Nicknames as identity badges: How self-reflective nicknames can facilitate users’ online social interactions

An Internet nickname is essential in facilitating online interpersonal interactions. It is a pivotal element of interaction that provides other users with an initial impression and affects the decisions and behaviors of users during their online interactions. Although the existing literature has found that nicknames are relevant to users’ psychology and behavior, direct research on the motivation for and the effect of choosing a particular Internet nickname has been very limited. Borrowing from relevant theories on avatars, this paper summarizes the motivations behind Internet nickname choices in terms of three aspects: virtual exploration, social navigation and contextual adaptation. Moreover, from the perspective of collective self-esteem (CSE) and group identification, this research explores the influence mechanism between the motivations underlying Internet nickname choices and users’ online social interactions. A total of 394 samples were obtained from online communities in China to test our research model. We found that virtual exploration, social navigation and contextual adaptation are three motivations for creating Internet nicknames, and they have direct effects on the online social interactions of users. CSE has a full mediating effect between virtual exploration and online social interaction and acts as a mediated variable between social navigation and online social interaction. Group identification fully mediates the relationship between social navigation and online social interaction as well as that between contextual adaptation and online social interaction. The conclusion of this paper provides not only a new perspective for the study of Internet nicknames but also a potentially feasible way to stimulate users’ online social interactions.     

The Relationship Between Consumer-Brand Identification and Brand Extension

The present research aims at examining the role of consumer-brand identification (CBI) in attitude toward brand extension regarding the congruency between the values of consumers and brands. In this way, the benefits of CBI to consumers and brand managers are outlined. This research has been conducted through designing a survey and collecting data through a questionnaire. For data mining and investigating the model, the SEM approach is implemented. According to the findings, high levels of CBI lead to positive attitudes toward the brand extension, and that value congruity positively impacts this relationship. The results outline that CBI impacts fit and tie in separate ways, so that CBI effect on fit is more substantial. Furthermore, according to the results, in comparison to the role of tie, the role of fit is more significant in predicting attitudes toward brand extension. The sample from university students is appropriate for testing theory, but limits generalizing the results of the research. In addition, this research has studied one product category and is limited in this perspective. The findings have remarkable recommendations for implementing brand extension as they emphasize the role of perceived fit. Moreover, by reexamining CBI in a less known market, the research has outlined its positive outcomes for firms. This research has used perceived tie in a brand extension context and, similar to perceived fit, has implemented it as another factor to predict attitude toward brand extension. In addition, this research is unique, as it has investigated CBI in a new context.     

NUMERICAL MODEL FOR DYNAMIC SIMULATION OF TRAFFIC FLOW

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竹坑水库大坝安全鉴定分析

在竹坑水库大坝安全鉴定中,认真分析了该工程存在的主要问题,并针对大坝的结构稳定和渗流稳定作了详细的分析论证.     

基于支持向量机的岩爆识别模型

岩爆的发生受多种因素的影响,这些因素关系错综复杂,以不同的特征和组合对岩爆产生综合影响,为科学、充分地利用现有的有限工程资料,本文从地下洞室围岩最大切向应力、岩石的单轴抗压强度和抗拉强度以及岩石冲击倾向指数四个主要因素出发,提出了基于支持向量机的岩爆识别模型,较好地解决小样本、非线性、高维数、局部极小点等的实际问题。     

Measurement error and panel data1

The underidentification of linear models with measurement error does not necessarily extend to panel data models, as has been shown by GAiliches and Hausman (1986). We discuss and extend some of their results for a simple case and address particular issues concerning identification and asymptotic variances.     

论企业核心竞争力与企业自生能力

文章阐述了核心竞争力和企业自生能力的内涵及其特征,总结了企业核心竞争力识别的方法,对企业如何解决自生能力进行了论述,并提出围绕主导产业,培植企业核心竞争能力和构筑企业自生能力的方法,同时给出了比较详细的实现途径,希望对现代企业的运作能起到一定的指导作用。     

Prediction of the Indian summer monsoon using a stacked autoencoder and ensemble regression model

The study of climatic variables that govern the Indian summer monsoon has been widely explored. In this work, we use a non-linear deep learning-based feature reduction scheme for the discovery of skilful predictors for monsoon rainfall with climatic variables from various regions of the globe. We use a stacked autoencoder network along with two advanced machine learning techniques to forecast the Indian summer monsoon. We show that the predictors such as the sea surface temperature and zonal wind can predict the Indian summer monsoon one month ahead, whereas the sea level pressure can predict ten months before the season. Further, we also show that the predictors derived from a combination of climatic variables can outperform the predictors derived from an individual variable. The stacked autoencoder model with combined predictors of sea surface temperature and sea level pressure can predict the monsoon (June-September) two months ahead with a 2.8% error. The accuracy of the identified predictors is found to be superior to the state-of-the-art predictions of the Indian monsoon.     

改进的蚁群算法在溶质运移参数识别问题中的应用

非饱和土壤水分和溶质运移参数(扩散率、导水率和水动力弥散系数)取值范围较大,往往跨越几个数量级。采用传统离散化蚁群算法求解此类问题,所需节点较多,这会造成算法收敛时间较长。该文在传统蚁群算法基础上,对蚂蚁搜索路径进行改进,改进后的蚁群算法寻优路径由参数精度位数(整数位和小数位)、参数个数以及0–9十个数字构成,并将路径解码公式修改为具有判别参数正负功能的解码公式。采用改进的连续蚁群算法对非饱和溶质运移参数识别优化模型进行求解。数值模拟表明相同迭代次数下改进的蚁群算法比传统蚁群算法耗时少,算法计算时间与迭代次数满足线性关系,含水率和溶质浓度实测值与计算值吻合较好、相关性较高。     

考虑卸荷损伤的柱状节理岩体开挖特性研究

通过建立柱状节理岩体径向应力比与力学劣化系数的数学关系,获取柱状节理岩体卸荷损伤效应的定量表达式。结合FLAC~(3D)提出一种考虑卸荷损伤的柱状节理岩体开挖分析方法,实现岩体单元的力学参数在开挖过程中随实际应力状态得到动态调整。在此基础上开展柱状节理岩体试验洞的开挖特性分析。结果表明,考虑卸荷损伤的计算数据与实测数据基本吻合,试验洞围岩的位移、体积应变增量和塑性区均随开挖断面的逐步增加而增大,且呈边墙最大,底板次之,拱顶最小的分布规律。最后采用体积应变增量大于0.10%和塑性区作为岩体松弛的综合判别指标,其分析结果与钻孔声波测试数据相一致。