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Estimating the value of a statistical life in China: A contingent valuation study in six representative cities

Chinese Journal of Population,Resources and Environment 2023年第4期21卷 269-278页

作者： Chaoji Cao Xinke Song Wenjia Cai Yichao Li Jianhui Cong Xueying Yu Mengzhao Gao Can Wang Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System ScienceTsinghua UniversityBeijing100084China Center for Healthy Cities Institute for China Sustainable UrbanizationTsinghua UniversityBeijing100084China Tsinghua Urban Institute Tsinghua UniversityBeijing100084China State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijing100084China Center for Statistical Science Tsinghua UniversityBeijing100084China Department of Industrial Engineering Tsinghua UniversityBeijing100084China School of Economics and Management Shanxi UniversityTaiyuan030000China School of Economics and Management Beihang UniversityBeijing100191China Beijing Advanced Innovation Center for Big Data-based Precision Medicine Beihang UniversityBeijing100191China Key Laboratory of Big Data-Based Precision Medicine(Beihang University) Ministry of Industry and Information TechnologyBeijing100191China

The value of a statistical life(VSL)is a crucial tool for monetizing health *** explore the VSL in China,this study examines people’s willingness to pay(WTP)to reduce death risk from air pollution in six representative cities in China based on face-to-face contingent valuation interviews(n=3936)from March 7,2019 to September 30,*** results reveal that the WTP varied from CNY 455 to 763 in 2019(USD 66-111),corresponding to a VSL range of CNY 3.79-6.36 million(USD 549395-921940).The VSL in China in 2019 is estimated to be CNY 4.76 million(USD 689659).The statistics indicate that monthly expenditure levels,environmental concerns,risk attitudes,and assumed market acceptance,which have seldom been dis‐cussed in previous studies,significantly impact WTP and *** findings will serve as a reference for ana‐lyzing mortality risk reduction benefits in future research and for policymaking.

关键词： Air pollution Contingent valuation method Willingness to pay Value of a statistical life China

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Real-Time Epileptic Seizure Detection Based on Deep Learning*

Real-Time Epileptic Seizure Detection Based on Deep Learning...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Tianshu Zhou Yulang Feng Jianda Wang Yu Tian Jianhua Feng Jingsong Li Research Center for Healthcare Data Science Zhejiang Laboratory China Engineering Research Center of EMR and Intelligent Expert System Ministry of Education Zhejiang University China Department of Pediatrics The Second Affiliated Hospital of Zhejiang University School of Medicine China

Epilepsy is one of the most common neurological diseases, and video EEG is the most commonly used examination method for epilepsy diagnosis. However, since the video EEG examination lasts for hours, the escort has a heavy burden, and the large amount of video EEG data needs to be visually checked by the doctor. The real-time detection of epileptic seizures can reduce the stress of the escort and provide a mark for the doctor to check the EEG efficiently. In this paper, we propose a deep neural network with specified signal representation for real-time seizure detection and add a smoothing filter on the model output to enhance performance. First, we compare the performance of real-time epileptic seizure detection model under different signal representations. Then we use the best signal representation for further analysis in real-time scenario. In the experiment, the EEG data of 9 patients in the CHB-MIT public data set was used, and a patient-specific neural network was trained for each individual. The recall was 97%, the false alarm was 0.219 times per hour, and the latency time was 3.4s for real-time seizure event detection. The results show that this method can realize the real-time detection of epileptic seizures, which is of great significance to the subsequent system design combined with actual scenes.

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Rainfall-Induced Landslide Prediction using Graph-Based Convolutional Network Models

Rainfall-Induced Landslide Prediction using Graph-Based Conv...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： P. Rajasekar K. M. Devendraiah A Tamizhselvi Droupti Yadav A. Priyadharshini D. Suganthi Department of Data Science and Business System SRM Institute of Science and Technology India V. V. Sangha's Independent P U College Ballari India Department of Information Technology St. Joseph's College of Engineering Chennai India Department of Environmental Science Technology School of Basic Sciences CSJM University Kanpur India Department of Computer Science and Engineering (Cyber Security) Nandha Engineering College Erode India Saveetha College of Liberal Arts and Sciences SIMATS Chennai India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

Landslides are natural disasters that are devastating and unpredictable. Community notification and casualty reduction require early warning systems. Using data analytics and machine learning technology (MLT), IoT-enabled early warning models can monitor environmental variables and predict landslides. However, current models struggle to anticipate *** innovative strategy suggested by this work involves feature extraction, preprocessing, and model training. The first step to high-quality data is preparing the landslip grid points with Multicollinearity Analysis. Simulating salp chains improves feature selection during feature extraction with the Salp Swarm Algorithm (SSA). During training, a Graph-Based Convolutional Neural Network (G-CNN) is used for optimum predictions. The suggested strategy was more predictive than before, averaging 92.76% Due to its accuracy, the G-CNN outperforms graph-based methods and standard CNNs in handling complex environmental *** SSA-based feature extraction with G-CNN training improves landslip prediction accuracy, a promising early warning system development. This technique could improve natural catastrophe prediction and response by using IoT and ML advances.

