The Main Determinants of the Use of the Cost Accounting System in Portuguese Local Government

This paper explores the main determinants of the use of the cost accounting system (CAS) in Portuguese local government (PLG). Regression analysis is used to study the fit of a model of accounting changes in PLG, focused on cost accounting systems oriented to activities and outputs. Based on survey data gathered from PLG, we have found that the use of information in decision-making and external reporting is still a mirage. We obtain evidence about the influence of the internal organizational context (especially the lack of support and difficulties in the CAS implementation) in the use for internal purposes, while the institutional environment (like external pressures to implement the CAS) appears to be more deterministic of the external use. Results strengthen the function of external reporting to legitimate the organization’s activities to external stakeholders. On the other hand, some control variables (like political competition, usefulness and experience) also evidence some explanatory power in the model. Some mixed results were found that appeal to further research in the future. Our empirical results contribute to understand the importance of interconnecting the contingency and institutional approaches to gain a clear picture of cost accounting changes in the public sector.

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.