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181.Understanding Music Piracy Behaviours in China: A Mixed Method Approach

Author:Chang Xiong 2019
Abstract:Purpose Music piracy can be defined as an unethical consumer behaviour in the digital domain that has been facilitated by advancements in information and communication technologies. It can be regarded as a crucial issue in terms of the sustainable development of the global music industry. The issue of music piracy has attracted scholars’ attention to examine the antecedents of music piracy behaviours. The Theory of Planned Behaviour (TPB) has been widely employed in explaining and predicting various unethical consumer behaviours from a social-psychological perspective. However, extant music piracy studies based on TPB, which take a post positivist stance, are mainly quantitative in nature. Considering the distinctive differences between Chinese and Western music markets, the purpose of this project is to investigate Chinese music consumers and their music piracy intentions by employing an exploratory sequential mixed methods approach.   Design/methodology/approach To fill the gap in the music piracy literature, this project has examined the music piracy intentions of Chinese music consumers using an exploratory sequential mixed methods design based on a pragmatic worldview which emphasizes the importance of the research question. Specifically, in the first phase of the study, a qualitative project was conducted. Data was collected and analysed using semi-structured interviews with 36 Chinese music consumers based in China. The measurement scale of an emerging variable named normative ambiguity has been developed accordingly, and the development process is embedded in the mixed methods design. In the second phase of the study, a quantitative project based on data from 346 surveys and a Structural Equation Modelling based on Partial Least Squares estimates (PLS-SEM) technique has been adopted to test the proposed theoretical framework and mechanism of how normative ambiguity affects piracy behaviours.     Findings A new theme, named normative ambiguity, has emerged as one of the potential impactors on music piracy intentions. The quantitative results confirmed that the findings and insights of the qualitative investigation could be generalised to a wider population, which offers a more comprehensive understanding of the music piracy behaviours among Chinese music consumers or music consumers from other less-developed music markets, as these music markets might share similar characteristics in terms of the music piracy issue. It is expected that the research findings could be applied to the study of other unethical consumer behaviours within the digital domain such as movie piracy and cybercrime.    Practical implications This study provides novel insights to the practitioners and policy makers within the music industry. Designing and implementing consumer educational programmes in the purpose of reducing normative ambiguity may lead to lower piracy intentions among music consumers. This, in turn, would reduce the occurrence of music piracy behaviours and perhaps encourage music consumers to subscribe legal digital music services. Moreover, relevant music market participants should use descriptive norm-based appeals as music consumers’ perceived descriptive norms are correlated with their music piracy intentions.   Originality/value This study is among the first few researches attempts in understanding the music piracy intentions based on mixed methods design. The current research contributes to the existing literature by investigating the relationship between the impact of social norms and intentions to conduct unethical consumer behaviours. Also, by bringing in data from an under-studied context of an emerging economy, this study offers a more comprehensive understanding of the piracy issues from a social-psychological perspective, as the music consumers’ viewpoints are taken into considerations when investigating the issue. In addition, the thesis demonstrated the effectiveness of using an exploratory sequential mixed methods design in consumer research.

182.Characterization of CD39 expression on neoantigen-specific T cells for adoptive cell transfer

Author:Yuan Feng 2022
Abstract:The advancements of immune checkpoint blockade therapy have revolutionized the field of cancer immunotherapies to treat many types of human cancers. Remarkably, the blockade of T-cell inhibitory signals rejuvenated pre-existing tumor-reactive T cells targeted to neoantigens. Mutation-derived neoantigens are highly tumor-specific and patient-specific, which can induce protective immune responses against cancers. The recent preclinical studies in neoantigen-based vaccines and adoptive T-cell transfer therapies have demonstrated the efficiency of inducing robust neoantigen-specific T cells and anti-tumor immunity against cancers. In the present study, we identified neoantigens-derived neo-peptides from MC38 cell lines and validated the immunogenicity of these predicted neo-peptides through bioinformatic and immunological approaches. Moreover, we explored the role of CD39 on T cells in human colorectal cancer and tumor-bearing mice, which correlate with the recent observations regarding neoantigen specificity and T-cell functionality. Our results indicated that CD39+ T cells are reactive to tumor cells, which can recognize neoantigens. However, CD39+ T cells exhibit an exhausted phenotype, as characterized by the impaired secretion of effector cytokines and increased expression of checkpoint molecules such as PD-1 and Tim-3. Notably, CD39+ T cells display a significantly higher anti-tumor activity in vitro but fail to suppress tumor overgrowth in vivo. Cell tracing analysis revealed that CD39- T cells give birth to CD39+ T cells, and CD39+ T cells display less in vivo proliferative capacity than CD39- T cells. Furthermore, the phenotypic analysis revealed a divergent composition of CD39+ T cells in tumor-bearing mice. CD39+ T cells are non-na?ve T cells (effector and memory T cells), which have experienced antigenic activation. Besides, Treg cells are mainly enriched in CD4+CD39+ T-cell subsets, and CD8+CD39+ T cells exhibit less progenitor phenotype compared to CD8+CD39- T cells. The expression of CD39 is up-regulated in response to TCR engagement. The increased expression of CD39 on T cells could suppress IL-2 production via generating immunosuppressive adenosine when T cells were cultured under a high concentration of extracellular ATP (eATP). Pharmacological blockade of CD39 or adenosine receptor A2A resulted in the attenuation of  IL-2 suppression. Additionally, the establishment of CD39-overexpressing tumors consumes eATP within the tumor microenvironment resulting in poorer T-cell infiltration and response to PD-L1 therapy. Finally, neoantigen-specific T cells or tumor-reactive T cells were generated from the CD39-/- mice through vaccination of neo-peptides or irradiated tumor cells. The adoptive transfer of these tumor-reactive CD39-/- T cells also suppressed tumor overgrowth, indicating CD39 itself might not be required for T cells to elicit the protective immune responses. Together, our studies explain the underlying mechanisms between CD39 expression and neoantigen specificity, suggesting that CD39+ T cells can be employed to screen neoantigens, while CD39- T cells are better for adoptive transfer to patients.

