Supercritical water gasification of RDF and its components over RuO2/γ-Al2O3 catalyst:new insights into RuO2 catalytic reaction mechanisms

Five samples including a composite refuse derived fuel (RDF) and four combustible components of municipal solid wastes (MSW) have been reacted under supercritical water conditions in a batch reactor. The reactions have been carried out at 450 °C for 60 min reaction time, with or without 20 wt% RuO2/gamma-alumina catalyst. The reactivities of the samples depended on their compositions; with the plastic-rich samples, RDF and mixed waste plastics (MWP), giving similar product yields and compositions, while the biogenic samples including mixed waste wood (MWW) and textile waste (TXT) also gave similar reaction products. The use of the heterogeneous ruthenium-based catalyst gave carbon gasification efficiencies (CGE) of up to 99 wt%, which was up by at least 83% compared to the non-catalytic tests. In the presence of RuO2 catalyst, methane, hydrogen and carbon dioxide became the dominant gas products for all five samples. The higher heating values (HHV) of the gas products increased at least two-fold in the presence of the catalyst compared to non-catalytic tests. Results show that the ruthenium-based catalyst was active in feedstock steam reforming, methanation and possible direct hydrogenolysis of C-C bonds. This work provides new insights into the catalytic mechanisms of RuO2 during SCWG of carbonaceous materials, along with the possibility of producing high yields of methane from MSW fractions.

Natural language processing as a foundation of the semantic Web

The main argument of this paper is that Natural Language Processing (NLP) does, and will continue to, underlie the Semantic Web (SW), including its initial construction from unstructured sources like the World Wide Web (WWW), whether its advocates realise this or not. Chiefly, we argue, such NLP activity is the only way up to a defensible notion of meaning at conceptual levels (in the original SW diagram) based on lower level empirical computations over usage. Our aim is definitely not to claim logic-bad, NLP-good in any simple-minded way, but to argue that the SW will be a fascinating interaction of these two methodologies, again like the WWW (which has been basically a field for statistical NLP research) but with deeper content. Only NLP technologies (and chiefly information extraction) will be able to provide the requisite RDF knowledge stores for the SW from existing unstructured text databases in the WWW, and in the vast quantities needed. There is no alternative at this point, since a wholly or mostly hand-crafted SW is also unthinkable, as is a SW built from scratch and without reference to the WWW. We also assume that, whatever the limitations on current SW representational power we have drawn attention to here, the SW will continue to grow in a distributed manner so as to serve the needs of scientists, even if it is not perfect. The WWW has already shown how an imperfect artefact can become indispensable.

BBC3 - Grime Scene Investigation - Episode 1

Grime Scene Investigation was an eight part television series broadcast on BBC3 during Autumn 2006. In each episode a team of scientists from Aston University would visit a member of the public in their mobile laboratory to reveal the hidden world of microbes living in, on and around them. In this way microbiology was communicated in an informative and entertaining way. In this episode, Grime Scene Investigation dives head first into the murky world of Hooligan Swamp - a Bristolian band who pride themselves on living the rock'n'roll lifestyle to the full. The Swamp are facing an eviction notice and the environmental health authorities are threatening to brand their home a health hazard.

Automatic identification of best answers in online enquiry communities

Online communities are prime sources of information. The Web is rich with forums and Question Answering (Q&A) communities where people go to seek answers to all kinds of questions. Most systems employ manual answer-rating procedures to encourage people to provide quality answers and to help users locate the best answers in a given thread. However, in the datasets we collected from three online communities, we found that half their threads lacked best answer markings. This stresses the need for methods to assess the quality of available answers to: 1) provide automated ratings to fill in for, or support, manually assigned ones, and; 2) to assist users when browsing such answers by filtering in potential best answers. In this paper, we collected data from three online communities and converted it to RDF based on the SIOC ontology. We then explored an approach for predicting best answers using a combination of content, user, and thread features. We show how the influence of such features on predicting best answers differs across communities. Further we demonstrate how certain features unique to some of our community systems can boost predictability of best answers.

Browsing for information by highlighting automatically generated annotations:a user study and evaluation

The realization of the Semantic Web is constrained by a knowledge acquisition bottleneck, i.e. the problem of how to add RDF mark-up to the millions of ordinary web pages that already exist. Information Extraction (IE) has been proposed as a solution to the annotation bottleneck. In the task based evaluation reported here, we compared the performance of users without access to annotation, users working with annotations which had been produced from manually constructed knowledge bases, and users working with annotations augmented using IE. We looked at retrieval performance, overlap between retrieved items and the two sets of annotations, and usage of annotation options. Automatically generated annotations were found to add value to the browsing experience in the scenario investigated. Copyright 2005 ACM.

EPCIS Event-Based traceability in pharmaceutical supply chains via automated generation of linked pedigrees

In this paper we show how event processing over semantically annotated streams of events can be exploited, for implementing tracing and tracking of products in supply chains through the automated generation of linked pedigrees. In our abstraction, events are encoded as spatially and temporally oriented named graphs, while linked pedigrees as RDF datasets are their specific compositions. We propose an algorithm that operates over streams of RDF annotated EPCIS events to generate linked pedigrees. We exemplify our approach using the pharmaceuticals supply chain and show how counterfeit detection is an implicit part of our pedigree generation. Our evaluation results show that for fast moving supply chains, smaller window sizes on event streams provide significantly higher efficiency in the generation of pedigrees as well as enable early counterfeit detection.