Travelling with golf clubs: the influence of baggage on the trip decision-making process

Sports participation often requires the use of specialist equipment and for many sport tourists this is transported to the destination to aid convenience and enjoyment of participation. Yet, to date there has been little consideration of the influence that travelling with sporting equipment can have on the trip decisions making process. This paper focuses on golf tourism, said to be the largest sector of the sports tourism market and examines the influence that traveling with golf equipment has on aspects of the trip such as travel mode and opportunities for participation. Based on a longitudinal grounded theory study this paper concludes that packing sporting equipment can stimulate negotiations associated with participation. Furthermore the nature of the sporting equipment to be carried can determine the choices made regarding the travel modes used to reach and move around holiday destinations and thus directly influence the trip decision making process

SAM: Speech-Aware Applications in Medicine to Support Structured Data Entry

In the last two years, improvements in speech recognition technology has directed the medical community’s interest to porting and using such innovations in clinical systems. The acceptance of speech recognition systems in clinical domains increases with recognition speed, large medical vocabulary, high accuracy, continuous speech recognition, and speaker independence. Although some commercial speech engines approach these requirements, the greatest benefit can be achieved in adapting a speech recognizer to a specific medical application. The goals of our work are first, to develop a speech-aware core component which is able to establish connections to speech recognition engines of different vendors. This is realized in SAM. Second, with applications based on SAM we want to support the physician in his/her routine clinical care activities. Within the STAMP project (STAndardized Multimedia report generator in Pathology), we extend SAM by combining a structured data entry approach with speech recognition technology. Another speech-aware application in the field of Diabetes care is connected to a terminology server. The server delivers a controlled vocabulary which can be used for speech recognition