The ecological metaphor of industrial ecology is a proven conceptual tool, having spawned an entire field of interdisciplinary research that explores the intimate linkages between industry and its underlying natural systems. Besides its name and a number of borrowed concepts, however, industrial ecology has no formal relationship with the ecological sciences. This study explores the potential for further interdisciplinary collaboration by testing whether some of the same quantitative analysis techniques used in community ecology research can have meaning in an industrial context. Specifically, we applied the ecological concepts of connectance and diversity to an analysis of Burnside Industrial Park in Halifax, Nova Scotia. Our results demonstrate that these ecological tools show promise for use in industrial ecology. We discuss the meaning of connectance and diversity concepts in an industrial context and suggest next steps for future studies. We hope that this research will help to lay the groundwork of an ecologically inspired tool kit for analyzing industrial ecosystems.

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In this paper, we describe a set of experiments to examine the effect of various attributes of web genre on the automatic identification of the genre of web pages. Four different genres are used in the data set, namely, FAQ, News, E-Shopping and Personal Home Pages. The effects of the number of features used to represent the web pages (5, 20, or 100) as well as the types of attributes, , singly and in various combinations are examined.The results indicate that fewer features produce better precision but more features produce better recall, and that attributes in combinations will always perform better than single attributes.

Information Filtering systems learn user preferences either through explicit or implicit feedback. However, requiring users to explicitly rate items as part of the interface interaction can place a large burden on the user. Implicit feedback removes the burden of explicit user ratings by transparently monitoring user behavior such as time spent reading, mouse movements and scrolling behavior. Previous research has shown that task may have an impact on the effectiveness of some implicit measures. In this work we report both qualitative and quantitative results of an initial study examining the relationship between user time spent reading and relevance for three web search tasks: relevance judgment, simple question answering and complex question answering. This study indicates that the usefulness of time spent as a measure of user interest is related to task and is more useful for more complex web search tasks. Future directions for this research are presented. IntroductionThe rapid growth of the World Wide Web has highlighted the need for systems that help users sift through the wealth of information available online in order to find documents relevant to their needs. Information Filtering (IF) systems select documents for users based on their previous preferences. In order to learn user preferences, IF systems must determine user interest through either explicit or implicit user feedback. The next generation of IF systems must respond to the context of past user preferences as well as their current task, and it must do so without incurring additional overhead fiom the user.Explicit feedback may come in the form of user defined rules or more commonly as user ratings. Users are often required to give binary judgments after reading each document, such as "relevant" or "not relevant", or rate items on a five point Likert scale. Implicit feedback transparently monitors user behavior in order to determine user preferences. Common user behaviors that have been found to indicate interest include time spent reading, mouse movements, scrolling behavior and interactions with a document, such as saving, forwarding and printing.Implicit feedback has a number of advantages over explicit feedback. Unobtrusively monitoring user behavior allows users to focus on the task at hand without the interruption of having to give ratings for documents. Requiring users to stop and explicitly rate items before moving on to the next document disrupts a user's normal reading and browsing behavior (Middleton et al., 2003;Nichols, 1997) and complicates the design. It is difficult to motivate users to continuously give explicit ratings (Kim et al., 2002; Konstan et al., 1997) even when the benefits are obvious (i.e., personalized recommendations). Implicit feedback may also help to eliminate inconsistencies in explicit user ratings.A hurdle for IF systems is user attitude towards having their behavior monitored. If users are not comfortable with having some behaviors monitored in order to make recommendations then an IF system with implicit fe...

This article presents a study to evaluate the accuracy of drug interaction (DI) alerts triggered by two electronic medical record (EMR) systems in primary healthcare. A scenario-based software architecture analysis methodology (SAAM) was used with drug-drug interaction (DDI) pairs in hypothetical patient scenarios. A literature search identified common drugs used in the management of conditions in the elderly population. Three reference programs determined the level of severity of drug interactions, and a common severity rating scale was adapted. The EMR systems showed a limited potential to identify 'severe' clinically significant DDIs and considerable probability for triggering spurious alerts. This may explain the overriding of DI alerts and the interruption of the workflow of users of EMR systems. Reasons for EMR system deficiency included unavailable updates or programming, database functioning discrepancies, and controversies in the clinical evidence.