大学生网络圈群化对高校主流话语的消解与重构

随着通讯技术和网络媒体的迅速发展,大学生在互联网上的社会交往、休闲娱乐、信息获取的圈群化特征日益显著。一方面,网络圈群化产生的权力主体去中心化、价值观念复杂化、信息交互多样化、准入过程排他化、网络话语泛娱乐化,一定程度上消解了高校主流话语的权威性;另一方面,网络圈群化可以重置高校主流话语的主体结构、拓展高校主流话语的受众范围、创新高校主流话语的传播载体、重构高校主流话语的传播体系、改变高校主流话语的传播情境,给高校主流话语的重构带来了新的机遇。为有效应对大学生网络圈群化对高校主流话语权威性的消解,高校应该努力提升话语主体权威、深化话语情感认同、转变话语表达方式、优化主流话语内容、契合圈群传播规律,确保主流话语在大学生网络圈群中占据主导地位、发挥引领作用。

网络圈群化背景下青年主流意识形态的认同困境及破解策略

文章首先简述了网络圈群化的主要特征。其次,分析了网络圈群化背景下青年主流意识形态认同的困境,主要为:窄化青年认知,影响主流意识形态的辐射性;冲击主导思想,解构主流意识形态的权威;话语表达伪自由化,弱化青年对主流意识形态的认同。最后,总结出破解策略,即做好舆论回应,提升主流意识形态的舆论主导力;强化制度规范,构建网络圈群管理体系与机制;提升媒介素养,培养青年自我养成的能力。

大学生网络圈群化对高校网络思想政治教育的挑战与应对

随着网络的快速发展,已经被大多数人接受并融入了网络圈层化的生活。在高校的网络普及和大学生的好奇心理就导致了大学生网络圈群化。对网络圈群化和高校思想政治进行全面的了解,才能更好地应对大学生圈群化给高校思想政治教育带来的挑战。大学生圈群化给高校思想政治教育带来挑战,对大学生网络圈群化解析,与高校网络思想政治教育相比较,形成高校网络思想政治教育的新方法。

网络圈群化对高校思想政治教育的挑战及其应对之策

网络圈群化已经成为影响大学生人生价值观念的重要因素。大学生网络圈群化的特点为网络圈群影响的广泛性与复杂性、直接性与闭环性、网络圈群构成的虚拟性与真实性、嵌套性与叠加化。网络圈群化对思想政治教育的实效性、凝聚力、亲和力、主导性都造成了影响。因此,高校的思想政治教育应从树立理念、打造清朗网络空间、实现产品的有效供给、建立制度保障四个方面的策略加以应对。

大学生网络圈群化及高校网络思想政治教育研究

网络圈群化已经成为影响大学生人生价值观念的重要因素。大学生网络圈群化的特点为网络圈群影响的广泛性与复杂性、网络圈群影响的深入性与封闭性、网络圈群构成的虚拟性与真实性、网络圈群构成的嵌套性与叠加化。网络圈群化使高校思想政治教育者无法全面掌握大学生所属的网络圈群情况、无法及时了解与把控大学生出现的网络舆情、无法精准掌握与利用网络圈群文化与话语。因此,高校应从建构网络圈群管理体系与机制、建构介入网络圈群的网络思想政治教育队伍、增强网络思想政治教育信息平台的吸引力三个方面的策略应对大学生网络圈群化对高校网络思想政治教育的挑战。

高职英语网络社群化教学探析:以微信为例

网络社群化趋势带来了教育方式的革新。教学目标的项目化分解、课程知识的碎片化处理、教学集体的组织化重构与教师角色的多元化塑造,构成了英语网络社群化教学的特点和要求。高职英语网络社群化教学有助于密切师生关系,开发网络教学资源,增强学习效能感,推进课程教学改革。基于微信的高职英语网络社群化教学对此作出了尝试。

对教师网络社群化学习的调研分析

在信息高速发展,社会快速进步的今天。新时期的教师应该创新培训与学习模式,建立彼此信任的专业性对话关系,主动分享学习成果和优质教学资源,使教师由被动学习变为主动探究,实现线上线下相结合,教师之间、学校之间相链接,构建网络社群化学习的文化氛围。激励教师积极寻求变革与创新,实现自我由平凡到优秀的成长轨迹,让不断成长成为教师发展的内驱力,从而提升教师专业素养。

