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African swine fever and meat prices fluctuation:An empirical study in China based on TVP-VAR model

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Journal of Integrative Agriculture 2021年第8期20卷 2289-2301页

作者： LI Hui-shang HU Chen-pei LÜZheng LI Mei-qi GUO Xin-zhu Agricultural Information institute/Key Laboratory of big Agri-data of Ministry of Agriculture and Rural Affairs Chinese Academy of Agricultural SciencesBeijing 100081P.R.China International Statistical Information Center National Bureau of StatisticsBeijing 100826P.R.China School of Statistics and Mathematics Central University of Finance and EconomicsBeijing 100081P.R.China

African swine fever(ASF),a fatal disease outbroken in China in August 2018,has widely attracted social concern especially in the information era.The occurrence of ASF led to an imbalance between supply and demand in pork and other meat markets.As a result,meat prices fluctuated greatly during the past year in 2019.To measure ASF quantitatively,the internet public concern index about ASF was created using web crawler methods.The relationships between ASF and meat prices were analyzed based on time-varying parameter vector auto-regressive(TVP-VAR)model.The results showed that there were some differences in the impact size,direction and duration of ASF on the prices of pork,chicken,beef and mutton,and the characteristics of time variability and heterogeneity were obvious.At the same time,the impact of ASF on meat prices is not consistent with the trend and degree of ASF.The impulse intensity is strongly correlated with the strength and duration of ASF,and it is generally weak in the early stage and much stronger in the middle and late periods.The results indicate that macro regulations,monitoring and early-warning system,standardizing production and circulation,and the public opinion monitoring and guidance about ASF should be given more attention in future to stabilize the market expectations and to promote a smooth functioning of the livestock markets.

关键词： African swine fever meat prices dynamic transmission TVP-VAR model

A Parallel High-Utility Itemset Mining Algorithm Based on Hadoop

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Complex System Modeling and Simulation 2023年第1期3卷 47-58页

作者： Zaihe Cheng Wei Shen Wei Fang Jerry Chun-Wei Lin School of internet of Things Wuxi Institute of TechnologyWuxi 214121China School of Artificial Intelligence and Computer Science Jiangnan UniversityWuxi 214122China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan UniversityWuxi 214122China Department of Computer Science Electrical Engineering and Mathematical SciencesWestern Norway University of Applied SciencesBergen 5020Norway

High-utility itemset mining(HUIM)can consider not only the profit factor but also the profitable factor,which is an essential task in data mining.However,most HUIM algorithms are mainly developed on a single machine,which is inefficient for big data since limited memory and processing capacities are available.A parallel efficient high-utility itemset mining(P-EFIM)algorithm is proposed based on the Hadoop platform to solve this problem in this paper.In P-EFIM,the transaction-weighted utilization values are calculated and ordered for the itemsets with the MapReduce framework.Then the ordered itemsets are renumbered,and the low-utility itemsets are pruned to improve the dataset utility.In the Map phase,the P-EFIM algorithm divides the task into multiple independent subtasks.It uses the proposed S-style distribution strategy to distribute the subtasks evenly across all nodes to ensure load-balancing.Furthermore,the P-EFIM uses the EFIM algorithm to mine each subtask dataset to enhance the performance in the Reduce phase.Experiments are performed on eight datasets,and the results show that the runtime performance of P-EFIM is significantly higher than that of the PHUI-Growth,which is also HUIM algorithm based on the Hadoop framework.

关键词： pattern mining data mining Hadoop parallel high-utility itemset mining big data

A Deletable and Modifiable Blockchain Scheme Based on Record Verification Trees and the Multisignature Mechanism

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Computer Modeling in Engineering & Sciences 2021年第7期128卷 223-245页

作者： Daojun Han Jinyu Chen Lei Zhang Yatian Shen Yihua Gao Xueheng Wang Henan Key Laboratory of big data Analysis and Processing Henan UniversityKaifeng47500China institute of data and Knowledge Engineering Henan UniversityKaifeng47500China School of Computer and Information Engineering Henan UniversityKaifeng47500China Department of Network and Finance Agricultural Bank of China Limited Xuchang BranchXuchang461000China School of Software Pingdingshan UniversityPingdingshan467000China Department of Network and Finance Agricultural Bank of China Limited Nanyang BranchNanyang473000China

