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arXiv 2022年

作者： Chen, Gang Canonaco, Francesco Tobler, Anna Aas, Wenche Alastuey, Andres Allan, James Atabakhsh, Samira Aurela, Minna Baltensperger, Urs Bougiatioti, Aikaterini De Brito, Joel F. Ceburnis, Darius Chazeau, Benjamin Chebaicheb, Hasna Daellenbach, Kaspar R. Ehn, Mikael Haddad, Imad El Eleftheriadis, Konstantinos Favez, Olivier Flentje, Harald Font, Anna Fossum, Kirsten Freney, Evelyn Gini, Maria Green, David C. Heikkinen, Liine Herrmann, Hartmut Kalogridis, Athina-Cerise Keernik, Hannes Lhotka, Radek Lin, Chunshui Lunder, Chris Maasikmets, Marek Manousakas, Manousos I. Marchand, Nicolas Marin, Cristina Marmureanu, Luminita Mihalopoulos, Nikolaos Močnik, Griša Nęcki, Jaroslaw O'Dowd, Colin Ovadnevaite, Jurgita Peter, Thomas Petit, Jean-Eudes Pikridas, Michael Platt, Stephen Matthew Pokorná, Petra Poulain, Laurent Priestman, Max Riffault, Véronique Rinaldi, Matteo Różański, Kazimierz Schwarz, Jaroslav Sciare, Jean Simon, Leïla Skiba, Alicja Slowik, Jay G. Sosedova, Yulia Stavroulas, Iasonas Styszko, Katarzyna Teinemaa, Erik Timonen, Hilkka Tremper, Anja Vasilescu, Jeni Via, Marta Vodička, Petr Wiedensohler, Alfred Zografou, Olga Minguillón, María Cruz Prévôt, André S.H. Laboratory of Atmospheric Chemistry Paul Scherrer Institute Villigen5232 Switzerland Datalystica Ltd. Park innovAARE Villigen5234 Switzerland NILU - Norwegian Institute for Air Research Kjeller2007 Norway Barcelona08034 Spain Department of Earth and Environmental Sciences University of Manchester Manchester United Kingdom National Centre for Atmospheric Science University of Manchester Manchester United Kingdom Department of Chemistry The Atmosphere Leibniz Institute for Tropospheric Research Permoser Straße 15 Leipzig04318 Germany Atmospheric Composition Research Finnish Meteorological Institute P.O. Box 503 Helsinki00101 Finland Institute for Environmental Research and Sustainable Development National Observatory of Athens Palaia Penteli Athens15236 Greece IMT Nord Europe Institut Mines-Télécom Univ. Lille Centre for Energy and Environment Lille59000 France School of Physics Ryan Institute's Centre for Climate and Air Pollution Studies National University of Ireland Galway University Road GalwayH91 CF50 Ireland Aix Marseille Univ. CNRS LCE Marseille France AtmoSud Regional Network for Air Quality Monitoring of Provence-Alpes-Côte-d'Azur Marseille France Institut National de l'Environnement Industriel et des Risques Parc Technologique ALATA Verneuil en Halatte 60550 France /Physics University of Helsinki Helsinki Finland Environmental Radioactivity Laboratory Institute of Nuclear & Radiological Sciences & Technology Energy & Safety N.C.S.R. "Demokritos" Athens15310 Greece Deutscher Wetterdienst Meteorologisches Observatorium Hohenpeißenberg Hohenpeißenberg 82383 Germany MRC Centre for Environment and Health Environmental Research Group Imperial College London 86 Wood Lane LondonW12 0BZ United Kingdom HPRU in Environmental Exposures and Health Imperial College London United Kingdom Laboratoire de Météorologie Physique UMR6016 Université Clermont Auvergne-CNRS Aubière France TallinnMarja 4D Estonia Department of Software Scienc

