Productive Synergies in the City

The chapter focuses on the development of sustainable growing infrastructure in the city at two scales. Firstly the development of a large-scale city wide fuel productive landscape through the development of algae arrays in Liverpool and their connection through urban agriculture systems to develop a closed-cycle food and energy system where waste is food and secondly a hyper-localised neighbourhood food production system in Salford UK that utilises a closed cycle aquaponic system to re-invigorate an urban food desert.

The author develops a three-part model for the implementation of urban agriculture based on hardware (the technological system), software (the biological components) and interface (the links to food and other social networks). The conclusion being that it is possible to develop urban agriculture in cities if their implementation is seen as a process, rather than a static design. Also that as the benefits of such systems are wider than purely the physical outputs of the system in terms of energy and food, and thus we should re-evaluate the purely economic model of appraisal to include these.

Efficient Network in Player and Partner Heterogeneity Models

This paper characterizes efficient networks in player and partner heterogeneity models for both the one-way flow and the two-way flow models. Player (partner) dependent network formation allows benefits and costs to be player (partner) heterogeneous which is an important extension for modeling social networks in the real world. Employing widely used assumptions, I show that efficient networks in the two way flow model are minimally connected and have star or derivative of star type architectures, whereas efficient networks in the one way flow model have wheel architectures.

Locally Unwanted Sea Use: The Case of Syrian Chemical weapons disposal in the Mediterranean

This paper discusses the opposition to the disposal of Syrian chemical weapons in the Mediterranean Sea. Following insights from Green criminology and recent calls in that discipline for the inclusion of new social movements and resistance, it discusses in detail how the issue was framed in terms of environmental and ecological justice by different protest actors. This process is aided by an analytical model that brings together the sociology of protest and social movements, insights from reflexive modernisation and the study of southern European civil societies. Methodologically, the focus is on mobilisations that took place in Greece in general and the island of Crete in particular. Data have been harvested through the examination of online sources, such as newspapers, blogs and dedicated social networks. The analysis of the findings suggests that these mobilisations were initially stimulated by real concern, but subsequently these were only carried through by certain movement entrepreneurs who didn’t hesitate to pepper these concerns with false claims and/or linkages to an already active anti-imperialist discourse.

Effects of peer network interactions on adolescent cannabis use

Purpose
This study capitalises on three waves of longitudinal data from a cohort of 4351 secondary school pupils to examine the effects on individuals’ cannabis use uptake of both peer cannabis use and position within a peer network.

Design/methodology/approach
Both cross-sectional and individual fixed effects models are used to estimate the effect on cannabis use of nominated friends’ cannabis use, of reciprocity and transitivity of nominations across the friendship cluster, and of interactions between these nominated friends. Post hoc analyses parsed the behaviour of reciprocating and non-reciprocating friends.

Findings
Cannabis use varied depending on the stability of friendship network and the degree of reciprocity and interconnectedness within the group. Behavioural influence was strong, but interaction effects were observed between the prevalence of cannabis use among friends, the structure of the friendship group and ego’s proximity to group members. These interactions demonstrate that behavioural influence is more salient in more cohesive groups. When reciprocating and non-reciprocating friends’ mean cannabis use were separated, influence from reciprocating friends was estimated at twice the magnitude of other friends.

Originality/value
While preventing any one individual from using cannabis is likely to have a multiplier effect on classmates, the bonds and interactions between classmates will determine which classmates are affected by this multiplier and the salience of that effect.

Young People’s Early Offending: The Context of Strained Leisure Careers

Criminology has witnessed a growth of interest in the later stages of criminal careers with less attention given to providing an understanding of the onset of offending which goes beyond identifying the precipitative or ‘risk’ factors. Drawing on findings from a study of young people’s offending careers in Ireland, this article provides a contextualized understanding of the onset of crime located in young people’s biographical experiences and transition through youth more specifically. It focuses on one particular dimension of this process, suggesting that early offending can be understood as emerging in the context of strained leisure careers. The findings also highlight the close interaction between the development of young people’s leisure careers and their experiences of the local neighbourhood and social networks. It argues that responses to the early stages of youth offending must widen their focus from the individual to incorporate an understanding of broader socio-economic and cultural contexts.

Inferring and Exploiting Categories for Next Location Prediction

Predicting the next location of a user based on their previous visiting pattern is one of the primary tasks over data from location based social networks (LBSNs) such as Foursquare. Many different aspects of these so-called “check-in” profiles of a user have been made use of in this task, including spatial and temporal information of check-ins as well as the social network information of the user. Building more sophisticated prediction models by enriching these check-in data by combining them with information from other sources is challenging due to the limited data that these LBSNs expose due to privacy concerns. In this paper, we propose a framework to use the location data from LBSNs, combine it with the data from maps for associating a set of venue categories with these locations. For example, if the user is found to be checking in at a mall that has cafes, cinemas and restaurants according to the map, all these information is associated. This category information is then leveraged to predict the next checkin location by the user. Our experiments with publicly available check-in dataset show that this approach improves on the state-of-the-art methods for location prediction.

The VINEYARD Approach: Versatile, Integrated, Accelerator-Based, Heterogeneous Data Centres.

Emerging web applications like cloud computing, Big Data and social networks have created the need for powerful centres hosting hundreds of thousands of servers. Currently, the data centres are based on general purpose processors that provide high flexibility buts lack the energy efficiency of customized accelerators. VINEYARD aims to develop an integrated platform for energy-efficient data centres based on new servers with novel, coarse-grain and fine-grain, programmable hardware accelerators. It will, also, build a high-level programming framework for allowing end-users to seamlessly utilize these accelerators in heterogeneous computing systems by employing typical data-centre programming frameworks (e.g. MapReduce, Storm, Spark, etc.). This programming framework will, further, allow the hardware accelerators to be swapped in and out of the heterogeneous infrastructure so as to offer high flexibility and energy efficiency. VINEYARD will foster the expansion of the soft-IP core industry, currently limited in the embedded systems, to the data-centre market. VINEYARD plans to demonstrate the advantages of its approach in three real use-cases (a) a bio-informatics application for high-accuracy brain modeling, (b) two critical financial applications, and (c) a big-data analysis application.

A spatial approach to network generation for three properties:Degree distribution, clustering coefficient and degree assortativity

Social networks generally display a positively skewed degree distribution and higher values for clustering coefficient and degree assortativity than would be expected from the degree sequence. For some types of simulation studies, these properties need to be varied in the artificial networks over which simulations are to be conducted. Various algorithms to generate networks have been described in the literature but their ability to control all three of these network properties is limited. We introduce a spatially constructed algorithm that generates networks with constrained but arbitrary degree distribution, clustering coefficient and assortativity. Both a general approach and specific implementation are presented. The specific implementation is validated and used to generate networks with a constrained but broad range of property values. © Copyright JASSS.