Tourism impacts of a portuguese world heritage historic center: residents’ perspectives

The aim of this study is twofold. First, the study analyzes local community perspectives of the importance of the WHS classification of the historic center of Évora. Second, the study analyzes local residents’ perceived tourism impacts on the municipality of Évora. The methodology comprises quantitative research based on a self-administered survey applied to convenience samples of local residents of Évora in the beginning of 2014. The main results reveal that local residents have a strongly positive perception of the WHS designation. With regard to the perceived tourism impacts, a principal component factor analysis delineated three positive and three negative tourism impacts. The comparison of the mean scores of these factors across residents that live near and far from the historic center reveals that the most valued and least valued factors are common to all groups of residents. Nevertheless, in terms of positive impacts, the residents that live near the historic center revealed higher means than the residents that live far from it, whereas in terms of negative impacts, the latter group revealed higher means than former group.

Residents’ perspectives on tourism impacts of portuguese world heritage historic centers: Angra do Heroísmo and Évora

The aim of this study is twofold. First, the study analyzes local community perspectives of the importance of the World Heritage Site (WHS) classification of the historic centers of Angra do Heroísmo and Évora. Second, the study analyzes local residents’ perceived tourism impacts on the municipalities of Angra do Heroísmo and Évora. The methodology comprises quantitative research based on a self-administered survey applied to convenience samples of local residents of the two Portuguese municipalities in 2014. The main results reveal that local residents have a strongly positive perception of the WHS designation in both municipalities. With regard to the perceived tourism impacts, residents from Angra do Heroísmo have a stronger agreement about the impacts of tourism on their city than the residents of Évora, except for the negative social and cultural impacts. The comparison of the mean scores of these impacts across residents that live near and far from the historic centers reveals that the most valued and least valued impacts in the three categories of impacts (economic, social and cultural, and environmental) are common to all groups of residents. Nevertheless, residents living in or near the historic center of Angra do Heroísmo have higher means in the majority of tourism impacts (in all categories), with only one negative impact to concern the majority of respondents. Among the residents from Évora, residents living in or near the historic center have higher means in the majority of economic impacts but lower means in almost social and cultural impacts. With regard to the environmental impacts, residents living in or near the historic center have higher means scores in the positive impacts and lower means scores in the negative environmental impacts.

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.