Introducing SYPRENE: An International Practice Research Network for Strategic and Systemic Therapists and Researchers

SYPRENE, a new international Systemic Practice Research Network (PRN), has been established to fill the gap in practice-based research on the effectiveness and efficiency of strategic therapies. This article presents the rationale for the creation of SYPRENE and describes data collection methods, and the encoding system implemented within this PRN. More developments are expected in the recruitment of practitioners, the types of data collected, findings, and the implementation of SYPRENE in supervision, trainings, and professional schools.     

Segment routing based energy aware routing for software defined data center

Despite the fact that most of the data centers are software-defined, the multifaceted network architecture and increase in network traffic make data centers to suffer from overhead. Multipath TCP supports multiple paths for a single routing session and ensures proper utilization of bandwidth over all available links. As rise in number of nodes in data center is frequent and drastic, scalability issue limits the performance of many existing techniques. Segment Routing is vibrant in reducing scalability disputes and routing overhead. Segment routing approach combined with MPTCP traffic result in efficient routing approach. The downfall of the link capacity due to drastic incoming traffic remains as a major concern in data center network which enforces preventing link energy depletion due to high network traffic. Our proposed work, segment routing based energy aware routing approach for software defined data center aims to achieve throughput maximization through preserving link residual capacity and proper utilization of links. As well, our approach shows a decrease in length of segment label stack with respect to maximum segment label depth. Analysis is done by comparing the executions of other existing approaches in a single-controller environment with our energy-aware routing approach in a distributed environment. Distributed controller setup prevents network from single point of failure. It helps to prevent controller overhead and provides improved network performance through throughput.     

Self‐Disclosure Characteristics and Motivations in Social Media: Extending the Functional Model to Multiple Social Network Sites

The purpose of this study was to examine differences in self‐disclosure goals, privacy concerns, and self‐disclosure characteristics between Facebook and Twitter. These sites were compared in terms of audience representations, based on structural cues that suggest potential audiences for a user. We conceptualized audience representations in 2 ways: based on privacy boundaries that imply bounded versus unbounded audiences, and on network characteristics such as size and diversity for audiences within the boundary. Results revealed that self‐disclosure goals, privacy concerns, and self‐disclosure intimacy were different depending on the privacy boundary. Network characteristics were also important, but effects were moderated by the privacy boundary type, suggesting a complex interplay between the 2 types of audience representations in shaping self‐disclosure in social media.     

The Individual or the Group: A Multilevel Analysis of Cyberbullying in School Classes

In this study, we focus on the relevance of social influence to explain cyberbullying experiences among German high school students. Social influence is discussed in the context of computer‐mediated communication. To obtain individual and sociostructural data, we conducted a survey study among German high school students (N = 4,282). Using multilevel modeling, we found that the attributes of the school class only contributed to the risk of being involved in cyberbullying to a small extent. Still, procyberbullying norms in class did enhance the risk of perpetration and victimization for students, even more so than their individual beliefs. Previous experiences with bullying and intensive, unrestricted use of the Internet were the strongest individual predictors of cyberbullying involvement.     

A Network Perspective on the Processes of Empowered Organizations

Organizational empowerment is a multi-faceted concept that involves processes occurring both within and between organizations that facilitate achievement of their goals. This paper takes a closer look at three interorganizational processes that lead to empowered organizations: building alliances, getting the word out, and capturing others’ attention. These processes are located within the broader nomological network of empowerment and organizational empowerment, and are linked to particular patterns of interorganizational relationships that facilitate organizations’ ability to engage in them. A new network-based measure, γ-centrality, is introduced to capture the particular network structure associated with each process to be assessed. It is demonstrated first in a hypothetical organizational network, then applied to take a closer look at organizational empowerment in the context of a coordinating council composed of human service agencies. The paper concludes with a discussion of the implications of relationships between these processes, and the potential for unintended consequences in the empowerment of organizations.     

The network of conscientiousness

According to the network perspective, the coalescence of several personality characteristics into major personality dimensions results from a pattern of complex interactions that can be modeled as a network. We focused on one personality dimension, conscientiousness, and on its main facets. We administered a large battery of questionnaires to two samples (N = 210 and N = 230) and analyzed them by means of network analysis. The results showed that some elements of the network, such as general self-control and orientation toward the future, characterized all facets. These “shared” elements could be responsible for the facets to clump into one major dimension. Other elements of the network uniquely characterized different facets. These “unique” elements could underlie the main differences among conscientiousness facets.     

An adaptive temporal-causal network model to analyse extinction of communication over time

The persistence of information communicated between humans is difficult to measure as it is affected by many features. This paper presents an approach to computationally model the cognitive processes of information sharing to describe persistence or extinction of communication in Twitter over time. The adaptive mental network model explains, for example, how an individual can experience information overflow on a topic, and how this affects the sharing of information. Parameter tuning by Simulated Annealing is used to identify characteristics of the network model that fit to empirical data from Twitter. The data collected is related to the independentism in Catalunya, Spain, which is considered a global issue with repercussion in Europe.     

On the dynamics and adaptivity of mental processes: Relating adaptive dynamical systems and self-modeling network models by mathematical analysis

In this paper, it is addressed by mathematical analysis how network-oriented modeling relates to the dynamical systems perspective on mental processes. It has been mathematically proven that any dynamical system can be modeled as a temporal-causal network model and that any adaptive dynamical system (of any order) can be modeled by a self-modeling network (of the same order).     

Global and Local Feature Distinctiveness Effects in Language Acquisition

Various aspects of semantic features drive early vocabulary development, but less is known about how the global and local structure of the overall semantic feature space influences language acquisition. A feature network of English words was constructed from a large database of adult feature production norms such that edges in the network represented feature distances between words (i.e., Manhattan distances of probability distributions of features elicited for each pair of words). A word's global feature distinctiveness is measured with respect to all other words in the network and a word's local feature distinctiveness is measured relative to words in sub-networks derived from clustering analyses. This paper investigates how feature distinctiveness of individual words at local and global scales of the network influences language acquisition. Regression analyses indicate that global feature distinctiveness was associated with earlier age of acquisition ratings, and was a stronger predictor of age of acquisition than local feature distinctiveness. These results suggest that the global structure of the semantic feature network could play an important role in language acquisition, whereby globally distinctive concepts help to structure vocabulary development over the lifespan.     

Computational modeling of organisational learning by self-modeling networks

Within organisational learning literature, mental models are considered a vehicle for both individual learning and organizational learning. By learning individual mental models (and making them explicit), a basis for formation of shared mental models for the level of the organization is created, which after its formation can then be adopted by individuals. This provides mechanisms for organizational learning. These mechanisms have been used as a basis for an adaptive computational network model. The model is illustrated by a not too complex but realistic case study.