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Am Musikvideo „Kiss“ von Prince and the Revolution (1986) werden exemplarisch verschiedene Verfahren beleuchtet, wie Gender durch „Stimme“ im konkreten und abstrakten Sinn auf ambivalente Weise inszeniert wird. Michael Bachtins Metapher der fremden Stimme für das intertexuelle Verweisgeflecht von Sprache im Allgemeinen und Literatur im Besonderen wird hier sowohl im übertragenen als auch im wörtlichen Sinne verwendet und auf audiovisuelle Musikformate übertragen. Das Spiel mit Gender entsteht durch eine komplexe Verwebung von intertextuellen und intermedialen Verweisen auf andere Liedtexte, Filme, Gesangstraditionen, Subkulturen etc. Dabei wird vestimentäres Cross-Dressing mit gesanglichem Cross-Dressing kombiniert.
Thermoelectric generators (TEGs) have the ability to convert waste heat into electrical energy under unfavorable conditions and are becoming increasingly popular in academia, but have not yet achieved a broad commercial success, due to the still comparably low efficiency. To increase the efficiency and economic viability of TEGs, research is performed on the materials on one hand and on the system connection on the other. In the latter case, the net output power of the cooling system plays a key role. At first glance, passive cooling seems preferable to active cooling because it does not affect the net electrical output power. However, as shown in the present review, the active cooling is to be preferred for net output power. The situation is similar in air and water-cooling. Even though air-cooling is easier to set up, the water-cooling should be preferred to achieve higher net output power. It is shown that microchannel cooling has similar hydraulic performance to conventional cooling and inserts increase the net output power of TEG. As the review reveals that active water-cooling should be the method of choice to achieve high net output power, it also shows that a careful optimization is necessary to exploit the potential.
The Sound of Success: Investigating Cognitive and Behavioral Effects of Motivational Music in Sports
(2017)
Listening to music before, during, or after sports is a common phenomenon, yet its functions and effects on performance, cognition, and behavior remain to be investigated. Listening to music before, during, or after sports is a common phenomenon, yet its functions and effects on performance, cognition, and behavior remain to be investigated. In this study we present a novel approach to the role of music in sports and exercise that focuses on the notion of musical self-enhancement (Elvers, 2016). We derived the following hypotheses from this framework: listening to motivational music will (i) enhance self-evaluative cognition, (ii) improve performance in a ball game, and (iii) evoke greater risk-taking behavior. To evaluate the hypotheses, we conducted a between-groups experiment (N = 150) testing the effectiveness of both an experimenter playlist and a participant-selected playlist in comparison to a no-music control condition. All participants performed a ball-throwing task developed by Decharms and Davé (1965), consisting of two parts: First, participants threw the ball from fixed distances into a funnel basket. During this task, performance was measured. In the second part, the participants themselves chose distances from the basket, which allowed their risk-taking behavior to be assessed. The results indicate that listening to motivational music led to greater risk taking but did not improve ball-throwing performance. This effect was more pronounced in male participants and among those who listened to their own playlists. Furthermore, self-selected music enhanced state self-esteem in participants who were performing well but not in those who were performing poorly. We also discuss further implications for the notion of musical self-enhancement. // Listening to music before, during, or after sports is a common phenomenon, yet its functions and effects on performance, cognition, and behavior remain to be investigated. In this study we present a novel approach to the role of music in sports and exercise that focuses on the notion of musical self-enhancement (Elvers, 2016). We derived the following hypotheses from this framework: listening to motivational music will (i) enhance self-evaluative cognition, (ii) improve performance in a ball game, and (iii) evoke greater risk-taking behavior. To evaluate the hypotheses, we conducted a between-groups experiment (N = 150) testing the effectiveness of both an experimenter playlist and a participant-selected playlist in comparison to a no-music control condition. All participants performed a ball-throwing task developed by Decharms and Davé (1965), consisting of two parts: First, participants threw the ball from fixed distances into a funnel basket. During this task, performance was measured. In the second part, the participants themselves chose distances from the basket, which allowed their risk-taking behavior to be assessed. The results indicate that listening to motivational music led to greater risk taking but did not improve ball-throwing performance. This effect was more pronounced in male participants and among those who listened to their own playlists. Furthermore, self-selected music enhanced state self-esteem in participants who were performing well but not in those who were performing poorly. We also discuss further implications for the notion of musical self-enhancement.
