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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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On the Ft-Icr Mass Spectrometry Analysis of Dissolved Organic Matter Released by Adsorbent During Coal Chemical Wastewater Treatment

SSRN

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SSRN 2024年

作者： Wei, Qi Chen, Cai Ma, Hong-Peng Liu, Tao Liu, Bing Wang, Tian-Xiang Zhang, Sen Yang, Dan-Hui Lv, Wen-Jie Wang, Hua-Lin National Engineering Research Center of Industrial Wastewater Detoxication and Resource Recovery East China University of Science and Technology Shanghai200237 China State Environmental Protection Key Laboratory of Environmental Risk Assessment and Control on Chemical Process East China University of Science and Technology Shanghai200237 China Key Laboratory of Special Functional and Smart Polymer Materials Ministry of Industry and Information Technology Northwestern Polytechnical University Shaan Xi 710072 China Methanol to Olefins Center Shaanxi Yanchang Petroleum Yan'an Energy Chemical Co. Ltd. Shaan Xi 727500 China

This study analyzed the dissolved organic matter (DOM) released by adsorbent during wastewater treatment. It was found that the adsorption method resulted in an organic removal efficiency of over 97% for coal-to-olefin (CTO) wastewater, with the lowest value of 15.7 mg/L. The Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS) detected 4111 DOM in the wastewater, 4052 remaining DOM after first-stage anthracite (ANC) adsorption, and 1013 after second-stage macroporous adsorption resin (MAR). The removal degree of lipids in wastewater was the highest, followed by proteins and lignin. During the adsorption process, the proportion of halogenated compounds (HCs) declined from 59.86% to 38.63% and 21.67%. Additionally, freshly produced 2035 and 311 DOMs were found in the adsorption effluent of ANC and MAR, respectively, with HCs accounting for 34.71% and 67.96%. Upon flowing ultra-pure water through ANC and MAR, the effluent dissolved organic carbon (DOC) ranges were 1.118-3.574 mg/L and 1.014-2.557 mg/L, respectively. There were 159 and 131 kinds of DOM detected, respectively, with HCs content of 59.06% and 45.02%. Comparative experiments revealed the complex components of the wastewater promoting the release of organic matter on the adsorbent surface that further reacted to generate organic matter. However, fewer substances were released by the adsorbent. © 2024, The Authors. All rights reserved.

关键词： Adsorption

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An Anomaly Detection Approach Based on Integrated LSTM for IoT Big Data

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Security and Communication Networks 2023年第1期2023卷

作者： Li, Chao Fu, Yuhan Zhang, Rui Liang, Hai Wang, Chonghua Li, Junjian Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou510700 China Guangxi Key Laboratory of Cryptography and Information Security Guilin541004 China COMAC Shanghai Aircraft Manufacturing Co. Ltd Beijing100005 China China Industrial Control Systems Cyber Emergency Response Team Beijing100040 China

Due to the expanding scope of Industry 4.0, the Internet of Things has become an important element of the information age. Cyber security relies heavily on intrusion detection systems for Internet of Things (IoT) devices. In the face of complex network data and diverse intrusion methods, today's network security environment requires more suitable machine learning methods to meet its security needs, and the current machine learning methods are hardly competent. In part because of network attacks by intruders using cutting-edge techniques and the constrained environment of IoT devices themselves, the most widely used algorithms in recent years include CNN and LSTM, with the former being particularly good at extracting features from the original data space and the latter concentrating more on temporal features of the data. We aim to address the issue of merging spatial and temporal variables in intrusion detection models by introducing a fusion model CNN and C-LSTM in this paper. Fusion features enhanced parallelism in the training process and better results without a very deep network, giving the model a shorter training time, fast convergence, and computational speed for emerging resource-limited network entities. This model is more suitable for anomaly detection tasks in the resource-constrained and time-sensitive big data environment of the Internet of Things. KDDCup-99, a publicly available IBD dataset, was applied in our experiments to demonstrate the model's validity. In comparison to existing deep learning implementations, our proposed multiclass classification model delivers higher accuracy, precision, and recall. © 2023 Chao Li et al.

