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Implications of Sea Breezes on Air Quality Monitoring in a Coastal Urban Environment: Evidence From High Resolution Modeling of NO2and O3

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Journal of Geophysical Research: Atmospheres 2023年第11期128卷 e2022JD037860-e2022JD037860页

作者： Bo Wang Jeffrey A. Geddes Taylor J. Adams Elena S. Lind Brian C. McDonald Jian He Colin Harkins Dan Li Gabriele G. Pfister Department of Earth and Environment Boston University MA Boston USA B. Wang Contribution: Conceptualization Methodology Formal analysis Investigation Resources Writing - original draft Writing - review & editing Visualization Contribution: Conceptualization Methodology Formal analysis Investigation Resources Data curation Writing - review & editing Supervision Project administration Funding acquisition Contribution: Resources Writing - review & editing Department of Electrical and Computer Engineering Virginia Tech VA Blacksburg USA Now at NASA Goddard Space Flight Center Greenbelt MD USA Chemical Sciences Laboratory NOAA Earth System Research Laboratories CO Boulder USA Cooperative Institute for Research in Environmental Sciences University of Colorado CO Boulder USA Contribution: Methodology Resources Writing - review & editing Atmospheric Chemistry Observations and Modeling Laboratory NCAR CO Boulder USA Contribution: Conceptualization Resources Writing - review & editing

Coastal urban environments face unique challenges associated with air quality-meteorology interactions. In this study, high resolution chemical transport modeling over the Greater Boston area was performed to improve our understanding of sea breezes impacts on the spatiotemporal variability of primary and secondary pollutants. We perform WRF-Chem simulations at 3 km resolution over June 22 to 10 July 2019 (a period that included 10 sea breeze occurrences), and use Pandora tropospheric NO 2 column, surface air quality monitoring, and vertical meteorological aircraft profiles for evaluation. The model generally reproduces observed spatiotemporal variability of air pollution during sea breezes well. Tropospheric columns of NO 2 predicted by the model and observed by the Pandora instrument show that sea breezes are associated with rapid increases and steep gradients in tropospheric NO 2 and confirm accumulation of local primary emissions. Spatial heterogeneity in tropospheric NO 2 is strongly governed by inland penetration lengths of the sea breeze front. Process diagnostics show that three sea-breeze days where O 3 observations recorded hourly concentrations >70 ppb have both efficient net chemical O 3 production in the boundary layer (>10 ppb/hr) and rapid O 3 convergence in the near-surface convergence zone (>20 ppb/hr). During sea breezes, interactions between photochemistry, the convergence zone inland penetration, and urban NO x titration effects, contribute to strong heterogeneity and high O 3 inland that is not captured by the current monitoring network. We discuss monitoring needs and model applications for the sea breeze scenarios, with broad implications for air quality monitoring in coastal urban environments. High resolution WRF-Chem modeling reproduces observed spatiotemporal variability of NO 2 and O 3 during sea breezes in the Greater Boston area Model results suggest high O 3 pollution and NO 2 column spatial variability associated with sea breezes is n

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Fully Automated and Standardized Segmentation of Adipose Tissue Compartments by Deep Learning in Three-dimensional Whole-body MRI of Epidemiological Cohort Studies

arXiv

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arXiv 2020年

作者： Küstner, Thomas Hepp, Tobias Fischer, Marc Schwartz, Martin Fritsche, Andreas Häring, Hans-Ulrich Nikolaou, Konstantin Bamberg, Fabian Yang, Bin Schick, Fritz Gatidis, Sergios Machann, Jürgen University Hospital Tübingen Diagnostic and Interventional Radiology Medical Image and Data Analysis Tübingen Germany University of Stuttgart Department for Signal Processing and System Theory Stuttgart Germany School of Biomedical Engineering & Imaging Sciences King’s College London St. Thomas’ Hospital London United Kingdom Max-Planck Institute for Intelligent Systems Empirical Inference Department Tübingen Germany University Hospital Tübingen Diagnostic and Interventional Radiology Section on Experimental Radiology Tübingen Germany Department of Internal Medicine IV Eberhard Karls University Tübingen Germany Helmholtz Zentrum München University of Tübingen Tübingen Germany Germany Department of Diagnostic and Interventional Radiology Medical Center – University of Freiburg Faculty of Medicine University of Freiburg Germany

