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Corrigendum to “Manufacturer’s selling mode choice in a platform-oriented dual channel supply chain” [Expert Syst. Appl. 198 (2022) 116842]

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Expert Systems with Applications 2022年 207卷

作者： Tong-Yuan Wang Zhen-Song Chen Kannan Govindan Kwai-Sang Chin Institute of Modern Logistics Henan University Kaifeng 475004 China School of Business Henan University Kaifeng 475004 China School of Transportation and Logistics Southwest Jiaotong University Chengdu 611756 China Department of Engineering Management School of Civil Engineering Wuhan University Wuhan 430072 China School of Data Science City University of Hong Kong Hong Kong China Institute of FTZ Supply Chain Shanghai Maritime University Shanghai 201306 China Yonsei Frontier Lab Yonsei University Seoul South Korea Center for Sustainable Supply Chain Engineering Department of Technology and Innovation Danish Institute for Advanced Study University of Southern Denmark Campusvej 55 Odense M Denmark Department of Advanced Design and Systems Engineering City University of Hong Kong 83 Tat Chee Avenue Kowloon Tong Hong Kong City University of Hong Kong Shenzhen Research Institute Shenzhen China

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Daily CO_(2) Emission Reduction Indicates the Control of Activities to Contain COVID-19 in China

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The Innovation 2020年第3期1卷 24-36页

作者： Rong Wang Yuankang Xiong Xiaofan Xing Ruipu Yang Jiarong Li Yijing Wang Junji Cao Yves Balkanski Josep Peñuelas Philippe Ciais Didier Hauglustaine Jordi Sardans Jianmin Chen Jianmin Ma Tang Xu Haidong Kan Yan Zhang Tomohiro Oda Lidia Morawska Renhe Zhang Shu Tao Department of Environmental Science and Engineering Fudan UniversityShanghai 200438P.R.China Institute of Atmospheric Sciences Fudan UniversityShanghai 200438P.R.China Big Data Institute for Carbon Emission and Environmental Pollution Fudan UniversityShanghai 200438P.R.China Key Laboratory of Aerosol Chemistry and Physics Institute of Earth EnvironmentChinese Academy of SciencesXi'an 710075P.R.China Laboratoire des Sciences du Climat et de l’Environnement CEA CNRS UVSQ91190 Gif-sur-YvetteFrance CREAF Cerdanyola del Vallès08193 CataloniaSpain CSIC Global Ecology Unit CREAF-CSIC-UABBellaterra08193 CataloniaSpain College of Urban and Environmental Sciences Laboratory for Earth Surface ProcessesPeking University100871 BeijingP.R.China School of Public Health Key Laboratory of Public Health Safety of the Ministry of Education and National Health Commission Key Laboratory of Health Technology AssessmentFudan UniversityShanghai 200438P.R.China Universities Space Research Association ColumbiaMDUSA Global Modeling and Assimilation Office NASA Goddard Space Flight CenterGreenbeltMDUSA Queensland University of Technology 2 George StreetBrisbaneQLD 4001Australia Shanghai Institute of Pollution Control and Ecological Security Shanghai 200092P.R.China

Lockdown measures are essential to containing the spread of coronavirus disease 2019(COVID-19),but they will slow down economic growth by reducing industrial and commercial ***,the benefits of activity control from containing the pandemic have not been examined and *** we use daily carbon dioxide(CO_(2))emission reduction in China estimated from statistical data for energy consumption and satellite data for nitrogen dioxide(NO_(2))measured by the Ozone Monitoring Instrument(OMI)as an indicator for reduced activities consecutive to a *** perform a correlation analysis to show that a 1%day-1 decrease in the rate of COVID-19 cases is associated with a reduction in daily CO_(2) emissions of 0.22%±0.02%using statistical data for energy consumption relative to emissions without COVID-19,or 0.20%±0.02%using satellite data for atmospheric column NO_(2).We estimate that swift action in China is effective in limiting the number of COVID-19 cases<100,000 with a reduction in CO_(2) emissions of up to 23%by the end of February 2020,whereas a 1-week delay would have required greater containment and a doubling of the emission reduction to meet the same *** analyzing the costs of health care and fatalities,we find that the benefits on public health due to reduced activities in China are 10-fold larger than the loss of gross domestic *** findings suggest an unprecedentedly high cost of maintaining activities and CO_(2) emissions during the COVID-19 pandemic and stress substantial benefits of containment in public health by taking early actions to reduce activities during the outbreak of COVID-19.

