Layered Event Ontology Modes
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- disruption ,: Global convergence vs nationalSustainable - ,practices and dynamic capabilities in the food industry: A critical analysis of the literature5 Mesoscopic - simulation6 Firm size and sustainable performance in food -s: Insights from Greek SMEs7 An analytical method for cost analysis in multi-stage -s: A stochastic / model approach8 A Roadmap to Green - System through Enterprise Resource Planning (ERP) Implementation9 Unidirectional transshipment policies in a dual-channel -10 Decentralized and centralized model predictive control to reduce the bullwhip effect in - ,11 An agent-based distributed computational experiment framework for virtual - / development12 Biomass-to-bioenergy and biofuel - optimization: Overview, key issues and challenges13 The benefits of - visibility: A value assessment model14 An Institutional Theory perspective on sustainable practices across the dairy -15 Two-stage stochastic programming - model for biodiesel production via wastewater treatment16 Technology scale and -s in a secure, affordable and low carbon energy transition17 Multi-period design and planning of closed-loop -s with uncertain supply and demand18 Quality control in food - ,: An analytical model and case study of the adulterated milk incident in China19 - information capabilities and performance outcomes: An empirical study of Korean steel suppliers20 A game-based approach towards facilitating decision making for perishable products: An example of blood -21 - design under quality disruptions and tainted materials delivery22 A two-level replenishment frequency model for TOC - replenishment systems under capacity constraint23 - dynamics and the ―cross-border effect‖: The U.S.–Mexican border’s case24 Designing a new - for competition against an existing -25 Universal supplier selection via multi-dimensional auction mechanisms for two-way competition in oligopoly market of -26 Using TODIM to evaluate green - practices under uncertainty27 - downsizing under bankruptcy: A robust optimization approach28 Coordination mechanism for a deteriorating item in a two-level - system29 An accelerated Benders decomposition algorithm for sustainable - / design under uncertainty: A case study of medical needle and syringe -30 Bullwhip Effect Study in a Constrained -31 Two-echelon multiple-vehicle location–routing problem with time windows for optimization of sustainable - / of perishable food32 Research on pricing and coordination strategy of green - under hybrid production mode33 Agent-system co-development in - research: Propositions and demonstrative findings34 Tactical ,for coordinated -s35 Photovoltaic - coordination with strategic consumers in China36 Coordinating supplier׳s reorder point: A coordination mechanism for -s with long supplier lead time37 Assessment and optimization of forest biomass -s from economic, social and environmental perspectives – A review of literature38 The effects of a trust mechanism on a dynamic - /39 Economic and environmental assessment of reusable plastic containers: A food catering - case study40 Competitive pricing and ordering decisions in a multiple-channel -41 Pricing in a - for auction bidding under information asymmetry42 Dynamic analysis of feasibility in ethanol - for biofuel production in Mexico43 The impact of partial information sharing in a two-echelon -44 Choice of - governance: Self-managing or outsourcing?45 Joint production and delivery lot sizing for a make-to-order producer–buyer - with transportation cost46 Hybrid algorithm for a vendor managed inventory system in a two-echelon -47 Traceability in a food -: Safety and quality perspectives48 Transferring and sharing exchange-rate risk in a risk-averse - of a multinational firm49 Analyzing the impacts of carbon regulatory mechanisms on supplier and mode selection decisions: An application to a biofuel -50 Product quality and return policy in a - under risk aversion of a supplier51 Mining logistics data to assure the quality in a sustainable food -: A case in the red wine industry52 Biomass - optimisation for Organosolv-based biorefineries53 Exact solutions to the - equations for arbitrary, time-dependent demands54 Designing a sustainable closed-loop - / based on triple bottom line approach: A comparison of metaheuristics hybridization techniques55 A study of the LCA based biofuel - multi-objective optimization model with multi-conversion paths in China56 A hybrid two-stock inventory control model for a reverse -57 Dynamics of judicial service -s58 Optimizing an integrated vendor-managed inventory system for a single-vendor two-buyer - with determining weighting factor for vendor׳s ordering59 Measuring - Resilience Using a Deterministic Modeling Approach60 A LCA Based Biofuel - Analysis Framework61 A neo-institutional perspective of -s and energy security: Bioenergy in the UK62 Modified penalty function method for optimal social welfare of electric power - with transmission constraints63 Optimization of blood - with shortened shelf lives and ABO compatibility64 Diversified firms on dynamical - cope with financial crisis better65 Securitization of energy -s in China66 Optimal design of the auto parts - for JIT operations: Sequential bifurcation factor screening and multi-response surface methodology67 Achieving sustainable -s through energy justice68 - agility: Securing performance for Chinese manufacturers69 Energy price risk and the sustainability of demand side -s70 Strategic and tactical mathematical programming models within the crude oil - context - A review71 An analysis of the structural complexity of - /s72 Business process re-design methodology to support - integration73 Could - technology improve food operators’ innovativeness? A developing country’s perspective74 RFID-enabled process reengineering of closed-loop -s in the healthcare industry of Singapore75 Order-Up-To policies in Information Exchange -s76 Robust design and operations of hydrocarbon biofuel - integrating with existing petroleum refineries considering unit cost objective77 Trade-offs in - transparency: the case of Nudie Jeans78 Healthcare - operations: Why are doctors reluctant to consolidate?79 Impact on the optimal design of bioethanol -s by a new European Commission proposal80 Managerial research on the pharmaceutical - – A critical review and some insights for future directions81 - performance evaluation with data envelopment analysis and balanced scorecard approach82 Integrated - design for commodity chemicals production via woody biomass fast pyrolysis and upgrading83 Governance of sustainable -s in the fast fashion industry84 Temperature ,for the quality assurance of a perishable food -85 Modeling of biomass-to-energy - operations: Applications, challenges and research directions86 Assessing Risk Factors in Collaborative - with the Analytic Hierarchy Process (AHP)87 Random / models and sensitivity algorithms for the analysis of