Music-related decision-making encompasses a wide range of behaviours including those associated with listening choices, composition and performance, and decisions involving music education and therapy. Although research programmes in psychology and economics have contributed to an improved understanding of music-related behaviour, historically, these disciplines have been unconnected. Recently, however, researchers have begun to bridge this gap by employing tools from behavioural economics. This article contributes to the literature by providing a discussion about the benefits of using behavioural economics in music-decision research. We achieve this in two ways. First, through a systematic review, we identify the current state of the literature within four key areas of behavioural economics-heuristics and biases, social decision-making, behavioural time preferences, and dual-process theory. Second, taking findings of the literature as a starting point, we demonstrate how behavioural economics can inform future research. Based on this, we propose the Behavioural Economics of Music (BEM), an integrated research programme that aims to break new ground by stimulating interdisciplinary research in the intersection between music, psychology, and economics.
The Long Tail theory serves as an explanation for market change triggered through evolving online market conditions. However, it is often referred to without empirical validation, or with inconsistent findings on its applicability. Therefore, this paper analyses the applicability of the Long Tail theory to German online media offerings as of 2014 and 2016, focusing on offerings that serve information purposes (information offerings). Based on a unique dataset of commercially oriented online media offerings (Longitudinal IntermediaPlus 2014–2016), an analysis for three Long Tail characteristics (variety increase, opposing concentration between hit and niche offerings and niche market share increase) is conducted. By additionally distinguishing between political and entertainment-oriented information offerings as boundaries of democratized online market conditions the analysis reveals that the Long Tail theory is not fully applicable to any market sample, and especially not to politically oriented offerings.
Heterogeneous catalysts usually consist of porous, inorganic materials. Especially in the mobile application, e.g., in exhaust gas catalysis as a part of the gas line of an engine, there are loads due to vibrations and high gas velocities. It can be assumed that physical stress will lead to a loss of the catalytically active coating. The present contribution describes experimental setups and methods for a systematic and reproducible investigation of the main influences to the mass loss due to vibrational and fluid‐mechanical stress caused by particle‐free gas flows.
Synthesis and Analysis of a Novel Linkage Mechanism with the Helical Motion of the End-Effector
(2020)
We present a system that exploits mobile rotational tracking and photospherical imagery to allow users to share their environment with remotely connected peers “on the go.” We surveyed related interfaces and developed a unique groupware application that shares a mixed reality space with spatially-oriented live video feeds. Users can collaborate through realtime audio, video, and drawings in a virtual space. The developed system was tested in a preliminary user study, which confirmed an increase in spatial and situational awareness among viewers as well as reduction in cognitive workload. Believing that our system provides a novel style of collaboration in mixed reality environments, we discuss future applications and extensions of our prototype.
In the present contribution, the utilization of steel-mill gases for an industrial methanol synthesis process is described. The focus of this work is on the influence of syngas compositions derived from steel-mill gases on an industrial Cu/ZnO/Al2O3 catalyst. Elevated CO2 and H2O concentrations seem to have an irreversible impact on the activity of the catalyst. In order to obtain realistic gas composition for the catalyst tests, a process simulation of the methanol synthesis was conducted. The calculated gas compositions at the reactor inlet were applied in a practical test in order to evaluate the catalyst stability under steel mill gas conditions. No noticeable deactivation was observed.
The optimization of full-scale biogas plant operation is of great importance to make biomass a competitive source of renewable energy. The implementation of innovative control and optimization algorithms, such as Nonlinear Model Predictive Control, requires an online estimation of operating states of biogas plants. This state estimation allows for optimal control and operating decisions according to the actual state of a plant. In this paper such a state estimator is developed using a calibrated simulation model of a full-scale biogas plant, which is based on the Anaerobic Digestion Model No.1. The use of advanced pattern recognition methods shows that model states can be predicted from basic online measurements such as biogas production, CH4 and CO2 content in the biogas, pH value and substrate feed volume of known substrates. The machine learning methods used are trained and evaluated using synthetic data created with the biogas plant model simulating over a wide range of possible plant operating regions. Results show that the operating state vector of the modelled anaerobic digestion process can be predicted with an overall accuracy of about 90%. This facilitates the application of state-based optimization and control algorithms on full-scale biogas plants and therefore fosters the production of eco-friendly energy from biomass.
