Refine
Year of publication
Document type
- Article (peer-reviewed) (596) (remove)
Has full text
- Yes (596) (remove)
Keywords
- Electrical impedance tomography (49)
- Mechanical ventilation (14)
- Acute respiratory distress syndrome (12)
- Exercise (11)
- Machine learning (11)
- Bone mineral density (8)
- ARDS (7)
- Biomechanics (7)
- Convolutional neural network (7)
- Convolutional neural network (CNN) (7)
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.
Data processed in context is more meaningful, easier to understand and has higher information content, hence it derives its semantic meaning from the surrounding context. Even in the field of acoustic signal processing. In this work, a Deep Learning based approach using Ensemble Neural Networks to integrate context into a learning system is presented. For this purpose, different use cases are considered and the method is demonstrated using acoustic signal processing of machine sound data for valves, pumps and slide rails. Mel-spectrograms are used to train convolutional neural networks in order to analyse acoustic data using image processing techniques.
In this paper, we derive set constraints for a reduced order model and augment them into a model predictive control (MPC) scheme to ensure safe operation of the large-scale ensemble system. For the control feedback, only the aggregated information of the whole system is required. For the constraint satisfaction, we consider an adaptive tube formulation to characterize the deviation between the reduced order model and the ensemble system. Employing the robust control invariant set, we ensure recursive feasibility and initial feasibility under an easily verifiable condition.
Influence of Reconstruction Algorithms on Harmonic Analysis in Electrical Impedance Tomography
(2023)
The absolute value of recruitment-to-inflation ratio does not correlate with the recruited volume
(2023)
Seit 2021 läuft in Baden-Württemberg das Landesprojekt Hochschulweiterbildung@BW, in dessen Mittelpunkt die strukturelle Weiterentwicklung der wissenschaftlichen und künstlerischen Weiterbildung steht.
Im folgenden Beitrag wird die dritte Säule des Projektes, die Initiierung und Etablierung einer Struktur von Regional-und Fachvernetzungsstellen an den beteiligten Hochschulen, in den Blick genommen. Dieses neue Instrument wird zunächst in seiner Grundstruktur vorgestellt, eine Zwischenbilanz nach Ablauf der ersten Projekthälfte gezogen und dann der Frage nachgegangen, wie die Arbeit der Regional- und Fachvernetzungsstellen dazu beiträgt, die Bedarfe aus Wirtschaft und Gesellschaft und die Weiterbildungsangebote der Hochschulen noch besser in Passung zu bringen.
Laparoscopic Video Analysis Using Temporal, Attention, and Multi-Feature Fusion Based-Approaches
(2023)
Existing literature (Erling & Hingeldorf, 2006; Earls, 2014) indicates that there is a lack of formal policies at the macro- or meso-level governing the use of English in German higher education. This has led to a situation in which higher education institutions (HEIs) are required to formulate and implement their own policies and guidelines regarding English-medium instruction (EMI). As a growing number of HEIs adopt EMI (Wächter & Maiworm, 2014; Macaro et al., 2018) without access to policy guidelines, there is an urgent need to scrutinize the policy formulation and implementation processes at the institutional level. Such investigation is crucial to understand the complexities that come with tailoring EMI to unique institutional contexts, objectives, and stakeholder needs. We believe that this will enable more effective and equitable implementations, while also providing insights that could inform future policy recommendations. In this article, we analyze the motivations for drafting a language policy at a medium-sized German university of applied sciences1 (UAS) and investigate the attitudes and opinions towards EMI of three stakeholder groups: faculty members, administrative staff, and the student body. We were especially interested in exploring the rationales for implementing Bilingual Degree Programs (BDPs), as a variant of EMI, and how each stakeholder group influenced the formulation and implementation of the policy. To get an initial overview, we read institutional policy documents outlining the proposed language policy. We then complemented the documentary analysis by conducting a survey investigating the attitudes and opinions of the stakeholder groups using a questionnaire format (n=207). Finally, to gain deeper insights and triangulate data from the questionnaire, we conducted semi-structured interviews (n=18). Analysis of the data indicates that the primary motivation for implementing BDPs is to attract greater numbers of international as well as domestic applicants to make up for an ongoing decline in student numbers. We also discovered that stakeholder groups hold different beliefs about BDPs, impacting their level of support for their implementation. We argue this is due to some groups within the institution being more influential in policy formulation, leading to feelings of disempowerment in individuals tasked with implementing BDPs, but not being consulted in the policy formulation process. Finally, it also seems that the institutional policy is driven by experience in implementation, resulting in policy enhancement over time. We assume this approach is a direct outcome of the lack of policy guidelines and consider the issues that arise from such an approach and share implications of the current practice.
Quality assurance (QA) plays a crucial role in manufacturing to ensure that products meet their specifications. However, manual QA processes are costly and time-consuming, thereby making artificial intelligence (AI) an attractive solution for automation and expert support. In particular, convolutional neural networks (CNNs) have gained a lot of interest in visual inspection. Next to AI methods, the explainable artificial intelligence (XAI) systems, which achieve transparency and interpretability by providing insights into the decision-making process of the AI, are interesting methods for achieveing quality inspections in manufacturing processes. In this study, we conducted a systematic literature review (SLR) to explore AI and XAI approaches for visual QA (VQA) in manufacturing. Our objective was to assess the current state of the art and identify research gaps in this context. Our findings revealed that AI-based systems predominantly focused on visual quality control (VQC) for defect detection. Research addressing VQA practices, like process optimization, predictive maintenance, or root cause analysis, are more rare. Least often cited are papers that utilize XAI methods. In conclusion, this survey emphasizes the importance and potential of AI and XAI in VQA across various industries. By integrating XAI, organizations can enhance model transparency, interpretability, and trust in AI systems. Overall, leveraging AI and XAI improves VQA practices and decision-making in industries.
Assessing the impact of a structural prior mask on EIT images with different thorax excursion models
(2023)
Multifrequency bioimpedance device based on the Analog Discovery 2: performance and characterization
(2023)
Microstructure of Selective Laser Melted 316L under Non-Equilibrium Solidification Conditions
(2023)
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.
A special generative manufacturing (AM) process was developed for the partial integration of active ingredients into open-porous matrix structures. A mixture of a silver-containing solution as an antibacterial material with an alginate hydrogel as a carrier system was produced as the active ingredient. The AM process developed was used to introduce the active ingredient solution into an open-porous niobium containing a β-titanium matrix structure, thus creating a reproducible active ingredient delivery system. The matrix structure had already been produced in a separate AM process by means of selective laser melting (SLM). The main advantage of this process is the ability to control porosity with high precision. To determine optimal surface conditions for the integration of active ingredients into the matrix structure, different surface conditions of the titanium substrate were tested for their impact on wetting behaviour of a silver-containing hydrogel solution. The solution-substrate contact angle was measured and evaluated to determine the most favourable surface condition. To develop the generative manufacturing process, an FDM printer underwent modifications that permitted partial application of the drug solution to the structure in accordance with the bioprinting principle. The modified process enabled flexible control and programming of both the position and volume of the applied drug. Furthermore, the process was able to fill up to 95% of the titanium matrix body pore volume used. The customised application of drug carriers onto implants as a drug delivery system can be achieved via the developed process, providing an alternative to established methods like dip coating that lack this capability.
Harnessing Wearable Devices for Emotional Intelligence: Therapeutic Applications in Digital Health
(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.