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arXiv 2024年

作者： Liao, Yuan Gil, Jorge Yeh, Sonia Pereira, Rafael H.M. Alessandretti, Laura Department of Space Earth and Environment Chalmers University of Technology Gothenburg Sweden Department of Architecture and Civil Engineering Chalmers University of Technology Gothenburg Sweden - Brazil Data Science Division Brazil Department of Applied Mathematics and Computer Science Technical University of Denmark Lyngby Denmark

Segregation is a key challenge in promoting more diverse and inclusive cities. Research based on large-scale mobility data indicates that segregation between majority and minority groups persists in daily activities beyond residential areas, like visiting shops and restaurants. Aspects including lifestyle differences, homophily, and mobility constraints have been proposed as drivers of this phenomenon, but their contributions remain poorly quantified. Here, we elucidate how different mechanisms influence segregation outside home, looking at the distinctive segregation experienced by native and foreign-born individuals. Our study is based on the movement of ∼320,000 individual smartphone devices collected in Sweden, where immigration creates profound divides. We find that while day-to-day activities lead to mixing for native-born individuals, foreign-born individuals remain segregated in their out-of-home activities. Using counterfactual simulations, we show that this heterogeneous effect of mobility on experienced segregation results mainly from two mechanisms: homophily and limited travel, i.e. foreign-born individuals (i) tend to visit destinations visited by similar individuals, and (ii) have limited mobility ranges. We show that homophily, as represented by destination preference, plays a minor role, while limited mobility, associated with reduced transport access, limits opportunities for foreign-born minorities to diversify their encounters. Our findings suggest that enhancing transport accessibility in foreign-born concentrated areas could reduce social segregation. © 2024, CC BY-SA.

关键词： Smartphones

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Introducing QuantConn: Overcoming Challenging Diffusion Acquisitions with Harmonization 15th

Introducing QuantConn: Overcoming Challenging Diffusion Acqu...

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15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Newlin, Nancy Schilling, Kurt Koudoro, Serge Chandio, Bramsh Qamar Kanakaraj, Praitayini Moyer, Daniel Kelly, Claire E. Genc, Sila Yang, Joseph Yuan-Mou Wu, Ye Adluru, Nagesh Nath, Vishwesh Pathak, Sudhir Schneider, Walter Gade, Anurag Consagra, William Rathi, Yogesh Hendriks, Tom Vilanova, Anna Chamberland, Maxime Pieciak, Tomasz Ciupek, Dominika Vega, Antonio Tristán Aja-Fernández, Santiago Malawski, Maciej Ouedraogo, Gani Machnio, Julia Thompson, Paul M. Jahanshad, Neda Garyfallidis, Eleftherios Landman, Bennett Department of Computer Science Vanderbilt University NashvilleTN United States Department of Radiology and Radiological Sciences Vanderbilt University Medical Center Nashville United States Indiana University Bloomington BloomingtonIN United States Mark and Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine of USC Los AngelesCA United States Developmental Imaging Murdoch Children’ Research Institute Melbourne Australia Murdoch Children’s Research Institute Melbourne Australia Turner Institute for Brain and Mental Health School of Psychological Sciences Monash University Melbourne Australia Department of Neurosurgery Royal Children’s Hospital Melbourne Australia Neuroscience Research Murdoch Children’s Research Institute Melbourne Australia Department of Paediatrics University of Melbourne Melbourne Australia School of Computer Science and Technology Nanjing University of Science and Technology Nanjing China Waisman Center Department of Radiology University of Wisconsin Madison United States NVIDIA Santa Clara United States Learning Research and Development Center University of Pittsburgh Pittsburgh United States Brigham and Women’s Hospital Boston United States Brigham and Women’s Hospital Harvard Medical School Boston United States Department of Computer Science and Mathmatics Eindhoven University of Technology Eindhoven Netherlands LPI ETSI Telecomunicación Universidad de Valladolid Castilla y León Spain Sano Centre for Computational Medicine Kraków30-054 Poland Aix-Marseille Université Marseille France Vanderbilt University Institute of Imaging Science Vanderbilt University NashvilleTN United States Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN United States

