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Estimating wetland CH4 emissions on the Qinghai-Tibetan Plateau based on a processed model coupled with inundation dynamics between 1960 and 2100

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science of the Total Environment 2024年 955卷 177169页

作者： Zhang, Jiang Chen, Huai Peng, Changhui Huang, Yuanyuan Wang, Yike Liu, Xinwei Wang, Meng Wang, Le Yu, Dongxue Zhu, Qiuan Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China Chengdu Institute of Biology Chinese Academy of Sciences Chengdu610041 China Institute of Environment Sciences Department of Biology Sciences University of Quebec at Montreal Case Postale 8888 Succursale Centre-Ville MontrealQCH3C 3P8 Canada School of Geography Science Hunan Normal University Changsha410081 China Key Laboratory of Geographical Processes and Ecological Security in Changbai Mountains Ministry of Education School of Geographical Sciences Northeast Normal University Changchun130024 China Key Laboratory of Soil and Water Processes in Watershed College of Geography and Remote Sensing Hohai University Nanjing210098 China National Earth System Science Data Center National Science & Technology Infrastructure of China Beijing100101 China

Quantifying the impacts of climate change and wetland area dynamics on methane (CH4) emissions in the Qinghai-Tibetan Plateau (QTP) is crucial for enhancing our understanding of regional and global CH4 budgets. However, a major source of uncertainty in estimating wetland methane emissions stems from the limited delineation of wetland area dynamics. In this study, we integrated dynamic wetland inundation schemes into a process-based model to quantify the contributions of wetland area variations to natural wetland CH4 emissions on the QTP, both historically and in the future. The results revealed that climate change significantly influenced the spatiotemporal distribution of wetland CH4 emissions, with wetland inundation dynamics contributing between 21.84 % and 27.18 % of the increase in CH4 emissions from 1960 to 2020. Under future climate change scenarios, wetland CH4 emissions are projected to exhibit a more pronounced increasing trend, with wetland inundation dynamics projected to contribute 18.94 % to 21.80 % of the increase in CH4 emissions in the QTP under seven Shared Socioeconomic Pathways scenarios. © 2024 Elsevier B.V.

关键词： Greenhouse gas emissions

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Editorial: AI, data analytics, and mechanism design for DER integration toward net zero

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Frontiers in Energy Research 2022年 10卷

作者： Wang, Hao Ye, Yujian Chen, Yue Chen, Yize Yang, Qing Cui, Qiushi You, Pengcheng Department of Data Science and AI Faculty of Information Technology and Monash Energy Institute Monash University MelbourneVIC Australia School of Electrical Engineering Southeast University Nanjing China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong Guangzhou China College of Electronics and Information Engineering Shenzhen University Shenzhen China State Key Laboratory of Power Transmission Equipment and System Security and New Technology School of Electrical Engineering Chongqing University Chongqing China Department of Industrial Engineering and Management College of Engineering Peking University Beijing China

The energy system is undergoing a fundamental energy transition by integrating low-carbon distributed 5 energy resources (DERs) in distribution networks to accelerate net zero. The increased DER uptake 6 poses significant challenges in operating energy systems to achieve net zero with high reliability and low 7 cost. In particular, the inherent variability of renewable generation, such as solar photovoltaic systems, 8 brings significant uncertainties to the energy system, causing reliability concerns. DERs also cause power 9 quality issues, such as voltage fluctuations. But the distribution system was not designed to support large Our goal in putting together this research topic is to provide a glance at the wide variety of aspects, 51 spanning from technical modeling and assessment to market-based solutions enabling energy transition 52 towards net zero by integrating DERs. We are also aware that the four papers selected are a small fraction 53 of works focusing on DER integration. We hope that these articles will succeed in attracting readers for 54 further research on this significant research topic of smart grids.

