Abstract
One of the main goals of any power grid is sustainability. The given study proposes a new method, which aims to reduce users' anxiety especially at slow charging stations and improve the smart charging model to increase the benefits for the electric vehicles' owners, which in turn will increase the grid stability. The issue under consideration is modelled as an optimisation problem to minimise the cost of charging. This approach levels the load effectively throughout the day by providing power to charge EVs' batteries during the off-peak hours and drawing it from the EVs' batteries during peak-demand hours of the day. In order to minimise the costs associated with EVs' charging in the given optimisation problem, an improved version of an intelligent algorithm is developed. In order to evaluate the effectiveness of the proposed technique, it is implemented on several standard models with various loads, as well as compared with other optimisation methods. The superiority and efficiency of the proposed method are demonstrated, by analysing the obtained results and comparing them with the ones produced by the competitor techniques.

Abstract
[No abstract available]

Abstract
Background: The human hand is the part of the body most frequently injured in work related accidents, accounting for a third of all accidents at work and often involving surgery and long periods of rehabilitation. Several applications of Augmented Reality (AR) and Virtual Reality (VR) have been used to improve the rehabilitation process. However, there is no sound evidence about the effectiveness of such applications nor the main drivers of therapeutic success. Objectives: The objective of this study was to review the efficacy of AR and VR interventions for hand rehabilitation. Methods: A systematic search of publications was conducted in October 2019 in IEEE Xplore, Web of Science, Cochrane library, and PubMed databases. Search terms were: (1) video game or videogame, (2) hand, (3) rehabilitation or therapy and (4) VR or AR. Articles were included if (1) were written in English, (2) were about VR or AR applications, (3) were for hand rehabilitation, (4) the intervention had tests on at least ten patients with injuries or diseases which affected hand function and (5) the intervention had baseline or intergroup comparisons (AR or VR intervention group versus conventional physical therapy group). PRISMA protocol guidelines were followed to filter and assess the articles. Results: From the eight selected works, six showed improvements in the intervention group, and two no statistical differences between groups. We were able to identify motivators of patients’ adherence, namely real-time feedback to the patients, challenge, and increased individualized difficulty. Automated tracking, easy integration in the home setting and the recording of accurate metrics may increase the scalability and facilitate healthcare professionals’ assessments. Conclusions: This systematic review provided advantages and drivers for the success of AR/VR application for hand rehabilitation. The available evidence suggests that patients can benefit from the use of AR or VR interventions for hand rehabilitation. © 2020 Elsevier Inc.

Abstract
Unmanned aerial vehicles (UAVs) have become popular in recent years and are now used in a wide variety of applications. This is the logical result of certain technological developments that occurred over the last two decades, allowing UAVs to be equipped with different types of sensors that can provide high-resolution data at relatively low prices. However, despite the success and extraordinary results achieved by the use of UAVs, traditional remote sensing platforms such as satellites continue to develop as well. Nowadays, satellites use sophisticated sensors providing data with increasingly improving spatial, temporal and radiometric resolutions. This is the case for the Sentinel-2 observation mission from the Copernicus Programme, which systematically acquires optical imagery at high spatial resolutions, with a revisiting period of five days. It therefore makes sense to think that, in some applications, satellite data may be used instead of UAV data, with all the associated benefits (extended coverage without the need to visit the area). In this study, Sentinel-2 time series data performances were evaluated in comparison with high-resolution UAV-based data, in an area affected by a fire, in 2017. Given the 10-m resolution of Sentinel-2 images, different spatial resolutions of the UAV-based data (0.25, 5 and 10 m) were used and compared to determine their similarities. The achieved results demonstrate the effectiveness of satellite data for post-fire monitoring, even at a local scale, as more cost-effective than UAV data. The Sentinel-2 results present a similar behavior to the UAV-based data for assessing burned areas.

Abstract
As a response to rapidly increasing penetration of wind power generation in modern electric power grids, accurate prediction models are crucial to deal with the associated uncertainties. Due to the highly volatile and chaotic nature of wind power, employing complex intelligent prediction tools is necessary. Accordingly, this article proposes a novel improved version of empirical mode decomposition (IEMD) to decompose wind measurements. The decomposed signal is provided as input to a hybrid forecasting model built on a bagging neural network (BaNN) combined with K-means clustering. Moreover, a new intelligent optimization method named ChB-SSO is applied to automatically tune the BaNN parameters. The performance of the proposed forecasting framework is tested using different seasonal subsets of real-world wind farm case studies (Alberta and Sotavento) through a comprehensive comparative analysis against other well-known prediction strategies. Furthermore, to analyze the effectiveness of the proposed framework, different forecast horizons have been considered in different test cases. Several error assessment criteria were used and the obtained results demonstrate the superiority of the proposed method for wind forecasting compared to other methods for all test cases.

Abstract
Efficient, large-scale genomic analysis is facilitated on the cloud by a computational tool with error-diagnosing and self-healing capabilities. We present Butler, a computational tool that facilitates large-scale genomic analyses on public and academic clouds. Butler includes innovative anomaly detection and self-healing functions that improve the efficiency of data processing and analysis by 43% compared with current approaches. Butler enabled processing of a 725-terabyte cancer genome dataset from the Pan-Cancer Analysis of Whole Genomes (PCAWG) project in a time-efficient and uniform manner.