183.Depth-Map-Assisted Texture and Depth Map Super-Resolution

Author:Zhi JIN 2016
Abstract:With the development of video technology, high definition video and 3D video applications are becoming increasingly accessible to customers. The interactive and vivid 3D video experience of realistic scenes relies greatly on the amount and quality of the texture and depth map data. However, due to the limitations of video capturing hardware and transmission bandwidth, transmitted video has to be compressed which degrades, in general, the received video quality. This means that it is hard to meet the users’ requirements of high definition and visual experience; it also limits development of future applications. Therefore, image/video super-resolution techniques have been proposed to address this issue. Image super-resolution aims to reconstruct a high resolution image from single or multiple low resolution images captured of the same scene under different conditions. Based on the image type that needs to be super-resolved, image super-resolution includes texture and depth image super-resolutions. If classified based on the implementation methods, there are three main categories: interpolation-based, reconstruction-based and learning-based super-resolution algorithms. This thesis focuses on exploiting depth data in interpolation-based super-resolution algorithms for texture video and depth maps. Two novel texture and one depth super-resolution algorithms are proposed as the main contributions of this thesis. The first texture super-resolution algorithm is carried out in the Mixed Resolution (MR) multiview video system where at least one of the views is captured at Low Resolution (LR), while the others are captured at Full Resolution (FR). In order to reduce visual uncomfortableness and adapt MR video format for free-viewpoint television, the low resolution views are super-resolved to the target full resolution by the proposed virtual view assisted super resolution algorithm. The inter-view similarity is used to determine whether to fill the missing pixels in the super-resolved frame by virtual view pixels or by spatial interpolated pixels. The decision mechanism is steered by the texture characteristics of the neighbors of each missing pixel. Thus, the proposed method can recover the details in regions with edges while maintaining good quality at smooth areas by properly exploiting the high quality virtual view pixels and the directional correlation of pixels. The second texture super-resolution algorithm is based on the Multiview Video plus Depth (MVD) system, which consists of textures and the associated per-pixel depth data. In order to further reduce the transmitted data and the quality degradation of received video, a systematical framework to downsample the original MVD data and later on to super-resolved the LR views is proposed. At the encoder side, the rows of the two adjacent views are downsampled following an interlacing and complementary fashion, whereas, at the decoder side, the discarded pixels are recovered by fusing the virtual view pixels with the directional interpolated pixels from the complementary downsampled views. Consequently, with the assistance of virtual views, the proposed approach can effectively achieve these two goals. From previous two works, we can observe that depth data has big potential to be used in 3D video enhancement. However, due to the low spatial resolution of Time-of-Flight (ToF) depth camera generated depth images, their applications have been limited. Hence, in the last contribution of this thesis, a planar-surface-based depth map super-resolution approach is presented, which interpolates depth images by exploiting the equation of each detected planar surface. Both quantitative and qualitative experimental results demonstrate the effectiveness and robustness of the proposed approach over benchmark methods.

184.The International Strategies of Family Firms

Author:Fei Tang 2022
Abstract:Extant studies have reached an agreement that family firm internationalization is unique, not only because of the complex trade-off between economic and non-economic wealth involved in the decision-making of such firms but also because of the opportunity they present to examine and enrich theories in the field of management and international business. Motivated by the uniqueness of family firm internationalization, this thesis focuses on the impact of the unique governance features of family firms on their international activities, and it presents three empirical studies to address the research gaps identified by a systematic literature review. This thesis investigates why and how family involvement influences international resource allocation. In family firms where socioemotional wealth (SEW) preservation has a higher priority than financial performance, family firms are suggested to treat different types of resources asymmetrically. This thesis empirically supports the view that family firms are less likely than non-family firms to allocate financial slack to foreign markets but are more likely to allocate human resource (HR) slack to such markets. Moreover, the thesis also asserts that when family firms receive negative performance feedback, their priority in decision-making will be altered from SEW preservation to bolstering financial performance. Leveraging the psychological ownership theory, the thesis finds that internally promoted non-family CEOs tend to engage in internationalization because they have stronger psychological ownership with the firm through their knowledge, control and devotion to the firm. However, the involvement of the incoming family generation and institutional investors reduces the internally promoted non-family CEOs’ tendency to conduct internationalization. The thesis also pays attention to tax haven internationalization by analysing how family owners balance the costs and benefit associated with SEW preservation when family firms establish tax haven subsidiaries. The positive relationship found between family ownership and the likelihood of establishing tax haven subsidiaries reveals a dark side of SEW preservation that can also promote family firms’ unethical behaviours. In sum, this thesis examines the impact of family and non-family involvement on internationalization and contributes to family firm literature and international businessliterature in a number of ways. In particular, this thesis contributes to the family firm literature by explaining the asymmetric treatments that family firms hold towards different resources, by examining the boundary conditions of prioritizing SEW preservation, by revealing the dark side of SEW preservation, and by distinguishing between different types of non-family CEO. Meanwhile, the thesis explores the impact of slack resource on internationalization from the perspective of strategic intent and takes a balanced view to analyse the costs and benefits associated with tax haven internationalization, thus contributing to the international business literature.