关键词： Training Landslides Analytical models Accuracy Biological system modeling Disasters Predictive models Feature extraction Prediction algorithms Convolutional neural networks

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An Analysis of Load-Balancing Algorithms on Edge-Markovian Evolving Graphs

SSRN

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SSRN 2023年

作者： Shiraga, Takeharu Kijima, Shuji Department of Information and System Engineering Chuo University 1-13-27 Kasuga Bunkyo-ku Tokyo112-8551 Japan Faculty of Data Science Shiga University 1-1-1 Banba Hikone Shiga522-8522 Japan

The edge-Markovian is a simple and comprehensive model of time-varying networks (often called evolving graphs). Analysis of algorithms on the edge-Markovian graphs is, however, quite tough comparing with other models of evolving graphs with independent random graphs, and very few is known about the subject. This paper presents a new technique for analysis of algorithms on the edge-Markovian evolving graphs. Specifically, we give an analysis of random matching algorithms, which is a standard scheme for load-balancing on networks, and provide a nearly optimal load balance on edge-Markovian evolving graphs. To avoid the difficulty of an analysis caused by a complex correlation with the history of execution, we develop a simple proof technique based on history-independent bounds. © 2023, The Authors. All rights reserved.

关键词： Graphic methods

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Development of Modern Forecasting Models 16

Development of Modern Forecasting Models

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16th International Conference Management of Large-Scale system Development, MLSD 2023

作者： Ivanyuk, Vera Shuvalov, Konstantin Akhobadze, Gurami Malekova, Victoria Mikhailov, Alexey Levchenko, Kiril Financial University under the Government of the Russian Federation Department of Data Analysis and Machine Learning Moscow Russia Bauman Moscow State Technical University Department of Higher Mathematics Moscow Russia V.A.Trapeznikov Institute of Control Sciences of Ras Laboratory of Management of Large-Scale System Development Moscow Russia V. A.Trapeznikov Institute of Control Sciences of Ras Laboratory of Network Systems Control Moscow Russia Financial University under the Government of the Russian Federation Department of Financial Markets and Financial Engineering Moscow Russia Financial University under the Government of the Russian Federation Department of Mathematics Moscow Russia

ISBN: (纸本)9798350337907

关键词： Forecasting

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Analysis and Optimization of Servo Antenna Equipment Based on Finite Element Method

Analysis and Optimization of Servo Antenna Equipment Based o...

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Mechatronics Technology and Intelligent Manufacturing (ICMTIM), International Conference on

作者： Yunlei Hou Yong Zhang Dongliang Geng Mechanical and Electrical Engineering Department Tianjin Zhongwei Aerospace Data System Technology Co. Ltd. Tian Jin China Passenger car test research department CATARC Automotive Test Center (Tian Jin) Co. Ltd Tian Jin China

The deceleration curve of vehicle emergency braking conditions during equipment transportation was collected, and the structural strength and stiffness of the servo equipment were analyzed and optimized. The finite element analysis results indicate that in the case of emergency braking of transportation vehicles, the base part of the servo equipment is prone to plastic deformation, and the structure needs to be optimized and strengthened. Based on engineering experience, modifications have been made to the servo equipment base. Simulation was conducted on the improved structure, and the results showed that some optimized servo base structures met the design requirements for stiffness and strength.

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Cascading Waves of Fluctuation in Time-delay Multi-agent Rendezvous

Cascading Waves of Fluctuation in Time-delay Multi-agent Ren...

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American Control Conference (ACC)

作者： Guangyi Liu Vivek Pandey Christoforos Somarakis Nader Motee Department of Mechanical Engineering and Mechanics Lehigh University Bethlehem PA USA Intelligent System Laboratory Palo Alto Research Center - Xerox During the Production of This Paper Applied Mathematics and Data Science Group Merck West Point PA USA

We develop a framework to assess the risk of cascading failures when a team of agents aims to solve the distributed rendezvous problem in time in the presence of exogenous noise and communication time-delay. The notion of value-at-risk (VaR) measure is used to evaluate the risk of cascading failures (i.e., network-based waves of large fluctuations) when some agents have failed to rendezvous. Furthermore, an explicit formula is obtained to calculate the risk of higher-order cascading failures recursively. Finally, from a risk-aware design perspective, we report an evaluation of the most vulnerable sequence of agents in various communication graphs.

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Vibration signal characterization of underpass bridge using time-frequency representation method for condition-based maintenance purpose 30

Vibration signal characterization of underpass bridge using ...