Purpose: To enable fast and reliable assessment of subcutaneous and visceral adipose tissue compartments derived from whole-body MRI. Methods: Quantification and localization of different adipose tissue compartments from whole-body MR images is of high interest to examine metabolic conditions. For correct identification and phenotyping of individuals at increased risk for metabolic diseases, a reliable automatic segmentation of adipose tissue into subcutaneous and visceral adipose tissue is required. In this work we propose a 3D convolutional neural network (DCNet) to provide a robust and objective segmentation. In this retrospective study, we collected 1000 cases (66±13 years;523 women) from the Tuebingen Family Study and from the German Center for Diabetes research (TUEF/DZD), as well as 300 cases (53±11 years;152 women) from the German National Cohort (NAKO) database for model training, validation, and testing with a transfer learning between the cohorts. These datasets had variable imaging sequences, imaging contrasts, receiver coil arrangements, scanners and imaging field strengths. The proposed DCNet was compared against a comparable 3D UNet segmentation in terms of sensitivity, specificity, precision, accuracy, and Dice overlap. Results: Fast (5-7seconds) and reliable adipose tissue segmentation can be obtained with high Dice overlap (0.94), sensitivity (96.6%), specificity (95.1%), precision (92.1%) and accuracy (98.4%) from 3D whole-body MR datasets (field of view coverage 450x450x2000mm3). Segmentation masks and adipose tissue profiles are automatically reported back to the referring physician. Conclusion: Automatic adipose tissue segmentation is feasible in 3D whole-body MR data sets and is generalizable to different epidemiological cohort studies with the proposed DCNet. Copyright © 2020, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Wireless Image Transmission Using Deep Source Channel Coding With Attention Modules

arXiv

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arXiv 2020年

作者： Xu, Jialong Ai, Bo Chen, Wei Yang, Ang Sun, Peng Rodrigues, Miguel The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China Frontiers Science Center for Smart High-Speed Railway System Beijing100044 China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen518055 China Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China vivo Communication Research Institute Beijing100015 China The Department of Electronic and Electrical Engineering University College London LondonWC1E 7JE United Kingdom

Recent research on joint source-channel coding (JSCC) for wireless communications has achieved great success owing to the employment of deep learning (DL). However, the existing work on DL based JSCC usually trains the designed network to operate under a specific signal-to-noise ratio (SNR) regime, without taking into account that the SNR level during the deployment stage may differ from that during the training stage. A number of networks are required to cover the scenario with a broad range of SNRs, which is computational inefficiency (in the training stage) and requires large storage. To overcome these drawbacks our paper proposes a novel method called Attention DL based JSCC (ADJSCC) that can successfully operate with different SNR levels during transmission. This design is inspired by the resource assignment strategy in traditional JSCC, which dynamically adjusts the compression ratio in source coding and the channel coding rate according to the channel SNR. This is achieved by resorting to attention mechanisms because these are able to allocate computing resources to more critical tasks. Instead of applying the resource allocation strategy in traditional JSCC, the ADJSCC uses the channel-wise soft attention to scaling features according to SNR conditions. We compare the ADJSCC method with the state-of-the-art DL based JSCC method through extensive experiments to demonstrate its adaptability, robustness and versatility. Compared with the existing methods, the proposed method takes less storage and is more robust in the presence of channel mismatch. © 2020, CC BY.