关键词： net zero energy transition smart grid distributed energy resources (DER) artificial intelligence data analytics optimization

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µVulDeePecker: A deep learning-based system for multiclass vulnerability detection

arXiv

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arXiv 2020年

作者： Zou, Deqing Wang, Sujuan Xu, Shouhuai Li, Zhen Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Big Data Security Engineering Research Center School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518057 China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Big Data Security Engineering Research Center School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science University of Texas at San Antonio San AntonioTX78249 United States

Fine-grained software vulnerability detection is an important and challenging problem. Ideally, a detection system (or detector) not only should be able to detect whether or not a program contains vulnerabilities, but also should be able to pinpoint the type of a vulnerability in question. Existing vulnerability detection methods based on deep learning can detect the presence of vulnerabilities (i.e., addressing the binary classification or detection problem), but cannot pinpoint types of vulnerabilities (i.e., incapable of addressing multiclass classification). In this paper, we propose the first deep learning-based system for multiclass vulnerability detection, dubbed µVulDeePecker. The key insight underlying µVulDeePecker is the concept of code attention, which can capture information that can help pinpoint types of vulnerabilities, even when the samples are small. For this purpose, we create a dataset from scratch and use it to evaluate the effectiveness of µVulDeePecker. Experimental results show that µVulDeePecker is effective for multiclass vulnerability detection and that accommodating control-dependence (other than data-dependence) can lead to higher detection capabilities. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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VulDeeLocator: A deep learning-based fine-grained vulnerability detector

arXiv

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arXiv 2020年

作者： Li, Zhen Zou, Deqing Xu, Shouhuai Chen, Zhaoxuan Zhu, Yawei Jin, Hai The National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Big Data Security Engineering Research Center School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Cyber Security and Computer Hebei University Baoding071002 China The Department of Computer Science University of Colorado Colorado Springs CO80918 United States University of Texas San Antonio United States

Automatically detecting software vulnerabilities is an important problem that has attracted much attention from the academic research community. However, existing vulnerability detectors still cannot achieve the vulnerability detection capability and the locating precision that would warrant their adoption for real-world use. In this paper, we present a vulnerability detector that can simultaneously achieve a high detection capability and a high locating precision, dubbed Vulnerability Deep learning-based Locator (VulDeeLocator). In the course of designing VulDeeLocator, we encounter difficulties including how to accommodate semantic relations between the definitions of types as well as macros and their uses across files, how to accommodate accurate control flows and variable define-use relations, and how to achieve high locating precision. We solve these difficulties by using two innovative ideas: (i) leveraging intermediate code to accommodate extra semantic information, and (ii) using the notion of granularity refinement to pin down locations of vulnerabilities. When applied to 200 files randomly selected from three real-world software products, VulDeeLocator detects 18 confirmed vulnerabilities (i.e., true-positives). Among them, 16 vulnerabilities correspond to known vulnerabilities;the other two are not reported in the National Vulnerability database (NVD) but have been "silently" patched by the vendor of Libav when releasing newer versions. Copyright © 2020, The Authors. All rights reserved.

关键词： Location

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Skill Assessment of North American Multi-Models Ensemble (NMME) for June-September (JJAS) Seasonal Rainfall over Ethiopia

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Atmospheric and Climate sciences 2022年第1期12卷 54-73页

作者： Asaminew Teshome Jie Zhang Qianrong Ma Stephen E. Zebiak Teferi Demissie Tufa Dinku Asher Siebert Jemal Seid Nachiketa Acharya National Meteorological Agency of Ethiopia Addis Ababa Ethiopia Key Laboratory of Meteorological Disaster Ministry of Education (KLME)/International Joint Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD) Nanjing University of Information Science & Technology (NUIST) Nanjing China International Research Institute for Climate and Society (IRI) The Earth Institute Columbia University New York USA NORCE Norwegian Research Center Bjerknes Center for Climate Research Bergen Norway CGIAR Research Program on Climate Change Agriculture and Food Security (CCAFS) East Africa Addis Ababa Ethiopia Ethiopian Institute of Agricultural Research Addis Ababa Ethiopia Scuola Superiore Sant’Anna Pisa Italy Center for Earth System Modeling Analysis and Data Department of Meteorology and Atmospheric Science Pennsylvania State University University Park PA USA

