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SSRN 2023年

作者： Wang, Yuwei Zhao, Na Wu, Chaoyang Quan, Jinling Chen, Mingxing State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China College of Resources and Environment University of Chinese Academy of Sciences Beijing100049 China Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application Nanjing210023 China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China Key Laboratory of Regional Sustainable Development Modelling Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China

Global warming leads to more frequent and intense heatwaves, putting urban populations at greater risk. Previous related studies considered only surface air temperature or 1-2 shared socioeconomic pathways (SSPs), and were limited to specific regions. Moreover, no research focused on heatwave exposure in highly-populated global megacities facing severe threats. This study is the first to project future population exposure to heatwaves in 83 global megacities by 2100 using fine-resolution data, suitable indices reflecting human comfort in heatwaves by incorporating temperature and humidity, and a future population exposure projection and analysis framework. The results show that (1) the global frequency of extreme heatwave events and average change rate in each megacity sequentially increase from SSP126 to SSP585, and the change rate is generally larger in megacities in the Southern Hemisphere;(2) the increases in heatwave exposure are greatest under SSP370, and the change rates are generally larger for megacities in southern Asia;(3) there is a high degree of inequality (Gini of 0.6 to 0.63) in future heatwave exposure globally, with the highest inequality under SSP585 and the lowest under SSP370;(4) the average exposure, increase rate and change are highest in low-income megacities and lowest in high-income megacities. The distribution of exposure is the most balanced in middle-income megacities and the least balanced in high-income megacities;and (5) population growth contributes more to the change in exposure than total warming in high-income megacities under SSP126;and total urban warming contributes much more than population growth in all other cases. Every effort should be made to avoid SSP370 scenario and pursue sustainable and rational urban economic development. Mumbai, Manila, Kolkata, and Jakarta warrant particular attention due to their rapid exposure growth. Additionally, policymakers and urban planners must focus on improving sustainable development pl

关键词： Population statistics

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Association of PM_(2.5)Reduction with Improved Kidney Function:A Nationwide Quasiexperiment among Chinese Adults

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Health Data Science 2022年第1期2022卷 115-123页

作者： Yiqun Han Tao Xue Frank J.Kelly Yixuan Zheng Yao Yao Jiajianghui Li Jiwei Li Chun Fan Pengfei Li Tong Zhu Environmental Research Group MRC Centre for Environment and HealthImperial College LondonLondonUK BIC-ESAT and SKL-ESPC College of Environmental Sciences and EngineeringCenter for Environment and HealthPeking UniversityBeijing 100871China Institute of Reproductive and Child Health/Ministry of Health Key Laboratory of Reproductive Health and Department of Epidemiology and Biostatistics School of Public HealthPeking UniversityBeijingChina Center of Air Quality Simulation and System Analysis Chinese Academy of Environmental Planning100012 BeijingChina China Center for Health Development Studies Peking UniversityBeijingChina College of Computer Science and Technology Zhejiang UniversityHangzhouChina Computer Center Peking University and Peng Cheng LaboratoryChina Advanced Institute of Information Technology Peking UniversityChina

*** evidence from human studies has revealed the adverse impact of ambient fine particles(PM_(2.5))on health outcomes related to metabolic disorders and distant *** exposure to ambient PM_(2.5)leads to kidney impairment remains *** rapid air quality improvement driven by the clean air actions in China since 2013 provides an opportunity for a quasiexperiment to investigate the beneficial effect of PM_(2.5)reduction on kidney *** on two repeated nationwide surveys of the same population of 5115 adults in 2011 and 2015,we conducted a difference-indifference *** in long-term exposure to ambient PM_(2.5)were associated with changes in kidney function biomarkers,including estimated glomerular filtration rate by serum creatinine(GFRscr)or cystatin C(GFRcys),blood urea nitrogen(BUN),and uric acid(UA).*** a 10μg/m^(3)reduction in PM_(2.5),a significant improvement was observed for multiple kidney functional biomarkers,including GFRscr,BUN and UA,with a change of 0.42(95%confidence interval[CI]:0.06,0.78)mL/min/1.73m^(2),-0.38(-0.64,-0.12)mg/dL,and-0.06(-0.12,-0.00)mg/dL,respectively.A lower socioeconomic status,indicated by rural residence or low educational level,enhanced the adverse effect of PM_(2.5)on kidney *** results support a significant nephrotoxicity of PM_(2.5)based on multiple serum biomarkers and indicate a beneficial effect of improved air quality on kidney function.

