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Institution

Nanjing University of Information Science and Technology

EducationNanjing, China
About: Nanjing University of Information Science and Technology is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Precipitation & Aerosol. The organization has 14129 authors who have published 17985 publications receiving 267578 citations. The organization is also known as: Nan Xin Da.


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Journal ArticleDOI
TL;DR: In this article, the main aim of the study was to estimate the target values of agrochemical use, and its impact on crop productivity, and human health, using a standardised questionnaire, cross-sectional data of 480 rice growers from September to October, 2017 from the Hafizabad and Sheikhupura districts of Punjab, Pakistan.

99 citations

Journal ArticleDOI
TL;DR: In this article, a series of rare earth (La, Ce, Sm, and Pr) doped Ni based mesoporous materials had been facilely fabricated by the one-pot evaporation induced self-assembly (EISA) strategy and directly employed as the catalysts for CO 2 methanation.

99 citations

Journal ArticleDOI
TL;DR: Wang et al. as mentioned in this paper studied the air pollution condition of a severe haze episode occurred on 6-16 January 2013 over eastern China, and found that this severe pollution episode of large area haze was accompanied with low visibility, high PM10 and AOD in eastern China.

99 citations

Journal ArticleDOI
TL;DR: The efficiency of the multi-agent technology for WSN based the SHM applications on the large aircraft structures is shown and implementation in the validation work for two typical kinds of structure states are presented.
Abstract: Much attention has been focused on the research of structural health monitoring (SHM), since it could increase the safety and reduce the maintenance costs of engineering structures. In recent years, wireless sensor network (WSN) has been explored for adoption to improve the centralized cable-based SHM system performances. This paper presents a multi-agent design method and system evaluation for wireless sensor network based structural health monitoring to validate the efficiency of the multi-agent technology. Through the cooperation of six different agents for SHM applications, the distributed wireless sensor network can automatically allocate SHM tasks, self-organize the sensor network and aggregate different sensor information. In the evaluation work, the strain gauge and PZT sensors are used to monitor strain distribution change and joint failure of an experimental aluminum plate structure. A dedicated sensor network platform including the wireless strain node, wireless PZT node and wireless USB station is designed for the evaluation system. Based on the hardware platform, the multi-agents software architecture is defined. The multi-agent monitoring principle and implementation in the validation work for two typical kinds of structure states are presented. This paper shows the efficiency of the multi-agent technology for WSN based the SHM applications on the large aircraft structures.