关键词： Internet of things

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Identification of Predictive Biomarkers for the Progression to Severe COVID-19 Caused by Omicron Variants Using Metabolomics and Machine Learning Models

SSRN

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SSRN 2024年

作者： Zhang, Shuaijie Wang, Chunhua Zhang, Lu Ye, Fan Mao, Xiaogang Xia, Ying Lu, Junjie Yu, Tingyu Zhao, Jianzhong Jia, Yanlong Tang, Mengjun Yin, Lijuan Yang, Yang Xing, Hui Tan, Wenjie Shen, Liang Cao, Fengsheng Department of Central Laboratory Xiangyang Central Hospital Affiliated Hospital of Hubei University of Arts and Science Hubei Province Xiangyang441021 China Department of Clinical Laboratory Xiangyang No. 1 People's Hospital Hubei University of Medicine Hubei Province Xiangyang441000 China Department of Critical Care Medicine Xiangyang Central Hospital Affiliated Hospital of Hubei University of Arts and Science Hubei Province Xiangyang441021 China State Key Laboratory of Food Nutrition and Safety Key Laboratory of Industrial Microbiology Ministry of Education Tianjin Key Laboratory of Industry Microbiology National and Local United Engineering Lab of Metabolic Control Fermentation Technology China International Science and Technology Cooperation Base of Food Nutrition/Safety and Medicinal Chemistry College of Biotechnology Tianjin University of Science & Technology Tianjin300457 China Shenzhen Key Laboratory of Pathogen and Immunity National Clinical Research Center for infectious disease State Key Discipline of Infectious Disease Shenzhen Third People's Hospital Second Hospital Affiliated to Southern University of Science and Technology Shenzhen China NHC Key Laboratory of Biosafety National Institute for Viral Disease Control and Prevention Chinese Center for Disease Control and Prevention Beijing102206 China Xiangyang Central Hospital China

Background: The ongoing pandemic of COVID-19 has entered a new phase. Despite the reduced pathogenicity of the currently prevalent Omicron variant of SARS-CoV-2, there is still a risk of severe illness and death. Currently, there are no specific biomarkers available to accurately predict the progression and outcomes of Omicron induced COVID-19. Previous studies have mainly focused on untargeted or lipid metabolism analysis of individuals infected with the original strain of SARS-CoV-2 or Omicron with mild to moderate symptoms. Methods: We conducted a comprehensive targeted serum metabolomics analysis to analyze the metabolic profiles of COVID-19 patients infected with Omicron. We also correlated differentially expressed metabolites with laboratory test parameters. Finally, we developed a machine learning model that 'predict ' the progression and prognosis of severe COVID-19. Findings: We identified 450 DEMs between the non-severe and severe groups of COVID-19 patients. These differentially regulated metabolites are closely related to laboratory indicators such as inflammatory response, immune function. The results of the KEGG enrichment analysis on DEMs imply the association between the severity of COVID-19 in patients and host carbon homeostasis and resistance to oxidative stress, such as butyric acid metabolism, fructose and mannose metabolism. Subsequently, this study was able to accurately predict the progression of COVID-19 infection caused by the Omicron variant using three metabolites, Moreover, we first reported using five specific metabolites for predicting the outcomes of severe COVID-19 patients caused by the Omicron variant. Interpretation: 450 metabolites differ significantly between severe and non-severe Omicron-induced COVID-19 groups. We accurately predict the progression of COVID-19 infection caused by the Omicron variant using three metabolites. Funding: This research was supported by national Natural Science Foundation of China (82002192, 32302218

关键词： COVID-19

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Edge Accelerated Robot Navigation With Collaborative Motion Planning

arXiv

引用

arXiv 2023年

作者： Li, Guoliang Han, Ruihua Wang, Shuai Gao, Fei Eldar, Yonina C. Xu, Chengzhong Department of Computer and Information Science University of Macau 999078 China The Department of Computer Science The University of Hong Kong Hong Kong The Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China The State Key Laboratory of Industrial Control Technology The Huzhou Institute Zhejiang University Zhejiang 310027 China The Faculty of Mathematics and Computer Science Weizmann Institute of Science Rehovot7610001 Israel

