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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.
Seismic design of buried pipeline systems for energy and water supply is not only important for plant and operational safety but also for the maintenance of the supply infrastructure after an earthquake. The present paper shows special issues of the seismic wave impacts on buried pipelines, describes calculation methods, proposes approaches and gives calculation examples. This paper regards the effects of transient displacement differences and resulting tensions within the pipeline due to the wave propagation of the earthquake. However, the presented model can also be used to calculate fault rupture induced displacements. Based on a three-dimensional Finite Element Model parameter studies are performed to show the influence of several parameters such as incoming wave angle, wave velocity, backfill height and synthetic displacement time histories. The interaction between the pipeline and the surrounding soil is modeled with non-linear soil springs and the propagating wave is simulated affecting the pipeline punctually, independently in time and space. Special attention is given to long-distance heat pipeline systems. Here, in regular distances expansion bends are arranged to ensure movements of the pipeline due to high temperature. Such expansion bends are usually designed with small bending radii, which during the earthquake lead to high bending stresses in the cross-section of the pipeline. Finally, an interpretation of the results and recommendations are given for the most critical parameters.
In steps of the production chain of carbide inserts, such as unloading or packaging, the conformity test of the insert type is done manually, which causes a statistic increase of errors due to monotony and fatigue of the worker and the wide variety of the insert types. A machine vision system is introduced that captures digital frames of the inserts in the production line, analyses inspects automatically and measures four quality features: coating colour, edge radius, plate shape and chip-former geometry. This new method has been tested on several inserts of different types and has shown that the prevalent insert types can be inspected and robustly classified in real production environment and therefore improves the manufacturing automation.
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.
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.
Often, research results from collaboration projects are not transferred into productive environments even though approaches are proven to work in demonstration prototypes. These demonstration prototypes are usually too fragile and error-prone to be transferred
easily into productive environments. A lot of additional work is required.
Inspired by the idea of an incremental delivery process, we introduce an architecture pattern, which combines the approach of Metrics Driven Research Collaboration with microservices for the ease of integration. It enables keeping track of project goals over the course of the collaboration while every party may focus on their expert skills: researchers may focus on complex algorithms,
practitioners may focus on their business goals.
Through the simplified integration (intermediate) research results can be introduced into a productive environment which enables
getting an early user feedback and allows for the early evaluation of different approaches. The practitioners’ business model benefits throughout the full project duration.
This paper presents the laser-based powder bed fusion (L-PBF) using various glass powders (borosilicate and quartz glass). Compared to metals, these require adapted process strategies. First, the glass powders were characterized with regard to their material properties and their processability in the powder bed. This was followed by investigations of the melting behavior of the glass powders with different laser wavelengths (10.6 µm, 1070 nm). In particular, the experimental setup of a CO2 laser was adapted for the processing of glass powder. An experimental setup with integrated coaxial temperature measurement/control and an inductively heatable build platform was created. This allowed the L-PBF process to be carried out at the transformation temperature of the glasses. Furthermore, the component’s material quality was analyzed on three-dimensional test specimen with regard to porosity, roughness, density and geometrical accuracy in order to evaluate the developed L-PBF parameters and to open up possible applications.
Compared to peripheral pain, trigeminal pain elicits higher levels of fear, which is assumed to enhance the interruptive effects of pain on concomitant cognitive processes. In this fMRI study we examined the behavioral and neural effects of trigeminal (forehead) and peripheral (hand) pain on visual processing and memory encoding. Cerebral activity was measured in 23 healthy subjects performing a visual categorization task that was immediately followed by a surprise recognition task. During the categorization task subjects received concomitant noxious electrical stimulation on the forehead or hand. Our data show that fear ratings were significantly higher for trigeminal pain. Categorization and recognition performance did not differ between pictures that were presented with trigeminal and peripheral pain. However, object categorization in the presence of trigeminal pain was associated with stronger activity in task-relevant visual areas (lateral occipital complex, LOC), memory encoding areas (hippocampus and parahippocampus) and areas implicated in emotional processing (amygdala) compared to peripheral pain. Further, individual differences in neural activation between the trigeminal and the peripheral condition were positively related to differences in fear ratings between both conditions. Functional connectivity between amygdala and LOC was increased during trigeminal compared to peripheral painful stimulation. Fear-driven compensatory resource activation seems to be enhanced for trigeminal stimuli, presumably due to their exceptional biological relevance.
