Battery energy storage (BES) can provide many grid services, such as power flow management to reduce distribution grid overloading. It is desirable to minimise BES storage capacities to reduce investment costs. However, it is not always clear how battery sizing is affected by battery siting and power flow simultaneity (PFS). This paper describes a method to compare the battery capacity required to provide grid services for different battery siting configurations and variable PFSs. The method was implemented by modelling a standard test grid with artificial power flow patterns and different battery siting configurations. The storage capacity of each configuration was minimised to determine how these variables affect the minimum storage capacity required to maintain power flows below a given threshold. In this case, a battery located at the transformer required 10–20% more capacity than a battery located centrally on the grid, or several batteries distributed throughout the grid, depending on PFS. The differences in capacity requirements were largely attributed to the ability of a BES configuration to mitigate network losses. The method presented in this paper can be used to compare BES capacity requirements for different battery siting configurations, power flow patterns, grid services, and grid characteristics.
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This paper presents the design of the offshore energy simulation CEL as a flow network, and its integration in the MSP Challenge 2050 simulation game platform. This platform is designed to aid learning about the key characteristics and complexity of marine or maritime spatial planning (MSP). The addition of CEL to this platform greatly AIDS MSP authorities in learning about and planning for offshore energy production, a highly topical and big development in human activities at sea. Rather than a standard flow network, CEL incorporates three additions to accommodate for the specificities of energy grids: an additional node for each team's expected energy, a split of each node representing an object into input and output parts to include the node's capacity, and bidirectional edges for all cables to enable more complex energy grid designs. Implemented with Dinic's algorithm it takes less than 30ms for the simulation to run for the average amount of grids included in an MSP Challenge 2050 game session. In this manner CEL enables MSP authorities and their energy stakeholders to use MSP Challenge 2050 for designing and testing more comprehensive offshore energy grids.
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From the article: Abstract: An overview of neural network architectures is presented. Some of these architectures have been created in recent years, whereas others originate from many decades ago. Apart from providing a practical tool for comparing deep learning models, the Neural Network Zoo also uncovers a taxonomy of network architectures, their chronology, and traces back lineages and inspirations for these neural information processing systems.
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Onderzoeksplatform ‘Connected Learning: ’Al ruim vijftien jaar houdt De Haagse Hogeschool zich bezig met onderzoek als deel van haar missie. Terwijl onderwijs vaak geworteld is in monodisciplinaire vakgebieden, kan met onderzoek wat makkelijker gekeken worden naar domeinen in de samenleving (zorg, veiligheid, ondernemen, etc.) waarin complexe problematiek steeds vaker wél dan niet een multidisciplinaire aanpak vereist. Bijna niemand werkt nog alleen of met alleen vakgenoten aan problemen of uitdagingen. En die veranderende beroepspraktijk is bij uitstek het domein van het hoger beroepsonderwijs. Daar leiden we voor op. Het onderzoeken van en experimenteren met nieuwe uitdagingen in de praktijk verbindt ons sterker met de samenleving, het stelt ons in staat om ons beroepsonderwijs te vernieuwen en geeft docenten, onderzoekers en studenten de kans om zich te ontwikkelen door samen te werken aan vragen en uitdagingen die de toekomst van de beroepspraktijk vorm geven. Veel onderzoek wordt uitgevoerd onder begeleiding van lectoren die samenwerken met docent-onderzoekers, studenten, en professionals in het werkveld aan veelal meerjarige onderzoeksagenda’s die lijn aanbrengen in verschillende deelactiviteiten. Een van de manieren waarop De Haagse Hogeschool onderzoek organiseert is in de vorm van onderzoeksplatforms die zich richten op verschillende domeinen van de samenleving. Wij zijn ‘Connected Learning’, een onderzoeksplatform dat zich richt op leren in de netwerksamenleving - in de samenleving zelf, maar ook in de beroepspraktijk en in ons onderwijs. Aangenaam. Wat wij doen? Daar gaat dit boek over, dus daar verklappen we hier nog niets over. Wat verwacht u als u nadenkt over onze naam? Enig idee? Geen idee? Benieuwd? Lees verder om te ontdekken wat ons inspireert, uitdaagt en nieuwsgierig maakt. Sommige van onze ideeën zijn doordacht en doorleefd omdat we er al jaren onderzoek naar doen, andere zijn nieuw en dagen ons uit om er grip op te krijgen. Wij geven met dit boek een beeld van waar we staan in 2018. Zie het als een eerste kennismaking, met de nadruk op ‘eerste’: we werken graag met veel en verschillende partners. Zie het als visitekaartje van onze onderzoeksagenda. We hopen van harte dat u zich als lezer uitgenodigd voelt om met ons samen op zoek te gaan—misschien wel naar een gezamenlijke toekomst. ‘Connected Learning’ Research Platform: For over fifteen years, The Hague University of Applied Sciences has been carrying out research as part of its mission. While education is often rooted in monodisciplinary subject areas, research allows for a broader look at areas of society (care, security, entrepreneurship etc.), where complex problems more often than not require a multidisciplinary approach. Today, barely anyone works on problems or challenges alone or solely with colleagues from within the same subject area. Universities of applied sciences are uniquely placed to deal with these changes in professional practices; after all, we train the professionals who will one day enter that field. Researching and experimenting with new challenges in professional practice allows us to connect more strongly with society, enables us to be innovative in our professional training and gives lecturers, researchers and students the opportunity to develop themselves by cooperating on the challenges and issues that will shape the future of that professional practice. Most research is carried out under the guidance of professors who cooperate with lecturers/researchers, students and the professional field, mainly on long-term research agendas that provide an outline for various sub-activities. One of the ways in which research is organised at The Hague University of Applied Sciences is in the form of research platforms that focus on various areas of society. We are ‘Connected Learning’, a research platform focusing on learning in the network society — in that society as such, but also in professional practice and our education. Nice to meet you! So, what do we do? That’s what this book is about, so we’re not going to give anything away just yet. Just thinking about our name, what do you expect we do? Any ideas? Or not a clue at all? If you’d like to find out, keep reading to find out what inspires us, what challenges we face and what drives our curiosity. Some of our ideas are well-established because we’ve been researching them for years, while other, newer ideas are more challenging to grasp. This book provides an overview of where we stand in 2018. You could see it as an initial introduction, with the emphasis on “initial”; we work with many different partners, and we enjoy doing so. Alternatively, you could see it as a calling card for our research agenda. We sincerely hope that, as a reader, you feel encouraged to join us in our quest — possibly towards a joint future.
