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Fruits (follicles) of Hakea salicifolia and Hakea sericea (Proteaceae) are characterised by pronounced lignification and open via a ventral suture and the dorsal side. The opening along both sides is unique within the Proteaceae. Both serotinous species are obligate seeders, whose spreading benefits from bush fire events. The different tissues and the course of the vascular bundles must allow the opening mechanism. While their 2D-arrangements are known to some extent from light-microscopy images of cross-sections, this work presents their three-dimensional structures and discusses their contribution to the opening of Hakea fruits. For this purpose, 3D greyscale images, reconstructed from µCT-projection data of both fruits are segmented, assisted by a deep learning algorithm (AI algorithm). 3D renderings from these segmentations show strongly interconnected vascular bundles that build a double-dome shaped network in each valve of H. salicifolia and a dome shaped honeycomb-structure in each valve of H. sericea. However, the vascular bundles of both species show no interconnection between the two lateral valves of the fruit but leave gaps for predetermined fracture tissues on the ventral and dorsal side. The opening of the fruits after a fire or after separation from the mother plant can be explained by the anisotropic shrinkage in the two valves of the fruit.
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.