ordering time and inventory state in multi-stage -s88 Information sharing and collaborative behaviors in enabling - performance: A social exchange perspective89 The coordinating contracts for a fuzzy - with effort and price dependent demand90 Criticality analysis and the -: Leveraging representational assurance91 Economic model predictive control for inventory ,in -s92 - ,ontology from an ontology engineering perspective93 Surplus division and investment incentives in -s: A biform-game analysis94 Biofuels for road transport: Analysing evolving -s in Sweden from an energy security perspective95 - ,executives in corporate upper echelons Original Research Article96 Sustainable - ,in the fast fashion industry: An analysis of corporate reports97 An improved method for managing catastrophic - disruptions98 The equilibrium of closed-loop - super/ with time-dependent parameters99 A bi-objective stochastic programming model for a centralized green - with deteriorating products100 Simultaneous control of vehicle routing and inventory for dynamic inbound -101 Environmental impacts of roundwood - options in Michigan: life-cycle assessment of harvest and transport stages102 A recovery mechanism for a two echelon - system under supply disruption103 Challenges and Competitiveness Indicators for the Sustainable Development of the - in Food Industry104 Is doing more doing better? The relationship between responsible - ,and corporate reputation105 Connecting product design, process and - decisions to strengthen global - capabilities106 A computational study for common / design in multi-commodity -s107 Optimal production and procurement decisions in a - with an option contract and partial backordering under uncertainties108 Methods to optimise the design and ,of biomass-for-bioenergy -s: A review109 Reverse - coordination by revenue sharing contract: A case for the personal computers industry110 SCOlog: A logic-based approach to analysing - operation dynamics111 Removing the blinders: A literature review on the potential of nanoscale technologies for the ,of -s112 Transition inertia due to competition in -s with remanufacturing and recycling: A systems dynamics mode113 Optimal design of advanced drop-in hydrocarbon biofuel - integrating with existing petroleum refineries under uncertainty114 Revenue-sharing contracts across an extended -115 An integrated revenue sharing and quantity discounts contract for coordinating a - dealing with short life-cycle products116 Total JIT (T-JIT) and its impact on - competency and organizational performance117 Logistical - design for bioeconomy applications118 A note on ―Quality investment and inspection policy in a supplier-manufacturer -‖119 Developing a Resilient -120 Cyber - risk ,: Revolutionizing the strategic control of critical IT systems121 Defining value chain architectures: Linking strategic value creation to operational - design122 Aligning the sustainable - to green marketing needs: A case study123 Decision support and intelligent systems in the textile and apparel -: An academic review of research articles124 - ,capability of small and medium sized family businesses in India: A multiple case study approach125 - collaboration: Impact of success in long-term partnerships126 Collaboration capacity for sustainable - ,: small and medium-sized enterprises in Mexico127 Advanced traceability system in aquaculture -128 - information systems strategy: Impacts on - performance and firm performance129 Performance of - collaboration – A simulation study130 Coordinating a three-level - with delay in payments and a discounted interest rate131 An integrated framework for agent basedinventory–production–transportation modeling and distributed simulation of -s132 Optimal - design and ,over a multi-period horizon under demand uncertainty. Part I: MINLP and MILP models133 The impact of knowledge transfer and complexity on - flexibility: A knowledge-based view134 An innovative - performance measurement system incorporating Research and Development (R&D) and marketing policy135 Robust decision making for hybrid process - systems via model predictive control136 Combined pricing and - operations under price-dependent stochastic demand137 Balancing - competitiveness and robustness through ―virtual dual sourcing‖: Lessons from the Great East Japan Earthquake138 Solving a tri-objective - problem with modified NSGA-II algorithm 139 Sustaining long-term - partnerships using price-only contracts 140 On the impact of advertising initiatives in -s141 A typology of the situations of cooperation in -s142 A structured analysis of operations and - ,research in healthcare (1982–2011143 - practice and information quality: A - strategy study144 Manufacturer's pricing strategy in a two-level - with competing retailers and advertising cost dependent demand145 Closed-loop - / design under a fuzzy environment146 Timing and eco(nomic) efficiency of climate-friendly investments in -s147 Post-seismic - risk ,: A system dynamics disruption analysis approach for inventory and logistics planning148 The relationship between legitimacy, reputation, sustainability and branding for companies and their -s149 Linking - configuration to - perfrmance: A discrete event simulation model150 An integrated multi-objective model for allocating the limited sources in a multiple multi-stage lean -151 Price and leadtime competition, and coordination for make-to-order -s152 A model of resilient - / design: A two-stage programming with fuzzy shortest path153 Lead time variation control using reliable shipment equipment: An incentive scheme for - coordination154 Interpreting - dynamics: A quasi-chaos perspective155 A production-inventory model for a two-echelon - when demand is dependent on sales teams׳ initiatives156 Coordinating a dual-channel - with risk-averse under a two-way revenue sharing contract157 Energy supply planning and - optimization under uncertainty158 A hierarchical model of the impact of RFID practices on retail - performance159 An optimal solution to a three echelon - / with multi-product and multi-period160 A multi-echelon - model for municipal solid waste ,system 161 A multi-objective approach to - visibility and risk162 An integrated - model with errors in quality inspection and learning in production163 A fuzzy AHP-TOPSIS framework for ranking the solutions of Knowledge ,adoption in - to overcome its barriers164 A relational study of - agility, competitiveness and business performance in the oil and gas industry165 Cyber - security practices DNA – Filling in the puzzle using a diverse set of disciplines166 A three layer - model with multiple suppliers, manufacturers and retailers for multiple items167 Innovations in low input and organic dairy -s—What is acceptable in Europe168 Risk Variables in Wind Power -169 An analysis of - strategies in the regenerative medicine industry—Implications for future development170 A note on - coordination for joint determination of order quantity and reorder point