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Design and fabrication of a novel 4D-printed customized hand orthosis to treat cerebral palsy
(2024)
Pulmonary response prediction through personalized basis functions in a virtual patient model
(2024)
Introduction: The present study investigated the role of training intensity in the dose–response relationship between endurance training and cardiorespiratory fitness (CRF). The hypothesis was that beginners would benefit from an increase in training intensity after an initial training phase, even if the energy expenditure was not altered. For this purpose, 26 weeks of continuous moderate training (control group, CON) was compared to training with gradually increasing intensity (intervention group, INC) but constant energy expenditure.
Methods: Thirty-one healthy, untrained subjects (13 men, 18 women; 46±8 years; body mass index 25.4 ± 3.3 kg m−2; maximum oxygen uptake, VO2max −1 −1 34 ± 4 ml min kg ) trained for 10 weeks with moderate intensity [3 days/week for 50 min/session at 55% heart rate reserve (HRreserve)] before allocation to one of two groups. A minimization technique was used to ensure homogeneous groups. While group CON continued with moderate intensity for 16 weeks, the INC group trained at 70% HRreserve for 8weeks and thereafter participated in a 4 × 4 training program (high-intensity interval training, HIIT) for 8 weeks. Constant energy expenditure was ensured by indirect calorimetry and corresponding adjustment of the training volume. Treadmill tests were performed at baseline and after 10, 18, and 26 weeks.
Results: The INC group showed improved VO2max (3.4 ± 2.7 ml kg−1 min−1) to a significantly greater degree than the CON group (0.4 ± 2.9 ml kg−1 min−1) (P = 0.020). In addition, the INC group exhibited improved Vmax (1.7 ± 0.7 km h−1) to a significantly greater degree than the CON group (1.0 ± 0.5 km h−1) (P = 0.001). The reduction of resting HR was significantly larger in the INC group (7±4bpm) than in the CON group (2±6bpm) (P=0.001). The mean heart rate in the submaximal exercise test was reduced significantly in the CON group (5±6bpm; P=0.007) and in the INC group (8±7bpm; P=0.001), without a significant interaction between group and time point.
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.