5,385 research outputs found

    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

    COVID-19: Current Challenges and Future Perspectives

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    On March 11, 2020, the World Health Organization declared COVID-19 a global pandemic and the disease now affects nearly every country and region. Caused by SARS-CoV-2, COVID-19 presents significant challenges to health systems and public health in both hemispheres as well as to the economies of each country. The morbidity and mortality due to infections caused by SARS-CoV-2 have been significant despite the short duration since its discovery and initially overwhelmed many hospitals and clinics. It influences everyone, and countermeasures have been dramatic in their impact on employment, social systems, and mental health. This Special Issue provides an avenue for authors from various disciplines to provide feedback on our responses and preparedness to COVID-19 globally as well as to disseminate critical information about the SARS-CoV-2 virus and the associated COVID-19 pandemic. It consists of 22 peer-reviewed papers that cover worldwide perspectives encompasses the following: Original articles about COVID-19 (including epidemiology, modelling, clinical data, treatment, prevention, countermeasures, impacts on tropical regions, response, and preparedness);Original articles about SARS-CoV-2 (microbiology, virology, transmission, pathology, and vaccinology);Perspectives about COVID-19 and SARS-CoV-2 (comparisons with past coronavirus outbreaks, impactful local initiatives, novel responses, and commentaries);Reviews on COVID-19 (based on systematic and narrative reviews);and Innovations (vaccine development, drug trials, and original countermeasures)

    Advanced Modeling, Control, and Optimization Methods in Power Hybrid Systems - 2021

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    The climate changes that are becoming visible today are a challenge for the global research community. In this context, renewable energy sources, fuel cell systems and other energy generating sources must be optimally combined and connected to the grid system using advanced energy transaction methods. As this reprint presents the latest solutions in the implementation of fuel cell and renewable energy in mobile and stationary applications such as hybrid and microgrid power systems based on the Energy Internet, blockchain technology and smart contracts, we hope that they will be of interest to readers working in the related fields mentioned above

    Neutrons for Sciences

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    IN 1967, France and Germany agreed to cooperate on the construction and commissioning of a nuclear reactor dedicated to research in physics, chemistry and biology. Thus was born the Institut Laue-Langevin, a project whose aim was to provide research scientists with an extremely intense source of neutron beams, a fundamental tool for probing the mysteries of matter. Britain soon joined the project, followed gradually by other countries both from western and eastern Europe, making the Institut Laue-Langevin a particularly successful example of European cooperation. This success is a clear illustration of how, by joining forces and skills in this way, it was possible to provide scientists from “the old continent” with the means to tackle ambitious projects by giving them the best neutron source in the world. Neutrons for Science tells the story of the beginnings of this project and shows how, with the right organisation, it was possible to optimise the use of the reactor. The book also paints the portraits of three eminent figures, Jules Horowitz, Heinz Maier-Leibnitz and Louis Néel, who played a key role in this success. In this English edition, a chapter has been added covering the period 2004-2018 in order to create a link with the modern era and highlight the dynamism that has marked the Institute since it was founded

    TOWARDS AN UNDERSTANDING OF EFFORTFUL FUNDRAISING EXPERIENCES: USING INTERPRETATIVE PHENOMENOLOGICAL ANALYSIS IN FUNDRAISING RESEARCH

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    Physical-activity oriented community fundraising has experienced an exponential growth in popularity over the past 15 years. The aim of this study was to explore the value of effortful fundraising experiences, from the point of view of participants, and explore the impact that these experiences have on people’s lives. This study used an IPA approach to interview 23 individuals, recognising the role of participants as proxy (nonprofessional) fundraisers for charitable organisations, and the unique organisation donor dynamic that this creates. It also bought together relevant psychological theory related to physical activity fundraising experiences (through a narrative literature review) and used primary interview data to substantiate these. Effortful fundraising experiences are examined in detail to understand their significance to participants, and how such experiences influence their connection with a charity or cause. This was done with an idiographic focus at first, before examining convergences and divergences across the sample. This study found that effortful fundraising experiences can have a profound positive impact upon community fundraisers in both the short and the long term. Additionally, it found that these experiences can be opportunities for charitable organisations to create lasting meaningful relationships with participants, and foster mutually beneficial lifetime relationships with them. Further research is needed to test specific psychological theory in this context, including self-esteem theory, self determination theory, and the martyrdom effect (among others)

