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Memento mori!
(2022)
Das plötzliche Ende des romantischen Komponisten Felix Mendelssohn Bartholdy (1809–1847) gibt uns auch heute noch Rätsel auf. Einiges deutet auf ein rupturiertes zerebrales Aneurysma mit konsekutiver Subarachnoidalblutung hin. Das Quellenmaterial zu den Symptomen seiner Todeskrankheit wird in dieser Arbeit ausführlich vorgestellt und diskutiert. Eine mögliche familiäre Disposition im Sinne eines Ehlers-Danlos-Syndroms Typ IV wird erörtert.
Dieser Beitrag beschreibt, wie mit Campbells Schema der „Heldenreise“ personalisierte Narrative der obersten Führungsebene aufgebaut werden können, um für interne und externe Stakeholder eine Orientierung zu bieten und die Unternehmenskultur bewusst zu prägen und zu beeinflussen. Das Beispiel der Preisträgerportraits des Manager Magazins zeigt, dass diese Methode breite Anwendung findet und dabei auch unterschiedliche funktionale Zuschreibungen der Führungsrolle erfolgen können.
Heat generation that is coupled with electricity usage, like combined heat and power generators or heat pumps, can provide operational flexibility to the electricity sector. In order to make use of this in an optimized way, the flexibility that can be provided by such plants needs to be properly quantified. This paper proposes a method for quantifying the flexibility provided through a cluster of such heat generators. It takes into account minimum operational time and minimum down-time of heat generating units. Flexibility is defined here as the time period over which plant operation can be either delayed or forced into operation, thus providing upward or downward regulation to the power system on demand. Results for one case study show that a cluster of several smaller heat generation units does not provide much more delayed operation flexibility than one large unit with the same power, while it more than doubles the forced operation flexibility. Considering minimum operational time and minimum down-time of the units considerably limits the available forced and delayed operation flexibility, especially in the case of one large unit.
Purpose: Participation and accessibility issues faced by gamers with multi-sensory disabilities are themes yet to be fully understood by accessible technology researchers. In this work, we examine the personal experiences and perceptions of individuals with deafblindness who play games despite their disability, as well as the reasons that lead some of them to stop playing games.
Materials and methods: We conducted 60 semi-structured interviews with individuals living with deafblindness in five European countries: United Kingdom, Germany, Netherlands, Greece and Sweden.
Results: Participants stated that reasons for playing games included them being a fun and entertaining hobby, for socialization and meeting others, or for occupying the mind. Reasons for stop playing games included essentially accessibility issues, followed by high cognitive demand, changes in gaming experience due their disability, financial reasons, or because the accessible version of a specific game was not considered as fun as the original one.
Conclusions: We identified that a considerable number of individuals with deafblindness enjoy playing casual mobile games such as Wordfeud and Sudoku as a pastime activity. Despite challenging accessibility issues, games provide meaningful social interactions to players with deafblindness. Finally, we introduce a set of user-driven recommendations for making digital games more accessible to players with a diverse combination of sensory abilities.
IMPLICATIONS FOR REHABILITATION
- Digital games were considered a fun and entertaining hobby by participants with deafblindness. Furthermore, participants play games for socialization and meeting others, or for occupying the mind.
- Digital games provide meaningful social interactions and past time to persons with deafblindness.
- On top of accessibility implications, our findings draw attention to the importance of the social element of gaming for persons with deafblindness.
- Based on interviews, we introduce a set of user-driven recommendations for making digital games more accessible to players with a diverse combination of sensory abilities.
Surface acoustic waves are propagated toward the edge of an anisotropic elastic medium (a silicon crystal), which supports leaky waves with a high degree of localization at the tip of the edge. At an angle of incidence corresponding to phase matching with this leaky wedge wave, a sharp peak in the reflection coefficient of the surface wave was found. This anomalous reflection is associated with efficient excitation of the leaky wedge wave. In laser ultrasound experiments, surface acoustic wave pulses were excited and their reflection from the edge of the sample and their partial conversion into leaky wedge wave pulses was observed by optical probe-beam deflection. The reflection scenario and the pulse shapes of the surface and wedge-localized guided waves, including the evolution of the acoustic pulse traveling along the edge, have been confirmed in detail by numerical simulations.