using a credit option171 Implementation of a responsive - strategy in global complexity: The case of manufacturing firms172 - scheduling at the manufacturer to minimize inventory holding and delivery costs173 GBOM-oriented ,of production disruption risk and optimization of - construction175 Alliance or no alliance—Bargaining power in competing reverse -s174 Climate change risks and adaptation options across Australian seafood -s – A preliminary assessment176 Designing contracts for a closed-loop - under information asymmetry 177 Chemical - modeling for analysis of homeland security178 Chain liability in multitier -s? Responsibility attributions for unsustainable supplier behavior179 Quantifying the efficiency of price-only contracts in push -s over demand distributions of known supports180 Closed-loop - / design: A financial approach181 An integrated - / design problem for bidirectional flows182 Integrating multimodal transport into cellulosic biofuel - design under feedstock seasonality with a case study based on California183 - dynamic configuration as a result of new product development184 A genetic algorithm for optimizing defective goods - costs using JIT logistics and each-cycle lengths185 A - / design model for biomass co-firing in coal-fired power plants 186 Finance sourcing in a -187 Data quality for data science, predictive analytics, and big data in - ,: An introduction to the problem and suggestions for research and applications188 Consumer returns in a decentralized -189 Cost-based pricing model with value-added tax and corporate income tax for a - /190 A hard nut to crack! Implementing - sustainability in an emerging economy191 Optimal location of spelling yards for the northern Australian beef -192 Coordination of a socially responsible - using revenue sharing contract193 Multi-criteria decision making based on trust and reputation in -194 Hydrogen - architecture for bottom-up energy systems models. Part 1: Developing pathways195 Financialization across the Pacific: Manufacturing cost ratios, -s and power196 Integrating deterioration and lifetime constraints in production and - planning: A survey197 Joint economic lot sizing problem for a three—Layer - with stochastic demand198 Mean-risk analysis of radio frequency identification technology in - with inventory misplacement: Risk-sharing and coordination199 Dynamic impact on global -s performance of disruptions propagation produced by terrorist acts。
名词解释中英文对比<using_information_sources> social networks 社会网络abductive reasoning 溯因推理action recognition(行为识别)active learning(主动学习)adaptive systems 自适应系统adverse drugs reactions(药物不良反应)algorithm design and analysis(算法设计与分析) algorithm(算法)artificial intelligence 人工智能association rule(关联规则)attribute value taxonomy 属性分类规范automomous agent 自动代理automomous systems 自动系统background knowledge 背景知识bayes methods(贝叶斯方法)bayesian inference(贝叶斯推断)bayesian methods(bayes 方法)belief propagation(置信传播)better understanding 内涵理解big data 大数据big data(大数据)biological network(生物网络)biological sciences(生物科学)biomedical domain 生物医学领域biomedical research(生物医学研究)biomedical text(生物医学文本)boltzmann machine(玻尔兹曼机)bootstrapping method 拔靴法case based reasoning 实例推理causual models 因果模型citation matching (引文匹配)classification (分类)classification algorithms(分类算法)clistering algorithms 聚类算法cloud computing(云计算)cluster-based retrieval (聚类检索)clustering (聚类)clustering algorithms(聚类算法)clustering 聚类cognitive science 认知科学collaborative filtering (协同过滤)collaborative filtering(协同过滤)collabrative ontology development 联合本体开发collabrative ontology engineering 联合本体工程commonsense knowledge 常识communication networks(通讯网络)community detection(社区发现)complex data(复杂数据)complex dynamical networks(复杂动态网络)complex network(复杂网络)complex network(复杂网络)computational biology 计算生物学computational biology(计算生物学)computational complexity(计算复杂性) computational intelligence 智能计算computational modeling(计算模型)computer animation(计算机动画)computer networks(计算机网络)computer science 计算机科学concept clustering 概念聚类concept formation 概念形成concept learning 概念学习concept map 概念图concept model 概念模型concept modelling 概念模型conceptual model 概念模型conditional random field(条件随机场模型) conjunctive quries 合取查询constrained least squares (约束最小二乘) convex programming(凸规划)convolutional neural networks(卷积神经网络) customer relationship management(客户关系管理) data analysis(数据分析)data analysis(数据分析)data center(数据中心)data clustering (数据聚类)data compression(数据压缩)data envelopment analysis (数据包络分析)data fusion 数据融合data generation(数据生成)data handling(数据处理)data hierarchy (数据层次)data integration(数据整合)data integrity 数据完整性data intensive computing(数据密集型计算)data management 数据管理data management(数据管理)data management(数据管理)data miningdata mining 数据挖掘data model 数据模型data models(数据模型)data partitioning 数据划分data point(数据点)data privacy(数据隐私)data security(数据安全)data stream(数据流)data streams(数据流)data structure( 数据结构)data structure(数据结构)data visualisation(数据可视化)data visualization 数据可视化data visualization(数据可视化)data warehouse(数据仓库)data warehouses(数据仓库)data warehousing(数据仓库)database management systems(数据库管理系统)database management(数据库管理)date interlinking 日期互联date linking 日期链接Decision analysis(决策分析)decision maker 决策者decision making (决策)decision models 决策模型decision models 决策模型decision rule 决策规则decision support system 决策支持系统decision support systems (决策支持系统) decision tree(决策树)decission tree 决策树deep belief network(深度信念网络)deep learning(深度学习)defult reasoning 默认推理density estimation(密度估计)design methodology 设计方法论dimension reduction(降维) dimensionality reduction(降维)directed graph(有向图)disaster management 灾害管理disastrous event(灾难性事件)discovery(知识发现)dissimilarity (相异性)distributed databases 分布式数据库distributed databases(分布式数据库) distributed query 分布式查询document clustering (文档聚类)domain experts 领域专家domain knowledge 领域知识domain specific language 领域专用语言dynamic databases(动态数据库)dynamic logic 动态逻辑dynamic network(动态网络)dynamic system(动态系统)earth mover's distance(EMD 距离) education 教育efficient algorithm(有效算法)electric commerce 电子商务electronic health records(电子健康档案) entity disambiguation 实体消歧entity recognition 实体识别entity recognition(实体识别)entity resolution 实体解析event detection 事件检测event detection(事件检测)event extraction 事件抽取event identificaton 事件识别exhaustive indexing 完整索引expert system 专家系统expert systems(专家系统)explanation based learning 解释学习factor graph(因子图)feature extraction 特征提取feature extraction(特征提取)feature extraction(特征提取)feature selection (特征选择)feature selection 特征选择feature selection(特征选择)feature space 特征空间first order logic 一阶逻辑formal logic 形式逻辑formal meaning prepresentation 形式意义表示formal semantics 形式语义formal specification 形式描述frame based system 框为本的系统frequent itemsets(频繁项目集)frequent pattern(频繁模式)fuzzy clustering (模糊聚类)fuzzy clustering (模糊聚类)fuzzy clustering (模糊聚类)fuzzy data mining(模糊数据挖掘)fuzzy logic 模糊逻辑fuzzy set theory(模糊集合论)fuzzy set(模糊集)fuzzy sets 模糊集合fuzzy systems 模糊系统gaussian processes(高斯过程)gene expression data 基因表达数据gene expression(基因表达)generative model(生成模型)generative model(生成模型)genetic algorithm 遗传算法genome wide association study(全基因组关联分析) graph classification(图分类)graph classification(图分类)graph