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Selected case studies regarding research-based education in the area of machine and civil assemblies
(2023)
Relationships between External, Wearable Sensor-Based, and Internal Parameters: A Systematic Review
(2023)
Specification of Neck Muscle Dysfunction through Digital Image Analysis Using Machine Learning
(2023)
The Present and Future of a Digital Montenegro: Analysis of C-ITS, Agriculture, and Healthcare
(2023)
Der Rehabilitationsprozess von Patientinnen und Patienten mit neurologischen Langzeitschäden ist eine große Herausforderung. Bei zentralnervalen chronischen Schädigungen arbeiten Therapierende mit Patient*innen und pflegenden Angehörigen mitunter über Jahre intensiv zusammen. Alexa von Bosse hat in einer empirischen Studie die Wichtigkeit einer gelingenden Kommunikation und Beziehungsgestaltung in diesem Beziehungsdreieck verdeutlichen können.
Novel method for plasma etching of printed circuit boards as alternative for fluorocarbon gases
(2023)
The common corpus optimization method “stop words removal” is based on the assumption that text tokens with high occurrence frequency can be removed without affecting classification performance. Linguistic information regarding sentence structure is ignored as well as preferences of the classification technology. We propose the Weighted Unimportant Part-of-Speech Model (WUP-Model) for token removal in the pre-processing of text corpora. The weighted relevance of a token is determined using classification relevance and classification performance impact. The WUP-Model uses linguistic information (part of speech) as grouping criteria. Analogous to stop word removal, we provide a set of irrelevant part of speech (WUP-Instance) for word removal. In a proof-of-concept we created WUP-Instances for several classification algorithms. The evaluation showed significant advantages compared to classic stop word removal. The tree-based classifier increased runtime by 65% and 25% in performance. The performance of the other classifiers decreased between 0.2% and 2.4%, their runtime improved between −4.4% and −24.7%. These results prove beneficial effects of the proposed WUP-Model.
Feasibility of Parylene C for encapsulating piezoelectric actuators in active medical implants
(2023)
Parylene C is well-known as an encapsulation material for medical implants. Within the approach of miniaturization and automatization of a bone distractor, piezoelectric actuators were encapsulated with Parylene C. The stretchability of the polymer was investigated with respect to the encapsulation functionality of piezoelectric chips. We determined a linear yield strain of 1% of approximately 12-μm-thick Parylene C foil. Parylene C encapsulation withstands the mechanical stress of a minimum of 5×105 duty cycles by continuous actuation. The experiments demonstrate that elongation of the encapsulation on piezoelectric actuators and thus the elongation of Parylene C up to 0.8 mm are feasible.