Application of improved BP neural network based on e-commerce supply chain network data in the forecast of aquatic product export volume

Aiming at the existing problems in the production and export scale prediction of aquaculture, a model of yield prediction based on BP Neural network algorithm is proposed, and a set of algorithms is proposed to optimize BP neural network (BPNN). Based on the traditional BP neural network, it is easy to get into the local optimal problem due to the long training time of the model. By using the simple Johnson algorithm, the dimensionality of the input neuron is reduced, and then the hidden layer neural network is determined by this method. At the same time, the data mining method is used to filter the Data.Particle swarm optimization algorithm is used to optimize the parameters. At the same time, based on the domestic e-commerce Sales network data, the results show that the average square root error of the model is less than the traditional BP neural network and the learning efficiency is higher than the traditional BP neural network. The results show that the model has a great advantage in building up a large number of historical data, and it can shorten the modeling time and get good prediction result by combining the sales data of e-commerce. It provides a new feasible method for the export prediction of aquatic products.     

Brainwave classification without the help of limb movement and any stimulus for character-writing application

Recently, Brain-Computer Interfaces (BCIs) have been extensively popular for employing Electroencephalography (EEG) signals to control devices with different applications. The use of BCIs currently involves for lots of applications to help the disabilities who cannot communicate with other people, as it is an alternative way for communication by passing the need of speech. Although the applications to spell the character with BCI systems (e.g., P300-speller, SSVEP-speller, Hex-O-spell) have been already developed, but these techniques are not flexible in the real scenarios because they require the stimulus all the time or stopping the activity to focus on the limb movement in order to provide the accuracy of brain responses. In this paper, the feasibility of brainwave classification for the applications of character-writing by considering only the EEG signals without the need of stimulus unlike the literature is newly introduced. This paper adopts a classification technique named Artificial Neural Network (ANN) and focuses on two different characters; straight line and circle. From the experimental results, the suitable position of electrodes are the pair of electrodes (F3 and F4) at the frontal lobe, which provide the best result as compared to other areas due to its important role in perception, maintenance and revival of the information. The experimental results indicate that the classification accuracy of the proposed technique is about 70%, which in turn leads to a significant achievement for the development of character-writing applications.     

Personal trust extends cooperation beyond trustees: A Mexican study

We studied 2 groups of workers from Oaxaca (Mexico) with different levels of income and education to investigate the role that the affective‐based psychological mechanism of personal trust, as evolutionarily acquired, plays on group cooperation. We measured trust levels through some questionnaires and cooperative behaviour through an iterated prisoner's dilemma under different conditions and analysed trust networks of group members. While these groups did not differ in trust levels or cooperation among trustees, they did differ in terms of cooperation with other group members. Such differences are related to dissimilarities in the trust network topology—as a measure of group cohesion. These results suggest that some personal trust networks extend cooperation within a group beyond trustees in a way that complements the role of the reputation for indirect reciprocity.     

A Community's Response to Adverse Childhood Experiences: Building a Resilient,Trauma‐Informed Community

Adverse childhood experiences, or ACEs, may be mitigated by trauma‐informed social environments—programs, services, systems, communities—that offer responses to trauma that promote healing, recovery, and resilience. However, there is currently little empirical evidence to support the use of specific approaches to do so. Guided by a population health perspective, this paper describes a participatory community change process in response to ACEs that seeks to build a resilient, trauma‐informed community in Pottstown, PA. We examine the initial implementation phase of this change process, centered originally on the education sector and the social and behavioral health services sector, and then eventually expanding to 14 community sectors across two years. A variety of data sources and methods are used to track individual and organizational processes, as well as service system network processes. A central feature of this research is the use of data to generate hypotheses rather than test them. Data were also used to guide understanding and decision‐making during implementation. The results show that moving forward the community is well‐positioned to establish stronger inter‐agency and system supports for trauma‐informed practice in the service system and in the broader community. We discuss results for their implications for building resilient, trauma‐informed communities.     