关键词： Signal to noise ratio

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Analysis of railway accidents' narratives using deep learning

arXiv

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arXiv 2018年

作者： Heidarysafa, Mojtaba Kowsari, Kamran Barnes, Laura E. Brown, Donald E. Department of System and Information Engineering University of Virginia CharlottesvilleVA United States Data Science Institute University of Virginia CharlottesvilleVA United States

Automatic understanding of domain specific texts in order to extract useful relationships for later use is a nontrivial task. One such relationship would be between railroad accidents' causes and their correspondent descriptions in reports. From 2001 to 2016 rail accidents in the U.S. cost more than $4.6B. Railroads involved in accidents are required to submit an accident report to the Federal Railroad Administration (FRA). These reports contain a variety of fixed field entries including primary cause of the accidents (a coded variable with 389 values) as well as a narrative field which is a short text description of the accident. Although these narratives provide more information than a fixed field entry, the terminologies used in these reports are not easy to understand by a non-expert reader. Therefore, providing an assisting method to fill in the primary cause from such domain specific texts (narratives) would help to label the accidents with more accuracy. Another important question for transportation safety is whether the reported accident cause is consistent with narrative description. To address these questions, we applied deep learning methods together with powerful word embeddings such as Word2Vec and GloVe to classify accident cause values for the primary cause field using the text in the narratives. The results show that such approaches can both accurately classify accident causes based on report narratives and find important inconsistencies in accident reporting1. Copyright © 2018, The Authors. All rights reserved.

关键词： Railroads

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Correction to: Remote effect of model systematic bias in tropical SST on the cold bias over the Tibetan Plateau

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Climate Dynamics 2022年第11期60卷 4193-4193页

作者： Wu, Yuting Hu, Xiaoming Li, Zhenning Cai, Ming Lu, Mengmeng Yang, Song Chongqing Research Institute of Big Data Peking University Chongqing China School of Atmospheric Sciences Sun Yat-Sen University Zhuhai China Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) Zhuhai China Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies Sun Yat-Sen University Zhuhai China Division of Environment and Sustainability The Hong Kong University of Science and Technology Hong Kong China Department of Earth Ocean and Atmospheric Science Florida State University Tallahassee USA State Key Laboratory of Severe Weather Institute of Climate System Chinese Academy of Meteorological Sciences Beijing China

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Anomalously low thermal conductivity in superhard cubic Si3N4

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Physical Review B 2019年第6期100卷 064303-064303页

作者： Jing Liu Shenghong Ju Norimasa Nishiyama Shiomi Junichiro Department of Mechanical Engineering University of Tokyo 7-3-1 Hongo Bunkyo Tokyo 113-8656 Japan Center for Materials research by Information Integration (CMI2) Research and Services Division of Materials Data and Integrated System (MaDIS) National Institute for Materials Science (NIMS) 1-1 Namiki Tsukuba Ibaraki 305-0044 Japan Laboratory for Materials and Structures Tokyo Institute of Technology 4259 Nagatsuta-cho Midori-ku Yokohama 226-8503 Japan

Superhard bulk materials with large bulk modulus like diamond and cubic BN (c-BN) show high thermal conductivity (κ) reaching 2000–3000 Wm−1K−1. The large modulus means large group velocity, which contributes to high lattice thermal conductivity. However, whether the hardening of phonon bands would increase or decrease the phonon scattering rate through the phonon-phonon scattering phase space is not evident and should depend on the material type. In this work, we target cubic silicon nitride (c−Si3N4), which was recently developed and found to be the third superhardest material next to the diamond and c-BN. The κ of polycrystalline c−Si3N4 was measured with the time-domain thermoreflectance method, and thermal conductivity of 23 Wm−1K−1 was obtained at room temperature. The first-principles thermal conductivity calculations identified that this translates to a single-crystal value of about 88 Wm−1K−1, which is more than an order of magnitude smaller than those of diamond and c-BN. The calculation further identified that difference arises from the large scattering phase space and anharmonic amplitude of c−Si3N4, which are two to three times larger than the values of diamond. It is found that the scattering phase space of c−Si3N4 optical phonons with small dispersion anomalously increases with the frequency.

