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- 2019 (289) (remove)
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- Lehrbuch (4)
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Eine Organisationspflichtverletzung des Arbeitgebers, aus der keine gesundheitsschädigende Einwirkung beim Versicherten resultiert, sondern nur eine Nothilfemaßnahme für eine berufsunabhängige Gesundheitsstörung unterbleibt, erfüllt insoweit nicht die Voraussetzungen eines Arbeitsunfalls. Das Fehlen eines Defibrillators an der Betriebsstätte begründet schon kein Organisationsverschulden des Arbeitgebers, denn die Vorhaltung eines Defibrillators zur Anwendung durch Laien ist nicht verpflichtend gesetzlich geregelt.
This work addresses the issue of finding an optimal flight zone for a side-by-side tracking and following Unmanned Aerial Vehicle(UAV) adhering to space-restricting factors brought upon by a dynamic Vector Field Extraction (VFE) algorithm. The VFE algorithm demands a relatively perpendicular field of view of the UAV to the tracked vehicle, thereby enforcing the space-restricting factors which are distance, angle and altitude. The objective of the UAV is to perform side-by-side tracking and following of a lightweight ground vehicle while acquiring high quality video of tufts attached to the side of the tracked vehicle. The recorded video is supplied to the VFE algorithm that produces the positions and deformations of the tufts over time as they interact with the surrounding air, resulting in an airflow model of the tracked vehicle. The present limitations of wind tunnel tests and computational fluid dynamics simulation suggest the use of a UAV for real world evaluation of the aerodynamic properties of the vehicle’s exterior. The novelty of the proposed approach is alluded to defining the specific flight zone restricting factors while adhering to the VFE algorithm, where as a result we were capable of formalizing a locally-static and a globally-dynamic geofence attached to the tracked vehicle and enclosing the UAV.
This work aims to create a natural language generation (NLG) base for further development of systems for automatic examination questions generation and automatic summarization in Hochschule Bonn-Rhein-Sieg and Fraunhofer IAIS, respectively. Nowadays both tasks are very relevant. The first can significantly simplify the university teachers' work and the second to be of assistance for a faster retrieval of knowledge from an excessively large amount of information that people often work with. We focus on the search for an efficient and robust approach to the controlled NLG problem. Therefore, though the initial idea of the project was the usage of the generative adversarial neural networks (GANs), we switched our attention to more robust and easily-controllable autoencoders. Thus, in this work we implement an autoencoder for unsupervised discovery of latent space representations of text, and show the ability of the system to generate new sentences based on this latent space. Apart from that, we apply Gaussian mixture techniques in order to obtain meaningful text clusters and thereby try to create a tool that would allow us to generate sentences relevant to the semantics of the Gaussian clusters, e.g. positive or negative reviews or examination questions on certain topic. The developed system is tested on several datasets and compared to GANs' performance.
Are quality diversity algorithms better at generating stepping stones than objective-based search?
(2019)
The route to the solution of complex design problems often lies through intermediate "stepping stones" which bear little resemblance to the final solution. By greedily following the path of greatest fitness improvement, objective-based search overlooks and discards stepping stones which might be critical to solving the problem. Here, we hypothesize that Quality Diversity (QD) algorithms are a better way to generate stepping stones than objective-based search: by maintaining a large set of solutions which are of high-quality, but phenotypically different, these algorithms collect promising stepping stones while protecting them in their own "ecological niche". To demonstrate the capabilities of QD we revisit the challenge of recreating images produced by user-driven evolution, a classic challenge which spurred work in novelty search and illustrated the limits of objective-based search. We show that QD far outperforms objective-based search in matching user-evolved images. Further, our results suggest some intriguing possibilities for leveraging the diversity of solutions created by QD.
Due to global ecological and economic challenges that have been correlated to the transition from fossil-based to renewable resources, fundamental studies are being performed worldwide to replace fossil fuel raw materials in plastic production. One aspect of current research is the development of lignin-derived polyols to substitute expensive fossil-based polyol components for polyurethane and polyester production. This article describes the synthesis of bioactive lignin-based polyurethane coatings using unmodified and demethylated Kraft lignins. Demethylation was performed to enhance the reaction selectivity toward polyurethane formation. The antimicrobial activity was tested according to a slightly modified standard test (JIS Z 2801:2010). Besides effects caused by the lignins themselves, triphenylmethane derivatives (brilliant green and crystal violet) were used as additional antimicrobial substances. Results showed increased antimicrobial capacity against Staphylococcus aureus. Furthermore, the coating color could be varied from dark brown to green and blue, respectively.