The SG1-mediated solution polymerization of methyl methacrylate (MMA) and oligo(ethylene glycol) methacrylate (OEGMA, Mₙ = 300 g mol⁻¹) in the presence of a small amount of functional/reactive styrenic comonomer is investigated. Moieties such as pentafluorophenyl ester, triphenylphosphine, azide, pentafluorophenyl, halide, and pyridine are considered. A comonomer fraction as low as 5 mol% typically results in a controlled/living behavior, at least up to 50% conversion. Chain extensions with styrene for both systems were successfully performed. Variation of physical properties such as refractive index (for MMA) and phase transition temperature (for OEGMA) were evaluated by comparing to 100% pure homopolymers. The introduction of an activated ester styrene derivative in the polymerization of OEGMA allows for the synthesis of reactive and hydrophilic polymer brushes with defined thickness. Finally, using the example of pentafluorostyrene as controlling comonomer, it is demonstrated that functional PMMA-b-PS are able to maintain a phase separation ability, as evidenced by the formation of nanostructured thin films.
Detection and identification of free radicals in hydrocarbon pyrolysis by an iodine trapping method
(1992)
Risk factors for cardiovascular calcifications in non-diabetic Caucasian haemodialysis patients
(2009)
The potential of SMART climbing robot combined with a weatherproof cabin for rotor blade maintenance
(2016)
Short term effects of magnetic resonance imaging on excitability of the motor cortex at 1.5T and 7T
(2010)
Rationale and Objectives
The increasing spread of high-field and ultra-high-field magnetic resonance imaging (MRI) scanners has encouraged new discussion of the safety aspects of MRI. Few studies have been published on possible cognitive effects of MRI examinations. The aim of this study was to examine whether changes are measurable after MRI examinations at 1.5 and 7 T by means of transcranial magnetic stimulation (TMS).
Materials and Methods
TMS was performed in 12 healthy, right-handed male volunteers. First the individual motor threshold was specified, and then the cortical silent period (SP) was measured. Subsequently, the volunteers were exposed to the 1.5-T MRI scanner for 63 minutes using standard sequences. The MRI examination was immediately followed by another TMS session. Fifteen minutes later, TMS was repeated. Four weeks later, the complete setting was repeated using a 7-T scanner. Control conditions included lying in the 1.5-T scanner for 63 minutes without scanning and lying in a separate room for 63 minutes. TMS was performed in the same way in each case. For statistical analysis, Wilcoxon's rank test was performed.
Results
Immediately after MRI exposure, the SP was highly significantly prolonged in all 12 subjects at 1.5 and 7 T. The motor threshold was significantly increased. Fifteen minutes after the examination, the measured value tended toward normal again. Control conditions revealed no significant differences.
Conclusion
MRI examinations lead to a transient and highly significant alteration in cortical excitability. This effect does not seem to depend on the strength of the static magnetic field.
Purpose
To assess potential cognitive deficits under the influence of static magnetic fields at various field strengths some studies already exist. These studies were not focused on attention as the most vulnerable cognitive function. Additionally, mostly no magnetic resonance imaging (MRI) sequences were performed.
Materials and Methods
In all, 25 right-handed men were enrolled in this study. All subjects underwent one MRI examination of 63 minutes at 1.5 T and one at 7 T within an interval of 10 to 30 days. The order of the examinations was randomized. Subjects were referred to six standardized neuropsychological tests strictly focused on attention immediately before and after each MRI examination. Differences in neuropsychological variables between the timepoints before and after each MRI examination were assessed and P-values were calculated
Results
Only six subtests revealed significant differences between pre- and post-MRI. In these tests the subjects achieved better results in post-MRI testing than in pre-MRI testing (P = 0.013–0.032). The other tests revealed no significant results.
Conclusion
The improvement in post-MRI testing is only explicable as a result of learning effects. MRI examinations, even in ultrahigh-field scanners, do not seem to have any persisting influence on the attention networks of human cognition immediately after exposure.