关键词： Lattice thermal conductivity Phonons Thermal properties Thermoreflectance

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Errata to “On-Edge Multi-Task Transfer Learning: Model and Practice With data-Driven Task Allocation”

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IEEE Transactions on Parallel and Distributed systems 2020年第11期31卷 2569-2569页

作者： Qiong Chen Zimu Zheng Chuang Hu Dan Wang Fangming Liu National Engineering Research Center for Big Data Technology and System Huazhong University of Science and Technology Wuhan China Edge Cloud Innovation Lab Technical Innovation Department Cloud BU Huawei Technologies Company Ltd. Shenzhen China Department of Computing Hong Kong Polytechnic University Kowloon Hong Kong

Presents corrections to author information for the above named paper.

关键词： Task analysis Resource management Technological innovation Computational modeling Big data Service computing Computer science

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Small P values may not yield robust findings:an example using REST-meta-PD

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Science Bulletin 2021年第21期66卷 2148-2152,M0003页

作者： Xi-Ze Jia Na Zhao Hao-Ming Dong Jia-Wei Sun Marek Barton Roxana Burciu Nicolas Carrière Antonio Cerasa Bo-Yu Chen Jun Chen Stephen Coombes Luc Defebvre Christine Delmaire Kathy Dujardin Fabrizio Esposito Guo-Guang Fan Federica Di Nardo Yi-Xuan Feng Brett W.Fling Saurabh Garg Moran Gilat Martin Gorges Shu-Leong Ho Fay BHorak Xiao Hu Xiao-Fei Hu Biao Huang Pei-Yu Huang Ze-Juan Jia Christina Jones Jan Kassubek Lenka Krajcovicova Ajay Kurani Jing Li Qing Li Ai-Ping Liu Bo Liu Hu Liu Wei-Guo Liu Renaud Lopes Yu-Ting Lou Wei Luo Tara Madhyastha Ni-Ni Mao Grainne McAlonan Martin J.McKeown Shirley Pang Andrea Quattrone Irena Rektorova Alessia Sarica Hui-Fang Shang James M.Shine Priyank Shukla Tomas Slavicek Xiao-Peng Song Gioacchino Tedeschi Alessandro Tessitore David Vaillancourt Jian Wang Jue Wang Z.Jane Wang Lu-Qing Wei Xia Wu Xiao-Jun Xu Lei Yan Jing Yang Wan-Qun Yang Nai-Lin Yao De-Long Zhang Jiu-Quan Zhang Min-Ming Zhang Yan-Ling Zhang Cai-Hong Zhou Chao-Gan Yan Xi-Nian Zuo Mark Hallett Tao Wu Yu-Feng Zang Center for Cognition and Brain Disorders the Affiliated HospitalHangzhou Normal UniversityHangzhou 310015China Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments Hangzhou 311121China National Basic Science Data Center Beijing 100190China State Key Laboratory of Cognitive Neuroscience and Learning&McGovern Institute for Brain Research Beijing Normal UniversityBeijing 100875China School of Information and Electronics Technology Jiamusi UniversityJiamusi 154007China Neuroscience Program Central European Institute of TechnologyCEITECMasaryk UniversityBrno 62500Czech Republic Department of Applied Physiology and Kinesiology University of FloridaGainesville FL 32611USA Univ.Lille InsermCHU LilleU1172-LilNCog-Lille Neuroscience&CognitionLille F-59000France The Institute for Biomedical Research and Innovation National Research CouncilMangone CS 87050Italy Department of Radiology The First Affiliated Hospital of China Medical UniversityShenyang 110001China Department of Radiology Guangdong Provincial Hospital of Chinese MedicineGuangzhou 510120China Department of Medicine Surgery and DentistryScuola Medica SalernitanaUniversity of SalernoFiscianoSA 132-84084Italy Department of Advanced Medical and Surgery Sciences University of Campania“Luigi Vanvitelli”Caserta 81100Italy Eye Center of the 2nd Affiliated Hospital Medical College of Zhejiang UniversityHangzhou 310020China Zhejiang Provincial Key Lab of Ophthalmology Hangzhou 310020China Department of Health and Exercise Science Colorado State UniversityFort Collins CO 80523USA Pacific Parkinson’s Research Centre University of British ColumbiaVancouver BC V6E 2M6Canada Department of Medicine(Neurology)University of British Columbia Vancouver BC V6T 1B7Canada Brain and Mind Center The University of SydneySydney NSW 2006Australia Department of Neurology Ulm UniversityUlm 89081Germany Division of Neurology Department of MedicineQueen Mary HospitalUniversity of Hong KongHong Kong 999077China Department of