关键词： Kidney organs ambient

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CM-UNet++: A Multi-Level Information Optimized Network for Urban Water Body Extraction from High-Resolution Remote Sensing Imagery

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Remote Sensing 2025年第6期17卷

作者： Cai, Jiangchen Tao, Liufeng Li, Yang National Engineering Research Center of Geographic Information System China University of Geosciences Wuhan 430074 China School of Computer Science China University of Geosciences Wuhan 430074 China Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural Resource Shenzhen518000 China Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software Ministry of Education Wuhan 430074 China School of Geophysics and Geomatics China University of Geosciences Wuhan 430074 China Anhui Urban & Rural Planning and Design Institute Co. Ltd. Hefei230022 China

Urban water bodies are crucial in urban planning and flood detection, and they are susceptible to changes due to climate change and rapid urbanization. With the development of high-resolution remote sensing technology and the success of semantic segmentation using deep learning in computer vision, it is possible to extract urban water bodies from high-resolution remote sensing images. However, many urban water bodies are small, oddly shaped, silted, or spectrally similar to other objects, making their extraction extremely challenging. In this paper, we propose a neural network named CM-UNet++, a combination of the dense-skip module based on UNet++ and the CSMamba module to encode different levels’ information with interactions and then extract global and local information at each level. We use a size-weighted auxiliary loss function to balance feature maps of different levels. Additionally, features beyond RGB are incorporated into the input of the neural network to enhance the distinction between water bodies and other objects. We produced a labeled urban water extraction dataset, and experiments on this dataset show that CM-UNet++ attains 0.8781 on the IOU (intersection over union) metric, which indicates that this method outperforms other recent semantic segmentation methods and achieves better completeness, connectivity, and boundary accuracy. The proposed dense-skip module and CSMamba module significantly improve the extraction of small and spectrally indistinct water bodies. Furthermore, experiments on a public dataset confirm the method’s robustness. © 2025 by the authors.

关键词： Semantic Segmentation

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ExpoNAS: Using Exposure-based Candidate Exclusion to Reduce NAS Space for Heterogeneous Pipeline of CNNs

ExpoNAS: Using Exposure-based Candidate Exclusion to Reduce ...

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International Conference on Parallel and Distributed systems (ICPADS)

作者： Huijuan Duan Danghui Wang Xingting Zhao Kuizhi Mei Jihe Wang School of Computer Science Northwestern Polytechnical University Xi'an China Engineering Research Center of Embedded System Integration Ministry of Education Northwestern Polytechnical University Xi'an China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Northwestern Polytechnical University Xi'an China

Efficiently deploying neural network models on heterogeneous edge systems faces unique challenges. Two major issues are underutilized pipelines caused by hardware heterogeneity and explosive growth of architecture search space. To address these challenges, we propose ExpoNAS, a framework enabling fast deployment-oriented neural architecture search on heterogeneous platforms. ExpoNAS evaluates model partitioning and deployability before search to meet accuracy and latency constraints. However, repeated partitioning leads to exponential growth in architecture search space. We first observe a strong correlation between operational exposure and accuracy improvement, and then we propose an exposure-based reduction technique that excludes the operations of low exposure. By removing these non-critical operations, the exposure-based pruning successfully reduces architecture search time. Our experiments on popular datasets show that the ExpoNAS pipelined design improves execution efficiency by a factor of 2 compared to traditional batch processing. Without compromising accuracy, ExpoNAS is able to reduce the architecture search time by 47.5% for discovering high-accuracy networks, compared to the SOTA NAS strategies.