98 citations

Journal ArticleDOI
Pierre Friedlingstein1, Sönke Zaehle2, Corinne Le Quéré3, Christian Rödenbeck2, Bronte Tilbrook, Henry C. Bittig4, Denis Pierrot5, Louise Chini6, Jan Ivar Korsbakken7, Nicolas Bellouin8, Toste Tanhua9, Benjamin Poulter10, Peter Landschützer11, Francesco N. Tubiello12, Judith Hauck13, Are Olsen14, Vivek K. Arora15, Colm Sweeney16, Almut Arneth17, Marion Gehlen18, Hiroyuki Tsujino19, Daniel P. Kennedy20, Yosuke Iida19, Luke Gregor21, Jiye Zeng22, George C. Hurtt6, Nicolas Mayot23, Giacomo Grassi24, Shin-Ichiro Nakaoka22, Frédéric Chevallier18, Clemens Schwingshackl7, Wiley Evans25, Meike Becker26, Thomas Gasser27, Xu Yue28, Katie Pocock25, Stephanie Falk29, Thanos Gkritzalis11, Naiqing Pan30, Ingrid T. van der Laan-Luijkx31, Fraser Holding32, Carlos Gustavo Halaburda, Guanghong Zhou33, Peter Angele34, Jianling Chen1, e6gehqc68135, Carlos Muñoz Pérez23, Hiroshi Niinami36, Zongwe Binesikwe Crystal Hardy, Samuel Bourne37, Ralf Wüsthofen38, Paulo Brito, Christian Liguori39, Juan A. Martin-Ramos, Rattan Lal, kensetyrdhhtml2mdcom40, Staffan Furusten, Luca Miceli41, Eric Horster16, V. Miranda Chase, Field Palaeobiology Lab30, Living Tree Cbd Gummies, Lifeng Qin34, Yong Tang42, Annie Phillips43, Nathalie Fenouil26, mark, Karina Querne de Carvalho44, Satya Wydya Yenny, Maja Bak Herrie, Silvia Ravelli45, Andreas Gerster46, Denise Hottmann47, Wui-Lee Chang, Andreas Lutz48, Olga D. Vorob'eva49, Pallavi Banerjee1, Verónica Undurraga50, Jovan Babić, Michele D. Wallace9, Mònica Ginés-Blasi, 에볼루션카지노51, James Kelvin29, Christos Kontzinos1, Охунова Дилафруз Муминовна, Isabell Diekmann, Emily Burgoyne16, Vilemina Čenić52, Naomi Gikonyo26, CHAO LUAN21, Benjamin Pfluger53, Benjamin Pfluger54, A. J. Shields, Kobzos, Laszlo55, Adrian Langer56, Stuart L. Weinstein55, Abdullah ÖZÇELİK57, Yi Chen58, Anzhelika Solodka59, Valery Vasil'evich Kozlov60, Н.С. Рыжук, Roshan Vasant Shinde, Dr Sandeep Haribhau Wankhade, Dr Nitin Gajanan Shekapure, Mr Sachin Shrikant …61, Mylene Charon7, David Seibt62, Kobi Peled, None Rahmi52 
University of Exeter1, Max Planck Institute for Biogeochemistry2, Tyndall Centre3, Leibniz Institute for Baltic Sea Research4, Atlantic Oceanographic and Meteorological Laboratory5, University of Maryland, College Park6, CICERO Center for International Climate Research7, University of Reading8, Leibniz Institute of Marine Sciences9, Goddard Space Flight Center10, Flanders Marine Institute11, Food and Agriculture Organization12, Alfred Wegener Institute for Polar and Marine Research13, Geophysical Institute14, University of Victoria15, National Oceanic and Atmospheric Administration16, Karlsruhe Institute of Technology17, Laboratoire des Sciences du Climat et de l'Environnement18, Japan Meteorological Agency19, Indiana University20, ETH Zurich21, National Institute for Environmental Studies22, University of East Anglia23, European Commission24, Tula Foundation25, Bjerknes Centre for Climate Research26, Hertie Institute for Clinical Brain Research27, Nanjing University of Information Science and Technology28, Ludwig Maximilian University of Munich29, Auburn University30, Wageningen University and Research Centre31, University of Western Sydney32, Cooperative Institute for Research in Environmental Sciences33, Tsinghua University34, University of Florida35, Center for Neuroscience and Regenerative Medicine36, Woods Hole Research Center37, University of Alaska Fairbanks38, Princeton University39, Michigan State University40, University of Washington41, Appalachian State University42, Sun Yat-sen University43, Imperial College London44, University of Groningen45, University of Tennessee46, Washington University in St. Louis47, Jilin Medical University48, Tohoku University49, Rutgers University50, Centre for Research on Ecology and Forestry Applications51, Institut Pierre-Simon Laplace52, North West Agriculture and Forestry University53, Northwest A&F University54, Pacific Marine Environmental Laboratory55, Xi'an Jiaotong University56, Stanford University57, National Center for Atmospheric Research58, University of Edinburgh59, Max Planck Institute for Meteorology60, Utrecht University61, Oak Ridge National Laboratory62
TL;DR: Friedlingstein et al. as mentioned in this paper presented and synthesized data sets and methodologies to quantify the five major components of the global carbon budget and their uncertainties, including fossil CO2 emissions, land use and land-use change data and bookkeeping models.
Abstract: Abstract. Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize data sets and methodologies to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly, and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) is estimated with global ocean biogeochemistry models and observation-based data products. The terrestrial CO2 sink (SLAND) is estimated with dynamic global vegetation models. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the year 2021, EFOS increased by 5.1 % relative to 2020, with fossil emissions at 10.1 ± 0.5 GtC yr−1 (9.9 ± 0.5 GtC yr−1 when the cement carbonation sink is included), and ELUC was 1.1 ± 0.7 GtC yr−1, for a total anthropogenic CO2 emission (including the cement carbonation sink) of 10.9 ± 0.8 GtC yr−1 (40.0 ± 2.9 GtCO2). Also, for 2021, GATM was 5.2 ± 0.2 GtC yr−1 (2.5 ± 0.1 ppm yr−1), SOCEAN was 2.9 ± 0.4 GtC yr−1, and SLAND was 3.5 ± 0.9 GtC yr−1, with a BIM of −0.6 GtC yr−1 (i.e. the total estimated sources were too low or sinks were too high). The global atmospheric CO2 concentration averaged over 2021 reached 414.71 ± 0.1 ppm. Preliminary data for 2022 suggest an increase in EFOS relative to 2021 of +1.0 % (0.1 % to 1.9 %) globally and atmospheric CO2 concentration reaching 417.2 ppm, more than 50 % above pre-industrial levels (around 278 ppm). Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2021, but discrepancies of up to 1 GtC yr−1 persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows (1) a persistent large uncertainty in the estimate of land-use change emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extratropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set. The data presented in this work are available at https://doi.org/10.18160/GCP-2022 (Friedlingstein et al., 2022b).

98 citations


Authors

Showing all 14448 results

NameH-indexPapersCitations
Ashok Kumar1515654164086
Lei Zhang135224099365
Bin Wang126222674364
Shuicheng Yan12381066192
Zeshui Xu11375248543
Xiaoming Li113193272445
Qiang Yang112111771540
Yan Zhang107241057758
Fei Wang107182453587
Yongfa Zhu10535533765
James C. McWilliams10453547577
Zhi-Hua Zhou10262652850
Tao Li102248360947
Lei Liu98204151163
Jian Feng Ma9730532310
Network Information
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023173
2022552
20213,001
20202,492
20192,221
20181,822