Abstract
The road transportation sector is responsible for 87% of the human CO2 emissions. The estimation and prediction of fuel consumption plays a key role in the development of systems that foster the reduction of those emissions through trip planing. In this paper, we present a predictive regression model of instantaneous fuel consumption for diesel and gasoline light-duty vehicles, based on their instantaneous speed and acceleration and on road inclination. The parameters are extracted from GPS data, thus the models do not require data from dedicated vehicle sensors. We use data collected by 17 drivers during their daily commutes using the SenseMyCity crowdsensor. We perform an empyrical comparison of several regression algorithms for prediction across trips of the same vehicle and for prediction across vehicles. The results show that models trained for a vehicle show similar RMSE when are applied to other vehicles with similar characteristics. Relying on these results, we propose fuel type specific models that provide an accurate prediction for vehicles with similar characteristics to those on which the models were trained.

Abstract
Group work is an essential activity during both graduate and undergraduate formation. Although there is a vast theoretical literature and numerous case studies about group work, we haven’t yet seen much development concerning the assessment of individual group participants. The problem relies on the difficulty to have the perception of each student’s contribution towards the whole work. We propose and describe a novel tool to manage and assess individual group. Using the collected interactions from the tool usage we create a model for predicting ill-conditioned interactions which generate alerts. We also describe a functionality to predict the final activity grading, based on the interaction patterns and on an automatic classification of these interactions.

Abstract

Abstract
A nonlinear model predictive formation controller (NMPFC) was used to converge a group of middle sized mobile soccer robots towards a desired target using the concept of active target tracking. This paper presents a novel approach on the formation controller's weight tuning in order to minimize an objective function that reflects the controller's efficiency with respect to a given criteria. This method is here called Iterative Weight Tuning (IWT). In this paper the effectiveness from the proposed method is shown by the results of simulations and experiment with real robots, compared to the tuning performed using genetic algorithms approach. The results demonstrated that the IWT method was successful in achieving a better set of weights that influenced the formation controller to converge the robots into formation in a better fashion regarding the agents' objective function.

Abstract
Purpose - The purpose of this paper is to estimate retailing efficiency in a 26-store women clothing retail chain and to decompose it in several measures in order to contribute to the performance improvement of this retail service firm, as well as to compare the efficiency of the different decision making units. Design/methodology/approach - This paper uses the following measures to estimate efficiency: sigma convergence analysis; efficiency analysis; technical efficiency (TE) analysis; pure technical efficiency (PTE) analysis and scale efficiency (SE) analysis for a set of 26 stores of a women retail service brand operating in Portugal. A cross-section input-oriented data envelopment analysis (DEA) is used to analyse quarterly data sets from 2010 to 2013. Findings - The results show that costs with personnel are slightly increasing when analysed using the sigma convergence method, although there are some geographical differences. Moreover, it is possible to witness that the retail store chain's TE diminishes as the operations outputs do not grow as fast as input savings. On the other hand, there are no SE problems as the levels of SE are larger than pure efficiency levels. Research limitations/implications - The main limitation of the study stems from the fact that the analysis is based on a simple retail chain, which makes it a single case study. Therefore, the generalisation of the conclusions for other firms or for other periods of analysis should be made cautiously. Practical implications - It is shown that some stores have a good TE and other stores have some SE advantage. As such, it is possible to select some stores as benchmarks to deploy internal efficiency throughout the retail chain. Originality/value - The contribution of this paper is based on the application of the sigma conversion and DEA techniques to evaluate efficiency in retail service store.

Abstract
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 three-step 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+/-34 mu m and 108 mu m, and angular misfits of 0.15+/-0.08 degrees and 1.4 degrees, 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.