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Abstract Healthcare organizations operate within a network of governments, insurers, inspection services and other healthcare organizations to provide clients with the best possible care. The parties involved must collaborate and are accountable to each other for the care provided. This has led to a diversity of administrative processes that are supported by a multi-system landscape, resulting in administrative burdens among healthcare professionals. Management methods, such as Enterprise Architecture (EA), should help to develop and manage such landscapes, but they are systematic, while the network of healthcare parties is dynamic. The aim of this research is therefore to develop an EA framework that fits the dynamics of network organizations (such as long-term healthcare). This research proposal outlines the practical and scientific relevance of this research and the proposed method. The current status and next steps are also described.
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Passenger flow management is an important issue at many airports around the world. There are high concentrations of passengers arriving and leaving the airport in waves of large volumes in short periods, particularly in big hubs. This might cause congestion in some locations depending on the layout of the terminal building. With a combination of real airport data, as well as synthetic data obtained through an airport simulator, a Long Short-Term Memory Recurrent Neural Network has been implemented to predict the possible trajectories that passengers may travel within the airport depending on user-defined passenger profiles. The aim of this research is to improve passenger flow predictability and situational awareness to make a more efficient use of the airport, that could also positively impact communication with public and private land transport operators.
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Background: Adverse outcome pathway (AOP) networks are versatile tools in toxicology and risk assessment that capture and visualize mechanisms driving toxicity originating from various data sources. They share a common structure consisting of a set of molecular initiating events and key events, connected by key event relationships, leading to the actual adverse outcome. AOP networks are to be considered living documents that should be frequently updated by feeding in new data. Such iterative optimization exercises are typically done manually, which not only is a time-consuming effort, but also bears the risk of overlooking critical data. The present study introduces a novel approach for AOP network optimization of a previously published AOP network on chemical-induced cholestasis using artificial intelligence to facilitate automated data collection followed by subsequent quantitative confidence assessment of molecular initiating events, key events, and key event relationships. Methods: Artificial intelligence-assisted data collection was performed by means of the free web platform Sysrev. Confidence levels of the tailored Bradford-Hill criteria were quantified for the purpose of weight-of-evidence assessment of the optimized AOP network. Scores were calculated for biological plausibility, empirical evidence, and essentiality, and were integrated into a total key event relationship confidence value. The optimized AOP network was visualized using Cytoscape with the node size representing the incidence of the key event and the edge size indicating the total confidence in the key event relationship. Results: This resulted in the identification of 38 and 135 unique key events and key event relationships, respectively. Transporter changes was the key event with the highest incidence, and formed the most confident key event relationship with the adverse outcome, cholestasis. Other important key events present in the AOP network include: nuclear receptor changes, intracellular bile acid accumulation, bile acid synthesis changes, oxidative stress, inflammation and apoptosis. Conclusions: This process led to the creation of an extensively informative AOP network focused on chemical-induced cholestasis. This optimized AOP network may serve as a mechanistic compass for the development of a battery of in vitro assays to reliably predict chemical-induced cholestatic injury.
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We present a novel anomaly-based detection approach capable of detecting botnet Command and Control traffic in an enterprise network by estimating the trustworthiness of the traffic destinations. A traffic flow is classified as anomalous if its destination identifier does not origin from: human input, prior traffic from a trusted destination, or a defined set of legitimate applications. This allows for real-time detection of diverse types of Command and Control traffic. The detection approach and its accuracy are evaluated by experiments in a controlled environment.
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Passenger flow management is an important issue at many airports around the world. There are high concentrations of passengers arriving and leaving the airport in waves of large volumes in short periods, particularly in big hubs. This might cause congestion in some locations depending on the layout of the terminal building. With a combination of real airport data, as well as synthetic data obtained through an airport simulator, a Long Short-Term Memory Recurrent Neural Network has been implemented to predict the possible trajectories that passengers may travel within the airport depending on user-defined passenger profiles. The aim of this research is to improve passenger flow predictability and situational awareness to make a more efficient use of the airport, that could also positively impact communication with public and private land transport operators.
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