10,636 research outputs found

    Modeling Travel Behavior in Mobility Systems with an Atomic Routing Game and Prospect Theory

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    In this paper, we present a game-theoretic modeling framework for studying the travel behavior in mobility systems, by incorporating prospect theory. As part of our motivation, we conducted an experiment in a scaled smart city to investigate the frequency of errors in actual and perceived probabilities of a highway route under free flow conditions. Based on these findings, we provide a game that captures how travelers distribute their traffic flows in a transportation network with splittable traffic, utilizing the Bureau of Public Roads function to establish the relationship between traffic flow and travel time cost. Given the inherent non-linearities, we propose a smooth approximation function that helps us estimate the prospect-theoretic cost functions. As part of our analysis, we characterize the best-fit parameters and derive an upper bound for the error. We then show a Nash Equilibrium existence. Finally, we present a numerical example and simulations to verify the theoretical results and demonstrate the effectiveness of our approximation.Comment: arXiv admin note: text overlap with arXiv:2202.0769

    Endogenous measures for contextualising large-scale social phenomena: a corpus-based method for mediated public discourse

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    This work presents an interdisciplinary methodology for developing endogenous measures of group membership through analysis of pervasive linguistic patterns in public discourse. Focusing on political discourse, this work critiques the conventional approach to the study of political participation, which is premised on decontextualised, exogenous measures to characterise groups. Considering the theoretical and empirical weaknesses of decontextualised approaches to large-scale social phenomena, this work suggests that contextualisation using endogenous measures might provide a complementary perspective to mitigate such weaknesses. This work develops a sociomaterial perspective on political participation in mediated discourse as affiliatory action performed through language. While the affiliatory function of language is often performed consciously (such as statements of identity), this work is concerned with unconscious features (such as patterns in lexis and grammar). This work argues that pervasive patterns in such features that emerge through socialisation are resistant to change and manipulation, and thus might serve as endogenous measures of sociopolitical contexts, and thus of groups. In terms of method, the work takes a corpus-based approach to the analysis of data from the Twitter messaging service whereby patterns in users’ speech are examined statistically in order to trace potential community membership. The method is applied in the US state of Michigan during the second half of 2018—6 November having been the date of midterm (i.e. non-Presidential) elections in the United States. The corpus is assembled from the original posts of 5,889 users, who are nominally geolocalised to 417 municipalities. These users are clustered according to pervasive language features. Comparing the linguistic clusters according to the municipalities they represent finds that there are regular sociodemographic differentials across clusters. This is understood as an indication of social structure, suggesting that endogenous measures derived from pervasive patterns in language may indeed offer a complementary, contextualised perspective on large-scale social phenomena

    Estudo da remodelagem reversa miocárdica através da análise proteómica do miocárdio e do líquido pericárdico

