Abstract

Abstract
Entrepreneurship education is increasingly being promoted, driven by a wide consensus in modern societies concerning its benefits in fostering the development of several professional and personal attitudes and skills, such as business expertise, creativity, risk assessment or responsibility. In this context, several authorities have been actively developing policies and activities to empower entrepreneurship culture in young people. Serious Games are recognized as having an important role and potential in education and social networks emerged in the last decade as the platform preferred by many people to socialize, play games or conduct professional activities. This paper presents a proposal for BIZZY, a serious game to be developed and implemented as a Facebook application, to enable young people in the range 12-18 years old to learn entrepreneurial skills progressively, by guiding them to develop a business project from the early idea to the business plan.

Abstract
Walking robots are well known for being able to walk over rough terrain and adapt to various environments. Hexapod robots are chosen because of their better stability and higher number of different gaits. However, having to hold the whole weight of the body and a large number of actuators makes all walking robots less energetically efficient than wheeled machines. Special methods for energy consumption optimization must be found. In this paper, hexapod robot energy consumption dependence on body elevation and step height is presented. Three main hexapod gaits are used: tripod, tetrapod and wave. Experimental results show that energy consumption does not depend on body elevation or gait. Although, higher steps increases the power consumption. Therefore, when walking over even terrain, lower step heights along with higher body elevation must be selected for tripod or tetrapod gait in order to surpass ground irregularities but still maintain maximum energetic efficiency.

Abstract
BACKGROUND: In a preliminary study the oxidative stability and tocopherol content of pork meat were shown to be improved by olive leaf (OL) feed supplementation at 50 and 100 g kg(-1). However, growth performance was affected negatively. Therefore the objective of the present study was to assess the influence of OL supplementation at a lower level on feed digestibility, growth performance and meat quality. RESULTS: Pigs were fed a basal diet (control), a basal diet with 25 g OL kg(-1) (OL2.5) or a basal diet with 50 g OL kg(-1) (OL5). The incorporation of OL significantly decreased growth rates (P = 0.010) and backfat thickness (P = 0.035) and increased feed/gain ratio (P = 0.032) in the OL5 group. Feed/gain ratio increased more for females (P = 0.001). The incorporation of OL decreased the crude fat (P = 0.006) and protein (P = 0.037) digestibility of both OL diets. Nevertheless, OL was effective in increasing the tocopherol content of meat (P = 0.009). However, meat from pigs fed the OL diets showed similar conjugated diene content, pH and colour parameters to that from pigs fed the control diet, even after 6 days of storage at 4 degrees C. CONCLUSION: The data indicate that olive leaves may be included in pig diets at 25 g kg(-1) in order to improve the tocopherol content of meat without excessively compromising growth performance. (C) 2014 Society of Chemical Industry

Abstract
Performance evaluation increasingly assumes a more important role in any organizational environment. In the transport area, the drivers are the company’s image and for this reason it is important to develop and increase their performance and commitment to the company goals. This evaluation can be used to motivate driver to improve their performance and to discover training needs. This work aims to create a performance appraisal evaluation model of the drivers based on the multi-criteria decision aid methodology. The MMASSI (Multicriteria Methodology to Support Selection of Information Systems) methodology was adapted by using a template supporting the evaluation according to the freight transportation company in study. The evaluation process involved all drivers (collaborators being evaluated), their supervisors and the company management. The final output is a ranking of the drivers, based on their performance, for each one of the scenarios used.

Abstract
Machine or system failures have high impact both at technical and economic levels. Most modern equipment has logging systems that allow us to collect a diversity of data regarding their operation and health. Using data mining models for novelty detection enables us to explore those datasets, building classification systems that can detect and issue an alert when a failure starts evolving, avoiding the unknown development up to breakdown. In the present case we use a failure detection system to predict train doors breakdowns before they happen using data from their logging system. We study three methods for failure detection: outlier detection, novelty detection and a supervised SVM. Given the problem's features, namely the possibility of a passenger interrupting the movement of a door, the three predictors are prone to false alarms. The main contribution of this work is the use of a low-pass filter to process the output of the predictors leading to a strong reduction in the false alarm rate.