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Today’s Industry 4.0 Smart Factories involve complicated and highly automated processes. Nevertheless, certain crucial activities such as machine maintenance remain that require human involvement. For such activities, many factors have to be taken into account, like worker safety or worker qualification. This adds to the complexity of selection and assignment of optimal human resources to the processes and overall coordination. Contemporary Business Process Management (BPM) Systems only provide limited facilities regarding activity resource assignment. To overcome these, this contribution pro- poses a BPM-integrated approach that applies fuzzy sets and rule processing for activity assignment. Our findings suggest that our approach has the potential for improved work distribution and cost savings for Industry 4.0 production processes. Furthermore, the scalability of the approach provides efficient performance even with a large number of concurrent activity assignment requests and can be applied to complex production scenarios with minimal effort.
Identification and quantitative segmentation of individual blood vessels in mice visualized with preclinical imaging techniques is a tedious, manual or semiautomated task that can require weeks of reviewing hundreds of levels of individual data sets. Preclinical imaging, such as micro-magnetic resonance imaging (μMRI) can produce tomographic datasets of murine vasculature across length scales and organs, which is of outmost importance to study tumor progression, angiogenesis, or vascular risk factors for diseases such as Alzheimer’s. Training a neural network capable of accurate segmentation results requires a sufficiently large amount of labelled data, which takes a long time to compile. Recently, several reasonably automated approaches have emerged in the preclinical context but still require significant manual input and are less accurate than the deep learning approach presented in this paper—quantified by the Dice score. In this work, the implementation of a shallow, three-dimensional U-Net architecture for the segmentation of vessels in murine brains is presented, which is (1) open-source, (2) can be achieved with a small dataset (in this work only 8 μMRI imaging stacks of mouse brains were available), and (3) requires only a small subset of labelled training data. The presented model is evaluated together with two post-processing methodologies using a cross-validation, which results in an average Dice score of 61.34% in its best setup. The results show, that the methodology is able to detect blood vessels faster and more reliably compared to state-of-the-art vesselness filters with an average Dice score of 43.88% for the used dataset.
The direct ophthalmoscope is a retinal screening tool that has been in existence and development for more than 150 years, yet, the rapid influence of technological evolution in screening tools, has left the direct ophthalmoscope untouched. The main purpose of this master thesis is to determine if the direct ophthalmoscope has reached its maximum potential of development and, additionally, to determine if a further development, including a more electronic input, would be feasible.
Soft magnetic Fe-Al alloys have been a subject of research in the past. However, they never saw the same reception in technical applications as the Fe-Si or Fe-Ni alloys, which is, to some extent, due to a low ductility level and difficulties in manufacturing. Additive manufacturing (AM) technology could be a way to avoid issues in conventional manufacturing and produce soft magnetic components from these alloys, as has already been shown with similarly brittle Fe-Si alloys. While AM has already been applied to certain Fe-Al alloys, no magnetic properties of AM Fe-Al alloys have been reported in the literature so far. Therefore, in this work, a Fe-12Al alloy was additively manufactured through laser powder bed fusion (L-PBF) and characterized regarding its microstructure and magnetic properties. A comparison was made with the materials produced by casting and rolling, prepared from melts with an identical chemical composition. In order to improve the magnetic properties, a heat treatment at a higher temperature (1300 °C) than typically applied for conventionally manufactured materials (850–1150 °C) is proposed for the AM material. The specially heat-treated AM material reached values (HC: 11.3 A/m; µmax: 13.1 × 103) that were close to the heat-treated cast material (HC: 12.4 A/m; µmax: 20.3 × 103). While the DC magnetic values of hot- and cold-rolled materials (HC: 3.2 to 4.1 A/m; µmax: 36.6 to 40.4 × 103) were not met, the AM material actually showed fewer losses than the rolled material under AC conditions. One explanation for this effect can be domain refinement effects. This study shows that it is possible to additively manufacture Fe-Al alloys with good soft magnetic behavior. With optimized manufacturing and post-processing, further improvements of the magnetic properties of AM L-PBF Fe-12Al may still be possible.
Purpose: The aim is to be able to advise patients on the choice of sports and exercises regarding the effects on the intraocular pressure.
Methods: The search engines Google Scholar and PubMed were used to search for suitable studies. The studies were summarized, and the most important data were collected in one table for each study. The effect on the IOP was extracted or, if not given in the article, calculated by the difference of means of the IOP after or during exercise, and the baseline IOP before, whenever these values were available.
Findings: A total of 47 studies out of the years 1990 to 2020 that investigated the influence on the IOP of the most popular sports actively practiced in Germany were reviewed and summarized: twelve for running, sixteen for fitness/ weight training, one for swimming/diving, twelve for cycling, four for hiking, and two for yoga.
Conclusions: Throughout all studies and sports it was seen that physical fitness stabilized the IOP. Higher
intensity of exercise led to higher fluctuations of the IOP. Moderate endurance training keeps the IOP fluctuations low and may lead to a lower baseline IOP if practiced on a regular base. Fitness and weight training lead to fluctuations of the IOP in a pronounced manner when performed at moderate and high intensity. Therefore, only a moderate training can be recommended if there is need to keep the IOP stable. Isometric exercise is not recommended as it provokes a rise of the IOP even when performed with light loads. The Valsalva Maneuver should always be avoided as it leads to additional fluctuations of the IOP. Also, the IOP behaved more stable during resistance training when higher fitness was present.
Purpose
The purpose of this study was to evaluate the validity of the iPad Aniseikonia Test for
measurement size lens-induced aniseikonia. The iPad Aniseikonia Test is a new
computer-based test designed for measuring aniseikonia in vertical direction. The iPad
Test uses red-green anaglyphs.
Methods
Aniseikonia was induced in 21 subjects by means of afocal size lenses. Resulting
aniseikonia was measured in vertical direction by the iPad Aniseikonia Test. The
measurement was performed in dark condition with appropriate correction of refractive
error. All subject were patients with normal vision with no anisometropia or other
ocular problem.
Results:
Afocal size lenses of known magnification were used to induce aniseikonia. 5
measurements were taken in each subject, ranging from zero to 7 % magnification.
When using the regression analysis, the slope of the fitted line significantly differs from
1. The average slope of regression line is 0,58.
Conclusions:
Only moderate accuracy was found for tested target size and orientation. In all cases the
iPad Aniseikonia Test underestimates the level of aniseikonia. However for gross
assessment of anisometropia in clinical practice it could be successfully used. Further
study with different target size should be addressed.
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.
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.
Production processes in Industry 4.0 settings are usually highly automated. However, many complicated tasks, such as machine maintenance, must be executed by human workers. In current smart factories, such tasks can be supported by Augmented Reality (AR) devices. These AR tasks rely on high numbers of contextual factors like live data from machines or work safety conditions and are mostly not well integrated into the global production process. This can lead to various problems like suboptimal task assignment, over-exposure of workers to hazards like noise or heat, or delays in the production process. Current Business Process Management (BPM) Systems (BPMS) are not capable of readily taking such factors into account. There- fore, this contribution proposes a novel approach for context- integrated modeling and execution of processes with AR tasks. Our practical evaluations show that our AR Process Framework can be easily integrated with prevalent BPMS. Furthermore, we have created a comprehensive simulation scenario and our findings suggest that the application of this system can lead to various benefits, like better quality of AR task execution and cost savings regarding the overall Industry 4.0 processes.