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    Valve replacement remains as the standard therapeutic option for aortic stenosis patients, aiming at abolishing pressure overload and triggering myocardial reverse remodeling. However, despite the instant hemodynamic benefit, not all patients show complete regression of myocardial hypertrophy, being at higher risk for adverse outcomes, such as heart failure. The current comprehension of the biological mechanisms underlying an incomplete reverse remodeling is far from complete. Furthermore, definitive prognostic tools and ancillary therapies to improve the outcome of the patients undergoing valve replacement are missing. To help abridge these gaps, a combined myocardial (phospho)proteomics and pericardial fluid proteomics approach was followed, taking advantage of human biopsies and pericardial fluid collected during surgery and whose origin anticipated a wealth of molecular information contained therein. From over 1800 and 750 proteins identified, respectively, in the myocardium and in the pericardial fluid of aortic stenosis patients, a total of 90 dysregulated proteins were detected. Gene annotation and pathway enrichment analyses, together with discriminant analysis, are compatible with a scenario of increased pro-hypertrophic gene expression and protein synthesis, defective ubiquitinproteasome system activity, proclivity to cell death (potentially fed by complement activity and other extrinsic factors, such as death receptor activators), acute-phase response, immune system activation and fibrosis. Specific validation of some targets through immunoblot techniques and correlation with clinical data pointed to complement C3 β chain, Muscle Ring Finger protein 1 (MuRF1) and the dual-specificity Tyr-phosphorylation regulated kinase 1A (DYRK1A) as potential markers of an incomplete response. In addition, kinase prediction from phosphoproteome data suggests that the modulation of casein kinase 2, the family of IκB kinases, glycogen synthase kinase 3 and DYRK1A may help improve the outcome of patients undergoing valve replacement. Particularly, functional studies with DYRK1A+/- cardiomyocytes show that this kinase may be an important target to treat cardiac dysfunction, provided that mutant cells presented a different response to stretch and reduced ability to develop force (active tension). This study opens many avenues in post-aortic valve replacement reverse remodeling research. In the future, gain-of-function and/or loss-of-function studies with isolated cardiomyocytes or with animal models of aortic bandingdebanding will help disclose the efficacy of targeting the surrogate therapeutic targets. Besides, clinical studies in larger cohorts will bring definitive proof of complement C3, MuRF1 and DYRK1A prognostic value.A substituição da válvula aórtica continua a ser a opção terapêutica de referência para doentes com estenose aórtica e visa a eliminação da sobrecarga de pressão, desencadeando a remodelagem reversa miocárdica. Contudo, apesar do benefício hemodinâmico imediato, nem todos os pacientes apresentam regressão completa da hipertrofia do miocárdio, ficando com maior risco de eventos adversos, como a insuficiência cardíaca. Atualmente, os mecanismos biológicos subjacentes a uma remodelagem reversa incompleta ainda não são claros. Além disso, não dispomos de ferramentas de prognóstico definitivos nem de terapias auxiliares para melhorar a condição dos pacientes indicados para substituição da válvula. Para ajudar a resolver estas lacunas, uma abordagem combinada de (fosfo)proteómica e proteómica para a caracterização, respetivamente, do miocárdio e do líquido pericárdico foi seguida, tomando partido de biópsias e líquidos pericárdicos recolhidos em ambiente cirúrgico. Das mais de 1800 e 750 proteínas identificadas, respetivamente, no miocárdio e no líquido pericárdico dos pacientes com estenose aórtica, um total de 90 proteínas desreguladas foram detetadas. As análises de anotação de genes, de enriquecimento de vias celulares e discriminativa corroboram um cenário de aumento da expressão de genes pro-hipertróficos e de síntese proteica, um sistema ubiquitina-proteassoma ineficiente, uma tendência para morte celular (potencialmente acelerada pela atividade do complemento e por outros fatores extrínsecos que ativam death receptors), com ativação da resposta de fase aguda e do sistema imune, assim como da fibrose. A validação de alguns alvos específicos através de immunoblot e correlação com dados clínicos apontou para a cadeia β do complemento C3, a Muscle Ring Finger protein 1 (MuRF1) e a dual-specificity Tyr-phosphoylation regulated kinase 1A (DYRK1A) como potenciais marcadores de uma resposta incompleta. Por outro lado, a predição de cinases a partir do fosfoproteoma, sugere que a modulação da caseína cinase 2, a família de cinases do IκB, a glicogénio sintase cinase 3 e da DYRK1A pode ajudar a melhorar a condição dos pacientes indicados para intervenção. Em particular, a avaliação funcional de cardiomiócitos DYRK1A+/- mostraram que esta cinase pode ser um alvo importante para tratar a disfunção cardíaca, uma vez que os miócitos mutantes responderam de forma diferente ao estiramento e mostraram uma menor capacidade para desenvolver força (tensão ativa). Este estudo levanta várias hipóteses na investigação da remodelagem reversa. No futuro, estudos de ganho e/ou perda de função realizados em cardiomiócitos isolados ou em modelos animais de banding-debanding da aorta ajudarão a testar a eficácia de modular os potenciais alvos terapêuticos encontrados. Além disso, estudos clínicos em coortes de maior dimensão trarão conclusões definitivas quanto ao valor de prognóstico do complemento C3, MuRF1 e DYRK1A.Programa Doutoral em Biomedicin