Properties of higher-order surface acoustic wave modes in Al(1-x)Sc(x)N / sapphire structures
(2021)
In this work, surface acoustic wave (SAW) modes and their dependence on propagation directions in epitaxial Al0.68Sc0.32N(0001) films on Al2O3(0001) substrates were studied using numerical and experimental methods. In order to find optimal propagation directions for higher-order SAW modes, phase velocity dispersion branches of Al0.68Sc0.32N on Al2O3 with Pt mass loading were computed for the propagation directions <11-20> and <1-100> with respect to the substrate. Experimental investigations of phase velocities and electromechanical coupling were performed for comparison with the numerical results. Simulations carried out with the finite element method (FEM) and with a Green function approach allowed identification of each wave type, including Rayleigh, Sezawa and shear horizontal wave modes. For the propagation direction <1-100>, significantly increased wave guidance of the Sezawa mode compared to other directions was observed, resulting in enhanced electromechanical coupling (k2eff = 1.6 %) and phase velocity (vphase = 6 km/s). We demonstrated, that selecting wave propagation in <1-100> with high mass density electrodes results in increased electromechanical coupling without significant reduction in phase velocities for the Sezawa wave mode. An improved combination of metallization, Sc concentration x, and SAW propagation direction is suggested which exhibits both high electromechanical coupling (k2eff > 6 %) and high velocity (vphase = 5.5 km/s) for the Sezawa mode.
It is important to minimize the unscheduled downtime of machines caused by outages of machine components in highly automated production lines. Considering machine tools such as, grinding machines, the bearing inside of spindles is one of the most critical components. In the last decade, research has increasingly focused on fault detection of bearings. In addition, the rise of machine learning concepts has also intensified interest in this area. However, up to date, there is no single one-fits-all solution for predictive maintenance of bearings. Most research so far has only looked at individual bearing types at a time.
This paper gives an overview of the most important approaches for bearing-fault analysis in grinding machines. There are two main parts of the analysis presented in this paper. The first part presents the classification of bearing faults, which includes the detection of unhealthy conditions, the position of the error (e.g. at the inner or at the outer ring of the bearing) and the severity, which detects the size of the fault. The second part presents the prediction of remaining useful life, which is important for estimating the productive use of a component before a potential failure, optimizing the replacement costs and minimizing downtime.
In the last decade, deep learning models for condition monitoring of mechanical systems increasingly gained importance. Most of the previous works use data of the same domain (e.g., bearing type) or of a large amount of (labeled) samples. This approach is not valid for many real-world scenarios from industrial use-cases where only a small amount of data, often unlabeled, is available.
In this paper, we propose, evaluate, and compare a novel technique based on an intermediate domain, which creates a new representation of the features in the data and abstracts the defects of rotating elements such as bearings. The results based on an intermediate domain related to characteristic frequencies show an improved accuracy of up to 32 % on small labeled datasets compared to the current state-of-the-art in the time-frequency domain.
Furthermore, a Convolutional Neural Network (CNN) architecture is proposed for transfer learning. We also propose and evaluate a new approach for transfer learning, which we call Layered Maximum Mean Discrepancy (LMMD). This approach is based on the Maximum Mean Discrepancy (MMD) but extends it by considering the special characteristics of the proposed intermediate domain. The presented approach outperforms the traditional combination of Hilbert–Huang Transform (HHT) and S-Transform with MMD on all datasets for unsupervised as well as for semi-supervised learning. In most of our test cases, it also outperforms other state-of-the-art techniques.
This approach is capable of using different types of bearings in the source and target domain under a wide variation of the rotation speed.