    Z-Numbers-Based Approach to Hotel Service Quality Assessment

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    In this study, we are analyzing the possibility of using Z-numbers for measuring the service quality and decision-making for quality improvement in the hotel industry. Techniques used for these purposes are based on consumer evalu- ations - expectations and perceptions. As a rule, these evaluations are expressed in crisp numbers (Likert scale) or fuzzy estimates. However, descriptions of the respondent opinions based on crisp or fuzzy numbers formalism not in all cases are relevant. The existing methods do not take into account the degree of con- fidence of respondents in their assessments. A fuzzy approach better describes the uncertainties associated with human perceptions and expectations. Linguis- tic values are more acceptable than crisp numbers. To consider the subjective natures of both service quality estimates and confidence degree in them, the two- component Z-numbers Z = (A, B) were used. Z-numbers express more adequately the opinion of consumers. The proposed and computationally efficient approach (Z-SERVQUAL, Z-IPA) allows to determine the quality of services and iden- tify the factors that required improvement and the areas for further development. The suggested method was applied to evaluate the service quality in small and medium-sized hotels in Turkey and Azerbaijan, illustrated by the example

    LEADERSHIP TO ENABLE 21ST-CENTURY TEAMS TO SOLVE ILL-STRUCTURED PROBLEMS

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    Although contemporary literature overwhelmingly shows that high-performing teams are greater than the sum of their parts, the current Marine Corps manpower model systematically creates ad-hoc teams. Ad-hoc teams are temporary teams, which are formed to accomplish a specific task, in contrast to enduring, cohesive teams, which possess teamwork skills, share mental models, and have refined team processes to successfully accomplish a range of tasks. Due to the changing character of war, ad-hoc teams are tasked to operate in an increasingly complex environment. While manpower model initiatives have begun under Talent Management, the effect and timeline of these initiatives are yet to be seen. This thesis addresses current team challenges by synthesizing cross-discipline scholarly research findings into four recommendations for tactical-level Marine teams. The thesis presents a two-part, decision-forcing case study and teaching note that provides a mechanism to train teams in practical methods to improve team performance. Tactical Marine units cannot afford to wait for structural changes to address team dynamics. The tactical leader should use contemporary scholarly research findings to augment their current team practices to create an environment for high-performing teams to solve the ill-structured problems they will face.Outstanding ThesisMajor, United States Marine CorpsApproved for public release. Distribution is unlimited

    ECOS 2012

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    The 8-volume set contains the Proceedings of the 25th ECOS 2012 International Conference, Perugia, Italy, June 26th to June 29th, 2012. ECOS is an acronym for Efficiency, Cost, Optimization and Simulation (of energy conversion systems and processes), summarizing the topics covered in ECOS: Thermodynamics, Heat and Mass Transfer, Exergy and Second Law Analysis, Process Integration and Heat Exchanger Networks, Fluid Dynamics and Power Plant Components, Fuel Cells, Simulation of Energy Conversion Systems, Renewable Energies, Thermo-Economic Analysis and Optimisation, Combustion, Chemical Reactors, Carbon Capture and Sequestration, Building/Urban/Complex Energy Systems, Water Desalination and Use of Water Resources, Energy Systems- Environmental and Sustainability Issues, System Operation/ Control/Diagnosis and Prognosis, Industrial Ecology

    Uncertain Multi-Criteria Optimization Problems

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    Most real-world search and optimization problems naturally involve multiple criteria as objectives. Generally, symmetry, asymmetry, and anti-symmetry are basic characteristics of binary relationships used when modeling optimization problems. Moreover, the notion of symmetry has appeared in many articles about uncertainty theories that are employed in multi-criteria problems. Different solutions may produce trade-offs (conflicting scenarios) among different objectives. A better solution with respect to one objective may compromise other objectives. There are various factors that need to be considered to address the problems in multidisciplinary research, which is critical for the overall sustainability of human development and activity. In this regard, in recent decades, decision-making theory has been the subject of intense research activities due to its wide applications in different areas. The decision-making theory approach has become an important means to provide real-time solutions to uncertainty problems. Theories such as probability theory, fuzzy set theory, type-2 fuzzy set theory, rough set, and uncertainty theory, available in the existing literature, deal with such uncertainties. Nevertheless, the uncertain multi-criteria characteristics in such problems have not yet been explored in depth, and there is much left to be achieved in this direction. Hence, different mathematical models of real-life multi-criteria optimization problems can be developed in various uncertain frameworks with special emphasis on optimization problems

    The Impact of Alcoholic Beverages on Human Health

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    Alcohol is often perceived as an under-rated risk factor for human health. This book corrects these misperceptions and misinformation by providing up to date reviews and publications that consider the impact of alcoholic beverages on human health in the domains of toxicity, carcinogenicity, genotoxicity, foetal toxicity, neurotoxicity, impacts of alcohol on the gastro-intestinal system (including nutrient deficiencies), cardiovascular system, injuries, body weight and communicable diseases. The reprint considers how the impact of alcohol on human health can be mitigated – through, for example, improved labelling on nutrients and health warnings, better policy measures, and actions by alcohol producers on their products through reformulation to lower alcoholic strength
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