Spec2Vec: Improved mass spectral similarity scoring through learning of structural relationships
(2021)
Spectral similarity is used as a proxy for structural similarity in many tandem mass spectrometry (MS/MS) based metabolomics analyses such as library matching and molecular networking. Although weaknesses in the relationship between spectral similarity scores and the true structural similarities have been described, little development of alternative scores has been undertaken. Here, we introduce Spec2Vec, a novel spectral similarity score inspired by a natural language processing algorithm-Word2Vec. Spec2Vec learns fragmental relationships within a large set of spectral data to derive abstract spectral embeddings that can be used to assess spectral similarities. Using data derived from GNPS MS/MS libraries including spectra for nearly 13,000 unique molecules, we show how Spec2Vec scores correlate better with structural similarity than cosine-based scores. We demonstrate the advantages of Spec2Vec in library matching and molecular networking. Spec2Vec is computationally more scalable allowing structural analogue searches in large databases within seconds.
We present a novel metric Spatial Recall Index to assess the performance of machine-learning (ML) algorithms for automotive applications, focusing on where in the image which performance occurs. Typical metrics like intersection-over-union (IoU), precisionrecallcurves or average precision (AP) quantify the performance over a whole database of images, neglecting spatial performance variations. But as the optics of camera systems are spatially variable over the field of view, the performance of ML-based algorithms is also a function of space, which we show in simulation: A realistic objective lens based on a Cooke-triplet that exhibits typical optical aberrations like astigmatism and chromatic aberration, all variable over field, is modeled. The model is then applied to a subset of the BDD100k dataset with spatially-varying kernels. We then quantify local changes in the performance of the pre-trained Mask R-CNN algorithm. Our examples demonstrate the spatial dependence of the performance of ML-based algorithms from the optical quality over field, highlighting the need to take the spatial dimension into account when training ML-based algorithms, especially when looking forward to autonomous driving applications.
Spatial precision and recall indices to assess the performance of instance segmentation algorithms
(2022)
Singing in different rooms: Common or individual adaptation patterns to the acoustic conditions?
(2020)
The rock material pentlandite with the composition Fe4.5Ni4.5S8 was synthesized via high temperature synthesis from the elements. The structure and composition of the material was characterized via powder X-ray diffraction (PXRD), Mössbauer spectroscopy (MB), scanning electron microscopy (SEM), differential scanning calorimetry (DSC) and energy dispersive X-ray spectroscopy (EDX). Two preparation methods of pentlandite bulk electrodes are presented. In the first approach a piece of synthetic pentlandite rock is directly contacted via a wire ferrule. The second approach utilizes pentlandite pellets, pressed from finely ground powder, which is immobilized in a Teflon casing. Both electrodes, whilst being prepared by an additive-free method, reveal high durability during electrocatalytic conversions in comparison to common drop-coating methods. We herein showcase the striking performance of such electrodes to accomplish the hydrogen evolution reaction (HER) and present a standardized method to evaluate the electrocatalytic performance by electrochemical and gas chromatographic methods. Furthermore, we report stability tests via potentiostatic methods at an overpotential of 0.6 V to explore the material limitations of the electrodes during electrolysis under industrial relevant conditions.
Despite its impressive complexity the cytoskeleton succeeds to persistently organize itself and thus the cells' interior. In contrast to classical man-made machines, much of the cellular organization originates from inherent self-assembly and self-organization allowing a high degree of autonomy for various functional units. Recent experimental and theoretical studies revealed numerous examples of cytoskeleton components that arrange and organize in a self-regulative way. In the present review we want to shortly summarize some of the principle mechanisms that are able to inherently trigger and regulate the cytoskeleton organization. Although taken individually most of these regulative principles are rather simple with intuitively predictable consequences, combinations of two or more of these mechanisms can quickly give rise to very complex, unexpected behavior and might even be able to explain the formation of different functional units out of a common pool of available building blocks.
