Forecasting the Spreading of Technologies in Research Communities

Technologies such as algorithms, applications and formats are an important part of the knowledge produced and reused in the research process. Typically, a technology is expected to originate in the context of a research area and then spread and contribute to several other fields. For example, Semantic Web technologies have been successfully adopted by a variety of fields, e.g., Information Retrieval, Human Computer Interaction, Biology, and many others. Unfortunately, the spreading of technologies across research areas may be a slow and inefficient process, since it is easy for researchers to be unaware of potentially relevant solutions produced by other research communities. In this paper, we hypothesise that it is possible to learn typical technology propagation patterns from historical data and to exploit this knowledge i) to anticipate where a technology may be adopted next and ii) to alert relevant stakeholders about emerging and relevant technologies in other fields. To do so, we propose the Technology-Topic Framework, a novel approach which uses a semantically enhanced technology-topic model to forecast the propagation of technologies to research areas. A formal evaluation of the approach on a set of technologies in the Semantic Web and Artificial Intelligence areas has produced excellent results, confirming the validity of our solution

Social dynamics in conferences: analyses of data from the Live Social Semantics application

Popularity and spread of online social networking in recent years has given a great momentum to the study of dynamics and patterns of social interactions. However, these studies have often been confined to the online world, neglecting its interdependencies with the offline world. This is mainly due to the lack of real data that spans across this divide. The Live Social Semantics application is a novel platform that dissolves this divide, by collecting and integrating data about people from (a) their online social networks and tagging activities from popular social networking sites, (b) their publications and co-authorship networks from semantic repositories, and (c) their real-world face-to-face contacts with other attendees collected via a network of wearable active sensors. This paper investigates the data collected by this application during its deployment at three major conferences, where it was used by more than 400 people. Our analyses show the robustness of the patterns of contacts at various conferences, and the influence of various personal properties (e.g. seniority, conference attendance) on social networking patterns

Potentials and Limits of Bayesian Networks to Deal with Uncertainty in the Assessment of Climate Change Adaptation Policies

Bayesian networks (BNs) have been increasingly applied to support management and decision-making processes under conditions of environmental variability and uncertainty, providing logical and holistic reasoning in complex systems since they succinctly and effectively translate causal assertions between variables into patterns of probabilistic dependence. Through a theoretical assessment of the features and the statistical rationale of BNs, and a review of specific applications to ecological modelling, natural resource management, and climate change policy issues, the present paper analyses the effectiveness of the BN model as a synthesis framework, which would allow the user to manage the uncertainty characterising the definition and implementation of climate change adaptation policies. The review will let emerge the potentials of the model to characterise, incorporate and communicate the uncertainty, with the aim to provide an efficient support to an informed and transparent decision making process. The possible drawbacks arising from the implementation of BNs are also analysed, providing potential solutions to overcome them.Adaptation to Climate Change, Bayesian Network, Uncertainty

Exploring pig trade patterns to inform the design of risk-based disease surveillance and control strategies

An understanding of the patterns of animal contact networks provides essential information for the design of risk-based animal disease surveillance and control strategies. This study characterises pig movements throughout England and Wales between 2009 and 2013 with a view to characterising spatial and temporal patterns, network topology and trade communities. Data were extracted from the Animal and Plant Health Agency (APHA)’s RADAR (Rapid Analysis and Detection of Animal-related Risks) database, and analysed using descriptive and network approaches. A total of 61,937,855 pigs were moved through 872,493 movements of batches in England and Wales during the 5-year study period. Results show that the network exhibited scale-free and small-world topologies, indicating the potential for diseases to quickly spread within the pig industry. The findings also provide suggestions for how risk-based surveillance strategies could be optimised in the country by taking account of highly connected holdings, geographical regions and time periods with the greatest number of movements and pigs moved, as these are likely to be at higher risk for disease introduction. This study is also the first attempt to identify trade communities in the country, information which could be used to facilitate the pig trade and maintain disease-free status across the country in the event of an outbreak

Agroecological aspects of evaluating agricultural research and development:

In this paper we describe how biophysical data can be used, in conjunction with agroecological concepts and multimarket economic models, to systematically evaluate the effects of agricultural R&D in ways that inform research priority setting and resource allocation decisions. Agroecological zones can be devised to help estimate the varying, site-specific responses to new agricultural technologies and to evaluate the potential for research to spill over from one agroecological zone to another. The application of agroecological zonation procedures in an international agricultural research context is given special attention.Agricultural research., Technological innovations., Agricultural economics and policies.,

What personal and environmental factors determine frequency of urban greenspace use?

For many people, urban greenspaces are the only places where they encounter the natural world. This is concerning as there is growing evidence demonstrating that human well-being is enhanced by exposure to nature. There is, therefore, a compelling argument to increase how frequently people use urban greenspaces. This may be achieved in two complementary ways by encouraging: (I) non-users to start visiting urban greenspaces; (II) existing users to visit more often. Here we examine the factors that influence frequency of greenspace visitation in the city of Sheffield, England. We demonstrate that people who visit a site least frequently state lower self-reported psychological well-being. We hypothesised that a combination of socio-demographic characteristics of the participants, and the biophysical attributes of the greenspaces that they were visiting, would be important in influencing visit frequency. However, socio-demographic characteristics (income, age, gender) were not found to be predictors. In contrast, some biophysical attributes of greenspaces were significantly related to use frequency. Frequent use was more likely when the time taken to reach a greenspace was shorter and for sites with a higher index of greenspace neglect, but were unrelated to tree cover or bird species richness. We related these results to the motivations that people provide for their visits. Infrequent users were more likely to state motivations associated with the quality of the space, while frequent users gave motivations pertaining to physical, repeated activities. This suggests that there may be no simple way to manage greenspaces to maximise their use across user cohorts as the motivations for visits are very different

The National Transport Data Framework

Report by Professor Peter Landshoff (Cambridge University) and Professor John Polak (Imperial College London) on a project for the Department for Transport. emails: [email protected] [email protected] NTDF is designed to be a resource for data owners to deposit descriptions into a central catalogue, so that people can search for data and find data and understand their characteristics. The value of this is to individuals, to commercial organizations, and to public bodies. For example, services that provide better information to travellers will help to make their journey less stressful and persuade them to make more use of public transport. Transport operators need very diverse information to help them plan developments to their services: demographic, geographical, economic etc. And policy makers need a similar range of information to help them decide how to divide their budget and afterwards to evaluate how valuable it has been.This work was supported by the Department for Transport (DfT)

Technical efficiency and spatial externalities: evidence from spanish small and middle-sized industrial firms

The aim of this paper is to analyze whether firms located inside a hypothetic cluster have a different performance in terms of technical efficiency from firms located outside. To undertake this analysis we present a methodology to overcome the restrictions suffered by this kind of studies, basically the radial efficiency measures. The methodological approach we propose is based on non-radial efficiency measures, also referred as the Russell measure. We present the main results obtained on a sample of small and middle-sized industrial firms located in the Valencian Community (Spain) to show the advantages of this methodology. Furthermore, we assess the links between the spatial location and some management characteristics at firm level.