谈钱还是谈情:企业如何引导消费者分享自媒体营销

社交网络的发展弱化了传统媒体的影响力,强化了消费者的影响力;在这种情况下,企业需要思考如何借助消费者传播营销信息,以影响其他消费者。本文从企业如何设计营销信息入手,探究消费者与企业所处的关系范式对消费者信息转发行为的影响。通过二手数据分析与实验法,本文得出如下结论:经济类刺激对触发交易关系范式下消费者的转发行为更有效;情感类刺激对触发共有关系范式下消费者的转发行为更有效;交易关系范式下消费者出于回报企业的动机分享,共有关系范式下消费者出于利己动机而分享。     

The effects of sport dance on brain connectivity and body intelligence

Dancing is characterised by physical movement in accordance with rhythm perception. Twelve sport dancers and 12 age- and sex-matched young adults who had no dance experience (control group) were recruited. Body intelligence and brain activity were assessed in both groups using the Body Intelligence Scale (BIS) and resting state functional magnetic resonance imaging. BIS scores of dancers were higher than those of control subjects. The dancer group showed increased functional connectivity from the precentral gyrus to the right cingulate gyrus, right occipital fusiform gyrus, right inferior frontal gyrus, right medial frontal gyrus, left inferior frontal gyrus, right parietal postcentral gyrus, and right frontal lobe compared with control subjects. Sport dancers had increased body intelligence sensitivity compared with matched controls. In addition, the characteristics of dance, including physical movement in accordance with rhythm perception, might be associated with increased brain activity in the somatosensory and rhythm perception networks.     

Plagues,Pagans, and Christians: Differential Survival,Social Networks,and the Rise of Christianity

In his book, The Rise of Christianity, Rodney Stark argues that the early Christian church indirectly benefitted from the plagues that struck the early Roman Empire in the 2nd and 3rd centuries of the Common Era. In particular, he argues that the early church's doctrines concerning love, charity, and social service would have led Christians to enjoy higher survival rates than pagans, which would have left the social networks of the former relatively intact, while those of the latter in disarray. Moreover, since recruitment to new religious movements occurs largely through social ties, the probability that an average pagan would have converted to Christianity would have been greater after the plagues than before. In this article, we use computer modeling to test Stark's hypothesis. Not only do we find support for his hypothesis, we also show that higher survival rates for either group not only would have benefitted their respective social networks but also would have benefitted their competitor's social networks, highlighting how network effects often flow in multiple and unexpected ways. We conclude with suggestions for future research, in particular how scholars can build upon our analyses and explore alternative assumptions.     

Determinants of assessing efficiency within auditory attention networks

The attention network test (ANT) assesses efficiency across alerting, orienting, and executive components of visual attention. This study examined approaches to assessing auditory attention networks, and performance was compared to the visual ANT. Results showed (1) alerting was sufficiently elicited in a pitch discrimination and sound localization task, although these effects were unrelated, (2) weak orienting of attention was elicited through pitch discrimination, which varied based on ISI and conflict level, but robust orienting of attention was found through sound localization, and (3) executive control was sufficiently assessed in both pitch discrimination and sound localization tasks, but these effects were unrelated. Correlation analysis suggested that, unlike alerting and orienting, sound localization auditory executive control functions tap a shared attention network system. Overall, the results suggest that auditory ANT measures are largely task and modality specific, with sound localization offering potential to assess all three attention networks in a single task.     

Rolling element bearing fault diagnosis using convolutional neural network and vibration image

Detecting in prior bearing faults is an essential task of machine health monitoring because bearings are the vital components of rotary machines. The performance of traditional intelligent fault diagnosis methods depend on feature extraction of fault signals, which requires signal processing techniques, expert knowledge, and human labor. Recently, deep learning algorithms have been applied widely in machine health monitoring. With the capacity of automatically learning complex features of input data, deep learning architectures have great potential to overcome drawbacks of traditional intelligent fault diagnosis. This paper proposes a method for diagnosing bearing faults based on a deep structure of convolutional neural network. Using vibration signals directly as input data, the proposed method is an automatic fault diagnosis system which does not require any feature extraction techniques and achieves very high accuracy and robustness under noisy environments.     

Law,discipline and governance based on cognitive process simulation – analysis of legal forms in the modern rights relationship

People are becoming more and more humanized in the process of understanding the law. According to the right to discipline, the law has its own core setting factors, while some limits can't reach people's desire. Therefore, the legal and illegal mode of transcending rights is very important. In order to analyze the legal form of modern rights, in this paper, the cognitive learning and memory process of human brain were simulated through the artificial neural network and the understanding of human brain structure, and the role of law, discipline and governance was reflected. In the study, the structure and algorithm of the model neural network were optimized, the memory forgetting curve mechanism that can simulate the human brain was introduced, and thus the network recognition rate was improved. And in the algorithm, the calculation of matching degree was avoided, and the computational complexity was reduced to the sample. Then the sample was compared with the SOM, ART1, and PNN algorithms. The experimental simulation results show that the recognition speed of this sample is 1.9 times faster than that of ART1, 58 times than that of SOM, and 1.5 times than that of the PNN network.     

基于安戈夫法的青少年问题性移动社交网络使用评估标准

摘 要 使用安戈夫法界定青少年问题性移动社交网络使用评估标准。结果发现,筛查评估问卷中8个及以上题目持肯定回答即可评估为问题性移动社交网络使用,实证区分效度中正常使用者检出率为12%,问题性使用者检出率为91.1%。基于安戈夫法的青少年问题性移动社交网络使用评估工具有较好的心理学测量属性,可用于青少年问题性移动社交网络使用的筛查评估。     

社会排斥的心理行为特征及其脑机制

社会排斥损害个体基本的归属需要, 对个体的心理和生理会造成严重影响。根据需要威胁时间模型, 社会排斥后的心理和行为特征可以分为三个阶段。社交媒体的发展使社会排斥出现了新的心理和行为特征。近年来功能性磁共振成像研究发现, 突显网络和默认网络等网络中的一些核心脑区都参与了社会排斥各阶段的情绪和认知加工过程。未来研究应以需要威胁时间模型为基础, 以脑网络方法为手段, 探索社会排斥神经机制, 预测排斥后的心理和行为反应模式。     

Multi-layer architecture for adaptive fuzzy inference system with a large number of input features

The Sugeno adaptive fuzzy neural network using training data is a good approximation to model different systems. The large number of adaptive neuro-fuzzy inference system (ANFIS) input features is a major challenge in using ANFIS and is not applicable with increased parameters. We present a solution for many input features solving modular problems; we created a multi-layer architecture of SUB-ANFIS (MLA-ANFIS) for this purpose. Different topologies were created with various combinations of multiple input features, and an error indicator was calculated for each combination of topologies. Finally, the best topology was chosen among the states with the highest possible performance. We implemented a multi-layered approach based on 365-day concrete compressive strength data with eight input features and the optimized MLA-ANFIS topology (5-3-1) for this purpose from different ANFIS topologies and neural networks. Finally, the results from five other datasets prove the impact of the proposed MLA-ANFIS approach compared to the neural network method.