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Institute
VR-EDStream+EDA
(2023)
With increasing digitalization, the importance of data and events, which comprise its most fundamental level, cannot be overemphasized. All types of organizations, including enterprises, business, government, manufacturing, and the supporting IT, are dependent on these fundamental building blocks. Thus, evidence-based comprehension and analysis of the underlying data and events, their stream processing, and correlation with enterprise events and activities becomes vital for an increasing set of (grassroot or citizen) stakeholders. Thus, further investigation of accessible alternatives to visually support analysis of data and events is needed. This paper contributes VR-EDStream+EDA, a solution for immersively visualizing and interacting with data and event streams or pipelines and generically visualizing Event-Driven Architecture (EDA) in Virtual Reality (VR). Our realization shows its feasibility, and a case-based evaluation provides insights into its capabilities.
Purpose: The purpose of this thesis is to provide a comprehensive literature review about albinism as an inherited metabolic disorder of melanin synthesis along with those related conditions impacting the visual system. As such, it addresses eye care emphasizing the visual consequences of albinism along with diagnostic and treatment options.
Methods: Background knowledge about ocular development is given as well as information about etiological biochemical and genetic processes. The current classification, clinical findings and their assessment and management options are presented based on recent results of research. In conclusion, two case reports are described as examples of visual care options.
Results: Melanin plays a big role in the retinal and chiasmal development. Melanin biosynthesis can be disrupted by different genes in various ways which leads to the current classification of albinism. Clinical findings include fundus hypopigmenta-tion, nystagmus, iris transillumination, photophobia, foveal hypoplasia, excessive chiasmal decussation, reduced visual acuity, high astigmatism (with-the-rule), strabismus and decreased stereopsis. Treatment options to improve visual acuity, fixation and binocularity are (tinted) prescription lenses and contact lenses, low vision aids, surgical procedures and vision therapy. Medication and supplementa-tion for increased pigmentation are currently being tested on mice.
Conclusions: Albinism is caused by genetic mutations resulting in ocular and cutaneous hypopigmentation. It establishes various phenotypes that require different therapy approaches in order to improve vision and therefore quality of life.
Ophthalmic lenses are ideally measured in accordance with the center of rotation of the eye. Therefore a measuring device was constructed due to this principle to measure lenses with a focimeter. In this work that measuring device was validated. Lenses of ± 4 dpt in spherical and aspherical design were measured across a field of 9x9 measuring points being at 5° distance from each other. This corresponds to a field of view of 40°. The measurement points in x- and y- direction were theoretically calculated to validate the measurement results. Regarding angles of incidence up to 20° it was supposed that the main optical aberration depends on a change in the sagittal and tangential sphere powers which is also defined as astigmatism. Therefore the calculation presents the tangential and sagittal oblique sphere powers depending on the different angles of the line of vision. On average the measurement results and the calculated data of the spherical designed lenses coincide quite good (correlation at 0,98), the systematic deviation of both values on average is 0.01 dpt and the random error (standard deviation) amounts 0.03 dpt on average. The minimum deviation is -0.06 dpt and the maximum is 0.09 dpt. Common focimeters have a measuring inaccuracy of up to 0.06 dpt (Diepes, Blendowske 2002). Therefore the quality of the measured data should be reliable. The aspherical designed lenses were compared to the spherical designed lenses. With increased angles of incidence the astigmatism of the aspherical lenses leads to lower values than the astigmatism of the spherical lenses
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.