I need to be your only friend: the effect of salesperson network centrality on opportunistic behavior

Transaction cost economics can be used to detect opportunistic behavior by one or more actors within a sales network. Using a sample of firms in the medical industry, where the role of salespeople is important, this study examines network centrality as a determinant of a salesperson’s opportunistic behavior in intrafirm relationships. The results indicate that opportunistic behavior is affected differently depending on a salesperson’s relational and positional centralities. While relational centrality decreases opportunistic behavior by providing motivation to protect one’s reputation, positional centrality negatively moderates the effect of relational centrality on opportunistic behavior. However, with a high relational centrality, a salesperson is less likely to be opportunistic when she or he is low in positional centrality than when her or his positional centrality is high.     

Using network science to analyze concept maps of psychology undergraduates

Network science is an emerging area of complexity science that uses mathematical techniques to study complex systems and could represent a new way of quantifying and investigating the internal structure of domain‐specific knowledge as approximated by students' concept maps. Students enrolled in introductory psychology constructed concept maps to represent their understanding of a psychology chapter. Concept networks were constructed based on the concept maps generated by students. Network analysis revealed that the structure of concept networks differed across students (i.e., some networks were better connected than others), and network structure significantly predicted quiz scores, such that concept networks with larger average shortest path lengths (a network metric representing the average of the shortest paths between two nodes in a network) were associated with higher quiz scores, after controlling for network size. This paper illustrates how network science techniques can be used to quantify the conceptual structure of a learner's knowledge.     

自闭症谱系障碍者抑制控制的影响因素及神经机制

自闭症谱系障碍(ASD)是一种起源于儿童期的神经发育障碍, ASD的重复刻板行为与其抑制控制发展有密切关系。使用Go/No-go、Flanker及Stroop等范式的研究发现, ASD者抑制控制受损主要表现为反应抑制和干扰抑制能力存在缺陷; 其主要影响因素是任务状态、被试年龄及取样; 涉及的脑区和脑网络集中在前额皮层、前扣带回和基底神经节; 涉及的基因集中在谷氨酸盐、γ-氨基丁酸与5-羟色胺三种神经递质。未来应从脑机制研究入手, 综合考虑任务状态、个体特征对研究结果造成的影响, 并着力开发更有效的干预模式。     

人脑自适应多尺度功能连接的性别差异

已有脑成像研究展示了男女脑功能差异, 但功能磁共振信号的频率划分通常基于主观经验, 使脑功能性别差异的生物学解释遭遇瓶颈。本文提出人脑自适应多尺度功能连接算法, 刻画功能连接的时空多尺度特性, 揭示出0.06~0.10 Hz的性别差异：男性较强的连接主要与边缘网络和腹侧注意网络有关, 女性较强的连接主要与腹侧注意网络、视觉网络和额顶网络有关。     

Personality and personal network type

The association between personality and personal relationships is mostly studied within dyadic relationships. We examined these variables within the context of personal network types. We used Latent Class Analysis to identify groups of students with similar role relationships with three focal figures. We performed Latent Class Logistic Regression to explore the relationships of the latent classes with the Big Five personality factors. Personality was assessed with the Five Factor Personality Inventory. We found three personal network types: a primarily family oriented network, a primarily peer oriented network, and a mixed family/peer oriented network. We found significant associations between personality and personal network type. Extraverted students were more likely to have a primarily peer oriented network relative to a primarily family oriented network. Autonomous students were more likely to have a primarily family oriented network relative to a primarily peer oriented network. Autonomous students were also more likely to have a mixed family/peer oriented network relative to a primarily peer oriented network. Conscientious students were more likely to have a primarily family oriented network relative to a mixed family/peer oriented network.     

Social capital, self-esteem, and use of online social network sites: A longitudinal analysis

A longitudinal analysis of panel data from users of a popular online social network site, Facebook, investigated the relationship between intensity of Facebook use, measures of psychological well-being, and bridging social capital. Two surveys conducted a year apart at a large U.S. university, complemented with in-depth interviews with 18 Facebook users, provide the study data. Intensity of Facebook use in year one strongly predicted bridging social capital outcomes in year two, even after controlling for measures of self-esteem and satisfaction with life. These latter psychological variables were also strongly associated with social capital outcomes. Self-esteem served to moderate the relationship between Facebook usage intensity and bridging social capital: those with lower self-esteem gained more from their use of Facebook in terms of bridging social capital than higher self-esteem participants. We suggest that Facebook affordances help reduce barriers that lower self-esteem students might experience in forming the kinds of large, heterogeneous networks that are sources of bridging social capital.