In a healthcare context, the success of a fire safety procedure in a real-life emergency mainly depends on staff decisions and actions. One of the factors influencing staff decision-making is their training. In most healthcare facilities, safety educators use slide-based lectures as a training tool. Virtual Reality (VR) is gaining fire safety community attention for being an interesting training tool. However, few studies have assessed the effectiveness of VR-based fire safety training simulators compared with a slide-based lecture. The present research proposes a novel non-immersive VR-based training for healthcare fire safety education. This paper describes the prototyping steps required to develop a nonimmersive VR serious game (SG) to train the staff of Vincent Van Gogh (VVG) hospital in Belgium.The paper finally validates the VR SG comparing its effectiveness against slide-based lecture training. 78 staff from VVG hospital in Belgium participated in this study. They were divided into two groups: Group A was trained using a slide-based lecture, and Group B was trained using the VR SG. The results indicated that the VR SG was more effective than the slide-based lecture in terms of knowledge acquisition and retention and in terms of self-efficacy increment in short and long terms than the slidebased lecture.

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Assessing the fire safety of buildings is fundamental to reduce the impact of this threat on their occupants. Such an assessment can be done by combining existing models and existing knowledge on how occupants behave during fires. Although many studies have been carried out for several types of built environment, only few of those investigate healthcare facilities and hospitals. In this study, we present a new behavioural data-set for hospital evacuations. The data was collected from the North Shore Hospital in Auckland (NZ) during an unannounced drill carried out in May 2017. This drill was recorded using CCTV and those videos are analysed to generate new evacuation model inputs for hospital scenarios. We collected pre-movement times, exit choices and total evacuation times for each evacuee. Moreover, we estimated pre-movement time distributions for both staff members and patients. Finally, we qualitatively investigated the evacuee actions of patients and staff members to study their interaction during the drill. The results show that participants were often independent from staff actions with a majority able to make their own decision.

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The design step of multibody systems requires in some specific cases an optimization process, in order to determine the set of parameters which lead to optimal kinematic or dynamic performances. The aim of this paper is to propose an optimal design method adapted to general multibody systems and submitted to kinematic and/or dynamic time-dependent criteria. The optimization process is based on stochastic techniques referred to genetic algorithms, which are inspired from natural evolution and can often overtop classical optimization methods when applied to practical problems. Illustrative examples are given in the context of the optimization of the kinematics of a motorcar suspension and the lateral dynamics of an urban railway vehicle.