Characteristics of college students who text while driving: Do their perceptions of a significant other influence their decisions?

IntroductionTexting while driving is a significant risk factor for automobile collisions. The use of cell phones is prevalent among young people and commonly reported when they drive.MethodA web-based survey of 861 college student drivers determined how texting was associated with other forms of risky driving, perceptions of risk, and their driving and texting interactions with a significant other.ResultsTexting drivers were more likely to engage in other risky driving behaviors, perceived less risk in texting and driving, felt more immune to traffic risks, and had friends who text and drive. Logistic regression analyses showed that even after adjusting for risky driving behaviors and perceived risk, texting drivers were significantly more likely to do so if they saw their significant other text and drive.ConclusionsTraffic safety campaigns need to address important social influences on this behavior.     

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.     

Information matrix estimation procedures for cognitive diagnostic models

Two new methods to estimate the asymptotic covariance matrix for marginal maximum likelihood estimation of cognitive diagnosis models (CDMs), the inverse of the observed information matrix and the sandwich-type estimator, are introduced. Unlike several previous covariance matrix estimators, the new methods take into account both the item and structural parameters. The relationships between the observed information matrix, the empirical cross-product information matrix, the sandwich-type covariance matrix and the two approaches proposed by de la Torre (2009, J. Educ. Behav. Stat., 34, 115) are discussed. Simulation results show that, for a correctly specified CDM and Q-matrix or with a slightly misspecified probability model, the observed information matrix and the sandwich-type covariance matrix exhibit good performance with respect to providing consistent standard errors of item parameter estimates. However, with substantial model misspecification only the sandwich-type covariance matrix exhibits robust performance.     

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.