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Health benefits from the rapid reduction in ambient exposure to air pollutants after China's clean air actions:progress in efficacy and geographic equality

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national Science Review 2024年第2期11卷 103-113页

作者： Tao Xue Ruohan Wang Meng Wang Yanying Wang Dan Tong Xia Meng Conghong Huang Siqi Ai Fangzhou Li Jingyuan Cao Mingkun Tong Xueqiu Ni Hengyi Liu Jianyu Deng Hong Lu Wei Wan Jicheng Gong Shiqiu Zhang Tong Zhu Institute of Reproductive and Child Health National Health Commission Key Laboratory of Reproductive Health/Department of Epidemiology and Biostatistics Ministry of Education Key Laboratory of Epidemiology of Major Diseases(PKU) School of Public Health Peking University Health Science Centre State Environmental Protection Key Laboratory of Atmospheric Exposure and Health Risk Management and Center for Environment and Health Peking University Advanced Institute of Information Technology Peking University Department of Epidemiology and Environmental Health School of Public Health and Health ProfessionsUniversity at Buffalo State Key Joint Laboratory of Environment Simulation and Pollution Control College of Environmental Sciences and EngineeringPeking University Department of Earth System Science Tsinghua University School of Public Health Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of HealthFudan University College of Land Management Nanjing Agricultural University National&Local Joint Engineering Research Center for Rural Land Resources Use and Consolidation Clean Air Asia

Clean air actions （CAAs） in China have been linked to considerable benefits in public ***,whether the beneficial effects of CAAs are equally distributed geographically is *** high-resolution maps of the distributions of major air pollutants (fine particulate matter[PM2.5]and ozone[O3]) and population,we aimed to track spatiotemporal changes in health impacts from,and geographic inequality embedded in,the reduced exposures to PM2.5and O3from 2013 to *** used a method established by the Global Burden of Diseases *** analyzing the changes in loss of life expectancy（LLE） attributable to PM2.5and O3,we calculated the gain of life expectancy （GLE） to quantify the health benefits of the air-quality ***,we assessed the geographic inequality embedded in the GLE using the Gini index （GI）.Based on risk assessments of PM2.5and O3,during the first stage of CAAs（2013 to 2017）,the mean GLE was 1.87 *** of the sum of the GLE was disproportionally distributed in about one quarter of the population exposed （GI 0.44）.During the second stage of CAAs（2017 to 2020）,the mean GLE increased to 3.94 months and geographic inequality decreased （GI 0.18）.According to our assessments,CAAs were enhanced,from the first to second stages,in terms of not only preventing premature mortality but also ameliorating health *** enhancements were related to increased sensitivity to the health effects of air pollution and synergic control of PM2.5and *** findings will contribute to optimizing future CAAs.

关键词： clean air action public health inequality fine particulate matter ozone

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Multi-periodicity dependency Transformer based on spectrum offset for radio frequency fingerprint identification

arXiv

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arXiv 2024年

作者： Xiao, Jing Ding, Wenrui Shao, Zeqi Zhang, Duona Ma, Yanan Wang, Yufeng Wang, Jian School of Electronics and Information Engineering Beihang University Beijing100191 China Institute of Unmanned System Beihang University Beijing100191 China School of Information Science and Technology North China University of Technology Beijing100144 China National Innovation Institute of Defense Technology Academy of Military Sciences Beijing100071 China

Radio Frequency Fingerprint Identification (RFFI) has emerged as a pivotal task for reliable device authentication. Despite advancements in RFFI methods, background noise and intentional modulation features result in weak energy and subtle differences in the RFF features. These challenges diminish the capability of RFFI methods in feature representation, complicating the effective identification of device identities. This paper proposes a novel Multi-Periodicity Dependency Transformer (MPDFormer) to address these challenges. The MPDFormer employs a spectrum offset-based periodic embedding representation to augment the discrepency of intrinsic features. We delve into the intricacies of the periodicity-dependency attention mechanism, integrating both inter-period and intra-period attention mechanisms. This mechanism facilitates the extraction of both long and short-range periodicity-dependency features, accentuating the feature distinction whilst concurrently attenuating the perturbations caused by background noise and weak-periodicity features. Empirical results demonstrate MPDFormer’s superiority over established baseline methods, achieving a 0.07s inference time on NVIDIA Jetson Orin NX. © 2024, CC BY-NC-ND.

