variety in its own right, which includes different text types identified in CB discourse, and some linguistic features realized in the context of situation. This highlights the fact that corporate blogs cannot be regarded as a simple discourse; rather, they vary according to text types and context of situation.

Abstract:With the continuous progress in science and technology, a myriad of implantable medical devices (IMDs) have been invented aimed at improving public health and wellbeing. One of the main problems with these devices is their limited battery lifetime. This results in otherwise unnecessary surgeries to replace depleted batteries leading to excessive medical expenses. Wireless power transfer (WPT), as a promising technology, could be used to remedy this. Wireless power technologies, both through the transfer of transmitted radio frequency (RF) power or the harvesting of RF energy from the ambient environment and its subsequent conversion to useable electrical energy, are emerging as important features for the future of electronic devices in general and have attracted an upsurge in research interest. Unfortunately, the path to realising this wire free charging dream is paved with many thorns and there still exist critical challenges to be addressed. This thesis aims to deal with some of these challenges, developing an efficient WPT system for IMDs. The work begins with a comprehensive study of currently applied methods of WPT, which broadly fall into two categories: far-field (radiative) WPT and near field (non-radiative) WPT. The review includes a brief history of WPT, comparisons between current methodologies applied and a comprehensive literature review. Magnetic resonance coupling (MRC) WPT is emphasised due to its advantages for the desired application making it the technology of choice for system development. Design of an MRC-WPT system requires an understanding of the performance of the four basic topologies available for the MRC method. Following an investigation of these, it is found that series primary circuits are generally most suitable for WPT and that the choice of a series or parallel secondary circuit is dependent on the relative size of the load impedance. Importantly, design parameters must be optimised to avoid the phenomena of frequency splitting to simultaneously obtain maximum power transfer efficiency (PTE) and load power. The use of printed spiral coils (PSCs) as inductors in the construction of WPT circuits for IMDs, which can save space and be integrated with other circuit boards, is then investigated. The challenges and issues of PSCs present for WPT mainly relate to maintaining an inductive characteristic at frequencies in the Medical Implant Communication Service (MICS) band and to maximising the PTE between primary and secondary circuits. Investigations of PSC design parameters are performed to obtain inductive characteristics at high frequencies and the split-ring loop is proposed to increase the Quality factor relative to that offered by the PSC, which is shown to enhance WPT performance. To simplify the necessary resonating circuit configuration for MRC-WPT, a self-resonating split-ring loop with a series inductor-capacitor characteristic has been developed. A pair of these self-resonators has been adopted into a series primary-series secondary WPT system operating at high frequency. This is different to traditional planar self-resonators, which offer parallel self-resonance characteristics that are less desirable due to their reduced system power insertion as a parallel primary resonator. Finally, a system for implantable devices is developed using the split-ring loop in consideration of the effects of body tissues, whose dielectric characteristics have a significant influence on WPT performance. Due concern is also paid to human safety from radiated RF power. A series resonating split-ring loop for transmitting power is formed at the desired frequency through the addition of a lumped element capacitor. A single loop as a receiving resonator with a low Specific Absorption Rate (SAR), is designed to allow greater transmit power to be used in comparison to previous work, whilst satisfying the relevant standards relating to human safety. A rectifier circuit is also designed to convert the received RF energy into useable electrical energy allowing the realisation of the proposed WPT system. In a nutshell, this thesis places emphasis on solutions to overcome challenges relating to the use of MRC-WPT for IMDs. An efficient near-field WPT system for such devices is successfully demonstrated and should have profound significance in pushing forward the future development of this topic.

Abstract:Recently, organo-lead-halide perovskite solar cells have attracted growing and wide attention due to their remarkable photoelectric properties, low cost and ease of fabrication. However, the development of perovskite solar cells is still limited by several factors, such as strict fabrication conditions, low stability, small active area and poor reproducibility etc. The nature of perovskite film formation is argued as a process of a series of chemical reactions and crystallization processes, where the Pb(II) coordination chemistry involves in. We thus set out to improve the performance of perovskite films from the point of Pb(II) coordination chemistry.   By using the solvent engineering strategy, a series of inverted perovskite solar cells (PSCs) with a device structure as ITO/PEDOT: PSS/CH3NH3PbI3-xClx/PCBM/Al via one-step coating were fabricated had been successfully fabricated using simple one-step method from the solutions of chloric precursors in the mixtures of N, N-dimethylformamide (DMF) and -butyrolactone (GBL) at different ratios. The highest average PCE (power conversion efficiency) of 11.251 % was achieved when the solvent with DMF : GBL = 3.5 : 6.5 (v : v) was used for precursor preparation, while the average PCEs for the devices from precursors with pure GBL and DMF as the solvent were 8.600 % and 8.082 %, respectively. The detailed SEM (scanning electron microscope), XRD (X-ray Diffraction) and UV-Vis (UV-Visible spectroscopy) studies showed that the great increase of the PCE of the PSC was led by the apparent quality improvement of the perovskite film, owing to the fast nucleation and the slow crystal growth introduced by the dual solvent system. Plausible formation mechanisms of perovskite films from different solvents were proposed.   The film formation processes from different precursors were also studied, and several intermediates in the perovskite film formation processes were isolated and structurally characterized. The single crystals were successfully grew and the crystal structures of MAPbI3·DMF, MAPbI2Cl·DMF and MAPb1.5I3Br·DMF were solved. The crystal structures of MAPbI2Cl·DMF and MAPb1.5I3Br·DMF were identified for the first time. Meanwhile, the recrystallization process of MAPbI2Cl·DMF were founded that happened before spin-coating or at the early stage of annealing was the key to produce the perovskite film with high crystallinity and high orientation from chloride precursors. Based on the structures and chemical properties of the intermediates, the version of chemical reactions and mechanisms for perovskite film formation with different precursors were proposed.   In addition, several groups of PSCs from lead acetate trihydrate-based precursors were constructed by varying hydrate number, finely tuning spin-coating method, and applying DMSO as additive. It was found that the H2O molecules in precursors can greatly improve the film coverage, and the pre-heating method can avoid the low crystallinity while ensuring the high coverage of perovskite thin films. In addition, the adding of DMSO as additive influenced the formation kinetics of perovskite films and improved the reproducibility of devices. As a result, PSCs with PCE of 15.714 % had been achieved.

Abstract:Supply chains are operating in increasingly volatile business environments. Supply chain resilience development has become the core task of supply chain risk management for companies to maintain effectiveness and efficiency. To achieve this, research has predominantly focused on looking for approaches to improve companies’ ability to resist, respond to, and recover from influences of disruptive events. Fundamentally, developing resilience for risk management considers three phases, including pre-, during, and post-disruption phases. Specifically, supply chain resilience research primarily investigates supply chain readiness, responsiveness, and recovery. Among the main streams of supply chain resilience research, studies predominantly focus on the conceptual development of various capabilities for each phase. Business practices only serve to elucidate the definition of supply chain resilience and capabilities. Thus, the extant research area lacks sufficient empirical understanding of how resilience is achieved in a volatile environment in industrial sectors. To address the gaps in the literature, three papers have been developed, including one literature review paper (conceptual paper) and two empirical papers. Companies from the manufacturing industry, especially the machinery sector, were selected for investigation in this research. This study investigates different aspects of supply chain resilience for efficient risk management in uncertain and volatile environments, such as international sourcing strategies to respond to a global pandemic and the role of government interventions in achieving collaborative relationships during a pandemic. Therefore, this research makes a significant theoretical contribution to the supply chain resilience and risk management literature, providing insightful, practical implications for supply chains operating in the turbulent business environment.

Abstract:The thesis presents multimodal conceptual frameworks and their applications in improving the robustness and the performance of big data analytics through cross-modal interaction or integration. A joint interpretation of several knowledge renderings such as stream, batch, linguistics, visuals and metadata creates a unified view that can provide a more accurate and holistic approach to data analytics compared to a single standalone knowledge base. Novel approaches in the thesis involve integrating multimodal framework with state-of-the-art computational models for big data, cloud computing, natural language processing, image processing, video processing, and contextual metadata. The integration of these disparate fields has the potential to improve computational tools and techniques dramatically. Thus, the contributions place multimodality at the forefront of big data analytics; the research aims at mapping and under- standing multimodal correspondence between different modalities. The primary contribution of the thesis is the Multimodal Analytics Framework (MAF), a collaborative ensemble framework for stream and batch processing along with cues from multiple input modalities like language, visuals and metadata to combine benefits from both low-latency and high-throughput. The framework is a five-step process: Data ingestion. As a first step towards Big Data analytics, a high velocity, fault-tolerant streaming data acquisition pipeline is proposed through a distributed big data setup, followed by mining and searching patterns in it while data is still in transit. The data ingestion methods are demonstrated using Hadoop ecosystem tools like Kafka and Flume as sample implementations. Decision making on the ingested data to use the best-fit tools and methods. In Big Data Analytics, the primary challenges often remain in processing heterogeneous data pools with a one-method-fits all approach. The research introduces a decision-making system to select the best-fit solutions for the incoming data stream. This is the second step towards building a data processing pipeline presented in the thesis. The decision-making system introduces a Fuzzy Graph-based method to provide real-time and offline decision-making. Lifelong incremental machine learning. In the third step, the thesis describes a Lifelong Learning model at the processing layer of the analytical pipeline, following the data acquisition and decision making at step two for downstream processing. Lifelong learning iteratively increments the training model using a proposed Multi-agent Lambda Architecture (MALA), a collaborative ensemble architecture between the stream and batch data. As part of the proposed MAF, MALA is one of the primary contributions of the research.The work introduces a general-purpose and comprehensive approach in hybrid learning of batch and stream processing to achieve lifelong learning objectives. Improving machine learning results through ensemble learning. As an extension of the Lifelong Learning model, the thesis proposes a boosting based Ensemble method as the fourth step of the framework, improving lifelong learning results by reducing the learning error in each iteration of a streaming window. The strategy is to incrementally boost the learning accuracy on each iterating mini-batch, enabling the model to accumulate knowledge faster. The base learners adapt more quickly in smaller intervals of a sliding window, improving the machine learning accuracy rate by countering the concept drift. Cross-modal integration between text, image, video and metadata for more comprehensive data coverage than a text-only dataset. The final contribution of this thesis is a new multimodal method where three different modalities: text, visuals (image and video) and metadata, are intertwined along with real-time and batch data for more comprehensive input data coverage than text-only data. The model is validated through a detailed case study on the contemporary and relevant topic of the COVID-19 pandemic. While the remainder of the thesis deals with text-only input, the COVID-19 dataset analyzes both textual and visual information in integration.  

Abstract:Increasing evidence has suggested that RNA modifications regulate many important biological processes. To date, more than 170 types of post-transcriptional RNA modifications have been discovered. With recent advances in sequencing techniques, tens of thousands of modification sites are identified in a typical high-throughput experiment, posing a key challenge to distinguish the functional modified sites from the remaining ‘passenger’ (or ‘silent’) sites. To ensure that the massive epitranscriptome datasets are properly taken advantage of, annotated, and shared, bioinformatics solutions are developed with various focuses. In this thesis, we first described a comparative conservation analysis of the human and mouse m6A epitranscriptome at single-site resolution. A novel scoring framework, ConsRM, was devised to quantitatively measure the degree of conservation of individual m6A sites. ConsRM integrates multiple information sources and a positive-unlabeled learning framework, which integrated genomic and sequence features to trace subtle hints of epitranscriptome layer conservation. With a series of validation experiments in mouse, fly and zebrafish, we showed that ConsRM outperformed well-adopted conservation scores (phastCons and phyloP) in distinguishing the conserved and non-conserved m6A sites. Additionally, the m6A sites with a higher ConsRM score are more likely to be functionally important. To further unveil the functional epitranscriptome, we investigated the potential influence of genetic factors on epitranscriptome disturbance. Recent studies have found close associations between RNA modifications and multiple pathophysiological disorders, the precise identification and large-scale prediction of disease-related modification sites can truly contribute to understanding potential disease mechanisms. Consequently, we developed a computational pipeline to systemically identify RNA modification-associated variants and their affected modification regions, with emphasis on their disease- and trait-associations. Furthermore, we described the next research considering the dynamics of RNA methylome across different tissues by elucidating the tissue-specific impact of the somatic variant on m6A methylation. The TCGA cancer mutations (derived from 27 cancer types) that may lead to the gain or loss of m6A sites in corresponding cancer-originating tissues were systemically evaluated and collected. Token together, the proposed bioinformatics pipelines and databases should serve as useful resources for functional discrimination and annotation of the massive epitranscriptome data, with implications for the potential disease mechanisms functioning through epitranscriptome layer.