185.Statistic oriented Video Coding and Streaming Methods with Future Insight

Author:Li YU 2017
Abstract:As indicated by Cisco, IP video traffic represents 70 percent of all consumer Internet traffic in 2015 globally, and it is expected to reach 82 percent by 2020. Given this, research works related to video compression, video transmission, and interactive play- back are of vital importance. Most existing works solve one step of these tasks based on the currently and/or previously acquired information. One common challenge behind all these tasks is the uncertainty in the future. For example, the dynamic adaptive video streaming over HTTP (DASH) standard provides multiple quality levels for each video block to choose. The benefit of various options is that it can adapt to the band- width fluctuation and various client device capacity. Most methods predict the bitrate of future video blocks according to the already downloaded ones, which is usually un- precise. As a result, the mismatch between the predicted and actual bitrate of the chosen video block leads to latency or inefficient usage of the bandwidth. Thus, one of our work proposes to send the exact bitrate information of all video blocks to the client at the beginning to avoid such problems. To sum up, the focus of this thesis is to solve the video coding and streaming problems with future insight. By analyzing the uncertainties of future information in a statistical way, more efficient and suitable solutions are derived. In this thesis, how each problem is solved with future insight is described respectively. As for video compression, inter prediction is one of the biggest contributors to the compression ratio, which removes temporal redundancies between frames. However, it is also one of the most computational complex processes. Thus, the ideal scenario is that the inter prediction is only performed within necessary areas, where there exist similar contents for reference. However, the existing encoding standards, such as H.264 and H.265, simply uses the inter prediction for all reference frames following a fixed prediction structure. Thus, it is a waste of resources to perform inter prediction in these unnecessary areas that have less probability of being referenced. Inspired by this i idea, a statistical approach for motion estimation skipping (SAMEK) is proposed to recognize these unnecessary areas and avoid using them in the motion estimation stage while encoding future frames. By doing so, the overall complexity and encoding time are reduced. After the compression process (source coding), the channel coding is needed to pro- tect video contents when they are transmitted over unreliable networks. Reed-Solomon (RS) erasure code is one of the most popular errors correcting codes, which detects and recovers the erasures by adding parity packets. These parity packets should be optimally allocated according to the importance of each video packet. The impor- tance of each packet can be evaluated through its influence on the quality of the whole video. Thus, by knowing the future potential influence of each packet, a rate-distortion optimized redundancy allocation scheme is proposed to automatically allocate parity packets based on the network conditions and video characteristics. RS based error control mechanisms are usually used for real-time streaming over the unreliable networks, such as IP, UDP; whereas for delay insensitive video streaming over reliable protocol TCP, DASH is commonly adopted. The DASH is the de-facto video delivery mechanism nowadays, which takes advantage of the existing low cost and widespread HTTP platforms. So far, most DASH works focus on the CBR (constant bitrate) video delivery. The bit rate of CBR video is kept constant over each segment. In this thesis, VBR (various bitrate) video delivery is investigated instead. Since the quality is kept constant in VBR video, the bit rate of each segment fluctuates. Thus, it is important to know the instant bit rate of future segments beforehand. In the proposed method, such accurate bit rate information of every segment is sent at the beginning of a streaming session. Then, the proposed internal QoE (Quality of Experience) goal function would take the expected future influence of each request over buffer reservation into consideration. In addition to effective video streaming, user demands are increasing with the emer- gence of interactive multiview video streaming platforms, which provides immersive vi- sion, seamless view switching, and interactive involvement. A probabilistic navigation model, which predicts the views that might be watched by the user, is incorporated in the proposed convolutional neural network (CNN) assisted seamless multiview video streaming and navigation system to guide the download of future views. In addition, a bit allocation mechanism under the guidance of the navigation model is developed ii to prefetch all possibly being watched views and adapt to the network fluctuations at the same time. Besides, a convolutional neural network assisted multiview representa- tion method is proposed to prepare the multiview videos at the server. The proposed representation method would maintain a satisfactory compression efficiency and allow random access to any subset of views with dynamic qualities at the same time. All the above methods work closely to provide a seamless viewing experience to users. They can be fused into any existing multiview video streaming frameworks to enhance the overall performance. The main contribution of this thesis is incorporating future insight into various tasks related to video coding and streaming. By leveraging the methods proposed in this the- sis, efficient results could be obtained in different application scenarios. For example, with the proposed SAMEK method, up to 9.5% encoding time (averagely 6.87%) is saved with negligible rate-distortion losses (in average 0.006 dB) when compared with classical HEVC encoder. With the proposed RS redundancy allocation scheme, an aver- age gain of 1dB over the state-of-the-art approach is achieved. The proposed multiview video streaming and navigation system enhances the overall quality over benchmark with averagely 0.6 dB with a lower bitrate.

186.Intelligent Planning for Refractive Surgeries: A Modelling and Visualisation-based Approach

Author:Wei Wang 2020
Abstract:Laser refractive surgeries have been commonly used in ophthalmic operations. Considerable research has been carried out and encouraging progress made in recent years. It covers properties of the cornea and behaviour of tissue in different parts of the eye, topography and material expression of individual patient's eyes, prediction using finite element (FE) analysis to estimate the corneal shape change and the change in refractive power. Further effort is still required to advance the research to aid the decision making for laser refractive surgeries. This study comprehensively reviews the latest techniques of refractive surgery and research on computational analysis and modelling techniques and their applications, especially the current prediction and planning techniques for laser refractive surgeries. The aim of this study is to develop an intelligent assistant tool for the laser refractive surgeries with prediction and visualisation functions. For this aim, two objectives will be achieved: prediction with the clinical dataset and human vision simulation. Due to clinical statistics, the clinical dataset is often incomplete, imbalanced, and sparse. Three methods are proposed to predict surgery parameters and outcomes using the clinical dataset. A multiple imputation method, with multiple regression, is proposed for imputing the missing data. For the imbalance of data distribution in the clinical dataset, an over-sampling of the minority data method is proposed. The accuracy of predicted minority data is close to the accuracy of predicted majority data. Finally an ensemble learning method which is optimised by the genetic algorithm is proposed to improve the accuracy of the prediction results with a sparse dataset. According to the distribution of the sample in the clinical data, the percentage of unacceptable results is 23.02%. The methods in this study could provide an accuracy of 79.02% to find the possible unacceptable cases, that is, the method could reduce the percentage of unacceptable results from 23.02% to 4.82%. In human vision simulation, the study focuses on how the human vision simulation could be determined and obtained accurately within a required timeframe. The ray tracing technique can provide more precise results than the rasterisation technique, especially for the simulation of light reflection and refraction in the human eyeball. However, the thin lens assumption affects the accuracy of the pathological vision simulation with the ray tracing technique. An improved schematic human eye model is proposed to obtain a numerical model predicting the size of the defocus blur for the pathological vision, which wraps the shape of the ray intersection area. In order to generalise this model to other healthy and pathological vision, an intelligent blur range derivation method is proposed. On the other hand, ray tracing scene rendering requires repeated iterative computing which takes a significant amount of computation time. A GPU-based ray tracing computing method is proposed to accelerate and optimise the rendering of scenes. With this method, the scene rendering speed is about 75 times faster than using the CPU.

187.Multi-agent Near Real-Time Simulation of Light Train Network Energy Sustainability Analysis

Author:Yida Guo 2021
Abstract:As an attractive transportation mode, rail transit consumes a lot of energy while transporting a large number of passengers annually. Most energy-aimed research in rail transit focuses on optimizing the train timetable and speed trajectory offline. However, some disturbances during travel will cause the train to fail to follow the offline optimized control strategy, thus invalids the offline optimization. In the typical rail transit control framework, the moving authority of trains is calculated by the zone controller based on the moving/fixed block system in the zone. The zone controller is used to ensure safety when the travel plan of trains changes due to disturbance. Safety is guaranteed during the process, but the change of travel plan leads to extra energy costs. The energy-aimed optimization problem in rail transit requires ensuring safety, pursuing punctuality with considering track slope, travel comfort, energy transferring efficiency, and speed limit, etc. The complex constraints lead to high computational pressure. Therefore, it is difficult for the regional controller to re-optimize the travel plan for all affected trains in near real-time. Multi-agent systems are widely used in many other fields, which show decent performance in solving complex problems by coordinating multiple agents. This study proposes a multi-agent system with multiple optimization algorithms to realize energy-aimed re-optimization in rail transit under different disturbances. The system includes three types of agents, train agents, station agents and central agents. Each agent exchanges information by following the time trigger mechanism (periodically) and the event trigger mechanism (occasionally). Trigger mechanism ensures that affected agents receive necessary information when interference occurs, and their embedded algorithms can achieve necessary optimization. Four types of cases are tested, and each case has plenty of scenarios. The tested results show that the proposed system provides encouraging performance on energy savings and computational speed.  

