Abstract
Little is known about the impact of the addition of each stimulus in multisensory augmented reality experiences in cultural heritage contexts. This paper investigates the impact of different sensory conditions on a users sense of presence, enjoyment, knowledge about the cultural site, and value of the experience. Five different multisensory conditions, namely, Visual, Visual+ Audio, Visual +Smell, and Visual + Audio + Smell conditions, and regular visit referred to as None condition, were evaluated by a total of 60 random visitors distributed across the specified conditions. According to the results, the addition of particular types of stimuli created a different impact on the sense of presence subscale scores, namely, on spatial presence, involvement, and experienced realism, but did not influence the overall presence score. Overall, the results revealed that the addition of stimuli improved enjoyment and knowledge scores and did not affect the value of the experience scores. We concluded that each stimulus has a differential impact on the studied variables, demonstrating that its usage should depend on the goal of the experience: smell should be used to privilege realism and spatial presence, while audio should be adopted when the goal is to elicit involvement.

Abstract
When the COVID-19 pandemic hits Portugal in early March 2020, medical doctors, engineers and researchers, with the encouragement of the Northern Region Health Administration, teamed up to develop and build, locally and in a short time, a ventilator that might eventually be used in extreme emergency situations in the hospitals of northern Portugal. This letter tells you the story of Pneuma, a low-cost emergency ventilator designed and built under harsh isolation constraints, that gave birth to derivative designs in Brazil and Morocco, has been industrialized with 200 units being produced, and is now looking forward to the certification as a medical device that will possibly support a go-tomarket launch. Open intellectual property (IP), multi disciplinarity teamwork, fast prototyping and product engineering have shortened to a few months an otherwise quite longer idea-to-product route, clearly demonstrating that when scientific and engineering knowledge hold hands great challenges can be successfully faced.

Abstract
This study presents a conceptual model addressing the establishment of connection requirements for power-converter-interfaced renewable generation in islanded power systems. The replacement of fossil-fuel generation by time-variable renewable sources implies important changes in the system operation philosophy with respect the dynamic security resulting from the proliferation of power electronic interfaced units and reduction of regulation capacity existing in conventional units. Taking into consideration such an environment, this work focuses on the Madeira Island study case, which is facing a tremendous challenge regarding medium-term plans for renewables integration that lead to operational scenarios with 100% of power infeed from renewables in a system characterised by a diversity of resources. The requirements for the future renewable generation systems were identified via extensive dynamic simulations of large disturbances performed in those operating scenarios.

Abstract
Classic feature selection techniques remove irrelevant or redundant features to achieve a subset of relevant features in compact models that are easier to interpret and so improve knowledge extraction. Most such techniques operate on the whole dataset, but are unable to provide the user with useful information when only instance-level information is required; in other words, classic feature selection algorithms do not identify the most relevant information in a sample. We have developed a novel feature selection method, called saliency-based feature selection (SFS), based on deep-learning saliency techniques. Our algorithm works under any architecture that is trained by using gradient descent techniques (Neural Networks, SVMs, ...), and can be used for classification or regression problems. Experimental results show our algorithm is robust, as it allows to transfer the feature ranking result between different architectures, achieving remarkable results. The versatility of our algorithm has been also demonstrated, as it can work either in big data environments as well as with small datasets.

Abstract
Grid-connected photovoltaic (PV) systems are considered as the best options for home solar electric system applications. Compared to other alternatives, grid-connected PV systems offer the least expensive and lowest-maintenance choice for residential usage. The PV systems are constructed using some solar cells to extract the energy from sun radiations and power converters to convert the output DC voltage into AC one. In the present study a hybrid scheme is suggested to control the grid-connected PV converters. The developed scheme is able to inject the power created by solar arrays into the network. Also, it can rectify the problems associated with the reactive power and load harmonics in the system. Moreover, the suggested technique can secure the load active and reactive power during the network failure conditions. One of the contributions of the proposed method is that it can manage the power of solar arrays to supply the load, filter the harmonics and compensate the reactive power in a situation where the power produced by solar arrays is less than the power of converters. In this condition, the proposed technique can make the current drawn from the grid completely sine at unity power factor.

Abstract
Currently, the use of free and open-source software is increasing. The flexibility, availability, and maturity of this software could be a key driver to develop useful and interesting solutions. In general, open-source solutions solve specific tasks that can replace commercial solutions, which are often very expensive. This is even more noticeable in areas requiring analysis and manipulation/visualization of a large volume of data. Considering that there is a major gap in the development of web applications for photogrammetric processing, based on open-source technologies that offer quality results, the application presented in this article is intended to explore this niche. Thus, in this article a solution for photogrammetric processing is presented, based on the integration of MicMac, GeoServer, Leaflet, and Potree software. The implemented architecture, focusing on open-source software for data processing and for graphical manipulation, visualization, measuring, and analysis, is presented in detail. To assess the results produced by the proposed web application, a case study is presented, using imagery acquired from an unmanned aerial vehicle in two different areas.