Abstract
In this paper, the integration of a solar power plant to an electric vehicle (EV) Parking Lot is analyzed in terms of reduction of power consumption and losses for various scenarios and operating conditions in a distribution system. The parking lot is designed for EVs and is fed by both grid and roof mounted photovoltaic (PV) panels. The energy management system is designed for charging EVs for various scenarios combined with solar radiation data varying during daytime and the seasons. The energy transactions are simulated in accordance with daytime Solar Power Plant (SPP) generation and EV energy response on Electrical Power System Analysis Software (ETAP) environment. EVs based power consumption is calculated by considering the variation of charging sequences of different car brands. The study represents the results for daily change of power consumption for summer and winter conditions along with the reduction of power consumption, reduction of power losses, decreased main feeder ampacity, restoration of voltage level during SPP operation, and comparison of different scenarios and operation sequences. Finally, the effect of the SPP is presented in terms of reducing the peak and continuous power demand of the parking lot for various EV operations.

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Abstract
Traditional image segmentation algorithms operate by iteratively working over an image, as if refining a segmentation until a stopping criterion is met. Deep learning has replaced traditional approaches, achieving state-of-the-art performance in many problems, one of them being image segmentation. However, the concept of segmentation refinement is not present anymore, since usually the images are segmented in a single step. This work focuses on the refinement of image segmentations using deep convolutional neural networks, with the addition of a quality prediction output. The output from a state-of-the-art base segmenter is refined, simultaneously improving it and trying to predict its quality. We show that the quality concept can be used as a regularizer while training a network for direct segmentation refinement.

Abstract
Assuring the quality control of publications in the scientific literature is one of the main challenges of the peer review process. Consequently, there has been an increasing demand for computing solutions that will help to maintain the quality of this process. Recently, the use of Artificial Intelligence techniques has been highlighted, applied in the detection of plagiarism, bias, among other functions. The assessment of the reviewer's review has also been considered as important in the process, but, little is known about it, for instance, which techniques have been applied in this assessment or which criteria have been assessed. Therefore, this systematic literature review aims to find evidence regarding the computational approaches that have been used to evaluate reviewers' reports. In order to achieve this, five online databases were selected, from which 72 articles were identified that ma the inclusion criteria of this review, all of which have been published since 2000. The result returned 10 relevant studies meeting the evaluation requirements of scientific article reviews. The review revealed that mechanisms to rank review reports according to a score, as well as the word analysis, are the most common tools, and that there is no consensus on quality criteria. The systematic literature review has shown that reviewers' report assessment is a valid tool for maintaining quality throughout the process. However, it still needs to be further developed if it is to be used as a resource which surpass a single conference or journal, making the peer review process more rigorous and less based on random choice.

Abstract
Scientists spend huge efforts ensuring the highest standards of their research but fail in efforts to spread their achievements. They face time and effort constraints as they have to accomplish a myriad of tasks in their daily routines. If researchers on different disciplines have trouble communicating with each other around a specific topic of mutual interest, then cross-field collaborations will be problematic. Graphical Abstracts (GAs) are a specific type of infographics that summarize in one image what a research paper is about. Such visual communication tools convey facts, ideas and relationships more clearly and faster than written language. The usage and quality of GAs being produced for research papers, as well as the perception of their impact as a communication tool, is a growing topic of debate. We used i3S - one of the biggest biomedical research institutes in Europe - as a case study to build knowledge on that. A total of 994 scientific articles were analyzed, and 19% of the publications contain GAs while 26% neglected their use. With regard to graphic design principles, misuse of color is often encountered, as well as basic errors in space arrangement of the constituent elements of the graphic composition. Interestingly, it was observed that articles that use GAs have highest impact in terms of citations and readers but not in terms of tweets. In conclusion, the use of GAs falls short of their maximum potential as an inclusive and effective tool for communication between scientists.