In recent years, there has been increasing demand for high-resolution seasonal climate forecasts at sufficient lead times to allow response planning from users in agriculture, hydrology, disaster risk management, and health, among others. This paper examines the forecasting skill of the North American Multi-model Ensemble (NMME) over Ethiopia during the June to September (JJAS) season. The NMME, one of the multi-model seasonal forecasting systems, regularly generates monthly seasonal rainfall forecasts over the globe with 0.5 - 11.5 months lead time. The skill and predictability of seasonal rainfall are assessed using 28 years of hindcast data from the NMME models. The forecast skill is quantified using canonical correlation analysis (CCA) and root mean square error. The results show that the NMME models capture the JJAS seasonal rainfall over central, northern, and northeastern parts of Ethiopia while exhibiting weak or limited skill across western and southwestern Ethiopia. The performance of each model in predicting the JJAS seasonal rainfall is variable, showing greater skill in predicting dry conditions. Overall, the performance of the multi-model ensemble was not consistently better than any single ensemble member. The correlation of observed and predicted seasonal rainfall for the better performing models—GFDL-CM2p5-FLOR-A06, CMC2-CanCM4, GFDL-CM2p5-FLOR-B01 and NASA-GMAO-062012—is 0.68, 0.58, 0.52, and 0.5, respectively. The COLA-RSMAS-CCSM4, CMC1- CanCM3 and NCEP-CFSv2 models exhibit less skill, with correlations less than 0.4. In general, the NMME offers

关键词： Ethiopia Ensemble June-September Correlation Coefficient Skill

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Unbalanced incomplete multi-view clustering via the scheme of view evolution: Weak views are meat;strong views do eat

arXiv

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arXiv 2020年

作者： Fang, Xiang Hu, Yuchong Zhou, Pan Wu, Dapeng Oliver The School of Computer Science and Technology Key Laboratory of Information Storage System Ministry of Education of China Huazhong University of Science and Technology Wuhan430074 China The Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China The Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States

Incomplete multi-view clustering is an important technique to deal with real-world incomplete multi-view data. Previous methods assume that all views have the same incompleteness, i.e., balanced incompleteness. However, different views often have distinct incompleteness, i.e., unbalanced incompleteness, which results in strong views (low-incompleteness views) and weak views (high-incompleteness views). The unbalanced incompleteness prevents us from directly using previous methods. In this paper, inspired by the effective biological evolution theory, we design the novel scheme of view evolution to cluster strong and weak views. Moreover, we propose an Unbalanced Incomplete Multi-view Clustering method (UIMC), which is the first effective method based on view evolution for unbalanced incomplete multi-view clustering. Compared with previous methods, UIMC has two unique advantages: 1) it proposes weighted multi-view subspace clustering to integrate unbalanced incomplete views, which effectively solves the unbalanced incomplete multi-view clustering problem;2) it designs the low-rank representation to recover the data, which diminishes the impact of the incompleteness and noises. Extensive experimental results demonstrate that UIMC improves the clustering performance by up to 40% on three evaluation metrics over other state-of-the-art methods. We provide codes for all of our experiments in https://***/ZeusDavide/TETCI_UIMC. Copyright © 2020, The Authors. All rights reserved.

关键词： Clustering algorithms

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Front Cover: International Journal of Intelligent systems, Volume 37 Issue 11 November 2022

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International Journal of Intelligent systems 2022年第11期37卷

作者： Xianjia Meng Yong Yang Ximeng Liu Nan Jiang School of Information Science and Technology Northwest University Xi'an People's Republic of China Fujian Province Key Laboratory of Information Security of Network System and College of Computer and Data Science Fuzhou University Fuzhou People's Republic of China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen People's Republic of China Department of Internet of Things School of Information Engineering East China Jiao Tong University Nanchang People's Republic of China

