Abstract
Doctoral programmes are facing several challenges in modern societies. The societal role of the University, funded by the state, requires it to: a) increase the offer and admission of third cycle students; b) to reach industry/companies expectations; c) to ensure reasonable employability prospects for the PhD candidates. With the current demography, most candidates can only find a job in industry/companies. Therefore, significant pressure is being put on doctoral programmes to include transferable skills in their curriculum. This paper presents a course "Fit for Industry?" aiming at filling this need. The course design methodology is presented in detail. It includes: a) the involvement of industry since its inception; b) the joint identification of a small number of key competencies to be addressed; c) the inclusion of assessment and feedback mechanisms in its design; d) an immersive and international dimension. It was found that the course had a profound impact on the candidates' perceptions of industry and valued by industry participants. Other stakeholders, such as PhD supervisors, also had a positive perception. The paper concludes with recommendations for those willing to replicate the course locally.

Abstract
The increasing acceptance of WiFi has created unprecedented levels of congestion in the unlicensed frequency bands, especially in densely populated areas. This results mainly because of the unmanaged interference and uncoordinated operation between WiFi access points. Radio Environment Maps (REM) have been suggested as a support for coordination strategies that optimize the overall WiFi network performance. In this context, the main objective of this experiment is to assess the benefit of a coordinated management of radio resources in dense WiFi networks at 5 GHz band, using REMs for indoor scenarios. It was shown that REMs can detect the presence of interfering links on the network or coverage holes, and a suitable coordination strategy can use this information to reconfigure Access Points (AP) channel assignment and re-establish the client connection, at a cost of diminishing the aggregate throughput of the network. The technique of AP hand-off was tested to balance the load from one AP to another. Using REMs, the Radio Resource Management (RRM) strategy could reconfigure the network to optimize the client distribution among available APs. Although the aggregate throughput is lower after load balancing, the RRM could increase the throughput of the overloaded AP. © 2018 IEEE.

Abstract
This article describes part of the development cycle of an educational robotic platform to be used as an interdisciplinary teaching tool integrated in the curriculum. We focus on the creation of the alpha and beta versions of our prototype and it’s evaluation by representative users. The SUS score of 92.5 points, Best Imaginable, show a very stable and satisfactory robotic platform, with almost no usability problems detected.

Abstract
The aim of this study was to investigate the effect chitosan (Ch) porous 3D scaffolds embedded with resolvin D1 (RvD1), an endogenous pro-resolving lipid mediator, on bone tissue healing. These scaffolds previous developed by us have demonstrated to have immunomodulatory properties namely in the modulation of the macrophage inflammatory phenotypic profile in an in vivo model of inflammation. Herein, results obtained in an in vivo rat femoral defect model demonstrated that two months after Ch+RvD1 scaffolds implantation, an increase in new bone formation, in bone trabecular thickness, and in collagen type I and Coll I/Coll III ratio were observed. These results suggest that Ch scaffolds embedded with RvD1 were able to lead to the formation of new bone with improvement of trabecular thickness. This study shows that the presence of RvD1 in the acute phase of the inflammatory response to the implanted biomaterial had a positive role in the subsequent bone tissue repair, thus demonstrating the importance of innovative approaches for the control of immune responses to biomedical implants in the design of advanced strategies for regenerative medicine. (c) 2018 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 106A: 1626-1633, 2018.

Abstract
This paper looks as initial results of research into the validity of application of selected priority rules in the development of a levelled production plan. The development of a levelled production plan is the key stage of the authors' own methodology of levelling production to mitigate the adverse impact of variable demand. A levelled production plan permits to maximize effects, defined as being able to deliver diverse products in a timely manner and at the same time reduce stocks and optimize efficient use of manufacturing resources. Application of an appropriate priority rule in the development of a levelled production plan is the key factor determining the effectiveness of the developed methodology. Initial research has been conducted for twenty automatically generated task sets. One hundred manufacturing schedules have been developed in total-five schedules for each set, according to the selected priority rules (shortest task time, longest task time, shortest processing time, longest processing time, first in first out). The schedules have been assessed in terms of meeting the selected key criteria for the objectives of levelled production.

Abstract
Machine Learning (ML) progressed significantly in the last decade, evolving the computer-based learning/prediction paradigm to a much more effective class of models known as Deep learning (DL). Since then, hyperspectral data processing relying on DL approaches is getting more popular, competing with the traditional classification techniques. In this paper, a valid ML/DL-based works applied to hyperspectral data processing is reviewed in order to get an insight regarding the approaches available for the effective meaning extraction from this type of data. Next, a general DL-based methodology focusing on hyperspectral data processing to provide farmers and winemakers effective tools for earlier threat detection is proposed.