具有前置反馈特征的社群化制造网络评价方法

将社群化制造网络作为一种适应社群化制造模式、匹配海量社会化服务资源的组织形式,应用综合评价理论与技术、通过调节网络结构参数,从微观至中观层面粗粒化评价过程,应用社会行为映射理论假设讨论具有前置反馈特征的社群化制造网络优化方法。首先,模拟测算不同假设下的社群化制造网络结构参数及其变化,如假设不同边缘分布随机变量通过高斯copula函数得到群体判断的联合分布,应用犹豫模糊集表述社群判断。其次,通过测算不同参数下基于社群间节点调整的网络结构熵和基于连接的冯诺依曼熵,对社群设置前置反馈,包括不同参数条件下,社群状态分布的占优形式,社群规模控制范围和连接形式。最后,讨论了前置反馈对社群组织的潜在影响,以期进一步改进社群化制造网络组织管理的适用性和有效性。

网络圈群化对大学生交际行为影响的研究

全媒体时代,网络圈群化因其独特的传播生态,引起了人们生存方式,尤其是交往方式的“革命”,由社群化网络交往而衍生的网络圈群正在成为大学生人际交往、信息获取以及思想交流等活动的主要阵地,深刻地改变了人际交往的内容、手段和状态,表现出鲜明的特征。文章深入分析和揭示这些特点,梳理出网络圈群化对大学生交际行为的影响,努力探寻网络人际交往的正确方式,提出网络圈群化对大学生消极影响的应对策略。

基于群特征的通信自组织网优化研究

文章在分析通信自组织网中节点群体特征的基础上,结合群化现象,提出无线自组织网的网络群化概念,围绕这一概念深入研究自组织网网络协作中节点位置管理优化方案、网络群化节点和基于群化节点的网络聚合、网络协作等问题,并依托横向课题中真实移动自组织网案例对设计方案进行仿真实验,充分验证了该方案的可行性和实用性,具有较好的应用和推广价值。

Springback prediction for incremental sheet forming based on FEM-PSONN technology

In the incremental sheet forming （ISF） process, springback is a very important factor that affects the quality of parts. Predicting and controlling springback accurately is essential for the design of the toolpath for ISF. A three-dimensional elasto-plastic finite element model （FEM） was developed to simulate the process and the simulated results were compared with those from the experiment. The springback angle was found to be in accordance with the experimental result, proving the FEM to be effective. A coupled artificial neural networks （ANN） and finite element method technique was developed to simulate and predict springback responses to changes in the processing parameters. A particle swarm optimization （PSO） algorithm was used to optimize the weights and thresholds of the neural network model. The neural network was trained using available FEM simulation data. The results showed that a more accurate prediction of s!oringback can be acquired using the FEM-PSONN model.

Energy-efficient mechanism based on ACO for the coverage problem in sensor networks

An energy-efficient heuristic mechanism is presented to obtain the optimal solution for the coverage problem in sensor networks. The mechanism can ensure that all targets are fully covered corresponding to their levels of importance at minimum cost, and the ant colony optimization algorithm （ACO） is adopted to achieve the above metrics. Based on the novel design of heuristic factors, artificial ants can adaptively detect the energy status and coverage ability of sensor networks via local information. By introducing the evaluation function to global pheromone updating rule, the pheromone trail on the best solution is greatly enhanced, so that the convergence process of the algorithm is speed up. Finally, the optimal solution with a higher coverage- efficiency and a longer lifetime is obtained.

A novel internet traffic identification approach using wavelet packet decomposition and neural network

Internet traffic classification plays an important role in network management, and many approaches have been proposed to classify different kinds of internet traffics. A novel approach was proposed to classify network applications by optimized back-propagation (BP) neural network. Particle swarm optimization (PSO) algorithm was used to optimize the BP neural network. And in order to increase the identification performance, wavelet packet decomposition (WPD) was used to extract several hidden features from the time-frequency information of network traffic. The experimental results show that the average classification accuracy of various network applications can reach 97%. Moreover, this approach optimized by BP neural network takes 50% of the training time compared with the traditional neural network.

Research and Implementation of Decreasing the Acetic Acid Consumption in Purified Terephthalic Acid Solvent System

Decreasing the acetic acid consumption in purified terephthalic acid （PTA） solvent system has become a hot issue with common concern. In accordance with the technical features, the electrical conductivity is in direct proportion to the acetic acid content. General regression neural network （GRNN） is used to establish the model of electrical conductivity on the basis of mechanism analysis, and then particle swarm optimization （PSO） algorithm with the improvement of inertia weight and population diversity is proposed to regulate the operating conditions. Thus, the method of decreasing the acid loss is derived and applied to PTA solvent system in a chemical plant. Cases studies show that the precision of modeling and optimization are higher. The results also provide the optimal operating conditions, which decrease the cost and improve the profit.

