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Purpose: Recent studies found a reduction of myopia progression with multifocal contact lenses, however, with yet unclear mechanism. This raises the hypothesis that the addition zones of the multifocal contact lenses induce myopic defocus on the retina, which consequentially leads to choroidal thickening and therefore inhibited eye growth. We tested the effect of the optical design of multifocal contact lenses on choroidal thickness.
Methods: 18 myopic students wore four different contact lenses ((1) single-vision lens corrected for distance, (2) single-vision lens with +2.50 D full-field defocus, (3) “Multifocal center-distance” design, addition +2.50 D, (4) “Multifocal center-near” design, addition +2.50 D) for each 30 minutes on their right eye. Automated analysis of the macular choroidal thickness, vitreous chamber depth and eccentric photorefraction were performed before and after each contact lens.
Results: Choroidal thickness and vitreous chamber depth showed no significant differences to baseline with none of the contact lenses. Choroidal thickness increased the most with the “Multifocal center-distance” and the full-field defocus lens, followed by the “Multifocal center-near” and the single-vision contact lens (+2.1 ± 11.1 μm, +2.0 ± 11.1 μm, +1.6 ± 11.3 μm, +0.9 ± 11.2 μm, respectively). The “Multifocal center-distance” design showed an overall more myopic refractive profile than the other lenses. Changes of vitreous chamber depth occurred in anti-phase to these of choroidal thickness.
Conclusion: Multifocal contact lenses have no significant influence on choroidal thickness and after short-term wear. Therefore, it is assumed that it is not the main contributor to the protective effect of multifocal contact lenses in myopia control.
The effect of personality and gender on individuals' propensity for corruption and corrupt behavior
(2020)
The Dimensional Accuracy of Thin-Walled Parts Manufactured by Laser-Powder Bed Fusion Process
(2020)
In this study, we investigate the use of artificial neural networks as a potentially efficient method to determine the rate capability of electrodes for lithium-ion batteries with different porosities. The performance of a lithium-ion battery is, to a large extent, determined by the microstructure (i.e., layer thickness and porosity) of its electrodes. Tailoring the microstructure to a specific application is a crucial process in battery development. However, unravelling the complex correlations between microstructure and rate performance using either experiments or simulations is time-consuming and costly. Our approach provides a swift method for predicting the rate capability of battery electrodes by using machine learning on microstructural images of electrode cross-sections. We train multiple models in order to predict the specific capacity based on the batteries’ microstructure and investigate the decisive parts of the microstructure through the use of explainable artificial intelligence (XAI) methods. Our study shows that even comparably small neural network architectures are capable of providing state-of-the-art prediction results. In addition to this, our XAI studies demonstrate that the models are using understandable human features while ignoring present artefacts.