Lateinisches und Romanisches in den Reichenauer Glossen

In today’s complex networks, timely identification and resolution of performance problems is extremely challenging. Current diagnostic practices to identify the root causes of such problems primarily rely on human intervention and investigation. Fully automated and scalable systems, which are capable of identifying complex problems are needed to provide rapid and accurate diagnosis. The study presented in this thesis creates the necessary scientific basis for the automatic diagnosis of network performance faults using novel intelligent inference techniques based on machine learning. We propose three new techniques for characterisation of network soft failures, and by using them, create the Intelligent Automated Network Diagnostic (IAND) system. First, we propose Transmission Control Protocol (TCP) trace characterisation techniques that use aggregated TCP statistics. Faulty network components embed unique artefacts in TCP packet streams by altering the normal protocol behaviour. Our technique captures such artefacts and generates a set of unique fault signatures. We first introduce Normalised Statistical Signatures (NSSs) with 460 features, a novel representation of network soft failures to provide the basis for diagnosis. Since not all 460 features contribute equally to the identification of a particular fault, we then introduce improved forms of NSSs called EigenNSS and FisherNSS with reduced complexity and greater class separability. Evaluations show that we can achieve dimensionality reduction of over 95% and detection accuracies up to 95% while achieving micro-second diagnosis times with these signatures. Second, given NSSs have features that are dependent on link properties, we introduce a technique called Link Adaptive Signature Estimation (LASE) using regression-based predictors to artificially generateNSSs for a large number of link parameter combinations. Using LASE, the system can be trained to suit the exact networking environment, however dynamic, with a minimal set of sample data. For extensive performance evaluation, we collected 1.2 million sample traces for 17 types of device failures on 8 TCP variants over various types of networks using a combination of fault injection and link emulation techniques. Third, to automate fault identification, we propose a modular inference technique that learns from the patterns embedded in the signatures, and create Fault Classifier Modules (FCMs). FCMs use support vector machines to uniquely identify individual faults and are designed using soft class boundaries to provide generalised fault detection capability. The use of a modular design and generic algorithm that can be trained and tuned based on the specific faults, offers scalability and is a key differentiator from the existing systems that use specific algorithms to detect each fault. Experimental evaluations show that FCMs can achieve detection accuracies of between 90% – 98%. The signatures and classifiers are used as the building blocks to create the IAND system with its two main sub-systems: IAND-k and IAND-h. The IANDk is a modular diagnostic system for automatic detection of previously known problems using FCMs. The IAND-k system is applied for accurately detecting faulty links and diagnosing problems in end-user devices in a wide range of network types (IAND-kUD, IAND-kCC). Extensive evaluation of the systems demonstrated high overall detection accuracies up to 96.6% with low false positives and over 90% accuracy even in the most difficult scenarios. Here, the FCMs use supervised machine learning methods and can only detect previously known problems. To extend the diagnostic capability to detect previously unknown problems, we propose IAND-h, a hybrid classifier system that uses a combination of unsupervised machine learning-based clustering and supervised machine learning-based classification. The evaluation of the system shows that previously unknown faults can be detected with over 92% accuracy. The IAND-h system also offers real-time detection capability with diagnosis times between 4 μs and 66 μs. The techniques and systems proposed during this research contribute to the state of the art of network diagnostics and focus on scalability, automation and modularity with evaluation results demonstrating a high degree of accuracy

Leistungs- und Kostenrechnung in der Datenverarbeitung:Am Beispiel der Hochschulrechenzentren in NRW

Für Hochschulrechenzentren wird eine Leistungs- und Kostenrechnung vorgeschlagen, die Dienstleistungen und ihre Qualitätsmerkmale neben eine Prozeßkostenrechnung stellt. Mit der Prozeßkostenrechnung entstehen Steuerungs- und Vergleichsmöglichkeiten in und zwischen Hochschulrechenzentren. Über "Kostentreiber" werden die Leistungen weitgehend mengenabhängig. Eine Zuordnung der Kosten zu ihren Verursachern wird möglich. Die Prozesse werden für ein Hochschulrechenzentrum definiert, die einzelnen Kostenfaktoren erläutert und zusammengestellt. Es wird ein Berechnungsschema vorgeschlagen, Ausführungshinweise werden gegeben und die Ergebnisse diskutiert. Als Fallstudien werden die Prozeßkosten eines kleinen und eines großen Rechenzentrums angesprochen

The Influence of Distance and Lateral Offset of Follow Me Robots on User Perception

Robots that are designed to work in close proximity to humans are required to move and act in a way that ensures social acceptance by their users. Hence, a robot's proximal behavior toward a human is a main concern, especially in human-robot interaction that relies on relatively close proximity. This study investigated how the distance and lateral offset of “Follow Me” robots influences how they are perceived by humans. To this end, a Follow Me robot was built and tested in a user study for a number of subjective variables. A total of 18 participants interacted with the robot, with the robot's lateral offset and distance varied in a within-subject design. After each interaction, participants were asked to rate the movement of the robot on the dimensions of comfort, expectancy conformity, human likeness, safety, trust, and unobtrusiveness. Results show that users generally prefer robot following distances in the social space, without a lateral offset. However, we found a main influence of affinity for technology, as those participants with a high affinity for technology preferred closer following distances than participants with low affinity for technology. The results of this study show the importance of user-adaptiveness in human-robot-interaction.DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli

Stresstest städtischer Infrastrukturen – Ein Experiment zur Wahrnehmung des Alters im öffentlichen Raum