新媒体下电视生活服务类节目的回归与创新——以社交服务节目《四大名助》为例

从我国电视产生之初到今天,伴随着中国电视事业的栏目化、专业化、产业化发展,生活服务类节目作为重要的电视节目形态之一,一直活跃在电视荧屏。本文在梳理传统电视生活服务类节目的基础上,聚焦社交服务节目《四大名助》,探讨新媒体视野下生活服务类节目内容与形式的回归与创新,试图分析网络社群化在电视节目中的呈现机制、表现形式与深层原因。

Component based ant routing protocols analysis over mobile ad hoc networks

To deeply exploit the mechanisms of ant colony optimization （ACO） applied to develop routing in mobile ad hoe networks （MANETS）,some existing representative ant colony routing protocols were analyzed and compared.The analysis results show that every routing protocol has its own characteristics and competitive environment.No routing protocol is better than others in all aspects.Therefore,based on no free lunch theory,ant routing protocols were decomposed into three key components：route discovery,route maintenance （including route refreshing and route failure handling） and data forwarding.Moreover,component based ant routing protocol （CBAR） was proposed.For purpose of analysis,it only maintained basic ant routing process,and it was simple and efficient with a low overhead.Subsequently,different mechanisms used in every component and their effect on performance were analyzed and tested by simulations.Finally,future research strategies and trends were also summarized.

Boiler combustion optimization based on ANN and PSO-Powell algorithm

To improve the thermal efficiency and reduce nitrogen oxides （NOx ） emissions in a power plant for energy conservation and environment protection, based on the reconstructed section temperature field and other related parameters, dynamic radial basis function （RBF） artificial neural network （ANN） models for forecasting unburned carbon in fly ash and NO, emissions in flue gas ware developed in this paper, together with a multi-objective optimization system utilizing particle swarm optimization and Powell （PSO-Powell） algorithm. To validate the proposed approach, a series of field tests were conducted in a 350 MW power plant. The results indicate that PSO-Powell algorithm can improve the capability to search optimization solution of PSO algorithm, and the effectiveness of system. Its prospective application in the optimization of a pulverized coal （ PC ） fired boiler is presented as well.

A fuzzy neural network evolved by particle swarm optimization

A cooperative system of a fuzzy logic model and a fuzzy neural network(CSFLMFNN)is proposed,in which a fuzzy logic model is acquired from domain experts and a fuzzy neural network is generated and prewired according to the model.Then PSO-CSFLMFNN is constructed by introducing particle swarm optimization(PSO)into the cooperative system instead of the commonly used evolutionary algorithms to evolve the prewired fuzzy neural network.The evolutionary fuzzy neural network implements accuracy fuzzy inference without rule matching.PSO-CSFLMFNN is applied to the intelligent fault diagnosis for a petrochemical engineering equipment,in which the cooperative system is proved to be effective.It is shown by the applied results that the performance of the evolutionary fuzzy neural network outperforms remarkably that of the one evolved by genetic algorithm in the convergence rate and the generalization precision.

Global Optimization for the Synthesis of Integrated Water Systems with Particle Swarm Optimization Algorithm

The problem of optimal synthesis of an integrated water system is addressed in this study, where water using processes and water treatment operations are combined into a single network such that the total cost of fresh water and wastewater treatment is globally minimized. A superstructure that incorporates all feasible design alterna- tives for wastewater treatment, reuse and recycle, is synthesized with a non-linear programming model. An evolutionary approach--an improved particle swarm optimization is proposed for optimizing such systems. Two simple examples are .Presented.to illustrate the global op.timization of inte.grated water networks using the proposed algorithm.

Prediction of Flash Point Temperature of Organic Compounds Using a Hybrid Method of Group Contribution ＋ Neural Network ＋ Particle Swarm Optimization

The flash points of organic compounds were estimated using a hybrid method that includes a simple group contribution method (GCM) implemented in an artificial neural network (ANN) with particle swarm optimization (PSO). Different topologies of a multilayer neural network were studied and the optimum architecture was determined. Property data of 350 compounds were used for training the network. To discriminate different substances the molecular structures defined by the concept of the classical group contribution method were given as input variables. The capabilities of the network were tested with 155 substances not considered in the training step. The study shows that the proposed GCM+ANN+PSO method represent an excellent alternative for the estimation of flash points of organic compounds with acceptable accuracy (AARD = 1.8%; AAE = 6.2 K).