Abstract
Wavefront sensors (WFSs) encode phase information of an incoming wavefront into an intensity pattern that can be measured on a camera. Several kinds of WFSs are used in astronomical adaptive optics. Among them, Fourier-based WFSs perform a filtering operation on the wavefront in the focal plane. The most well-known example of a WFS of this kind is the Zernike WFS. The pyramid WFS also belongs to this class. Based on this same principle, WFSs can be proposed, such as the n-faced pyramid (which ultimately becomes an axicon) or the flattened pyramid, depending on whether the image formation is incoherent or coherent. To test such concepts, the LAM/ONERA on-sky pyramid sensor (LOOPS) adaptive optics testbed hosted at the Laboratoire d'Astrophysique de Marseille has been upgraded by adding a spatial light modulator (SLM). This device, placed in a focal plane produces high-definition phase masks that mimic otherwise bulk optic devices. We first present the optical design and upgrades made to the experimental setup of the LOOPS bench. Then, we focus on the generation of the phase masks with the SLM and the implications of having such a device in a focal plane. Finally, we present the first closed-loop results in either static or dynamic mode with different WFS applied on the SLM.

Abstract
The topic of stress is nowadays a very important one, not only in research but on social life in general. People are increasingly aware of this problem and its consequences at several levels: health, social life, work, quality of life, etc. This resulted in a significant increase in the search for devices and applications to measure and manage stress in real-time. Recent technological and scientific evolution fosters this interest with the development of new methods and approaches. In this paper we survey these new methods for stress assessment, focusing especially on those that are suited for the workplace: one of today's major sources of stress. We contrast them with more traditional methods and compare them between themselves, evaluating nine characteristics. Given the diversity of methods that exist nowadays, this work facilitates the stakeholders' decision towards which one to use, based on how much their organization values aspects such as privacy, accuracy, cost-effectiveness or intrusiveness.

Abstract

Abstract
The integration of renewable energy resources (RES) (such as wind and photovoltaic (PV)) on large or small scales, in addition to small generation units, and individual producers, has led to a large variation in energy production, adding uncertainty to power systems (PS) due to the inherent stochasticity of natural resources. The implementation of demand-side management (DSM) in distribution grids (DGs), enabled by intelligent electrical devices and advanced communication infrastructures, ensures safer and more economical operation, giving more flexibility to the intelligent smart grid (SG), and consequently reducing pollutant emissions. Consumers play an active and key role in modern SG as small producers, using RES or through participation in demand response (DR) programs. In this work, the proposed DSM model follows a two-stage stochastic approach to deal with uncertainties associated with RES (wind and PV) together with demand response aggregators (DRA). Three types of DR strategies offered to consumers are compared. Nine test cases are modeled, simulated, and compared in order to analyze the effects of the different DR strategies. The purpose of this work is to minimize DG operating costs from the Distribution System Operator (DSO) point-of-view, through the analysis of different levels of DRA presence, DR strategies, and price variations.

Abstract

Abstract
Over the last years, there has been an increase in hearing-impaired students who use sign language as their main form of communication attending higher education institutions around the world. The knowledge that their comprehension of texts is reduced due to sentence structure differences causes a need for more solutions to improve communication and support students in environments where they are unable to be accompanied by sign interpreters. This article details the improvements and current structure of the VirtualSign platform, a bidirectional sign language to text translation tool that has been in development since 2015. The platform is divided into two main parts, sign to text and text to sign, and both components are described and explained. The solution has received positive feedback on several tests and a pilot experiment, and is being developed with partnerships with sign interpreters from six different European countries. Some planned improvements and future functionalities for the tool are also mentioned and detailed.