188.Model Checking the Reliability of Smart Grid

Author:Kai Zheng 2019
Abstract:In recent years, air pollution in China become more serious than before. Thermal power generation is one of the main pollution sources, so the Chinese government wants to develop distributed energy systems (DESs) to solve the problem. However, the monitoring and control of the DESs become a challenge. In order to solve the problem, the concept of smart microgrid is introduced. Smart microgrid can monitor and optimize the running of DESs in an intelligent way. Smart microgrid systems usually include the following components: DESs, converters, inverters, sensors, gateways, and servers. The control algorithms are imported into the inverters and converters to realize the optimal control of DESs. The internet of things (IoT) network in a smart microgrid is used for monitoring the operation of the DESs. However, the reliability of smart microgrid is still a challenge. In recent years, some of the researchers also focus on the reliability of smart microgrid[53]. But the research about the reliability of the smart microgrid is still not enough. Most of the researchers focus on the power quality reliability of the microgrid. However, few research concentrates on optimizing the structure design of smart microgrid. In this project, we will optimize the architecture design of smart microgrid. Continuous- time Markov chain (CTMC) models will be used to evaluate the reliability of smart micro- grid. The architectures of the IoT system and DC microgrid will be evaluated respectively. Then the analysis results will show our optimized architecture is better. The optimized design of smart microgrid in this project will help the designer to improve the architecture design of smart microgrid in real cases. In this project, Monte Carlo method, reliability block diagram (RBD) method and case study system are used as benchmarks.

189.Spatial Workspace Co-presence Awareness and Proxemics of Users in Collaborative Interaction

Author:Lei Chen 2022
Abstract:Computer Supported Cooperative Work (CSCW) is an emerging technology that integrates human collaborative behaviors and computer-aided techniques. Augmented Reality (AR), Virtual Reality (VR), mobile devices, and large interactive technologies can be used to create unique collaborative experiences, especially for spatial problem-solving tasks. Therefore, this dissertation makes exploratory research on techniques for supporting collaborative activities with consideration of users' performance and experience. It aims to explore two research issues in CSCW: spatial workspace co-presence awareness and proxemics issues. Accordingly, four research questions are answered: RQ1 - Can pointing visual cues support collaborative interaction and improve the awareness of social presence? RQ2 - Is it beneficial to share operations between workspaces for collaborators throughout the sense-making process? RQ3 - Do the proxemics influence collaborative behaviors and task performance? How do position arrangements affect performance, engagement, and collaborative behaviors? and RQ4 - How to give full play to the advantages of different platforms? To address these research questions, four user studies have been conducted to investigate the effect of sharing visualized information and position/device arrangements on technique usability, performance, and user experience in collaborative activities. Four platforms, AR, VR, tablet, and large display, are involved as exploratory environments in this dissertation. The results of this dissertation show that sharing visualized information provides benefits for improving spatial/workspace co-presence awareness to enhance collaboration, and proxemics issues indeed have an influence on task performance, user experience, and behavior during collaboration. Specifically, by exploring the effect of pointing visual cues in AR-supported collaboration, the first study answered RQ1 - pointing visual cues can improve social presence to support collaborative interaction. By exploring the effect of shared operations between workspaces and position/device arrangements in VR, mobile device, and large display collaboration, the other three studies answered RQ2 - sharing operations between workspaces is beneficial for sense-making tasks, and RQ3 - proxemics will influence collaborative behaviors and task performance. Finally, RQ4 was answered by deriving a set of guidelines and design implications for different platforms in co-located collaboration to exploit their advantages. Overall, this research offers some insights into how to design effective multi-user interfaces that are supportive of exploratory tasks.

190.Recycled Materials for Concrete Paving Blocks

Author:Xinyi Wang 2019
Abstract:This research aims to study the properties’ variations of concrete paving blocks mixed with multiple construction wastes, including recycled concrete coarse aggregate (RCCA), recycled concrete fine aggregate (RCFA), crushed glass (CG), crumb rubber (CR), and ground granulated blast furnace slag (GGBS), with different percentage, and design the optimal mix proportion for concrete paving blocks mixed with those waste materials. Concrete paving blocks that contain a single type of recycled material with different percentages were manufactured to obtain the ranges of replacement levels for each type of waste material that ensure the blocks’ five properties, including compressive strength, tensile splitting strength, water absorption, slip resistance, and abrasion resistance, can meet the British standard BS EN 1338: 2003’s requirements. Then a series of orthogonal experimental designs were carried out to study the impact of recycled material’s replacement levels on the five properties of concrete paving blocks that contain multiple types of recycled materials. Plus, two optimal mix proportions for concrete paving blocks that contain multiple waste materials were obtained, and two batches of concrete paving blocks were cast according to those two mix proportions. A series of static loading tests for a segment of pavement were carried out to study the application performances of those two types of blocks. A finite element analysis model was also carried out to simulate the behaviours of a segment of recycled concrete block pavement and predicted the safety compression of the block pavement. According to the test results, the recommended replacement levels for RCCA was from 0% to 60%, for RCFA was from 0% to 20%, for CG was from 0% to 60%, for GGBS from was 0% to 70%, and the CR was not recommended to use as fine aggregate in concrete paving blocks. For the blocks mixed with RCCA, RCFA, and GGBS, the optimal replacement levels for the three waste materials were 20%, 0% and 30% separately. For the blocks mixed with CG, RCFA, and GGBS, the optimal replacement levels for the three waste materials were 20%, 0% and 30% separately. A series of index equations were deduced to predict and judge properties performances of blocks mixed with multiple waste materials. According to the experimental test results and the numerical analysis results, the two optimal recycled concrete paving blocks were suitable for the practical applications.