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ANIMC: A soft approach for auto-weighted noisy and incomplete multi-view clustering

arXiv

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arXiv 2020年

作者： Fang, Xiang Hu, Yuchong Zhou, Pan Wu, Dapeng Oliver Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Computer Science and Technology Key Laboratory of Information Storage System Ministry of Education of China Huazhong University of Science and Technology Wuhan430074 China Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States

Multi-view clustering has wide real-world applications because it can process data from multiple sources. However, these data often contain missing instances and noises, which are ignored by most multi-view clustering methods. Missing instances may make these methods difficult to use directly, and noises will lead to unreliable clustering results. In this paper, we propose a novel Auto-weighted Noisy and Incomplete Multi-view Clustering approach (ANIMC) via a soft auto-weighted strategy and a doubly soft regular regression model. Firstly, by designing adaptive semi-regularized nonnegative matrix factorization (adaptive semi-RNMF), the soft auto-weighted strategy assigns a proper weight to each view and adds a soft boundary to balance the influence of noises and incompleteness. Secondly, by proposing θ-norm, the doubly soft regularized regression model adjusts the sparsity of our model by choosing different θ. Compared with previous methods, ANIMC has three unique advantages: 1) it is a soft algorithm to adjust our approach in different scenarios, thereby improving its generalization ability;2) it automatically learns a proper weight for each view, thereby reducing the influence of noises;3) it performs doubly soft regularized regression that aligns the same instances in different views, thereby decreasing the impact of missing instances. Extensive experimental results demonstrate its superior advantages over other state-of-the-art methods. Impact Statement-As an effective method to process data from multiple sources, multi-view clustering has attracted more and more attention. However, most previous works ignore missing instances and noises in original multi-view data, which limits the applications of these works. By a soft approach, our proposed ANIMC can effectively reduce the negative influence of missing instances and noises. Moreover, ANIMC outperforms the stateof- the-art works by about 20% in representative cases. With satisfactory performance on multiple rea

关键词： Regression analysis

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China's greenhouse gas budget during 2000-2023

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National science Review 2025年第04期 24-36页

作者： Wenping Yuan Minqi Liang Yuanyi Gao Ling Huang Li Dan Hongtao Duan Songbai Hong Fei Jiang Weimin Ju Tingting Li Ziyang Lou Shilong Luan Xiao Lu Zhangcai Qin Lishan Ran Lulu Shen Fei Teng Xiangjun Tian Yilong Wang Jing Wei Jiangzhou Xia Xiaosheng Xia Lijun Yu Xu Yue Haicheng Zhang Wen Zhang Yuzhong Zhang Xu Zhao Qiuan Zhu Shilong Piao Xuhui Wang Institute of Carbon Neutrality Sino-French Institute for Earth System Science College of Urban and Environmental Sciences Peking University School of Atmospheric Sciences Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle Sun Yat-sen University Key Laboratory of Regional Climate-Environment for Temperate East Asia Institute of Atmospheric PhysicsChinese Academy of Sciences Key Laboratory of Lake and Watershed Science for Water Security Nanjing Institute of Geography and LimnologyChinese Academy of Sciences State Key Laboratory of Lake Science and Environment Nanjing Institute of Geography and LimnologyChinese Academy of Sciences School of Urban Planning and Design Shenzhen Graduate School Peking University Frontiers Science Center for Critical Earth Material Cycling Nanjing University Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology International Institute for Earth System Science Nanjing University Key Laboratory of Atmospheric Environment and Extreme Meteorology Institute of Atmospheric PhysicsChinese Academy of Sciences Shanghai Engineering Research Center of Solid Waste Treatment and Resource Recovery School of Environmental Science & Engineering Shanghai Jiao Tong University Department of Geography The University of Hong Kong Department of Atmospheric and Oceanic Sciences School of PhysicsPeking University Institute of Energy Environment and Economy Tsinghua University State Key Laboratory of Tibetan Plateau Earth System Resources and Environment (TPESRE)Institute of Tibetan Plateau ResearchChinese Academy of Sciences Tianjin Key Laboratory of Water Resources and Environment Tianjin Normal University State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC) Institute of Atmospheric PhysicsChinese Academy of Sciences Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Collaborative Innovation Center of Atmospheric Environment and Equipment Technology School of