Abstract:The overall objective of this work is to investigate the effect of industrial waste and low-cost material as catalysts in fast pyrolysis products upgrading and to obtain valuable chemicals. Red mud, a by-product of the Bayer process in the aluminium industry, was catalysed with beechwood for the in-situ upgrading of fast pyrolysis vapour products. It was revealed that the catalysis of beechwood with thermally pre-treated red mud enhanced the vapour upgrading effect. Individual oxides (α-Al2O3, Fe2O3, SiO2, and TiO2), the main constituents of red mud were also tested for the identification of their individual impact on the upgrading process. A biomass/catalyst weight ratio (wt. ratio) of 1:4, on the basis of relative peak area, showed the strongest effect on the product distribution. Red mud was found to reduce phenolic compounds and promote the formation of cellulose- and hemicellulose-derived furfurals and hemicellulose-derived acetic acid, which can be used for the production of a broad range of chemicals. α-Al2O3 and Fe2O3 reduced the relative yield of phenols as well, whereas the formation of furfurals was promoted by Fe2O3 and TiO2. SiO2 showed a negligible effect on fast pyrolysis vapours. The impact of catalysts on the product distribution was discussed for phenols, furfurals, and acids, for which the strongest effects were observed. This work also investigated the activity of CaO as a catalyst in the aliphatic and cyclic ketonisation reaction and depletion of phenolic compounds in the catalytic fast pyrolysis of OW. Three basic aspects were investigated: The heterogeneous character of CaO in different wt. ratios for catalytic fast pyrolysis of OW, the stability of the catalyst by re-utilisation in successive runs, and the role of H2O and CO2 in the deterioration of the catalytic performance by contact with atmospheric air. CaO catalyst promoted the selectivity for ketonisation reactions with acetone and cyclic ketones formation, whereas most of phenolic compounds were declined. The characterisation by X-ray diffraction (XRD) and Fourier transform-infrared (FT-IR) spectroscopies led to the conclusion that CaO chemisorbs significant amount of H2O and CO2 by contact with room air. It was demonstrated that CO2 was the main deactivating agent, whereas the negative effect of water was less important. The catalyst reused several runs without significant deactivation. The activation by outgassing at temperatures 950 oC was required to revert the CO2 poisoning. In order to investigate the impact of crystal structures in fast pyrolysis products upgrading, five single-phase compounds (CaTiO3, CaSiO3, Ca2Fe2O5, Ca2FeAlO5 and CaAl2O4) were synthesised and employed for catalytic upgrading of biomass fast pyrolysis vapours. All compounds did not show strong catalytic activity on the transformation of undesirable compounds into valuable compounds. However, their impact were seen in decreasing the overall yield of pyrolysis products. Finally, two types of composites (CaTiO3/CaO and Ca2Fe2O5/CaO) in different mol % were synthesised to check synergy and to prevent sintering over multiple carbonations and decarbonisation cycles on CaO catalyst and results were compared with the catalytic capability of pure CaO. It was found that synergy of CaTiO3 with CaO did not impact the catalytic performance of CaO. Besides, CaTiO3/CaO composites were found to further asssist the CaO catalytic activity in the selectivity of ketonisation reactions for acetone and cyclic ketones formation. On the contrary, the selectivity for ketonisation reactions for acetone formation decreased with incomplete conversion of acetic acid in Ca2Fe2O5/CaO composites. Furfural transformation and phenols depletion were also impacted over the presence of Ca2Fe2O5 with CaO.

Abstract:This work presents the study of a three-dimensional (3D) simulation of the concrete behaviour in a uni-axial compressive test and flexural test using discrete element modelling (DEM). The proposed numerical models are namely, unreinforced cylindrical concrete under a uni-axial compressive test, unreinforced concrete beam under three-point flexural test and lastly, steel reinforced concrete beam under four-point flexural test. Those models were built up with fish programming language and python programming language (see Appendix A1 for the code created) and run into a computer program namely Particle flow code (PFC 3D). The main aim of this paper is to validate those numerical models developed and to study the cracking initiation and failure process in order to understand the fracture behaviour of concrete. The particles were distributed using an algorithm that is based on the sieve test analysis. The parameters were set up in order to validate the numerical model with the experimental result. It was observed that all the three models developed show a strong correlation with the laboratory experiment in term of stress-strain response, load-displacement response, crack pattern and macroscopic cracks development. Once, the bond between the spheres is broken, it leads to the formation of microscopic cracks which is not visible in laboratory experiment. DEM can help to identify which part is more prone to the evolution of microscopic cracks to macroscopic cracks under the discrete fracture network. In addition to, the rosette plot allows identifying the orientation that leads to a significant amount of micro cracks which is essential for designing structures. From the observation recorded in this research, it was observed that DEM is capable to reproduce concrete behaviour both quantitatively and qualitatively. It is also possible to measure the strain energy stored in the linear contact bond and parallel bond. At yield point which corresponds to the maximum amount of microcracks recorded, that strain energy is released in the form of kinetic energy, frictional slip energy, energy of dashpot, local damping. This can be extended further to compute fracture energy in the future work. Hence, it can be concluded DEM can be used to study the heterogeneous nature of concrete and as well as randomness nature of the fracturing of concrete structure.

Abstract:Tropilaelaps mercedesae (small mite) is one of two major honey bee ectoparasitic mite species responsible for the colony losses of Apis mellifera in Asia. Although T. mercedesae mites are still restricted in Asia (except Japan), they may diffuse all over the world due to the ever-increasing global trade of live honey bees (ex. Varroa destructor). Understanding the ecological characteristics of T. mercedesae at molecular level could potentially result in improving the management and control programs. However, molecular and genomic characterization of T. mercedesae remains poorly studied, and even no genes have been deposited in Genbank to date. Therefore, I conducted T. mercedesae genome and transcriptome sequencing. By comparing T. mercedesae genome with other arthropods, I have gained new insights into evolution of Parasitiformes and the evolutionary changes associated with specific habitats and life history of honey bee ectoparasitic mite that could potentially improve the control programs of T. mercedesae. Finally, characterization of T. mercedesae transient receptor potential channel, subfamily A, member 1 (TmTRPA1) would also help us to develop a novel control method for T. mercedesae.

Abstract:In the 2008 financial crisis, many investors endured heavy financial losses caused by sharply increased volatility. The growing demand of hedging volatility via trading volatility derivatives contributes to the rapid development of VIX futures market in recent years. In this high-volatility market, initial margin is the first defense for exchanges to fight against potential default losses. Adequate initial margin can cover most losses caused by unexpected price movements, while the liquidity of market may be squeezed in the presence of high margins. Therefore, a trade-off need be balanced for exchanges in setting appropriate margins. The primary aim of this thesis is to develop an option-based framework of margin setting, standards, and evaluation in VIX futures market. I further conduct a series of comprehensive empirical studies on stochastic behavior and term structure, and margin adequacy of VIX futures. This research consists of three separate studies with distinct focuses in each. In the first study, I investigate jumps in VIX futures market, as jumps usually result in extreme stochastic behavior in VIX futures prices, which is the primary concern of both investors and regulators. I provide the empirical evidence of jumps in this market by employing three non-parametric methods for statistical testing, and further propose a nonparametric framework which is rooted in the generalized moment method (GMM) and the extreme value theory (EVT) in order to investigate the properties of jumps in VIX futures prices. As such, the magnitudes of risk premiums are quantified at price and variation levels by incorporating both VIX options and futures, showing that relatively higher premiums are paid by investors against large upward jumps in VIX futures prices, commentated with smaller premiums gaining from the variations of VIX futures. In the second study, I propose multi-factor models for VIX options and futures to study their performance in pricing VIX options and margin management on VIX futures, equipped with a set of hump-shaped volatility functions. A procedure of the generalized moment method (GMM) is developed to estimate these models by incorporating both the “forward-looking” information from VIX options and the “backward looking” information from VIX futures. The empirical results suggest that the three-factor model outperforms other candidate models in pricing VIX options, well characterize the stochastic behavior and capture the dynamics of the term structure of VIX futures. Moreover, the option-incorporated VaR and ES risk estimates can Granger-cause initial margins imposed by the Chicago Futures Exchange (CFE). In the third study, I develop an option-based framework to study margin setting, standards, and evaluation for VIX futures. More specially, the payoffs involving the trading (long/short) positions in VIX futures are converted into the ones to barrier options with moderate assumptions. By virtue of this idea, the adapted framework transforms the tier initial margin requirements to the prices of corresponding barrier options for long and short positions. I hence propose two standards of margin setting (the zero-NPV standard and zero-default-loss one) to examine adequacy of initial margins of VIX futures. These standards eventually deliver the bounds on initial margins of VIX futures, which are estimated in the risk-neutral measure. The empirical results suggest that the tier margins imposed by the CFE are sufficient to reject positive net present value (NPV) of futures positions but still not high enough to cover any possible default losses caused by the fluctuations in VIX futures prices. Furthermore, I explore the appropriate margin bounds for VIX futures with various maturities. The proposed lower and upper bounds respectively indicate the minimum and maximum required margin standards for VIX futures. The margin bounds are evaluated from the perspectives of prudentiality and opportunity cost in the physical measure, showing that the more attentions are paid to capital burden on investors in the VIX futures market, despite its volatile nature.

Abstract:In the background of China’s economic development mode being focused the worldwide attention, there is a growing trend to study the risk transmission pattern and the crisis forecasting mechanism for China’s financial markets by domestic and global academics. The study progress, however, is observed to be affected by two gaping research problems: 1) few studies construct comparative contagion models and integrated crisis forecasting systems for China’s financial markets and 2) current econometric models hired to the risk spreading effects detection and the financial crisis forecasts are yet deterministically investigated in terms of the effectiveness on China. To fill the gaps, this research proposes two hybrid contagion models and prototypes the early warning systems with motivations of first analyzing the crisis linkages and transmission channels across domestic markets in hierarchical frameworks, and then predicting the market turbulence by integrating the crisis identifying techniques and time-dependent deep learning neuron networks. To accomplish our aims, the full project is progressed in phases by solving four technical challenges that portray two literature gaps of A) the crisis identification on the basis of price volatility state distinction, B) the decomposition for multivariate correlated patterns to infer the interdependence structure and risk spillover dynamics respectively, C) the real-time warning signals generation in comparison of between traditional and stylized predictive models and D) the contagion information fusion in the EWS frameworks to distinguish the leading indicators from between internal macroeconomic factors and external risk transmitters in statistical validation metrics. The research mainly contributes to the comparative analysis on financial contagion effects detection and market turbulence prediction through the hybrid model innovations for CM and EWS development, and meanwhile brings practical significance to improve the risk management in investing activities and support the crisis prevention in policy-making. In addition, the model experimented results corroborate the China-characterized mode on risk transmissions and crisis warnings that 1) the stocks and real estate markets are verified to play the central role among risk transmitters, while the managed floating foreign exchange rate and the non-fully liberalized bond market are peripheral during the crisis; and 2) the all-round opening up policy increases the possibility of domestic security markets being exposed to external risk factors, especially relating to the cash flows, energy commodities and precious metals.