关键词： COVID-19 PANDEMIC CARBON EMISSION ENERGY CONSUMPTION SOCIAL CARBON COST CONTAINMENT EFFICACY PUBLIC HEALTH CORRELATION ANALYSIS INTEGRATED MODEL

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Programming the Demirci-Selçuk Meet-in-the-Middle Attack with Constraints 24th

Programming the Demirci-Selçuk Meet-in-the-Middle Attack wi...

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24th Annual International Conference on Theory and Application of Cryptology and Information security, ASIACRYPT 2018

作者： Shi, Danping Sun, Siwei Derbez, Patrick Todo, Yosuke Sun, Bing Hu, Lei State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China Data Assurance and Communication Security Research Center Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Univ Rennes CNRS IRISA Rennes France NTT Secure Platform Laboratories Tokyo Japan College of Liberal Arts and Sciences National University of Defense Technology Changsha China

ISBN: (纸本)9783030033286

Cryptanalysis with SAT/SMT, MILP and CP has increased in popularity among symmetric-key cryptanalysts and designers due to its high degree of automation. So far, this approach covers differential, linear, impossible differential, zero-correlation, and integral cryptanalysis. However, the Demirci-Selçuk meet-in-the-middle (DS-MITM) attack is one of the most sophisticated techniques that has not been automated with this approach. By an in-depth study of Derbez and Fouque’s work on (DS-MITM) analysis with dedicated search algorithms, we identify the crux of the problem and present a method for automatic (DS-MITM) attack based on general constraint programming, which allows the cryptanalysts to state the problem at a high level without having to say how it should be solved. Our method is not only able to enumerate distinguishers but can also partly automate the key-recovery process. This approach makes the (DS-MITM) cryptanalysis more straightforward and easier to follow, since the resolution of the problem is delegated to off-the-shelf constraint solvers and therefore decoupled from its formulation. We apply the method to SKINNY, TWINE, and LBlock, and we get the currently known best (DS-MITM) attacks on these ciphers. Moreover, to demonstrate the usefulness of our tool for the block cipher designers, we exhaustively evaluate the security of 40320 versions of LBlock instantiated with different words permutations in the F functions. It turns out that the permutation used in the original LBlock is one of the 64 permutations showing the strongest resistance against the (DS-MITM) attack. The whole process is accomplished on a PC in less than 2 h. The same process is applied to TWINE, and similar results are obtained. © 2018, International Association for Cryptologic research.

关键词： Constraint programming

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Transportation, Germs, Culture: A Dynamic Graph Model of COVID-19 Outbreak

SSRN

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SSRN 2020年

作者： Yang, Xiaofei Xu, Tun Jia, Peng Xia, Han Guo, Li Zhang, Lei Ye, Kai School of Computer Science and Technology Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an710049 China MOE Key Lab for Intelligent Networks & Networks Security Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an710049 China School of Automation Science and Engineering Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an710049 China Hugobiotech Co. Ltd. Beijing100000 China Genome Institute The First Affiliated Hospital of Xi’an Jiaotong University Xi’an710061 China The School of Life Science and Technology Xi’an Jiaotong University Xi’an710049 China China-Australia Joint Research Center for Infectious Diseases School of Public Health Xi’an Jiaotong University Health Science Center Shaanxi Xi’an710061 China Melbourne Sexual Health Centre Alfred Health Melbourne Australia Central Clinical School Faculty of Medicine Nursing and Health Sciences Monash University MelbourneVIC Australia Department of Epidemiology and Biostatistics College of Public Health Zhengzhou University Henan Zhengzhou450001 China