As one of the most valuable technologies,blockchains have received extensive attention from researchers and industry circles and are widely applied in various scenarios.However,data on a blockchain cannot be deleted.As a result,it is impossible to clean invalid and sensitive data and correct erroneous data.This,to a certain extent,hinders the application of blockchains in supply chains and internet of Things.To address this problem,this study presents a deletable and modifiable blockchain scheme(DMBlockChain)based on record verification trees(RVTrees)and the multisignature scheme.(1)In this scheme,an RVTree structure is designed and added to the block structure.The RVTree can not only ensure that a record is true and valid but,owing to its unique binary structure,also verify whether modification and deletion requests are valid.(2)In DMBlockChain,the multisignature mechanism is also introduced.This mechanism requires the stakeholders’signatures for each modification or deletion request and thus ensures that a record will not be modified arbitrarily.A user’s request is deemed valid only if it is dually verified by the RVTree and the multisignature mechanism.The analysis finds that DMBlockChain can provide a secure and valid means for modifying and deleting records in a block while ensuring the integrity of the block and that DMBlockChain can effectively save space in some scenarios that require frequent records modification.

关键词： Blockchain record verification trees multisignature DMBlockChain

database Commons:A Catalog of Worldwide Biological databases

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Genomics, Proteomics & Bioinformatics 2023年第5期21卷 1054-1058页

作者： Lina Ma Dong Zou Lin Liu Huma Shireen Amir AAbbasi Alex Bateman Jingfa Xiao Wenming Zhao Yiming Bao Zhang Zhang National Genomics data Center Beijing Institute of GenomicsChinese Academy of Sciences and China National Center for BioinformationBeijing 100101China CAS Key Laboratory of Genome Sciences and Information Beijing Institute of GenomicsChinese Academy of Sciences and China National Center for BioinformationBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China National Center for Bioinformatics Programme of Comparative and Evolutionary GenomicsFaculty of Biological SciencesQuaid-i-Azam UniversityIslamabad 45320Pakistan European Molecular Biology Laboratory European Bioinformatics Institute(EMBL-EBI)HinxtonCambridge CB101SDUnited Kingdom

Biological databases serve as a global fundamental infrastructure for the worldwide scientific community,which dramatically aid the transformation of big data into knowledge discovery and drive significant innovations in a wide range of research fields.Given the rapid data production,biological databases continue to increase in size and importance.To build a catalog of worldwide biological databases,we curate a total of 5825 biological databases from 8931 publications,which are geographically distributed in 72 countries/regions and developed by 1975 institutions(as of September 20,2022).We further devise a z-index,a novel index to characterize the scientific impact of a database,and rank all these biological databases as well as their hosting institutions and countries in terms of citation and z-index.Consequently,we present a series of statistics and trends of worldwide biological databases,yielding a global perspective to better understand their status and impact for life and health sciences.An up-to-date catalog of worldwide biological databases,as well as their curated meta-information and derived statistics,is publicly available at database Commons(https://***/databasecommons/).

关键词： Biological database Catalog database Commons Citation z-index

Research on the ecological compensation standard of the basin pollution control project based on evolutionary game theory and by taking Xiangjiang River as a case

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Frontiers of Engineering Management 2019年第4期6卷 575-583页

作者： Dongbin HU Huiwu LIU Xiaohong CHEN Yang CHEN School of Business Resource-Conserving&Environment-Friendly Society and Ecological Civilization 2011 Collaborative Innovation Center of Hunan ProvinceCentral South UniversityChangsha 410083China School of Business Central South UniversityChangsha 410083China institute of big data and internet Innovation Hunan University of Technology and BusinessChangsha 410205China

Ecological compensation is a new resource and environment management model.As one of the main areas for implementing ecological compensation policies,basin ecological compensation has become an important measure for encouraging basin pollution control projects and improving the quality of regional economic development.By applying the basic game analysis of evolutionary game theory and building an evolutionary game model with a“reward-punishment”mechanism,this paper compares the interest-related decision-making behaviors of the upstream and downstream stakeholders of basin ecological compensation.By using data on the water quality of Xiangjiang River Basin,this paper calculates the rewards and penalties in different intervals by building a parametric regression mathematical model and employing the local linear regression method.Results show that a decline in water quality should be fined RMB 925500 yuan,an improvement in water quality should be awarded RMB 1227800 yuan,and a deteriorating water quality should be severely fined RMB 5087600 yuan.