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Global Future Population Exposure to Heatwaves

SSRN

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SSRN 2023年

作者： Wang, Yuwei Zhao, Na Yin, Xiaozhe Wu, Chaoyang Chen, Mingxing Jiao, Yimeng Yue, Tianxiang State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China College of Resources and Environment University of Chinese Academy of Sciences Beijing100101 China Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application Nanjing210023 China University of Southern California United States Key Laboratory of Land Surface Pattern and Simulation Institute Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China Key Laboratory of Regional Sustainable Development Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China

The increasing exposure to extreme heatwaves in urban areas from both climate change and the urban heat island (UHI) effect is posing multiple threats and challenges to human society. Despite a growing number of studies focusing on extreme exposure, research advances are still limited in some aspects like oversimplifies of some inter-individual factors of human exposure, resulting in unreliable and unrealistic estimates of future results. In addition, little research performed comprehensive and fine-resolution global analysis in future scenarios. In this study, we present the first global fine-resolution projection of future changing urban population exposure to heatwaves by 2100 under four shared socioeconomic pathways (SSPs) by considering the urban development at global, regional, and national scales. Overall, global urban population exposure to heatwaves is rising under the four SSPs. Temperate and tropical zones predictably have the greatest exposure among all climate zones. Coastal cities are projected to have the greatest exposure, followed closely by cities at low altitudes. Middle-income countries have the lowest exposure and the lowest inequality of exposure among countries. Individual climate effects contributed the most (about 46.4%) to future changes in exposure, followed by the interactive effect between climate and urbanization (about 18.5%). Our results indicate that more attention needs to be paid to policy improvements and sustainable development planning of global coastal cities and some low-altitude cities, especially in low- and high-income countries. Meanwhile, this study also highlights the impact of continued future urban expansion on population exposure to heatwaves. © 2023, The Authors. All rights reserved.

关键词： Climate change

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Global Future Population Exposure to Heatwaves

SSRN

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SSRN 2023年

关键词： Climate change

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Global Future Population Exposure to Heatwaves

SSRN

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SSRN 2023年

关键词： Climate change

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Coupled Pacific Rim megadroughts contributed to the fall of the MingDynasty’s capital in 1644 CE

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Science Bulletin 2024年第19期69卷 3106-3114页

作者： Feng Chen Tao Wang Xiaoen Zhao Jan Esper Fredrik Charpentier Ljungqvist Ulf Büntgen Hans W.Linderholm David Meko Hongna Xu Weipeng Yue Shijie Wang Yujiang Yuan Jingyun Zheng Wei Pan Fidel Roig Martín Hadad Mao Hu Jiachang Wei Fahu Chen Yunnan Key Laboratory of International Rivers and Transboundary Eco-Security Institute of International Rivers and Eco-SecurityYunnan UniversityKunming 650504China Southwest United Graduate School Kunming 650504China Climate Change Research Center and Nansen-Zhu International Research Centre Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China Department of Geography Johannes Gutenberg UniversityMainz 55099Germany Global Change Research Institute(CzechGlobe) Czech Academy of SciencesBrno 60300Czech Republic Department of History Stockholm UniversityStockholm 10691Sweden Bolin Centre for Climate Research Stockholm UniversityStockholm 10691Sweden Swedish Collegium for Advanced Study LinneanumUppsala 75238Sweden Department of Geography University of CambridgeCambridge CB23ENUK Department of Geography Faculty of ScienceMasaryk UniversityBrno 61137Czech Republic Swiss Federal Research Institute(WSL) Birmensdorf 8903Switzerland Regional Climate Group Department of Earth SciencesUniversity of GothenburgGothenburg 40530Sweden Laboratory of Tree-Ring Research University of ArizonaTucson AZ 85721USA Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC FEMD) Nanjing University of Information Science and TechnologyNanjing 210044China Key Laboratory of Tree-ring Physical and Chemical Research Institute of Desert MeteorologyChina Meteorological AdministrationUrumqi 830002China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China Key Laboratory of Digital Human Technology R&D and Application of Yunnan Provincial Department of Education Yunnan UniversityKunming 650504China Laboratorio de Dendrocronología e Historia Ambiental IANIGLA-CCT CONICET-Universidad Nacional de CuyoMendoza 5500Argentina Hémera Centro de Observación de La Tierra Escuela de Ingeniería ForestalFacultad de CienciasUniversidad MayorHuechuraba 85

