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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The inefficiency of maintaining static and long-lasting safety zones in environments where actual risks are limited is likely to increase in the coming decades, as autonomous systems become more common and human workers fewer in numbers. Nevertheless, an uncompromising approach to safety remains paramount, requiring the introduction of novel methods that are simultaneously more flexible and capable of delivering the same level of protection against potentially hazardous situations. We present such a method to create dynamic safety zones, the boundaries of which can be redrawn in real-time, taking into account explicit positioning data when available and using conservative extrapolation from last known location when information is missing or unreliable. Simulation and statistical methods were used to investigate performance gains compared to static safety zones. The use of a more advanced probabilistic framework to further improve flexibility is also discussed, although its implementation would not offer the same level of protection and is currently not recommended.
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In today’s intellectual capital literature, we see a shift from identifying intangibles towards understanding the dynamics of value creation. As it is not clear what “dynamic” stands for, the aim of this explorative and conceptual paper is to contribute to a better understanding of the dynamic dimension of IC. Based on a review of the early IC literature, the dynamic dimension (or dynamics) of intellectual capital seems to refer to the logic that value creation is the product of interaction between different types of (intangible) resources. As the idea of value creation through combination of knowledge resources is closely related to the New Growth Theory (Romer, 1990, 1994), this paper explores the New Growth Theory and its implications for the dynamic dimension of intellectual capital. Based on the exploration of the New Growth Theory, a conceptual model is presented in which the elements that constitute the dynamic dimension of intellectual capital are integrated. These elements are ideas, things, the process of knowledge creation, the process of continuous innovation, and institutions. The main conclusion of this paper is that the concept of knowledge is more closely related to the dynamic dimension of IC, than the concept of intellectual capital. Therefore, further research would probably benefit from approaching this topic from a knowledge management point of view. It is suggested that further research should focus on exploring the metaphors that contribute to a better understanding of the dynamics of IC, on the contribution that ideas can make to increase the effectiveness of knowledge management, and finally on the institutional arrangements that support the process of knowledge creation and innovation.
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Abstract Background: COVID-19 was first identified in December 2019 in the city of Wuhan, China. The virus quickly spread and was declared a pandemic on March 11, 2020. After infection, symptoms such as fever, a (dry) cough, nasal congestion, and fatigue can develop. In some cases, the virus causes severe complications such as pneumonia and dyspnea and could result in death. The virus also spread rapidly in the Netherlands, a small and densely populated country with an aging population. Health care in the Netherlands is of a high standard, but there were nevertheless problems with hospital capacity, such as the number of available beds and staff. There were also regions and municipalities that were hit harder than others. In the Netherlands, there are important data sources available for daily COVID-19 numbers and information about municipalities. Objective: We aimed to predict the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants per municipality in the Netherlands, using a data set with the properties of 355 municipalities in the Netherlands and advanced modeling techniques. Methods: We collected relevant static data per municipality from data sources that were available in the Dutch public domain and merged these data with the dynamic daily number of infections from January 1, 2020, to May 9, 2021, resulting in a data set with 355 municipalities in the Netherlands and variables grouped into 20 topics. The modeling techniques random forest and multiple fractional polynomials were used to construct a prediction model for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants per municipality in the Netherlands. Results: The final prediction model had an R2 of 0.63. Important properties for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants in a municipality in the Netherlands were exposure to particulate matter with diameters <10 μm (PM10) in the air, the percentage of Labour party voters, and the number of children in a household. Conclusions: Data about municipality properties in relation to the cumulative number of confirmed infections in a municipality in the Netherlands can give insight into the most important properties of a municipality for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants in a municipality. This insight can provide policy makers with tools to cope with COVID-19 and may also be of value in the event of a future pandemic, so that municipalities are better prepared.
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Business process modeling and system dynamics are different approaches that are used in the design and management of organizations. Both approaches are concerned with the processes in, and around, organizations with the aim to identify, design and understand their behavior as well as potential improvements. At the same time, these approaches differ considerably in their methodological focus. While business process modeling specifically takes the (control flow of) business processes as its primary focus, system dynamics takes the analysis of complex and multi-faceted systems as its core focus. More explicitly combining both approaches has the potential to better model and analyze (by way of simulation) complex business processes, while specifically also including more relevant facets from the environment of these business processes. Furthermore, the inherent ability for simulation of system dynamics models, can be used to simulate the behavior of processes over time, while also putting business processes in a broader multi-faceted context. In this paper, we report on initial results on making such a more explicit combination of business process modeling and system dynamics. In doing so, we also provide a step-by-step guide on how to use BPMN based models and system dynamics models together to model and analyze complex business processes, while illustrating this in terms of a case study on the maintenance of building facades.