Background: Various forms of model have been applied to predict the trend of the epidemic since the outbreak of COVID-19 at the hardest-hit city of Wuhan. Methods: In this study, we designed a dynamic graph model, not for precisely predicting the number of infected cases, but for a glance of the dynamics under a public epidemic emergency situation and of different contributing factors. Findings: We demonstrated the impact of asymptomatic transmission in this outbreak and showed the effectiveness of city lockdown to halt virus spread within a city. We further illustrated that sudden emergence of a large number of cases could overwhelm the city medical system, and external medical aids are critical to not only containing the further spread of the virus but also reducing fatality. Interpretation: Our model simulation showed that highly populated modern cities are particularly vulnerable and lessons learned in China could facilitate other countries to plan the proactive and decisive actions. We shall pay close attention to the asymptomatic transmission being suggested by rapidly accumulating evidence as dramatic changes in quarantine protocol are required to contain SARS-CoV-2 from spreading globally. Funding Statement: This study was supported by the National Science Foundation of China (grand NO. 61702406 31671372, 31701739, and 8191101420), the National Key R&D Program of China (grand NO. 2018YFC0910400, 2017YFC0907500), the National Science and technology Major Project of China (grand NO. 2018ZX10302205), the 'World-Class Universities and the Characteristic Development Guidance Funds for the Central Universities', Thousand Talents Plan Professorship for Young Scholars (3111500001), Xi'an Jiaotong University Young Talent Support Program and Xi’an Jiaotong University Basic research and Profession Grant (xtr022019003). Declaration of Interests: None. © 2020, The Authors. All rights reserved.

关键词： SARS

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Augmentation of wastewater-based epidemiology with machine learning to support global health surveillance

Nature Water

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Nature Water 2025年 1-11页

作者： Eva Aßmann Christopher Irrgang Hugues Richard Martin Hölzer Timo Greiner Alexander Ullrich Matthew Wade Shelesh Agrawal Susanne Lackner Fabian Amman Sindy Böttcher Markus Landthaler Emanuel Wyler Serghei Mangul Viorel Munteanu Fotis Psomopoulos Maureen Smith Maria Trofimova Max von Kleist Centre for Artificial Intelligence in Public Health Research (ZKI-PH) Robert Koch Institute Berlin Germany Genome Competence Center (MF1) Robert Koch Institute Berlin Germany Department of Infectious Disease Epidemiology Robert Koch Institute Berlin Germany Chief Data Officer Group UK Health Security Agency London UK Chair of Water and Environmental Biotechnology Institute IWAR Department of Civil and Environmental Engineering Sciences Technical University of Darmstadt Darmstadt Germany CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences Vienna Austria Institute for Hygiene and Applied Immunology Medical University of Vienna Vienna Austria Gastroenteritis and Hepatitis Pathogens and Enteroviruses (FG15) Robert Koch Institute Berlin Germany Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC) Berlin Germany Institute for Biology Humboldt-Universität zu Berlin Berlin Germany Titus Family Department of Clinical Pharmacy USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences University of Southern California Los Angeles CA USA Department of Biological and Morphofunctional Sciences College of Medicine and Biological Sciences Stefan cel Mare University of Suceava Suceava Romania Department of Computers Informatics and Microelectronics Technical University of Moldova Chisinau Moldova Institute of Applied Biosciences Center for Research and Technology Hellas Thessaloniki Greece Systems Medicine of Infectious Disease (P5) Robert Koch Institute Berlin Germany Department of Mathematics and Computer Science Freie Universität Berlin Berlin Germany

Wastewater-based epidemiology (WBE) has proven to be a valuable tool for monitoring the evolution and spread of global health threats, from pathogens to antimicrobial resistances. Throughout the COVID-19 pandemic, multiple wastewater surveillance programmes have advanced statistical and machine learning methods for detecting pathogens from wastewater sequencing data and correlating measured targets with the represented population to infer meaningful conclusions for public health. Integrating contextual data can account for measurement uncertainties across the WBE workflow that affect the reliability of analyses. However, the broader availability and harmonization of data are major obstacles to method development. Here we review the benefits and limitations of wastewater-related data streams, highlighting the potential of machine learning to leverage these streams for normalization and other WBE applications. We emphasize the relevance of developing global frameworks for integrating WBE with other health surveillance systems and discuss next steps to address current and foreseeable challenges for robust and interpretable machine learning-enhanced WBE.

