The effects of place of residence on tourist motivations: a revised case of the Urban Quadrangle of Minho

This study seeks to capture the underlying reasons for the travel decisions of residents of the Urban Quadrangle of Minho (composed of the municipalities of Barcelos, Braga, Guimarães, and Vila Nova de Famalicão). The aim of the research is three-fold. Firstly, the study identifies the push and pull motivational factors of residents of the Urban Quadrangle of Minho. Secondly, the study examines whether there are differences between the tourist motivations of residents of the four different municipalities of the Urban Quadrangle. Finally, the study investigates if there are any differences in the motivations of those who choose national and international destinations. The methodology comprises quantitative research based on questionnaires administered in 2012 to residents of the Urban Quadrangle of Minho. A principal component factor analysis is employed to identify six push and seven pull factors. The comparison of the mean scores of these factors across municipalities and across residents that choose national and international destinations reveals that the most valued and least valued factors are common to all four municipalities and both groups of residents (that choose national and international destinations).

The effects of place of residence on tourist motivations: the case of Urban Quadrangle of Minho

The aim of this preliminary study is three-fold. Firstly, the study identifies the push and pull motivational factors of residents of the Urban Quadrangle of Minho. Secondly, the study examines whether there are differences between the tourist motivations of residents of the four different municipalities. Finally, the study investigates if there are any differences in the motivations of those who choose national and international destinations.

Automated traffic route identification through the shared nearest neighbour algorithm

Many organisations need to extract useful information from huge amounts of movement data. One example is found in maritime transportation, where the automated identification of a diverse range of traffic routes is a key management issue for improving the maintenance of ports and ocean routes, and accelerating ship traffic. This paper addresses, in a first stage, the research challenge of developing an approach for the automated identification of traffic routes based on clustering motion vectors rather than reconstructed trajectories. The immediate benefit of the proposed approach is to avoid the reconstruction of trajectories in terms of their geometric shape of the path, their position in space, their life span, and changes of speed, direction and other attributes over time. For clustering the moving objects, an adapted version of the Shared Nearest Neighbour algorithm is used. The motion vectors, with a position and a direction, are analysed in order to identify clusters of vectors that are moving towards the same direction. These clusters represent traffic routes and the preliminary results have shown to be promising for the automated identification of traffic routes with different shapes and densities, as well as for handling noise data.

An approach on place attachment, involvement and behavioural intentions in cross-border tourist regions

This study aims at gaining a deeper understanding of customer profiling and behaviour in cross-border tourism destinations. The study is developed under a niche marketing perspective. It is our view that niche marketing is not confined to the limits of national markets. Previous studies suggest that cross-border regions are an attractive notion, yet they require further theoretical and empirical research. There is still a gap in the understanding of destination management in cross-border regions and the customer profile and motivations. Overall this research attempts to produce a deeper understanding of the profile and behaviour of consumers in tourism settings, addressing the predisposition for the destination in specific contexts (cross-border tourism regions).

Computer-aided recognition of dental implants in X-ray images

Dental implant recognition in patients without available records is a time-consuming and not straightforward task. The traditional method is a complete user-dependent process, where the expert compares a 2D X-ray image of the dental implant with a generic database. Due to the high number of implants available and the similarity between them, automatic/semi-automatic frameworks to aide implant model detection are essential. In this study, a novel computer-aided framework for dental implant recognition is suggested. The proposed method relies on image processing concepts, namely: (i) a segmentation strategy for semi-automatic implant delineation; and (ii) a machine learning approach for implant model recognition. Although the segmentation technique is the main focus of the current study, preliminary details of the machine learning approach are also reported. Two different scenarios are used to validate the framework: (1) comparison of the semi-automatic contours against implant’s manual contours of 125 X-ray images; and (2) classification of 11 known implants using a large reference database of 601 implants. Regarding experiment 1, 0.97±0.01, 2.24±0.85 pixels and 11.12±6 pixels of dice metric, mean absolute distance and Hausdorff distance were obtained, respectively. In experiment 2, 91% of the implants were successfully recognized while reducing the reference database to 5% of its original size. Overall, the segmentation technique achieved accurate implant contours. Although the preliminary classification results prove the concept of the current work, more features and an extended database should be used in a future work.

Multi-centre validation of an automatic algorithm for fast 4D myocardial segmentation in cine CMR datasets

Quantitative analysis of cine cardiac magnetic resonance (CMR) images for the assessment of global left ventricular morphology and function remains a routine task in clinical cardiology practice. To date, this process requires user interaction and therefore prolongs the examination (i.e. cost) and introduces observer variability. In this study, we sought to validate the feasibility, accuracy, and time efficiency of a novel framework for automatic quantification of left ventricular global function in a clinical setting.