从“馒头事件”看网络传播受众的言论自由权利与义务

本文通过对网络传播的受众及其在网络传播中的言论自由权利进行论述,结合对“馒头事件“引发的一边倒网络舆论的分析,试图探讨网络传播的受众在享受言论自由权利的同时,应当履行相应义务的必要性。     

大众传播与通俗文学的关系研究——以鸳鸯蝴蝶派为例

鸳鸯蝴蝶派文学是典型的近现代通俗文学。鸳鸯蝴蝶派的兴盛与近代大众传媒密不可分。本文从报刊传播和电影传播两种典型的大众传播模式入手来研究鸳鸯蝴蝶派的传播概况,来探究大众传播与通俗文学的关系,揭示大众传播对文学发展的重要性。     

民间药膳植物猫尾射的繁育技术

猫尾射为民间药膳植物,由于过分挖掘导致自然资源面临枯竭。从采种、浸种、播种、播后管理及移苗定植等方面总结了猫尾射的种子繁育技术,从苗床准备、插穗剪取与处理、扦插、苗床智能化管理、移苗等方面介绍了猫尾射的种苗智能化快繁技术,为珍稀猫尾射植物的人工栽培提供技术支撑。     

试论电视节目主持人的传播策略

电视节目主持人是电视台及栏目的形象代言人与信息传播者。讲求传播策略与技巧,对于增加主持人的魅力,提升整个栏目乃至媒体的传播水平和效果有着至关重要的意义。     

AlN/ZL101原位复合材料的正交实验研究

通过正交实验分析,利用气液反应法制备原位AlN/ZL101复合材料并确定了其最佳成分,测试了复合材料的力学性能并对该材料进行了显微金相分析和透射电子显微分析。研究结果表明:AlN/ZL101原位复合材料中原位生成增强体AlN的尺寸为30nm左右,均匀分布于共晶体内部,同基体结合良好;由于细小增强相的作用,热处理后复合材料中的共晶硅以粒状形态均匀分布于基体中。     

大花蕙兰原球茎诱导与增殖研究

为研究大花蕙兰（Cymbidium hybridum）原球茎诱导与增殖的最佳配方,用大花蕙兰无菌苗为材料,以MS、1/2MS为基本培养基,应用6-BA、2,4-D、NAA植物生长调节剂对大花蕙兰无菌苗茎段切块进行原球茎诱导和原球茎增殖研究。结果表明：在1/2MS＋6-BA 8.0 mg/L＋2,4-D 0.2 mg/L＋琼脂10g/L＋蔗糖15g/L＋活性炭1.5g/L的培养基（PH=5.8）上诱导出原球茎的效果最好,其诱导率高达77.78%;原球茎接种于MS＋6-BA6.0mg/L＋NAA0.2mg/L＋琼脂10g/L＋蔗糖10g/L＋活性炭1.5g/L＋香蕉泥100g/L（PH=5.8）培养基上原球茎增殖效果最佳。低水平的2,4-D（0.2 mg/L）、较高水平的6-BA（8.0mg/L）对大花蕙兰原球茎诱导具有显著的促进作用。     

Tension in In a Station of a Metro to Explore the Energy in Language

Pound’s poem In a Station of a Metro is a masterpiece of imagism. Pound once talked about the energy in language, and American scholar Allen Tate told us that the meaning of poetry is its tension. Base...     

从简单迁移到系统重构——信息技术支持的教师培训管理研究与实践

信息技术是助推教师培训变革的核心要素。当前信息技术支持的教师培训管理主要是基于软件的功能再造以及工具组合这种"简单迁移"的信息化方式开展的。受制于现有工具的设计局限,教师培训管理中各个环节的数据很难实现关联互动,无法满足当前培训发展对管理工作所提出的需求,信息化支持的教师培训管理亟需进行"系统重构"。信息技术支持的教师培训管理框架包括过程管理、资源管理、评估管理、团队沟通管理四个方面,体现了教师培训管理的深层内涵——即从被动适应到主动追求。采用"护航型"管理模式、丰富资源的分类框架、强化信息数据的挖掘、展开多维度分析等,这是信息技术支持教师培训管理深层次的作为空间,也体现了其应用价值。随着信息技术的快速发展,大数据、学习分析技术在培训管理中的应用前景值得期待。     

深海声传播信道和目标被动定位研究现状

随着我国海洋技术逐渐从近浅海向深远海的战略性转移,深海目标远程探测技术逐渐受到重视。海洋中目前已知的可用于远距离传播的信号是声信号,海洋是"声透明"的。基于水声传播特性的新型水声技术是实现装备探测能力突破的关键。文章主要概述了目前研究比较集中的深海表面波导、表面波导泄露、会聚区、海底弹射和可靠声路径5种深海声传播信道,以及基于可靠声路径的目标被动定位方法。旨在通过梳理深海声传播规律及相关被动定位技术,为水声技术的快速发展提供思路。     

Revisiting the cluster-based paradigm for implicit search result diversification

To cope with ambiguous and/or underspecified queries, search result diversification (SRD) is a key technique that has attracted a lot of attention. This paper focuses on implicit SRD, where the subtopics underlying a query are unknown. Many existing methods appeal to the greedy strategy for generating diversified results. A common practice is using a heuristic criterion for making the locally optimal choice at each round. As a result, it is difficult to know whether the failures are caused by the optimization criterion or the setting of parameters. Different from previous studies, we formulate implicit SRD as a process of selecting and ranking k exemplar documents through integer linear programming (ILP). The key idea is that: for a specific query, we expect to maximize the overall relevance of the k exemplar documents. Meanwhile, we wish to maximize the representativeness of the selected exemplar documents with respect to the non-selected documents. Intuitively, if the selected exemplar documents concisely represent the entire set of documents, the novelty and diversity will naturally arise. Moreover, we propose two approaches ILP4ID (Integer Linear Programming for Implicit SRD) and AP4ID (Affinity Propagation for Implicit SRD) for solving the proposed formulation of implicit SRD. In particular, ILP4ID appeals to the strategy of bound-and-branch and is able to obtain the optimal solution. AP4ID being an approximate method transforms the target problem as a maximum-a-posteriori inference problem, and the message passing algorithm is adopted to find the solution. Furthermore, we investigate the differences and connections between the proposed models and prior models by casting them as different variants of the cluster-based paradigm for implicit SRD. To validate the effectiveness and efficiency of the proposed approaches, we conduct a series of experiments on four benchmark TREC diversity collections. The experimental results demonstrate that: (1) The proposed methods, especially ILP4ID, can achieve substantially improved performance over the state-of-the-art unsupervised methods for implicit SRD. (2) The initial runs, the number of input documents, query types, the ways of computing document similarity, the pre-defined cluster number and the optimization algorithm significantly affect the performance of diversification models. Careful examinations of these factors are highly recommended in the development of implicit SRD methods. Based on the in-depth study of different types of methods for implicit SRD, we provide additional insight into the cluster-based paradigm for implicit SRD. In particular, how the methods relying on greedy strategies impact the performance of implicit SRD, and how a particular diversification model should be fine-tuned.