Accurate assessment of the brake torque on a rope-braked cycle ergometer

Sports Engineering - A Monark cycle ergometer is a device globally used in physiological studies to measure the work and energy levels of exercising humans. In this paper a rope-braked cycle...     

Ensuring Content‐Area Learning by Secondary Students with Learning Disabilities

Three factors tied with secondary student success in content‐area reading are demonstrated: (a) validated teacher‐focused and student‐focused interventions, (b) integrated and comprehensive service delivery systems, and (c) well‐designed, data‐based professional developmental programs. Difficult challenges face secondary students with LD and their teachers with regard to these students’ participation and success in required general education classes. Recently, instructional methods and materials have been developed and validated for promoting these students’ success. Some of them focus on how general education teachers plan and teach their content‐area courses; others focus on giving students the strategies they need to respond independently to the demands of their courses. This article describes these instructional methods, a service‐delivery model for implementing these interventions in secondary schools, and professional‐development mechanisms and administrative support that must be in place for the model to be maintained effectively.     

Verteilte Metadatenverwaltung und Anfragebearbeitung für Internet-Datenquellen

Zusammenfassung.   Wir pr?sentieren in dieser Arbeit das ObjectGlobe-System, ein offenes und verteiltes Anfragebearbeitungssystem auf Internet-Datenquellen. ObjectGlobe erweitert die begrenzten F?higkeiten des Internets bezüglich Anfragebearbeitung, indem es einen offenen Marktplatz schafft, in dem eine Vielzahl von Anbietern Rechenzeit, Daten und Anfrageoperatoren bereitstellen. Den Schwerpunkt dieses Beitrags bildet die im Rahmen des ObjectGlobe-Projekts entwickelte Metadatenverwaltung MDV, ein verteiltes Metadaten-Management-System. Es besitzt eine 3-schichtige Architektur und unterstützt Caching und Replikation in der Mittelschicht, so dass Anfragen lokal ausgewertet werden k?nnen. Benutzer spezifizieren die Informationen, die sie ben?tigen und die repliziert werden, mit Hilfe einer besonderen Regelsprache. Um Replikate aktuell zu halten und die Replikation von neuen und relevanten Informationen anzusto?en, verwendet das MDV-System einen neuartigen Publish & Subscribe-Algorithmus. Wir beschreiben diesen Algorithmus im Detail und zeigen, wie er mit Hilfe eines normalen relationalen Datenbanksystems implementiert werden kann. Eingegangen am 19. November 2001 / Angenommen am 30. Juli 2002     

Cross-cultural argument interactions between Israeli-Jews and Palestinians

This study examined the argument patterns that result when Israeli-Jews and Palestinians confront each other during group dialogues. We tested predictions derived from two theories. The first was a theory of cultural communication which predicted that Israeli-Jews and Palestinians would argue in a manner consistent with their respective cultural communication codes known as dugri and musayra respectively. Thus, the Israeli-Jews were expected to be assertive and the Palestinians more accommodating. The second theory was rooted in majority/minority power relations and predicted the opposite. The data were generated from reconciliation-based dialogue groups of Israeli-Jews and Palestinians. Communicative acts were coded according to the Conversational Argument Coding Scheme. Analysis of covariance was used to determine which patterns of argument distinguished the groups, and to identify variance attributable to lag sequences and individuals. The results were supportive of predictions from majority/minority power relations.     

Design and evaluation of a personal digital assistant- based alerting service for clinicians.

PURPOSE: This study describes the system architecture and user acceptance of a suite of programs that deliver information about newly updated library resources to clinicians' personal digital assistants (PDAs). DESCRIPTION: Participants received headlines delivered to their PDAs alerting them to new books, National Guideline Clearinghouse guidelines, Cochrane Reviews, and National Institutes of Health (NIH) Clinical Alerts, as well as updated content in UpToDate, Harrison's Online, Scientific American Medicine, and Clinical Evidence. Participants could request additional information for any of the headlines, and the information was delivered via e-mail during their next synchronization. Participants completed a survey at the conclusion of the study to gauge their opinions about the service. RESULTS/OUTCOME: Of the 816 headlines delivered to the 16 study participants' PDAs during the project, Scientific American Medicine generated the highest proportion of headline requests at 35%. Most users of the PDA Alerts software reported that they learned about new medical developments sooner than they otherwise would have, and half reported that they learned about developments that they would not have heard about at all. While some users liked the PDA platform for receiving headlines, it seemed that a Web database that allowed tailored searches and alerts could be configured to satisfy both PDA-oriented and e-mail-oriented users.     

Analysis of Statistical Question Classification for Fact-Based Questions

Question classification systems play an important role in question answering systems and can be used in a wide range of other domains. The goal of question classification is to accurately assign labels to questions based on expected answer type. Most approaches in the past have relied on matching questions against hand-crafted rules. However, rules require laborious effort to create and often suffer from being too specific. Statistical question classification methods overcome these issues by employing machine learning techniques. We empirically show that a statistical approach is robust and achieves good performance on three diverse data sets with little or no hand tuning. Furthermore, we examine the role different syntactic and semantic features have on performance. We find that semantic features tend to increase performance more than purely syntactic features. Finally, we analyze common causes of misclassification error and provide insight into ways they may be overcome.