Euler-based induced drag estimation for highly non-planar lifting systems during conceptional design
(2013)
The impact of wake model effects is investigated for two highly
non-planar lifting systems. Dependent on the geometrical
arrangement of the configuration, the wake model shape is found
to considerably affect the estimation. Particularly at higher angles
of attack, an accurate estimation based on the common linear wake
model approaches is involved.
Reasoning with Qualitative Positional Information for Domestic Domains in the Situation Calculus
(2011)
In this paper we present CAESAR, an intelligent domestic service robot. In domestic settings for service robots complex tasks have to be accomplished. Those tasks benefit from deliberation, from robust action execution and from flexible methods for human–robot interaction that account for qualitative notions used in natural language as well as human fallibility. Our robot CAESAR deploys AI techniques on several levels of its system architecture. On the low-level side, system modules for localization or navigation make, for instance, use of path-planning methods, heuristic search, and Bayesian filters. For face recognition and human–machine interaction, random trees and well-known methods from natural language processing are deployed. For deliberation, we use the robot programming and plan language READYLOG, which was developed for the high-level control of agents and robots; it allows combining programming the behaviour using planning to find a course of action. READYLOG is a variant of the robot programming language Golog. We extended READYLOG to be able to cope with qualitative notions of space frequently used by humans, such as “near” and “far”. This facilitates human–robot interaction by bridging the gap between human natural language and the numerical values needed by the robot. Further, we use READYLOG to increase the flexible interpretation of human commands with decision-theoretic planning. We give an overview of the different methods deployed in CAESAR and show the applicability of a system equipped with these AI techniques in domestic service robotics
In the RoboCup@Home domestic service robot competition, complex tasks such as "get the cup from the kitchen and bring it to the living room" or "find me this and that object in the apartment" have to be accomplished. At these competitions the robots may only be instructed by natural language. As humans use qualitative concepts such as "near" or "far", the robot needs to cope with them, too. For our domestic robot, we use the robot programming and plan language Readylog, our variant of Golog. In previous work we extended the action language Golog, which was developed for the high-level control of agents and robots, with fuzzy concepts and showed how to embed fuzzy controllers in Golog. In this paper, we demonstrate how these notions can be fruitfully applied to two domestic service robotic scenarios. In the first application, we demonstrate how qualitative fluents based on a fuzzy set semantics can be deployed. In the second program, we show an example of a fuzzy controller for a follow-a-person task.
In this paper we present an extension of the action language Golog that allows for using fuzzy notions in non-deterministic argument choices and the reward function in decision-theoretic planning. Often, in decision-theoretic planning, it is cumbersome to specify the set of values to pick from in the non-deterministic-choice-of-argument statement. Also, even for domain experts, it is not always easy to specify a reward function. Instead of providing a finite domain for values in the non-deterministic-choice-of-argument statement in Golog, we now allow for stating the argument domain by simply providing a formula over linguistic terms and fuzzy uents. In Golog’s forward-search DT planning algorithm, these formulas are evaluated in order to find the agent’s optimal policy. We illustrate this in the Diner Domain where the agent needs to calculate the optimal serving order.
In this work, we report on our attempt to design and implement an early introduction to basic robotics principles for children at kindergarten age. One of the main challenges of this effort is to explain complex robotics contents in a way that pre-school children could follow the basic principles and ideas using examples from their world of experience. What sets apart our effort from other work is that part of the lecturing is actually done by a robot itself and that a quiz at the end of the lesson is done using robots as well. The humanoid robot Pepper from Softbank, which is a great platform for human–robot interaction experiments, was used to present a lecture on robotics by reading out the contents to the children making use of its speech synthesis capability. A quiz in a Runaround-game-show style after the lecture activated the children to recap the contents they acquired about how mobile robots work in principle. In this quiz, two LEGO Mindstorm EV3 robots were used to implement a strongly interactive scenario. Besides the thrill of being exposed to a mobile robot that would also react to the children, they were very excited and at the same time very concentrated. We got very positive feedback from the children as well as from their educators. To the best of our knowledge, this is one of only few attempts to use a robot like Pepper not as a tele-teaching tool, but as the teacher itself in order to engage pre-school children with complex robotics contents.