Im Zuge des demographischen Wandels ist ein deutlicher Anstieg des Bevölkerungsanteils an Seniorinnen und Senioren (Personen ab 65 Jahren) in Deutschland abzusehen. Dadurch wächst auch die Anzahl der mobilitätseingeschränkten und behinderten Menschen. Insbesondere für die Gruppe der über 80-Jährigen spielen barrierearme Wege eine große Rolle, sie stellen dementsprechend eine zunehmend wichtiger werdende Herausforderung für die räumliche Planung dar. In einer Pilotstudie wurde hierfür im Bochumer Stadtteil Querenburg versucht, Barrieren (nach DIN 18040-3 etc.) in einem „barrierefreien Stadtplan“ zu erfassen. Es stellt sich jedoch die Frage, ob es tatsächlich diese Barrieren sind, die den mobilitätseingeschränkten Verkehrsteilnehmerinnen und -teilnehmern Stress verursachen. Um diese Problematik zu klären, wurde die Technik des „EmoMapping“ (dt. Emotionskartierung) verwendet. Dieses Verfahren dient dazu, Emotionen von einzelnen Personen mit einer Georeferenz zu erfassen. Zu diesem Zweck wurden Testpersonen mit einem Alterssimulationsanzug, einem GPS-Logger zur Erfassung der geographischen Position sowie einem Sensorband, welches die Hautleitfähigkeit und die Hauttemperatur misst, ausgestattet. In Testläufen wurden durch Erkennung von Mustern besondere Situationen, in denen die Probanden Stress verspürten, positionsgenau erfasst. Anschließend wurden die Messdaten des GPS-Loggers und des Sensorbands ausgewertet und kombiniert. Die daraus resultierenden Ergebnisse wurden als Dichtekarten (Heatmaps) in einem Geoinformationssystem dargestellt. Aus diesen wurde ersichtlich, an welchen Stellen im Straßennetz die Probanden Stress empfunden haben. Im Anschluss daran wurden die Daten des barrierefreien Stadtplans mit den Ergebnissen des EmoMappings validiert und die einzelnen Barrierearten hinsichtlich ihrer Relevanz für eine barriere- und stressfreie Navigation eingeschätzt. Da die Auswertung der Daten aus dem EmoMapping bislang nicht automatisiert und über verschiedene Software durchgeführt wurde, konnten die Daten nicht sehr zeitnah ausgewertet werden. Auch der Arbeitsaufwand war als relativ hoch einzustufen. Deshalb wurde im Rahmen einer Bachelorarbeit ein Werkzeug entwickelt, mit welchem sich die einzelnen Arbeitsschritte der Auswertung automatisiert durchführen lassen. Das Tool wurde in der frei verfügbaren Programmiersprache „R“ entwickelt und erleichtert den bisherigen Arbeitsablauf der Auswertung deutlich. Das lässt in Zukunft eine erhöhte Nutzung der EmoMapping-Methode zu

Oligosaccharyltransferase is highly specific for the hydroxy amino acid in Asn-Xaa-Thr/Ser

AbstractPig liver oligosaccharyltransferase (OST), which is involved in the en bloc transfer of the Dol-PP-linked GlcNAc2-Man9-Glc3 precursor on to asparagine residues in the Asn-Xaa-Thr/Ser sequence, is highly stereospecific for the conformation of the 3-carbon atom in the hydroxy amino acid. Moreover, substitution of the hydroxy group by either SH as in cysteine, or NH2 as in β,γ-diamino-butanoic acid as reported previously [Bause, E. et al., Biochem. J. 312 (1995) 979–985], followed by the determination of the pH optimum for enzymatic activity, indicates that neither a negative nor a positive charge in the hydroxy amino acid position is tolerated by the enzyme. Binding of the threonine β-methyl group by OST is also specific, with serine, L-threo-β-hydroxynorvaline and L-β-hydroxynorleucine containing tripeptides all bound much less efficiently than the threonine peptide itself. The data are interpreted in terms of a highly stereospecific hydrophobic binding pocket for the threonine CH3-CH(OH) group

Material Extrusion 3D Printing of PEEK-Based Composites

High-performance thermoplastics like polyetheretherketone (PEEK), with their outstanding thermal stability, mechanical properties and chemical stability, have great potential for various structural applications. Combining with additive manufacturing methods extends further PEEK usage, e.g., as a mold insert material in polymer melt processing like injection molding. Mold inserts must possess a certain mechanical stability, a low surface roughness as well as a good thermal conductivity for the temperature control during the molding process. With this in mind, the commercially available high-performance thermoplastic PEEK was doped with small amounts of carbon nanotubes (CNT, 6 wt%) and copper particles (10 wt%) targeting enhanced thermomechanical properties and a higher thermal conductivity. The composites were realized by a commercial combined compounder and filament maker for the usage in a material extrusion (MEX)-based 3D-printer following the fused filament fabrication (FFF) principle. Commercial filaments made from PEEK and carbon fiber reinforced PEEK were used as reference systems. The impact of the filler and the MEX printing conditions like printing temperature, printing speed and infill orientation on the PEEK properties were characterized comprehensively by tensile testing, fracture imaging and surface roughness measurements. In addition, the thermal conductivity was determined by the laser-flash method in combination with differential scanning calorimetry and Archimedes density measurement. The addition of fillers did not alter the measured tensile strength in comparison to pure PEEK significantly. The fracture images showed a good printing quality without the MEX-typical voids between and within the deposited layers. Higher printing temperatures caused a reduction of the surface roughness and, in some cases, an enhanced ductile behavior. The thermal conductivity could be increased by the addition of the CNTs. Following the given results, the most critical process step is the compounding procedure, because for a reliable process–parameter–property relationship, a homogeneous particle distribution in the polymer matrix yielding a reliable filament quality is essential