关键词： evolutionary game ecological compensation standard Xiangjiang River Basin

Entity perception of Two-Step-Matching framework for public opinions

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Journal of Safety Science and Resilience 2020年第1期1卷 36-43页

作者： Ren-De Li Hao-Tian Ma Zi-Yi Wang Qiang Guo Jian-Guo Liu Library and Research Center of Computer Systems Science University of Shanghai for Science and TechnologyShanghai 200093PR China School of Accountancy and Shanghai Key Laboratory of Financial Information Technology Shanghai University of Finance and EconomicsShanghai 200433PR China School of Humanities Shanghai University of Finance and EconomicsShanghai 200433PR China institute of Sina WRD big data Shanghai 201204PR China

Entity perception of ambiguous user comments is a critical problem of target identification for huge amount of public opinions.In this paper,a Two-Step-Matching method is proposed to identify the precise target entity from multiple entities mentioned.Firstly,potential entities are extracted by BiLSTM-CRF model and characteristic words by TF-IDF model from public comments.Secondly,the first matching is implemented between potential entities and an official business directory by Jaro-Winkler distance algorithm.Then,in order to find the pre-cise one,an industry-characteristic dictionary is developed into the second matching process.The precise entity is identified according to the count of characteristic words matching to industry-characteristic dictionary.In addition,associated rate(global indicator)and accuracy rate(sample indicator)are defined for evaluation of matching accuracy.The results for three data sets of public opinions about major public health events show that the highest associated rate and accuracy rate arrive at 0.93 and 0.95,averagely enhanced by 32%and 30%above the case of using the first matching process alone.This framework provides the method to find the true target entity of really wanted expression from public opinions.

关键词： Entity perception BiLSTM-CRF model Jaro-Winkler distance algorithm User comments Public opinions

MMLUP: Multi-Source & Multi-Task Learning for User Profiles in Social Network

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Computers, Materials & Continua 2019年第9期61卷 1105-1115页

作者： Dongjie Zhu Yuhua Wang Chuiju You Jinming Qiu Ning Cao Chenjing Gong Guohua Yang Helen Min Zhou School of Computer Science and Technology Harbin Institute of TechnologyWeihai264209China College of Information Engineering Sanming University365004SanmingChina Fujian Province University Key Lab for Industry big data Analysis and Application FujianChina College of Information Engineering Qingdao Binhai UniversityQingdaoChina Jiangsu Province Wireless Sensing System Application Engneering Technology Research and Development Centre China School of Engineering Manukau Institute of TechnologyAuckland2241New Zealand

With the rapid development of the mobile internet,users generate massive data in different forms in social network every day,and different characteristics of users are reflected by these social media data.How to integrate multiple heterogeneous information and establish user profiles from multiple perspectives plays an important role in providing personalized services,marketing,and recommendation systems.In this paper,we propose Multi-source&Multi-task Learning for User Profiles in Social Network which integrates multiple social data sources and contains a multi-task learning framework to simultaneously predict various attributes of a user.Firstly,we design their own feature extraction models for multiple heterogeneous data sources.Secondly,we design a shared layer to fuse multiple heterogeneous data sources as general shared representation for multi-task learning.Thirdly,we design each task’s own unique presentation layer for discriminant output of specific-task.Finally,we design a weighted loss function to improve the learning efficiency and prediction accuracy of each task.Our experimental results on more than 5000 Sina Weibo users demonstrate that our approach outperforms state-of-the-art baselines for inferring gender,age and region of social media users.

