Abstract
Most path planning algorithms used presently in multi-robot systems are based on offline planning. The Timed Enhanced A* (TEA*) algorithm gives the possibility of planning in real time, rather than planning in advance, by using a temporal estimation of the robot's positions at any given time. In this article, the implementation of a control system for multi-robot applications that operate in environments where communication faults can occur and where entire sections of the environment may not have any connection to the communication network will be presented. This system uses the TEA* to plan multiple robot paths and a supervision system to control communications. The supervision system supervises the communication with the robots and checks whether the robot's movements are synchronized. The implemented system allowed the creation and execution of paths for the robots that were both safe and kept the temporal efficiency of the TEA* algorithm. Using the Simtwo2020 simulation software, capable of simulating movement dynamics and the Lazarus development environment, it was possible to simulate the execution of several different missions by the implemented system and analyze their results.

Abstract
Adjuvant systemic therapies are now routinely used following resection of stage III melanoma, however accurate prognostic information is needed to better stratify patients. We use differential expression analyses of primary tumours from 204 RNA-sequenced melanomas within a large adjuvant trial, identifying a 121 metastasis-associated gene signature. This signature strongly associated with progression-free (HR=1.63, p=5.24 x 10(-5)) and overall survival (HR=1.61, p=1.67 x 10(-4)), was validated in 175 regional lymph nodes metastasis as well as two externally ascertained datasets. The machine learning classification models trained using the signature genes performed significantly better in predicting metastases than models trained with clinical covariates (p(AUROC) = 7.03 x 10(-4)), or published prognostic signatures (p(AUROC) < 0.05). The signature score negatively correlated with measures of immune cell infiltration (=-0.75, p<2.2 x 10(-16)), with a higher score representing reduced lymphocyte infiltration and a higher 5-year risk of death in stage II melanoma. Our expression signature identifies melanoma patients at higher risk of metastases and warrants further evaluation in adjuvant clinical trials. The identification of prognostic biomarkers can help stratify cancer patients. Here, the authors apply deep RNA sequencing from primary melanomas coupled with long-term clinical outcome data from a prospective multicentre phase III trial, to develop and validate a 121 metastasis-associated gene signature identifying early-stage melanoma patients at higher risk of metastasis and worse survival.

Abstract
While living in a digital era, both teachers and students of Engineering Courses were not ready for the drastic change associated with the Covid-19 first confinement (March 2020). This forced change from a presential mode to a fully on-line mode provided teaching/leaning difficulties as well as new opportunities. Moreover, as most engineering courses require laboratory practice, on-line teaching raised additional challenges. This paper reports two different experiences in two different Control Engineering university courses in the North of Portugal. The goal is to share some learning tools that are particularly relevant in the pandemic time we are living: pocket-sized laboratory kits that students can easily take home and experience real-world control contents; an open Mural that can serve as an exchange of knowledge. Perceptions received both from students and lecturers regarding these two experiments are presented.

Abstract
The traditional task of locating suspects using forensic sketches posted on public spaces, news, and social media can be a difficult task. Recent methods that use computer vision to improve this process present limitations, as they either do not use end-to-end networks for sketch recognition in police databases (which generally improve performance) or/and do not offer a photo-realistic representation of the sketch that could be used as alternative if the automatic matching process fails. This paper proposes a method that combines these two properties, using a conditional generative adversarial network (cGAN) and a pre-trained face recognition network that are jointly optimised as an end-to-end model. While the model can identify a short list of potential suspects in a given database, the cGAN offers an intermediate realistic face representation to support an alternative manual matching process. Evaluation on sketch-photo pairs from the CUFS, CUFSF and CelebA databases reveal the proposed method outperforms the state-of-the-art in most tasks, and that forcing an intermediate photo-realistic representation only results in a small performance decrease.

Abstract
This research examines the relationship of Export Promotion Programs (EPPs), sponsored by a Portuguese Regional Association for Development on Trade and Industry, with firms' export performance. To investigate this, two models were applied using panel data analyses that relate export performance to variables such as participation in EPPs, age and size of firms. Data on 198 firms for the sampling period 2010 and 2018 was drawn from a Portuguese Regional Development Association and SABI (Iberian Balance sheet Analysis System) database. The results show that participation in EPPs does have a positive influence on the export performance of firms, especially for those firms with previous export experience, demonstrating the importance of using these programs. Findings also document that firm size relates positively export performance, whereas firms age show a negative effect on export performance, suggesting that larger firms with more resources positively influence exports and an increase in firms' age may increase their cultural inertia, inability to change strategies, decreasing their export performance, respectively. Finally, the participation in EPPs seems not to influence export performance for firms without previous export experience. The research contributes to the literature providing evidence on the role of EPP on firms' export performance behavior promoted by regional development associations.

Abstract
The actual learning process in a school, college or university should take full advantage of the digital transformation. Computers, mobile phones, tablets or other electronic devices can be used in learning environments to improve learning experience and students performance. However, in a university campus, there are some activities where the use of connected devices, might be discouraged or even forbidden. Students should be discouraged to use their own devices in classes where they may become alienated or when their devices may cause any disturbance. Ultimately, their own devices should be forbidden in activities such as closed-book exams. This paper proposes a system architecture to detect or block unwanted wireless signals by students' mobile phones in a classroom. This architecture focuses on specific wireless signals from Wi-Fi and Bluetooth interfaces, and it is based on Software-Defined Radio (SDR) modules and a set of antennas with two configuration modes: detection mode and blocking mode. When in the detection mode, the architecture processes signals from the antennas, detects if there is any signal from Wi-Fi or Bluetooth interfaces and infers a position of the unwanted mobile device. In the blocking mode, the architecture generates noise in the same frequency range of Wi-Fi or Bluetooth interfaces, blocking any possible connection. The proposed architecture is designed to be used by professors to detect or block unwanted wireless signals from student devices when supervising closed-book exams, during specific periods of time. © Daniel Barros, Paulo Barros, Emanuel Lomba, Vítor Ferreira, and Pedro Pinto; licensed under Creative Commons License CC-BY 4.0 Second International Computer Programming Education Conference (ICPEC 2021).