191.Intelligent Train Operation with On-Board Energy Storage Device: An Energy-Saving Perspective

Author:Chaoxian Wu 2022
Abstract:Railway transportation is applied extensively in urban transportation to satisfy the increasing travel demand as well as reduce CO2 emission from the road transportation. However, energy consumption of the railway transportation is observed to increase due to its boosting construction and usage. In this case, energy efficiency of the railway transportation has become a popular research topic in the past few decades. At the same time, as an emerging technology, the on-board energy storage device (OESD) is utilised in many modern railway systems to help improve the energy efficiency. This thesis focuses on the investigation of the intelligent train operation with OESD, in which the optimal train operation strategy, OESD discharging/charging strategy, mutual in influence between the train operation strategy and OESD, and study on different types of energy storage as OESD in train operation are given. First, the intelligent train operation with OESD in single inter-station section is discussed. An integrated mathematical model to optimise the train speed trajectory with OESD to minimise the net energy consumption of the system for a single inter-station section is established. The in influence of the OESD capacity, energy status of OESD and OESD degradation on optimal train operation solution is revealed based on the proposed model. The results show that with a general railway case, the net energy consumption can be signifficantly reduced by the intelligent management of both the train operation mode and OESD discharging/charging process. Second, the intelligent train operation with OESD in a service cycle is discussed. The OESD is allowed to discharge/be charged when the train dwells at each station. A two-step method, which overcomes the drawbacks of the originally proposed mathematical programming model, is proposed to locate the optimal train speed trajectory, timetable and OESD discharging/charging strategy for both inter-station running and dwelling at stations with high computation efficiency. Beijing Yizhuang line as numerical experiment is given in this thesis, where the reduced energy consumption is observed when compared to other scenarios by using the optimised solution, showing the effectiveness of the proposed method. Third, the intelligent train operation with OESD in a network is investigated. By considering the train network and power network as the environment information, an agent-environment model for the train with OESD is proposed. In the model, the available regenerative braking energy from other trains in the railway power network is formulated into the time-variant expectation based on the stochastic running time distribution of the train network, which can be used by train and OESD during the running. This further utilises the OESD to receive as much as the regenerative braking energy in the environment to reduce the energy waste. The results shows that the proposed method is able to improve the utilisation of the regenerative braking energy in the power network, and also lead to the significantly reduced energy consumption. Fourth, impact of the different types of energy storage as OESD on the optimal train operation strategy is studied. The dynamic discharging/charging power limits with respect to the energy status of each type of OESD, supercapacitors, ywheels and Li-ion batteries, are taken into account. In addition, the optimal sizing problems of the above three types of OESD are also investigated. The results show that the introduction of different type of OESD will lead to the change of the optimal train operation strategy and the resulted energy-saving performance. Using Beijing Changping line as numerical experiment, it is found that choosing the right type and right sized OESD are important due to the significantly different engineering characteristics, energy cost and monetary cost brought to the system when introducing different OESD. In summary, this thesis gives a systematic discussion and exploration on the intelligent train operation with OESD in a perspective of energy saving from small operation scale to large operation scale, and the OESD is also studied as general/no specific type to multiple/specific type to ensure both of the academic and industrial value of the thesis.

192.Climate Change Adaptation and Human Health: Reducing Climate Change Health Risks in the Ageing Chinese Population, a KAP study

Author:Pelin Kinay 2021
Abstract:Adaptation to climate change is now firmly on the agenda of policymakers. Climate change effects have been discernible for over 20 years. The numerous weather-related extreme events that have occurred in recent years around the world demonstrate beyond doubt that we are not prepared for the major future changes projected. Over the next few decades, societies will be faced with living in very different climatic conditions. Climate change, through its impacts on local weather, will adversely impact some areas more or less intensely than others. All sectors of the economy and society will be affected as the climate changes, precipitation patterns change, and extreme weather events become more frequent leading to adverse effects on infrastructure, food production (agriculture, fisheries), business activity, ecosystem services, and human health. Adapting to climate change from a human health perspective is becoming urgent. The World Health Organization has identified climate change as a key health risk and is pursuing several projects to facilitate adaptation. China with its rapidly ageing population will be experiencing health impacts resulting from climate change. The original contribution of this thesis is presenting the health and weather concerns of Chinese elderly using their knowledge, attitude and practices towards climate change weather and health extremes, and contributing to the literature with wider knowledge in this particular field. This study aimed to evaluate the climate change and health related knowledge, attitudes and practices (KAP) of the elderly population Suzhou, Hefei and Xiamen cities of China. This cross-sectional study included 3466 participants in total. Data regarding demographic characteristics, KAP, and climate change perceptions were collected using a semi-structured questionnaire. The potential impacts of climate change on health are a concern of the elderly in China and a majority of the elderly in all three cities said that it poses significant risks. When asked about the potential impacts of climate change over majority of participants stated that climate change affected their lifestyle. Participants were most concerned about heatwaves, flooding and drought. The main health risks cited included heatstroke and respiratory diseases. Finally, majority of participants of Suzhou city did not report receiving government assistance for climate change issues. These findings provide insights for potential adaptation strategies targeting the elderly. It is recommended that government should take responsibility in creating awareness strategies to improve the coping capacity of the elderly in China to climate change and its health impacts, and further develop climate change adaptation strategies. Public health communication initiatives should be taken and local governments should pay specific attention to vulnerable segments of the population and constitute and implement effective adaptive strategies for the elderly.

193.The Investigation on Electrical and Artificial Synaptic Performance of Resistive Random Access Memory Fabricated with Solution-Processed Materials

Author:Zongjie Shen 2022
Abstract:For now, extensive effort has been put on resistive random access memory devices (RRAM) due to the advantages of low energy consumption, fast writing & erasing speed, large storage capacity, and excellent scalability. To explore the resistive switching (RS) of the RRAM device with lower economic cost and higher manufacturing efficiency, the emerging solution-processed (SP) spin-coating technique was utilized to fabricate RS layers. The primary research work in this thesis mainly focused on the electrical and artificial synaptic performance of RRAM devices with their RS layers fabricated by the SP spin-coating technique, which was divided into Phases 1, 2, and 3). In Phase 1(Chapter 2) of this thesis, RRAM devices with the structures of Ni/SP-AlOx/Pt and TiN/SP-AlOx/Pt were investigated with their performance variation and the Ni/SP-AlOx/Pt device showed better performance with lower operation voltage, which was associated with the different work functions (ΦM) of Ni and TiN. The electrical performance of the Ni/SP-AlOx/Pt devices was evaluated with the SP-AlOx layers annealed at 225°C, 250°C, and 275°C. The final results revealed that the performance of the SP-AlOx layers and the RRAM devices was influenced by the concentration of hydroxyl group (-OH) in the RS layer, higher -OH concentration in the SP-AlOx layer indicated that more oxygen vacancies were generated during the RS process. In Phase 2 (Chapter 3) of this thesis, improvement methods were used to enhance the electrical performance of the new RRAM device. Ni was replaced by Ag, and Pt was replaced by ITO. The RS layer with a single SP-AlOx thin film was replaced with a stacked layer with the SP-GaOx/SP-AlOx thin film. The improved RRAM device with the structure of Ag/SP-GaOx/SP-AlOx/ITO was fabricated and the device showed a lower operation voltage, a larger ON/OFF ratio, longer retention time, and more endurance cycles. In Phase 3 (Chapter 4) of this thesis, based on the enhanced electrical performance in Phase 2, the multi-level states of current and conductance for the Ag/SP-GaOx/SP-AlOx/ITO RRAM device were confirmed and indicated the great potential of the Ag/SP-GaOx/SP-AlOx/ITO RRAM device as the artificial synaptic devices. Related artificial synaptic behaviors of the Ag/SP-GaOx/SP-AlOx/ITO RRAM device were investigated based on the multi-level conductance response to electrical pulses, including excitatory postsynaptic current (EPSC), short-term plasticity like paired-pulse facilitation (PPF), long-term plasticity like long-term potentiation (LTP) and long-term depression (LTD), and spiking-timing-dependent plasticity (STDP). By modulating input pulses, the short-term plasticity of the Ag/SP-GaOx/SP-AlOx/ITO RRAM device transit to the long-term potentiation, an integrated RRAM array comprising of multiple devices emulated the biomimetic human-brain-like behaviors of ‘learning - forgetting - relearning - memorizing’. In addition, with significant parameters obtained in LTP and LTD performance of the Ag/SP-GaOx/SP-AlOx/ITO RRAM device, a pattern recognition system based on the artificial neuron network (ANN) algorithm was carried on handwriting Arabic numbers 0 ~ 9. The final average accuracy was about 94% and the highest accuracy was ~ 98%.

