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Biomechanical simulation of different prosthetic meshes for repairing uterine/vaginal vault prolapse
(2017)
The integration of frequently changing, volatile product data from different manufacturers into a single catalog is a significant challenge for small and medium-sized e-commerce companies. They rely on timely integrating product data to present them aggregated in an online shop without knowing format specifications, concept understanding of manufacturers, and data quality. Furthermore, format, concepts, and data quality may change at any time. Consequently, integrating product catalogs into a single standardized catalog is often a laborious manual task. Current strategies to streamline or automate catalog integration use techniques based on machine learning, word vectorization, or semantic similarity. However, most approaches struggle with low-quality or real-world data. We propose Attribute Label Ranking (ALR) as a recommendation engine to simplify the integration process of previously unknown, proprietary tabular format into a standardized catalog for practitioners. We evaluate ALR by focusing on the impact of different neural network architectures, language features, and semantic similarity. Additionally, we consider metrics for industrial application and present the impact of ALR in production and its limitations.
The integration of product data from heterogeneous sources and manufacturers into a single catalog is often still a laborious, manual task. Especially small- and medium-sized enterprises face the challenge of timely integrating the data their business relies on to have an up-to-date product catalog, due to format specifications, low quality of data and the requirement of expert knowledge. Additionally, modern approaches to simplify catalog integration demand experience in machine learning, word vectorization, or semantic similarity that such enterprises do not have. Furthermore, most approaches struggle with low-quality data. We propose Attribute Label Ranking (ALR), an easy to understand and simple to adapt learning approach. ALR leverages a model trained on real-world integration data to identify the best possible schema mapping of previously unknown, proprietary, tabular format into a standardized catalog schema. Our approach predicts multiple labels for every attribute of an inpu t column. The whole column is taken into consideration to rank among these labels. We evaluate ALR regarding the correctness of predictions and compare the results on real-world data to state-of-the-art approaches. Additionally, we report findings during experiments and limitations of our approach.
Chemische Sensoren mit Bariumstrontiumtitanat als funktionelle Schicht zur Multiparameterdetektion
(2013)
Design and implementation aspects of a 3D reconstruction algorithm for the Jülich TierPET system
(1997)
Detection of Adrenaline Based on Bioelectrocatalytical System to Support Tumor Diagnostic Technology
(2017)
The discovery of human induced pluripotent stem cells reprogrammed from somatic cells [1] and their ability to differentiate into cardiomyocytes (hiPSC-CMs) has provided a robust platform for drug screening [2]. Drug screenings are essential in the development of new components, particularly for evaluating the potential of drugs to induce life-threatening pro-arrhythmias. Between 1988 and 2009, 14 drugs have been removed from the market for this reason [3]. The microelectrode array (MEA) technique is a robust tool for drug screening as it detects the field potentials (FPs) for the entire cell culture. Furthermore, the propagation of the field potential can be examined on an electrode basis. To analyze MEA measurements in detail, we have developed an open-source tool.
We propose a stochastic programming method to analyse limit and shakedown of structures under random strength with lognormal distribution. In this investigation a dual chance constrained programming algorithm is developed to calculate simultaneously both the upper and lower bounds of the plastic collapse limit or the shakedown limit. The edge-based smoothed finite element method (ES-FEM) using three-node linear triangular elements is used.
DNA-hybridization detection using light-addressable potentiometric sensor modified with gold layer
(2014)
Conventional EEG devices cannot be used in everyday life and hence, past decade research has been focused on Ear-EEG for mobile, at-home monitoring for various applications ranging from emotion detection to sleep monitoring. As the area available for electrode contact in the ear is limited, the electrode size and location play a vital role for an Ear-EEG system. In this investigation, we present a quantitative study of ear-electrodes with two electrode sizes at different locations in a wet and dry configuration. Electrode impedance scales inversely with size and ranges from 450 kΩ to 1.29 MΩ for dry and from 22 kΩ to 42 kΩ for wet contact at 10 Hz. For any size, the location in the ear canal with the lowest impedance is ELE (Left Ear Superior), presumably due to increased contact pressure caused by the outer-ear anatomy. The results can be used to optimize signal pickup and SNR for specific applications. We demonstrate this by recording sleep spindles during sleep onset with high quality (5.27 μVrms).