Organic aerosol (OA) is a key component to total submicron particulate matter (PM1), and comprehensive knowledge of OA sources across Europe is crucial to mitigate PM1 levels. Europe has a well-established air quality research infrastructure from which yearlong datasets using 21 aerosol chemical speciation monitors (ACSMs) and 1 aerosol mass spectrometer (AMS) were gathered during 2013-2019. It includes 9 non-urban and 13 urban sites. This study developed a state-of-the-art source apportionment protocol to analyse long-term OA mass spectrum data by applying the most advanced source apportionment strategies (i.e., rolling PMF, ME-2, and bootstrap). This harmonised protocol was followed strictly for all 22 datasets, making the source apportionment results more comparable. In addition, it enables the quantifications of the most common OA components such as hydrocarbon-like OA (HOA), biomass burning OA (BBOA), cooking-like OA (COA), more oxidised-oxygenated OA (MO-OOA), and less oxidised-oxygenated OA (LO-OOA). Other components such as coal combustion OA (CCOA), solid fuel OA (SFOA: mainly mixture of coal and peat combustion), cigarette smoke OA (CSOA), sea salt (mostly inorganic but part of the OA mass spectrum), coffee OA, and ship industry OA could also be separated at a few specific sites. Oxygenated OA (OOA) components make up most of the submicron OA mass (average = 71.1%, range from 43.7 to 100%). Solid fuel combustion-related OA components (i.e., BBOA, CCOA, and SFOA) are still considerable with in total 16.0% yearly contribution to the OA, yet mainly during winter months (21.4%). Overall, this comprehensive protocol works effectively across all sites governed by different sources and generates robust and consistent source apportionment results. Our work presents a comprehensive overview of OA sources in Europe with a unique combination of high time resolution (30-240 minutes) and long-term data coverage (9-36 months), providing essential information to improve/

关键词： Aerosols

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Hypergraph learning for identification of COVID-19 with CT imaging

arXiv

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arXiv 2020年

作者： Di, Donglin Shi, Feng Yan, Fuhua Xia, Liming Mo, Zhanhao Ding, Zhongxiang Shan, Fei Li, Shengrui Wei, Ying Shao, Ying Han, Miaofei Gao, Yaozong Sui, He Gao, Yue Shen, Dinggang BNRist THUIBCS KLISS School of Software Tsinghua University Beijing China Department of Research and Development Shanghai United Imaging Intelligence Co. Ltd. Shanghai China Department of Radiology Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Department of Radiology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan Hubei China Department of Radiology China-Japan Union Hospital Jilin University Changchun Department of Radiology Hangzhou First Peoples Hospital Zhejiang University School of Medicine Zhejiang Department of Radiology Shanghai Public Health Clinical Center Fudan University Shanghai China

The coronavirus disease, named COVID-19, has become the largest global public health crisis since it started in early 2020. CT imaging has been used as a complementary tool to assist early screening, especially for the rapid identification of COVID-19 cases from community acquired pneumonia (CAP) cases. The main challenge in early screening is how to model the confusing cases in the COVID-19 and CAP groups, with very similar clinical manifestations and imaging features. To tackle this challenge, we propose an Uncertainty Vertex-weightedHypergraph Learning (UVHL) method to identify COVID-19 from CAP using CT images. In particular,multiple types of features (including regional features and radiomics features) are first extracted from CT image for each case. Then, the relationship among different cases is formulated by a hypergraph structure, with each case represented as a vertex in the hypergraph. The uncertainty of each vertex is further computed with an uncertainty score measurement and used as a weight in the hypergraph. Finally, a learning process of the vertex-weighted hypergraph is used to predict whether a new testing case belongs to COVID-19 or not. Experiments on a large multi-center pneumonia dataset, consisting of 2,148 COVID-19 cases and 1,182 CAP cases from five hospitals, are conducted to evaluate the performance of the proposed method. Results demonstrate the effectiveness and robustness of our proposed method on the identification of COVID-19 in comparison to state-of-the-art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Graph theory

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Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for COVID-19

arXiv

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arXiv 2020年

作者： Shi, Feng Wang, Jun Shi, Jun Wu, Ziyan Wang, Qian Tang, Zhenyu He, Kelei Shi, Yinghuan Shen, Dinggang Department of Research and Development Shanghai United Imaging Intelligence Co. Ltd. Shanghai200232 Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai Institute for Advanced Communication and Data Science School of Communication and Information Engineering Shanghai University Shanghai200444 United Imaging Intelligence CambridgeMA02140 United States Institute for Medical Imaging Technology School of Biomedical Engineering Shanghai Jiao Tong University Shanghai200030 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 Medical School of Nanjing University Nanjing China National Institute of Healthcare Data Science Nanjing University Nanjing210093 China National Key Laboratory for Novel Software and Technology Nanjing University Nanjing China