National greenhouse gas（GHG） budget,including CO2,CH4and N2O has increasingly become a topic of concern in international climate *** is paying increasing attention to reducing GHG emissions and increasing land sinks to effectively mitigate climate *** estimates of GHG fluxes are crucial for monitoring progress toward mitigating GHG emissions in *** study used comprehensive methods,including emission factor methods,process-based models,atmospheric inversions,and data-driven models,to estimate the long-term trends of GHG sources and sinks from all anthropogenic and natural sectors in China's mainland during 2000-2023,and produced an up-to-date China GHG Budget dataset（CNGHG）.The total gross emissions of the three GHGs show a 3-fold increase from 5.0（95% CI:4.9-5.1） Gt CO2-eq yr-1（in 2000） to 14.3（95% CI:13.8-14.8） Gt CO2-eqyr-1（in2023）.CO2emissions represented 81.8% of the GHG emissions in 2023,while 12.7% and 5.5% were for CH4and N2O,*** the largest CO2source,the energy sector contributed 87.4% *** contrast,the agriculture,forestry and other land use sector was the largest sector of CH4and N2O,representing 50.1% and 6 6.3% emissions,***,China's terrestrial ecosystems serve as a net CO2sink(1.0 Gt CO2yr-1,9596 CI:0.2-1.9 Gt CO2yr-1) during 2012 to 2021,equivalent to an average of 14.3% of fossil *** GHG emission estimates showed a general consistency with national GHG inventories,with gridded and sector-specific estimates of GHG fluxes over China,providing the basis for curtailing GHG emissions for each region and sector.

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Spring migration duration exceeds that of autumn migration in Far East Asian Greater White-fronted Geese (Anser albifrons)

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Avian Research 2019年第3期10卷 319-329页

作者： Xueqin Deng Qingshan Zhao Lei Fang Zhenggang Xu Xin Wang Haoren He Lei Cao Anthony David Fox State Key Laboratory of Urban and Regional Ecology Research Center for Eco-Environmental Sciences Chinese Academy of SciencesBeijing 100085China University of Chinese Academy of Sciences Beijing 100049China School of Life Science University of Science and Technology of ChinaHefei 230026AnhuiPeople’s Republic of China Key Laboratory of Forestry Remote Sensing Based Big Data and Ecological Security for Hunan Province Central South University of Forestry and TechnologyChangsha 410004China Information Management and System Department IBSSXi’an Jiaotong-Liverpool UniversitySuzhou 215123China Department of Bioscience Aarhus UniversityKalo Grenavej 148410 RondeDenmark

Background:Migration theory suggests,and some empirical studies show, that in order to compete for the best breeding sites and increase reproductive success,long-distance avian migrants tend to adopt a time minimization strategy during spring migration, resulting in shorter duration spring migration compared to that in ***:Using GPS/GSM transmitters,we tracked the full migrations of 11 Greater White-fronted Geese (Anser albifrons) between southeast China and the Russian Arctic,to reveal the migration timing and routes of the East Asian population, and compare the difference in duration between spring and autumn migration of this ***:We found that migration in spring (79 ± 12 days) took more than twice as long to cover the same distance as in autumn (35 ±7 days).This difference in migration duration was mainly determined by significantly more time spent in spring (59±16 days) than in autumn (23± 6days) at significantly more stopover sites. Conclusions:We suggest that these geese, thought to be partial capital breeders, spent almost three quarters of total migration time at spring stopover sites to acquire energy stores for ultimate investment in reproduction, although we cannot reject the hypothesis that timing of the spring thaw also contributed to stopover *** autumn,they acquired necessary energy stores on the breeding grounds sufficient to reach Northeast China staging areas almost without stop, which reduced stopover times in autumn and resulted in the faster autumn migration than spring.

关键词： Greater White-fronted Goose GPS/GSM tracking Migration Spring staging Stopover duration

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