Pulmonary arterial cannulation is a common and effective method for percutaneous mechanical circulatory support for concurrent right heart and respiratory failure [1]. However, limited data exists to what effect the positioning of the cannula has on the oxygen perfusion throughout the pulmonary artery (PA). This study aims to evaluate, using computational fluid dynamics (CFD), the effect of different cannula positions in the PA with respect to the oxygenation of the different branching vessels in order for an optimal cannula position to be determined. The four chosen different positions (see Fig. 1) of the cannulas are, in the lower part of the main pulmonary artery (MPA), in the MPA at the junction between the right pulmonary artery (RPA) and the left pulmonary artery (LPA), in the RPA at the first branch of the RPA and in the LPA at the first branch of the LPA.
Effectiveness of the edge-based smoothed finite element method applied to soft biological tissues
(2012)
Beim Ausbau nachhaltiger, regenerativer Energieversorgung hat die Umwandlung von organischer Biomasse in Biogas ein großes Potential. Der zugrundeliegende, komplexe biologische Prozess wird noch immer unzureichend verstanden und bedarf systematischer Untersuchungen der Prozessparameter, um einen hohen Ertrag bei guter Gasqualität zu ermöglichen. Die Fragestellungen zur Entschlüsselung des Prozesses sind sowohl verfahrenstechnischer als auch mikrobiologischer Natur. Aus mikrobiologischer Sicht ist die Kenntnis der tatsächlich beteiligten prozesstragenden Mikroorganismen von erheblicher Bedeutung, aus verfahrenstechnischer Sicht die Kenntnis der physikalischen und chemischen Faktoren, welche die mikrobiologischen Prozesse und kontrollieren. Im Zusammenspiel aller dieser Parameter wird die Biogasbildung befördert oder behindert, bis zum Abbruch des Prozesses.
Eine mögliche Kontrollmethode ist die Messung der metabolischen Aktivität prozesstragender Organismen.
Diese soll, beruhend auf fundierten Prozessdaten, gewonnen durch eine Parallelanlage, mit einem lichtadressierbaren potentiometrischen Sensor-System (LAPS) realisiert werden. Dieser Sensor ist in der Lage, pH-Wert-änderungen zu detektieren, die durch den Stoffwechsel der auf dem Chip immobilisierten Organismen hervorgerufen werden, um eine Online-Überwachung von Biogasanlagen zu ermöglichen.
We compare four different algorithms for automatically estimating the muscle fascicle angle from ultrasonic images: the vesselness filter, the Radon transform, the projection profile method and the gray level cooccurence matrix (GLCM). The algorithm results are compared to ground truth data generated by three different experts on 425 image frames from two videos recorded during different types of motion. The best agreement with the ground truth data was achieved by a combination of pre-processing with a vesselness filter and measuring the angle with the projection profile method. The robustness of the estimation is increased by applying the algorithms to subregions with high gradients and performing a LOESS fit through these estimates.
In recent years, the development of large pretrained language models, such as BERT and GPT, significantly improved information extraction systems on various tasks, including relation classification. State-of-the-art systems are highly accurate on scientific benchmarks. A lack of explainability is currently a complicating factor in many real-world applications. Comprehensible systems are necessary to prevent biased, counterintuitive, or harmful decisions.
We introduce semantic extents, a concept to analyze decision patterns for the relation classification task. Semantic extents are the most influential parts of texts concerning classification decisions. Our definition allows similar procedures to determine semantic extents for humans and models. We provide an annotation tool and a software framework to determine semantic extents for humans and models conveniently and reproducibly. Comparing both reveals that models tend to learn shortcut patterns from data. These patterns are hard to detect with current interpretability methods, such as input reductions. Our approach can help detect and eliminate spurious decision patterns during model development. Semantic extents can increase the reliability and security of natural language processing systems. Semantic extents are an essential step in enabling applications in critical areas like healthcare or finance. Moreover, our work opens new research directions for developing methods to explain deep learning models.
