Sober social networks: The role of online support groups in recovery from alcohol addiction

In the context of addiction research, positive recovery outcomes are affected by the quality of people's social interactions and perhaps to an even greater extent, by the defining norms of the groups they identify with—that is, using versus recovery groups. Here, we examine the role of online supportive networks in the process of recovery from alcohol addiction. We analyse the relationship between negative and positive aspects of recovery capital (i.e., self‐stigma, and respectively, positive recovery identity and self‐efficacy) as they relate to well‐being outcomes among alcohol users in an online recovery support group. Based on a computerised linguistic analysis of the naturally occurring data extracted from an online recovery support group (i.e., 257 posts made by 237 group members), we found that self‐stigma negatively predicts self‐efficacy and well‐being, and social identification with a recovery identity mediates these relationships. Overall, these findings highlight that positive engagement with supportive recovery networks is central to an effective and sustainable recovery.     

Social network members' responses to friends' breakup false alarms

Social networks influence romantic relationships through friends' support or lack of support of the union. However, the consequences of friends repeatedly telling other friends their relationship was going to end but failing to actually terminate the relationship are unclear. Guided by the false alarm effect (FAE), and data from both student (N = 273) and noncollege (N = 226) samples, it was found that breakup false alarms were associated with increases in both positive and negative responses. In addition, when participants were friends with both partners, breakup false alarms were associated with “feeling caught” between the friends. Findings generally support the FAE and paint a complex picture of how social network members react to their close friends' tumultuous relationships.     

On simulating one-trial learning using morphological neural networks

“Learning once, remembering forever”, this wonderful cognitive phenomenon sometimes occurs in the learning process of human beings. Psychologists call this psychological phenomenon “one-trial learning”. The traditional artificial neural networks can simulate the psychological phenomenon of “implicit learning”, but can’t simulate the cognitive phenomenon of “one-trial learning”. Therefore, cognitive psychology gives a challenge to the traditional artificial neural networks. From two aspects of theory and practice in this paper, the possibility of simulating this kind of psychological phenomenon was explored by using morphological neural networks. This paper takes advantage of morphological associative memory networks to realize the simulation of “one-trial learning” for the first time, and gives 5 simulating practical examples. Theoretical analysis and simulation experiments show that the morphological associative memory networks are a higher effective machine learning method, and can better simulate the cognitive phenomenon of “one-trial learning”, therefore provide a theoretical basis and technological support for the study of intelligent science and cognitive science.     

An improved energy efficient cluster head selection protocol using the double cluster heads and data fusion methods for IoT applications

The more energy consumption is the major issue for wireless sensor network. In wireless sensor network (WSN), sensor nodes are fixed in various routing algorithms. In the same networks, mobile sensor and fixed sensor nodes are combine and its used in few applications. The function is corruption while mobility was achieved, since these nodes have minimum battery power, lower range of communication and a lessor amount of memory. To overcome this issue, Improved Energy Efficient Cluster Head Selection protocol (IEECHS-WSN) is proposed, in these technique is used to transfer the received information by using energy efficient routing protocol. In the CH election method, two cluster heads are selected in a separated cluster and its work in various functions, this can be prolong the network lifetime and decrease the energy consumption of IoT applications. Proposed technique is described on clustering of dual CHs in the method of data fusion for data entropy. This information entropy is used for fusion and classification, the result of fusion and classification are accurate and efficient for data transmission. Our proposed IEECHS protocol has better throughput, lifetime of network and energy consumption compared than the existing technique.     

Populations of spiking neurons for reservoir computing: Closed loop control of a compliant quadruped

Compliant robots can be more versatile than traditional robots, but their control is more complex. The dynamics of compliant bodies can however be turned into an advantage using the physical reservoir computing framework. By feeding sensor signals to the reservoir and extracting motor signals from the reservoir, closed loop robot control is possible. Here, we present a novel framework for implementing central pattern generators with spiking neural networks to obtain closed loop robot control. Using the FORCE learning paradigm, we train a reservoir of spiking neuron populations to act as a central pattern generator. We demonstrate the learning of predefined gait patterns, speed control and gait transition on a simulated model of a compliant quadrupedal robot.     