194.Design, synthesis, characterization, and application of non-fullerene based small molecules bearing A-π-D-π-A framework

Author:Xiaochen Liu 2022
Abstract:Since climate issues have been increasingly raised in recent years, the tendency of developing novel renewable energy source reached agreements among most countries in the world. In the development of solar energy, Organic semiconductors with light weight, easy synthesis, and less toxicity has attracted many interests in application to OSCs, and a series of OSCs were and developed to have excellent PCE over 18%. In this research, four kinds of organic acceptor materials bearing A-π-D-π-A framework were synthesized and characterized. Their corresponding synthetic chemistry, optoelectrical properties were discussed and optimized. In the first part, two spiro-type molecules SDF-based species and one type linear-based molecules were synthesized and characterized. In the synthetic work, we developed a new approach for synthesis of SCT-based molecules. Compared to Pozzi’s work, our synthetic pathway is simplified in a total yield at 8.3%. Device performance optimization involves donor/acceptor ratio, annealing temperature, spin-coating speed, and additives. Although linear molecules were synthesized as reference, their fabricated device demonstrated better efficiency than spiro-type-based molecules. To further explore potential higher efficiency of linear molecule-based devices, additional optimized conditions were investigated. The best record for linear molecule-based device performance is 5.19%. The plausible reasons for better PCE of linear molecule are better solubility of material and better combability with donor materials. To improve the solubility of spiro-molecules, we aimed to add more alkyl side chains on SCT-based molecules. In order to synthesize such molecule, we developed two possible pathways by retrosynthesis analysis. Therefore, in the second part, synthesis of SCTC6-Br4 via two pathways were attempted. The first pathway involves nucleophilic attack to alkylated ketone. Because of the steric hindrance, the reaction showed low yield of product and low atomic economy. In the second pathway, attack of alkylated-dithiophene to ketone rapidly, while selectivity features a “diol” type intermediate, finally a macromolecule comprising di-spiro structure was obtained by accident. However, blending device performance did not achieve an efficiency over 2%, which is accounted by low current density as well as low fill factor. To improve the current density, we designed and synthesized a fused ring-based large conjugated molecule in the third part. The synthesis features six steps towards final product. To improve the solubility of the molecule, alkyl-sidechains over C10 was added. Because electron withdrawing character of aldehyde group, the peripherical functionalization was achieved in three steps with a total yield over 43%. The total yield for the six-step synthesis was 21%-23%. For higher device efficiency, the prospect works in terms of design molecules are large, fused ring system with hetero atoms to enhance planarity and crystallinity. For device engineering, more donor materials, additives, processing solvents will be screened. Carrier mobility and morphology measurement will also help for the device mechanistic study.

195.Deep Learning-based RNA Modification Prediction Method Development

Author:Daiyun Huang 2022
Abstract:Increasing evidence suggests that post-transcriptional ribonucleic acid (RNA) modifications regulate essential biological processes and are related to the pathogenesis of various diseases. Precise identification of RNA modification sites is essential for a better understanding of RNA-mediated biological functions and mechanisms, but technical limitations often hinder these efforts. Recently, many machine learning methods have been developed to in silico identify RNA modification sites in tandem with high-throughput experimental methods. Despite recent advances, most of these methods can only predict a single type of RNA modification, ignoring potential interplays between different modifications. Existing methods are mainly based on RNA primary sequences only. The use of other biologically explainable predictive features remains underexplored. Almost all current models require precise locations of modification sites for training. However, such high-resolution epitranscriptome data may not always be available. To capture joint features among modifications, we developed MultiRM, an attention-based deep learning framework for integrated prediction and interpretation of mRNA modifications from RNA sequences. By adopting a multi-label architecture, MultiRM serves as the first approach to simultaneously predict twelve most widely occurring human RNA modifications, revealing potential associations among multiple types based on sequence context. To supplement sequence information, we developed Geo2vec, a general encoding framework that depicts the geographic information of transcripts and the relative positions of the modification sites under studied. Deep learning architectures for combining new encodings with sequence-derived features and jointly learning from multiple transcript variants are developed. When high-resolution data are not available, we developed WeakRM, the first weakly supervised learning framework for RNA modification prediction. WeakRM learns from region-level coarse-grained labels instead of base-level and predicts subregions that are most likely to be modifiable. We also extended Geo2vec to the region level and combined it with WeakRM to perform tissue-specific m6A prediction from low-resolution data.

196.Migraine pathophysiology: NR2A/SFKs signaling in cortical spreading depression