Low-cost distributed robots suffer from limited onboard computing power, resulting in excessive computation time when navigating in cluttered environments. This paper presents Edge Accelerated Robot Navigation (EARN), to achieve real-time collision avoidance by adopting collaborative motion planning (CMP). As such, each robot can dynamically switch between a conservative motion planner executed locally to guarantee safety (e.g., path-following) and an aggressive motion planner executed non-locally to guarantee efficiency (e.g., overtaking). In contrast to existing motion planning approaches that ignore the interdependency between low-level motion planning and high-level resource allocation, EARN adopts model predictive switching (MPS) that maximizes the expected switching gain with respect to robot states and actions under computation and communication resource constraints. The MPS problem is solved by a tightly-coupled decision making and motion planning framework based on bilevel mixed-integer nonlinear programming and penalty dual decomposition. We validate the performance of EARN in indoor simulation, outdoor simulation, and real-world environments. Experiments show that EARN achieves significantly smaller navigation time and higher success rates than state-of-the-art navigation approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： Motion planning

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Effect of calcium chelation on the physio-chemical characteristics and rehydration behavior of spray-dried casein-rich powder

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Journal of Food Engineering 2025年 400卷

作者： Zhong, Qiantong Luo, Shucan Fizpatrick, John Jiang, Langye Guo, Zonglin Lin, Jie Zheng, Hua Zeng, Haili Wu, Canhu Zheng, Songbai Gu, Yan Turchiuli, Christelle Wang, Yunna Lei, Hongtao Wu, Shaozong Guangdong Provincial Key Laboratory of Food Quality and Safety National-Local Joint Engineering Research Center for Processing and Safety Control of Livestock and Poultry Products College of Food Science South China Agricultural University Guangzhou510642 China Universite Paris-Saclay INRAE AgroParisTech UMR SayFood Palaiseau91120 France Guangdong Provincial Engineering & Technology Research Center for Conservation and Utilization of the Genuine Southern Medicinal Resources Guangzhou510640 China Process & Chemical Engineering School of Engineering University College Cork Cork Ireland Key Laboratory of Agro-Products Quality and Safety Control in Storage and Transport Process Ministry of Agriculture and Rural Affairs Institute of Food Science and Technology Chinese Academy of Agricultural Sciences Beijing100193 China School of Food Science and Engineering South China University of Technology Guangdong Guangzhou510640 China Guangzhou Dublin International College of Life Sciences and Technology South China Agricultural University Guangzhou510642 China

Calcium chelators may solve the rehydration difficulty of casein micelle rich powder. However, the thermal effect during spray drying process may modify the calcium-chelated casein structure and affect the powder functionality. This study therefore investigated the effect of calcium chelation and spray drying on casein powder and the corresponding casein structure. Trisodium citrate (TC) was added to 20 % solutions of MCI (micellar casein isolate) powders to bring the TC concentrations up to 10–100 mM before spray drying. The results indicated that TC effectively decreased the particle size of casein clusters and increased zeta potential of casein solutions. Noticeably, high TC (80 & 100 mM) led to high viscosity unsuitable for spray drying. The chelated casein (TCC) powder size decreased significantly from 37.779 ± 1.407 μm to a minimum 12.815 ± 0.660 μm (P © 2025

关键词： Chelation

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Robust Optimization for Quantum Reinforcement Learning control using Partial Observations

arXiv

引用

arXiv 2022年

作者： Jiang, Chen Pan, Yu Wu, Zheng-Guang Gao, Qing Dong, Daoyi State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou310027 China The School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China The School of Engineering and Information Technology University of New South Wales CanberraACT2600 Australia

The current quantum reinforcement learning control models often assume that the quantum states are known a priori for control optimization. However, full observation of quantum state is experimentally infeasible due to the exponential scaling of the number of required quantum measurements on the number of qubits. In this paper, we investigate a robust reinforcement learning method using partial observations to overcome this difficulty. This control scheme is compatible with near-term quantum devices, where the noise is prevalent and predetermining the dynamics of quantum state is practically impossible. We show that this simplified control scheme can achieve similar or even better performance when compared to the conventional methods relying on full observation. We demonstrate the effectiveness of this scheme on examples of quantum state control and quantum approximate optimization algorithm. It has been shown that high-fidelity state control can be achieved even if the noise amplitude is at the same level as the control amplitude. Besides, an acceptable level of optimization accuracy can be achieved for QAOA with noisy control Hamiltonian. This robust control optimization model can be trained to compensate the uncertainties in practical quantum computing. © 2022, CC BY.