ISBN: (纸本)9783031869198

White matter alterations are increasingly implicated in neurological diseases and their progression. Diffusion-weighted magnetic resonance imaging (DW-MRI) has been included in many international-scale studies to identify alterations in white matter microstructure and connectivity. Yet, quantitative investigation of DW-MRI data is hindered by a lack of consistency due to variations in acquisition protocols, sites, and scanners. Specifically, there is a need to harmonize the preprocessing of DW-MRI datasets to ensure that compatible and reproducible quantitative metrics are derived from each site, including (1) bundle-wise microstructure measures, (2) features of white matter fiber bundles, and (3) connectomics measures. In the MICCAI CDMRI 2023 QuantConn challenge, participants are provided raw data from the same individuals taken with two different acquisition protocols on a single 4 tesla scanner in the same scanning session and asked to preprocess the data in order to minimize acquisition differences while retaining biological variation. Here, we outline the testing framework, provide baseline pre-harmonized results, and discuss the learning implications of this challenge. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Photointerpretation

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Optimal Design of Heliostat Field Using Random Search Algorithm

Optimal Design of Heliostat Field Using Random Search Algori...

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IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS)

作者： Chenglin Mou Kaiqi Wu Pengcheng Du Kaibo Yang Department of Engineering Mechanics School of Civil Engineering and Architecture Henan University of Science and Technology Luoyang China Department of Computer Science and Technology School of Information Engineering Henan University of Science and Technology Luoyang China Department of Automation School of Information Engineering Henan University of Science and Technology Luoyang China Department of Civil Engineering School of International Education Henan University of Science and Technology Luoyang China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

Tower solar thermal power generation is a new type of low-carbon and environmentally friendly clean energy technology. In this paper, a single-objective optimization model is established with the maximum annual average thermal output per unit mirror area of the heliostat field as the objective function. By optimizing the heliostat width, heliostat height and heliostat coordinate, the annual average output thermal power can be maximized while meeting the rated power. The heliostat field is distributed in a circular shape. As the seasons change, the light intensity received by the heliostat field is uneven, so the optical efficiency of the heliostats is also different. We simplify the heliostat field into S area (area with higher optical efficiency) and W area (area with lower optical efficiency) based on the optical efficiency distribution of heliostats. At different locations, According to the heliostat width, heliostat height and heliostat coordinate generated by random functions, substituting these parameters into the efficiency model in this paper, and the random search algorithm is used to traverse the maximum annual output thermal power and the corresponding heliostat parameters. In the shadow occlusion loss model, we have grid to set up the heliostat and use the coordinate conversion to solve the shadow occlusion loss efficiency.

关键词： Integrated optics Green energy Shape Optimization models Computational modeling Poles and towers Linear programming Mirrors Power generation

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SOCIO-SPATIAL SEGREGATION AND HUMAN MOBILITY: A REVIEW OF EMPIRICAL EVIDENCE

arXiv

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arXiv 2024年

Socio-spatial segregation is the physical separation of different social, economic, or demographic groups within a geographic space, often resulting in unequal access to resources, services, and opportunities. The literature has traditionally focused on residential segregation, examining how individuals' residential locations are distributed differently across neighborhoods based on various social attributes, e.g., race, ethnicity, and income. However, this approach overlooks the complexity of spatial segregation in people's daily activities, which often extend far beyond residential areas. Since the 2010s, emerging mobility data sources have enabled a new understanding of socio-spatial segregation by considering daily activities such as work, school, shopping, and leisure visits. From traditional surveys to GPS trajectories, diverse data sources reveal that daily mobility can result in spatial segregation levels that differ from those observed in residential segregation. This literature review focuses on three critical questions: (a) What are the strengths and limitations of segregation research incorporating extensive mobility data? (b) How do human mobility patterns relate to individuals' residential vs. experienced segregation levels? and (c) What key factors explain the relationship between one's mobility patterns and experienced segregation? Our literature review enhances the understanding of socio-spatial segregation at the individual level and clarifies core concepts and methodological challenges in the field. Our review explores studies of key themes: segregation, activity space, co-presence, and the built environment. By synthesizing their findings, we aim to offer actionable insights for reducing segregation. © 2024, CC BY-SA.