Thousands of resting state functional magnetic resonance imaging(RS-f MRI)articles have been published on brain *** precise localization of abnormal brain activity,a voxel-level comparison is *** of the large number of voxels in the brain,multiple comparison correction(MCC)must be performed to reduce false positive rates,and a smaller P value(usually including either liberal or stringent MCC)is widely recommended[1].

关键词：帕金森病 precise correction

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Author Correction: Site selection of desert solar farms based on heterogeneous sand flux

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npj Climate and Atmospheric Science 2024年第1期7卷 1页

作者： Guoshuai Li Tao Che Ying Zhang Chunlin Huang Rui Jin Wenjun Tang Xin Li Lihai Tan Bao Yang Guangcai Feng Fredrik Charpentier Ljungqvist Yayong Luo Heqiang Du Hui Zhao Ning Huang Heihe Remote Sensing Experimental Research Station Northwest Institute of Eco-Environment and Resources Chinese Academy of Sciences Lanzhou China National Tibetan Plateau Data Center State Key Laboratory of Tibetan Plateau Earth System Environment and Resources Institute of Tibetan Plateau Research Chinese Academy of Sciences Beijing China Dunhuang Gobi Desert Research Station Northwest Institute of Eco-Environment and Resources Chinese Academy of Sciences Lanzhou China School of Geography and Ocean Science Nanjing University Nanjing China School of Geosciences and Info-physics Central South University Changsha China Department of History Stockholm University Stockholm Sweden Bolin Centre for Climate Research Stockholm University Stockholm Sweden Swedish Collegium for Advanced Study Uppsala Sweden Naiman Desertification Research Station Northwest Institute of Eco-Environment and Resources Chinese Academy of Sciences Lanzhou China Key Laboratory of Desert and Desertification Northwest Institute of Eco-Environment and Resources Chinese Academy of Sciences Lanzhou China Department of Mechanics College of Civil Engineering and Mechanics Lanzhou University Lanzhou China

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A study on the improvement and application of system dynamics model for demand forecasting of ships

A study on the improvement and application of system dynamic...

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RINA, Royal Institution of Naval Architects - International Conference on Computer Applications in Shipbuilding 2017, ICCAS 2017

作者： Wada, Y. Hamada, K. Hirata, N. Knowledge and Data System Department National Maritime Research Institute Japan Graduate School of Engineering Hiroshima University Japan

ISBN: (纸本)9781909024670

Shipbuilding is an industry in which the change in demand has been extremely drastic. Therefore, it is important to develop a method of demand forecasting for new ships in order to realize sustainable development of the shipbuilding industry. Recently, sufficient actual data on the shipbuilding market has become available, and it is expected to define an accurate demand-forecasting model by using these data. In this study, the system dynamics model of previous study is improved by using latest market data, and optimal measures planning system for shipbuilding industry is developed based on the proposed model. Thereby, some simulations using the proposed demand-forecasting model and system are conducted, and the effectiveness of proposed model and system are shown. © 2017: The Royal Institution of Naval Architects.

关键词： Shipbuilding

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