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Future Changes in Rainy Seasons in the Upper Blue Nile Basin: Impacts on Agriculture and Water Resources

Natural Hazards Research

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Natural Hazards research 2025年

作者： Fekadie Bazie Enyew Dejene Sahlu Gashaw Bimrew Tarekegn Yakob Umer Belen Marti-Cardona Bedassa R. Cheneka Daniel Asfaw Bekele Daniel Ayalew Mengistu Sarkawt Hama Zinaw D. Shenga Sisay E. Debele Institute of Disaster Risk Management and Food Security Studies Bahir Dar University P.O. Box 5501 Bahir Dar Ethiopia International Water Management Institute Hatfield Gardens 333 Grosvenor Street Hatfield Pretoria South Africa School of Sustainability Civil and Environmental Engineering Faculty of Engineering and Physical Sciences University of Surrey Guildford GU2 7XH United Kingdom Biology Department Utrecht University The Netherlands University of Antwerp Belgium Department of Geography and Environmental Studies Bahir Dar University P.O. Box 79 Bahir Dar Ethiopia Geospatial Data and Technology Center (GDTC) Bahir Dar University P.O. Box 79 Bahir Dar Ethiopia Global Centre for Clean Air Research Department of Civil and Environmental Engineering Faculty of Engineering and Physical Sciences University of Surrey Guildford GU2 7XH United Kingdom Department of Chemistry College of Science University of Sulaimani Sulaimani City 46001 Kurdistan Region Iraq Slovak Hydrometeorological Institute Jeséniova 17 833 15 Bratislava Slovak Republic Department of Global Health and Development Faculty of Public Health and Policy London School of Hygiene & Tropical Medicine London WC1H 9SH UK

Changing climate is increasingly influencing the rainy seasons and posing significant challenges for agriculture and water resource management in vulnerable regions. The study examines the spatiotemporal variation of rainy seasons in the Upper Blue Nile Basin (UBNB) of Northwestern Ethiopia using projections from the CMIP6 climate models to assess potential impacts on agricultural and water planning. We analyzed observed and projected precipitation data across three Shared Socioeconomic Pathway (SSP) scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5, spanning near-term (2015-2044), mid-term (2045-2074), and long-term (2075-2100) periods. To correct systematic biases in model outputs, a power transformation technique was applied to precipitation data from seven CMIP6 models. The finding of the study showed that the INM-CM5-0 model is the most accurate in simulating precipitation patterns, forming the basis for our detailed analysis. Our findings reveal significant shifts in the timing and duration of rainy seasons across all scenarios, with more pronounced changes under the highest emission pathways, SSP5-8.5. The SSP5-8.5 scenario indicates the most substantial extension in the length of rainy season, particularly in lowland areas, due to both earlier onset and delayed cessation. Conversely, highland and midland regions are projected to experience shorter rainy seasons, lasting between 82 and 130 days, driven by delayed onset and earlier cessation. These shifts could profoundly affect agricultural productivity, necessitating adjustments in planting and harvesting schedules by several months. Under high-emission scenarios, crop cycles may become misaligned with traditional planting windows, especially in highland areas where shorter rainy seasons may constrain crop viability. This study highlights the urgent need for adaptive strategies, such as the practice of early-maturing crop varieties, to enhance resilience to the change of projected seasonal rainfall patterns. Proac

关键词： Rainy Season Patterns Climate Change Impact CMIP6 Models Bias Correction Upper Blue Nile Basin

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The 2021 report of the Lancet Countdown on health and climate change: code red for a healthy future

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The Lancet 2021年第10311期398卷 1619-1662页