关键词： Palmprint recognition

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Liquid Metal-Based Epidermal Flexible Sensor for Wireless Breath Monitoring and Diagnosis Enabled by Highly Sensitive SnS2 Nanosheets

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Research 2021年第1期2021卷 1319-1331页

作者： Yifan Huang Fan Yang Sanhu Liu Rongguo Wang Jinhong Guo Xing Ma National Key Laboratory of Science and Technology on Advanced Composites in Special Environments Harbin Institute of TechnologyHarbin 150086China Sauvage Laboratory for Smart Materials School of Materials Science and EngineeringHarbin Institute of Technology(Shenzhen)Shenzhen 518055China Shenzhen STRONG Advanced Materials Research Institute Co. Ltd.China School of Communication and Information Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Shenzhen Key Laboratory of Flexible Printed Electronics Technology Harbin Institute of Technology(Shenzhen)Shenzhen 518055China Shenzhen Bay Laboratory No.9 Duxue RoadShenzhen 518055China

Real-time wireless respiratory monitoring and biomarker analysis provide an attractive vision for noninvasive telemedicine such as the timely prevention of respiratory arrest or for early diagnoses of chronic ***,wearable respiratory sensors are in high demand as they meet the requirement of portability in digital healthcare ***,high-performance sensing material plays a crucial role for the precise sensing of specific markers in exhaled air,which represents a complex and rather humid ***,we present a liquid metal-based flexible electrode coupled with SnS_(2)nanomaterials as a wearable gas-sensing device,with added Bluetooth capabilities for remote respiratory monitoring and *** flexible epidermal device exhibits superior skin compatibility and high responsiveness(1092%/ppm),ultralow detection limits(1.32 ppb),and a good selectivity of NO gas at ppb-level *** advantage of the fast recovery kinetics of SnS_(2)responding to H_(2)O molecules,it is possible to accurately distinguish between different respiratory patterns based on the amount of water vapor in the exhaled ***,based on the different redox types of H_(2)O and NO molecules,the electric signal is reversed once the exhaled NO concentration exceeds a certain threshold that may indicate the onset of conditions like asthma,thus providing an early warning system for potential lung ***,by integrating the wearable device into a wireless cloud-based multichannel interface,we provide a proof-of-concept that our device could be used for the simultaneous remote monitoring of several patients with respiratory diseases,a crucial field in future digital healthcare management.

关键词： Highly reversed integrating

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Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning 24

Towards High-resolution 3D Anomaly Detection via Group-Level...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9798400706868

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3D-AD. We provide the code and supplementary information on our website: https://***/M-3LAB/Group3AD. © 2024 ACM.

关键词： Contrastive Learning

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AI Empowered Channel Semantic Acquisition for 6G Integrated Sensing and Communication Networks

arXiv

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arXiv 2024年

作者： Zhang, Yifei Gao, Zhen Zhao, Jingjing He, Ziming Zhang, Yunsheng Lu, Chen Xiao, Pei The Advanced Research Institute of Multidisciplinary Science Beijing Institute of Technology Beijing100081 China The School of Electronics and Information Engineering Beihang University Beijing100191 China The National Key Laboratory of CNS/ATM Beijing100191 China The Samsung Cambridge Solution Centre System LSI Samsung Electronics Cambridge United Kingdom The Shenzhen Institute of Information Technology 518109 China University of Surrey GU2 7XH United Kingdom

Motivated by the need for increased spectral efficiency and the proliferation of intelligent applications, the sixth-generation (6G) mobile network is anticipated to integrate the dual-functions of communication and sensing (C&S). Although the millimeter wave (mmWave) communication and mmWave radar share similar multiple-input multiple-output (MIMO) architecture for integration, the full potential of dual-function synergy remains to be exploited. In this paper, we commence by overviewing state-of-the-art schemes from the aspects of waveform design and signal processing. Nevertheless, these approaches face the dilemma of mutual compromise between C&S performance. To this end, we reveal and exploit the synergy between C&S. In the proposed framework, we introduce a two-stage frame structure and resort artificial intelligence (AI) to achieving the synergistic gain by designing a joint C&S channel semantic extraction and reconstruction network (JCASCasterNet). With just a cost-effective and energy-efficient single sensing antenna, the proposed scheme achieves enhanced overall performance while requiring only limited pilot and feedback signaling overhead. In the end, we outline the challenges that lie ahead in the future development of integrated sensing and communication networks, along with promising directions for further research. Copyright © 2024, The Authors. All rights reserved.

