The evolution of emerging technologies that use Radio Frequency Electromagnetic Field (RF-EMF) has increased the interest of the scientific community and society regarding the possible adverse effects on human health and the environment. This article provides NextGEM’s vision to assure safety for EU citizens when employing existing and future EMF-based telecommunication technologies. This is accomplished by generating relevant knowledge that ascertains appropriate prevention and control/actuation actions regarding RF-EMF exposure in residential, public, and occupational settings. Fulfilling this vision, NextGEM commits to the need for a healthy living and working environment under safe RF-EMF exposure conditions that can be trusted by people and be in line with the regulations and laws developed by public authorities. NextGEM provides a framework for generating health-relevant scientific knowledge and data on new scenarios of exposure to RF-EMF in multiple frequency bands and developing and validating tools for evidence-based risk assessment. Finally, NextGEM’s Innovation and Knowledge Hub (NIKH) will offer a standardized way for European regulatory authorities and the scientific community to store and assess project outcomes and provide access to findable, accessible, interoperable, and reusable (FAIR) data.
The evolution of emerging technologies that use Radio Frequency Electromagnetic Field (RF-EMF) has increased the interest of the scientific community and society regarding the possible adverse effects on human health and the environment. This article provides NextGEM’s vision to assure safety for EU citizens when employing existing and future EMF-based telecommunication technologies. This is accomplished by generating relevant knowledge that ascertains appropriate prevention and control/actuation actions regarding RF-EMF exposure in residential, public, and occupational settings. Fulfilling this vision, NextGEM commits to the need for a healthy living and working environment under safe RF-EMF exposure conditions that can be trusted by people and be in line with the regulations and laws developed by public authorities. NextGEM provides a framework for generating health-relevant scientific knowledge and data on new scenarios of exposure to RF-EMF in multiple frequency bands and developing and validating tools for evidence-based risk assessment. Finally, NextGEM’s Innovation and Knowledge Hub (NIKH) will offer a standardized way for European regulatory authorities and the scientific community to store and assess project outcomes and provide access to findable, accessible, interoperable, and reusable (FAIR) data.
Summary: Xpaths is a collection of algorithms that allow for the prediction of compound-induced molecular mechanisms of action by integrating phenotypic endpoints of different species; and proposes follow-up tests for model organisms to validate these pathway predictions. The Xpaths algorithms are applied to predict developmental and reproductive toxicity (DART) and implemented into an in silico platform, called DARTpaths.
In order to stay competitive and respond to the increasing demand for steady and predictable aircraft turnaround times, process optimization has been identified by Maintenance, Repair and Overhaul (MRO) SMEs in the aviation industry as their key element for innovation. Indeed, MRO SMEs have always been looking for options to organize their work as efficient as possible, which often resulted in applying lean business organization solutions. However, their aircraft maintenance processes stay characterized by unpredictable process times and material requirements. Lean business methodologies are unable to change this fact. This problem is often compensated by large buffers in terms of time, personnel and parts, leading to a relatively expensive and inefficient process. To tackle this problem of unpredictability, MRO SMEs want to explore the possibilities of data mining: the exploration and analysis of large quantities of their own historical maintenance data, with the meaning of discovering useful knowledge from seemingly unrelated data. Ideally, it will help predict failures in the maintenance process and thus better anticipate repair times and material requirements. With this, MRO SMEs face two challenges. First, the data they have available is often fragmented and non-transparent, while standardized data availability is a basic requirement for successful data analysis. Second, it is difficult to find meaningful patterns within these data sets because no operative system for data mining exists in the industry. This RAAK MKB project is initiated by the Aviation Academy of the Amsterdam University of Applied Sciences (Hogeschool van Amsterdan, hereinafter: HvA), in direct cooperation with the industry, to help MRO SMEs improve their maintenance process. Its main aim is to develop new knowledge of - and a method for - data mining. To do so, the current state of data presence within MRO SMEs is explored, mapped, categorized, cleaned and prepared. This will result in readable data sets that have predictive value for key elements of the maintenance process. Secondly, analysis principles are developed to interpret this data. These principles are translated into an easy-to-use data mining (IT)tool, helping MRO SMEs to predict their maintenance requirements in terms of costs and time, allowing them to adapt their maintenance process accordingly. In several case studies these products are tested and further improved. This is a resubmission of an earlier proposal dated October 2015 (3rd round) entitled ‘Data mining for MRO process optimization’ (number 2015-03-23M). We believe the merits of the proposal are substantial, and sufficient to be awarded a grant. The text of this submission is essentially unchanged from the previous proposal. Where text has been added – for clarification – this has been marked in yellow. Almost all of these new text parts are taken from our rebuttal (hoor en wederhoor), submitted in January 2016.
Brandweermensen lopen het meeste gevaar als ze onder tijdsdruk een gebouw moeten verkennen, of een brand moeten blussen terwijl de situatie nog niet goed kan worden overzien. Omvallende muren, instortende plafonds of gewoon gestruikeld over door de rook onzichtbare brokstukken leiden tot vermijdbare letsels of zelfs slachtoffers. Met name de inzet bij branden in stedelijke parkeergarages onder woontorens vormen een enorm risico. Het inzetten van onbemande, op afstand bestuurbare voertuigen voor verkenning en bluswerk is een oplossing die binnen de brandweer breed wordt gedragen. De brandweer moet deze innovatieve technologie echter zien te omarmen. Zij werken nu vanuit hun intuïtie en weten direct hoe te acteren op basis van wat zij waarnemen. Praktijkgericht onderzoek heeft echter uitgewezen dat scepsis over de inzet van blusplatforms bij incidenten plaats heeft gemaakt voor zeker vertrouwen. Een blusplatform, voorzien van juiste sensoren kan de Officier van Dienst (OVD) ondersteunen bij het nemen van een beslissing om al dan niet tot een ‘aanval’ over te gaan. Praktijktesten hebben echter laten zien dat de huidige blusplatforms nog niet optimaal functioneren om als volwaardig ‘teamlid’ te kunnen worden ingezet. Dit heeft enerzijds met technologische ontwikkelingen (sensoren en communicatieverbindingen) te maken, maar anderzijds moet de informatievoorziening (human-machine interfacing) naar de brandweer beter worden afgestemd. In dit project gaan Saxion, het instituut fysieke veiligheid, de universiteit Twente, het bedrijfsleven en vijf veiligheidsregio’s onderzoeken hoe en wanneer innovatieve blusplatforms op een intuïtieve manier kunnen worden ingezet door training én (kleine) productaanpassing zodat deze een volwaardig onderdeel kunnen zijn van het brandweerkorps. Een blusplatform kan letselschade en slachtoffers voorkomen, mits goed ingezet en vertrouwd door de mensen die daarvan afhankelijk zijn. Het vak van brandweer, als beroeps of vrijwilliger, is een van de gevaarlijkste die er is. Laten we er samen voor zorgen dat het iets veiliger kan worden.
The proposed study is focused on finding out whether Virtual Reality is a feasible method to train for composite manufacturing. The demand for cost-effective training methods for composite production is growing. The current training methods are not satisfying the demands of the fast-growing industry. This could be solved with the help of Virtual Reality (VR), potentially cutting down training time and use of material, hence reducing costs. This project will create insight into the technical and economic feasibility of this idea. This will be achieved with interns from Inholland, lecturer and researchers.