The antiradical and antimicrobial activity of lignin and lignin-based films are both of great interest for applications such as food packaging additives. The polyphenolic structure of lignin in addition to the presence of O-containing functional groups is potentially responsible for these activities. This study used DPPH assays to discuss the antiradical activity of HPMC/lignin and HPMC/lignin/chitosan films. The scavenging activity (SA) of both binary (HPMC/lignin) and ternary (HPMC/lignin/chitosan) systems was affected by the percentage of the added lignin: the 5% addition showed the highest activity and the 30% addition had the lowest. Both scavenging activity and antimicrobial activity are dependent on the biomass source showing the following trend: organosolv of softwood > kraft of softwood > organosolv of grass. Testing the antimicrobial activities of lignins and lignin-containing films showed high antimicrobial activities against Gram-positive and Gram-negative bacteria at 35 °C and at low temperatures (0-7 °C). Purification of kraft lignin has a negative effect on the antimicrobial activity while storage has positive effect. The lignin release in the produced films affected the activity positively and the chitosan addition enhances the activity even more for both Gram-positive and Gram-negative bacteria. Testing the films against spoilage bacteria that grow at low temperatures revealed the activity of the 30% addition on HPMC/L1 film against both B. thermosphacta and P. fluorescens while L5 was active only against B. thermosphacta. In HPMC/lignin/chitosan films, the 5% addition exhibited activity against both B. thermosphacta and P. fluorescens.
Lower back pain is one of the most prevalent diseases in Western societies. A large percentage of European and American populations suffer from back pain at some point in their lives. One successful approach to address lower back pain is postural training, which can be supported by wearable devices, providing real-time feedback about the user’s posture. In this work, we analyze the changes in posture induced by postural training. To this end, we compare snapshots before and after training, as measured by the Gokhale SpineTracker™. Considering pairs of before and after snapshots in different positions (standing, sitting, and bending), we introduce a feature space, that allows for unsupervised clustering. We show that resulting clusters represent certain groups of postural changes, which are meaningful to professional posture trainers.
More and more devices will be connected to the internet [3]. Many devicesare part of the so-called Internet of Things (IoT) which contains many low-powerdevices often powered by a battery. These devices mainly communicate with the manufacturers back-end and deliver personal data and secrets like passwords.
Analytische Chemie I
(2019)
Analytical pyrolysis
(2019)
Analytical pyrolysis deals with the structural identification and quantitation of pyrolysis products with the ultimate aim of establishing the identity of the original material and the mechanisms of its thermal decomposition. The pyrolytic process is carried out in a pyrolyzer interfaced with analytical instrumentation such as gas chromatography (GC), mass spectrometry (MS), gas chromatography coupled with mass spectrometry (GC/MS), or with Fourier-transform infrared spectroscopy (GC/FTIR). By measurement and identification of pyrolysis products, the molecular composition of the original sample can often be reconstructed.This book is the outcome of contributions by experts in the field of pyrolysis and includes applications of the analytical pyrolysis-GC/MS to characterize the structure of synthetic organic polymers and lignocellulosic materials as well as cellulosic pulps and isolated lignins, solid wood, waste particle board, and bio-oil. The thermal degradation of cellulose and biomass is examined by scanning electron micrography, FTIR spectroscopy, thermogravimetry (TG), differential thermal analysis, and TG/MS. The calorimetric determination of high heating values of different raw biomass, plastic waste, and biomass/plastic waste mixtures and their by-products resulting from pyrolysis is described.
This paper proposes an approach to an ANN-based temperature controller design for a plastic injection moulding system. This design approach is applied to the development of a controller based on a combination of a classical ANN and integrator. The controller provides a fast temperature response and zero steady-state error for three typical heaters (bar, nozzle, and cartridge) for a plastic moulding system. The simulation results in Matlab Simulink software and in comparison to an industrial PID regulator have shown the advantages of the controller, such as significantly less overshoot and faster transient (compared to PID with autotuning) for all examined heaters. In order to verify the proposed approach, the designed ANN controller was implemented and tested using an experimental setup based on an STM32 board.
Herein we report an update to ACPYPE, a Python3 tool that now properly converts AMBER to GROMACS topologies for force fields that utilize nondefault and nonuniform 1–4 electrostatic and nonbonded scaling factors or negative dihedral force constants. Prior to this work, ACPYPE only converted AMBER topologies that used uniform, default 1–4 scaling factors and positive dihedral force constants. We demonstrate that the updated ACPYPE accurately transfers the GLYCAM06 force field from AMBER to GROMACS topology files, which employs non-uniform 1–4 scaling factors as well as negative dihedral force constants. Validation was performed using β-d-GlcNAc through gas-phase analysis of dihedral energy curves and probability density functions. The updated ACPYPE retains all of its original functionality, but now allows the simulation of complex glycomolecular systems in GROMACS using AMBER-originated force fields. ACPYPE is available for download at https://github.com/alanwilter/acpype.