Historical documents provide evidence for regional droughts preceding the political turmoil and fall of Beijing in 1644 CE,when more than 20 million people died in northern China during the late Ming famine ***,the role climate and environmental changes may have played in this pivotal event in Chinese history remains ***,we provide tree-ring evidence of persistent megadroughts from1576 to 1593 CE and from 1628 to 1644 CE in northern China,which coincided with exceptionally cold summers just before the fall of *** analysis reveals that these regional hydroclimatic extremes are part of a series of megadroughts along the Pacific Rim,which not only impacted the ecology and society of monsoonal northern China,but likely also exacerbated external geopolitical and economic *** finding is corroborated by last millennium reanalysis data and numerical climate model simulations revealing internally driven Pacific sea surface temperature variations and the predominance of decadal scale La Ni?a-like conditions to be responsible for precipitation decreases over northern China,as well as extensive monsoon regions in the *** teleconnection patterns provide a mechanistic explanation for reoccurring drought spells during the late Ming Dynasty and the environmental framework fostering the fall of Beijing in 1644 CE,and the subsequent demise of the Ming Dynasty.

关键词： Paleoclimate Beijing Precipitation reconstruction Climate-society interactions Tree rings Ming Dynasty

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Time-varying Characteristics of mmWave Channel based on the Clustered Sparsity Model

Time-varying Characteristics of mmWave Channel based on the ...

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IEEE Conference on Vehicular Technology (VTC)

作者： Lijun Yang Haitao Lu Xinchao Ge Zhixin Sun Pan Cao School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing China RAN System Design Dept. ZTE Corporation Shenzhen China Shenzhen Key Laboratory of 5G RAN Security Technology Research and Application Shenzhen China School of Modern Posts Nanjing University of Posts and Telecommunications Nanjing China School of Physics Engineering and Computer Science University of Hertfordshire Hatfield UK

A better understanding of the mmWave channel coherence time will play a significant role in both communication and sensing. Traditional literatures consider that the channel coherence time in mmWave communication will be very short due to high frequency, which makes the channel estimation more difficult. However, we believe that the propagation characteristics of the mmWave channel will affect its coherence time, i.e., channel sparsity may limit its time variability. In this paper, we investigate the time-varying nature of mmWave channel by analyzing its Doppler spread based on the clustered sparsity model. After the derivation of Doppler spectrum DPS and the simulation analysis based on the two-cluster channel, we found that the maximum angle between clusters and the power distribution between clusters are two main factors limiting the Doppler spread. Finally, the realistic mmWave NYUSIM model is used for Doppler spread comparative analysis with Clarke’s model and find that Doppler spread of NYUSIM channel is far less than that estimated based on Clarke’s model. Thus, it is demonstrated that even if the mmWave frequency is high, the clustered sparsity characteristics makes its channel coherence time not as short as thought intuitively, which is meaningful for mmWave vehicular sensing and communication.

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ATASI-Net: An Efficient Sparse Reconstruction Network for Tomographic SAR Imaging with Adaptive Threshold

arXiv

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arXiv 2022年

作者： Wang, Muhan Zhang, Zhe Qiu, Xiaolan Gao, Silin Wang, Yue Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology China Suzhou Aerospace Information Research Institute Jiangsu Suzhou China Aerospace Information Research Institute Chinese Academy of Sciences China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China The Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China Electrical and Computer Engineering Department George Mason University FairfaxVA United States

Tomographic SAR technique has attracted remarkable interest for its ability of three-dimensional resolving along the elevation direction via a stack of SAR images collected from different cross-track angles. The emerged compressed sensing (CS)-based algorithms have been introduced into TomoSAR considering its super-resolution ability with limited samples. However, the conventional CS-based methods suffer from several drawbacks, including weak noise resistance, high computational complexity, and complex parameter fine-tuning. Aiming at efficient TomoSAR imaging, this paper proposes a novel efficient sparse unfolding network based on the analytic learned iterative shrinkage thresholding algorithm (ALISTA) architecture with adaptive threshold, named Adaptive Threshold ALISTA-based Sparse Imaging Network (ATASI-Net). The weight matrix in each layer of ATASI-Net is pre-computed as the solution of an offline optimization problem, leaving only two scalar parameters to be learned from data, which significantly simplifies the training stage. In addition, adaptive threshold is introduced for each azimuth-range pixel, enabling the threshold shrinkage to be not only layer-varied but also element-wise. Moreover, the final learned thresholds can be visualized and combined with the SAR image semantics for mutual feedback. Finally, extensive experiments on simulated and real data are carried out to demonstrate the effectiveness and efficiency of the proposed method. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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