clustering(图聚类)graph data(图数据)graph data(图形数据)graph database 图数据库graph database(图数据库)graph mining(图挖掘)graph mining(图挖掘)graph partitioning 图划分graph query 图查询graph structure(图结构)graph theory(图论)graph theory(图论)graph theory(图论)graph theroy 图论graph visualization(图形可视化)graphical user interface 图形用户界面graphical user interfaces(图形用户界面)health care 卫生保健health care(卫生保健)heterogeneous data source 异构数据源heterogeneous data(异构数据)heterogeneous database 异构数据库heterogeneous information network(异构信息网络) heterogeneous network(异构网络)heterogenous ontology 异构本体heuristic rule 启发式规则hidden markov model(隐马尔可夫模型)hidden markov model(隐马尔可夫模型)hidden markov models(隐马尔可夫模型) hierarchical clustering (层次聚类) homogeneous network(同构网络)human centered computing 人机交互技术human computer interaction 人机交互human interaction 人机交互human robot interaction 人机交互image classification(图像分类)image clustering (图像聚类)image mining( 图像挖掘)image reconstruction(图像重建)image retrieval (图像检索)image segmentation(图像分割)inconsistent ontology 本体不一致incremental learning(增量学习)inductive learning (归纳学习)inference mechanisms 推理机制inference mechanisms(推理机制)inference rule 推理规则information cascades(信息追随)information diffusion(信息扩散)information extraction 信息提取information filtering(信息过滤)information filtering(信息过滤)information integration(信息集成)information network analysis(信息网络分析) information network mining(信息网络挖掘) information network(信息网络)information processing 信息处理information processing 信息处理information resource management (信息资源管理) information retrieval models(信息检索模型) information retrieval 信息检索information retrieval(信息检索)information retrieval(信息检索)information science 情报科学information sources 信息源information system( 信息系统)information system(信息系统)information technology(信息技术)information visualization(信息可视化)instance matching 实例匹配intelligent assistant 智能辅助intelligent systems 智能系统interaction network(交互网络)interactive visualization(交互式可视化)kernel function(核函数)kernel operator (核算子)keyword search(关键字检索)knowledege reuse 知识再利用knowledgeknowledgeknowledge acquisitionknowledge base 知识库knowledge based system 知识系统knowledge building 知识建构knowledge capture 知识获取knowledge construction 知识建构knowledge discovery(知识发现)knowledge extraction 知识提取knowledge fusion 知识融合knowledge integrationknowledge management systems 知识管理系统knowledge management 知识管理knowledge management(知识管理)knowledge model 知识模型knowledge reasoningknowledge representationknowledge representation(知识表达) knowledge sharing 知识共享knowledge storageknowledge technology 知识技术knowledge verification 知识验证language model(语言模型)language modeling approach(语言模型方法) large graph(大图)large graph(大图)learning(无监督学习)life science 生命科学linear programming(线性规划)link analysis (链接分析)link prediction(链接预测)link prediction(链接预测)link prediction(链接预测)linked data(关联数据)location based service(基于位置的服务) loclation based services(基于位置的服务) logic programming 逻辑编程logical implication 逻辑蕴涵logistic regression(logistic 回归)machine learning 机器学习machine translation(机器翻译)management system(管理系统)management( 知识管理)manifold learning(流形学习)markov chains 马尔可夫链markov processes(马尔可夫过程)matching function 匹配函数matrix decomposition(矩阵分解)matrix decomposition(矩阵分解)maximum likelihood estimation(最大似然估计)medical research(医学研究)mixture of gaussians(混合高斯模型)mobile computing(移动计算)multi agnet systems 多智能体系统multiagent systems 多智能体系统multimedia 多媒体natural language processing 自然语言处理natural language processing(自然语言处理) nearest neighbor (近邻)network analysis( 网络分析)network analysis(网络分析)network analysis(网络分析)network formation(组网)network structure(网络结构)network theory(网络理论)network topology(网络拓扑)network visualization(网络可视化)neural network(神经网络)neural networks (神经网络)neural networks(神经网络)nonlinear dynamics(非线性动力学)nonmonotonic reasoning 非单调推理nonnegative matrix factorization (非负矩阵分解) nonnegative matrix factorization(非负矩阵分解) object detection(目标检测)object oriented 面向对象object recognition(目标识别)object recognition(目标识别)online community(网络社区)online social network(在线社交网络)online social networks(在线社交网络)ontology alignment 本体映射ontology development 本体开发ontology engineering 本体工程ontology evolution 本体演化ontology extraction 本体抽取ontology interoperablity 互用性本体ontology language 本体语言ontology mapping 本体映射ontology matching 本体匹配ontology versioning 本体版本ontology 本体论open government data 政府公开数据opinion analysis(舆情分析)opinion mining(意见挖掘)opinion mining(意见挖掘)outlier detection(孤立点检测)parallel processing(并行处理)patient care(病人医疗护理)pattern classification(模式分类)pattern matching(模式匹配)pattern mining(模式挖掘)pattern recognition 模式识别pattern recognition(模式识别)pattern recognition(模式识别)personal data(个人数据)prediction algorithms(预测算法)predictive model 预测模型predictive models(预测模型)privacy preservation(隐私保护)probabilistic logic(概率逻辑)probabilistic logic(概率逻辑)probabilistic model(概率模型)probabilistic model(概率模型)probability distribution(概率分布)probability distribution(概率分布)project management(项目管理)pruning technique(修剪技术)quality management 质量管理query expansion(查询扩展)query language 查询语言query language(查询语言)query processing(查询处理)query rewrite 查询重写question answering system 问答系统random forest(随机森林)random graph(随机图)random processes(随机过程)random walk(随机游走)range query(范围查询)RDF database 资源描述框架数据库RDF query 资源描述框架查询RDF repository 资源描述框架存储库RDF storge 资源描述框架存储real time(实时)recommender system(推荐系统)recommender system(推荐系统)recommender systems 推荐系统recommender systems(推荐系统)record linkage 记录链接recurrent neural network(递归神经网络) regression(回归)reinforcement learning 强化学习reinforcement learning(强化学习)relation extraction 关系抽取relational database 关系数据库relational learning 关系学习relevance feedback (相关反馈)resource description framework 资源描述框架restricted boltzmann machines(受限玻尔兹曼机) retrieval models(检索模型)rough set theroy 粗糙集理论rough set 粗糙集rule based system 基于规则系统rule based 基于规则rule induction (规则归纳)rule learning (规则学习)rule learning 规则学习schema mapping 模式映射schema matching 模式匹配scientific domain 科学域search problems(搜索问题)semantic (web) technology 语义技术semantic analysis 语义分析semantic annotation 语义标注semantic computing 语义计算semantic integration 语义集成semantic interpretation 语义解释semantic model 语义模型semantic network 语义网络semantic relatedness 语义相关性semantic relation learning 语义关系学习semantic search 语义检索semantic similarity 语义相似度semantic similarity(语义相似度)semantic web rule language 语义网规则语言semantic web 语义网semantic web(语义网)semantic workflow 语义工作流semi supervised learning(半监督学习)sensor data(传感器数据)sensor networks(传感器网络)sentiment analysis(情感分析)sentiment analysis(情感分析)sequential pattern(序列模式)service oriented architecture 面向服务的体系结构shortest path(最短路径)similar kernel function(相似核函数)similarity measure(相似性度量)similarity relationship (相似关系)similarity search(相似搜索)similarity(相似性)situation aware 情境感知social behavior(社交行为)social influence(社会影响)social interaction(社交互动)social interaction(社交互动)social learning(社会学习)social life networks(社交生活网络)social machine 社交机器social media(社交媒体)social media(社交媒体)social media(社交媒体)social network analysis 社会网络分析social network analysis(社交网络分析)social network(社交网络)social network(社交网络)social science(社会科学)social tagging system(社交标签系统)social tagging(社交标签)social web(社交网页)sparse coding(稀疏编码)sparse matrices(稀疏矩阵)sparse representation(稀疏表示)spatial database(空间数据库)spatial reasoning 空间推理statistical