Abstract
The proposed model analyzes the profit of a demand response (DR) aggregator from trading DR in the day-ahead electricity market in a way that it tends to gain profit from the favorable deviations of the uncertain parameters. Two types of DR programs are implemented in this model, i.e., time-of-use and reward based DR program. The information-gap decision theory is being employed as a risk measure to address the uncertainties. Two uncertain parameters from both sides of the aggregator have been taken into account in this model, such as the participation rate of the consumers in reward-based DR program in the consumer-side of the aggregator and the day-ahead market prices in the wholesale-side of it. The program is simulated in GAMS software using the available commercial solver. Real data is considered to check the feasibility of the proposed program.

Abstract
The Light in Tiles (LiT) project is an innovation project funded by the European Commission and aims to develop a new technological solution for the manufacture of traditional flooring and tiles with integrated LED lighting technologies. The goal is to incorporate in traditional ceramics, without changing its characteristics or dimensions, light effects for lighting and decoration. These tiles must have an easy interconnecting method, both for assembly and for possible repair. The LiT project aims to offer the market a solution that breaks with traditional products from the point of view of their technical, functional and even aesthetic characteristics. This article briefly presents the first developments in the incorporation of LED lighting in traditional ceramic tiles. © 2019 IEEE.

Abstract
Industries are becoming more and more digitized to better implement intelligent and predictive maintenance support systems, aligned with Industry 4.0, which requires the progressive digitization of data collection and processes. Maintenance interventions, in an evolving technological context, are increasingly more complex and difficult for technicians to perform. In these environments, the use of Augmented Reality (AR) to help assist and guide in the maintenance operations, can accomplish a considerable gain in productivity. AR allows to superimpose information objects in real scenes, such as text, images, audiovisuals, and 2D/3D model animations, making available contextual information about the process, based on location and perspective. This paper describes the design and implementation of a prototype augmented reality application to support maintenance tasks inside a metal stamping production unit, that produces components for the automotive sector. It aims to train and guide personnel during the maintenance operations, and offering an extra channel to reach expert help.

Abstract
Spectral information is characterized by multi-scaled interference, convolution and variability. Spectral lines are fragmented and diffused along the spectra. In many cases, matrix and physical effects do not allow to determine specific bands. Despite this limitation, the observed spectra contains significant amounts of information about the sample composition and characteristics, which once understood, can make spectroscopy an ideal technology for analyzing complex samples, such as bodyfluids and tissues. Breaking down and deciphering the structure of spectral information is paramount for the development of reagent-free point-of-care devices. A self-learning artificial intelligence was developed to take advantage of spectral complex information structure. It determines the relationships between composition and/or spectral features in high-dimensional space, where different sub-spaces correlate to specific constituents or characteristics. It also establishes a knowledgebase, by feature space transformations and optimizing co-variance search direction under the correct 'matrix effect' context. An example of hemogram analysis with erythrocyte and leucocyte counts is presented to demonstrate the advantages of the developed methodology.

Abstract
This paper describes the implementation of an end-to-end Internet of Things (IoT) solution, focusing specifically in the multi-protocol sensor node with LoRaWAN and Wi-Fi connectivity options (Pycom's FiPy). A performance assessment will be presented, addressing a comparison between the different protocols (LoRaWAN vs. Wi-Fi) in terms radio coverage, timing issues, among others. Further, it will be investigated the integration onto the sensor node of sensor/actuator circuit blocks for energy metering, supported on Microchip's ATM90E26 single-phase meter. This will provide a practical use case in the field of Industry 4.0, leading to preliminary insights for power quality monitoring. © 2019 IEEE.

Abstract
Reinforcement learning techniques bring a new perspective to enduring problems. Developing skills from scratch is not only appealing due to the artificial creation of knowledge. It can also replace years of work and refinement in a matter of hours. From all the developed skills in the RoboCup 3D Soccer Simulation League, running is still considerably relevant to determine the winner of any match. However, current approaches do not make full use of the robotic soccer agents’ potential. To narrow this gap, we propose a way of leveraging the Proximal Policy Optimization using the information provided by the simulator for official RoboCup matches. To do this, our algorithm uses a mix of raw, computed and internally generated data. The final result is a sprinting and a stopping behavior that work in tandem to bring the agent from point a to point b in a very short time. The sprinting speed stabilizes at around 2.5 m/s, which is a great improvement over current solutions. Both the sprinting and stopping behaviors are remarkably stable. © 2019, Springer Nature Switzerland AG.