关键词： User profiles multi-source multi-task learning social network

DeepSun:machine-learning-as-a-service for solar flare prediction

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Research in Astronomy and Astrophysics 2021年第7期21卷 51-61页

作者： Yasser Abduallah Jason T.L.Wang Yang Nie Chang Liu Haimin Wang institute for Space Weather Sciences New Jersey Institute of TechnologyUniversity HeightsNewarkNJ 07102-1982USA Department of Computer Science New Jersey Institute of TechnologyUniversity HeightsNewarkNJ 07102-1982USA big Bear Solar Observatory New Jersey Institute of Technology40386 North Shore LaneBig Bear CityCA 92314-9672USA Center for Solar-Terrestrial Research New Jersey Institute of TechnologyUniversity HeightsNewarkNJ 07102-1982USA

Solar flare prediction plays an important role in understanding and forecasting space weather.The main goal of the Helioseismic and Magnetic Imager(HMI), one of the instruments on NASA's Solar Dynamics Observatory, is to study the origin of solar variability and characterize the Sun's magnetic activity.HMI provides continuous full-disk observations of the solar vector magnetic field with high cadence data that lead to reliable predictive capability;yet, solar flare prediction effort utilizing these data is still limited. In this paper, we present a machine-learning-as-a-service(MLaa S) framework, called Deep Sun,for predicting solar flares on the web based on HMI's data products. Specifically, we construct training data by utilizing the physical parameters provided by the Space-weather HMI Active Region Patch(SHARP)and categorize solar flares into four classes, namely B, C, M and X, according to the X-ray flare catalogs available at the National Centers for Environmental Information(NCEI). Thus, the solar flare prediction problem at hand is essentially a multi-class(i.e., four-class) classification problem. The Deep Sun system employs several machine learning algorithms to tackle this multi-class prediction problem and provides an application programming interface(API) for remote programming users. To our knowledge, Deep Sun is the first MLaa S tool capable of predicting solar flares through the internet.

关键词： Sun:flares Sun:activity methods:data analysis

赭曲霉毒素A和玉米赤霉烯酮二联定量胶体金免疫层析试纸条的制备及应用（英文）

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Journal of Zhejiang University-Science B(Biomedicine & Biotechnology) 2018年第11期19卷 871-883页

作者： Xian ZHANG Ke HE Yun FANG Tong CAO Narayan PAUDYAL Xiao-feng ZHANG Hou-hui SONG Xiao-liang LI Wei-huan FANG China-Australian Joint Laboratory for Animal Health big data Analytics Zhejiang Provincial Engineering Laboratory for Animal Health Inspection and Internet TechnologyCollege of Animal Science and TechnologyZhejiang A&F UniversityLin'an 311300China Zhejiang University institute of Preventive Veterinary Medicine Zhejiang Provincial Key Laboratory of Preventive Veterinary MedicineHangzhou 310058China Zhejiang Academy of Science and Technology for Inspection and Quarantine Hangzhou 310012China

目的:优化建立二联胶体金免疫层析试纸条,实现对玉米、面粉和饲料中赭曲霉毒素A和玉米赤霉烯酮的同时快速定量检测。创新点:采用纳米金颗粒标记真菌毒素单克隆抗体,基于竞争反应模式,通过对金颗粒尺寸、免疫层析相关组成材料及缓冲液配... 详细信息

目的:优化建立二联胶体金免疫层析试纸条,实现对玉米、面粉和饲料中赭曲霉毒素A和玉米赤霉烯酮的同时快速定量检测。创新点:采用纳米金颗粒标记真菌毒素单克隆抗体,基于竞争反应模式,通过对金颗粒尺寸、免疫层析相关组成材料及缓冲液配方的系列优化,同时借助手持式信号读取装备,实现对谷物和饲料中赭曲霉毒素A和玉米赤霉烯酮的同时定量检测。该方法简单、快速且成本低,与液相色谱串联质谱(LC-MS/MS)一致性良好,便于基层单位的推广使用。方法:采用柠檬酸钠还原法制备纳米金颗粒并标记获得金标抗体。通过在基于竞争反应的免疫层析检测体系中,同时设置两条检测线(T1、T2)以实现两种毒素的同时检测,最终采用金标信号读数仪实现定量检测。结论:制备的二联定量胶体金试纸条对赭曲霉毒素A和玉米赤霉烯酮的检测限分别为0.32和0.58 ng/ml(图5),在玉米、小麦和饲料样本中的加标回收率为77.3%～106.3%,且变异系数均小于15%(表2)。制备的二联定量胶体金免疫层析试纸条和LC-MS/MS对玉米、小麦和饲料等天然样本中赭曲霉毒素A和玉米赤霉烯酮的定量检测结果相关性较好(表3和图7),具有较好的应用价值。

关键词：免疫层析纳米金颗粒赭曲霉毒素A 玉米赤霉烯酮定量检测