Abstract
This paper presents CoopREP, a system that provides support for fault replication of concurrent programs based on cooperative recording and partial log combination. CoopREP uses partial logging to reduce the amount of information that a given program instance is required to store to support deterministic replay. This allows reducing substantially the overhead imposed by the instrumentation of the code, but raises the problem of finding a combination of logs capable of replaying the fault. CoopREP tackles this issue by introducing several innovative statistical analysis techniques aimed at guiding the search of the partial logs to be combined and needed for the replay phase. CoopREP has been evaluated using both standard benchmarks for multithreaded applications and real-world applications. The results highlight that CoopREP can successfully replay concurrency bugs involving tens of thousands of memory accesses, while reducing recording overhead with respect to state-of-the-art noncooperative logging schemes by up to 13x (and by 2.4x on average).

Abstract
We propose a deep learning-based pipeline that, given a low-dose computed tomography of a patient chest, recommends if a patient should be submitted to further lung cancer assessment. The algorithm is composed of a nodule detection block that uses the object detection framework YOLOv2, followed by a U-Net based segmentation. The found structures of interest are then characterized in terms of diameter and texture to produce a final referral recommendation according to the National Lung Screen Trial (NLST) criteria. Our method is trained using the public LUNA16 and LIDC-IDRI datasets and tested on an independent dataset composed of 500 scans from the Kaggle DSB 2017 challenge. The proposed system achieves a patient-wise recall of 89% while providing an explanation to the referral decision and thus may serve as a second opinion tool to speed-up and improve lung cancer screening.

Abstract
Motivating undergraduate engineering students for the area of control engineering can be a challenging task. Posicast control can be used as a simple technique to introduce both open-loop and closed-loop control systems. This paper addresses several approaches to teach Posicast control involving simulation and practical implementations. A demonstration experiment using the robotic arm (UR5) is reported here as an alternative practical system which can be used to demonstrate Posicast Control. Results obtained from student's perceptions of the reported experiment are presented.

Abstract
The use of energy from renewable sources is one of the major concerns of today’s society. In recent years, the European Union has been changing legislation and implementing policies aimed at promoting its investment and encouraging its use in order to reduce the emission of greenhouse gases [1].

Abstract
Exposing electric vehicles (EV) to extreme temperatures limits its performance and charging. For the foreseen adoption of EVs, it is not only important to study the technology behind it, but also the environment it will be inserted into. In Europe, temperatures ranging from -30°C to +40°C are frequently observed and the impacts on batteries are well-known. However, the impact on the grid due to the performance of fast-chargers, under such conditions, also requires analysis, as it impacts both on the infrastructure's dimensioning and design. In this study, six different fast-chargers were analysed while charging a full battery EV, under four temperature levels (-25 °C, -15 °C, +20 °C, and +40 °C). The current total harmonic distortion, power factor, standby power, and unbalance were registered. Results show that the current total harmonic distortion (THDI) tended to increase at lower temperatures. The standby consumption showed no trend, with results ranging from 210 VA to 1650 VA. Three out of six chargers lost interoperability at -25 °C. Such non-linear loads, present high harmonic distortion, and, hence, low power factor. The temperature at which the vehicle's battery charges is crucial to the current it withdraws, thereby, influencing the charger's performance.

Abstract
Usually, Aspect-Oriented Programming (AOP) languages are an extension of a specific target programming language (e.g., Aspect J for JAVA and Aspect C++ for C++). Although providing AOP support with target language extensions may ease the adoption of an approach, it may impose constraints related with constructs and semantics. Furthermore, by tightly coupling the AOP language to the target language the reuse potential of many aspects, especially the ones regarding non-functional requirements, is lost. LARA is a domain-specific language inspired by AOP concepts, having the specification of source-to-source transformations as one of its main goals. LARA has been designed to be, as much as possible, independent of the target language and to provide constructs and semantics that ease the definition of concerns, especially related to non-functional requirements. In this paper, we propose techniques to overcome some of the challenges presented by a multilanguage approach to AOP of cross-cutting concerns focused on non-functional requirements and applied through the use of a weaving process. The techniques mainly focus on providing well-defined library interfaces that can have concrete implementations for each supported target language. The developer uses an agnostic interface and the weaver provides a specific implementation for the target language. We evaluate our approach using 8 concerns with varying levels of language agnosticism that support 4 target languages (C, C++, JAVA and MATLAB) and show that the proposed techniques contribute to more concise LARA aspects, high reuse of aspects, and to significant effort reductions when developing weavers for new imperative, object-oriented programming languages.