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Facilities planning tools have been used by project managers for planning industry spaces for decades now, but applications in creative organizations are still sparse. This happens mainly because of a gap that exists between engineering and psychology fields, with the first developing planning tools for production but with little concern for creativity and the other developing spaces for creativity, but with a lack of tools. Both try to solve the same issue: fostering productivity in the workplace, but they need to be linked first, in order to accomplish it. How to close this gap? Can a project manager plan the facilities for a creative organizational in the best way possible? In this paper, a new approach is used for studying the design of physical environments that foster organizational creativity, combining the results of psychological studies on the impact of physical environment on creativity with the facilities planning body of knowledge applied by industrial engineers. In order to test the results, a single case study is developed in a small IT consulting firm. By using the systematic layout planning (SLP) step by step process, it is shown that by acting on the work environment of the company, the developers creative processes can be boosted and facilitated. It is also shown that both industrial engineering and creativity research have much to contribute to each other and future research topics in the field are presented.

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Tuberculosis (TB) is a contagious disease which is among the top 10 causes of death in the world. In order to eliminate the disease by 2050, the treatment of TB infection (TBI) is essential, which requires radiological reports to exclude active tuberculosis. The automatic X-ray classifiers used today are based on models that do not guarantee the retention of knowledge if they need to learn new tasks over time. This work proposes the introduction of the lifelong machine learning (LML) paradigm in automatic X-ray classifiers aimed at helping to diagnose active TB (ATB). Two LML algorithms, Efficient Lifelong Learning Algorithm (ELLA) and Learning without Forgetting (LwF), are applied to the TB and pneumonia classification tasks. The results show that it is possible to keep the performance in both tasks with the LML paradigm.