Peer victimization in single-grade and multigrade classrooms

Although peer victimization mainly takes place within classrooms, little is known about the impact of the classroom context. To this end, we examined whether single-grade and multigrade classrooms (referring to classrooms with one and two grades in the same room) differ in victim–bully relationships in a sample of elementary school children (646 students; age 8–12 years; 50% boys). The occurrence of victim–bully relationships was similar in single-grade and multigrade classrooms formed for administrative reasons, but lower in multigrade classrooms formed for pedagogical reasons. Social network analyses did not provide evidence that peer victimization depended on age differences between children in any of the three classroom contexts. Moreover, in administrative multigrade classrooms, cross-grade victim–bully relationships were less likely than same-grade victim–bully relationships. The findings did not indicate that children in administrative multigrade classrooms are better or worse off in terms of victim–bully relationships than are children in single-grade classrooms.     

企业组织的学习结构

在理论综述的基础上,通过访谈、预试和大规模调查,对企业组织学习结构进行了实证研究。根据访谈和预试,初步确定组织学习问卷的项目。采用探索性因素分析和验证性因素分析,对来自全国不同地区43家企业的管理者和员工的调查数据进行了分析。结果表明,我国企业组织学习是一个6因素的结构模型：组织间学习、组织层学习、集体学习、个体学习、利用式学习、开发式学习;组织学习问卷具有一定的信效度。组织学习的作用和影响因素以及这三者之间的复杂作用机制还有待更为深入的研究     

What can make the difference? Premature birth and maternal sensitivity at 3 months of age: the role of attachment organization, traumatic reaction and baby's medical risk

The influence of prematurity and maternal attachment organization on sensitivity was investigated in 40 mother–infant dyads. Results show a main effect of attachment and an interaction between attachment and prematurity. Sensitivity was differently associated with medical risk and mothers’ traumatic reaction, depending on the quality of attachment.     

Abductive reasoning in neural-symbolic systems

Abduction is or subsumes a process of inference. It entertains possible hypotheses and it chooses hypotheses for further scrutiny. There is a large literature on various aspects of non-symbolic, subconscious abduction. There is also a very active research community working on the symbolic (logical) characterisation of abduction, which typically treats it as a form of hypothetico-deductive reasoning. In this paper we start to bridge the gap between the symbolic and sub-symbolic approaches to abduction. We are interested in benefiting from developments made by each community. In particular, we are interested in the ability of non-symbolic systems (neural networks) to learn from experience using efficient algorithms and to perform massively parallel computations of alternative abductive explanations. At the same time, we would like to benefit from the rigour and semantic clarity of symbolic logic. We present two approaches to dealing with abduction in neural networks. One of them uses Connectionist Modal Logic and a translation of Horn clauses into modal clauses to come up with a neural network ensemble that computes abductive explanations in a top-down fashion. The other combines neural-symbolic systems and abductive logic programming and proposes a neural architecture which performs a more systematic, bottom-up computation of alternative abductive explanations. Both approaches employ standard neural network architectures which are already known to be highly effective in practical learning applications. Differently from previous work in the area, our aim is to promote the integration of reasoning and learning in a way that the neural network provides the machinery for cognitive computation, inductive learning and hypothetical reasoning, while logic provides the rigour and explanation capability to the systems, facilitating the interaction with the outside world. Although it is left as future work to determine whether the structure of one of the proposed approaches is more amenable to learning than the other, we hope to have contributed to the development of the area by approaching it from the perspective of symbolic and sub-symbolic integration.

The Social Networks of Women Experiencing Domestic Violence

The research literature has demonstrated that battered women living in shelters experience impaired social support. This study examines this phenomenon among battered women living in the community. This study compared a group of pregnant battered women (n = 145) and a group of pregnant nonbattered women (n = 58) in terms of their structural [e.g., total number of supporters, network members in violent relationships with their partners (an index of homophily or nonhomophily) and functional (e.g., emotional)] social support. Four reasons for impaired support in battered women were proposed and examined, including social isolation, failure to disclose abuse, homophily, and low SES. Only homophily was related to impaired support among battered women. In addition, the relationship between structural and functional support and mental health outcomes (e.g., depression, self-esteem) was examined. Criticism, practical support, homophily, and disclosure were all significant predictors of mental health for battered women. Implications for community-based interventions are discussed in the context of current intervention strategies with battered women.