Abstract:Recently, in computer vision, a branch of machine learning, called deep learning, has attracted high attention due to its superior performance in various computer vision tasks such as image classification, object detection, semantic segmentation, action recognition and image description generation. Deep learning aims at discovering multiple levels of distributed representations, which have been validated to be discriminatively powerful in many tasks. Visual attention is an ability of the vision system to selectively focus on the salient and relevant features in a visual scene. The core objective of visual attention is to achieve the least possible amount of visual information to be processed to solve the complex high-level tasks, e.g., object recognition, which can lead the whole vision process to become effective. The visual attention is not a new topic which has been addressed in the conventional computer vision algorithms for many years. The development and deployment of visual attention in deep learning algorithms are of vital importance since the visual attention mechanism matches well with the human visual system and also shows an improving effect in many real-world applications. This thesis is on the visual attention in deep learning, starting from the recent progress in visual attention mechanism, followed by several contributions on the visual attention mechanism targeting at diverse applications in computer vision, which include the action recognition from still images, action recognition from videos and image description generation. Firstly, the soft attention mechanism, which was initially proposed to combine with Recurrent Neural Networks (RNNs), especially the Long Short-term Memories (LSTMs), ii Visual Attention Mechanism in Deep Learning and Its Applications Shiyang Yan was applied in image description generation. In this thesis, instead, as one contribution to the visual attention mechanism, the soft attention mechanism is proposed to directly plug into the convolutional neural networks for the task of action recognition from still images. Specifically, a multi-branch attention network is proposed to capture the object that the human is intereating with and the scene in which the action is performing. The soft attention mechanism applying in this task plays a significant role in capturing multi-type contextual information during recognition. Also, the proposed model can be applied in two experimental settings: with and without the bounding box of the person. The experimental results show that the proposed networks achieved state-of-the-art performance on several benchmark datasets. For the action recognition from videos, our contribution is twofold: firstly, the hard at- tention mechanism, which selects a single part of features during recognition, is essentially a discrete unit in a neural network. This hard attention mechanism shows superior capacity in discriminating the critical information/features for the task of action recognition from videos, but is often with high variance during training, as it employs the REINFORCE algorithm as its gradient estimator. Hence, this brought another critical research question, i.e., the gradient estimation of the discrete unit in a neural network. In this thesis, a Gumbel-softmax gradient estimator is applied to achieve this goal, with much lower vari- ance and more stable training. Secondly, to learn a hierarchical and multi-scale structure for the multi-layer RNN model, we embed discrete gates to control the information be- tween each layer of the RNNs. To make the model differentiable, instead of using the REINFORCE-like algorithm, we propose to use Gumbel-sigmoid to estimate the gradient of these discrete gates. For the task of image captioning, there are two main contributions in this thesis: pri- marily, the visual attention mechanism can not only be used to reason on the global image features but also plays a vital role in the selection of relevant features from the fine-grained objects appear in the image. To form a more comprehensive image representation, as a iii Visual Attention Mechanism in Deep Learning and Its Applications Shiyang Yan contribution to the encoder network for image captioning, a new hierarchical attention network is proposed to fuse the global image and local object features through the con- struction of a hierarchical attention structure, to better the visual representation for the image captioning. Secondly, to solve an inherent problem called exposure-biased issue of the RNN-based language decoder commonly used in image captioning, instead of only relying on the supervised training scheme, an adversarial training-based policy gradient op- timisation algorithm is proposed to train the networks for image captioning, with improved results on the evaluation metrics. In conclusion, comprehensive research has been carried out for the visual attention mechanism in deep learning and its applications, which include action recognition and im- age description generation. Related research topics have also been discussed, for example, the gradient estimation of the discrete units and the solution to the exposure-biased issue in the RNN-based language decoder. For the action recognition and image captioning, this thesis presents several contributions which proved to be effective in improving existing methods.

Abstract: Many researchers have argued that the welfare effect of fiscal decentralisation (FD) theory is not clearly determined outside of democratic countries. To provide more empirical evidence for the welfare effect of FD, this study focuses on the effect of China’s FD on social welfare in the post-1994 fiscal structure. Because the 1994 tax reform effectively resulted in fiscal recentralisation on the revenue side and fiscal decentralisation on the expenditure side, I construct indicators of revenue decentralisation and expenditure decentralisation and include both sets of indicators to ascertain the marginal effect of each under China’s post-1994 fiscal structure. This study consists of three chapters. In the first chapter, I examine the impact of the post-1994 fiscal structure on provincial environmental spending from 1994 to 2017. The findings show that expenditure decentralisation caused a reduction in provincial governments’ environmental spending. Although revenue decentralisation led to an increase in provincial environmental spending, its effect on the latter was not as significant as the effect of expenditure decentralisation. On balance, it can be inferred that, given China’s official promotion system and other institutional and policy factors, its post-1994 fiscal structure has had a negative effect on provincial environmental spending. In the second chapter, I explore the impact of fiscal structure on the urban-rural income gap in China after 1994, using data from 2007-2018. The findings suggest that the post-1994 fiscal structure significantly reduced the urban-rural income gap. This result is reinforced when the influence of FD on the rural-urban income gap is examined via its impact on public investment in education, healthcare and social security. The third chapter focuses on how China’s post-1994 fiscal structure has affected housing affordability in China, based on panel data from 1999 to 2017. Due to the spectacular rise of housing prices in major cities, housing has become increasingly unaffordable in China. This chapter examines the role of the fiscal structure, by which most city governments rely heavily on revenues from land-lease to help finance their many fiscal responsibilities as well as expenditure on pro-growth projects important to city officials’ promotion under China’s GDP-focused promotion system. The findings show that China’s post-1994 fiscal structure has impeded the effectiveness of its affordable housing policies. This is because the fiscal structure, together with China’s promotion system, has been an important factor behind the sharp rise in housing prices, on the one hand, and the lack of investment in affordable housing programmes, on the other. Considering that the current level of welfares can be heavily determined by those past level, the dynamic effects in these processes have been modelled by including the lagged welfare-related dependent variables on the right-hand side of the regression equations. A system generalised method of moments (Sys-GMM) estimator has been used in this research to solve the autocorrelation problem caused by the presence of lagged dependent variables while considering the endogeneity of the FD variable. In summary, the findings suggest that China’s post-1994 fiscal structure had a mixed effect on social welfare. It had a negative effect on environmental spending and housing affordability, but a positive effect on reducing urban-rural income inequality. The conclusions from this research have implications for the current debate on China’s fiscal framework and the design of future fiscal reforms. The results suggest that Chinese policymakers should consider redistributing fiscal responsibilities to match the revenue and expenditure responsibilities of all levels of government. Furthermore, the results suggest that future fiscal reforms should not only involve fiscal policy. Given that China’s official promotion system is influencing the effectiveness of sub-national officials’ decision-making, policymakers should consider adjusting the assessment criteria of the official promotion system to incentivise local officials to provide higher levels of social welfare.  

Abstract:Tuberculosis infection is one of the leading causes of mortality worldwide and is caused by Mycobacterium tuberculosis (Mtb). With an upsurge of multidrug-resistant tuberculosis, it is a global threat. Therefore, development of new drugs need immediate attention, and this needs identification of potential drug targets. The cell envelope of mycobacteria is one such attractive drug target owing to its role in maintaining the structural integrity and pathogenicity of the bacterium. The LytR-CpsA-Psr (LCP) family of proteins in Mycobacterium spp. have been shown to catalyze the coupling of arabinogalactan and peptidoglycan and possess pyrophosphatase activity. The four LCP protein homologues present in Mycobacterium smegmatis (Msmeg), MSMEG_0107, MSMEG_1824, MSMEG_5775 and MSMEG_6421, have not been extensively investigated with the focus on the existence and interplay of multiple LCP proteins. In this study with this non-pathogenic model organism, all four LCP homologues were shown to possess pyrophosphatase activity, with a significant higher activity displayed by MSMEG_0107 and MSMEG_5775. In order to further study the role of the LCP proteins on the physiology of the bacterium, single and double deletion strains lacking of the three non-essential lcp genes were created along with the respective complemented strains. All the generated mutants showed different phenotypes in the different assays, but usually not very severe. However, the double-deletion lcp mutant, ΔΔ(0107+5775) was the most affected mutant strain and displayed a disrupted cell envelope as evident from deprived growth rate, slower cellular aggregation, diminished biofilm formation on air-liquid interface, altered morphology, as well as an increased susceptibility to surface detergent, lysozyme and a wide range of antibiotics. Thus, the loss of both MSMEG_0107 and MSMEG_5775 exhibited profound effects on the mycobacterial cell envelope, and therefore could be further investigated as a possible combined drug target by extending these studies in Mtb. A novel approach in this study is the detection of exposed mycobacterial Galf moieties of arabinogalactan by EB-A2 monoclonal antibody, in the double lcp deletion mutant ΔΔ(0107+5775). Transcription profiling of all the lcp genes in the wild type strain and the mutants exhibited differential expression of these genes under both standard and stress conditions. A loss of MSMEG_5775 resulted in an upregulation of the other three lcp genes in comparison to the wild type strain under standard conditions. Under both acid and lysozyme stress, the loss of MSMEG_5775 downregulated all other lcp genes while loss of MSMEG_6421 upregulated these genes. Lastly, an in silico approach led to the identification of putative transcriptional factors in mycobacteria and related species which could be further investigated and experimentally confirmed. This study helped to understand the role of the lcp homologues in Msmeg better. From the differential expression studies, role of regulator(s) might be a significant approach to understand this family of proteins much better.

Abstract:With the increased demand for developing energy storage technologies, lithium-ion batteries have been considered as one of the most promising candidates due to its high energy density, excellent cyclic performance, and environmental benignity. Indeed, extensive applications of lithium-ion batteries are witnessed in the market, for example, in portable electronic equipment. However, the commercialized graphite anodes for lithium-ion batteries exhibiting low theoretical specific capacity is far from meeting the tremendous demands created by the fast-growing market. Therefore, enormous efforts have been devoted to developing desirable electrode materials with better recyclability and advanced capacity for next-generation lithium-ion batteries. Although alloy anode materials like silicon have the highest gravimetric and volumetric capacity, its huge volume change and low electron and ions conductivity still hinder the broad application in other fields, such as large-scale energy storage systems. Similar challenges also impede the wide implementation of conversion materials in Li-ion batteries. This work is focused on employing different highly conductive materials to improve the electrical conductivity of the entire electrode. At the same time, the formation of the conductive framework is beneficial to accommodate the substantial volume change of the active materials. In Chapter 3, copper nanowires and multi-wall carbon nanotubes coated on the surface of Cu foils built a porous substrate to support the active materials. Silicon was deposited on the porous substrate by the template of copper nanowires and multi-wall carbon nanotubes. The formation of copper nanowires/silicon and multi-wall carbon nanotubes/silicon core-shell structures intrinsically reduces the volume expansion of active materials. Meanwhile, the poles created by the intertwined copper nanowires and multi-wall carbon nanotubes further accommodate the stress from volume change. In addition, the copper nanowires/silicon and multi-wall carbon nanotubes/silicon core-shell structure provide the highly efficient electrons and Li+ diffusion pathways. As a result, we have demonstrated that multi-wall carbon nanotubes/copper nanowires/silicon delivers a high specific capacity of 1845 mAh g-1 in a half cell at a current density of 3.5 A g-1 after 180 cycles with a capacity retention of 85.1 %. In Chapter 4, a free-standing silicon-based anode was developed by preparing a three-dimensional copper nanowires/silicon nanoparticles@carbon composite using freeze-drying. Silicon nanoparticles were uniformly attached along with the copper nanowires, which was reinforced by the carbon coatings. The three-dimensional conductive structure allows the silicon nanoparticles to distribute evenly as well as enhance the electrical and ionic conductivity of the whole electrode. Similarly, considerable interspace produced by the three-dimensional structure can relieve the stress produced by the vast volume expansion of silicon nanoparticles, which is also restricted by the carbon coating layers during the charge and discharge processes. Moreover, the outer layers strengthen the stability of the three-dimensional framework and the contact between the copper nanowires and silicon nanoparticles. The electrochemical performance of copper nanowires/silicon nanoparticles@carbon composite electrode has been measured, which exhibits excellent cycling performance. In Chapter 5, a new highly conductive material, MXene nanosheets, was introduced to promote the electrochemical performance in lithium-ion batteries. In this chapter, the cobalt oxides were chosen as the active material for its controllable and facile synthesis methods. Meanwhile, cobalt oxides, one of the conversion materials as anodes for lithium-ion batteries, face similar issues with silicon. Therefore, an anode involving cobalt oxides nanoparticles mixed with MXene nanosheets on Ni foams has been developed. Small-size cobalt oxides nanoparticles were uniformly distributed within the MXene nanosheets leading to high lithium ions and electrons transmission efficiency, as well as preventing restacking of MXene nanosheets and colossal volume change of the cobalt oxides nanoparticles. As shown in Chapter 5, cobalt oxides /MXene composite electrode remains a stable capacity of 307 mAh g-1 after 1000 cycles when the current density approaches 5 C, which indicates the enormous potential of cobalt oxides/MXene composite as an anode for the high-performance lithium-ion batteries.

Abstract:This thesis studies the characteristics of Chinese futures markets and the quantitative investment strategies. The main objective of this thesis is to provide a comprehensive analysis on the performance of quantitative investment strategies in the Chinese market. Furthermore, with an econometric analysis, the stylised facts of the Chinese futures markets are documented. Extensive backtesting results on the performance of momentum, reversal and pairs trading type strategies are provided. In the case of pairs trading type strategies, risk and return relationship is characterised by the length of the maximum holding periods, and thus re ected in the maximum drawdown risk. In line with the increasing holding periods, the pro tability of pairs trading increases over longer holding periods. Therefore, the abnormal returns from pairs trading in the Chinese futures market do not necessarily re ect market ine ciency. Momentum and reversal strategies are compared by employing both high- and low-frequency time series with precise estimation of transaction costs. The comparison of momentum and reversal investment strategies at the intra- and inter-day scales displays that the portfolio rebalancing frequency signi cantly impacts the pro tability of such strategies. Complementarily, the excess returns of inter-day momentum trading with the inclusion of precise estimates of transaction costs re ect that quantitative investment strategies consistently produce abnormal pro ts in the Chinese commodity futures markets. However, from a risk-adjusted view, the returns are obtained only by bearing additional drawdown risks. Finally, this thesis suggests that investor should choose quantitative trading strategies according to the investment horizon, tolerance for maximum drawdown and portfolio rebalancing costs.

