Abstract
The automatic assessment of visual quality on images of the eye fundus is an important task in retinal image analysis. A novel quality assessment technique is proposed in this paper. We propose to compute Mean-Subtracted Contrast-Normalized (MSCN) coefficients on local spatial neighborhoods of a given image and analyze their distribution. It is known that for natural images, such distribution behaves normally, while distortions of different kinds perturb this regularity. The combination of MSCN coefficients with a simple measure of local contrast allows us to design a simple but effective retinal image quality assessment algorithm that successfully discriminates between good and low-quality images, while delivering a meaningful quality score. The proposed technique is validated on a recent database of quality-labeled retinal images, obtaining results aligned with state-of-the-art approaches at a low computational cost.

Abstract

Abstract
Accurate solar PV power forecasting can provide expected future PV output power so as to help the system operator to dispatch traditional power plants to maintain the balance between supply and demand sides. However, under non-stationary weather conditions, such as cloudy or partly cloudy days, the variability of solar irradiance makes the accurate PV power forecasting a very hard task. Ensemble forecasting based on multiple models established by different theory has been proved as an effective means on improving forecasting accuracy. Classification modeling according to different patterns could reduce the complexity and difficulty of intro-class data fitting so as to improve the forecasting accuracy as well. When combining the two above points and focusing on the different fusion pattern specifically in terms of hourly time dimension, a time-section fusion pattern classification based day-ahead solar irradiance ensemble forecasting model using mutual iterative optimization is proposed, which contains multiple forecasting models based on wavelet decomposition (WD), fusion pattern classification model, and fusion models corresponding to each fusion pattern. First, the solar irradiance is forecasted using WD based models at different WD level. Second, the fusion pattern classification recognition model is trained and then applied to recognize the different fusion pattern at each hourly time section. At last, the final forecasting result is obtained using the optimal fusion model corresponding to the data fusion pattern. In addition, a mutual iterative optimization framework for the pattern classification and data fusion models is also proposed to improve the model's performance. Simulations show that the mutual iterative optimization framework can effectively enhance the performance and coordination of pattern classification and data fusion models. The accuracy of the proposed solar irradiance day-ahead ensemble forecasting model is verified when compared with a standard Artificial Neural Network (ANN) forecasting model, five WD based models and a single ensemble forecasting model without time-section fusion classification.

Abstract
In this chapter, we outline the issue of education in the field of Operations Research (OR) and discuss various educational resources that are currently available, with a main focus on the most important international resources, but also with an emphasis on what is currently done in our home country, Portugal. The identification of shortcomings of education in OR and opportunities for its development will follow from the analysis of these resources. By choosing the word “education” over “teaching”, the aim is to stress the fact that (formal) teaching is nothing but one of the multiple aspects of education, whatever the field may be. Finally, we conclude that the dissemination and promotion of the field of OR intimately relates to issues related to education in this field. It is shown that these activities create a direct impact on the ability to attract publics into educational activities in this area, such as students enrolment on courses and programs with a high OR content. © 2018, Springer International Publishing AG.

Abstract
Electrical distribution system operators (DSOs) are facing an increasing number of challenges, largely as a result of the growing integration of distributed energy resources (DERs), such as photovoltaic (PV) and wind power. Amid global climate change and other energy-related concerns, the transformation of electrical distribution systems (EDSs) will most likely go ahead by modernizing distribution grids so that more DERs can be accommodated. Therefore, new operational strategies that aim to increase the flexibility of EDSs must be thought of and developed. This action is indispensable so that EDSs can seamlessly accommodate large amounts of intermittent renewable power. One plausible strategy that is worth considering is operating distribution systems in a meshed topology. The aim of this work is, therefore, related to the prospects of gradually adopting such a strategy. The analysis includes the additional level of flexibility that can be provided by operating distribution grids in a meshed manner, and the utilization level of variable renewable power. The distribution operational problem is formulated as a mixed integer linear programming approach in a stochastic framework. Numerical results reveal the multi-faceted benefits of operating distribution grids in a meshed manner. Such an operation scheme adds considerable flexibility to the system and leads to a more efficient utilization of variable renewable energy source (RES)-based distributed generation.

Abstract
This paper describes a two-month summer collective internship conceived to provide a unique hands-on experience in robotics. The objective of the Robotics Craftsmanship International Academy, or RobotCraft for short, is to introduce higher education students in the full design cycle of a mobile robotic platform, providing training in computer-aided design (CAD), mechatronics, low-level programming of embedded systems, high-level development using the Robot Operating System (ROS), and artificial intelligence. This non-academic teaching, which successfully completed its second edition, already encompassed around 150 students and 100 universities, being evaluated by participants as challenging, engaging, and beneficial not only to their overall understanding of robotics, but also guiding them through their future academic and professional endeavors. © 2019, Springer Nature Switzerland AG.