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30th International Conference on Noise and Vibration engineering, ISMA 2022 and 9th International Conference on Uncertainty in Structural Dynamics, USD 2022

作者： Caesarendra, W. Surindra, M.D. Putra, S.A. Zaini, J. Universiti Brunei Darussalam Faculty of Integrated Technologies Jalan Tungku Link GadongBE1410 Brunei Universiti Brunei Darussalam Institute of Applied Data Analytics Jalan Tungku Link GadongBE1410 Brunei Politeknik Negeri Semarang Department of Mechanical Engineering Jalan Prof. Sudharto SH Semarang50278 Indonesia Telkom University School of Industrial and System Engineering Bandung40257 Indonesia

ISBN: (纸本)9789082893151

Bridges are among the important infrastructure to support social and economic activities in the South East Asia region. Monitoring the bridge dynamic is important for regular maintenance. Bridge health monitoring is highly dependent on environmental factors and their utilization, especially the influence of moving loads, such as large vehicles, so they are chosen as crucial parameter factors. There have been widely devoted methods for bridge condition monitoring based on vibration signals using certain signal processing methods. This study aims to investigate the dynamic characteristics of underpass bridges, in particular, the frequency response of the bridge. The initial study is to construct a numerical model of the bridge using the Finite Element Analysis (FEA) method. The frequency response of the numerical model is validated with the time-frequency representation method i.e. Empirical Mode Decomposition (EMD) for underpass bridge vibration data collected from the wireless accelerometer. The result shows the frequency response of the numerical method is detected in the frequency response of the EMD method. © 2022 Proceedings of ISMA 2022 - International Conference on Noise and Vibration engineering and USD 2022 - International Conference on Uncertainty in Structural Dynamics. All rights reserved.

关键词： Frequency response

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Towards understanding structure–property relations in materials with interpretable deep learning

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npj Computational Materials 2023年第1期9卷 157-168页

作者： Tien-Sinh Vu Minh-Quyet Ha Duong-Nguyen Nguyen Viet-Cuong Nguyen Yukihiro Abe Truyen Tran Huan Tran Hiori Kino Takashi Miyake Koji Tsuda Hieu-Chi Dam Japan Advanced Institute of Science and Technology NomiIshikawaJapan HPC SYSTEMS Inc. MinatoTokyoJapan Applied Artificial Intelligence Institute Deakin UniversityGeelongVICAustralia School of Materials Science and Engineering Georgia Institute of TechnologyAtlantaGAUSA Research and Services Division of Materials Data and Integrated System National Institute for Materials ScienceTsukubaIbarakiJapan Research Center for Computational Design of Advanced Functional Materials National Institute of Advanced Industrial Science and TechnologyTsukubaIbarakiJapan Department of Computational Biology and Medical Sciences Graduate School of Frontier SciencesUniversity of TokyoKashiwa-shiChiba-kenJapan International Center for Synchrotron Radiation Innovation Smart(SRIS) Tohoku UniversityAoba-kuSendaiJapan

Deep learning(DL)models currently employed in materials research exhibit certain limitations in delivering meaningful information for interpreting predictions and comprehending the relationships between structure and material *** address these limitations,we propose an interpretable DL architecture that incorporates the attention mechanism to predict material properties and gain insights into their structure–property *** proposed architecture is evaluated using two well-known datasets(the QM9 and the Materials Project datasets),and three in-house-developed computational materials ***–test–split validations confirm that the models derived using the proposed DL architecture exhibit strong predictive capabilities,which are comparable to those of current state-of-the-art ***,comparative validations,based on first-principles calculations,indicate that the degree of attention of the atoms’local structures to the representation of the material structure is critical when interpreting structure–property relationships with respect to physical *** properties encompass molecular orbital energies and the formation energies of *** proposed architecture shows great potential in accelerating material design by predicting material properties and explicitly identifying crucial features within the corresponding structures.

关键词： properties property structure

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Collaborative Reinforcement Learning Based Unmanned Aerial Vehicle (UAV) Trajectory Design for 3D UAV Tracking

arXiv

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arXiv 2024年

作者： Zhu, Yujiao Chen, Mingzhe Wang, Sihua Hu, Ye Liu, Yuchen Yin, Changchuan The Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Department of Industrial and System Engineering University of Miami Coral GablesFL33146 United States The Department of Computer Science North Carolina State University RaleighNC27695 United States

In this paper, the problem of using one active unmanned aerial vehicle (UAV) and four passive UAVs to localize a 3D target UAV in real time is investigated. In the considered model, each passive UAV receives reflection signals from the target UAV, which are initially transmitted by the active UAV. The received reflection signals allow each passive UAV to estimate the signal transmission distance which will be transmitted to a base station (BS) for the estimation of the position of the target UAV. Due to the movement of the target UAV, each active/passive UAV must optimize its trajectory to continuously localize the target UAV. Meanwhile, since the accuracy of the distance estimation depends on the signal-to-noise ratio of the transmission signals, the active UAV must optimize its transmit power. This problem is formulated as an optimization problem whose goal is to jointly optimize the transmit power of the active UAV and trajectories of both active and passive UAVs so as to maximize the target UAV positioning accuracy. To solve this problem, a Z function decomposition based reinforcement learning (ZD-RL) method is proposed. Compared to value function decomposition based RL (VD-RL), the proposed method can find the probability distribution of the sum of future rewards to accurately estimate the expected value of the sum of future rewards thus finding better transmit power of the active UAV and trajectories for both active and passive UAVs and improving target UAV positioning accuracy. Simulation results show that the proposed ZD-RL method can reduce the positioning errors by up to 39.4% and 64.6%, compared to VD-RL and independent deep RL methods, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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