Schule als sicherer Ort
(2017)
Cell migration is associated with the dynamic protrusion of a thin actin-based cytoskeletal extension at the cell front, which has been shown to consist of two different substructures, the leading lamellipodium and the subsequent lamellum. While the formation of the lamellipodium is increasingly well understood, organizational principles underlying the emergence of the lamellum are just beginning to be unraveled. We report here on a 1D mathematical model which describes the reaction-diffusion processes of a polarized actin network in steady state, and reproduces essential characteristics of the lamellipodium-lamellum system. We observe a steep gradient in filament lengths at the protruding edge, a local depolymerization maximum a few microns behind the edge, as well as a differential dominance of the network destabilizer ADF/cofilin and the stabilizer tropomyosin. We identify simple and robust organizational principles giving rise to the derived network characteristics, uncoupled from the specifics of any molecular implementation, and thus plausibly valid across cell types. An analysis of network length dependence on physico-chemical system parameters implies that to limit array treadmilling to cellular dimensions, network growth has to be truncated by mechanisms other than aging-induced depolymerization, e.g., by myosin-associated network dissociation at the transition to the cell body. Our work contributes to the analytical understanding of the cytoskeletal extension's bisection into lamellipodium and lamellum and sheds light on how cells organize their molecular machinery to achieve motility.
The importance of high data quality is increasing with the growing impact and distribution of ML systems and big data. Also, the planned AI Act from the European commission defines challenging legal requirements for data quality especially for the market introduction of safety relevant ML systems. In this paper, we introduce a novel approach that supports the data quality assurance process of multiple data quality aspects. This approach enables the verification of quantitative data quality requirements. The concept and benefits are introduced and explained on small example data sets. How the method is applied is demonstrated on the well-known MNIST data set based an handwritten digits.
Menschen mit Suchtverhalten, die Bürgergeld nach dem Sozialgesetzbuch II beziehen, zu ihren Wünschen an für sie hilfreiche Unterstützungsangebote zu befragen und aus ihren Schilderungen herauszuarbeiten, welche Bedeutung für sie Erwerbarbeit hat, fordert die Forschenden zu kritischen Selbstvergewisserungen heraus.Denn die qualitative Studie, die im Rahmen eines Modellprojekts, gefördert vom Bundesarbeitsministerium, durchgeführt wurde, berührt vier kritische Fachdebatten in der Sozialen Arbeit. Neben der (1) sozialpolitischen Aktivierungsprogrammatik sind hier v. a. die (2) Bezeichnung von Menschen mit Suchtverhalten sowie (3) methodologische Einwände gegen subjektorientierte Forschungsperspektiven und (4) Case Management bedeutsam. Nach der Positionierung in diesen Fachdebatten werden empirische Einblicke dazu gewährt, dass die Frage „Problembewältigung oder Erwerbsarbeit first?“ differenziert beantwortet wird.Ferner äußern die Bürgergeldbezieher:innen mit Suchtverhalten diverse Wünsche an die Unterstützung im Jobcenter und andere Hilfsangebote. Vor allem wünschen sie sich vom Jobcenter, dass es dort nicht nur um die Vermittlung in Erwerbsarbeit gehen sollte, sondern weitere und damit hybride Zielsetzungen verfolgt werden sollten.
The settling velocities of helminth eggs of three types, namely Ascaris suum (ASC), Trichuris suis (TRI), and Oesophagostomum spp. (OES), in clean tap water are computationally determined by means of computational fluid dynamics, using the general-purpose CFD software ANSYS Fluent 18.0. The previous measurements of other authors are taken as the basis for the problem formulation and validation, whereby the latter is performed by comparing the predicted sink velocities with those measured in an Owen tube. To enable a computational treatment, the measured shapes of the eggs are parametrized by idealizing them in terms of elementary geometric forms. As the egg shapes show a variation within each class, “mean” shapes are considered. The sink velocities are obtained through the computationally obtained drag coefficients. The latter are defined by means of steady-state calculations. Predicted sink velocities are compared with the measured ones. It is observed that the calculated values show a better agreement with the measurements, for ASC and TRI, compared to the theoretical sink values delivered by the Stokes theory. However, the observed agreement is still found not to be very satisfactory, indicating the role of further parameters, such as the uncertainties in the characterization of egg shapes or flocculation effects even in clean tap water.
