Universities in the United Kingdom that have installed Combined Heat and Power (CHP) technology are making good moves towards achieving their CO 2 reduction targets. However, CHP may not always be an economical option for a university campus due to numerous factors. Identification of such factors is highly important before making an investment decision. A detailed technical, economic, and environmental feasibility of CHP is, therefore, indispensable. This study aims to undertake a detailed assessment of CHP for a typical university campus and attempts to highlight the significance of such factors. Necessary data and information were collected through site visits, whereas the CHP sizing was performed using the London South Bank University (LSBU) CHP model. The results suggest that there is a strong opportunity of installing a 230 kW CHP that will offset grid electricity and boilers thermal supply by 47% and 75%, respectively, and will generate financial and environmental yearly savings of £51k and 395 t/CO 2 , respectively. A wider spark gap decreases the payback period of the project and vice versa. The capital cost of the project could affect the project's economics due to factors, such as unavailability of space for CHP, complex existing infrastructure, and unavailability of a gas connection.

No abstract

The analysis of potentially large volumes of crowd-sourced and social media data is central to meeting the requirements of the Athena project. Here, we discuss the various stages of the pipeline process we have developed, including acquisition of the data, analysis, aggregation, filtering and structuring. We highlight the challenges involved when working with unstructured, noisy data from sources such as Twitter, and describe the crisis taxonomies that have been developed to support the tasks and enable concept extraction. State of the art technology such as formal concept analysis and machine learning is used to create a range of capabilities including concept drill down, sentiment analysis, credibility assessment and assignment of priority. We present an evaluation of results obtained from a set of tweets which emerged from the Colorado wild fires of 2012.

This paper describes an approach for detecting the presence or emergence of organised crime (OC) signals on social media. It shows how words and phrases, used by members of the public in social media posts, can be treated as weak signals of OC, enabling information to be classified according to a taxonomy. Formal concept analysis is used to group information sources, according to crime-type and location, thus providing a means of corroboration and creating OC concepts that can be used to alert police analysts to the possible presence of OC. The analyst is able to 'drill down' into an OC concept of interest, discovering additional information that may be pertinent to the crime. The paper describes the implementation of this approach into a fully-functional prototype software system, incorporating a social media scanning system and a map-based user interface. The approach and system are illustrated using human trafficking and modern slavery as an example. Real data is used to obtain results that show that weak signals of OC have been detected and corroborated, thus alerting to the possible presence of OC.

The Web and social media nowadays play an increasingly significant role in spreading terrorism-related propaganda and content. In order to deploy counterterrorism measures, authorities rely on automated systems for analysing text, multimedia, and social media content on the Web. However, since each of these systems is an isolated solution, investigators often face the challenge of having to cope with a diverse array of heterogeneous sources and formats that generate vast volumes of data. Semantic Web technologies can alleviate this problem by delivering a toolset of mechanisms for knowledge representation, information fusion, semantic search, and sophisticated analyses of terrorist networks and spatiotemporal information. In the Semantic Web environment, ontologies play a key role by offering a shared, uniform model for semantically integrating information from multimodal heterogeneous sources. An additional benefit is that ontologies can be augmented with powerful tools for semantic enrichment and reasoning. This paper presents such a unified semantic infrastructure for information fusion of terrorism-related content and threat detection on the Web. The framework is deployed within the TENSOR EU-funded project, and consists of an ontology and an adaptable semantic reasoning mechanism. We strongly believe that, in the short-and long-term, these techniques can greatly assist Law Enforcement Agencies in their investigational operations.