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.     

青少年偏差行为的同伴选择和影响效应:基于纵向社会网络的元分析

以22篇纵向社会网络分析研究文献(30项独立样本研究,27746个被试)为对象,使用元分析技术探讨青少年偏差行为的同伴选择和同伴影响效应假设。结果发现:(1)同伴选择和同伴影响效应都显著,但同伴影响效应显著大于同伴选择效应;(2)青少年发展阶段在同伴选择和同伴影响过程中存在显著的调节效应。在同伴选择过程中,青少年早期阶段不显著,而中、晚期阶段显著;在同伴影响过程中,青少年早、中期显著,晚期阶段不显著。(3)偏差行为类型在同伴选择和同伴影响效应中的调节效应不显著。(4)网络数量在同伴影响过程中存在显著的调节效应,随着网络数量增加同伴影响效应越可能成立。结论,中晚期青少年会基于偏差行为相似性选择朋友,早中期青少年会受朋友的影响而产生偏差行为。     

Sensitive Topics,Missing Data,and Refusal in Social Network Studies: An Ethical Examination

We describe our ethics‐driven process of addressing missing data within a social network study about accountability for racism, classism, sexism, heterosexism, cis‐sexism, ableism, and other forms of oppression among social justice union organizers. During data collection, some would‐be participants did not return emails and others explicitly refused to engage in the research. All refusals came from women of color. We faced an ethical dilemma: Should we continue to seek participation from those who had not yet responded, with the hopes of recruiting more women of color from within the network so their perspectives would not be tokenized? Or, should we stop asking those who had been contacted multiple times, which would compromise the social network data and analysis? We delineate ways in which current discussions of the ethics of social network studies fell short, given our framework and our community psychology (CP ) values. We outline literature that was helpful in thinking through this challenge; we looked outside of CP to the decolonization literature on refusal. Lessons learned include listening for the possible meanings of refusals and considering the level of engagement and the labor required of participants when designing research studies.     

HUMAN PHENOTYPIC MORALITY AND THE BIOLOGICAL BASIS FOR KNOWING GOOD

Co‐creating knowledge takes a new approach to human phenotypic morality as a biologically based, human lineage specific (HLS) trait. Authors from very different backgrounds (anthropology and biology, on the one hand, and astronomy, philosophy, and theology, on the other) first review research on the nature and origins of morality using the social brain network, and studies of individuals who cannot “know good” or think morally because of brain dysfunction. They find these models helpful but insufficient, and turn to paleoanthropology, cognitive science, and neuroscience to understand human moral capacity and its origins long ago, in the genus Homo. An unusual narrative capturing “morality in action” takes the reader back 900,000 years, and then the authors analyze the essential features of moral thinking and behavior as expressed by early and later species on our lineage. In what has primarily been the province of philosophers to date, the authors’ morality model is presented for further scientific testing.