In Sensor-based Fault Detection and Diagnosis (SFDD) methods, spatial and temporal dependencies among the sensor signals can be modeled to detect faults in the sensors, if the defined dependencies change over time. In this work, we model Granger causal relationships between pairs of sensor data streams to detect changes in their dependencies. We compare the method on simulated signals with the Pearson correlation, and show that the method elegantly handles noise and lags in the signals and provides appreciable dependency detection. We further evaluate the method using sensor data from a mobile robot by injecting both internal and external faults during operation of the robot. The results show that the method is able to detect changes in the system when faults are injected, but is also prone to detecting false positives. This suggests that this method can be used as a weak detection of faults, but other methods, such as the use of a structural model, are required to reliably detect and diagnose faults.
The choice of suitable semiconducting metal oxide (MOX) gas sensors for the detection of a specific gas or gas mixture is time-consuming since the sensor’s sensitivity needs to be characterized at multiple temperatures to find its optimal operating conditions. To obtain reliable measurement results, it is very important that the power for the sensor’s integrated heater is stable, regulated and error-free (or error-tolerant). Especially the error-free requirement can be only be achieved if the power supply implements failure-avoiding and failure-detection methods. The biggest challenge is deriving multiple different voltages from a common supply in an efficient way while keeping the system as small and lightweight as possible. This work presents a reliable, compact, embedded system that addresses the power supply requirements for fully automated simultaneous sensor characterization for up to 16 sensors at multiple temperatures. The system implements efficient (avg. 83.3% efficiency) voltage conversion with low ripple output (<32 mV) and supports static or temperature-cycled heating modes. Voltage and current of each channel are constantly monitored and regulated to guarantee reliable operation. To evaluate the proposed design, 16 sensors were screened. The results are shown in the experimental part of this work.
Background 3-hydroxy-3-methylglutaryl-coenzyme A lyase deficiency (HMGCLD) is an autosomal recessive disorder of ketogenesis and leucine degradation due to mutations in HMGCL .
Method We performed a systematic literature search to identify all published cases. 211 patients of whom relevant clinical data were available were included in this analysis. Clinical course, biochemical findings and mutation data are highlighted and discussed. An overview on all published HMGCL variants is provided.
Results More than 95% of patients presented with acute metabolic decompensation. Most patients manifested within the first year of life, 42.4% already neonatally. Very few individuals remained asymptomatic. The neurologic long-term outcome was favorable with 62.6% of patients showing normal development.
Conclusion This comprehensive data analysis provides a systematic overview on all published cases with HMGCLD including a list of all known HMGCL mutations.
2-methylacetoacetyl-coenzyme A thiolase (beta-ketothiolase) deficiency: one disease - two pathways
(2019)
Background: 2-methylacetoacetyl-coenzyme A thiolase deficiency (MATD; deficiency of mitochondrial acetoacetyl-coenzyme A thiolase T2/ “beta-ketothiolase”) is an autosomal recessive disorder of ketone body utilization and isoleucine degradation due to mutations in ACAT1.
Methods: We performed a systematic literature search for all available clinical descriptions of patients with MATD. 244 patients were identified and included in this analysis. Clinical course and biochemical data are presented and discussed.
Results: For 89.6 % of patients at least one acute metabolic decompensation was reported. Age at first symptoms ranged from 2 days to 8 years (median 12 months). More than 82% of patients presented in the first two years of life, while manifestation in the neonatal period was the exception (3.4%). 77.0% (157 of 204 patients) of patients showed normal psychomotor development without neurologic abnormalities.
Conclusion: This comprehensive data analysis provides a systematic overview on all cases with MATD identified in the literature. It demonstrates that MATD is a rather benign disorder with often favourable outcome, when compared with many other organic acidurias.
For robots acting - and failing - in everyday environments, a predictable behaviour representation is important so that it can be utilised for failure analysis, recovery, and subsequent improvement. Learning from demonstration combined with dynamic motion primitives is one commonly used technique for creating models that are easy to analyse and interpret; however, mobile manipulators complicate such models since they need the ability to synchronise arm and base motions for performing purposeful tasks. In this paper, we analyse dynamic motion primitives in the context of a mobile manipulator - a Toyota Human Support Robot (HSR)- and introduce a small extension of dynamic motion primitives that makes it possible to perform whole body motion with a mobile manipulator. We then present an extensive set of experiments in which our robot was grasping various everyday objects in a domestic environment, where a sequence of object detection, pose estimation, and manipulation was required for successfully completing the task. Our experiments demonstrate the feasibility of the proposed whole body motion framework for everyday object manipulation, but also illustrate the necessity for highly adaptive manipulation strategies that make better use of a robot's perceptual capabilities.