Full text beschikbaar met HU-account. Since the 2010s, various companies have begun to manufacture wearable smartwatch devices, but the current sales of these products are not impressive. This study investigates how the limitations of the smartwatch are related to perceptual discomforts. Theoretically, this study evaluates the claim that the discomfort that users appear to have with the smartwatch stem from failed remediation. Users perceive the smartwatch more as a set of functional sensors rather than a watch or smartphone. Specifically, from the remediation perspective, the authors asked how users perceive the functions of the smartwatch. This study used dynamic topic modeling for topics on the smartwatch on Reddit. This study reports that the smartwatch has failed to provide a proper way to use the remediated content that it provides. Suggestions for future studies are addressed.
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In this paper, we focus on how the qualitative vocabulary of Dynalearn, which is used for describing dynamic systems, corresponds to the mathematical equations used in quantitative modeling. Then, we demonstrate the translation of a qualitative model into a quantitative model, using the example of an object falling with air resistance.
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Dynamic stall phenomena bring risk for negative damping and instability in wind turbine blades. It is crucial to model these phenomena accurately to reduce inaccuracies in predicting design driving (fatigue) loads. Inaccuracies in currentdynamic stall models may be due to the facts that they are not properly designed for high angles of attack, and that they do not 10 specifically describe vortex shedding behaviour. The Snel second order dynamic stall model attempts to explicitly model unsteady vortex shedding. This model could therefore be a valuable addition to DNV GL’s turbine design software Bladed. In this thesis the model has been validated with oscillating airfoil experiments and improvements have been proposed for reducing inaccuracies. The proposed changes led to an overall reduction in error between the model and experimental data. Furthermore the vibration frequency prediction improved significantly. The improved model has been implemented in Bladed and tested 15 against small scale turbine experiments at parked conditions. At high angles of attack the model looks promising for reducing mismatches between predicated and measured (fatigue) loading. Leading to possible lower safety factors for design and more cost efficient designs for future wind turbines.
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Collaborative networks for sustainability are emerging rapidly to address urgent societal challenges. By bringing together organizations with different knowledge bases, resources and capabilities, collaborative networks enhance information exchange, knowledge sharing and learning opportunities to address these complex problems that cannot be solved by organizations individually. Nowhere is this more apparent than in the apparel sector, where examples of collaborative networks for sustainability are plenty, for example Sustainable Apparel Coalition, Zero Discharge Hazardous Chemicals, and the Fair Wear Foundation. Companies like C&A and H&M but also smaller players join these networks to take their social responsibility. Collaborative networks are unlike traditional forms of organizations; they are loosely structured collectives of different, often competing organizations, with dynamic membership and usually lack legal status. However, they do not emerge or organize on their own; they need network orchestrators who manage the network in terms of activities and participants. But network orchestrators face many challenges. They have to balance the interests of diverse companies and deal with tensions that often arise between them, like sharing their innovative knowledge. Orchestrators also have to “sell” the value of the network to potential new participants, who make decisions about which networks to join based on the benefits they expect to get from participating. Network orchestrators often do not know the best way to maintain engagement, commitment and enthusiasm or how to ensure knowledge and resource sharing, especially when competitors are involved. Furthermore, collaborative networks receive funding from grants or subsidies, creating financial uncertainty about its continuity. Raising financing from the private sector is difficult and network orchestrators compete more and more for resources. When networks dissolve or dysfunction (due to a lack of value creation and capture for participants, a lack of financing or a non-functioning business model), the collective value that has been created and accrued over time may be lost. This is problematic given that industrial transformations towards sustainability take many years and durable organizational forms are required to ensure ongoing support for this change. Network orchestration is a new profession. There are no guidelines, handbooks or good practices for how to perform this role, nor is there professional education or a professional association that represents network orchestrators. This is urgently needed as network orchestrators struggle with their role in governing networks so that they create and capture value for participants and ultimately ensure better network performance and survival. This project aims to foster the professionalization of the network orchestrator role by: (a) generating knowledge, developing and testing collaborative network governance models, facilitation tools and collaborative business modeling tools to enable network orchestrators to improve the performance of collaborative networks in terms of collective value creation (network level) and private value capture (network participant level) (b) organizing platform activities for network orchestrators to exchange ideas, best practices and learn from each other, thereby facilitating the formation of a professional identity, standards and community of network orchestrators.
This Professional Doctorate (PD) research focuses on optimizing the intermittency of CO₂-free hydrogen production using Proton Exchange Membrane (PEM) and Anion Exchange Membrane (AEM) electrolysis. The project addresses challenges arising from fluctuating renewable energy inputs, which impact system efficiency, degradation, and overall cost-effectiveness. The study aims to develop innovative control strategies and system optimizations to mitigate efficiency losses and extend the electrolyzer lifespan. By integrating dynamic modeling, lab-scale testing at HAN University’s H2Lab, and real-world validation with industry partners (Fluidwell and HyET E-Trol), the project seeks to enhance electrolyzer performance under intermittent conditions. Key areas of investigation include minimizing start-up and shutdown losses, reducing degradation effects, and optimizing power allocation for improved economic viability. Beyond technological advancements, the research contributes to workforce development by integrating new knowledge into educational programs, bridging the gap between research, industry, and education. It supports the broader transition to a CO₂-free energy system by ensuring professionals are equipped with the necessary skills. Aligned with national and European sustainability goals, the project promotes decentralized hydrogen production and strengthens the link between academia and industry. Through a combination of theoretical modeling, experimental validation, and industrial collaboration, this research aims to lower the cost of green hydrogen and accelerate its large-scale adoption.