analysis(统计分析)statistical model 统计模型string matching(串匹配)structural risk minimization (结构风险最小化) structured data 结构化数据subgraph matching 子图匹配subspace clustering(子空间聚类)supervised learning( 有support vector machine 支持向量机support vector machines(支持向量机)system dynamics(系统动力学)tag recommendation(标签推荐)taxonmy induction 感应规范temporal logic 时态逻辑temporal reasoning 时序推理text analysis(文本分析)text anaylsis 文本分析text classification (文本分类)text data(文本数据)text mining technique(文本挖掘技术)text mining 文本挖掘text mining(文本挖掘)text summarization(文本摘要)thesaurus alignment 同义对齐time frequency analysis(时频分析)time series analysis( 时time series data(时间序列数据)time series data(时间序列数据)time series(时间序列)topic model(主题模型)topic modeling(主题模型)transfer learning 迁移学习triple store 三元组存储uncertainty reasoning 不精确推理undirected graph(无向图)unified modeling language 统一建模语言unsupervisedupper bound(上界)user behavior(用户行为)user generated content(用户生成内容)utility mining(效用挖掘)visual analytics(可视化分析)visual content(视觉内容)visual representation(视觉表征)visualisation(可视化)visualization technique(可视化技术) visualization tool(可视化工具)web 2.0(网络2.0)web forum(web 论坛)web mining(网络挖掘)web of data 数据网web ontology lanuage 网络本体语言web pages(web 页面)web resource 网络资源web science 万维科学web search (网络检索)web usage mining(web 使用挖掘)wireless networks 无线网络world knowledge 世界知识world wide web 万维网world wide web(万维网)xml database 可扩展标志语言数据库附录 2 Data Mining 知识图谱(共包含二级节点15 个,三级节点93 个)间序列分析)监督学习)领域 二级分类 三级分类。
高中英语学术论文研究方法练习题40题1.Which of the following topics is most suitable for a high school English academic paper?A.The history of video games.B.The influence of social media on teenagers' language learning.C.The development of artificial intelligence in the medical field.D.The architecture of ancient Rome.答案:B。
解析:选项 A 视频游戏历史与高中英语学术论文关联不大。
选项C 人工智能在医疗领域的发展与英语学科不相关。
选项D 古罗马建筑也与英语学科没有直接关系。
而选项B 社交媒体对青少年语言学习的影响既与英语语言相关,也适合高中学生进行研究。
2.In choosing a topic for an English academic paper, what should be considered first?A.Personal interest.B.Availability of resources.C.Relevance to the curriculum.D.Popularity of the topic.答案:C。
解析:选项 A 个人兴趣虽然重要,但不是首先要考虑的。
选项 B 资源的可获得性在确定选题后再考虑。
选项 D 话题的流行度不是关键因素。
首先应考虑与课程的相关性,这样才能确保论文在学科范围内有意义。
3.Which of the following is NOT a good criterion for choosing anacademic paper topic?A.Being too broad.B.Having enough research materials available.C.Being relevant to current events.D.Being easy to research.答案:A。
七年级科技创新英语阅读理解20题1<背景文章>Smartphones have become an essential part of our lives. In the past few years, smartphones have developed rapidly. They are not only used for making calls and sending messages, but also have many other functions.Smartphones can be used to take photos and record videos. With the improvement of camera technology, the quality of photos and videos taken by smartphones is getting better and better. People can use smartphones to record beautiful moments in their lives.Smartphones also can be used to play games and listen to music. There are many interesting games and wonderful music on smartphones. People can relax and have fun by using smartphones.Moreover, smartphones have a great impact on our lives. They make our lives more convenient and efficient. We can use smartphones to shop online, book tickets, and check the weather.1. Smartphones are not only used for making calls and sending messages, but also have many other ___.A. booksB. functionsC. pens答案:B。
《计算机英语(第4版)》练习参考答案Unit One: Computer and Computer ScienceUnit One/Section AI.Fill in the blanks with the information given in the text:1. Charles Babbage; Augusta Ada Byron2. input; output3. VLSI,4. workstations; mainframes5. vacuum; transistors6. instructions; software7. digit; eight; byte8. microminiaturization; chipII.Translate the following terms or phrases from English into Chinese and vice versa:1. artificial intelligence 人工智能,2. paper-tape reader 纸带阅读器3. optical computer 光计算机4. neural network 神经网络5. instruction set 指令集6. parallel processing 并行处理7. difference engine 差分机8. versatile logical element 通用逻辑元件9. silicon substrate 硅衬底—10. vacuum tube 真空管11. 数据的存储与处理the storage and handling of data12. 超大规模集成电路very large-scale integrated circuit13. 中央处理器central processing unit14. 个人计算机personal computer15. 模拟计算机analogue computer16. 数字计算机digital computer17. 通用计算机general-purpose computer~18. 处理器芯片processor chip19. 操作指令operating instructions20. 输入设备input deviceIII.Fill in each of the blanks with one of the words given in the following list, making changes if necessary:We can define a computer as a device that accepts input, processes data, stores data, and produces output. According to the mode of processing, computers are either analog or digital. They can also be classified as mainframes, minicomputers, workstations, or microcomputers. All else (for example, the age of the machine) being equal, this categorization provides some indication of t he computer’s speed, size, cost, and abilities.Ever since the advent of computers, there have been constant changes. First-generation computers of historic significance, such as UNIVAC (通用自动计算机), introduced in the early 1950s, were based on vacuum tubes. Second-generation computers, appearing in the early 1960s, were those in which transistors replaced vacuum tubes. In third-generation computers, dating from the 1960s, integrated circuits replaced transistors. In fourth-generation computers such as microcomputers, which first appeared in the mid-1970s, large-scale integration enabled thousands of circuits to be incorporated on one chip. Fifth-generation computers are expected to combine very-large-scale integration with sophisticated approaches to computing, including artificial intelligence and true distributed processing.<IV. Translate the following passage from English into Chinese:计算机将变得更加先进,也将变得更加容易使用。
◎2024年第4期◎基于事理图谱的典籍内容知识组织与应用——以《左传》为例李章超,何 琳,喻雪寒摘 要 在数字化背景下,整合海量、多源和异构的典籍内容知识资源,并从中抽取与典籍内容相关的知识单元,揭示知识之间的相互关系,成为还原历史事件所处复杂情境的关键。
文章尝试从知识组织角度出发,利用历史学者需求调查、LDA 主题模型聚类和本体复用等方法构建典籍内容知识表达模型;提出包括事件及其论元构成和事件关系抽取的事理图谱自动化构建方法,从内容和应用的维度实现事理图谱的质量评估。
在此基础上,从主题叙事、空间叙事和逻辑叙事的定义域视角,实现典籍内容知识应用。
本文构建的典籍内容事理图谱能从更细粒度实现事件与角色、地点、时间和万物的结构化和语义化描述,在实现典籍内容事件知识序化的同时,充分揭示历史事件的分布规律与发展趋势。
关键词 典籍内容知识 事理图谱 知识组织 知识应用引用本文格式 李章超,何琳,喻雪寒.基于事理图谱的典籍内容知识组织与应用——以《左传》为例[J].图书馆论坛,2024,44(4):125-137.Contextual Knowledge Organization and Application of Classics Based on Event Knowledge Graph ——Taking ZuoZhuan as an ExampleLI Zhangchao ,HE Lin & YU XuehanAbstract In the context of digitalization ,integrating massive ,multi-source ,heterogeneous knowledge ofancient books ,extracting the knowledge units ,and revealing the interrelationships among the knowledge have become the key to restoring the complex circumstances in which historical events took place. From the perspective of knowledge organization ,this study attempts to build a knowledge representation model of Chinese classics basedon a survey of historical scholars ’ needs ,using methods of LDA topic model clustering and ontology reuse. It proposes an automated method to extract events ,arguments and relationships ,and achieves the quality evaluation of the event knowledge graph in terms of content and application. Thus ,the knowledge in classical books can be used from the perspective of thematic ,spatial and logical narratives. The event knowledge graph proposed in thispaper gives a more fine-grained structural and semantic description of events ,roles ,places ,time and everything ,and fully reveals the distribution pattern and development trend of historical events while fulfilling the sequencing of event knowledge in ancient classics.Keywords knowledge in classical books ;event knowledge graph ;knowledge organization ;knowledgeapplication0 引言文化数字化背景下,国家典籍工作的重点由保护出版发展到应用转化,强调运用数字化技术深入挖掘典籍中蕴含的哲学思想、人文精神、价值理念和道德规范[1],推动中华优秀传统文化创125造性转化、创新性发展。
