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

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.

Abstract
The plug-in hybrid electric vehicles (PHEVs) integration into the electrical network introduces new challenges and opportunities for operators and PHEV owners. On the one hand, PHEVs can decrease environmental pollution. On the other hand, the high penetration of PHEVs in the network without charging management causes harmonics, voltage instability, and increased network problems. In this study, a charging management algorithm is presented to minimize the total cost and flatten the demand curve. The behavior of the PHEV owner in terms of arrival time and leaving time is modeled with a stochastic distribution function. The battery model and hourly power consumption of PHEV are modeled, and the obtained models are applied to determine the battery's state of charge. The proposed method is tested on a sample demand curve with and without a charging management algorithm to verify the efficiency. The results verify the efficiency of the proposed method in decreasing the total cost using the management algorithm for PHEVs, especially when the PHEVs sell the electricity to the network.