The pandemic of coronavirus disease 2019 (COVID-19) is spreading all over the world. Medical imaging such as X-ray and computed tomography (CT) plays an essential role in the global fight against COVID-19, whereas the recently emerging artificial intelligence (AI) technologies further strengthen the power of the imaging tools and help medical specialists. We hereby review the rapid responses in the community of medical imaging (empowered by AI) toward COVID-19. For example, AI-empowered image acquisition can significantly help automate the scanning procedure and also reshape the workflow with minimal contact to patients, providing the best protection to the imaging technicians. Also, AI ca n improve work efficiency by accurate delination of infections in X-ray and CT images, facilitating subsequent quantification. Moreover, the computer-aided platforms help radiologists make clinical decisions, i.e., for disease diagnosis, tracking, and prognosis. In this review paper, we thus cover the entire pipeline of medical imaging and analysis techniques involved with COVID-19, including image acquisition, segmentation, diagnosis, and follow-up. We particularly focus on the integration of AI with X-ray and CT, both of which are widely used in the frontline hospitals, in order to depict the latest progress of medical imaging and radiology fighting against COVID-19. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

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Conferring BiVO4Nanorods with Oxygen Vacancies to Realize Enhanced Sonodynamic Cancer Therapy

引用

Angewandte Chemie 2022年第44期134卷

作者： Dr. Zhuang Yang Dr. Meng Yuan Dr. Bin Liu Dr. Wenying Zhang Dr. Aziz Maleki Prof. Baolin Guo Prof. Ping'an Ma Prof. Ziyong Cheng Prof. Jun Lin State Key Laboratory of Rare Earth Resource Utilization Changchun Institute of Applied Chemistry Changchun 130022 P. R. China School of Applied Chemistry and Engineering University of Science and Technology of China Hefei 230026 P. R. China Contribution: Investigation (lead) Methodology (lead) Resources (lead) Supervision (lead) Writing - original draft (lead) Writing - review & editing (lead) Contribution: Conceptualization (equal) Investigation (equal) Software (lead) Contribution: Formal analysis (supporting) Funding acquisition (supporting) Methodology (supporting) Contribution: Data curation (supporting) Project administration (supporting) Resources (supporting) Zanjan Pharmaceutical Nanotechnology Research Center (ZPNRC) and Department of Pharmaceutical Nanotechnology School of Pharmacy Zanjan University of Medical Sciences Zanjan 45139-56184 Iran Contribution: Methodology (supporting) Resources (supporting) State Key Laboratory for Mechanical Behavior of Materials and Frontier Institute of Science and Technology and Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research College of Stomatology Xi'an Jiaotong University Xi'an 710049 P. R. China Contribution: Investigation (supporting) Validation (supporting) Contribution: Formal analysis (supporting) Software (supporting) Visualization (supporting) Guangdong Provincial Key Laboratory of Research and Development of Natural Drugs School of Pharmacy Guangdong Medical University Guangdong Medical University Key Laboratory of Research and Development of New Medical Materials Dongguan 523808 P. R. China Contribution: Validation (lead) Visualization (lead) Writing - original draft (lead) Writing - review & editing (lead) Contribution: Resources (lead) Visualization (lead) Writing - review & editing (lead)

Owing to the high depth of tissue penetration, non-invasiveness, and controllability, ultrasound (US)-mediated sonodynamic therapy (SDT) has shown broad application prospects for tumor treatment. However, the electron-hole separation inefficiency of sonosensitizers and the tumor hypoxia remain two major challenges limiting the effect of SDT. Here, ultrafine photoetched bismuth vanadate (BiVO 4 ) nanorods modified with DSPE-PEG 2000 (PEBVO@PEG NRs) were fabricated to achieve in situ self-supply of oxygen (O 2 ) and reactive oxygen species (ROS) for hypoxic tumor therapy. The photoetching approach could enhance the charge separation by inducing enriched oxygen vacancies on the surface of BiVO 4 , thereby improving the generation efficiency of ROS and O 2 . The PEBVO@PEG overcome the main obstacles of traditional sonosensitizers in the SDT process and show promising sonodynamic therapeutic effects, thus providing new strategies for improving the performance of sonosensitizer and hypoxic tumor elimination.