1. Interaction LayerThe Interaction Layer is responsible for transmission and reception of CAN messages according their transmission modes, timeout monitoring and setting of default values. It provides a signal interface to the application. Using the Interaction Layer you do not have to take care about the transmission or reception of signal or the data consistency. If you need the content of a signal, just read it, if a value changed, just write it,all the rest is done by the interaction layer.There are a set of so-called transmission modes. According to these modes thesignals and messages are being sent.The following signal transmission modes are selectable:_ Cyclic_ OnWrite_ OnWriteWithRepetition_ OnChange_ OnChangeWithRepetition_ IfActive_ IfActiveWithRepetition_ NoSigSendTypeAdditionally there are also transmission modes for messages:_ Cyclic_ IfActive_ NoMsgSendTypeThe resulting transmission mode is an OR between the message and the signal transmission mode. The application does not need to know the transmission mode of the signals. It just calls the function to write or read the signal value. Everything else will be done by the Signal Interface.In case of periodic transmission modes only two different cycle times could bechosen for signals combined in the same message. Therefore, the cycle time of a periodically transmitted signal depends on the cycle time of other signals defined for periodic transmission related to the same message. The application developer is responsible for choosing sensible combinations of signals for a message.1.1 Cyclic TransmissionA static period is used to transmit the signals cyclically using this transmission mode. This mode could be used to transmit signals which are frequently changing their values like the rpm of an engine for example. The period should be adapted to the speed the signals are changing their values. Short periods causes high bus load. As shown in Figure 7-1 signals could be updated asynchronously to the period of transmission. Each time the transmission takes place the driver checks for the current value of the message. This, of cause, could lead into the loss of data, if a signal was updated two or more times within a period. This Cyclic Transmission Mode actually just copies the signal data.1.2 OnEvent (OnWrite, OnChange)Signals using this transmission mode will be transmitted once each time when the Signal value was set. The transmission of the signals may be delayed by the delay timer (see chapter 4.4.7) to delimit the bus load. This transmission mode, for example, could be used for event triggered signals as the state of a switch. Figure 7-2 shows the timing diagram of the event triggered transmission mode._ Writing a signal which is related to the OnWrite transmission mode causes the transmission of the message which contains this signal._ Changing a signal which is related to the OnChange transmission mode causes the transmission of the message which contains this signal.The Task checks if the delay time elapsed and decides whether to transmit the message immediately or to delay the transmission until the delay time elapsed. This could cause the loss of data, if the signal was updated two or more times while delay time.The Diagram for OnEvent – OnChange looks like the same way but the decision on whether to send or not is met by a comparison between the old and the new signal value. It will only be sent if the value changes.1.3 OnEvent with Repetition (OnWrite, OnChange)The transmission of the signals using this transmission mode will be repeated n-times after the trigger is active. For example, this mode could be used to transmit important signals which have not to be missed like safety critical information. Each time the trigger of message transmission sets the repeat counter (repeat_counter [GenMsgNrOfRepetitions] = n). The repeat counter is decremented with each transmission of the signal. The transmission takes place each time the delay timer elapses and the repeat counter is still greater than 0. After the trigger the message will be sent n times.The Diagram for OnEvent – OnChange looks like the same way but the decision on whether to send or not is met by a comparison between the old and the new signal value.It will only be sent if the value changes.1.4 Transmit Fast if Signal is ActiveThis transmission mode is a Cyclic Transmission Mode with a trigger condition. Ifthe decision is met that the signal is active, the message will be send cyclically with a short period. In the Example in Figure 7-4 the condition is defined as x!=10. This will cause the transmission mode to transmit the signal with the period GenMsgCycleTimeFast. If the signal value is equal to 10 the signal is not sent.If two or more signals using this transmission mode are combined to the same message, a rule is needed to regulate switching between the periods. Figure 7-5 shows an example where three signals (A, B and C) are combined to the same message. If signal A was written and meet the defined condition, the transmission starts with the fast period. This state is stored in a flag presented by the three squares (grey = set, white = not set). The switch will cause the fast transmission of all signals combined to this message. A second write command for signal A won’t cause anything, if the value of A still meets the condition. If signal B was written and meet its condition the flag for signal B will be set. This should cause the transmission mode to switch to the fast period. But this was already done so nothing will happen. The signal value for B which is written next does not meet the condition so the transmission should stop. This won’t happen, because the flag for signal A is still set. To switch the transmission off all flags need to be reset. This is shown by setting the flag for signal C, reset the flag for signal A and reset the flag for signal C. After no set flag remains, the transmission stops.Short: If signals using the Transmit Fast if Signal Active Mode are combined to the same message, the message will be transmitted fast if one ore more of them meets its condition.It will not be transmitted if none of them meet its condition.。