Mit zunehmender Datenverfügbarkeit wird der Einsatz Maschinellen Lernens zur Steuerung und Optimierung von Supply Chains attraktiver, da die Qualität der Datenauswertung erhöht und gleichzeitig der Aufwand gesenkt werden kann. Anhand des SCOR-Modells werden exemplarische Ansätze als Orientierungshilfe eingeordnet und dazu passende Verfahren des Maschinellen Lernens vorgestellt.
IoT-Plattformen stellen ein zentrales Element für die Vernetzung von physischen Objekten und die Bereitstellung deren Daten für digitale Zwillinge dar. Der Markt für solche Plattformen ist in den vergangenen Jahren stark gewachsen. Bei inzwischen über 600 Anbietern ist die Wahl der „richtigen“ Plattform für das eigene Unternehmen keine triviale Aufgabe mehr. Dieser Beitrag soll Unternehmen im Auswahlprozess unterstützen, indem gängige Funktionen von IoT-Plattformen und Kriterien für die Auswahl von IoT-Plattformen aufgezeigt werden.
Wood juice, a liquid produced during wood processing, is a harmful waste that requires utilization. To achieve a circular economy, biowastes should be recycled, reducing fossil carbon usage. Therefore, the objective of this work was to examine the potential of wood juice as a feedstock for bioplastic synthesis by Bacillus sp. G8_19. Polyhydroxyalkanoate (PHA) syntheses using wood juice from Douglas fir trees and that from a mixture of spruce/fir trees were compared. It was found that the PHA content was higher after using wood juice from spruce/fir trees than that from Douglas fir trees (18.0% vs 6.1% of cell dry mass). Gas chromatography analysis showed that, with both wood juices, Bacillus sp. G8_19 accumulated poly(3-hydroxybutyrate-co-3-hydroxyvalerate). The content of 3-hydroxyvalerate (3HV) monomers was higher when spruce/fir wood juice was used (10.7% vs 1.9%). The C/N ratio did not have a statistically significant effect on the copolymer content in biomass, but it did significantly influence the 3HV content. The proposed concept may serve as an approach to wood waste valorization via production of biodegradable materials.
Interpreting seismic data requires the characterization of a number of key elements such as the position of faults and main reflections, presence of structural bodies, and clustering of areas exhibiting a similar amplitude versus angle response. Manual interpretation of geophysical data is often a difficult and time-consuming task, complicated by lack of resolution and presence of noise. In recent years, approaches based on convolutional neural networks have shown remarkable results in automating certain interpretative tasks. However, these state-of-the-art systems usually need to be trained in a supervised manner, and they suffer from a generalization problem. Hence, it is highly challenging to train a model that can yield accurate results on new real data obtained with different acquisition, processing, and geology than the data used for training. In this work, we introduce a novel method that combines generative neural networks with a segmentation task in order to decrease the gap between annotated training data and uninterpreted target data. We validate our approach on two applications: the detection of diffraction events and the picking of faults. We show that when transitioning from synthetic training data to real validation data, our workflow yields superior results compared to its counterpart without the generative network.
The work focuses on predictive capabilities of fundamental cyclic plasticity and fatigue life models, which can be calibrated using limited amount of experiments as specific ones needed for more advanced models are often absent. The analyses are conducted for the synthetic case of exhaust manifold made from cast iron. The thermal boundary conditions from the forced convection were obtained from the computational fluid dynamics considered as a conjugate heat transfer problem. Two rate-independent and temperature-dependent material models were calibrated for structural analyses. Both were validated with experiments on isothermal and anisothermal levels. Sequential thermal–mechanical finite element simulations were performed. Two fatigue life models were employed. The first was a temperature-dependent strain-based fatigue life criterion calibrated from uniaxial data. The second was a temperature-independent energy-based fatigue life criterion resulting in twice lower life than the strain-based criterion, while none of the plasticity models made a significant difference in that prediction.