关键词： Bismuth Vanadate Sonodynamic Therapy Sonosensitizers Surface Oxygen Vacancies Ultrasound

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A hierarchical similarity based job recommendation service framework for university students

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Frontiers of Computer Science 2017年第5期11卷 912-922页

作者： Rui LIU Wenge RONG Yuanxin OUYANG Zhang XIONG State Key Laboratory of Software Development Environment Beihang University Beijing 100191 China Engineering Research Center of Advanced Computer Application Technology Ministry of Education Beihang University Beijing 100191 China School of Computer Science and Engineering Beihang University Beijing 100191 China

When people want to move to a new job, it is often difficult since there is too much job information available. To select an appropriate job and then submit a resume is tedious. It is particularly difficult for university students since they normally do not have any work experience and also are unfamiliar with the job market. To deal with the informa- tion overload for students during their transition into work, a job recommendation system can be very valuable. In this research, after fully investigating the pros and cons of current job recommendation systems for university students, we propose a student profiling based re-ranking framework. In this system, the students are recommended a list of potential jobs based on those who have graduated and obtained job offers over the past few years. Furthermore, recommended employers are also used as input for job recommendation result re-ranking. Our experimental study on real recruitment data over the past four years has shown this method＇s potential.

关键词： job recommendation students similarity time,re-ranking

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Bi-directional mission offloading for agile space-air-ground integrated networks

arXiv

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arXiv 2019年

作者： Zhou, Sheng Wang, Guangchao Zhang, Shan Niu, Zhisheng Shen, Xuemin Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Tsinghua University Beijing100084 China School of Computer Science and Engineering Beihang Univesity Beijing100191 China State Key Laboratory of Software Development Environment Beijing Key Laboratory of Computer Networks Department of Electrical and Computer Engineering University of Waterloo 200 University Avenue West WaterlooONN2L 3G1 Canada

Space-air-ground integrated networks (SAGIN) provide great strengths in extending the capability of ground wireless networks. On the other hand, with rich spectrum and computing resources, the ground networks can also assist space-air networks to accomplish resource-intensive or power-hungry missions, enhancing the capability and sustainability of the space-air networks. Therefore, bi-directional mission offloading can make full use of the advantages of SAGIN and benefits both space-air and ground networks. In this article, we identify the key role of network reconfiguration in coordinating heterogeneous resources in SAGIN, and study how network function virtualization (NFV) and service function chaining (SFC) enable agile mission offloading. A case study validates the performance gain brought by bi-directional mission offloading. Future research issues are outlooked as the bi-directional mission offloading framework opens a new trail in releasing the full potentials of SAGIN. Copyright © 2019, The Authors. All rights reserved.

关键词： Network function virtualization

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Corrections to “Requirements Socio-Technical Graphs for Managing Practitioners’ Traceability Questions”

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IEEE Transactions on Computational Social Systems 2019年第1期6卷 190-190页

作者： Nan Niu Wentao Wang Arushi Gupta Mona Assarandarban Li Da Xu Juha Savolainen Jing-Ru C. Cheng Department of Electrical Engineering and Computer Science University of Cincinnati Cincinnati OH USA Old Dominion University Norfolk VA USA Software and Control Research and Development Danfoss Drives A/S 6300 Gråsten Denmark Information Technology Laboratory U.S. Army Engineer Research and Development Center Vicksburg MS USA

In [1] , Li Da Xu’s main affiliation should be Old Dominion University, Norfolk, VA 23529 USA.

关键词： research and development Computer science software Information technology

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An efficient and fast polarity optimization approach for mixed polarity Reed-Muller logic circuits

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Frontiers of Computer Science 2017年第4期11卷 728-742页

作者： Zhenxue HE Limin XIAO Fei GU Tongsheng XIA Shubin SU Zhisheng HUO Rong ZHANG Longbing ZHANG Li RUAN Xiang WANG State Key Laboratory of Software Development Environment Beihang University Beijing 100191 China School of Computer Science and Engineering Beihang University Beijing 100191 China School of Electronic and Information Engineering Beihang University Beijing 100191 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China National Engineering Research Center for Science & Technology Resources Sharing Service Beijing 100191 China