Die Begriffe „Nullenergiehaus“ oder „Plusenergiehaus“ sind in den letzten Jahren national und international zum Inbegriff für die konsequente Zusammenführung von Maßnahmen zur drastischen Energieeinsparung und optimierter, dezentraler Nutzung erneuerbarer Energien geworden. Dabei geht es nicht um energieautarke Gebäude und die dabei große Herausforderung der saisonalen Energiespeicherung. Grundidee ist, dass die von einem Gebäude in ein Netz eingespeiste Energiemenge in der Jahresbilanz mindestens dem Energiebezug entspricht. Sowohl im Kontext der Fortführung der europäischen Gebäuderichtline als auch im aktuellen Energiekonzept und Energieforschungsprogramm der Bundesregierung wird das Thema besetzt.
Refractive power measurements serve as the primary quality standard in the automotive glazing industry. In the light of autonomous driving new optical metrics are becoming more and more popular for specifying optical quality requirements for the windshield. Nevertheless, the link between those quantities and the refractive power needs to be established in order to ensure a holistic requirement profile for the windshield. As a consequence, traceable high-resolution refractive power measurements are still required for the glass quality assessment. Standard measurement systems using Moiré patterns for refractive power monitoring in the automotive industry are highly resolution limited, wherefore they are insufficient for evaluating the camera window area. Consequently, there is a need for more sophisticated refractive power measurement systems that provide a higher spatial resolution. In addition, a calibration procedure has to be developed in order to guarantee for comparability of the measurement results. For increasing the resolution, a measurement setup based on an auto-correlation algorithm is tested in this paper. Furthermore, a calibration procedure is established by using a single reference lens with a nominal refractive power of 100 km-1. For the calibration of the entire measurement range of the system, the lens is tilted by an inclination angle orthogonal to the optical axis. The effective refractive power is then given by the Kerkhof model. By adopting the measurement and calibration procedure presented in this paper, glass suppliers in the automotive industry will be able to detect relevant manufacturing defects within the camera window area more accurately paving the way for a holistic quality assurance of the windshield for future advanced driver-assistance system (ADAS) functionalities. Concurrently, the traceability of the measurement results is ensured by establishing a calibration chain based on a single reference lens, which is traced back to international standards.
Abstract The modulation-transfer function (MTF) is a fundamental optical metric to measure the optical quality of an imaging system. In the automotive industry it is used to qualify camera systems for ADAS/AD. Each modern ADAS/AD system includes evaluation algorithms for environment perception and decision making that are based on AI/ML methods and neural networks. The performance of these AI algorithms is measured by established metrics like Average Precision (AP) or precision-recall-curves. In this article we research the robustness of the link between the optical quality metric and the AI performance metric. A series of numerical experiments were performed with object detection and instance segmentation algorithms (cars, pedestrians) evaluated on image databases with varying optical quality. We demonstrate with these that for strong optical aberrations a distinct performance loss is apparent, but that for subtle optical quality differences – as might arise during production tolerances – this link does not exhibit a satisfactory correlation. This calls into question how reliable the current industry practice is where a produced camera is tested end-of-line (EOL) with the MTF, and fixed MTF thresholds are used to qualify the performance of the camera-under-test.
Mass spectrometry data is one of the key sources of information in many workflows in medicine and across the life sciences. Mass fragmentation spectra are generally considered to be characteristic signatures of the chemical compound they originate from, yet the chemical structure itself usually cannot be easily deduced from the spectrum. Often, spectral similarity measures are used as a proxy for structural similarity but this approach is strongly limited by a generally poor correlation between both metrics. Here, we propose MS2DeepScore: a novel Siamese neural network to predict the structural similarity between two chemical structures solely based on their MS/MS fragmentation spectra. Using a cleaned dataset of > 100,000 mass spectra of about 15,000 unique known compounds, we trained MS2DeepScore to predict structural similarity scores for spectrum pairs with high accuracy. In addition, sampling different model varieties through Monte-Carlo Dropout is used to further improve the predictions and assess the model's prediction uncertainty. On 3600 spectra of 500 unseen compounds, MS2DeepScore is able to identify highly-reliable structural matches and to predict Tanimoto scores for pairs of molecules based on their fragment spectra with a root mean squared error of about 0.15. Furthermore, the prediction uncertainty estimate can be used to select a subset of predictions with a root mean squared error of about 0.1. Furthermore, we demonstrate that MS2DeepScore outperforms classical spectral similarity measures in retrieving chemically related compound pairs from large mass spectral datasets, thereby illustrating its potential for spectral library matching. Finally, MS2DeepScore can also be used to create chemically meaningful mass spectral embeddings that could be used to cluster large numbers of spectra. Added to the recently introduced unsupervised Spec2Vec metric, we believe that machine learning-supported mass spectral similarity measures have great potential for a range of metabolomics data processing pipelines.