Abstract:Multi-carrier techniques play an important role in the fifth generation (5G) and beyond 5G (B5G) wireless communication systems, as they can support high data rate communications and exhibit high resilience to frequency selective fading. However, the presence of radio frequency (RF) impairments, such as carrier frequency offset (CFO), in-phase/quadrature-phase (IQ) imbalance, hinder the effectiveness of multi-carrier techniques. Thus, the estimation of RF impairments and channel are very essential. In this thesis, RF impairments(s) and channel(s), and their estimation together are considered for various multi-carrier 5G and B5G systems. This thesis consists of four main contributions as follows.  First, a joint multi-time of arrival (TOA) and multi-CFO estimation scheme is proposed for multi-user orthogonal frequency division multiplexing (OFDM) systems, where TOA is a key component of channel. With a carefully designed pilot, U TOAs and U CFOs of U users are separated jointly, dividing a complex 2U-dimensional estimation problem into 2U low-complexity one-dimensional estimation problems. Two CFO estimation approaches, including a low-complexity closed-form solution and a high-accuracy null-subcarrier assisted approach, are proposed to estimate the integer and fractional parts of each CFO as a whole. Each TOA estimate is robust against CFO by means of the features of the inter-carrier interference (ICI) matrix. Cramer-Rao lower bounds (CRLBs) of multi-TOA and mutli-CFO estimation are derived for multi-user OFDM systems. Extensive simulation results confirm the effectiveness of the proposed scheme.  Second, an iterative semi-blind (ISB) receiver structure is proposed for short-frame full-duplex (FD) OFDM systems with CFO. An equivalent system model with CFO included implicitly is first derived. A subspace-based blind channel estimation is proposed for the initial stage, followed by a single pilot assisted CFO estimation and channel ambiguities elimination. Then, channel and CFO are refined iteratively. The integer and fractional parts of CFO in the full range are extracted as a whole and in closed-form at each iteration. The proposed ISB receiver, with halved training overhead, demonstrates superior performances than the existing methods. CRLBs are derived to verify the effectiveness of the proposed receiver structure. It also demonstrates fast convergence speed.  Third, a robust semi-blind CFO and channel estimation scheme is proposed for generalised frequency division multiplexing (GFDM) systems. Based on an equivalent system model with CFO included implicitly, initial blind channel estimation is performed by subspace. Then, full-range CFO and channel ambiguity are estimated consecutively utilising a small number of nulls and pilots in a single subsymbol, respectively. Both CFO and channel estimates demonstrate high robustness against ICI and inter-symbol interference (ISI) caused by the nonorthogonal filters of GFDM. Simulation results verify that the bit error rate (BER) performance of the proposed scheme approaches the ideal case with perfect CFO and channel estimations.    Last but not least, a semi-blind joint estimation scheme of multiple channels, multiple CFOs and IQ imbalance is proposed for generalised frequency division multiple access (GFDMA) systems, with no constraints on carrier assignment scheme, modulation type, cyclic prefix length and symmetry of IQ imbalance. By means of subspace approach, CFOs and channels of U users are first separated into U groups. For each individual group, the CFO is estimated by minimising the smallest eigenvalue, whose corresponding eigenvector is utilised to determine channel. Then, IQ imbalance parameters and channel ambiguities are estimated jointly by very few pilots. Simulation results show that the proposed scheme significantly outperforms the existing methods, while at much lower training overhead. It also achieves a close performance to the derived CRLB.  To summarise, this thesis focuses on developing the estimation schemes of RF impairments and channels for 5G and B5G systems, by considering both OFDM and GFDM based multi-carrier techniques, half-duplex and full-duplex modes, single-user and multi-user systems. The developed estimation schemes are either pilot-aided with low complexity or semi-blind by subspace with high spectrum efficiency. This research work is an essential reference for academics and professionals involved in this topic. 

Abstract: The development of human activities has intensified and diversified the pressures applied to freshwater ecosystems. Particularly, land use stressors have been very pervasive and widespread. As a result, most freshwater systems are now under the influence of anthropogenic stressors. For instance, agricultural development and urbanisation have elevated the nutrients levels, facilitated the accumulation of chemicals, modified the natural flow velocities and promoted runoffs and sediment loads. Further, stressors often interact with each other, complicating the prediction of their effects on communities and ecosystem functioning; flow velocity and discharge reduction facilitate the accumulation of chemical and fine sediments. In order to evaluate the effect of multiple stressors and inform decision makers, investigations have been conducted worldwide on different trophic levels and ecosystem processes. Most notably, microbes, algae and macroinvertebrates have often been studied in isolation using taxonomic and now molecular methods. However, communities are made of complex population dynamics involving all trophic levels over time, and emergent ecosystem properties such as decomposition or net productivity are the result of multiple interactions between biotic and abiotic parameters. This calls for more holistic approaches encompassing as many facets of biodiversity as possible.   To investigate the effect of multiple land use stressors associated with agriculture and urbanisation, a highly replicated streamside field mesocosm experiment was built and performed in a near-pristine montane environment. The work was conducted in Autumn 2018 in the Jiulongfeng Nature Reserve, Huangshan, Anhui (China) and consisted of 64 experimental units naturally colonised by stream organisms for 3 weeks. I used a 4-factor full-factorial design, manipulating fine sediment deposition, flow velocity and nutrient concentration at two sampling times (2 and 3 weeks of exposure). Linear models were then applied to analyse the temporal response of microbial communities associated with both leaf litter decay and benthic biofilm formation, as well as the benthic macroinvertebrate communities. Additionally, to infer the emergent properties and functional characteristics of the different communities, four commonly used functional indices were investigated: (i) leaf litter decomposition in Chapter 2, (ii) databased predicted functional profile in Chapter 3, (iii) functional traits and (iv) functional diversity in Chapter 4. I then expanded my reflection from the knowledge acquired in the experimental side of my programme and outlined a novel framework to tackle multiple stressors interactions in riverine networks (Chapter 5).    The molecular analysis of microbial communities showed different impacts on species composition of the different stressors between microbes associated with leaf-litter decomposition and with biofilm development. Indeed, whilst nutrient enrichment and flow velocity reduction appeared to be the most pervasive factors affecting microbial decomposers communities on leaf substrates, fine sediment deposition and flow velocity reduction were most important for biofilm communities. Fine sediment deposition and flow velocity reduction were also the dominant factors driving macroinvertebrate community composition. Furthermore, both molecular analyses indicated that microbial clusters could be identified in response to the dominant stressors. In terms of interactions, 2-way interactions involving sediment and flow velocity reduction (sediment × flow velocity reduction) or nutrient enrichment and sediment (nutrient enrichment × sediment) were the most pervasive overall; 3-way interactions involving nutrient enrichment, sediment deposition and flow velocity reduction  (nutrient enrichment × sediment × flow velocity reduction) were also detected. Furthermore, temporal dynamics were also fairly widespread, highlighting the importance of integrating a temporal factor in multiple stressor studies. Finally, in accordance with the existing literature, changes in abiotic factors often led to functional rearrangements of the different communities underlying the environmental filtering and niche selection processes operating in the system.   From integrating the findings of this thesis into the wider subject area, I suggest ecosystem approach to multiple stressor interaction research. Specifically, I propose that future work adopt a spatiotemporal framework better integrating the energy fluxes across trophic levels and the flow of resources and material through riverine networks. Further, combining alpha diversity indices with functional traits aids understanding of the mechanisms that yield emergent ecosystem properties, such as productivity. Together, it is anticipated that spatiotemporal networks and functional measurements will facilitate prediction of the future stability of freshwater systems under stressor accumulation.  

Abstract:Lithium ion batteries (LIBs) have currently dominated the commercial market owing to the environmental benignity, suitable energy density, and long cycle lifetime. The commercial LIBs are commonly using graphite as anode materials, however, it has become clear that the theoretical capacity (~372 mAh g-1) of graphite has nearly reached the bottlenecks with little room for further exploration, and also the energy density and rate performance of existing LIBs are not sufficient for some advanced electronics equipment such as smart watch, and micro implantable biosensor system. With increasing demand and market potential, the worldwide academia researches and industrial community have been focused on investigating anode materials to achieve desirable power density, high rate performance, and long-term stability energy storage system, generating further impetus on flexible electrochemical applications, such as wearable devices, portable electronic devices especially for implant biological equipment. Alternative anode materials such as metal (Si, Ge and Sn) and metal oxide (Co3O4, SnO2 and GeO2) have been considered. Among them, the Si and germanium oxide have the highest theoretical gravimetric capacity in the elementary substance and oxide-based anode material respectively, which have been proposed as the best candidates for rechargeable battery anode. However, some challenges for these anode materials are also obvious due to the low conductivity and large volume expansion (> 300%) during the usage of LIBs. This expansion problem causes the pulverization of active materials and the repeated formation of the solid electrolyte interface (SEI) on that, resulting in the loss of interparticle electrical contact, and consequently deteriorating the battery cycle lifetime and capacity performance. In this work, we firstly demonstrated a facile method to fabricate a flexible alloyed copper/silicon core-shell nanoflowers structure anchored on the three-dimensional graphene foam as a current collector. In electrochemical testing, the resulting copper/silicon core-shell nanoflowered electrode demonstrates a high initial capacity of 1869 mAh g-1 at 1.6 A g-1, with a high retention rate of 66.6 % after 500 cycles. More importantly, at a high current density of 10 A g-1, this anode remains a high capacity retention > 63% (compared with the highest capacity 679 mAh g-1), offering enormous potential for energy storage applications. Secondly, we introduced a facile method to synthesize an amorphous GeOx-coated MXene nanosheet structure as the anode in lithium-ion batteries. For electrochemical performance, this GeOx/MXene nanosheet exhibited a reversible capacity of 950 mA h g-1 at 0.5 A g-1 after 100 cycles. It is indicated that the GeOx/MXene nanosheet structure can significantly improve the stability during the lithiation/delithiation prosses, with the enhanced capacity by the improvement of processes' kinetics. Thirdly, we built up a facile equipment to measure the high frequency capacitance change of silicon composite electrode. As this high frequency situation, the hypothesis circuit of the coin cell could be seemed as a combination of geometrical capacitance and resistance. For the alloy anodes which exhibited huge volume expanse during the lithiation/delithiation processes, the change of geometrical capacitance could be ascribed to the stress evolution and pulverization effect. Thereby the variation trend of the stress and pulverization could be determined by the change geometrical capacitance change.  To conclude, this project mainly focused on the pulverization and stress effect of the anode materials with alloying lithiation type. The strategies of first and second work were using the nanostructure engineering and 2D materials to release the stress and prevent the pulverization in the electrode. The results from these electrodes exhibited a stable electrochemical performance. Meanwhile, the rate performance of these electrodes was also improved by the additive of highly conductivity materials (e.g., copper, graphene, and MXene). To further investigate the consequence of severe volume expansion, we also built a high-frequency capacitance characterization system to perform the in-situ measurement of stress evolution and pulverization in coin cell with composite Si anode. That demonstrated the expected behavior corresponding to the electrode in the different states of charging.

Abstract:Solar photovoltaic (PV) energy is becoming an increasingly vital source in electricity grids for energy harvesting. Inspired by the regulatory incentives and plummeting cost, the integration of utility-scale PV systems into the power grid is boosting. Nonetheless, due to the natures of cloud movements, PV system exhibits rapid power ramp-rates (RR) in the output pro?les, which poses signi?cant challenges for system operators to maintain grid transient stability. In this context, this thesis focuses on the management of cloudinduced solar PV intermittency. Three aspects for coping with solar intermittency are addressed, namely, control, forecasting, and emulation. Firstly, from the control aspect, two predictive PV power RR control (PRRC) strategies are presented. To regulate system RRs, conventional methods are implemented either by active power curtailment (APC) or energy storage control (ESS). However, current APC method cannot deal with the ramp-down ?uctuations, and the integration of an ESS is still costly. On this point, two innovative PRRC strategies are proposed, which are based on a solar nowcasting system. The ?rst strategy does not require any ESS. With the prior knowledge of upcoming RRs, PV generation can be regulated before the actual shading occurs. The second strategy improves the conventional ESS method with minimal support of energy storage. The results show that both of the proposed strategies can e?ectively comply with RR regulations, and outperform the conventional methods. Then, in terms of forecasting, an improved sensor network-based spatio-temporal nowcasting method is developed. The proposed nowcasting method overcomes the shortcomings that typically associated with existing sensor network-based nowcasting methods, such as predictor mis-selection, inconsistent nowcasting, and poor model adaptability. The experimental results reveal that the proposed nowcasting method is more suitable for predicting system RRs. Subsequently, the operability of solar nowcasting for PRRC practice is demonstrated. To that end, temporal issues related to operational solar nowcasting are identi?ed, and their e?ects on nowcasting and PV control performance are evaluated. Lastly, from the emulation aspect, this thesis sets forth a partial shading emulator and a cloud shadow model, which can emulate the module-level responses of utility-scale PV systems under passing clouds. Based on the emulation tools, the characteristics of PV system RRs are comprehensively investigated across various system and cloud shadow attributions. The results indicate that a utility-scale PV system can frequently violate the RR limit imposed by grid operators. Hence, advanced RR control strategies should be essential for system operators to comply with the RR regulations.