2012 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2012) Layered Event Ontology ModelHUANG Mei-li1,2(School of Computer Engineering and Science, Shanghai University, Shanghai 200072, China) 1 (School of Information Science and Technology, Zhejiang Forestry University, Lin’an 311300,China ) 2LIU Zong-tian1(School of Information Science and Technology, Zhejiang Forestry University, Lin’an 311300,China ) 1Abstract— Event plays an important role in the field of semantic understanding that associates entities. Although traditional ontology is an effective model to represent domain concept hierarchies and semantics, it simply considers event same as static concept or describes it as relationship. This paper proposes a layered event ontology model which emphasis on event and layered the traditional ontology by excluding events from concepts have the static characterization, describing them at different levels respectively, meanwhile tie them by element relationship. It finally suggests the method to construct layered event ontology from existent ontology which is under the traditional model, and provides an example to explain it. Keywords-Entity; Event; Ontology; Event ontologyI. INTRODUCTIONOntology, originally from philosophy, is the objective existence of explanation or description of a system that concerned with the abstract nature of objective reality. More than a decade ago, the term was introduced into the computer science field to research on concepts and their relationships with one another to provide the standard and the formalized description. As the objective world model, ontology, which used to represent relationships between concepts, focus on the description of concepts, especially the entity concepts having static characteristics [1-3].However, the world is constituted by the entities and events [4]. Cognition psychologists believed that event is the basic unit of memory in terms of which to understand the real-world [5]. Therefore, traditional ontology model, which regards concept as a knowledge unit, in particular the static characteristic entity concept, not only limited in the ability to simulate human beings, but there still a lot of knowledge representation problems.This paper aims at highlighting the important roles of events through the process of differentiate between the dynamic characteristic event concepts and the static characteristic entity concepts at different layer. It will solve the problems in the existing model and increase the ability of ontology in the real world of modeling simulation of human cognitive processes in a certain extent. The rest of the paper is organized as following: the second section discusses the existing problems in the traditional ontology model, the third section puts forward a layered event ontology model, the fourth section gives the extension steps and an example to construct layered event ontology from the traditional ontology, and finally the conclusion.II. DRAWBACKS OF TRADITIONAL ONTOLOGYMODELUnder the traditional model, ontology is considered to be the conceptualization of a domain that represents concepts and relationships between them. Among them, the concept maybe the static characteristic entity concept or possibly be the dynamic characteristic event concept, and in some cases, it may also having the dynamic concept of events as the relationships of static entity concepts. Mixing the representation of static and dynamic concepts in practice mainly has the following disadvantages:z Redundancy of knowledge description. An entity concept may be related to multiple events, differentevents may have the same event element involved. Inthe traditional model, it needs to multiply describe thesame entity that resulting in redundancy in theknowledge description.z Inconsistency on the knowledge representation.Relatively complex concept of the event usuallyinvolves more than one entity concept, at this point, inthe traditional model usually express the multiplerelationships through converting it into a number ofbinary relations. It may lead to incomplete knowledgerepresentation and inconsistencies during the updateprocess.For example: Zhang San and Li Si published a paper entitled "Overview of Ontology" on Computer Science Journal. The published article event related to following elements: People: Zhang San and Li Si; Journal: Computer Science; Paper: Overview of Ontology. To express the published article event according to traditional model, it needsto have several binary relationships to express the published article event such as binary relation published (people, article) and binary relation published_on (article, Periodical) (Shownin Fig. 1).In this way, it not only cause knowledge redundancy, and there is no way to directly draw the conclusion that "Overview of ontology" is an article jointly published by Zhang San and Li Si; Meanwhile, if you want to know which journals have Zhang San published articles on, the reasoning engine has to search the articles published by Zhang San, then through the result articles to find the journal on which Zhang San had published articles on.A layered event ontology model is put forward in this paperin order to consistent with the cognitive scientists opinions that event is basic unit through which the human beings perceive the real world, and as much as possible to avoid problems in the traditional ontology model.Figure 1.Example of the “Published an article”III. LAYERED EVENT ONTOLOGY MODELA.EventScholars from different research areas have different viewpoints on the definition of event. From Philosophers point of view, the world is of material, material is of movement, movement is absolute and static is relative [4]. Linguistics set their event research focused on the observation of verbs and defined events in accordance with the classification of verbs and its modifiers. Meanwhile, some cognitive scientists study events from the human being’s memory principles and events structural aspects [5-7]. However, in the computer science related field such as information extraction and information retrieval supposed event definition and representation from the application point of view, and a considerable part of the study focused on event extraction and its applications [8-10].Although the definition of event is inconsistent, but the viewpoint that event is an important knowledge element is consistent. Based on the study of different field viewpoints comprehensively, we define event as following [12]:Definition 1. Event. Refers to a thing happens in a specific time and environment, which some actors take part in and show some action features. Formally, the event can be defined as a six-tuple:e=(A,O,T,V, P,L)A means action that is the process of event happens. O refers objects take part in the event, including all actors and entities involved in it. T is the period of time that the event lasing, it may be absolute time or relative time. V is the environment that the event occurred.P means assertions on the procedure of actions execution in an event. Assertions include pre-condition, post-condition and