Messenger apps like WhatsApp or Telegram are an integral part of daily communication. Besides the various positive effects, those services extend the operating range of criminals. Open trading groups with many thousand participants emerged on Telegram. Law enforcement agencies monitor suspicious users in such chat rooms. This research shows that text analysis, based on natural language processing, facilitates this through a meaningful domain overview and detailed investigations. We crawled a corpus from such self-proclaimed black markets and annotated five attribute types products, money, payment methods, user names, and locations. Based on each message a user sends, we extract and group these attributes to build profiles. Then, we build features to cluster the profiles. Pretrained word vectors yield better unsupervised clustering results than current
state-of-the-art transformer models. The result is a semantically meaningful high-level overview of the user landscape of black market chatrooms. Additionally, the extracted structured information serves as a foundation for further data exploration, for example, the most active users or preferred payment methods.
A new formulation to calculate the shakedown limit load of Kirchhoff plates under stochastic conditions of strength is developed. Direct structural reliability design by chance con-strained programming is based on the prescribed failure probabilities, which is an effective approach of stochastic programming if it can be formulated as an equivalent deterministic optimization problem. We restrict uncertainty to strength, the loading is still deterministic. A new formulation is derived in case of random strength with lognormal distribution. Upper bound and lower bound shakedown load factors are calculated simultaneously by a dual algorithm.
The structure of the female pelvic floor (PF) is an inter-related system of bony pelvis,muscles, pelvic organs, fascias, ligaments, and nerves with multiple functions. Mechanically, thepelvic organ support system are of two types: (I) supporting system of the levator ani (LA) muscle,and (II) the suspension system of the endopelvic fascia condensation [1], [2]. Significantdenervation injury to the pelvic musculature, depolimerization of the collagen fibrils of the softvaginal hammock, cervical ring and ligaments during pregnancy and vaginal delivery weakens thenormal functions of the pelvic floor. Pelvic organ prolapse, incontinence, sexual dysfunction aresome of the dysfunctions which increases progressively with age and menopause due toweakened support system according to the Integral theory [3]. An improved 3D finite elementmodel of the female pelvic floor as shown in Fig. 1 is constructed that: (I) considers the realisticsupport of the organs to the pelvic side walls, (II) employs the improvement of our previous FEmodel [4], [5] along with the patient based geometries, (III) incorporates the realistic anatomy andboundary conditions of the endopelvic (pubocervical and rectovaginal) fascia, and (IV) considersvarying stiffness of the endopelvic fascia in the craniocaudal direction [3]. Several computationsare carried out on the presented computational model with healthy and damaged supportingtissues, and comparisons are made to understand the physiopathology of the female PF disorders.
Useful market simulations are key to the evaluation of diferent market designs existing of multiple market mechanisms or rules. Yet a simulation framework which has a comparison of diferent market mechanisms in mind was not found. The need to create an objective view on different sets of market rules while investigating meaningful agent strategies concludes that such a simulation framework is needed to advance the research on this subject. An overview of diferent existing market simulation models is given which also shows the research gap and the missing capabilities of those systems. Finally, a methodology is outlined how a novel market simulation which can answer the research questions can be developed.
Messenger apps like WhatsApp and Telegram are frequently used for everyday communication, but they can also be utilized as a platform for illegal activity. Telegram allows public groups with up to 200.000 participants. Criminals use these public groups for trading illegal commodities and services, which becomes a concern for law enforcement agencies, who manually monitor suspicious activity in these chat rooms. This research demonstrates how natural language processing (NLP) can assist in analyzing these chat rooms, providing an explorative overview of the domain and facilitating purposeful analyses of user behavior. We provide a publicly available corpus of annotated text messages with entities and relations from four self-proclaimed black market chat rooms. Our pipeline approach aggregates the extracted product attributes from user messages to profiles and uses these with their sold products as features for clustering. The extracted structured information is the foundation for further data exploration, such as identifying the top vendors or fine-granular price analyses. Our evaluation shows that pretrained word vectors perform better for unsupervised clustering than state-of-the-art transformer models, while the latter is still superior for sequence labeling.