Although the genetic algorithm has been widely used in the polarity optimization of mixed polarity Reed- Muller （MPRM） logic circuits, few studies have taken into account the polarity conversion sequence. In order to im- prove the efficiency of polarity optimization of MPRM logic circuits, we propose an efficient and fast polarity optimiza- tion approach （FPOA） considering the polarity conversion se- quence. The main idea behind the FPOA is that, firstly, the best polarity conversion sequence of the polarity set wait- ing for evaluation is obtained by using the proposed hybrid genetic algorithm （HGA）; secondly, each of polarity in the polarity set is converted according to the best polarity con- version sequence obtained by HGA. Our proposed FPOA is implemented in C and a comparative analysis has been pre- sented for MCNC benchmark circuits. The experimental re- suits show that for the circuits with more variables, the FPOA is highly effective in improving the efficiency of polarity op- timization of MPRM logic circuits compared with the tradi- tional polarity optimization approach which neglects the po- larity conversion sequence and the improved polarity opti- mization approach with heuristic technique.

关键词： genetic algorithm polarity optimization mixed

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A deduplication algorithm based on data similarity and delta encoding 4th

A deduplication algorithm based on data similarity and delta...

引用

4th International Conference on Geo-Informatics in Resource Management and Sustainable Ecosystem, GRMSE 2016

作者： Song, Bin Xiao, Limin Qin, Guangjun Ruan, Li Qiu, Shida State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing100191 China National Engineering Research Center for Science and Technology Resources Sharing Service Beijing100191 China Space Star Technology Co. Ltd. Beijing100086 China

ISBN: (纸本)9789811039683

Satellite applications such as remote sensing application are overwhelmed with vast quantities of data. Nevertheless, the storage resources in the satellite are so limited that it should be used more efficient. The similarity between the remote sensing data is high, but the dissimilar parts of the data distribute irregularly. When using the traditional deduplication algorithm to split the file into chunks, a large amount of chunks are exactly similar but not the same, which results in the bad effect of data deduplication. We propose a deduplication algorithm based on data similarity and delta encoding to reduce the usage of storage resources. The data similarity analysis can find out the similar data. The delta encoding technology can reduce the usage of storage resources. Through experiments on remote sensing application data, we have achieved deduplication ratios up to 30:1, and analyzed how the chunksize affect the experiment results. © Springer Nature Singapore Pte Ltd. 2017.

关键词： Satellites

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A Power and Area Optimization Approach of Mixed Polarity Reed-Muller Expression for Incompletely Specified Boolean Functions

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Journal of Computer Science & technology 2017年第2期32卷 297-311页

作者： Zhen-Xue He Li-Min Xiao Li Ruan Fei Gu Zhi-Sheng Huo Guang-Jun Qin Ming-Fa Zhu F Long-Bing Zhang Rui Liu Xiang Wang State Key Laboratory of Software Development Environment Beihang University Beijing 100191 China School of Computer Science and Engineering Beihang University Beijing 100191 China School of Electronic and Information Engineering Beihang University Beijing 100191 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China National Engineering Research Center for Science and Technology Resources Sharing Service Beihang University Beijing 100191 China

The power and area optimization of Reed-Muller （RM） circuits has been widely concerned. However, almost none of the exiting power and area optimization approaches can obtain all the Pareto optimal solutions of the original problem and are efficient enough. Moreover, they have not considered the don＇t care terms, which makes the circuit performance unable to be further optimized. In this paper, we propose a power and area optimization approach of mixed polarity RM expression （MPRM） for incompletely specified Boolean functions based on Non-Dominated Sorting Genetic Algorithm II （NSGA-II）. Firstly, the incompletely specified Boolean function is transformed into zero polarity incompletely specified MPRM （ISMPRM） by using a novel ISMPRM acquisition algorithm. Secondly, the polarity and allocation of don＇t care terms of ISMPRM is encoded as chromosome. Lastly, the Pareto optimal solutions are obtained by using NSGA-II, in which MPRM corresponding to the given chromosome is obtained by using a chromosome conversion algorithm. The results on incompletely specified Boolean functions and MCNC benchmark circuits show that a significant power and area improvement can be made compared with the existing power and area optimization approaches of RM circuits.

关键词： power and area optimization Reed-Muller （RM） circuit Pareto optimal solution don＇t care term chromosomeconversion

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