Abstract
In this paper we present a Deep Learning-based methodology to automatically classify 12 of the most representative grapevarieties existing in the Douro Demarked region, Portugal. The dataset used consisted of images of leaves at different stages of development, collected on their natural environment. The development of such methodologies becomes particularly important, in a scenario in which ampeleographers are disappearing, creating a gap in the task of inspection of grape varieties. Our approach was based on the transfer learning of the Xcepetion model, using Focal Loss, adaptive learning rate decay and SGD. The model obtained a F1 score of 0.93. To clearly understand the predictions of the model, and realize which regions of the image contributed the most to the classification, the LIME library was used. This way it was possible to identify the parts of the images that were considered for and against each prediction.

Abstract
This tool paper presents the High-Assurance ROS (HAROS) framework. HAROS is a framework for the analysis and quality improvement of robotics software developed using the popular Robot Operating System (ROS). It builds on a static analysis foundation to automatically extract models from the source code. Such models are later used to enable other sorts of analyses, such as Model Checking, Runtime Verification, and Property-based Testing. It has been applied to multiple real-world examples, helping developers find and correct various issues.

Abstract
Application execution requests in cloud architecture and fog paradigm are generally heterogeneous and scheduling in these architectures is an optimization problem with multiple constraints. In this paper, we conducted a survey on the related works on scheduling in cloud architecture and fog paradigm, we identify their limitations, we explore some prospects for improvements and we propose a context-aware scheduling model for fog paradigm. The proposed solution uses Min-Max normalization, to solve heterogeneity and normalize the different context parameters. The priority of requests is set by applying the Multiple Linear Regression analysis technique and the scheduling is done using the Multiobjective Nonlinear Programming Optimization technique. The results obtained from simulations on iFogSim toolkit, show that our proposal performs better compared to the non-context-aware proposals.

Abstract
This paper presents a two-stage adaptive robust optimization approach to develop an optimal bidding strategy for a grid-connected solar photovoltaic (PV) plant with a coupled energy storage system (ESS). This study models the power flow through system elements as well as the exact interactions between the system and upstream network. The uncertainties of solar radiation, affecting the PV generation and market prices are characterized by bounded intervals in polyhedral uncertainty sets. A robust optimization is formed as a min-max-min problem characterizing both here-and-now and wait-and-see variables. This tri-level robust optimization is solved through a decomposition approach, where it is recast into a min master problem and a max-min subproblem. Unlike previous conventional robust optimization models, that utilise duality for solving the inner subproblem, a block coordinate decent (BCD) methodology is used in this study. Accordingly, instead of conducting duality theory, the subproblem is solved over a first-order Taylor series approximation of uncertainties. This results in a moderate computation/mathematical burden. Moreover, there is no need to linearize the dualized problem anymore, as no duality is conducted. Using BCD methodology in solving the robust optimization model also allows modelling binary variables as recourse actions, which differentiates this approach to conventional dual-based robust optimization models. An illustrative example is provided to demonstrate the performance of the proposed bidding strategy model.

Abstract
Distribution networks have a prominent role in electricity delivery to individual consumers. Nevertheless, their energy losses are higher than transmission systems, which this issue affects the distribution operational costs. Hence, the minimization of power losses in distribution networks has particular importance for the system operators. Distribution network reconfiguration (DNR) is an effective way to reduce energy losses. However, some research works regarding DNR have not considered load variations in power loss calculations. Load level has an essential role in network losses determination and significantly influences the energy losses amount. On the other hand, considering load variations in DNR increases the computational burden and processing time of the relevant algorithms. Therefore, this paper presents an effective reconfiguration framework for minimization of distribution losses, while the energy demand is changing. The simulation results show the effectiveness of the proposed strategy for optimal reconfiguration of distribution systems in presence of load variations.

Abstract
This letter presents a new optical fiber structure with the capability of measuring nano-displacement. This device is composed by a cleaved fiber and a drop-shaped microstructure that is connected to the fiber cladding. This optical structure is responsible for the light beam division and the formation of new optical paths. The operation mode consists of the Vernier effect that allows achieving higher sensitivity than the currently sensors. During the experimental execution, displacement sensitivities of 1.05 +/- 0.01 nm , 15.1 +/- 0.1 nm, 24.7 +/- 0.3 nm and 28.3 +/- 0.3 nm , were achieved for the carrier, the fundamental of the envelope, the first harmonic and the second harmonic, respectively. The M-factor of 27 was attained, allowing a minimum resolution of 0.7 nm. In addition to displacement sensing, the proposed optical sensor can be used as a cantilever enabling non-evasive measurements.