关键词： Robust control

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Labeled Graph Partitioning Scheme for Distributed Edge Caching

SSRN

引用

SSRN 2023年

作者： Wang, Pengfei Li, Shiqi Sun, Geng Zhou, Changjun Gao, Chengxi Qiu, Sen Tao, Tiwei Zhang, Qiang School of Computer Science and Technology Dalian University of Technology Dalian116024 China Changchun130012 China School of Computer Science and Technology Zhejiang Normal University Jinhua321000 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education Dalian116024 China School of Control Science and Engineering Dalian University of Technology Dalian116024 China School of Artificial Intelligence Dalian University of Technology Dalian116024 China

Distributed edge caching could address latency and congestion problems in large-scale data access effectively, improving system throughput and performance. However, the lack of specialized edge caching solutions for graph data results in low cache hit rates and high data access latency. This limitation stems from existing graph partitioning schemes that overlook the specific requirements of edge caching, such as communication overhead between edge servers, data query frequency, and query patterns. To overcome these challenges, we propose LGPE, a labeled graph partitioning scheme for distributed edge caching based on frequent query patterns. LGPE generates frequent query pattern according to the user’s historical query subgraphs, and then performs labeled graph partitioning based on the frequent pattern, ensuring that the labeled graph divided into edge servers has a high hit rate for frequent user queries while satisfying minimum edge-cut. Evaluation on real datasets from various application domains demonstrates that LGPE achieves approximately 40% higher cache hit rates compared to benchmarks like METIS and SCOTCH, and it also performs well in terms of edge cuts in special graph cases. © 2023, The Authors. All rights reserved.

关键词： Internet of things

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Actor Critic-based Dynamic Cost-Sensitive Classifier for Imbalanced Fault Classification

Actor Critic-based Dynamic Cost-Sensitive Classifier for Imb...

引用

第31届中国过程控制会议（CPCC 2020）

作者： Blind Review State Key Laboratory of Industrial Control Technology Institute of Industrial Process Control College of Control Science and Engineering Zhejiang University

Fault classification plays a crucial role in industrial processes. Most of fault classification algorithms are designed in the context of balanced data. However, in the actual industrial process, the data collected is usually imbalanced, that is, there are large amounts of normal data(majority) and only a few fault data(minority). This situation is also known as the imbalanced classification problem, which is a major obstacle to traditional classifiers. To handle the imbalanced classification problem, a novel actor critic-based dynamic cost-sensitive classifier(AC-DCC) is proposed in this work. In AC-DCC, a novel cost matrix learning strategy that can learn the cost matrix adaptively and yield an optimal cost matrix is developed. Thus, the proposed AC-DCC is more flexible and efficient in handling the imbalanced fault classification problem. The effectiveness and practicability of the proposed AC-DCC method are verified through its application to an industrial benchmark process.

关键词： Fault classification Actor-Critic Cost-Sensitive classifier Imbalanced classification Cost matrix

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Optimal Linear Deception Attacks on Remote State Estimation with Constrained Alarm Rates: A Low-Dimensional Case

Optimal Linear Deception Attacks on Remote State Estimation ...

引用

IEEE Conference on Decision and control

作者： Jun Shang Hanwen Zhang Jing Zhou Tongwen Chen Department of Control Science and Engineering Shanghai Research Institute for Intelligent Autonomous Systems National Key Laboratory of Autonomous Intelligent Unmanned Systems and Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai China Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This study addresses linear attacks on remote state estimation within the context of a constrained alarm rate. Smart sensors, which are equipped with local Kalman filters, transmit innovations instead of raw measurements through a wireless communication network. This transmission is vulnerable to malicious data interception and manipulation by attackers. The aim of this research is to identify the optimal attack strategy that degrades the system performance while adhering to stealthiness constraints. A notable innovation of this paper is the direct association of the attack’s stealthiness with the alarm rate, diverging from traditional approaches that rely on the covariance of the innovation or the Kullback–Leibler divergence, which are conventional metrics that have been extensively explored in previous studies. Our findings reveal that the optimal attack strategy exhibits some structural characteristics in systems of low dimensions. The performance of the proposed attack strategy is demonstrated through numerical examples.

关键词： Wireless communication Measurement Technological innovation System performance Kalman filters State estimation Intelligent sensors

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