美剧《老友记》中幽默言语解读机制的认知研究

该文试图结合认知语言学中的概念整合理论以及V-N理论,分析美剧《老友记》中幽默言语的解读机制,探讨剧中的幽默言语是如何被观众解读以产生幽默效果。在建立起理论模型后,该文将结合剧中的语料按照概念整合的四种类型依次进行分析,以揭示言语幽默解读过程中的认知机制。     

概念整合理论下的中国英语的词汇翻译研究

国内对中国英语以及对中国英语中的词汇的翻译研究发展了近30年的时间,不同的人对中国英语中词汇的翻译问题所持有的态度也不同,传统的翻译方法自身存在的问题并不能够凸显中国英语作为一种具有中国特色的英语变体的特点,概念整合理论以其强大的认知阐释力证明了中国英语的词汇翻译是一种中国人独有的认知活动,从而进一步证明了中国英语是一种普遍存在的具有中国特色的英语变体。     

概念整合理论下的英语新词研究

随着互联网的发展,英语新词层出不穷,它的创造及流行不仅丰富了语言本身,还为人们生活带来了乐趣。该文从概念整合理论出发,从认知层面解释英语新词的意义建构。我们认为：新词的新概念与语言中已有的概念相关联,而这种关联正是概念整合的过程。     

长江流域碳排放量的测算及减排研究

鉴于长江流域环境保护在我国转型发展中的重要战略地位,为定量研究长江流域碳排放量的变化趋势,文章在齐绍州和付坤建立的测算方法基础上,建立了分行业、分省区、分时段测算长江流域的碳排放量及其增长率的测算方法。长江流域碳排放量的结构及其增长率变化趋势表明,总的碳排放量增长率正在不断下降。这是由于长江流域各省区正在大力发展交通、物流、通讯业等惠及民生的产业,政府系列政策的实施效果不断得到体现。     

高职院校军事理论课程建设研究

军事理论课的课程建设是新时期高职院校国防教育的重要内容。文章分析了高职院校军事理论课程建设存在的问题,并从完善高职院校军事理论课程建设视角,提出加强师资队伍建设、加大师资培训力度、加强教学资源建设、增加课外实践教学环节等对策,以更好地促进高职院校军事理论课程建设的发展。     

基于山东省高等学校在线开放课程的教学改革--以“概率论与数理统计”课程为例

基于信息技术在教育领域的飞速提升,在线课程融入课堂势在必行。混合式教学是目前高等教育教学改革的热点。山东中医药大学教学改革团队依据培养应用型人才的毕业要求,对“概率论与数理统计”课程进行了混合式教学改革。“概率论与数理统计”课程知识点多且内容抽象,课堂容量较大,从而导致传统课堂重知识技能的传授,难以展开知识的应用,这进一步导致学生学习兴趣不高。另外,课程思政元素的融入也是一个亟待解决的问题。首先将知识点拆分,重构知识框架,建成山东省高等学校在线开放课程“医药数理统计”,在此基础上将智慧树平台翻转教学互动工具用于课堂教学,改革成果初见成效。     

由一元到多元:从五四纪念对鲁迅的评价看鲁迅精神——以《人民日报》为中心

鲁迅精神是中国共产党珍贵的精神资源。改革开放前后,鲁迅精神具有不同的内涵:社会主义革命和建设时期,鲁迅精神是一元的革命斗争精神;改革开放和社会主义现代化建设新时期以及中国特色社会主义新时代,鲁迅精神是由斗争精神、科学精神、创新精神、为民情怀等多种元素构成的一个整体。鲁迅精神由一元到多元的发展,是由中国共产党中心任务改变、话语体系转变、改革开放后各种错误思潮的泛滥、社会主义文化建设和文明交流互鉴的需要等多种因素促成的。中国共产党对鲁迅精神的阐释整合了鲁迅身上批判和建构、公共性和参与性的多种特质。新时代,应该从文明交流互鉴和社会主义文化建设的角度,深入挖掘和弘扬鲁迅精神的当代价值。     

浅析博弈论对中国企业发展的影响

博弈论理论成型于20世纪20年代,可以划分为合作博弈和非合作博弈,而经济学家一般相对更关注非合作博弈理论。"囚徒困境"和"纳什均衡"作为现代非合作博弈论的基石,对中国企业的发展具有深刻的借鉴意义。随着对博弈论研究的不断深入,其在中国微观经济中的实用性也不断显现,中国企业要想更好的发展,必须借助博弈论的知识体系,它可以帮助我们用全新的视角理解现实问题。     

Characterization of chaotic multiscale features on the time series of melt index in industrial propylene polymerization system

The chaos characteristics of melt index have been first explored, and the Hilbert–Huang transform method and time delay embedding method are applied to multiscale dynamic analysis on the time series of the melt index (MI) in the propylene polymerization industry. The research results show that the embedding delay is 2, the embedding dimension is 5, the correlation dimension D₂ is 1.57, and the maximum Lyapunov exponent is 0.143 for the melt index series, which provide clear evidence of chaotic multiscale features in the propylene polymerization process. Three intrinsic mode functions (IMFs) are decomposed from the melt index time series; the presence of non-integer fractal correlation dimension and positive finite maximum Lyapunov exponent are found in some IMF components. The PP melt index series are divided into two chaotic signals, a determined signal and a random signal respectively, and its complexity is therefore reduced. Furthermore, the coupling of subscale structures of the propylene polymerization is explored with the dimension of interaction dynamics and a robust algorithm for detecting interdependence. It is found that IMF(2) is the main driver in the coupling system of IMF(1)and IMF(2). All these provide a guideline for studying propylene polymerization process with chaotic multiscale theory and may offer more candidate tools to model and control propylene polymerization system in the future.     

Revisiting Cross-document Structure Theory for multi-document discourse parsing

Multi-document discourse parsing aims to automatically identify the relations among textual spans from different texts on the same topic. Recently, with the growing amount of information and the emergence of new technologies that deal with many sources of information, more precise and efficient parsing techniques are required. The most relevant theory to multi-document relationship, Cross-document Structure Theory (CST), has been used for parsing purposes before, though the results had not been satisfactory. CST has received many critics because of its subjectivity, which may lead to low annotation agreement and, consequently, to poor parsing performance. In this work, we propose a refinement of the original CST, which consists in (i) formalizing the relationship definitions, (ii) pruning and combining some relations based on their meaning, and (iii) organizing the relations in a hierarchical structure. The hypothesis for this refinement is that it will lead to better agreement in the annotation and consequently to better parsing results. For this aim, it was built an annotated corpus according to this refinement and it was observed an improvement in the annotation agreement. Based on this corpus, a parser was developed using machine learning techniques and hand-crafted rules. Specifically, hierarchical techniques were used to capture the hierarchical organization of the relations according to the proposed refinement of CST. These two approaches were used to identify the relations among texts spans and to generate multi-document annotation structure. Results outperformed other CST parsers, showing the adequacy of the proposed refinement in the theory.