Abstract
The recent report of distinct Xanthomonas lineages of Xanthomonas arboricola pv. juglandis and Xanthomonas euroxanthea within the same walnut tree revealed that this consortium of walnut-associated Xanthomonas includes both pathogenic and nonpathogenic strains. As the implications of this co-colonization are still poorly understood, in order to unveil niche-specific adaptations, the genomes of three X. euroxanthea strains (CPBF 367, CPBF 424(T), and CPBF 426) and of an X. arboricola pv. juglandis strain (CPBF 427) isolated from a single walnut tree in Loures (Portugal) were sequenced with two different technologies, Illumina and Nanopore, to provide consistent single scaffold chromosomal sequences. General genomic features showed that CPBF 427 has a genome similar to other X. arboricola pv. juglandis strains, regarding its size, number, and content of CDSs, while X. euroxanthea strains show a reduction regarding these features comparatively to X. arboricola pv. juglandis strains. Whole genome comparisons revealed remarkable genomic differences between X. arboricola pv. juglandis and X. euroxanthea strains, which translates into different pathogenicity and virulence features, namely regarding type 3 secretion system and its effectors and other secretory systems, chemotaxis-related proteins, and extracellular enzymes. Altogether, the distinct genomic repertoire of X. euroxanthea may be particularly useful to address pathogenicity emergence and evolution in walnut-associated Xanthomonas.

Abstract
In recent years we have seen a large adherence to social media by various Higher Education Institutions (HEI) with the intent of reaching their target audiences and improve their public image. These institutional publications are guided by a specific editorial strategy, designed to help them better accomplish and fulfill their mission. The current Covid-19 pandemic has had major consequences in many different fields (political, economic, social, educational) beyond the spread of the disease itself. In this paper, we attempt to determine the impact of the pandemic on the HEI content strategies by gauging if these social-economical, cultural and psychological changes that occurred during this global catastrophe are actively reflected in their publications. Furthermore, we identified the topics that emerge from the pandemic situation checking the trend changes and the concept drift that many topics had. We gathered and analyzed more than 18k Twitter publications from 12 of the top HEI according to the 2019 Center for World University Rankings (CWUR). Utilizing machine learning techniques, and topic modeling, we determined the emergent content topics for each institution before, and during, the Covid-19 pandemic to uncover any significant differences in the strategies.

Abstract
This paper proposes an energy hub management model for residential, commercial, and industrial hubs, considering demand response programs (DRPs). The network configuration and AC optimal power flow (ACOPF) constraints have been applied to the model to prevent any unreal power transaction in the system. The cost due to environmental emissions has also been taken into account and the problem is modeled as a dynamic optimization problem, solved using the CPLEX solver in the GAMS software, interfaced with MATLAB/MATPOWER for the power flow analysis. Besides, the problem is studied in two cases as coordinated and uncoordinated operation modes to investigate their impacts on the operating cost, emission, and power losses. The obtained results show that the coordinated operation would lead to reducing the operating cost, power losses, and emission. Moreover, the impacts of the coordinated and uncoordinated operation modes on the load demand-supply under contingent events and disconnection from the upstream grid are assessed. The results derived from the simulation verify the superior performance of the coordinated operation. It is also noted that the DRP leads to mitigating the operating costs.

Abstract

Abstract
In this work, we propose a deep learning-based prediction interval framework in order to model the forecasting uncertainties of tidal current datasets. The proposed model develops optimum prediction intervals (PIs) focused on the deep learning-based CNN-LSTM model (CLSTM), and non-parametric approach termed as the lower upper bound estimation (LUBE) model. On the other hand, due to the high complexity raises in designing manually the deep learning architectures, as well as the enhancing the performance of the prediction intervals, we develop a novel deep neuroevolution algorithm based on the two-stage modification of the Gaining-Sharing Knowledge (GSK) optimization algorithm to optimize the architecture of the CLSTM automatically without the procedure of trial and error. We also utilize coverage width criterion (CWC) to establish an excellent correlation appropriately between both the the PI coverage probability (PICP) and PI normalized average width (PINAW). We also indicate the searching efficiency and high accuracy of our proposed framework named as MGSK-CLSTM-LUBE by examining over the practical collected tidal current datasets from the Bay of Fundy, NS, Canada. The performance of the proposed model is examined on the practical tidal current data collected from the Bay of Fundy, NS, Canada.

Abstract
The demand for online services is increasing. Services that would require a long time to understand, use and master are becoming as transparent as possible to the users, that tend to focus only on the final goals. Combined with the advantages of the unmanned vehicles (UV), from the unmanned factor to the reduced size and costs, we found an opportunity to bring to users a wide variety of services supported by UV, through the Internet of Unmanned Vehicles (IoUV). Current solutions were analyzed and we discussed scalability and genericity as the principal concerns. Then, we proposed a solution that combines several services and UVs, available from anywhere at any time, from a cloud platform. The solution considers a cloud distributed architecture, composed by users, services, vehicles and a platform, interconnected through the Internet. Each vehicle provides to the platform an abstract and generic interface for the essential commands. Therefore, this modular design makes easier the creation of new services and the reuse of the different vehicles. To confirm the feasibility of the solution we implemented a prototype considering a cloud-hosted platform and the integration of custom-built small-sized cars, a custom-built quadcopter, and a commercial Vertical Take-Off and Landing (VTOL) aircraft. To validate the prototype and the vehicles' remote control, we created several services accessible via a web browser and controlled through a computer keyboard. We tested the solution in a local network, remote networks and mobile networks (i.e., 3G and Long-Term Evolution (LTE)) and proved the benefits of decentralizing the communications into multiple point-to-point links for the remote control. Consequently, the solution can provide scalable UV-based services, with low technical effort, for anyone at anytime and anywhere.