A Bayesian approach for the reliability of scientific co-authorship networks with emphasis on nodes

The co-authorship among members of a research group commonly can be represented by a (co-authorship) graph in which nodes represent the researchers that make up of this group and edges represent the connections between two agents (i.e., the co-authorship between these agents). Current study measures the reliability of networks by taking into consideration unreliable nodes (researchers) and perfectly reliable edges (co-authorship between two researchers). A Bayesian approach for the reliability of a network represented by the co-authorship among members of a real research group is proposed, obtaining Bayesian estimates and credibility intervals for the individual components (nodes or researchers) and the network. Weakly informative and non-informative prior distributions are assumed for those components and the posterior summaries are obtained by Monte Carlo-Markov Chain methods. The results show the relevance of an inferential approach for the reliability of scientific co-authorship network. The results also demonstrate that the contribution of each researcher is highly relevant for the maintenance of a research group. In addition, the Bayesian methodology was a feasible and easy computational implementation.     

Transitions of women’s substance use recovery networks and 12-month sobriety outcomes

Little is known about how the structure and composition of women’s personal social networks (PSNs) combine to support recovery from substance use disorders, how PSNs change during early recovery, or how known covariates such as trauma, co-occurring mental health disorders, or treatment modality impact this relationship. This study used latent profile and transition analyses with 6 recovery-specific PSN indicators in a sample of women in early recovery (N = 377) to identify three PSN typologies in relation to abstinence outcomes over 12 months, and track transitions between the typologies at 0–6 and 6–12 months. Women in the Highly Connected type (14.3 %) had tightly-knit networks, more sober alters, and fewer treatment-related alters. Women in the Treatment-Related type (49.3 %) had looser-knit networks with more sober and sobriety-supporting alters and alters they know from treatment. Women in the At-Risk type (36.3 %) had more isolates, few sobriety-supporting alters, and more alters with whom they used. Women in the Treatment-Related Sobriety Support type were significantly more likely to maintain sobriety by 12 months (B = −0.81; OR = 2.09, 95 % CI [1.23−3.56]) than women in the At Risk type. Higher mean Trauma Symptom Checklist scores were positively related to membership in the At Risk type. The majority of women who transitioned did so by 6 months, with 41.6 % transitioning then. Women in the At Risk group had the highest probability of transition (P = 0.55). Being in residential treatment (versus outpatient) predicted lower odds of transitioning (B = −0.81, p = .06). This study provides a framework for conducting longitudinal latent variable analysis with social network data, and offers a clinically-useful starting point for research on individualized, targeted, and stage-based interventions for women in recovery.     

Ethical implications of network data in business and management settings

Reflecting on the compilation and analysis of a range of network datasets drawn from our own work and some prominent examples, we consider the ethical challenges in dealing with network data in business and management settings. We argue that the managerial processes that characterize such settings introduce particular ethical sensitivities in the stages of commissioning and research design, and when collecting, analyzing and reporting network data. These sensitivities arise from the imperatives of business, motivations for commissioning network analyses and the legal authority that managers have over employees. We argue that ethical considerations are much more pervasive in business and management network research than in many other fields.In this contribution, we present a range of ethical challenges in network research in business and management settings that arise at several stages of the research process. For each issue identified, we describe the ethical problem and propose mitigation remedies. From this reflection, we suggest guidelines for other researchers to consider when designing research projects in this application area.     

The social economy of excluded families

Families with material, social and cultural resources can be seen as triply advantaged, while those without are thrice disadvantaged. The authors contend that families' connectedness or exclusion from their communities, and the processes that marginalize or substantially exclude families from the benefits of the wider society, are among the most important dimensions for practice in the family services field. Using selected theory relating to individuals and families in society, this paper proposes a conceptual framework for understanding all families in their social economy, with special attention to families who experience material, social and cultural poverty. These families are vulnerable to becoming excluded families, not only propelled into a survival mode of living that evokes distinct skills and strengths in family members, but also has profound deleterious effects on both children and their parents. When child and family services encounter these excluded families, they need to respond with complex linked strategies at individual, family, network and policy levels.     

The Facets of Social Capital

The emergence of the two facets of social capital, the individual and the collective, has contributed to much of the confusion in the field of social capital. The overall objective of this article is to elaborate on a theoretical model aiming at clarifying some bridges between the facets and dimensions of social capital previously suggested in the literature. Initially, the article shortly presents and discusses some important definitions of social capital. Furthermore, limitations and shortcomings of previous definitions are discussed. Moreover, a theoretical model is elaborated on suggesting that social capital comprises social resources that evolve in accessible social networks or social structures characterized by mutual trust. This model also emphasises some of the potential dark sides of social capital. The presented definition does not definitively address the theoretical uncertainties in the field; however, it suggests that a resource‐oriented notion of social capital could be useful in bridging the facets of social capital.     

平板热量传递实验设计

热量传递是化工专业的重要学习内容,但在实践教学中理论和实验却难以结合起来,目前在教学中没有进行平板热量传递的相关实验。设计了平板热量传递实验,并验证了由能量方程得出的温度分布方程。     

无线传感器网络功率控制的质心定位算法

在无线传感器网络的诸多应用中,迫切需要对事件发生区域或者数据来源进行定位,特别是在无锚节点的情况下。为此该文提出一种基于功率控制的质心定位算法(CLAPC),采用离散功率控制的方式对目标区域进行虚拟网格划分,从而确定包含未知节点的最小区域,在提升系统能效的同时,提高了节点的定位精度。算法无需部署锚节点,具有较高的实用性,仿真实验也进一步验证了算法的有效性。     

评价离散度敏感的P2P交易系统信任模型

提出了一种评价离散度敏感的P2P电子交易系统信任模型,该模型引入了基于推荐节点数量和推荐信息离散程度的全局声誉信度概念,以及直接声誉的相对信度概念,并给出了基于信度的信任度计算方法。通过仿真实验和分析,验证了该信任模型的有效性和抗恶意节点能力。与EigenRep进行比较,表明该文模型在抗恶意节点攻击能力上有了显著提高。     

负载均衡的多接入选择算法

针对异构无线分组网络进行多接入选择方法设计时,仅考虑已接入用户数及系统容量将存在缺陷的问题,提出了一种新的异构无线分组网络中的接入选择算法,在多接入选择时考虑了分组业务QoS和链路自适应机制(链路层重传及自适应调制)因素,来正确估计分组业务在接入后平均消耗的资源量以及分组网络当前的负载情况。最后对算法性能进行了仿真评估,并与传统多接入选择方法进行了比较,结果表明提出的算法优于传统多接入选择算法,能在保证分组业务QoS的基础上,同时达到不同网络间负载均衡的目的。     

满速率满分集优化的单频网发射信号

针对分布式发射天线单频网,设计了3种发射信号及针对第一种发射信号的优化方案。首先通过误对概率分析,推导出满速率单频网能获得的分集度,设计了一种能达到该分集度的发射信号和具有满分集度的2种发射信号。然后针对第一种发射信号,提出了基于异步时延的子载波交织分组的信号优化方法,在接收机位置与性能之间建立了一种折中关系。仿真结果表明,设计的满分集发射信号能获得8dB左右增益,优化后的发射信号能获得5～9dB增益。