Abstract
This paper presents a novel scenario-based stochastic mixed-integer second-order cone programming model to solve the problem of optimal reconfiguration of distribution systems with renewable energy sources considering short-circuit constraints. The proposed formulation minimizes technical losses by modifying the statuses of sectionalizing and tie switches, allowing the operation of distribution networks with radial and closed-loop topologies. Since the formation of loops could impact fault current levels, short-circuit constraints are considered in the problem formulation. Numerical experiments are carried out using an 84-node system and the results demonstrate the effectiveness of the proposed formulation to reduce technical losses notably when a closed-loop operation is allowed. Additionally, it is verified that short-circuit constraints prevent the adoption of network configurations with high short-circuit values.

Abstract
Robotic competitions are an excellent way to promote innovative solutions for the current industries' challenges and entrepreneurial spirit, acquire technical and transversal skills through active teaching, and promote this area to the public. In other words, since robotics is a multidisciplinary field, its competitions address several knowledge topics, especially in the STEM (Science, Technology, Engineering, and Mathematics) category, that are shared among the students and researchers, driving further technology and science. A new competition encompassed in the Portuguese Robotics Open was created according to the Industry 4.0 concept in the production chain. In this competition, RobotAtFactory 4.0, a shop floor, is used to mimic a fully automated industrial logistics warehouse and the challenges it brings. Autonomous Mobile Robots (AMRs) must be used to operate without supervision and perform the tasks that the warehouse requests. There are different types of boxes which dictate their partial and definitive destinations. In this reasoning, AMRs should identify each and transport them to their destinations. This paper describes an approach to the indoor localization system for the competition based on the Extended Kalman Filter (EKF) and ArUco markers. Different innovation methods for the obtained observations were tested and compared in the EKF. A real robot was designed and assembled to act as a test bed for the localization system's validation. Thus, the approach was validated in the real scenario using a factory floor with the official specifications provided by the competition organization.

Abstract

Abstract
Reasoning about spatial information is fundamental in natural language to fully understand relationships between entities and/or between events. However, the complexity underlying such reasoning makes it hard to represent formally spatial information. Despite the growing interest on this topic, and the development of some frameworks, many problems persist regarding, for instance, the coverage of a wide variety of linguistic constructions and of languages. In this paper, we present a proposal of integrating ISO-Space into a ISO-based multilayer annotation scheme, designed to annotate news in European Portuguese. This scheme already enables annotation at three levels, temporal, referential and thematic, by combining postulates from ISO 24617-1, 4 and 9. Since the corpus comprises news articles, and spatial information is relevant within this kind of texts, a more detailed account of space was required. The main objective of this paper is to discuss the process of integrating ISO-Space with the existing layers of our annotation scheme, assessing the compatibility of the aforementioned parts of ISO 24617, and the problems posed by the harmonization of the four layers and by some specifications of ISO-Space. © European Language Resources Association (ELRA).

Abstract
Exposure to airborne microorganisms has been linked to the development of health detriments, particularly in children. Microbial pollution can constitute a relevant health concern indoors, where levels of airborne microorganisms may be specially increased. This work aimed to characterize the airborne bacterial levels, and fungal concentration and diversity to which twins are exposed in their bedrooms (n = 30) during the first year of life. Bacterial and fungal levels varied widely across the studied bedrooms, with 10% of the rooms presenting values exceeding the national limit for both indoor bacterial and fungal counts. Cladosporium was the predominant genera, but Penicillium, Aspergillus, Alternaria, Trichoderma and Chrysonilia were also identified in the samples collected. In addition, two toxicogenic species, A. flavus and T. viride, were identified at counts that exceeded the established limit (12 CFU/m(3)) in 3 and 7% of the bedrooms surveyed, respectively. Based on indoor-to-outdoor concentration ratios, outdoor air seemed to be the main contributor to the total load of fungi found indoors, while airborne bacteria appeared to be mainly linked to indoor sources. Higher indoor nitrogen dioxide levels were negatively correlated with indoor fungi concentrations, whereas particulate matter and volatile organic compounds concentrations were associated with an increase in fungal prevalence. In addition, rooms with small carpets or located near outdoor agriculture sources presented significantly greater total fungal concentrations. Multiple linear regression models showed that outdoor levels were the single significant predictor identified, explaining 38.6 and 53.6% of the Cladosporium sp. and total fungi counts, respectively. The results also suggest the existence of additional factors contributing to airborne biologicals load in infants' bedrooms that deserve further investigation. Findings stress the need for investigating the existence of declared interactive effects between chemical and biological air pollutants to accurately understand the health risk that the assessed levels can represent to infants.

Abstract
Prosumers are agents that both consume and produce energy. This article studies the optimal energy management of a residential prosumer which consists of a renewable power plant and an energy storage unit. Energy could stream among power grid, renewable plant, storage unit, and demand, providing a highly flexible energy supply and the opportunity of arbitrage. To capture the uncertainty of renewable generation and electricity price, as well as the rolling horizon feature of the multiperiod energy management, the problem is formulated as a robust data-driven dynamic programming (RDDP). Kernel regression is utilized to build the empirical conditional distribution in a data-driven manner, and all candidates that reside in a Wasserstein metric-based ambiguity set are taken into account to tackle the inexactness of the empirical distribution. The RDDP can be transformed into a series of convex optimization problems with cost-to-go functions in their constraints. The piecewise linear expression of the cost-to-go function is retrieved from dual linear programs. Through such an analytical expression of cost-to-go functions, the RDDP can be solved via backward induction, unlike the popular stochastic dual dynamic programming technique that incorporates forward and backward passes. Case studies validate the performance and advantage of the proposed RDDP approach.