    Machine learning enabled millimeter wave cellular system and beyond

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    Millimeter-wave (mmWave) communication with advantages of abundant bandwidth and immunity to interference has been deemed a promising technology for the next generation network and beyond. With the help of mmWave, the requirements envisioned of the future mobile network could be met, such as addressing the massive growth required in coverage, capacity as well as traffic, providing a better quality of service and experience to users, supporting ultra-high data rates and reliability, and ensuring ultra-low latency. However, due to the characteristics of mmWave, such as short transmission distance, high sensitivity to the blockage, and large propagation path loss, there are some challenges for mmWave cellular network design. In this context, to enjoy the benefits from the mmWave networks, the architecture of next generation cellular network will be more complex. With a more complex network, it comes more complex problems. The plethora of possibilities makes planning and managing a complex network system more difficult. Specifically, to provide better Quality of Service and Quality of Experience for users in the such network, how to provide efficient and effective handover for mobile users is important. The probability of handover trigger will significantly increase in the next generation network, due to the dense small cell deployment. Since the resources in the base station (BS) is limited, the handover management will be a great challenge. Further, to generate the maximum transmission rate for the users, Line-of-sight (LOS) channel would be the main transmission channel. However, due to the characteristics of mmWave and the complexity of the environment, LOS channel is not feasible always. Non-line-of-sight channel should be explored and used as the backup link to serve the users. With all the problems trending to be complex and nonlinear, and the data traffic dramatically increasing, the conventional method is not effective and efficiency any more. In this case, how to solve the problems in the most efficient manner becomes important. Therefore, some new concepts, as well as novel technologies, require to be explored. Among them, one promising solution is the utilization of machine learning (ML) in the mmWave cellular network. On the one hand, with the aid of ML approaches, the network could learn from the mobile data and it allows the system to use adaptable strategies while avoiding unnecessary human intervention. On the other hand, when ML is integrated in the network, the complexity and workload could be reduced, meanwhile, the huge number of devices and data could be efficiently managed. Therefore, in this thesis, different ML techniques that assist in optimizing different areas in the mmWave cellular network are explored, in terms of non-line-of-sight (NLOS) beam tracking, handover management, and beam management. To be specific, first of all, a procedure to predict the angle of arrival (AOA) and angle of departure (AOD) both in azimuth and elevation in non-line-of-sight mmWave communications based on a deep neural network is proposed. Moreover, along with the AOA and AOD prediction, a trajectory prediction is employed based on the dynamic window approach (DWA). The simulation scenario is built with ray tracing technology and generate data. Based on the generated data, there are two deep neural networks (DNNs) to predict AOA/AOD in the azimuth (AAOA/AAOD) and AOA/AOD in the elevation (EAOA/EAOD). Furthermore, under an assumption that the UE mobility and the precise location is unknown, UE trajectory is predicted and input into the trained DNNs as a parameter to predict the AAOA/AAOD and EAOA/EAOD to show the performance under a realistic assumption. The robustness of both procedures is evaluated in the presence of errors and conclude that DNN is a promising tool to predict AOA and AOD in a NLOS scenario. Second, a novel handover scheme is designed aiming to optimize the overall system throughput and the total system delay while guaranteeing the quality of service (QoS) of each user equipment (UE). Specifically, the proposed handover scheme called O-MAPPO integrates the reinforcement learning (RL) algorithm and optimization theory. An RL algorithm known as multi-agent proximal policy optimization (MAPPO) plays a role in determining handover trigger conditions. Further, an optimization problem is proposed in conjunction with MAPPO to select the target base station and determine beam selection. It aims to evaluate and optimize the system performance of total throughput and delay while guaranteeing the QoS of each UE after the handover decision is made. Third, a multi-agent RL-based beam management scheme is proposed, where multiagent deep deterministic policy gradient (MADDPG) is applied on each small-cell base station (SCBS) to maximize the system throughput while guaranteeing the quality of service. With MADDPG, smart beam management methods can serve the UEs more efficiently and accurately. Specifically, the mobility of UEs causes the dynamic changes of the network environment, the MADDPG algorithm learns the experience of these changes. Based on that, the beam management in the SCBS is optimized according the reward or penalty when severing different UEs. The approach could improve the overall system throughput and delay performance compared with traditional beam management methods. The works presented in this thesis demonstrate the potentiality of ML when addressing the problem from the mmWave cellular network. Moreover, it provides specific solutions for optimizing NLOS beam tracking, handover management and beam management. For NLOS beam tracking part, simulation results show that the prediction errors of the AOA and AOD can be maintained within an acceptable range of ±2. Further, when it comes to the handover optimization part, the numerical results show the system throughput and delay are improved by 10% and 25%, respectively, when compared with two typical RL algorithms, Deep Deterministic Policy Gradient (DDPG) and Deep Q-learning (DQL). Lastly, when it considers the intelligent beam management part, numerical results reveal the convergence performance of the MADDPG and the superiority in improving the system throughput compared with other typical RL algorithms and the traditional beam management method

    Technologies and Applications for Big Data Value

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    This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems

    COVID-19 Outbreak and Beyond

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    The COVID-19 pandemic drastically changed our lifestyle when, on 30 January 2020, the World Health Organization declared the coronavirus disease outbreak a public health emergency of international concern. Since then, many governments have introduced unprecedented containment measures, hoping to slow the spread of the virus. International research suggests that both the pandemic and the related protective measures, such as lockdown, curfews, and social distancing, are having a profound impact on the mental health of the population. Among the most commonly observed psychological effects, there are high levels of stress, anxiety, depression, and post-traumatic symptoms, along with boredom and frustration. At the same time, the behavioral response of the population is of paramount importance to successfully contain the outbreak, creating a vicious circle in which the psychological distress impacts the willingness to comply with the protective measures, which, in turn, if prolonged, could exacerbate the population’s distress. This book includes: i) original studies on the worldwide psychological and behavioral impact of COVID-19 on targeted individuals (e.g., parents, social workers, patients affected by physical and mental disorders); ii) studies exploring the effect of COVID-19 using advanced statistical and methodological techniques (e.g., machine learning technologies); iii) research on practical applications that could help identify persons at risk, mitigate the negative effects of this situation, and offer insights to policymakers to manage the pandemic are also highly welcomed

    Sustainability Analysis and Environmental Decision-Making Using Simulation, Optimization, and Computational Analytics

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    Effective environmental decision-making is often challenging and complex, where final solutions frequently possess inherently subjective political and socio-economic components. Consequently, complex sustainability applications in the “real world” frequently employ computational decision-making approaches to construct solutions to problems containing numerous quantitative dimensions and considerable sources of uncertainty. This volume includes a number of such applied computational analytics papers that either create new decision-making methods or provide innovative implementations of existing methods for addressing a wide spectrum of sustainability applications, broadly defined. The disparate contributions all emphasize novel approaches of computational analytics as applied to environmental decision-making and sustainability analysis – be this on the side of optimization, simulation, modelling, computational solution procedures, visual analytics, and/or information technologies
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