Abstract
Following up the proposal of (Klimentova, Viana, Pedroso and Santos 2019), we consider the usage of a compensation scheme for multi-country kidney exchange programmes to balance out the benefits of cooperation. The novelty of our study is to base the target solution on the Shapley value of the corresponding TU-game, rather than on marginal contributions. We compare the long term performances of the above two fairness concepts by conducting simulations on realistically generated kidney exchange pools. © ECMS Mike Steglich, Christian Mueller, Gaby Neumann, Mathias Walther (Editors).

Abstract
The adoption of emergency alerting systems can bring countless benefits when managing urban areas, industrial plants, farms, roads and virtually any area that is subject to the occurrence of critical events, supporting in rescue operations and reducing their negative impacts. For such systems, a promising approach is to exploit scalar sensors to detect events of interest, allowing for the distributed monitoring of different variables. However, the use of cameras as visual sensors can enhance the detection of critical events, which can be employed along with scalar sensors for a more comprehensive perception of the environment. Although the particularities of visual sensing may be challenging in some scenarios, the combination of scalar and visual sensors for the early detection of emergency situations can be valuable for many scenarios, such as smart cities and industry 4.0, bringing promising results. Therefore, in this article, we extend a sensors-based emergency detection and alerting system to also exploit visual monitoring when identifying critical events. Implementation and experimental details are provided to reinforce the use of cameras as a relevant sensor unit, bringing promising results for emergencies management.

Abstract
Over the past decades, software engineering has reached a level of maturity which entails great challenges in its education. Universities must prepare students to real-life challenges by offering courses to aid students in developing several vital skills which go beyond hard skills (e.g., communication skills and self-management). At the Faculty of Engineering of the University of Porto, a pioneering course, dubbed Project Management Laboratory, offers the proper environment for students to develop such skills by inviting industry to be closely involved in the education of the students. This course integrates practice and theory in a setting close to what the students will face when they move into industry. This paper reports on the experience, results, and benefits of this innovative course.

Abstract
With the increasing technological advances that mark society daily, it is essential to analyze, not only the behavior of consumers, but also the behavior of companies facing market changes. It is necessary to distinguish a company that achieves success from one that is not able to succeed, over time. Nokia was the mobile phone market leader for about fourteen years. The launch of the iPhone, by Apple, in 2007, was one of the main reasons for the loss of that leadership. Nokia and its handset division were not able to adapt and demonstrate having dynamic capabilities. They had an excessive focus on their outdated product (their dumbphone), while not taking advantage of their internal innovative product developments to anticipate inevitable change in the market. To provide more depth to the present study a survey was performed and answered by 120 people.

Abstract
This paper addresses Visual Odometry (VO) estimation in challenging underwater scenarios. Robot visual-based navigation faces several additional difficulties in the underwater context, which severely hinder both its robustness and the possibility for persistent autonomy in underwater mobile robots using visual perception capabilities. In this work, some of the most renown VO and Visual Simultaneous Localization and Mapping (v-SLAM) frameworks are tested on underwater complex environments, assessing the extent to which they are able to perform accurately and reliably on robotic operational mission scenarios. The fundamental issue of precision, reliability and robustness to multiple different operational scenarios, coupled with the rise in predominance of Deep Learning architectures in several Computer Vision application domains, has prompted a great a volume of recent research concerning Deep Learning architectures tailored for visual odometry estimation. In this work, the performance and accuracy of Deep Learning methods on the underwater context is also benchmarked and compared to classical methods. Additionally, an extension of current work is proposed, in the form of a visual-inertial sensor fusion network aimed at correcting visual odometry estimate drift. Anchored on a inertial supervision learning scheme, our network managed to improve upon trajectory estimates, producing both metrically better estimates as well as more visually consistent trajectory shape mimicking.

Abstract
This study proposes a stochastic optimisation programming for scheduling a microgrid (MG) considering multiple energy devices and the uncertain nature of renewable energy resources and parking lot-based electric vehicles (EVs). Both thermal and electrical features of the multi-energy system are modelled by considering combined heat and power generation, thermal energy storage, and auxiliary boilers. Also, price-based and incentive-based demand response (DR) programs are modelled in the proposed multi-energy MG to manage a commercial complex including hospital, supermarket, strip mall, hotel and offices. Moreover, a linearised AC power flow is utilised to model the distribution system, including EVs. The feasibility of the proposed model is studied on a system based on real data of a commercial complex, and the integration of DR and EVs with multiple energy devices in an MG is investigated. The numerical studies show the high impact of EVs on the operation of the multi-energy MGs.