Men afflicted with chronic obstructive pulmonary disease (COPD) show signs of muscle dysfunction, such as increased muscle fatigue and acidosis during exercise. We hypothesize that muscle elasticity of the lower extremities differs between patients with COPD and age-related controls. The shear modulus of the soleus muscle was measured non-invasively using MR elastography (MRE) at 0%, 5%, 10%, 15%, and 20% of the subject’s maximum applied force. The results from this study showed statistical differences between the patients and controls, even though there were no statistical differences in the maximum applied force.
Music listening has become a highly individualized activity with smartphones and music streaming services providing listeners with absolute freedom to listen to any kind of music in any situation. Until now, little has been written about the processes underlying the selection of music in daily life. The present study aimed to disentangle some of the complex processes among the listener, situation, and functions of music listening involved in music selection. Utilizing the experience sampling method, data were collected from 119 participants using a smartphone application. For 10 consecutive days, participants received 14 prompts using stratified-random sampling throughout the day and reported on their music-listening behavior. Statistical learning procedures on multilevel regression models and multilevel structural equation modeling were used to determine the most important predictors and analyze mediation processes between person, situation, functions of listening, and music selection. Results revealed that the features of music selected in daily life were predominantly determined by situational characteristics, whereas consistent individual differences were of minor importance. Functions of music listening were found to act as a mediator between characteristics of the situation and music-selection behavior. We further observed several significant random effects, which indicated that individuals differed in how situational variables affected their music selection behavior. Our findings suggest a need to shift the focus of music-listening research from individual differences to situational influences, including potential person-situation interactions.
Methanol Synthesis with Steel Mill Gases: Performance Investigations in an On‐Site Technical Center
(2021)
Men afflicted with hypogonadism have reduced fat-free mass and in some cases a loss in muscle strength. We hypothesize that muscle elasticity of the lower extremities differs between patients with hypogonadism and healthy controls. The shear modulus of the soleus muscle was measured non-invasively using MR elastography (MRE) at 0%, 5%, 10%, 15%, and 20% of the subject’s maximum applied force. The results from this study showed statistical differences between the patients and controls, even though there were no statistical differences in applied force.
Management des Innovationsportfolios: Portfolio-Matrizen zumManagement von Innovationsprojekten
(2021)
Magneto-hydrothermal triple-convection in a W-shaped porous cavity containing oxytactic bacteria
(2022)
Bioconvective heat and mass transport phenomena have recently been the subject of interest in diverse fields of applications pertaining to the motion of fluids and their thermophysical properties. The transport processes in a system involving triple convective phenomena, irregular geometry, and boundary conditions constitute a complex phenomenon. This work aims to explore the mixed thermo-bioconvection of magnetically susceptible fluid containing copper nanoparticles and oxytactic bacteria in a novel W-shaped porous cavity. The buoyant convention is generated due to the isothermal heating at the wavy bottom wall, whereas the mixed convection is induced due to the shearing motion of the top-cooled sliding wall. Furthermore, the bioconvection is induced due to the manifestation of oxytactic bacteria or organisms. The inclined sidewalls are insulated. The geometry is packed with water based Cu nanoparticle mixed porous structure, which is subjected to a magnetizing field acted horizontally. The complex transport equations are transformed into nondimensional forms, which are then computed using the finite volume-based developed code. The coupled triple-convective flow physics are explored for a wide range of involved controlling parameters, which could provide helpful insight to the system designer for its proper operation. The shape of geometry can be considered one of the important parameters to control the heat and mass transport phenomena. In general, the influence of amplitude (δ) is more compared to the waviness number (m) of the undulations. The magnitude of heat (Nu) and mass (Sh) transfer rate for the W-shaped cavity is high compared to conventional square and trapezoidal-shaped cavities. The output of the analysis could be very helpful for the designer for modeling devices operating on nanotechnology-based bioconvection, microbial fuel cells, and others.