Abstract
Scand J Caring Sci; 2013; 27; 468474 Evaluation and cross-cultural adaptation of the Hendrich II Fall Risk Model to Portuguese Background: Several tools for the assessment of the risk of falling are used commonly by clinical nurses, but none have been validated in Portuguese. Aims: To adapt and evaluate the Hendrich II Fall Risk Model (HIIFRM) for use with elderly Portuguese inpatients. Method: We conducted a prospective study of 586 older inpatients in acute care hospitals, from November 2007 to May 2010. Results: The study involved 270 men and 316 women. The most frequent risk factor on admission and at discharge was a score 3 on the Get Up and Go' test. The adapted HIIFRM showed a sensitivity of 93.2% and 75.7%, and a specificity of 35% and 46.7%, on admission and at discharge, respectively. A positive predictive value of 17.2% on admission and 17% at discharge and a negative predictive value of 97.3% and 93%, respectively, were estimated. Conclusions: The HIIFRM was shown to be a useful tool in predicting falls by patients. Nevertheless, the research model suggested that only four risk factors affected the occurrence of falls significantly on admission and two risk factors at discharge. Further research is required in Portuguese hospital settings.

Abstract
The recent development of electric vehicles (EVs) has brought a new set of problems regarding their integration in power networks, particularly in terms of the potential growth of peak load. The peak growth leads to the increase of losses and braches charging and to voltage drops. Conversely, optimizing EV charging policy creates new opportunities for both network safety and energy trading through the markets. This paper presents a multi-level framework combining two representations of a medium voltage (MV) network in order to optimize the EV charging policy. A minimizing cost approach is set, modeling day-ahead markets, and taking into account losses. The proposed methodology is tested on a typical MV network.

Abstract
This paper presents a novel localization method for small mobile robots. The proposed technique is especially designed for the Robot@Factory which is a new robotic competition presented in Lisbon 2011. The real-time localization technique resorts to low-cost infra-red sensors, a map-matching method and an Extended Kalman Filter (EKF) to create a pose tracking system that is well-behaved. The sensor information is continuously updated in time and space through the expected motion of the robot. Then, the information is incorporated into the map-matching optimization in order to increase the amount of sensor information that is available at each moment. In addition, a particle filter based on Particle Swarm Optimization (PSO) relocates the robot when the map-matching error is high. Meaning that the map-matching is unreliable and robot is lost. The experiments conducted in this paper prove the ability and accuracy of the presented technique to localize small mobile robots for this competition. Therefore, extensive results show that the proposed method have an interesting localization capability for robots equipped with a limited amount of sensors.

Abstract
A relevant research topic in optimization of power distribution networks is to find the best relationship between system reliability and the allocation of maintenance resources. This paper presents a mathematical formulation that seeks the optimal preventive maintenance budget regarding the system reliability constraints. A dynamic programming approach is proposed to deal with this optimization problem. Some reductions are applied to the dynamic programming approach in order to avoid the combinatorial explosion. Case studies are presented to compare the performance of the dynamic programming approach with a hybrid genetic algorithm previously developed. © 2013 IEEE.

Abstract
More than ever, the economic globalization is creating the need to increase business competitiveness. Lean manufacturing is a management philosophy oriented to the elimination of activities that do not create any type of value and are thus considered a waste. One of the main differences from other management philosophies is the shop-floor focus and the operators' involvement. Therefore, the training of all organization levels is crucial for the success of lean manufacturing. Universities should also participate actively in this process by developing students' lean management skills and promoting a better and faster integration of students into their future organizations. This paper proposes a single realistic manufacturing platform, involving production and assembly operations, to learn by playing many of the lean tools such as VSM, 5S, SMED, poke-yoke, line balance, TPM, Mizusumashi, plant layout, and JIT/kanban. This simulation game was built in tight cooperation with experienced lean companies under the international program "Lean Learning Academy,"1 and its main aim is to make bachelor and master courses in applied sciences more attractive by integrating classic lectures with a simulated production environment that could result in more motivated students and higher study yields. The simulation game results show that our approach is efficient in providing a realistic platform for the effective learning of lean principles, tools, and mindset, which can be easily included in course classes of less than two hours. © 2013 IEEE.

Abstract
In an increasingly competitive and globalized world, companies need effective training methodologies and tools for their employees. However, selecting the most suitable ones is not an easy task. It depends on the requirements of the target group (namely time restrictions), on the specificities of the contents, etc. This is typically the case for training in Lean, the waste elimination manufacturing philosophy. This paper presents and compares two different approaches to lean training methodologies and tools: a simulation game based on a single realistic manufacturing platform, involving production and assembly operations that allows learning by playing; and a digital game that helps understand lean tools. This paper shows that both tools have advantages in terms of trainee motivation and knowledge acquisition. Furthermore, they can be used in a complementary way, reinforcing the acquired knowledge. © 2013 IEEE.