作者： Romanello, Marina McGushin, Alice Di Napoli, Claudia Drummond, Paul Hughes, Nick Jamart, Louis Kennard, Harry Lampard, Pete Solano Rodriguez, Baltazar Arnell, Nigel Ayeb-Karlsson, Sonja Belesova, Kristine Cai, Wenjia Campbell-Lendrum, Diarmid Capstick, Stuart Chambers, Jonathan Chu, Lingzhi Ciampi, Luisa Dalin, Carole Dasandi, Niheer Dasgupta, Shouro Davies, Michael Dominguez-Salas, Paula Dubrow, Robert Ebi, Kristie L Eckelman, Matthew Ekins, Paul Escobar, Luis E Georgeson, Lucien Grace, Delia Graham, Hilary Gunther, Samuel H Hartinger, Stella He, Kehan Heaviside, Clare Hess, Jeremy Hsu, Shih-Che Jankin, Slava Jimenez, Marcia P Kelman, Ilan Kiesewetter, Gregor Kinney, Patrick L Kjellstrom, Tord Kniveton, Dominic Lee, Jason K W Lemke, Bruno Liu, Yang Liu, Zhao Lott, Melissa Lowe, Rachel Martinez-Urtaza, Jaime Maslin, Mark McAllister, Lucy McMichael, Celia Mi, Zhifu Milner, James Minor, Kelton Mohajeri, Nahid Moradi-Lakeh, Maziar Morrissey, Karyn Munzert, Simon Murray, Kris A Neville, Tara Nilsson, Maria Obradovich, Nick Sewe, Maquins Odhiambo Oreszczyn, Tadj Otto, Matthias Owfi, Fereidoon Pearman, Olivia Pencheon, David Rabbaniha, Mahnaz Robinson, Elizabeth Rocklöv, Joacim Salas, Renee N Semenza, Jan C Sherman, Jodi Shi, Liuhua Springmann, Marco Tabatabaei, Meisam Taylor, Jonathon Trinanes, Joaquin Shumake-Guillemot, Joy Vu, Bryan Wagner, Fabian Wilkinson, Paul Winning, Matthew Yglesias, Marisol Zhang, Shihui Gong, Peng Montgomery, Hugh Costello, Anthony Hamilton, Ian Institute for Global Health University College London London UK United Kingdom Institute for Sustainable Resources University College London London UK United Kingdom UCL Energy Institute University College London London UK United Kingdom Institute for Environmental Design and Engineering University College London London UK United Kingdom Department of Geography University College London London UK United Kingdom The Bartlett School of Sustainable Construction University College London London UK United Kingdom Centre for Human Health and Performance University College London London UK United Kingdom School of Agriculture Policy and Development University of Reading Reading United Kingdom Department of Meteorology University of Reading Reading United Kingdom The Walker Institute University of Reading Reading United Kingdom Department of Health Sciences University of York York United Kingdom Institute for Environment and Human Security United Nations University Bonn Germany Centre on Climate Change and Planetary Health London School of Hygiene & Tropical Medicine London UK United Kingdom Centre for Mathematical Modelling of Infectious Diseases London School of Hygiene & Tropical Medicine London UK United Kingdom Department of Public Health Environments and Society London School of Hygiene & Tropical Medicine London UK United Kingdom Department of Earth System Science Tsinghua University Beijing China Department of Environment Climate Change and Health World Health Organization Geneva Switzerland Institute for Environmental Sciences World Health Organization Geneva Switzerland Centre for Climate Change and Social Transformations School of Psychology Cardiff University Cardiff United Kingdom Yale Center on Climate Change and Health Yale University New Haven CT United States School of Government University of Birmingham Birmingham United Kingdom Economic analysis of Climate Impacts and Policy Centro Euro-Mediterraneo sui Cambiamenti Climatic

