The Knowledge and Use of the Balanced Scorecard in Portugal: Empirical Evidence of the 250 Largest Companies

Balanced Scorecard (BSC) is recognized, both in the academic and business world, as one of the most powerful strategic management accounting tools. Thus, we launched in October 2004 a questionnaire survey applied to the 250 largest Portuguese companies aiming at observing the knowledge, use, and companies’ characteristics which are adopting this management instrument. Despite the majority of the companies inquired recognize BSC more as a strategic management tool than a performance valuation system, the results show that there is still a reduced and recent utilization of BSC in Portugal. Similarly to other countries Portugal is still in the initial state of BSC utilization. Our work has shown that the companies that use more BSC belong mainly to the secondary sector of industry. Nevertheless, unlike other studies, we did not get empirical evidence on the influence of variables such as geographical localization, dimension and internationalization, in the use and knowledge of BSC in Portugal.

Voxel-based registration of simulated and real patient CBCT data for accurate dental implant pose estimation

The success of dental implant-supported prosthesis is directly linked to the accuracy obtained during implant’s pose estimation (position and orientation). Although traditional impression techniques and recent digital acquisition methods are acceptably accurate, a simultaneously fast, accurate and operator-independent methodology is still lacking. Hereto, an image-based framework is proposed to estimate the patient-specific implant’s pose using cone-beam computed tomography (CBCT) and prior knowledge of implanted model. The pose estimation is accomplished in a threestep approach: (1) a region-of-interest is extracted from the CBCT data using 2 operator-defined points at the implant’s main axis; (2) a simulated CBCT volume of the known implanted model is generated through Feldkamp-Davis-Kress reconstruction and coarsely aligned to the defined axis; and (3) a voxel-based rigid registration is performed to optimally align both patient and simulated CBCT data, extracting the implant’s pose from the optimal transformation. Three experiments were performed to evaluate the framework: (1) an in silico study using 48 implants distributed through 12 tridimensional synthetic mandibular models; (2) an in vitro study using an artificial mandible with 2 dental implants acquired with an i-CAT system; and (3) two clinical case studies. The results shown positional errors of 67±34μm and 108μm, and angular misfits of 0.15±0.08º and 1.4º, for experiment 1 and 2, respectively. Moreover, in experiment 3, visual assessment of clinical data results shown a coherent alignment of the reference implant. Overall, a novel image-based framework for implants’ pose estimation from CBCT data was proposed, showing accurate results in agreement with dental prosthesis modelling requirements.