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
A cost-effective (less than 20€) and low-power device is present for in situ continuous monitoring of suspended sediments (SPM) concentration in estuarine and coastal areas. The sensor uses nephelometric technique for SPM values less than 20g/L and backscatter technique for higher ones. A transmitted infrared (IR) and ultraviolet (UV) channels are used to perform the distinguish of inorganic from organic matter in the suspended particles. It is explained the design and built of the sensor as its calibration and preparation for in field tests. The sensor was deployed for one week in a small dock in the estuarine zone of Cavado river (Esposende, Portugal) where is analyzed the SPM and organic/inorganic matter change with the tidal cycles.

Abstract
Enterprises have been challenged to adopt practices of sustainability to benefit shareholders and society with goods standing much beyond monetary profit or required by law. In combination with environmental and economic concerns, Corporate Social Responsibility (CSR) has become an option to leverage businesses with good reputation and to attract sustainability-aware market segments. In line with such a demand, this paper presents a systematic literature review of conceptual modeling studies referring explicitly to certifications, laws or norms of CSR. The more specific research goal of this work is to discover ontologies for representing CSR best practices, design patterns or policies. In total, 921 peer-reviewed papers were analyzed, from which only 17 were considered relevant for data extraction. The main result of this work is the identification of a research gap in explicit knowledge representation of CSR practices for Information Systems design, which ought to be filled to complement the (dominant) economic perspective on sustainability.

Abstract

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.