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The broad-lined Type-Ic supernova SN 2022xxf with extraordinary two-humped light curves

arXiv

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arXiv 2023年

作者： Kuncarayakti, H. Sollerman, J. Izzo, L. Maeda, K. Yang, S. Schulze, S. Angus, C.R. Aubert, M. Auchettl, K. Della Valle, M. Dessart, L. Hinds, K. Kankare, E. Kawabata, M. Lundqvist, P. Nakaoka, T. Perley, D. Raimundo, S.I. Strotjohann, N.L. Taguchi, K. Cai, Y.-Z. Charalampopoulos, P. Fang, Q. Fraser, M. Gutiérrez, C.P. Imazawa, R. Kangas, T. Kawabata, K.S. Kotak, R. Kravtsov, T. Matilainen, K. Mattila, S. Moran, S. Murata, I. Salmaso, I. Anderson, J.P. Ashall, C. Bellm, E.C. Benetti, S. Chambers, K.C. Chen, T.-W. Coughlin, M. De Colle, F. Fremling, C. Galbany, L. Gal-Yam, A. Gromadzki, M. Groom, S.L. Hajela, A. Inserra, C. Kasliwal, M.M. Mahabal, A.A. Martin-Carrillo, A. Moore, T. Müller-Bravo, T.E. Nicholl, M. Ragosta, F. Riddle, R.L. Sharma, Y. Srivastav, S. Stritzinger, M.D. Wold, A. Young, D.R. Tuorla Observatory Department of Physics and Astronomy University of Turku FI-20014 Finland University of Turku FI-20014 Finland Department of Astronomy Oskar Klein Centre AlbaNova Stockholm10691 Sweden DARK Niels Bohr Institute University of Copenhagen Copenhagen Denmark Department of Astronomy Kyoto University Kitashirakawa-Oiwake-cho Sakyo-ku Kyoto606-8502 Japan Henan Academy of Sciences Henan Zhengzhou450046 China The Oskar Klein Centre Department of Physics Stockholm University AlbaNova University Center StockholmSE 106 91 Sweden Université Clermont Auvergne CNRS IN2P3 LPC Clermont-Ferrand France School of Physics The University of Melbourne ParkvilleVIC3010 Australia Sydney2006 Australia Department of Astronomy and Astrophysics University of California Santa CruzCA95064 United States Capodimonte Astronomical Observatory INAF-Napoli Salita Moiariello 16 NapoliI-80131 Italy Institut d’Astrophysique de Paris CNRS-Sorbonne Université 98 bis boulevard Arago ParisF-75014 France Astrophysics Research Institute Liverpool John Moores University IC2 Liverpool Science Park 146 Brownlow Hill LiverpoolL3 5RF United Kingdom Turku Collegium for Science Medicine and Technology University of Turku TurkuFI-20014 Finland Hiroshima Astrophysical Science Center Hiroshima University 1-3-1 Kagamiyama Higashi-Hiroshima Hiroshima739-8526 Japan Department of Physics and Astronomy University of California Los AngelesCA United States Department of Physics & Astronomy University of Southampton Southampton United Kingdom Benoziyo Center for Astrophysics The Weizmann Institute of Science Rehovot76100 Israel Yunnan Observatories Chinese Academy of Sciences Kunming650216 China Key Laboratory for the Structure and Evolution of Celestial Objects Chinese Academy of Sciences Kunming650216 China School of Physics O’Brien Centre for Science North University College Dublin Belfield Dublin 4 Dublin Ireland Department of Physics Graduate School