Abstract:Wireless body and personal area networks have become commonplace in recent years in industrial, medical, and consumer-based applications, allowing a collection of devices such as medical sensors to be distributed around a person’s body or within their direct vicinity, to communicate with each other or a network controller to provide convenient personal services. Distributed devices are typically compact and can even be located within the human body. This produces several bottlenecks relating to RF ability and power availability which are addressed here. In this thesis, two antennas are developed. The first is designed for implantable and ingestible applications offering robust wideband performance, covering all the useable licenced operating bands, in the complex material characteristic environment of the human body. The radiation characteristics of the proposed antenna outperform other published work with a smaller size, achieved through the novel application of split-ring resonators. The second is an off-body antenna which concurrently provides appropriately polarised bands for indoor and outdoor localisation and data communication. For its minimised size and wide bandwidth, this antenna also outperforms other antennas for WPAN applications published in the literature. Two methods for simultaneous wireless information and power transfer have been proposed in this work, based on novel theoretical ideas and hardware implementations. A symbol splitting system separates the information- and non-information- carrying components of a signal, using each for data reception and energy harvesting, respectively. The second method makes use of the characteristic of the requisite rectifier in the power conversion from RF to DC, recycling the inevitable third harmonic for data reception. The hardware required to achieve both methodologies utilise couplers and each architecture has been proven feasible through simulation and measurement. They provide comparable performance to other published systems, offering a compact, efficient, and convenient route to simultaneous wireless information and power transfer.

Abstract:Discrete element method (DEM) is a useful numerical tool for analysing complex mechanical behaviour of granular materials as it considers the interaction at discrete contact points. In general, most of the DEM software packages use spherical particles by default because of easy contact detection and less computational cost. However, researchers confirmed that particle shape plays a significant role in exploring the mechanical behaviour of granular materials. Due to upgraded computation resources, nowadays it is possible to simulate the mechanical behaviour of granular materials considering true geometric shapes of particles. The key objective of the current research is to investigate the mechanical behaviour of granular materials considering particle shape characteristics. For that purpose, two basic geotechnical laboratory tests, i.e., direct shear test and triaxial test, are considered in this thesis.  The current research uses a commercial DEM code-named Particle Flow Code (PFC) developed by Itasca. An attempt was made to generate realistic particle shapes considering their major plane of orientations using a built-in clump mechanism in PFC. A series of DEM simulations were performed to investigate the sensitivity of the macroscopic specimen response to some specific parameter (e.g., particle numbers, loading rate). Based on the sensitivity analysis, selected microscopic parameters were selected to validate the DEM model with the experimental direct shear test results. To investigate the effects of particle elongations on the mechanical behaviour of granular materials, a series of simulations of direct shear tests and triaxial tests were performed using a range of dimensionless elongation parameters. The evolution of elongated particles was investigated at macro-and micro- scale levels. Moreover, the relationships between elongation parameter and critical state parameters were established.  A series of triaxial test simulations were performed considering two morphological descriptors and their mechanical behaviour was investigated at the macro- and micro-scale levels. In addition, a triaxial test environment was implemented to investigate the mechanical response of granular materials under different loading paths (i.e., axial compression (AC), axial extension (AE), lateral compression (LC) and lateral extension (LE)). The grain-scale interactions in terms of coordination number and deviator fabric were also investigated. Furthermore, the relationships were established among strength, dilatancy and state parameter concerning critical states. 

Abstract:Security is a significant problem for communications in many scenarios in Internet of Things (IoT), such as military applications, electronic payment, wireless reprogramming of smart devices and so on. To protect communications, a secret key shared by the communicating parties is often required. Authenticated key exchange (AKE) is one of the most widely used methods to provide two or more parties communicating over an open network with a shared secret key. It has been studied for many years. A large number of protocols are available by now. The majority of existing AKE protocols require the two communicating parties execute equivalent computational tasks. However, many communications take place between two devices with significantly different computational capabilities, such as a cloud center and a mobile terminal, a gateway and a sensor node, and so on. Most available AKE protocols do not perfectly match these scenarios. To further address the security problem in communications between parties with fairly unbalanced computational capabilities, this thesis studies AKE protocols with unbalanced computational requirements on the communicating parties. We firstly propose a method to unbalance computations in the Elliptic Curve Diffie-Hellman (ECDH) key exchange scheme. The resulting scheme is named as UECDH scheme. The method transfers one scalar multiplication from the computationally limited party to its more powerful communicating partner. It significantly reduces the computational burden on the limited party since scalar multiplication is the most time-consuming operation in the ECDH scheme. When applying the UECDH scheme to design AKE protocols, the biggest challenge is how to achieve authentication. Without authentication, two attacks (the man-in-the-middle attack and the impersonation attack) can be launched to the protocols. To achieve authentication, we introduce different measures that are suitable for a variety of use cases. Based on the authentication measures, we propose four suites of UECDH-based AKE protocols. The security of the protocols is discussed in detail. We also implement prototypes of these protocols and similar protocols in international standards including IEEE 802.15.6, Transport Layer Security (TLS) 1.3 and Bluetooth 5.0. Experiments are carried out to evaluate the performance. The results show that in the same experimental platform, the proposed protocols are more friendly to the party with limited computational capability, and have better performance than similar protocols in these international standards.

Abstract:The thesis contains three papers that focus on both private entrepreneurial firms and firms at the newly public stage. In regard to the privately entrepreneurial stage, Chapter 2 adopts an integrated signalling and screening perspective to investigate how investors perceive various signals sent by different firms across early financing stages. Through the use of multiple case studies of signaller?receiver dyads, Chapter 2 unexpectedly identifies a signal interpretation process model with three steps—extracting the fundamental signal; orchestrating signal compositions; and scrutinising signal consistency—and proposes differences among these three steps between the angel financing stage and the venture capital financing stage. Overall, Chapter 2 provides insights for the entrepreneurial financing literature by identifying a dynamic and temporal effect of different types of signals on high-technology entrepreneurial firms’ equity financing acquisition. Specifically, the findings of Chapter 2 indicate that some signals are persistent while others are temporary across different stages of a venture’s life cycle. Regarding the newly public stage, Chapter 3 examines how outside chief executive officer (CEO) succession affects newly public ventures’ growth. Building upon the evolutionary perspective, the thesis argues that outside CEOs can play a transformational role at the newly public stage because such CEOs are more aware of and motivated to break up organisational inertia for firm growth. The findings based on a sample of Chinese newly public firms between 2009 and 2018 indicate that newly public firms with outside CEO succession have stronger growth, and that this effect is stronger when these outside CEOs possess related experience in managing listed firms. The study further finds that following outside CEO succession, promotion of executives internally and adding of new senior executive roles can help outside CEOs to better play a transformational role, which strengthens firm growth. Based on the sample, Chapter 4 investigates the compromising decision arising from interactions between large shareholders based on the nature of different growth actions. Building on principal–principal agency theory, the thesis suggests that although acquisitive actions can promote growth, the second largest shareholder tends to discourage such growth action choices because of the potentially high agency risks. Instead, the second largest shareholder tends to encourage organic growth action choices even though such actions may produce a lower growth rate. The findings show that the second largest shareholder plays a dual role in monitoring the largest shareholder decision.

Abstract:RNA modification has emerged as an important layer for gene regulation, where biological functions are modulated by reversible post-transcriptional RNA modifications. N6-methyladenosine (m6A) is the most prevalent RNA modification on mRNAs and lncRNAs, and plays a pivotal role during various biological processes and disease pathogenesis. In this thesis, I presented the four bioinformatics approaches/applications to unravel the m6A epitranscriptome. We collected a total of 442,162 reliable m6A sites identified from seven base-resolution technologies and the quantified (rather than binary) epitranscriptome profiles estimated from 1,363 high-throughput sequencing samples to build the ‘m6A -Atlas’ database. As experimental approaches for studying the epitranscriptome are technically challenging and expensive, we used the collected base-resolution data to train a high-accuracy predictor ‘WHISTLE’ for m6A site identification from RNA sequences. Moreover, this prediction method was further extended to infer RNA modification-associated genetic variants to uncover potential epitranscriptome pathogenesis involving eight different types of RNA modification. In the last chapter, we presented a convenient measurement weighting strategy for enhanced detection of RNA co-methylation modules by tolerating the artifacts generated from epitranscriptome sequencing technology.

Abstract:Gonadotropin-releasing hormone (GnRH) is a pivotal regulator of the human reproductive system. Kisspeptin (KP) neurons act as the gate keeper of the GnRH neurons. GnRH and KP are both peptide hormones act on their cognate receptors, which are human GnRH receptor (hGnRHR) and KISS1 receptor (hKISS1R). They both belong to the superfamily of guanine nucleotide binding protein (G protein)-coupled receptors (GPCRs), which can activate G-protein dependent downstream signalling pathways, eventually lead to multiple cellular outcomes. In addition to their important roles in reproduction, they also inhibit proliferation and/or metastasis of cancer cells. Therefore, both of the receptors are important drug targets. NanoBRET-based ligand binding assays were developed to investigate ligand and receptor interactions. Fluorescently labelled peptide analogues, Fluorescent-GnRH and BODIPY630/650-GnRH, were firstly conceptualized, purchased, and evaluated. The Fluorescent-GnRH retained its receptor binding affinity, agonist activity and specificity, similar to that of the endogenous ligand, GnRH I. Thus, it was used to act as an energy acceptor in NanoBRET-based ligand binding assays, and also used in imaging studies of GnRHR. In addition, hGnRHR was tagged with Nluc at its N-terminus (N-Nluc-hGnRHR) to act as the energy donor in NanoBRET-based ligand binding assays. A secretory signal peptide (S) from interleukin-6 (IL-6) was added (S-N-Nluc-hGnRHR), to enhance its membrane expression. Similarly, a NanoBRET-based ligand binding assay with Fluorescent-KP-18 and S-N-Nluc-hKISS1R was also established. Overall,  a NanoBRET-based ligand binding assay format for high-throughput drug screening for hGnRHR has been established; thus, it has a great potential for drug development. Other NanoBRET-based assays were conducted to examine differential G protein coupling profiles of hGnRHR and hKISS1R, when stimulated with different ligands. The NanoBRET-based assay using hGnRHR-rTRHR tail-C-Nluc indicates that hGnRHR couples to Gi1, Gq, and G12, but not Gs, when stimulated with GnRH I and GnRH II. Overall, GnRH II seems to exhibit similar potency as GnRH I in activating Gq but seems to be less potent in activating Gi1 and G12. The reduction in activity is probably mainly caused by Tyr8 substitution in GnRH II. Similarly, after stimulation of KP-10 and KP-14, activated hKISS1R-C-Nluc showed a predominant coupling to Gq using the same assay. Additionally, activated hKISS1R-C-Nluc displayed a weak coupling to Gi1. However, whether this coupling is functional needs to be further investigated. To sum up, NanoBRET-based assays have been applied to determine ligand-induced selective signalling of hGnRHR and hKISS1R, and these findings could be important for the development of selective drugs that only activate desired receptor-mediated signalling pathways, while bypassing the others.