关键词： Leisure

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Safety Guaranteed Power-Delivered-to-Load Maximization for Magnetic Wireless Power Transfer

Safety Guaranteed Power-Delivered-to-Load Maximization for M...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Wangqiu Zhou Xinyu Wang Hao Zhou Shenyao Jiang Zhi Liu Yusheng Ji LINKE Lab School of Computer Science and Technology University of Science and Technology of China CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China Deqing Alpha Innovation Institute Huzhou China Department of Computer and Network Engineering The University of Electro-Communications Tokyo Japan Information Systems Architecture Science Research Division National Institute of Informatics Tokyo Japan

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Electromagnetic radiation (EMR) safety has always been a critical reason for hindering the development of magneticenabled wireless power transfer technology. People focus on the actual received energy at charging devices while paying attention to their health. Thus, we study this significant problem in this paper, and propose a universal safety guaranteed power-delivered-to-load (PDL) maximization scheme (called SafeGuard). Technically, we first utilize the off-the-shelf electromagnetic simulator to perform the EMR distribution analysis to ensure the universality of the method. Then, we innovatively introduce the concept of multiple importance sampling for achieving efficient EMR safety constraint extraction. Finally, we treat the proposed optimization problem as an optimal boundary point search problem from the perspective of space geometry, and devise a brand-new grid-based multi-constraint parallel processing algorithm to efficiently solve it. We implement a system prototype for SafeGuard, and conduct extensive experiments to evaluate it. The results indicate that our SafeGuard can obviously improve the achieved PDL by up to 1.75× compared with the state-of-the-art baseline while guaranteeing EMR safety. Furthermore, SafeGuard can accelerate the solution process by 29.12× compared with the traditional numerical method to satisfy the fast optimization requirement of wireless charging systems.

关键词： Performance evaluation Monte Carlo methods System performance Inductive charging Prototypes Wireless power transfer Parallel processing

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AI-based Fuzzy Clustering System for Improving Customer Relationship Management 6

AI-based Fuzzy Clustering System for Improving Customer Rela...

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6th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2022

作者： Krishnareddy, Kr Aravinda, T.V. Nair, Kiran Patel, Umesh Kumar Sadvokasova, Gaukhar Shyamala Susan, V. Sjm Institute of Technology Department of Computer Science and Engineering India College Abu Dhabi School of Management. Department of Management United Arab Emirates Chaudhary Charan SIngh University Department of Bca Noida India International Education Corporation Department of Architecture Almaty050042 Kazakhstan A P C Mahalaxmi College for Women Department of Computer Science Tamilnadu Tuticorin India

ISBN: (数字)9781665469418

ISBN: (纸本)9781665469418

Artificial Intelligence (AI) is a technology that is becoming more and more widely recognized with the addition of technologies such as personal helpers and chatbots. To get the most out of AI, your company needs to understand how to implement it better. Today, AI enhances almost every business segment and Customer Relationship Management (CRM), which is the area that has the most benefit in enhancing a better customer experience. The existing literature does not provide sufficient empirical evidence of how social media technologies affect a company's distribution chain relationships. Today, Artificial Intelligence empowers almost every business segment and CRM, providing the greatest benefit in enhancing a better customer experience. The main purpose of this study is to show how to successfully implement chatbot in CRM using AI-based Fuzzy Clustering system (AI-FCS), how to integrate chatbot with CRM and how chatbots affect customer relationship management. From forecast lead scoring to service chatbots to customized marketing operations. Chatbots can provide tools to provide a highly productive and better, personalized customer experience to all employees. Chatbots are now easily accessible to everyone in your organization, analyzing data, predicting and planning the next steps, and automating tasks and decisions. Create an optimal control model for Customer Relationship Management using the optimal control theory, depending on the characteristics of customer relationship management. With this model, it can gain effective management insights into Customer Relationship Management for optimal new customer acquisition and existing customer retention strategies. © 2022 IEEE.

