Abstract
Smart Transformers (STs) are being envisioned as a key element for the controllability of distribution networks in a future context of Renewable Energy Source (RES), Energy Storage System (ESS) and Electric Vehicle (EV) massification. Additionally, STs enable the deployment of hybrid AC/DC networks, which offer important advantages in this context. In addition to offering further degrees of controllability, hybrid AC/DC networks are more suited to integrate DC resources such as DC loads, PV generation, ESS and EV chargers. The purpose of the work developed in this paper is to address the feasibility of exploiting STs to actively coordinate a fleet of resources existing in a hybrid AC/DC network supplied by the ST aiming to provide active power-frequency regulation services to the upstream AC grid. The feasibility of the ST to coordinate the resources available in the hybrid distribution AC/DC network in order to provide active power-frequency regulation services is demonstrated in this paper through computational simulation. It is demonstrated that the aforementioned goal can be achieved using droop-based controllers that can modulate controlled variables in the ST.

Abstract
We present FGPE AuthorKit, a tool to author programming exercises featuring gamification elements that provide additional motivation for the students to intensify their learning effort. The tool allows the (1) creation of exercises and their associated metadata, (2) selection and parameterization of adequate gamification techniques for a specific exercise or their collection, (3) design of the content structure and sequencing rules, and (4) importing and exporting the content in the formats of choice. © 2020 ACM.

Abstract
Diabetic macular edema is a leading cause of visual loss for patients with diabetes. While diagnosis can only be performed by optical coherence tomography, diabetic macular edema risk assessment is often performed in eye fundus images in screening scenarios through the detection of hard exudates. Such screening scenarios are often associated with large amounts of data, high costs and high burden on specialists, motivating then the development of methodologies for automatic diabetic macular edema risk prediction. Nevertheless, significant dataset domain bias, due to different acquisition equipment, protocols and/or different populations can have significantly detrimental impact on the performance of automatic methods when transitioning to a new dataset, center or scenario. As such, in this study, a method based on residual neural networks is proposed for the classification of diabetic macular edema risk. This method is then validated across multiple public datasets, simulating the deployment in a multi-center setting and thereby studying the method’s generalization capability and existing dataset domain bias. Furthermore, the method is tested on a private dataset which more closely represents a realistic screening scenario. An average area under the curve across all public datasets of 0.891 ± 0.013 was obtained with a ResNet50 architecture trained on a limited amount of images from a single public dataset (IDRiD). It is also shown that screening scenarios are significantly more challenging and that training across multiple datasets leads to an improvement of performance (area under the curve of 0.911 ± 0.009). © Springer Nature Switzerland AG 2020.

Abstract
The adoption of sensors-based monitoring systems supported by Internet of Things technologies has opened new possibilities for data retrieving and processing in urban areas. Among such possibilities, emergencies management is expected to play an important role in how modern cities will evolve, reducing the negative impacts of critical events and improving the quality of life perceived by their inhabitants. Actually, when an emergency is detected and alerted, emergency vehicles, notably ambulances, fire trucks, police cars and transit agents vehicles, should be quickly assigned to respond to that situation, as soon as possible. In this context, we propose a dynamic algorithm to automatically assign emergency vehicles in smart city scenarios, exploiting for that a sensors-based emergency detection system to provide emergency alerts. The proposed algorithm can then be used to quickly assign a number of emergency vehicles in the first moments of an emergency, which can potentially save lives and improve existing crisis management applications in smart cities. © 2020 IEEE.

Abstract
Currently deployed in a wide variety of applicational scenarios, wireless sensor networks (WSNs) are typically a resource-constrained infrastructure. Consequently, characteristics such as WSN adaptability, low-overhead, and low-energy consumption are particularly relevant in dynamic and autonomous sensing environments where the measuring requirements change and human intervention is not viable. To tackle this issue, this article proposes e-LiteSense as an adaptive, energy-aware sensing solution for WSNs, capable of auto-regulate how data are sensed, adjusting it to each applicational scenario. The proposed adaptive scheme is able to maintain the sensing accuracy of the physical phenomena, while reducing the overall process overhead. In this way, the adaptive algorithm relies on low-complexity rules to establish the sensing frequency weighting the recent drifts of the physical parameter and the levels of remaining energy in the sensor. Using datasets from WSN operational scenarios, we prove e-LiteSense effectiveness in self-regulating data sensing accurately through a low-overhead process where the WSN energy levels are preserved. This constitutes a step-forward for implementing self-adaptive energy-aware data sensing in dynamic WSN environments.

Abstract
With the growing concern about decreasing CO2 emissions, renewable energy sources are being vastly integrated in the energy systems worldwide. This will bring new challenges to the network operators, which will need to find sources of flexibility to cope with the variable-output nature of these technologies. Demand response and multi-energy systems are being widely studied and considered as a promising solution to mitigate possible problems that may occur in the energy systems due to the large-scale integration of renewables. In this work, an optimal model to manage the resources and loads within residential and commercial buildings was developed, considering consumers preferences, electrical network restrictions and CO2 emissions. The flexibility that these buildings can provide was analyzed and quantified. Additionally, it was shown how this model can be used to solve technical problems in electrical networks, comparing the performance of two scenarios of flexibility provision: flexibility obtained only from electrical loads vs. flexibility obtained from multi-energy loads. It was proved that multi-energy systems bring more options of flexibility, as they can rely on non-electrical resources to supply the same energy needs and thus relieve the electrical network. It was also found that commercial buildings can offer more flexibility during the day, while residential buildings can offer more during the morning and evening. Nonetheless, Multi-Energy System (MES) buildings end up having higher CO2 emissions due to a higher consumption of natural gas.