关键词： Spectrum efficiency

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PAM-4 optical transmission beyond 224 Gbps based on an ultrahigh-bandwidth slow-light silicon modulator

PAM-4 optical transmission beyond 224 Gbps based on an ultra...

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CLEO: Applications and technology in CLEO 2024, CLEO: A and T 2024 - Part of Conference on Lasers and Electro-Optics

作者： Han, Changhao Qin, Jun Yang, Qipeng Zheng, Zhao Shu, Haowen Zhang, Yunhao Wu, Yichen Sun, Yu Lu, Junde Zhou, Yan Ge, Zhangfeng Wang, Lei He, Zhixue Yu, Shaohua Hu, Weiwei Peng, Chao Bowers, John E. Wang, Xingjun State Key Laboratory of Advanced Optical Communications System and Networks School of Electronics Peking University Beijing100871 China Department of Electrical and Computer Engineering University of California Santa BarbaraCA93106 United States Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing100192 China Frontiers Science Center for Nano-optoelectronics Peking University Beijing100871 China Peng Cheng Laboratory Shenzhen518055 China Peking University Yangtze Delta Institute of Optoelectronics Nantong226010 China

We experimentally demonstrate PAM-4 optical transmission beyond 224 Gbps based on an ultrahigh-bandwidth slow-light silicon modulator in C-band with the combination of the artificial neural network equalizers. © ... 详细信息

关键词： Light modulators

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PAM-4 optical transmission beyond 224 Gbps based on an ultrahigh-bandwidth slow-light silicon modulator

PAM-4 optical transmission beyond 224 Gbps based on an ultra...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Han, Changhao Qin, Jun Yang, Qipeng Zheng, Zhao Shu, Haowen Zhang, Yunhao Wu, Yichen Sun, Yu Lu, Junde Zhou, Yan Ge, Zhangfeng Wang, Lei He, Zhixue Yu, Shaohua Hu, Weiwei Peng, Chao Bowers, John E. Wang, Xingjun State Key Laboratory of Advanced Optical Communications System and Networks School of Electronics Peking University Beijing100871 China Department of Electrical and Computer Engineering University of California Santa BarbaraCA93106 United States Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing100192 China Frontiers Science Center for Nano-Optoelectronics Peking University Beijing100871 China Peng Cheng Laboratory Shenzhen518055 China Peking University Yangtze Delta Institute of Optoelectronics Nantong226010 China

ISBN: (纸本)9781957171395

关键词： Silicon photonics

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A Geometry-Based RIS-Assisted Multi-User Channel Model with Deep Reinforcement Learning

A Geometry-Based RIS-Assisted Multi-User Channel Model with ...

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IEEE Conference on Vehicular technology (VTC)

作者： Yuan Yuan Ruisi He Bo Ai Tong Wu Ruifeng Chen Zhengyu Zhang Yunwei Jin Zhangdui Zhong School of Electronics and Information Engineering Beijing Jiaotong University Beijing China Center for Advanced Metering Infrastructure National Institute of Metrology Beijing China China Academy of Railway Sciences Corporation Limited Institute of Computing Technology Beijing China

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

In this paper, a 3D geometry-based stochastic channel model (GBSM) is proposed for RIS-assisted multi-user communications. The proposed GBSM is divided into two sub-channels, that is, BS-RIS and RIS-Rx links, and propagation distances and angles of multipath components are derived to describe multi-user channels. In addition, optimization objective for multi-user channel is proposed, and deep reinforcement learning is introduced to solve high-dimensional RIS phase problem. Based on the proposed model and solved RIS phase, channel capacity and root mean square delay spread are derived. The simulation results show that RIS optimization parameters and channel parameters have major impact on channel characteristics. The conclusions can provide a reference for designing and developing of RIS-assisted multi-user systems.