关键词： Fuzzy clustering

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Health care data analysis and visualization using interactive data exploration for sportsperson

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science China(Information sciences) 2022年第6期65卷 85-97页

作者： Hao LIU Yang ZHANG Ke LIAN Yifei ZHANG Oscar Sanjuán MARTíNEZ Rubén González CRESPO Department of Sport and Exercise Sciences Kunsan National University Department of Naval Architecture and Ocean Engineering Ludong University Computer Science Department School of Engineering and Technology Universidad Internacional de La Rioja

Sports have scored significant attention among the public in this multifaceted world. Diverse training strategies are followed by many athletics and even flexible to adapt comfortable and optimal techniques. This fact has led physicians and educators to encourage remote health surveillance as one of the core strategies in athletic training. The need for innovative data exploration methodologies capable of facing Big Data's influence to make remote monitoring services viable has been raised by the growing ties of networks that deliver high quantities of real-time data. This paper presents an interactive healthcare data exploration and visualization(IHDEV) model to enhance multi-scaling data analysis and visualization in the athletic health vision platform. This paper aims to simplify optimization methods to measure sportsperson muscle tension. This model illustrates a three-layer architecture with a raw data acquisition layer, data analysis layer, and visualization layer. The first layer considers the acquisition of health-related data from the athletes for remote monitoring using IoT and stores it into the cloud. The data analysis layer adapts artificial intelligence(AI) in data mining. The final layer introduces an intelligent interactive data visualization model assisted by a reactive workflow mechanism, enabling analysis and visualization solutions to be composed in a personalized data flow appropriate to the athletic training. This experimental study extended with two healthcare datasets to show the feasibility of IHDEV in promoting healthcare based athletic monitoring and improves the accuracy ratio of 96.7%, prediction ratio of 96.2%, an efficiency ratio of 96.8%, Pearson correlation coefficient of 98.2%, and reduces the error rate of 18.7% compared to other conventional models.

关键词： artificial intelligence health monitoring data visualization data analysis athletics

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Flexible Control of the Interline DC Power Flow Controller Using Virtual Capacitor in VSC-HVDC Grids

Flexible Control of the Interline DC Power Flow Controller U...

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Power Electronic and Drive Systems and Technologies Conference (PEDSTC)

作者： Mirhamed Pourmirasghariyan Mirsajed Pourmirasghariyan Seyed Saeid Heidari Yazdi Seyyed Yousef Mousazadeh Mousavi Kumars Rouzbehi G. B. Gharehpetian Departement of Electrical and Computer Engineering Amirkabir University of Technology Tehran Iran School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Department of Electrical & Computer Engineering School of Engineering and Digital Science Nazarbayev University Nur-Sultan Kazakhstan Faculty of Engineering and Technology University of Mazandaran Sari Iran Department of Systems and Automatic Control Engineering University of Seville Seville Spain

The performance of Interline DC Power Flow Controllers (IDC-PFCs) in Voltage Source Converters (VSC)-based High Voltage Direct Current (HVDC) grids, can be affected due to different issues. The current limitation of HVDC lines, the voltage limitation of HVDC buses, and DC voltage of the IDC-PFC intermediary capacitor prevent effective and efficient operation of IDC-PFCs. In this paper, it is shown that this issue can be overcome by using a virtual capacitor in parallel with the IDC-PFC intermediary capacitor. Also, an energy control-based scheme is proposed for the operation of IDC-PFCs in VSC-HVDC grid. The benefits of using the virtual capacitor are: widening the operational area of the IDC-PFCs for the determined duty cycle and injecting more voltage in series to the interconnected HVDC line to control the related HVDC line current. The proposed solution is successfully evaluated on a CIGRE three-terminal VSC-HVDC grid which is modeled by linearized space-state equations.

