Image Analysis for Computer-Assisted Surgery of Hip Fractures

. This paper focuses on the task of automatic feature detection for computer-assisted fixation of hip fractures. The features of interest are the lateral cortex line, the femoral neck centre and the femoral head centre, the latter being the most challenging of all. A nonconventional "divide-and-conquer" knowledgebased approach produces more reliable and faster results than the standard global image processing routine. 1. Introduction Fractures at the neck of the femur, also referred to as hip fractures, are one of the most common orthopaedic fractures. The treatment involves a fixation by the use of a screw and plate combination (Figure 1). A variety of internal fixation devices exist but the surgical process is similar. The screw must be positioned accurately so that it passes through the neck, taps into the femoral head and terminates at a fixed distance from the hip joint surface. The margin for error is small as only a comparatively small region of cortical bone is available to a..

Understanding purchasing intentions in secondary sports ticket websites

The purpose of this study was to examine purchasing intentions in online sports ticketing websites. Based on previous research related to business-to-consumer (B2C) e-commerce, this study developed a conceptual model to test the effect of perceived risk, trust and the Technology Acceptance Model (TAM) on purchase intentions in online secondary ticket websites. College students (N = 251) from the northeastern region of the United States were chosen as the sample. Structural Equation Modeling (SEM) was used to investigate the proposed relationships among four major components (i.e. perceived risk, trust, TAM and behavioural intention). The results showed that there were positive effects of key TAM constructs (i.e. perceived usefulness and ease of use) and trust on purchasing intention, but perceived risk was not a significant indicator of purchase intention

Visual Analytics of Twitter Conversations about Corporate Sponsors of FC Barcelona and Juventus at the 2015 UEFA Final

Until now, little research has been conducted to analyse Twitter conversations about the corporate sponsors of football clubs. The conventional and most widely used method has been to use content analysis to assess the sentiment of the tweets that were sent. However, this approach may be inadequate because sports fans may be unlikely to mention a corporate sponsor in the text they tweet. This study demonstrates the use of visual analytics to assess conversations about corporate sponsors by examining the images people tweet