Abstract:This thesis presents research into the turbulent flow characteristic of open channels of complex cross-sections, with explicit attention to the interfacial region between the main channel and floodplain(s). The scope of the research includes two components: a series of detailed experimental investigations; the mathematical model development of coefficient of apparent shear stress for estimating zonal and overall discharge for complex asymmetric compound open channels. The primary goal of the experimental investigation was the procurement of high-quality data covering a well-defined and controlled range of hydrodynamic parameters. In support of the following objective, thirty-three sets of experiments have been undertaken and analysed in asymmetric compound channels to investigate flow behaviour over the floodplain and main channel interface. Laboratory experiments were performed under uniform flow conditions for new configurations with differential floodplain(s) width and multi-stage cases to fill the gap in the datasets of asymmetric compound open channels. Fundamental measurements were taken for estimating depth-averaged velocity, Reynolds shear stress, secondary currents, and apparent shear stress to examine the transverse current interaction of two-stage for new configurations of asymmetric compound channels using down-probe and side-probe acoustic Doppler velocimeter. The objective of the theoretical investigation was to obtain the generalized behaviour of the new configuration for the interaction mechanism. Flow interaction between two sub-sections affects the overall discharge capacity and conveyance distribution in compound open channels. Many investigators attempted to estimate flow interaction regarding apparent shear stress acting on the imaginary plane between the floodplain and main channel. However, previous models are neither generalized for asymmetric channels nor applied to a wide range of data sets, including field data, even though the apparent shear stress for asymmetric channels is higher than symmetric channels for the same flow depth and geometrical congruency. The momentum exchange models used in this thesis were motivated by scaling arguments and allowed a simple analytical solution for the zonal discharge in each section. However, it was found that the apparent shear models perform differently based on different depth ratios. None of the previous models performed well in channels with a low depth ratio. The different models for apparent shear based on width ratio and slope were found to give mixed results, discussed in detail. The resulting new models for the coefficient of the apparent shear stress are proposed to improve the zonal and overall discharge estimation for these new configurations. Models revealed that the coefficient is strongly dependent on the depth ratio for different ratios of bankfull height to floodplain width in these new configurations. The proposed new models can be applied to laboratory and field data without calibration.

Abstract:The modern smart grid requires flexible control ability, high transmission efficiency, and good robustness due to contingencies. Besides, a growing number of power stations and load is Direct Current (DC) power, such as photovoltaic power stations, battery energy storage stations, most consumer electronics like a computer. Thus the DC power transmission systems, such as DC solid-state transformers (SSTs) can be utilized to reduce the volume and losses of the transmission system. Among various DC SSTs structures, the DC SSTs based on dual active bridge (DAB) converter is considered as promising topology due to its symmetrical structures, bidirectional power flow capacity, wide soft switching region and flexible control ability. As a key component of DC-SSTs, the operation of the DAB converter will determine the overall performance of the whole system. Thus the improvement of DAB is essential to DC-SSTs and modern smart grid applications. In this thesis, steady-state and dynamic state operation, as well as soft switching behavior of DAB converter, have been studied. For improving the steady-state performance of the DAB converter, multiple optimizations are proposed to reduce the backflow current or reactive power and extend the soft switching region for improving the transmission efficiency. Besides, the frequency domain model is introduced to further reduce the complexity of the optimization model. The effectiveness of those optimization schemes has been verified by experimental results. Compared with traditional phase-shift control, these proposed optimization methods can significantly increase the transmission efficiency. Furthermore, the multiple natural switching surfaces boundary control is proposed to enhance the dynamic performance of the DAB converter, especially for start-up and voltage variation conditions. It can achieve a fast-dynamic response and eliminating DC bias current. Both simulation and experimental results have been presented to prove the superiority of the proposed method. Compared with traditional closed-loop control based on the PI controller, the proposed boundary control can dramatically accelerate the dynamic response. Moreover, the resonant transition for different switching conditions during the dead-time period has been investigated. Then, the phase correction method and variable dead-time are proposed to compensate the phase difference between the gate signal and actual waveform and power losses during dead-time. The effectiveness of those methods is validated by comparing the proposed method with a fixed dead-time method through the experimental result. It suggests that the proposed dead-time compensation and various dead-time method can correct the phase delay and improve transmission efficiency. 

Abstract:The primary objective of this research is to investigate various optimization problems connected with partial differential equations (PDE). In chapter 2, we utilize the tool of tangent cones from convex analysis to prove the existence and uniqueness of a minimization problem. Since the admissible set considered in chapter 2 is a suitable convex set in $L^infty(D)$, we can make use of tangent cones to derive the optimality condition for the problem. However, if we let the admissible set to be a rearrangement class generated by a general function (not a characteristic function), the method of tangent cones may not be applied. The central part of this research is Chapter 3, and it is conducted based on the foundation work mainly clarified by Geoffrey R. Burton with his collaborators near 90s, see [7, 8, 9, 10]. Usually, we consider a rearrangement class (a set comprising all rearrangements of a prescribed function) and then optimize some energy functional related to partial differential equations on this class or part of it. So, we call it rearrangement optimization problem (ROP). In recent years this area of research has become increasingly popular amongst mathematicians for several reasons. One reason is that many physical phenomena can be naturally formulated as ROPs. Another reason is that ROPs have natural links with other branches of mathematics such as geometry, free boundary problems, convex analysis, differential equations, and more. Lastly, such optimization problems also offer very challenging questions that are fascinating for researchers, see for example [2]. More specifically, Chapter 2 and Chapter 3 are prepared based on four papers [24, 40, 41, 42], mainly in collaboration with Behrouz Emamizadeh. Chapter 4 is inspired by [5]. In [5], the existence and uniqueness of solutions of various PDEs involving Radon measures are presented. In order to establish a connection between rearrangements and PDEs involving Radon measures, the author try to investigate a way to extend the notion of rearrangement of functions to rearrangement of Radon measures in Chapter 4.

Abstract:Clustering is one of the most widely used pattern recognition technologies for data analytics. Density-based clustering is a category of clustering methods which can find arbitrary shaped clusters. A well-known density-based clustering algorithm is Density- Based Spatial Clustering of Applications with Noise (DBSCAN). DBSCAN has three drawbacks: firstly, the parameters for DBSCAN are hard to set; secondly, the number of clusters cannot be controlled by the users; and thirdly, DBSCAN cannot directly be used as a classifier. With addressing the drawbacks of DBSCAN, a novel framework, Evolutionary and Swarm Algorithm optimised Density-based Clustering and Classification (ESA-DCC), is proposed. Evolutionary and Swarm Algorithm (ESA), has been applied in various different research fields regarding optimisation problems, including data analytics. Numerous categories of ESAs have been proposed, such as, Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), Differential Evaluation (DE) and Artificial Bee Colony (ABC). In this thesis, ESA is used to search the best parameters of density-based clustering and classification in the ESA-DCC framework to address the first drawback of DBSCAN. As method to offset the second drawback, four types of fitness functions are defined to enable users to set the number of clusters as input. A supervised fitness function is defined to use the ESA-DCC as a classifier to address the third drawback. Four ESA- DCC methods, GA-DCC, PSO-DCC, DE-DCC and ABC-DCC, are developed. The performance of the ESA-DCC methods is compared with K-means and DBSCAN using ten datasets. The experimental results indicate that the proposed ESA-DCC methods can find the optimised parameters in both supervised and unsupervised contexts. The proposed methods are applied in a product recommender system and image segmentation cases.

Abstract:As a lesson from the severe losses of $827 million by UK merchant bank Barings in 1995 , qualitative and quantitative modelling in operational risk started to attract more research attention in banking and insurance system. Such an operational risk would lead to serious consequence, even bankruptcy. Hence, it is necessary for financial institutions to model and avoid the operational risk. With this in mind, this thesis investigates important topics in quantitatively estimating of operational risk. We use heavy-tailed distribution functions to model the loss severities, and use several tools, such as copulas and multivariate regular variation, to model the dependence structures. Firstly, we consider both univariate and multivariate operational risk models, in which the loss severities are modelled by a series of weakly tail dependent and heavy-tailed positive random variables, and the loss frequency processes are general counting processes. In such models, we study the limit behaviors for the Value-at-Risk and Conditional Tail Expectation of aggregate operational risks. The methodology is based on capital approximation within the framework of the Basel II/III regulatory capital accords, which is the so-called Loss Distribution Approach. We also conduct simulation studies to check the accuracy of our obtained approximations and the (in)sensitivity due to different dependence structures or the heavy-tailedness of the severities. Next, in order to include both the weakly and strongly tail dependent case, we first consider a bivariate operational risk cell model, in which the loss severities are modelled by heavy-tailed and weakly (or strongly) dependent nonnegative random variables, and the frequency processes are described by two arbitrarily dependent general counting processes. In such a model, we then establish asymptotic formulas for the VaR and CTE of the total aggregate loss. Simulation studies are also conducted to check the accuracy of the obtained theoretical results via the Monte Carlo method. Later, we further extend the study of Gong and Yang (2021) to derive asymptotic approximations for VaR and CTE in a multivaraite operational risk cell model. Consider a multivariate operational risk cell model, in which the loss severities are modelled by heavytailed and weakly (or strongly) dependent nonnegative random variables, and the frequency processes are described by several arbitrarily dependent general counting processes. In this model, we establish asymptotic formulas for the VaR and CTE of the total aggregate loss. Numerical studies are conducted to examine the performance and to test the sensitivity of these asymptotic formulas. Lastly, to study the randomly weighted sums with infinitely many dependent terms, we consider the randomly weighted sums generated by a series of dependent subexponential primary random variables and a few arbitrarily dependent random weights. Then, we establish a Kesten-type upper bound for their tail probabilities in presence of subexponential primary random variables and under a certain dependence among them. As applications, then we derive asymptotic formulas for the tail probability and the VaR of total aggregate loss in a multivariate operational risk cell model.

Abstract:Vegetation plays a fundamental role in changing the flow characteristics of natural channels such as rivers. There are some studies which evaluated the impact of vegetation in open channel flows, but their modelling are not close to nature situation and usually only cover on single type of vegetation. Vegetation in natural channels is usually denser in lower layer and sparser in upper layer. For example, in riparian environments or floodplains, shorter vegetation (grasses or shrubs) is submerged, but the taller vegetation (e.g. trees) remains emergent. However, the impact of such mixed layer vegetation on the flow structure is not understood well, which is significant to reduce risk of flood and water environment. In this thesis, a series of laboratory experiments have been undertaken to study the impact of double layer vegetation in both emergent and submerged conditions. The vegetation was simulated by an array of PVC dowels with two different heights of 10 cm and 20 cm. The experiments were carried out in a rectangular hydraulic flume in Nanjing Hydraulic Research Institute and Xi’an Jiaotong-Liverpool University, respectively. Dowels were arranged in 5 different formations with each having 4 different flow depths to capture inflection of velocity over the mixing region between short and tall dowels. Velocity measurements were taken by using 3-D Acoustic Doppler Velocimeter (ADV) and Propeller Velocimeter in order to obtain key parameters such as turbulence intensity, Reynolds stress and turbulence kinetic energy. Ansys Fluent was used to simulate the same sets of vegetation configurations using K-? model with mesh sensitivity analysis to capture the inflection over the short vegetation region. The numerical study was explored for the double layer vegetation, and showed that the modelling results have good agreement with the experimental data for different vegetation configurations. New analytical models based on Reynolds-averaged closure principles have been also proposed to describe the vertical distribution of mean streamwise velocity in an open channel flow with double-layered vegetation. The proposed models were evaluated with extensive experimental data from our experiments and other published experiments available in the literature. The Root Mean Square Error (RMSE) of the velocity comparisons is found to be less than 0.0342 m/s, which is acceptable. In another series of experiments, vegetation dowels have been located only in one side of the channel while the other side is empty to simulate the partial vegetation and it has been noticed that a strong shear layer exists between non-vegetation and vegetation zones, indicating the reduction effect of vegetation on the velocity of flow. Furthermore, modifications are recommended to properly calculate the hydraulic radius and Manning's coefficient for the flow with double-layer vegetation. Finally it has been concluded that the flow in double layer vegetation is more complicated compare to flow through single layer vegetation and therefore the calibration of proposed models with three layers vegetation has been recommended.

