Refine
Year of publication
Institute
- Fachbereich Medizintechnik und Technomathematik (48) (remove)
Has Fulltext
- yes (48) (remove)
Document Type
- Article (48) (remove)
Keywords
- Einspielen <Werkstoff> (7)
- FEM (4)
- Finite-Elemente-Methode (4)
- Bauingenieurwesen (2)
- CAD (2)
- Capacitive field-effect sensor (2)
- Einspielanalyse (2)
- Label-free detection (2)
- Shakedown analysis (2)
- Traglastanalyse (2)
Originalausgabe: Orthopädische Praxis Jg. 47. 2011 H. 11; S. 536-543. Mit freundlicher Genehmigung des Verlags Zusammenfassung: Auf der Basis von Patientenabfragen mittels Fragebogen zum Schmerzempfinden und zur Einschränkung bei Aktivitäten des alltäglichen Lebens wird die Langzeitwirkung der MBST® KernspinResonanz-Therapie bei Gonarthrose untersucht. An der Studie nahmen 39 Patienten teil, bei denen die Therapie bis zu vier Jahre zurückliegt. Neben einer Gesamtbetrachtung wird der Erfolg auch in Abhängigkeit von Alter, Geschlecht und sportlicher Aktivität analysiert. Insgesamt weist die Studie auf eine anhaltende Verbesserung des Gesundheitszustands mit zum Teil deutlicher Schmerzlinderung auch noch nach vier Jahren hin, jedoch mit einer leichten Schmerzzunahme gegen Ende des Untersuchungszeitraums von vier Jahren. Eine tendenziell positivere Wirkung bei Frauen, älteren Menschen oder auch sportlich nicht-aktiven Patienten lässt auf eine mögliche Beeinflussung des Erfolgs der Therapie durch (Über-)Belastung im Alltag schließen. Ein zusätzlich positiver Effekt der Therapie auf die Knochendichte ist ebenfalls denkbar, dies bleibt jedoch offen.
This work is an attempt to answer the question: How to use convex programming in shakedown analysis of structures made of materials with temperature-dependent properties. Based on recently established shakedown theorems and formulations, a dual relationship between upper and lower bounds of the shakedown limit load is found, an algorithmfor shakedown analysis is proposed. While the original problem is neither convex nor concave, the algorithm presented here has the advantage of employing convex programming tools.
The importance of the availability of stored blood or blood cells, respectively, for urgent transfusion cannot be overestimated. Nowadays, blood storage becomes even more important since blood products are used for epidemiological studies, bio-technical research or banked for transfusion purposes. Thus blood samples must not only be processed, stored, and shipped to preserve their efficacy and safety, but also all parameters of storage must be recorded and reported for Quality Assurance. Therefore, blood banks and clinical research facilities are seeking more accurate, automated means for blood storage and blood processing.
Sleep spindles are neurophysiological phenomena that appear to be linked to memory formation and other functions of the central nervous system, and that can be observed in electroencephalographic recordings (EEG) during sleep. Manually identified spindle annotations in EEG recordings suffer from substantial intra- and inter-rater variability, even if raters have been highly trained, which reduces the reliability of spindle measures as a research and diagnostic tool. The Massive Online Data Annotation (MODA) project has recently addressed this problem by forming a consensus from multiple such rating experts, thus providing a corpus of spindle annotations of enhanced quality. Based on this dataset, we present a U-Net-type deep neural network model to automatically detect sleep spindles. Our model’s performance exceeds that of the state-of-the-art detector and of most experts in the MODA dataset. We observed improved detection accuracy in subjects of all ages, including older individuals whose spindles are particularly challenging to detect reliably. Our results underline the potential of automated methods to do repetitive cumbersome tasks with super-human performance.
Bacterial lipopolysaccharides (endotoxins) show strong biological effects at very low concentrations in human beings and many animals when entering the blood stream. These include affecting structure and function of organs and cells, changing metabolic functions, raising body temperature, triggering the coagulation cascade, modifying hemodynamics and causing septic shock. Because of this toxicity, the removal of even minute amounts is essential for safe parenteral administration of drugs and also for septic shock patients' care. The absence of a general method for endotoxin removal from liquid interfaces urgently requires finding new methods and materials to overcome this gap. Nanostructured carbonized plant parts is a promising material that showed good adsorption properties due to its vast pore network and high surface area. The aim of this study was comparative measurement of endotoxin- and blood proteins-related adsorption rate and adsorption capacity for different carboneous materials produced at different temperatures and under different surface modifications. As a main surface modificator, positively cbarged polymer, polyethileneimine (PEl) was used. Activated carbon materials showed good adsorption properties for LPS and some proteins used in the experiments. During the batch experiments, several techniques (dust removal, autoclaving) were used and optimized for improving the material's adsorption behavior. Also, with the results obtained it was possible to differentiate the materials according to their adsorption capacity and kinetic characteristics. Modification of the surface apparently has not affected hemoglobin binding to the adsorbent's surface. Obtained adsorption isotherms can be used as a powerful tool for designing of future column-based setups for blood purification from LPS, which is especially important for septic shock treatment.
An optimization method is developed to describe the mechanical behaviour of the human cancellous bone. The method is based on a mixture theory. A careful observation of the behaviour of the bone material leads to the hypothesis that the bone density is controlled by the principal stress trajectories (Wolff’s law). The basic idea of the developed method is the coupling of a scalar value via an eigenvalue problem to the principal stress trajectories. On the one hand this theory will permit a prediction of the reaction of the biological bone structure after the implantation of a prosthesis, on the other hand it may be useful in engineering optimization problems. An analytical example shows its efficiency.
Companies often build their businesses based on product information and therefore try to automate the process of information extraction (IE). Since the information source is usually heterogeneous and non-standardized, classic extract, transform, load techniques reach their limits. Hence, companies must implement the newest findings from research to tackle the challenges of process automation. They require a flexible and robust system that is extendable and ensures the optimal processing of the different document types. This paper provides a distributed microservice architecture pattern that enables the automated generation of IE pipelines. Since their optimal design is individual for each input document, the system ensures the ad-hoc generation of pipelines depending on specific document characteristics at runtime. Furthermore, it introduces the automated quality determination of each available pipeline and controls the integration of new microservices based on their impact on the business value. The introduced system enables fast prototyping of the newest approaches from research and supports companies in automating their IE processes. Based on the automated quality determination, it ensures that the generated pipelines always meet defined business requirements when they come into productive use.