We report on our study of supernova (SN) 2022xxf based on observations obtained during the first four months of its evolution. The light curves (LCs) display two humps of similar maximum brightness separated by 75 days, unprecedented for a broad-lined (BL) Type Ic supernova (SN IcBL). SN 2022xxf is the most nearby SN IcBL to date (in NGC 3705, z = 0.0037, at a distance of about 20 Mpc). Optical and near-infrared photometry and spectroscopy are used to identify the energy source powering the LC. Nearly 50 epochs of high signal-to-noise-ratio spectroscopy were obtained within 130 days, comprising an unparalleled dataset for a SN IcBL, and one of the best-sampled SN datasets to date. The global spectral appearance and evolution of SN 2022xxf points to typical SN Ic/IcBL, with broad features (up to ∼ 14000 km s−1) and a gradual transition from the photospheric to the nebular phase. However, narrow emission lines (corresponding to ∼ 1000−2500 km s−1) are present in the spectra from the time of the second rise, suggesting slower-moving circumstellar material (CSM). These lines are subtle, in comparison to the typical strong narrow lines of CSM-interacting SNe, for example, Type IIn, Ibn, and Icn, but some are readily noticeable at late times such as in Mg I λ5170 and [O I] λ5577. Unusually, the near-infrared spectra show narrow line peaks in a number of features formed by ions of O and Mg. We infer the presence of CSM that is free of H and He. We propose that the radiative energy from the ejecta-CSM interaction is a plausible explanation for the second LC hump. This interaction scenario is supported by the color evolution, which progresses to the blue as the light curve evolves along the second hump, and the slow second rise and subsequent rapid LC drop. SN 2022xxf may be related to an emerging number of CSM-interacting SNe Ic, which show slow, peculiar LCs, blue colors, and subtle CSM interaction lines. The progenitor stars of these SNe likely experienced an episode of m

关键词： Supernovae

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A Novel Texture Generation Super Resolution Model

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Procedia Computer Science 2019年 162卷 924-931页

作者： Biao Li Yong Shi Sujuan Li Bo Wang Zhiquan Qi Jiabin Liu School of Economics and Management University of Chinese Academy of Sciences Beijing 101408 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China College of Information Science and Technology University of Nebraska at Omaha NE 68182 USA Department of Otology the First Affiliated Hospital of Zhengzhou University No. 1 eastern Jianshe road Zhengzhou Henan 450052 China School of Information Technology and Management University of International Business and Economics Beijing 100029 China Department of Computer Science and Engineering Texas A&M University TX 77843 USA

Recently, super-resolution methods pursue visual pleasant details attract more attention in academic circle. Unlike former accurate driving models, they leverage new losses measured the difference of features extracting from a pre-trained deep learning model. Moreover, the popular GANs structure are introduced to approach better outputs. In general, these new visual feeling promoting losses are constrained by the learning ability of model and effected by the weakness of GANs. In order to address these problems, we propose a novel SR method to generating abundant details. Based on former SR methods and texture generated models, we combine new model constructure and integrated loss function to achieve our goal. The new constructure derives from the very thought that more information from the LR input leads to more accurate results. The new integrated loss try to decrease the weakness of each one by corresponding loss.. Experiments show that our model achieves higher quality generations than former methods.

关键词： Super Resolution Multi Scale Smooth Loss Visual Quality

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Classification of late-stage wheat powdery mildew based on Isomap and PNN analyses

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International Agricultural engineering Journal 2018年第4期 404-410页

作者： Huang, Linsheng Wang, Yulong Guo, Wei Zhang, Qing Yang, Xiaodong National Engineering Research Center for Agro-Ecological Big Data Analysis & Application Anhui University Hefei230601 China Beijing Research Center for Information Technology in Agriculture Beijing100097 China College of Information and Management Science Henan Agricultural University Zhengzhou450002 China

In order to provide assistance in precisely distinguishing different severities of wheat powdery mildew, imaging hyperspectral data of disease-infected and healthy wheat leaves were collected in the late growth stage. After conversion and enhancement of hyperspectral data were preprocessed, the isometric feature mapping (Isomap) was used to reduce the original normalized spectral data from 178 samples. Of these, 136 samples were used as training samples, and the rest were seen as test samples. The characteristics of the reduced dimension data were input into a probabilistic neural network (PNN). A model of classification and recognition was established, resulting in 100% prediction accuracy for healthy samples, 90% for samples with moderate diseased infection, and 80% for samples with mild and severe disease infection, and an overall recognition accuracy of 88% for the model. These results provide important theoretical basis for accurately real-time disease assessment, and also guide the agricultural production and disease prevention. © 2018, Asian Association for Agricultural engineering. All rights reserved.

关键词： Neural networks

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