Abstract:With the continuous physical size down scaling of Metal Oxide Semiconductor Field Effect Transistors (MOSFETs), silicon (Si) based MOS devices have reached their limits. To further decrease the minimum feature size of devices, high-k materials (with dielectric constants larger than that of silicon dioxide (SiO2), 3.9) have been employed to replace the SiO2 gate dielectric. However, there are higher densities of traps in high-k dielectrics than in the near trap free SiO2. Therefore, it is important to comprehensively investigate the defects and electron trapping/de-trapping properties of the oxides. Also, germanium (Ge) has emerged as a promising channel material to be used in high-speed metal-oxide-semiconductor (MOS) devices, mainly due to its high carrier mobility compared with that of silicon. However, due to the poor interface quality between the Ge substrate and gate dielectrics, it is difficult to fabricate high-performance germanium based devices. Therefore, an effective passivation method for the germanium substrate is a critical issue to be addressed to allow the fabrication of high quality Ge MOSFETs. To solve the above problems, the study of high-k materials and the passivation of germanium substrates was carried out in this research. In the first part of this work, lanthanide zirconium oxides (LaZrOx) were deposited on Si substrates using atomic layer deposition (ALD). The pulse capacitance-voltage (CV) technique, which can allow the completion of the CV sweep in several hundreds of microseconds, was employed to investigate oxide traps in the LaZrOx. The results indicate that: (1) more ii traps are observed in the LaZrOx when compared with measurements using the conventional CV characterization method; (2) the time-dependent trapping/de-trapping is influenced by edge times, pulse widths and peak to peak voltages (VPP) of the gate voltage pulses applied. Also, an anomalous behavior in the pulse CV curves, in which the relative positions of the forward and reverse CV traces are opposite to those obtained from the conventional measurements, was observed. A model relating to interface dipoles formed at the high-k/SiOx is proposed to explain this behavior. Formation of interface dipoles is caused by the oxygen atom density difference between the high-k materials and native oxides. In addition, a hump appears in the forward pulse CV traces. This is explained by the current displacement due to the pn junction formed between the substrate and inversion layer during the pulse CV measurement. Secondly, hafnium titanate oxides (TixHf1-xO2) with different concentrations of titanium oxide were deposited on p-type germanium substrates by ALD. X-ray Photoelectron Spectroscopy (XPS) was used to analyze the interface quality and chemical structure. The current-voltage (IV) and capacitance-voltage (CV) characteristics were measured using an Aglient B1500A semiconductor analyzer. The results indicate that GeOx and germinate are formed at the high-k/Ge interface and the interface quality deteriorates severely. Also, an increased leakage current is obtained when the HfO2 content in the TixHf1-xO2 is increased. A relatively large leakage current density (~10-3 A/cm2) is partially attributed to the deterioration of the interface between Ge and TixHf1-xO2 caused by the oxidation source from HfO2. The small band gap of iii the TiO2 also contributes to the observed leakage current. The CV characteristics show almost no hysteresis between the forward and reverse CV traces, which indicates low trap density in the oxide. Since deterioration of the interface quality was observed, an in-situ ZnO interfacial layer was deposited in the ALD system to passivate the germanium substrate. However, a larger distortion of the as-deposited sample was observed. Although the post deposition annealing (PDA) has a positive effect on the CV curves, there is an increase in frequency dispersion and the leakage current after PDA. Therefore, the ZnO interfacial layer is not an effective passivation layer for the germanium substrate. In addition, GeO is formed due to the reaction and GeO desorption from the gate oxide/Ge interface occurs, which also leads to the deterioration of the device performance. In the final part of this work, to circumvent the problems explored above, 0.1 mol/L propanethiol solution in 2-propanol, 0.1 mol/L octanethiol solution in 2-propanol, and 20% (NH4)2S solution in DI water were used to passivate the n-type germanium substrates before HfO2 dielectric thin films were deposited by ALD. The results show that an increase in the dielectric constant and a reduction in leakage current are obtained for the samples with chemical treatments. The sample passivated by octanethiol solution has the largest dielectric constant. The lowest leakage current density is observed for the sample passivated by (NH4)2S solution followed by the one passivated by octanethiol solution. In addition, effects of a TiN cap layer on the formation and suppression of GeO were investigated. It was found that the formation of GeO and iv desorption of the GeO form gate oxides/Ge interface are suppressed by the cap layer. As a result, an increase in dielectric constant from 8.2 to 13.5 and a lower leakage current density for a negatively applied voltage are obtained. Therefore, the passivation of the substrates by octanethiol or (NH4)2S solutions followed by the TiN cap layer is a useful technique for Ge based devices.

Abstract:This thesis aims to better understand the processes affecting the motivational dynamics of English for Academic Purposes (EAP) learners at a transnational education (TNE) university that uses English as its medium of instruction (EMI). It joins the ongoing discussion of how to leverage Complex Dynamic Systems Theory (CDST) to understand second language (L2) motivation and takes a special interest in understanding what demotivates students to study EAP. It employed a mixed methodology and two-stage research design to explore how EAP learners’ motivation changed over the course of a semester in their first year, as well as what the salient demotivating and motivating factors were for these students. First, motivation journals, motivation questionnaires, semi-structured interviews, and focus group discussions were leveraged to investigate how and why the motivation levels of 60 first year EAP students changed over a period of 10 weeks. Salient demotivating factors identified from the data were then further explored by means of a demotivation questionnaire that was administered to the larger student population (n=1517) in order to understand how frequently these factors were found to be a source of demotivation. Learners’ motivational disposition was found to be complex and multifaceted, changing frequently between motivated and demotivated states. Motivation constructs (e.g. L2 self guides, instrumentality, etc.) frequently used in previous L2 motivation studies did not sufficiently account for the changes in students’ motivational disposition from day to day. Instead, it was found that motivational disposition, or students’ willingness to expend effort to learn at any given moment, emerges from the complex and non-linear interaction of a multitude of factors internal and external to the language learner and language classroom. These factors exerted influences of different strengths on motivational disposition according to changes in time and context. Sources of demotivation were frequently associated with factors outside of the EAP classroom and sources of motivation were frequently associated with factors inside the EAP classroom. The study is significant for both theory and research methodology relating to L2 motivation. First, while CDST has been used as a metaphor for understanding dynamics of motivation, the current study provides evidence that characteristics of CDSs can be grounded in actual data (e.g. the emergent nature of motivation, sensitivity to initial conditions, etc.).  Second, based on these findings this thesis presents a new CDST informed model of language learning motivation. Third, it suggests it is necessary to move away from a binary way of thinking about motivational factors that categorizes them into a dichotomy of motivating/demotivating factors; a more complex and fluid understanding of motivational factors is needed. Lastly, it highlights the need for frequent sampling that ensures minimal time has passed between when students recollect motivating/demotivating experiences and the actual time those experiences occurred.

Abstract:Multiview videos, which are recorded from different viewpoints by multiple synchronized cameras, provide an immersive experience of 3D scene perception and more realistic 3D viewing experience. However, this imposes an enormous load on the acquisition, storage, compression, and transmission of multiview video data. Consequentially, new and advanced 3D video technologies for efficient representation and transmission of multiview data are important aspects for the success of multiview applications.  Various methods aiming at improving multiview video coding efficiency have been developed in this thesis, where convolutional neural networks are used as a core engine in these methods. The thesis includes two novel methods for accurate disparity estimation from stereo images. It proposes the use of convolutional neural networks with multi-scale correlation for disparity estimation. This method exploits the dependency between two feature maps by combining the benefits of using both a small correlating scale for fine details and a big scale for larger areas. Nevertheless, rendering accurate disparity maps for foreground and background objects with fine details in real scenarios is a challenging task. Thus, a framework with a three-stage strategy for the generation of high-quality disparity maps for both near and far objects is proposed. Furthermore, the current techniques for multiview data representation, even if they exploit inter-view correlation, require large storage size or bandwidth for transmission. Such bandwidth is almost linear with the number of transmitted views. To address thisproblem, we proposed a novel view synthesis method for multiview video systems. In this approach the intermediate views are solely represented using their edges while dropping their texture content. These texture contents can get synthesized using a convolutional neural network, by matching and exploiting the edges and other information in the central view. Experimental results verify the effectiveness of the proposed framework.  Finally, highly compressed multiview videos produce severe quality degradation. Thus, it is necessary to enhance the visual quality of highly compressed views at the decoder side. Consequentially,a novel method for multiview quality enhancement that directly learns an end-to-end mapping between the low-quality and high-quality views and recovers the details of the low-quality view is proposed.

Abstract:For reducing carbon emission and alleviating pollution, people are gradually replacing the fossil fuel-employing combustion engines with new energy devices. The secondary batteries with high energy storage have become a hot alternative to power sources due to its zero emission during their operation. In recent years, as the most popular energy storage equipment in the battery market for mobile devices, lithium-ion battery is gradually showing a decline in the field of power battery, because its energy density (~ 150 Wh kg-1) has been unable to meet the demands of power equipment, and the current research has almost reached the theoretical capacity of lithium-ion battery electrode materials, and leaves little space for improvement. Therefore, academic research began to seek a variety of new battery systems to meet the needs of the industry. As a battery system based on the non-topological reaction between lithium anode and sulfur anode, lithium sulfur battery has a very high theoretical energy density (2567 Wh kg-1) and theoretical specific capacity (1672 mAh g-1), which is good enough to meet the energy density requirements (500-600 Wh kg-1) of power battery. Meanwhile, sulfur is of low cost and environmentally friendly, which is suitable for large-scale commercialization. Therefore, it is considered as a strong competitor of the next generation power supply. However, a series of shortcomings of lithium sulfur battery limit its large-scale application at present stage; for examples, the sluggish reaction kinetics of active sulfur and the degraded cyclic stability from shuttle effect. The improvement of both can ameliorate the rate performance and cycle stability of lithium sulfur battery, which are crucial to the practical application of power battery. In this thesis, in order to solve the above problems, the author first used a facile and scalable method to prepare carbon black/ PEDOT:PSS. The modified separator was applied to lithium sulfur battery as an improved interlayer of the cathode. The principle of improving sulfur cathode by the interlayer was studied by the electrochemical analysis. The high conductivity and polysulfide adsorption ability of the coating delivers an initial specific capacity of 1315 mAh g-1 at 0.2 C current, and 699 mAh g-1 at a high rate of 2 C current; secondly, for the purpose of reducing the density of the cathode interlayer, a three-dimensional graphene foam was chosen as the conductive substrate of the interlayer, and modified with the zinc oxide by atomic layer deposition (ALD), creating the self-standing three-dimensional graphene foam / nano zinc oxide interlayer. This interlayer leads to an initial specific capacity of 1051 mAh g-1 at a 0.5 C rate. Its low area density (0.15 mg cm-2) also reduces the influence on the energy density of the cathode. As a step forward, the two-dimensional Ti3C2Tx nanosheet (MXene) with high conductivity and polysulfide adsorption characteristics was selected as an alternative material of zinc oxide to modify the graphene foam (GFMX), which simplifies the synthesis process and enhances the electronic conductivity of the interlayer. After 120 cycles at 0.2 C, the lithium sulfur batteries still maintain a specific capacity of 867 mAh g-1 and 755 mAh g-1 at 2 C high rate current with the GFMX interlayer. In light of the significant improvement of the interlayer by MXene, the modified the MXene by an in-situ growth of nitrogen and nickel doped carbon nanosheets has been studied. Results show that the stacking of MXene is greatly reduced and the specific surface area of the material is increased, moreover, the adsorption capacity of polysulfides has been largely improved by the nitrogen doping. When using the obtained composite material as the separator coating, the lithium sulfur batteries exhibit 943 mAh g-1 specific capacity after 100 cycles at 0.2 C current, and 588 mAh g-1 specific capacity after 500 cycles at 1 C. The average cycle capacity decay rate is 0.069%, and the specific capacity of the high sulfur loading cathode (3.8 mg cm-2) is 946 mAh g-1, highlighting its potential applications in the high-performance lithium sulfur batteries.

Abstract:Throughout the current development of video coding technologies, the main improvements are increasing the number of possible prediction directions and adding more sizes and more modes for blocks coding. However, there are no major substantial changes in video coding technology. The conventional video coding algorithms works well for video with motions of directions parallel to the image plane, but their efficiency drops for other kinds of motions, such as dolly motions. But increasing number of videos are captured by moving cameras as the video devices are becoming more diversified and lighter. Therefore, a higher efficient video coding tool has to be used to compress the video for new video technologies. In this thesis, a novel video coding tool, Global Motion Estimation using Motion Sensor (GMEMS), is proposed. Then, a series related approaches are researched and evaluated. The main target of this tool is using advanced motion sensor technology and computer graphics tools to improve and extend the traditional motion estimation and compensation method, which could finally enhance the video coding efficiency. Meanwhile, the computational complexity of motion estimation method is reduced as some differences have been compensated. Firstly, a Motion information based Coding method for Texture sequences (MCT) is proposed and evaluated using H.264/AVC standard. In this method, a motion sensor commonly-used in smart-phones is employed to get the panning motion (rotational motion). The proposed method could compensate panning motion by using frame projection using camera motion and a new reference allocation method. The experimental results demonstrate the average video coding gain is around 0.3 dB. In order to apply this method to other different types of motions for texture videos, the distance information of the object in the scene from the camera surface, i.e. depth map, has to be used according to the image projection principle. Generally, depth map contains fewer details than texture, especially for the low-resolution case. Therefore, a Motion information based Coding scheme using Frame-Skipping for Depth map sequence (MCFSD) is proposed. The experimental results show that this scheme is effective for low resolution depth map sequences, which enhances the performance by around 2.0 dB. The idea of motion information assisted coding is finally employed to both texture sequence and depth map sequence for different types of motions. A Motion information based Texture plus Depth map Coding (MTDC) scheme is proposed for 3D videos. Moreover, this scheme is applied to H.264/AVC and the last H.265/HEVC video coding standard and tested for VGA resolution and HD resolution. The results show that the proposed scheme improves the performance under all the conditions. For VGA resolution under H.264/AVC standard, the average gain is about 2.0 dB. As the last H.265/HEVC enhances the video encoding efficiency, the average gain for HD resolution under H.265/HEVC standard drops to around 0.4 dB. Another contribution of this thesis is that a software plus hardware experimental data acquisition method is designed. The proposed motion information based video coding schemes require video sequences with accurate camera motion information. However, it is difficult to find proper dataset. Therefore, an embedded hardware based experimental data acquisition platform is designed to obtain real scene video sequences, while a CG based method is used to produce HD video sequences with accurate depth map.