关键词： Vehicular and wireless technologies Three-dimensional displays Channel capacity Stochastic processes Reconfigurable intelligent surfaces Deep reinforcement learning Delays

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Deep Learning Based Vehicular Millimeter-Wave Channel Prediction Using Visual information

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IEEE Antennas and Wireless Propagation Letters 2025年

作者： Wang, Shirui He, Ruisi Yang, Mi Ai, Bo Zhang, Xuejian Zhong, Zhangdui Zhang, Haoxiang Han, Jiahui Liu, Bingcheng Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation the School of Electronics and Information Engineering the Frontiers Science Center for Smart High-speed Railway System Beijing100044 China Ministry of Industry and Information Technology China Academy of Industrial Internet Beijing100044 China Chinese Academy of Sciences Aerospace Information Research Institute Beijing100094 China

Millimeter-wave channel prediction requires detailed and accurate environmental information due to challenges of non-line-of-sight transmission and severe reflection and scattering. Image information conveniently reflects real-time changes of physical environment and provides a promising solution for millimeter-wave channel modeling. However, research on millimeter-wave channel prediction based on RGB images is still in its exploratory stage. Meanwhile, RGB images introduce new influencing factors, such as visual angle changes, leading to inaccurate prediction. This article proposes a millimeter-wave channel prediction model based on propagation knowledge and visual information, which enhances original RGB image features while combining manual features from specific domain prior knowledge in propagation. Experimental results show that the proposed model outperforms traditional RGB image-based prediction for a multi-scenario, multiview fused dataset, achieving a symmetric mean absolute percentage error as low as 4.16%. In addition, the proposed model demonstrates enhanced stability and interpretability. © 2002-2011 IEEE.

关键词： Prediction models

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Waveguide-multiplexed photonic matrix–vector multiplication processor using multiport photodetectors

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Optica 2025年第6期12卷 812-820页

作者： Rui Tang Makoto Okano Chao Zhang Kasidit Toprasertpong Shinichi Takagi Mitsuru Takenaka Department of Electrical Engineering and Information Systems The University of Tokyo Tokyo 113-8656 Japan Photonics-Electronics Integration Research Center National Institute of Advanced Industrial Science and Technology (AIST) Ibaraki 305-8569 Japan takenaka@mosfet.t.u-tokyo.ac.jp

The slowing down of Moore’s law has driven the development of application-specific processors for deep learning. Analog photonic processors offer a promising solution for accelerating matrix–vector multiplications (MVMs) in deep learning by leveraging parallel computations in the optical domain. Intensity-based photonic MVM processors, which do not utilize the phase information of light, are appealing due to their simplified operations. However, existing intensity-based schemes for such processors often employ wavelength multiplexing or mode multiplexing, both of which have limited scalability due to high insertion loss or wavelength crosstalk. In this work, we present a scalable intensity-based photonic MVM processor based on the concept of waveguide multiplexing. This scheme employs multiport photodetectors (PDs) to sum the intensities of multiple optical signals, eliminating the need for multiple wavelengths or modes. A 16-port Ge PD with a 3 dB bandwidth of 11.8 GHz at a bias voltage of ${-}{3}\;{\text{V}}$ is demonstrated, and it can be further scaled up to handle 250 ports while maintaining a 6.1 GHz operation bandwidth. A $4 \times 4$ circuit fabricated on a Si-on-insulator platform is used to perform MVMs in a three-layer neural network designed for classifying Iris flowers, achieving a classification accuracy of 93.3%. Furthermore, the performance of large-scale circuits in a convolutional neural network for Fashion-MNIST is simulated, resulting in a classification accuracy of 90.53%. This work provides a simplified and scalable approach to photonic MVM, laying a foundation for large-scale and multi-dimensional photonic matrix–matrix multiplication in optical neural networks.

关键词： Deep learning Erbium-doped fiber amplifiers Grating coupler Neural networks Optical neural systems Optical signals

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