Abstract
Probabilistic forecasting of distribution tails (i.e., quantiles below 0.05 and above 0.95) is challenging for non parametric approaches since data for extreme events are scarce. A poor forecast of extreme quantiles can have a high impact in various power system decision-aid problems. An alternative approach more robust to data sparsity is extreme value theory (EVT), which uses parametric functions for modelling distribution's tails. In this work, we apply conditional EVT estimators to historical data by directly combining gradient boosting trees with a truncated generalized Pareto distribution. The parametric function parameters are conditioned by covariates such as wind speed or direction from a numerical weather predictions grid. The results for a wind power plant located in Galicia, Spain, show that the proposed method outperforms state-of-the-art methods in terms of quantile score.

Abstract
Bird species identification is a relevant and time-consuming task for ornithologists and ecologists. With growing amounts of audio annotated data, automatic bird classification using machine learning techniques is an important trend in the scientific community. Analyzing bird behavior and population trends helps detect other organisms in the environment and is an important problem in ecology. Bird populations react quickly to environmental changes, which makes their real time counting and tracking challenging and very useful. A reliable methodology that automatically identifies bird species from audio would therefore be a valuable tool for the experts in different scientific and applicational domains. The goal of this work is to propose a methodology able to identify bird species by its chirp. In this paper we explore deep learning techniques that are being used in this domain, such as Convolutional Neural Networks and Recurrent Neural Networks to classify the data. In deep learning, audio problems are commonly approached by converting them into images using audio feature extraction techniques such as Mel Spectrograms and Mel Frequency Cepstral Coefficients. We propose and test multiple deep learning and feature extraction combinations in order to find the most suitable approach to this problem.

Abstract
Surface plasmon-polaritons are electromagnetic modes that can be excited at a conducting-dielec-tric interface [1]. The engineering of surface plasmon resonance (SPR) based devices is a milestone in the development of optical sensors. The ability to construct an all-optical system to confine lightwave power at subwavelength dimensions with higher levels of sensitivity and resolution in a broad spectral range are the central features that have attracted a rapid-growing interest in SPR sensors [2]. Particularly, minute variations in the refractive index of the surrounding medium (also known as analyte) change significantly the characteristics of the electromagnetic fields of a surface plasmon mode. As a consequence, the spectral shifts in the mode phase and also losses variations in the associated confined power can be used to detect analyte properties that are described in terms of the refractive index [3].

Abstract
This paper presents a novel mixed-integer second-order cone programming model to increase the photovoltaic (PV) hosting capacity and optimize the operation of distribution systems. The operational problem considers voltage control through the optimal operation of capacitors banks, substations' on-load tap changers, voltage regulators, and network reconfiguration with radial and closed-loop operation. The proposed formulation considers voltage-dependent models for loads and capacitor banks. The objective function maximizes the PV hosting capacity of the system. Numerical experiments are carried out using the 33-node system and results demonstrate the effectiveness of the proposed formulation to increase the penetration of PV sources, especially when the closed-loop operation is allowed, together with network reconfiguration.

Abstract
Task Scheduling assumes an integral topic in the efficiency of multiple mobile robots systems and is a key part in most modern manufacturing systems. Advances in the field of combinatorial optimisation have allowed the implementation of algorithms capable of solving the different variants of the vehicle routing problem in relation to different objectives. However few of this approaches are capable of taking into account the nuances associated with the coordinated path planning in multi-AGV systems. This paper presents a new study about the implementation of the Simulated Annealing algorithm to minimise the time and distance cost of executing a tasks set while taking into account possible pathing conflicts that may occur during the execution of the referred tasks. This implementation uses an estimation of the planned paths for the robots, provided by the Time Enhanced A* (TEA*) to determine where possible pathing conflicts occur and uses the Simulated Annealing algorithm to optimise the attribution of tasks to each robot, in order to minimise the pathing conflicts. Results are presented that validate the efficiency of this algorithm and compare it to an approach that does not take into account the estimation of the robots paths.

Abstract
An unprecedented outbreak pandemic caused disruption around the world. It had a strong impact on economic sector. Although, the pandemic accelerated the growth of e-commerce for specific categories as food retailer. As a result, several companies restructured their structures, in terms of IT and operations. During the first confinement, the operations and the website of SONAE MC were not prepared for the increase that existed due to the pandemic, COVID-19, causing disruption in the supply chain and long lead times. In this paper, it is explained how SONAE MC reduced its dependence on refrigerated vehicles, simplifying operations and reducing the costs of transporting products from online orders in vehicles with cargo space able to transport positive cold food and negative cold. It is also explained how innovation has ensured that products continue to be transported with quality and safety to all customers of the SONAE MC Darkstore. The result was the implementation of the proposed solution which may grow technologically once information and equipment are available.