Abstract:Since the building sector accounts for more than one third of global carbon emissions, it is imperative that the sector mitigate its emissions to help reach the goal of the COP26 climate conference of achieving a global net zero by mid-century. Building refurbishment (BR) is key to reducing carbon emissions in the building sector by reducing the operational energy consumption of existing buildings instead of demolishing them and building new ones. China is a good example of a country encouraging refurbishment, since it has prioritized BR in its 14th Five-Year Building Energy Efficiency and Green Building Development Plan (2021-2025). Since the number of BR projects in China is therefore likely to significantly increase in the coming years, it is important to evaluate the carbon emissions associated with construction and demolition (C&D) waste to find optimal waste management solutions. However, there are no studies that have considered the carbon emissions of C&D waste management of BR projects from a whole life cycle perspective. This study fills the research gap by developing a novel LCA-SD model, which integrates the features of life cycle assessment (LCA) and system dynamics (SD) to evaluate the carbon emissions of C&D waste management of BR projects through non-linear and dynamic analysis from a whole life cycle perspective. Variables for evaluating the carbon emissions were first identified in four life cycle stages of C&D waste management of BR projects. Causal loop diagrams were then developed to demonstrate the interrelations of the variables in the different life cycle stages, and the novel LCA-SD stock and flow model was formulated based on the causal loop diagrams. The model was validated through a case study of a typical BR project in China. The validated LCA-SD model was used to compare and analyze waste management scenarios for the case study BR project by performing simulations of selected scenarios. The simulation results reveal that the secondary material utilization rate is the most effective independent variable for reducing carbon emissions from C&D waste management of the case BR project, 11.28% of total carbon emissions could be reduced by using 31% of secondary materials to substitute natural raw materials; improving the combustible waste incineration rate to 100% could reduce 6.42% of total carbon emissions; reducing 50% of the on-site waste rate could reduce 1.28% of total carbon emissions; while improving the inert waste recycling rate to 90% could only reduce 1% of total carbon emissions. From the whole life cycle perspective, the refurbishment material stage accounts for the highest carbon emissions, followed by the refurbishment material EOL stage, and the dismantlement stage, the refurbishment construction stage accounts for the least carbon emissions. The findings not only highlight the importance of cradle to cradle life cycle C&D waste management for mitigating carbon emissions from BR projects, but also demonstrate the effectiveness of the novel integrated LCA-SD model as an “experimental laboratory” for BR C&D waste management decision makers to conduct “what-if” dynamic simulation analysis for various scenarios before embarking on a project.

Abstract:Thin-film transistors (TFTs), serving as the core components for the applications of the active matrix for liquid crystal displays (AMLCDs) and the active matrix for organic light emitting diodes (AMOLEDs), have been being intensively researched all over the world. For the past decades, in order to meet the display application requirements of high resolution, large screen size, and low power consumption, the metal oxides (MOs) semiconductors have been proposed and widely investigated for the fabrication of high-performance TFTs. Compared with the traditional TFTs based on the amorphous silicon (α-Si) technology, the MO based TFTs (MOTFTs) are reported to have much higher electron field-effect mobility (μFE) due to the large, spherical ns-orbitals (n≥4). Moreover, the MO semiconductor materials also have their advantages in transparent applications due to the wide bandgap (~3 eV). Therefore, the wide-bandgap MO semiconductors including indium (In), gallium (Ga), zinc (Zn), and Tin (Sn) based binary or multi-component oxides have gradually become the promising channel material candidates for advanced TFT based technologies. However, for the well-established vacuum-based MOs fabrication technologies such as magnetron sputtering, atomic layer deposition (ALD), and chemical vapor deposition (CVD), etc., the complex processes, high-demand equipment, and small depositing area heavily limit the development for low-cost MOs deposition. Therefore, the solution process, one feasible and facile route to deposit the MO films under an ambient condition was proposed and reported by the researchers. Nevertheless, every coin has two sides, there is often a trade-off between the low cost of solution methods and the high performance of TFTs. The solvent residues or incomplete annealing process may lead to the defects and ruin the performance of the devices. Accompanied by the challenges, many studies have been reported to deduce the side effects brought by solution process and plenty of breakthroughs also have been done. In another word, to fabricate the high electrical performance TFTs based on low-cost solution processes still have great room for development and are worthy of study. In this work, for environmental protection and cost reduction considerations, we mainly focus on the spin-coating based n-type In-free semiconductor zinc tin oxide (ZnSnO, ZTO). We firstly proposed a kind of deionized (DI) water solvent-based fabrication routine for ZTO semiconductor films. The fabrication process was operated under a low temperature (≤ 300℃) in air condition. Combining with the silicon dioxide (SiO2) dielectric layer, the TFTs with a μFE of 2 cm2V-1s-1 were successfully fabricated. Furthermore, with the help of the novel two-dimensional (2D) material MXene, we tuned the work function (WF) of the ZTO channel and optimized both the μFE (13.06 cm2V-1s-1) and the gate bias (GB) stability behaviors of the TFTs via depositing the homojunction structured channels. Subsequently, we replaced the SiO2 dielectric with the solution-processed high-k aluminum oxide (AlOx) films, the devices showed an increased μFE of 28.35 cm2V-1s-1 and applied to a resistor-load inverter successfully (Chapter 2). Secondly, besides the performance optimization, the solution-processed TFTs could also be applied to realize the advanced high-parallel neuromorphic network computing tasks. The TFTs that could meet this application requirement are regarded as the synaptic transistors (STs) and are decided to mimic the biological synapse. The operating basis is established on the hysteresis window in STs’ transfer characteristics and non-volatile multi-level variable channel conductance. Here we applied the MXene to the interface between the ZTO channel and the SiO2 dielectric layer and proposed a kind of floating-gate transistors (FGTs) with the functions of STs. The MXene induced FGTs (MXFGTs) successfully mimicked the typical behaviors of biological synapse under both the gate voltage (VGS) and channel incident ultraviolet (UV) light stimuli. To further explore the suitability of the MXFGTs in machine learning task, we utilized the classifier based on the artificial neural network (ANN) and the tested results of the devices to simulate the image classification process. The training and recognition results of the images based on the Modified National Institute of Standards and Technology (MNIST) database further proved the application potential of MXFGT in neural network (NN) system (Chapter 3). Finally, in Chapter 4, we further improved the light detecting behavior of the MXene based STs. A shell layer of germanium oxide (GeOx) was grown to cover the nanosheets of MXene through a facile solution method. The obtained GeOx-coated MXene (GMX) nanosheets were doped into the ZTO channel layer and fabricated into the GMX based STs (GMXSTs). Owning to the area enlarging function of the high electron density MXene core and the heterostructure of GeOx/ZTO bilayer, the GMXSTs showed excellent optoelectrical synaptic performance under visible light stimuli, which was highly improved over MXFGTs. Then, we applied the various responses of the devices under the different input lights into image target area detecting simulations. With the help of the detecting pre-process, the tasks of counting the fluorescent cells stained by 2-(4-Amidinophenyl)-6-indolecarbamidine dihydrochloride (DAPI) was correctly performed. Finally, the "night vision"-inspired and the brightness-adjusted image reconstruction results were presented, which further indicated the bright future of this kind of synaptic device in the application field of artificial visual perception.

Abstract:Background modeling is one of the fundamental tasks in the computer vision, which detects the foreground objects from the images. This is used in many applications such as object tracking, traffic analysis, scene understanding and other video applications. The easiest way to model the background is to obtain background image that does not include any moving objects. However, in some environment, the background may not be available and can be changed by the surrounding conditions like illumination changes (light switch on/off), object removed from the scene and objects with constant moving pattern (waving trees). The robustness and adaptation of the background are essential to this problem. Mixture of Gaussians (MOG) is one of the most widely used methods for background modeling using color information, whereas the depth map provides one more dimensional information of the images that is independent of the color. In this thesis, the color only based methods such as Gaussian Mixture Models (GMM), Hidden Markov Models (HMM), Kernel Density Estimation (KDE) are thoroughly reviewed firstly. Then the algorithm that jointly uses color and depth information is proposed, which uses MOG and single Gaussian model (SGM) to represent recent observations of the color and depth respectively. And the color-depth consistency check mechanism is also incorporated into the algorithm to improve the accuracy of the extracted background. The spatial resolution of the depth images captured from consumer depth camera is generally limited due to the element size of the senor. To overcome this limitation, depth image super-resolution is proposed to obtain the high resolution depth image from the low resolution depth image by making the inference on high frequency components. Deep convolution neural network has been widely successfully used in various computer vision tasks like image segmentation, classification and recognitions with remarkable performance. Recently, the residual network configuration has been proposed to further improve the performance. Inspired by this residual network, we redesign the popular deep model Super-Resolution Convolution Neural Network (SRCNN) for depth image super-resolution. Based on the idea of residual network and SRCNN structure, we proposed three neural network based approaches to address the problem of depth image super-resolution. In these approaches, we introduce the deconvolution layer into the network which enables the learning directly from original low resolution image to the desired high resolution image, instead of using conventional method like bicubic to interpolate the image before entering the network. Then in order to minimize the sharpness loss near the boundary regions, we add layers at the end of network to learn the residuals.

Abstract:Textiles are a vital and indispensable part of our clothing that we use daily. They are very flexible, often lightweight, and have a variety of application uses. Today, with the rapid developments in small and flexible sensing materials, textiles can be enhanced and used as input devices for interactive systems. Advances in fabric sensing technology allow us to combine multiple interface modalities together. However, most textile-based research uses unimodal approach and current input options have limitations such as gesture types and issues like low social acceptance when interactions are performed in public or in front of unfamiliar people. As an alternative, wrist-based gesture input has the extra benefit of supporting eyes-free interactions which are subtle, thus socially acceptable. In this research, we propose and develop two fabric-based multimodal interfaces (FABMMI) which supports, wrist, touch and combination of these gestures. To do that, we first investigated the acceptance and performance of using the wrist to perform multimodal inputs using FABMMIs for (1) in-vehicle controls and (2) handheld augmented reality (HAR) devices. Through the first user-elicitation study with 18 users, we devised a taxonomy of wrist and touch gestures for in-vehicle interactions using a wrist-worn FABMMI in a simulated driving setup. We provide an analysis of 864 gestures, the resulting in-vehicle gesture set with 10 unique gestures which represented 56% of the user preferred gestures. With our second user-elicitation study, we investigated the use of a fabric-based wearable device as an alternative interface option for performing interactions with HAR applications. We present results about users’ gestures preferences for hand-worn FABMMI by analysing 2,673 gestures from 33 participants for 27 HAR tasks. Our gesture set includes a total of 13 user-preferred gestures which are socially acceptable and comfortable to use for HAR devices and also derived an interaction vocabulary of the wrist and thumb-to-index touch gestures. To achieve the above-defined input possibilities, we developed strain sensors to capture wrist movements, and pressure sensors to detect touch inputs. Our sensors are graphene-modified polyester (PE) fabric and polyurethane (PU) foam respectively, on which the graphene loading is high, and it has better adhesion to both PE fabric and the PU foam, which can enhance the sensitivity and the lifetime of the sensors. Using our in-house developed sensors, we developed two prototypes: (1) WRISMMi, wrist-worn interface for in-vehicle interactions and (2) HARMMi, hand-worn device for HAR interactions. A linear regression model is used to set the global thresholds for different bending and pressing magnitude levels. We tested the suitability and performance of our prototypes for a set of interactions extrapolated from the two user-elicited studies. Our results suggest that FABMMIs are viable to support a variety of natural, eyes-free, and unobtrusive interactions in multitasking situations.