A case study and method development research of online simulation gaming to enhance youth care knowlegde exchange. Youth care professionals affirm that the application used has enough relevance as an additional tool for knowledge construction about complex cases. They state that the usability of the application is suitable, however some remarks are given to adapt the virtual environment to the special needs of youth care knowledge exchange. The method of online simulation gaming appears to be useful to improve network competences and to explore the hidden professional capacities of the participant as to the construction of situational cognition, discourse participation and the accountability of intervention choices.
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When it comes to hard to solve problems, the significance of situational knowledge construction and network coordination must not be underrated. Professional deliberation is directed toward understanding, acting and analysis. We need smart and flexible ways to direct systems information from practice to network reflection, and to guide results from network consultation to practice. This article presents a case study proposal, as follow-up to a recent dissertation about online simulation gaming for youth care network exchange (Van Haaster, 2014).
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A building block approach to simulation uses modules that are easily reusable and therefore speed up the simulation process. The authors assume that this approach can enhance complex decision making between stakeholders on infrastructure planning and design. The authors combined insights from process management and a simulation building block approach into an experimental interactive decision-making procedure and developed a simulation building block tool. The authors tested the procedure and the tool in the game CONTAINERS ADRIFT. Evaluation results indicate that the simulation tool is fast and easy to work with and that the combination of simulation building blocks and process management contributes to the quality and process of negotiation and generates mutual understanding.
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Granular materials (GMs) are simply a collection of individual particles, e.g., rice, coffee, iron-ore. Although straightforward in appearance, GMs are key to several processes in chemical-pharmaceutical, high-tech, agri-food and energy industry. Examples include laser sintering in additive manufacturing, tableting in pharma or just mixing of your favourite crunchy muesli mix in food industry. However, these bulk material handling processes are notorious for their inefficiency and ineffectiveness. Thereby, affecting the overall expenses and product quality. To understand and enhance the quality of a process, GMs industries utilise computer-simulations, much like how cars and aeroplanes have been designed and optimised since the 1990s. Just as how engineers utilise advanced computer-models to develop our fuel-efficient vehicle design, energy-saving granular processes are also developed utilising physics-based simulation-models, using a computer. Although physics-based models can effectively optimise large-scale processes, creating and simulating a fully representative virtual prototype of a GMs process is very iterative, computationally expensive and time intensive. On the contrary, given the available data, this is where machine learning (ML) could be of immense value. Like how ML has transformed the healthcare, energy and other top sectors, recent ML-based developments for GMs show serious promise in faster virtual prototyping and reduced computational cost. Enabling industries to rapidly design and optimise, enhancing real-time data-driven decision making. GranML aims to empower the GMs industries with ML. We will do so by (i) performing an in-depth GMs-ML literature review, (ii) developing open-access ML implementation guidelines; and (iii) an open-source proof-of-concept for an industry-relevant use case. Eventually, our follow-up mission is to build upon this vital knowledge by (i) expanding the consortium; (ii) co-developing a unified methodology for efficient computer-prototyping, unifying physics- and ML-based technologies for GMs; (iii) enhancing the existing computer-modelling infrastructure; and (iv) validating through industry focused demonstrators.
Project aimsNorthSEE aims to achieve greater coherence in Maritime Spatial Planning (processes; MSP) and in Maritime Spatial Plans (outcomes/solutions), capturing synergies and preventing incompatibilities in the North Sea Region (NSR). The project seeks to create better conditions for sustainable development of the area in the fields of shipping, energy and environmental protection. NorthSEE is possible thanks to the financial support from the Interreg North Sea Region programme of the European Union (European Regional Development Fund).Project tasks and resultsTo suggest a multi-level coordination framework capable of supporting ongoing coordination in MSP across the NSR in the long term. To develop an information and planning platform for MSP, enabling planners and stakeholders to share evidence for MSP and test different planning options in the form of scenarios based on real data. The MSP Challenge computer-supported simulation game will became this platform. To increase the capacity of stakeholders in key transnational sectors to actively contribute to MSP To align approaches for taking into account wider environmental issues in MSP To facilitate greater transnational coherence in MSP with respect to offshore energy infrastructure To achieve greater transnational coherence in using MSP to support environmental protection objectives. To facilitate greater transnational coherence in MSP with respect to shipping routes.Our roleThe Academy for Digital Entertainment (ADE) of Breda University of Applied Sciences is a full partner in this project. ADE is responsible for designing and developing the MSP Challenge simulation game concerning the NSR, as well as facilitating its application, all with the aim of developing insights befitting the project aims and thus Maritime Spatial Planning in the North Sea Region (see task 2). We therefore work closely with all NorthSEE partners to define the right requirements and ensure that the simulation game fulfills them. Multiple MSP Challenge sessions are planned to help develop insightful future scenarios and useful planning solutions for the NSR. More information about MSP Challenge is available on NorthSEE (https://northsearegion.eu/northsee) and on its own website (https://www.mspchallenge.info/).
Power Quality, ofwel de kwaliteit van spanning en stroom, is momenteel een veelbesproken onderwerp. Door de sterke toename van niet-lineaire en energiebesparende belastingen (denk bijv. aan spaar- en ledverlichting, computervoedingen, frequentieregelaars, solaromvormers, etc.) verslechtert de kwaliteit van de netspanning terwijl diezelfde apparatuur juist gevoeliger worden voor verstoringen. Dit heeft nadelige economische en technische gevolgen voor de levensduur, efficiëntie, betrouwbaarheid en veiligheid van zowel de energie infrastructuur als de aangesloten apparatuur. Het belang van Power Quality blijkt ook uit het recent aantal publicaties en conferenties op dit vakgebied. Desondanks is de technische en wetenschappelijke analyse van Power Quality problemen voornamelijk fenomenologisch van aard. Problemen worden doorgaans beschreven aan de hand van metingen. Oplossingen worden meestal gezocht in het ad hoc plaatsen van commerciële power conditioners die de spanning en stroom beogen te verbeteren. De Power Quality problemen waar MKB-er Kanters al jaren mee worstelt zijn typerend voor de vele Power Quality problemen waar MKB-er HyTEPS, marktleider in Power Quality en Energy Efficiency, dagelijks mee te maken heeft. De installaties kunnen worden doorgemeten, maar het blijft lastig om de veroorzaker(s) van het Power Quality probleem met zekerheid vast te stellen. Het is meestal niet toegestaan om ‘verdachte’ apparaten af te schakelen in dit proces. De optimale plaatsing van de power conditioner(s) blijft daarmee een open vraag. Derhalve ontstaat de dringende behoefte aan computersimulaties om de oorzaak van verstoring te analyseren en de mogelijke oplossingen te valideren. PQsim onderzoekt of middels modellering en simulatie de bron van Power Quality problemen kan worden gealloceerd zodat er efficiënt oplossingen kunnen worden ontwikkeld en toegepast. De kennisassimilatie tussen HyTEPS, Kanters, RUG en de HAN beoogt een solide basis te vormen voor een unieke systematische en regeltechnische benadering van Power Quality problemen. De verworven inzichten dienen voorts als input voor toekomstige SiA-RAAK/TKI projecten.