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Business process modeling and system dynamics are different approaches that are used in the design and management of organizations. Both approaches are concerned with the processes in, and around, organizations with the aim to identify, design and understand their behavior as well as potential improvements. At the same time, these approaches differ considerably in their methodological focus. While business process modeling specifically takes the (control flow of) business processes as its primary focus, system dynamics takes the analysis of complex and multi-faceted systems as its core focus. More explicitly combining both approaches has the potential to better model and analyze (by way of simulation) complex business processes, while specifically also including more relevant facets from the environment of these business processes. Furthermore, the inherent ability for simulation of system dynamics models, can be used to simulate the behavior of processes over time, while also putting business processes in a broader multi-faceted context. In this paper, we report on initial results on making such a more explicit combination of business process modeling and system dynamics. In doing so, we also provide a step-by-step guide on how to use BPMN based models and system dynamics models together to model and analyze complex business processes, while illustrating this in terms of a case study on the maintenance of building facades.
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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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Albeit the widespread application of recommender systems (RecSys) in our daily lives, rather limited research has been done on quantifying unfairness and biases present in such systems. Prior work largely focuses on determining whether a RecSys is discriminating or not but does not compute the amount of bias present in these systems. Biased recommendations may lead to decisions that can potentially have adverse effects on individuals, sensitive user groups, and society. Hence, it is important to quantify these biases for fair and safe commercial applications of these systems. This paper focuses on quantifying popularity bias that stems directly from the output of RecSys models, leading to over recommendation of popular items that are likely to be misaligned with user preferences. Four metrics to quantify popularity bias in RescSys over time in dynamic setting across different sensitive user groups have been proposed. These metrics have been demonstrated for four collaborative filteri ng based RecSys algorithms trained on two commonly used benchmark datasets in the literature. Results obtained show that the metrics proposed provide a comprehensive understanding of growing disparities in treatment between sensitive groups over time when used conjointly.
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An important issue in the field of motion control of wheeled mobile robots is that the design of most controllers is based only on the robot’s kinematics. However, when high-speed movements and/or heavy load transportation are required, it becomes essential to consider the robot dynamics as well. The control signals generated by most dynamic controllers reported in the literature are torques or voltages for the robot motors, while commercial robots usually accept velocity commands. In this context, we present a velocity-based dynamic model for differential drive mobile robots that also includes the dynamics of the robot actuators. Such model has linear and angular velocities as inputs and has been included in Peter Corke’s Robotics Toolbox for MATLAB, therefore it can be easily integrated into simulation systems that have been built for the unicycle kinematics. We demonstrate that the proposed dynamic model has useful mathematical properties. We also present an application of such model on the design of an adaptive dynamic controller and the stability analysis of the complete system, while applying the proposed model properties. Finally, we show some simulation and experimental results and discuss the advantages and limitations of the proposed model.
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The dynamic inflow effect describes the unsteady aerodynamic response to fast changes in rotor loading due to the inertia of the wake. Fast changes in turbine loading due to pitch actuation or rotor speed transients lead to load overshoots. The phenomenon is suspected to be also relevant for gust situations; however, this was never shown, and thus the actual load response is also unknown. The paper’s objectives are to prove and explain the dynamic inflow effect due to gusts, and compare and subsequently improve a typical dynamic inflow engineering model to the measurements. An active grid is used to impress a 1.8m diameter model turbine with rotor uniform gusts of the wind tunnel flow. The influence attributed to the dynamic inflow effect is isolated from the comparison of two experimental cases. Firstly, dynamic measurements of loads and radially resolved axial velocities in the rotor plane during a gust situation are performed. Secondly, corresponding quantities are linearly interpolated for the gust wind speed from lookup tables with steady operational points. Furthermore,simulations with a typical blade element momentum code and a higher-fidelity free-vortex wake model are performed. Both the experiment and higher-fidelity model show a dynamic inflow effect due to gusts in the loads and axial velocities. An amplification of induced velocities causes reduced load amplitudes. Consequently, fatigue loading would be lower. This amplification originates from wake inertia. It is influenced by the coherent gust pushed through the rotor like a turbulent box. The wake is superimposed on that coherent gust box, and thus the inertia of the wake and consequently also the flow in the rotor plane is affected. Contemporary dynamic inflow models inherently assume a constant wind velocity. They filter the induced velocity and thus cannot predict the observed amplification of the induced velocity. The commonly used Øye engineering model predicts increased gust load amplitudes and thus higher fatigue loads. With an extra filter term on the quasi-steady wind velocity, the qualitative behaviour observed experimentally and numerically can be caught. In conclusion, these new experimental findings on dynamic inflow due to gusts and improvements to the Øye model enable improvements in wind turbine design by less conservative fatigue loads.
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