Drawing off the technical flexibility of building polygeneration systems to support a rapidly expanding renewable electricity grid requires the application of advanced controllers like model predictive control (MPC) that can handle multiple inputs and outputs, uncertainties in forecast data, and plant constraints amongst other features. In this original work, an economic-MPC-based optimal scheduling of a real-world building energy system is demonstrated and its performance is evaluated against a conventional controller. The demonstration includes the steps to integrate an optimisation-based supervisory controller into a standard building automation and control system with off-the-shelf HVAC components and usage of state-of-art algorithms for solving complex nonlinear mixed integer optimal control problems. With the MPC, quantitative benefits in terms of 6–12% demand-cost savings and qualitative benefits in terms of better controller adaptability and hardware-friendly operation are identified. Further research potential for improving the MPC framework in terms of field-level stability, minimising constraint violations, and inter-system communication for its deployment in a prosumer-network is also identified.
The increasing use of artificial intelligence (AI) technologies across application domains has prompted our society to pay closer attention to AI’s trustworthiness, fairness, interpretability, and accountability. In order to foster trust in AI, it is important to consider the potential of interactive visualization, and how such visualizations help build trust in AI systems. This manifesto discusses the relevance of interactive visualizations and makes the following four claims: i) trust is not a technical problem, ii) trust is dynamic, iii) visualization cannot address all aspects of trust, and iv) visualization is crucial for human agency in AI.
Due to higher combustion chamber temperatures and pressures in efficient combustion engines, both the high-cycle and thermomechanical fatigue loads on service life-critical components, such as the cylinder head, are increasing. Material comparisons and analysis of damage behavior are very expensive and time-consuming using component tests. This study therefore develops a test method for cylinder head materials that takes into account the combined loading conditions from the above-mentioned loads and allows realistic temperature transients and gradients on near-component samples. The near-component cylinder head sample represents the failure-critical exhaust valve crosspiece and is tested in a test rig specially designed with the aid of conjugate heat transfer simulations. In the test rig, the sample is subjected to thermal stress by a hot gas burner and to mechanical stress by a high-frequency pulsator. Optical crack detection allows permanent observation of fatigue crack growth and crack closure during the test. Fractographic and metallo-graphic examinations of the fracture areas as well as analyses of the damage patterns show that loads close to engine operation can be set in this way and their influences on the damage can be monitored.
The aim of this review was to determine whether smartphone applications are reliable and valid to measure range of motion (RoM) in lower extremity joints. A literature search was performed up to October 2020 in the databases PubMed and Cochrane Library. Studies that reported reliability or validity of smartphone applications for RoM measurements were included. The study quality was assessed with the QUADAS-2 tool and baseline information, validity and reliability were extracted. Twenty-five studies were included in the review. Eighteen studies examined knee RoM, whereof two apps were analysed as having good to excellent reliability and validity for knee flexion (“DrGoniometer”, “Angle”) and one app showed good results for knee extension (“DrGoniometer”). Eight studies analysed ankle RoM. One of these apps showed good intra-rater reliability and excellent validity for dorsiflexion RoM (“iHandy level”), another app showed excellent reliability and moderate validity for plantarflexion RoM (“Coach’s Eye”). All other apps concerning lower extremity RoM had either insufficient results, lacked study quality or were no longer available. Some apps are reliable and valid to measure RoM in the knee and ankle joint. No app can be recommended for hip RoM measurement without restrictions.
Uphill training is applied to induce specific overload on the musculoskeletal system to improve sprinting mechanics. This study aimed to identify unique kinematic features of uphill sprinting at different slopes and to suggest practical implications based on comparisons we early stance phase. At take-off, steeper slopes induced significantly more extended joint angles and higher ROMs during the late stance phase. Compared with moderate slopes, more anti-phase coordination patterns were detected at steeper slopes. Thus, uphill sprinting at steeper slopes shares essential kinematic features with the early acceleration phase of level sprinting. Moderate inclinations induce biomechanical adaptations similar to those in the late acceleration phase of level sprinting. Hence, the specific transfer of uphill sprinting to acceleration depends on the slope inclinations.