Abstract
Soil erosion is a severe and complex issue in the agriculture area. The main objective of this study was to assess the soil loss in two regions, testing different methodologies and combining different factors of the Revised Universal Soil Loss Equation (RUSLE) based on Geographical Information Systems (GIS). To provide the methodologies to other users, a GIS open-source application was developed. The RUSLE equation was applied with the variation of some factors that compose it, namely the slope length and slope steepness (LS) factor and practices factor (P), but also with the use of different sources of information. Eight different erosion models (M1 to M8) were applied to the two regions with different ecological conditions: Montalegre (rainy-mountainous) and Alentejo (dry-flat), both in Portugal, to compare them and to evaluate the soil loss for 3 potential erosion levels: 0-25, 25-50 and >50 ton/ha center dot year. Regarding the methodologies, in both regions the behavior is similar, indicating that the M5 and M6 methodologies can be more conservative than the others (M1, M2, M3, M4 and M8), which present very consistent values in all classes of soil loss and for both regions. All methodologies were implemented in a GIS application, which is free and available under QGIS software.

Abstract
Epilepsy is a major neurological disorder affecting approximately 1% of the world population, where seizure semiology is an essential tool for clinical evaluation of seizures. This includes qualitative visual inspection of videos from the seizures in epilepsy monitoring units by epileptologists. In order to support this clinical diagnosis process, promising deep learning-based systems were proposed. However, these indicate that video datasets of epileptic seizures are still rare and limited in size. In order to enable the full potential of AI systems for epileptic seizure diagnosis support and research, a novel collaborative development framework is proposed for a scalable DL-assisted clinical research and diagnosis support of epileptic seizures. The designed cloud-based approach integrates our deployed and tested NeuroKinect data acquisition pipeline into an MLOps framework to scale data set extension and analysis to a multi-clinical utilization. The proposed development framework incorporates an MLOps approach, to ensure convenient collaboration between clinicians and data scientists, providing continuous advantages to both user groups. It addresses methods for efficient utilization of HW, SW and human resources. In the future, the system is going to be expanded with several AI-based tools. Such as DL-based automated 3D motion capture (MoCap), 3D movement analysis support, quantitative seizure semiology analysis tools, video-based MOI and seizure classification. © 2021 IEEE

Abstract
In this work, we propose a novel algorithm for heart sound segmentation. The proposed approach is based on the combination of two families of state-of-the-art solutions for such problem, hidden Markov models and deep neural networks, in a single training framework. The proposed approach is tested with heart sounds from the PhysioNet dataset and it is shown to achieve an average sensitivity of 93.9% and an average positive predictive value of 94.2% in detecting the boundaries of fundamental heart sounds.

Abstract
This paper investigates the issue of frequency regulation of a single-area alternating current (AC) power system connected to an electric vehicle (EV) aggregator through a nonideal communication network. It is assumed that the command control action is received by the EV aggregator with constant delay and the power system experiences uncertain parameters. A novel effective iterative algorithm, direct search, is proposed for the time-delayed system to design the gains of a proportional-integral (PI) controller. The proposed direct search algorithm can find a feasible solution whenever at least one solution lays in the space search. Thus, by choosing a wide space search, we can expect that the PI controller assures the closed-loop stability, theoretically. The proposed approach has low conservative results over the existing approaches. For the uncertain time-delayed system, a robust PI controller is designed, which is resilient against the system uncertainties and time delay. Numerical simulations are carried out to show the merits of the developed controller.

Abstract
We report the genome sequence of Xanthomonas arboricola pv. juglandis strain CPBF 427, which was isolated from early-season buds of a diseased walnut tree, suggesting overwinter potential. This study provides a consistent genomic reference for this pathovar and may contribute to addressing the overwinter survival of these walnut pathogens.

Abstract
The evolution of the electrical power sector due to the advances in digitalization, decarbonization and decentralization has led to the increase in challenges within the current distribution network. Therefore, there is an increased need to analyze the impact of the smart grid and its implemented solutions in order to address these challenges at the earliest stage, i.e., during the pilot phase and before large-scale deployment and mass adoption. Therefore, this paper presents the scalability and replicability analysis conducted within the European project InteGrid. Within the project, innovative solutions are proposed and tested in real demonstration sites (Portugal, Slovenia, and Sweden) to enable the DSO as a market facilitator and to assess the impact of the scalability and replicability of these solutions when integrated into the network. The analysis presents a total of three clusters where the impact of several integrated smart tools is analyzed alongside future large scale scenarios. These large scale scenarios envision significant penetration of distributed energy resources, increased network dimensions, large pools of flexibility, and prosumers. The replicability is analyzed through different types of networks, locations (country-wise), or time (daily). In addition, a simple replication path based on a step by step approach is proposed as a guideline to replicate the smart functions associated with each of the clusters.