Author:Fan BU 2017
Abstract:Cortical spreading depression (CSD) is a propagating neuronal/glial excitation, followed by depression in cerebral cortex and subcortical regions. It is known to be the underlying cause of migraine with aura in humans. CSD can also lead to migraine-like behavior by triggering pannexin1 (Panx1) channels opening and induce the release of calcitonin gene-related peptide (CGRP) that plays a key player in migraine patients. Increasing evidence points to an essential role of NR2A-containing NMDA receptors in CSD propagation in vitro; however whether NR2A also mediates CSD genesis and its downstream signaling associated with CSD is unknown. The purpose of this thesis is to clarify the contribution of NR2A-containing receptors to CSD propagation and determine their role in CSD genesis in vivo, and if so, to further explore the mechanism underlying the action of NR2A relevant to Panx1 channels opening and CGRP gene expression in rats. In the present study, CSD was induced both in vitro and in vivo. Multi-disciplinary methods were used including electrophysiology and intrinsic optical imaging for CSD recording, western blot, immunoprecipitation and immunohistochemistry for protein detection, and qPCR for gene expression analysis. The results demonstrated that NR2A-containing receptor inhibition using NR2A-preferring antagonist, TCN-201, suppressed CSD propagation in a concentration-dependent manner in the chick retina. In addition, both the NR2A antagonists, NVP-AAM077 and TCN-201, concentration-dependently suppressed CSD genesis but not propagation in the microdialysis-based CSD model in rats. Differently, perfusion of 0.3 nmol NVP-AAM077 into contralateral cerebroventricle considerably suppressed CSD propagation in rats. These data suggests a key role of NR2A in mediating both CSD genesis and propagation. Further mechanism study showed that CSD not only promoted sarcoma family kinases (SFKs) activation, but also SFKs-Panx1 interaction and neuronal Panx1 channels opening in the ipsilateral cortex of rats. Corresponds to this finding, inhibition of SFKs by intracerebroventricle (i.c.v.) perfusion of 2.5 nmol PP2 not only attenuated both SFKs activation and Panx1 channels opening induced by CSD, but also suppressed CSD propagation. Furthermore, the CSD-induced SFKs activation, SFKs-Panx1 interaction and neuronal Panx1 channels opening were significantly suppressed under NR2A inhibition by i.c.v. perfusion of 0.3 nmol NVP-AAM077. Finally, pre-treatment with 0.3 nmol NVP-AAM077 prevented the elevation of CGRP mRNA 24 hour after multiple CSD in the ipsilateral visual cortex of rats. In summary, this study provides strong evidence that NR2A-containing NMDA receptors contribute to CSD genesis and propagation, and reveals a previously unknown migraine mechanism of NR2A involving SFKs, Panx1 and CGRP during CSD, i.e. NR2A regulates single CSD-induced opening of neuronal Panx1 channels via coupling activated SFKs to Panx1 in cortex, and that NR2A regulates multiple CSD-induced CGRP gene expression in visual cortex. These findings provide new insights into CSD involving NR2A-containing receptor and downstream signals. Selectively antagonizing these elements might constitute a highly specific strategy treating migraine and other diseases associated with CSD.

197.Artificial Neural Network Design Approaches to Multi-Channel Information Analysis

Author:Jaehoon Cha 2021
Abstract:In recent years, a large amount of multi-channel data has been collected due to advances in technology such as with computers and the Internet. However, obtaining and labelling data are still laborious and time-consuming. Yeat another issue that adds to the difficulty is finding important channels and features from multi-channel data since having enough channels alone does not guarantee designing efficient algorithms due to scalability problems. In this thesis, a generative model and hierarchical learning models are introduced to deal with the aforementioned issues. First, the learning process of Variational Autoencoders is analysed. Taking into account the role of the mean and the standard deviation, which are used in the reparameterization trick, we propose a new generative model. The proposed model is modified from the original Autoencoder architecture which is used for dimensionality reduction. The model preserves the architecture of the Autoencoder by removing the reparameterization trick and becomes a generative model by extension of the mapping of the decoder from a discrete latent space to a continuous latent space. The model is compared with VAE and MMD on three benchmark datasets: MNIST, Fashion-MNIST and SVHN datasets. The experimental results show that the difference of the accuracy of the test set when training ANNs using synthetic data generated by the proposed model is less than 10% when training it using the original training set in MNIST and Fashion-MINST datasets. In addition, further experiments are carried out to investigate the impact of the number of the training set when training generative models. The results show that the accuracy of the test set decreases less than 10% when the number of the training set decreases in the NNIST and the Fashion-MNIST dataset. Second, two types of hierarchical learning models are proposed. Designing these models began with the idea of utilizing an innate hierarchy of targets. The first type of model, HAL, is proposed when targets are discrete. This model involves inserting the auxiliary block to output the auxiliary scores from the coarse classes. These scores are distributed based on the corresponding coarse classes. Although the model improves the accuracy of a test set, it has the disadvantage of requiring the coarse classes at the test phase. The second type of models are proposed when targets are continuous. C-FNNs and HADNNs are proposed to perform the regression task by utilizing the coarse classes. C-FNNs and HADNNs are evaluated on three benchmark indoor localization datasets, examples of multi-channel data. Results show that C-FNNs increase the floor accuracy by 30% at least and 60% at most in the three datasets. However, C-FNNs require more than three times the parameters than the baseline. HADNNs achieve better accuracy than C-FNNs and require 1.2 times the parameters than the baseline at most. Third, human motion data is analysed in order to show the importance of the relationship between sensor locations and motion types when identifying motion types. The data were gathered from patients and students in Inha University Hospital, Korea. Twenty-three subjects participated in the experiment and all had to perform nine motion types. Forty-eight total measurements were obtained from eight different body parts. The motion type detection algorithm is divided into five steps and is evaluated based on four metrics: recall, precision, accuracy and F-measure. The proposed detection algorithm has $0.8986$ average recall, 0.9071 average precision, 0.9739 average accuracy and 0.8977 average F-measure. The detection algorithm outperforms PCA, which is a popular method in feature extraction. This shows the importance of feature extraction based on the relationship between channels and targets in multi-channel data. Finally, the motion type detection process is proposed by integrating the proposed models. The process is divided into three: generation, labelling and classification. In generation, the proposed generative model is used to generate synthetic data. In labelling, SVM and PCA are used to label synthetic data. In classification, ResNet with C-FNNs and with HADNNs for a classification task are trained using the combination of the labelled synthetic data and the original training set, and the neural networks are used to detect motion types. The process is evaluated using InhaMotion and nine open source human motion datasets. The results show that training ANNs with synthetic data prevents overfitting, and the proposed generative model outperforms VAE, beta-VAE and MMD. In addition, the combination of ResNet and C-FNNs increase the accuracies of the test sets when coarse classes are available during the training phase. Since C-FNNs do not require coarse classes at the test phase, it is practical to use in daily life problems where hierarchy of targets should be considered.