关键词： Fluctuations HVDC transmission Capacitors High-voltage techniques Boosting Mathematical models Power conversion

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A Horizontal Federated Learning Approach to IoT Malware Traffic Detection: An Empirical Evaluation with N-BaIoT Dataset 26

A Horizontal Federated Learning Approach to IoT Malware Traf...

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26th International Conference on Advanced Communications technology, ICACT 2024

作者： Do, Phuc Hao Le, Tran Duc Vishnevsky, Vladimir Berezkin, Aleksandr Kirichek, Ruslan The Bonch-Bruevich Saint-Petersburg State University of Telecommunications Saint-Petersburg Russia Danang Architecture University Da Nang Viet Nam University of Telecommunications Department of Communication Networks and Data Transmission at Bonch-Bruevich Saint-Petersburg State Russia University of Science and Technology The University of Danang Da Nang Viet Nam The University of Danang University of Science and Technology Information Technology Faculty Danang Viet Nam V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Moscow Russia Institute of Control Sciences of Russian Academy of Sciences Russia Bonch Bruevich Saint Petersburg State University of Telecommunications Department of Programming Engineering and Computer Science Russia

ISBN: (纸本)9791188428120

The increasing prevalence of botnet attacks in IoT networks has led to the development of deep learning techniques for their detection. However, conventional centralized deep learning models pose challenges in simultaneously ensuring user data privacy and detecting botnet attacks. To address this issue, this study evaluates the efficacy of Federated Learning (FL) in detecting IoT malware traffic while preserving user privacy. The study employs N-BaIoT, a dataset of real-world IoT network traffic infected by malware, and compares the effectiveness of FL models using Convolutional Neural Network, Long Short-Term Memory, and Gated Recurrent Unit models with a centralized approach. The results indicate that FL can achieve high performance in detecting abnormal traffic in IoT networks, with the CNN model yielding the best results among the three models evaluated. The study recommends the use of FL for IoT malware traffic detection due to its ability to preserve data privacy. © 2024 Global IT Research Institute - GIRI.

关键词： Malware

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Joint Prediction of SOH and RUL for Lithium Batteries Considering Capacity Self Recovery and Model Drift

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IEEE Internet of Things Journal 2025年

作者： Jia, Zhen Li, Zhifei Wang, Zhengdong Liu, Zhenbao Vong, Chi-Man Xi'an University of Architecture and Technology School of Mechanical and Electrical Engineering Xi'an710055 China Northwestern Polytechnical University School of Civil Aviation Xi'an710072 China University of Macau Department of Computer and Informa tion Science Macau999078 China

Accurate monitoring of the state of health (SOH) and remaining useful life (RUL) of lithium batteries is a key technology to realize their stable and economic operation. The current data-driven methods have problems such as local prediction errors caused by capacity self-recovery effect, model drift phenomenon, and inaccurate trend prediction caused by complex degradation process. In this paper, we propose a joint SOH and RUL forecasting based on the fusion of Kolmogorov-Arnold Network (KAN), Deep Bidirectional Long Short-Term Memory Network (DBLSTM) and adaptive mechanism method (KAN-HDBLSTM-AM). The method innovatively introduces an adaptive mechanism that considers the capacity degradation trend for different prediction steps, suppresses the accumulation of errors in the model prediction process, and further improves the prediction accuracy by combining the nonlinear approximation capability of KAN and the mastery of data temporal characteristics of DBLSTM. Experimental validation shows that the proposed method can effectively overcome the prediction errors caused by capacity self-recovery and model drift, and can retain sufficient accuracy under long prediction steps, with the prediction trend being more in line with the actual change trend and the prediction error being smaller compared with other methods. © 2014 IEEE.

关键词： Prediction models

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