intermediate assertions. L is language expressions of event.Takes the "Published an article" event as an example, It can be represented in a six-tuple which is much more concise and simpler compared with the traditional model (The six-tuple representation see Fig. 2).Hence, to get the result of Zhang San has published articles on which journals, what the system needs to do is just find out the published event instance that has "Zhang San" as the object element. Obviously, it not only reduced redundancy, but also simplified the reasoning process.Figure 2.Six-tuple representation of "Published an article" eventyered Event Ontology ModelDefinition 2. Layered Event Ontology. Layered event ontology (LEO) is a formal, explicit specification of a shared event model that exists objectively and models events and entities in different layers respectively. It can be defined as a triple:LEO={C,R ,Rules }, in which C={SC,EC}C indicates concept sets, includes entity concept (SC) and event concept (EC). R indicates relations. Under this model, there are three kinds of relations. That is relations among entities, relations among events and relations between event and entity. Further, relations among entities, relations among events can be divided into the taxonomic and non-taxonomic relationships. Rules are expressed in logic languages, which can be used to describe the transformation and inference between events.Notice that entity concept or entity class is a common attribute set of entities, it can be a real person or thing(Such as "teacher" and "book" are the entity classes, but "teacher Zhang" is a specific entity instance), and can also be an abstract concept(Such as "thinking" is an entity class but "Marxist Thought" is a specific entity). Event concept or event class is a collection of event instances with similar elements (Such as "earthquake" is an event class, but the "5.12 WenChuan Earthquake" is a specific event instance). This article mainly carries on the discussion of ontology on classes but not instances, so, when it does not cause in the confusion situation, “the entity” and “the event” refers to “the entity class” and “the event class”.Based on the static and dynamic characteristics, in the layered event ontology model, the concepts are divided into the entities with static characteristics and events with dynamic properties. The domain is modeled by the related events (The graphical representation see Fig. 3).Among them, the lower layer is the static characteristics of the entity concepts and the relationships between them, and the upper layer is event concepts and the relationships between them.Figure 3.Layered event ontology model diagramExplanation of the symbols::Indicates the event concept :Indicates the entity concept:Taxonomic relations between concepts :Non-Taxonomic relations between concepts :Reference of event on its elementsIV. FROM THE TRADITIONAL DOMAIN ONTOLOGYTO LAYERED EVENT ONTOLOGY A. Construction MethodLayered event ontology of a specific domain can be constructed through the following three ways:z Constructed with the help of domain expertsmanually;zAcquired through a semi-automatic or automaticlearning method;z Obtained through expansion of the existing traditionaldomain ontologySpecifically, layered event ontology can be constructed from the existing ontology by following steps:z Reserve the static characteristics concepts and staticrelationships between them as the entity concepts and relationships between them to the lower layer of event domain ontology; z Extract dynamic concepts and dynamic relationsbetween concepts as events on the upper layer of event ontology. Meanwhile, let the related static entities be the elements of events, thus associating events with entities.z Organize taxonomic and non-taxonomic relationshipsbetween events and then receive the upper event layer and enrich the lower layer. By extending the existing domain ontology under the traditional model, it would reuse of existing knowledge to the utmost extent and simplify the complexity of building domain ontology.B. An Example Taking the fragment of Milan insect ontology constructed by Zhu Lijun in his doctoral dissertation of agricultural University in China as an example (show in Fig. 4), we extend it into layered event ontology according to the above method.Figure 4. The fragment of Milan insect ontologyFirst of all, retain the static concepts and their relationships such as " gardener"," flower"," Milan insect" and" Milan pesticide" in the lower layer of event ontology. Then, extract the dynamic characteristics relations like "planting", "sale" and "kill" as event concepts in the upper layer which accordingly associate the static entity concepts through their elements. Finally, expands event relationships with the help of domain experts. In this case, you can join the "disease" as the parent class of "insect harm" ,"sale" and "pest" as the following event of "planting" ,and obtains the expansion layered event ontology(as shown in Fig. 5).Figure 5. Layered event ontology of Milan pestThrough the above example you can see, the eventontology can be built from the existing domain ontology, thus simplifies the tedious construction process. On the other hand, it can also be seen from the above example that if it comes to represent domain ontology graphically, then the event concept in event ontology, essentially corresponds to a sub graph of the traditional domain ontology. That is, the process of construct event ontology from exist ontology is just as simply as select out the dynamic characteristic concepts and relations as events.V. CONCLUSIONSTraditional ontology model, as an efficient way to express concept hierarchies and semantics, mainly describes the domain concepts and relationships between them and can effectively reflect the rules of things in the objective world, especially the taxonomic relationships of things. However, consider the relatively simple and static entity concepts just same as the relatively complex and dynamic event concept will not only increase difficulties of the description of events, but also likely to result in the knowledge redundancy and easily lead to inconsistencies in the knowledge description.As the linkage of things, event having special role in the semantic understanding field. So, different from the traditional ontology model, a layered event ontology model is proposed by putting forward and highlighting the description of the event classes and their relationships. It finally suggests the method to construct layered event ontology from existent ontology and provides an example to explain it.It should be noted that there is no clear distinction between the boundaries of static and dynamic characteristics of concepts. Therefore in the concrete modeling process, some concepts are difficulty with assurance to whether take them as the entity concepts or the event concepts will be more perfect. 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