198.Occupancy-driven intelligent control of HVAC based on thermal comfort

Author:Mehdi Pazhoohesh 2017
Abstract:Nowadays, the building sector is a substantial consumer of world’s energy. The dominant energy share of Heating, Ventilation and Air-Conditioning (HVAC) systems, makes it the focus of research for saving energy. Current air conditioning systems often rely on maximum occupancy assumptions and fixed schedules to maintain sufficient comfort level. Having information regarding occupancy situation may lead to significant energy-savings. On the other hand, focusing on the reduction of energy only, may lead to sacrificing the thermal comfort of the occupants in a building. Moreover, due to the difference of preference of thermal comfort of individuals, particularly in a shared space, a fixed set point for HVAC systems, can cause discomfort. Therefore, a comprehensive technique is required to save energy while maintaining thermal comfort. The present research proposes an occupancy-driven HVAC control system based on thermal comfort analysis. A ZigBee-based indoor localization system is developed to monitor the location of occupants inside the buildings. Algorithms are used to improve the accuracy of positioning system, which include Near Neighbour Area (NNA), Principle Component Analysis (PCA) and Exponential Moving Average algorithms (EMA). Computational Fluid Dynamics (CFD) is used to simulate the thermal comfort through modelling the indoor air distribution and flows. Wind velocity and temperature are simulated in several scenarios and the Predicted Mean Vote (PMV) and the Predicted Percentage Dissatisfied (PPD) are computed. The simulation results are verified through a survey asking for occupants’ real feelings and consequently thermal comfort zones are identified with associated occupants, which are used for possible energy saving while providing satisfied level to all the occupants. To investigate different satisfaction feeling of occupants, a personalized thermal profile is created for individuals inside the test bed area. A fuzzy based approach is used to develop a fuzzy map of each occupant and as a result, a personal thermal preference profile is created. Based on the present occupants in the room, the minimum and maximum preferred temperatures are estimated and used for controlling the HVAC system. The Semi-hidden Markov chain method is used to create the occupants’ behavioural pattern which can reduce the frequencies of turning ON or OFF the HVAC systems. The real-time locations of the persons, estimated based on the NNA and MA localization method, are combined with their behavioural patterns and thermal preference profiles and their comfort zones to control the corresponding HVACs. The proposed method has been implemented to a shared office occupied by nine users and equipped with two individual air conditioners. The comparison of different control strategies show that the proposed intelligent control has a significant potential of saving energy and at the same time maintaining occupants in a reasonable thermal comfort range.

199.Adoption of Emerging Technologies in Chinese Supply Chains of Industry 4.0: How Past Experiences Assist in Shaping the Future

Author:Zengwen Yan 2021
Abstract:Practitioners and scholars have observed an increase in the importance of the supply chain at a fast pace, especially in the context of Industry 4.0. The supply chain provides a competitive advantage to collaborative corporations, which may result in higher profitability and a leading market position. However, a basic issue of the supply chain is efficient information sharing, which can be dealt with through technology adoption (TA). TA has a significant influence on the principles and practices of the supply chain. Emerging and advanced technologies are the foundation and driving forces of Industry 4.0, which is closely linked with, and rarely separate from, TA. Thus, it is natural to merge TA, the supply chain and Industry 4.0, and to research issues related to TA in supply chains under Industry 4.0. In a review of the extant literature, some major gaps were identified: (1) insufficient and inconsistent research on the environmental context of Technology-Organization-Environment Framework; (2) little and vague empirical evidence related to TA in Chinese supply chains; (3) the call for verification of results from developed countries in developing countries; (4) application of interdisciplinary theories. This thesis aims to fill these gaps and identify some lessons learnt from the past experience in Chinese supply chains in terms of TA. Thus, the authors mainly explore three research questions for achieving the targets: (1) whether the TA in Chinese supply chains could contribute significance and importance; (2) what detailed environmental factors exert their impacts on the TA in Chinese supply chains under the context of Industry 4.0; (3) how do the external and internal factors interact and syntegrate to exert their impacts on the TA in Chinese supply chains under the context of Industry 4.0. To answer these questions, this thesis adopted both qualitative and quantitative approaches with systematic review method and structural equation modelling method. The results revealed that the extant research proved the value of conducting the research regarding TA in Chinese supply chains under the context of Industry 4.0, and the expected benefits in Chinese supply chains led by emerging or advanced technologies have been confirmed; it is significant to explore the barriers and enablers in order to help Chinese corporations avoid failure and unnecessary losses as much as possible. Further, this thesis practically explored and examined the constrains from the aspects of environment, organization, and supply chain features; and discussed how they interact and impact the TA in Chinese supply chains. The results contribute to the literature in the environment context and suggest that the business innovation environment is an independent indicator in the environment context that affects TA in Chinese supply chains. Moreover, the results highlight the importance of these three aspects in corporations’ intention and capability of TA in Chinese supply chains under Industry 4.0, and they propose a new perspective to examine the causal relationship between firms’performance and TA.

200.Information Visualizaion Interfaces for Large Display Multi-device Colocated Synchronous Collaboration

Author:Yu Liu 2022
Abstract:The objective of this project is to investigate the use of information-visualisation interfaces to support co-located synchronous collaboration in large display multi-device environments. This is interface for people working together, at the same time in the same place, using interactive graphics with multiple connected computer devices, including large-displays as well as handheld devices such as smartphones and tablets. The technology to support this type of working is already commonplace and research indicates there are a significant advantages to this mode of working. There are however some significant challenges for the designers and developers of these types of system. Firstly, these interfaces need to deal with the inherent complexity of human-human as well as human-computer interaction. We have a limited understanding of how users work together in multi device environments, and need to manage a shared display space and shared control to accommodate different types of collaborative working. For example, at different times users can be working together closely, working separately, or even in disagreement. This introduces an additional level of complicity to the design of the interface that makes it in-practical to simply adapt traditional visualisation interfaces or apply established visual interface design methodologies for multi device collaboration. The work in this thesis investigates the processes of co-located synchronous collaboration in large display multi-device environments and how we can improve this type of activity through better interface design. par The first stage in our study, to improve the design of large display multi-device environment interfaces for co-located collaboration, was to observe how people tend to work in this type of environment using a simple interface with basic view coordination on a large display connected to two mobile devices. This revealed that the users would tend to move between patterns of individual and closer collaborative working. The next stage of our study was to look at ways to better support this type of working by making the transition between individual and collaborative working smoother for the user. This included looking at different ways of coordinating views between mobile devices and the large display, managing different user selections, and showing different users. Finally we used the results of these studies to develop an interface for a real-world collaborative visualisation activity. This validated some of the design decisions generated and demonstrates the potential of large display multi-device collaborative visualisation. Our study demonstrated the potential of a large-display to act as a shared space for users to view the context of their selections and share their insights into the data. We also present preliminary design guidelines on how to coordinate selections among the different displays for different types of collaborative activities. This includes the finding that users preferred a single shared view on a large display for close collaboration or activities that lend themselves naturally to turn taking and a more predictable split screen with separate views for activities that involve a large navigable area and more independent working. Users also preferred to have selections automatically sent to the large screen and interact to retrieve the selections of other users. It was also found that weak presentation of non-essential information was the most effective method for highlighting different user selections. These findings can help guide interface designers and developers for this sort of environment in the future. 
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