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A Crypto-Token Based Charging Incentivization
Scheme for Sustainable Light Electric Vehicle
Sharing
(2021)
The ecological impact of shared light electric vehicles (LEV) such as kick scooters is still widely discussed. Especially the fact that the vehicles and batteries are collected using diesel vans in order to charge empty batteries with electricity of unclear origin is perceived as unsustainable. A better option could be to let the users charge the vehicles themselves whenever it is necessary. For this, a decentralized,flexible and easy to install network of off-grid solar charging stations could bring renewable electricity where it is needed without sacrificing the convenience of a free float sharing system. Since the charging stations are powered by solar energy the most efficient way to utilize them would be to charge the vehicles when the sun is shining. In order to make users charge the vehicle it is necessary to provide some form of benefit for
them doing so. This could be either a discount or free rides. A
particularly robust and well-established mechanism is controlling incentives via means of blockchain-based cryptotokens. This paper demonstrates a crypto-token based scheme for incentivizing users to charge sharing vehicles during times of considerable solar irradiation in order to contribute to more sustainable mobility services.
Stereotactic frame systems are widely used in neurosurgery. The accuracy of frame devices is considered as a gold standard to which the accuracy of new frameless stereotactic navigation systems is compared. The purpose of this study is to develop a general approach for the prediction of the application accuracy of stereotactic systems. The approach will be applied to the frame‐based biopsy performed with three frame devices: Leksell G, Cosman–Roberts–Wells (CRW), and Brown–Roberts–Wells (BRW). A work‐flow analysis will be carried out demonstrating that the accuracy relevant for a clinical application comprises several error sources including imaging, target and entry point selection, image to frame coordinates registration, and the setting of mechanical parameters of the frame. These error sources will be postulated to obey a Gaussian distribution probability density. The linear, i.e., Gaussian, error propagation, will be used to link all error contributions thus to calculate the cumulative accuracy of the frame used in the application. Although the Gaussian approach is an approximation, it allows for an analytical treatment of the accuracy. Both the accuracy at the target point and the accuracy of the probe needle guidance along the planned trajectory have been investigated. Of great significance is the relationship found between accuracy, pixel dimension, and image slice thickness, the latter being the dominant factor for slices of more than 1.5 mm thickness, yielding inaccuracies larger than 1.5 mm. For target points the predictions for the application accuracy have been compared to the results of measurements, showing good agreement with the experimental data.
Robot arms are one of many assistive technologies used by people with motor impairments. Assistive robot arms can allow people to perform activities of daily living (ADL) involving grasping and manipulating objects in their environment without the assistance of caregivers. Suitable input devices (e.g., joysticks) mostly have two Degrees of Freedom (DoF), while most assistive robot arms have six or more. This results in time-consuming and cognitively demanding mode switches to change the mapping of DoFs to control the robot. One option to decrease the difficulty of controlling a high-DoF assistive robot arm using a low-DoF input device is to assign different combinations of movement-DoFs to the device’s input DoFs depending on the current situation (adaptive control). To explore this method of control, we designed two adaptive control methods for a realistic virtual 3D environment. We evaluated our methods against a commonly used non-adaptive control method that requires the user to switch controls manually. This was conducted in a simulated remote study that used Virtual Reality and involved 39 non-disabled participants. Our results show that the number of mode switches necessary to complete a simple pick-and-place task decreases significantl when using an adaptive control type. In contrast, the task completion time and workload stay the same. A thematic analysis of qualitative feedback of our participants suggests that a longer period of training could further improve the performance of adaptive control methods.