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Großstadt-Gärtnern Urban Gardening zwischen Subsistenz, urbaner Agrikultur und kreativer Stadtpraxis
(2016)
Building context-aware applications is an already widely researched topic. It is our belief that context awareness has the potential to supplement the Internet of Things, when a suitable methodology including supporting tools will ease the development of context-aware applications. We believe that a meta-model based approach can be key to achieving this goal. In this paper, we present our meta-model based methodology, which allows us to define and build application-specific context models and the integration of sensor data without any programming. We describe how that methodology is applied with the implementation of a relatively simple context-aware COVID-safe navigation app. The outcome showed that programmers with no experience in context-awareness were able to understand the concepts easily and were able to effectively use it after receiving a short training. Therefore, context-awareness is able to be implemented within a short amount of time. We conclude that this can also be the case for the development of other context-aware applications, which have the same context-awareness characteristics. We have also identified further optimization potential, which we will discuss at the conclusion of this article.
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.
Wechselwirkungen zwischen Druckluftspeichern und Netzengpässen in zukünftigen Energiesystemen
(2013)
An der Schnittstelle: Deutsche und internationale Perspektiven auf Community Music in Musikschulen
(2020)
Editorial
(2016)
Management des Innovationsportfolios: Portfolio-Matrizen zumManagement von Innovationsprojekten
(2021)
Das interdisziplinäre Team MIMO (Minimal Impact – Maximum Output) der Hochschule Düsseldorf beschäftigt sich mit der ganzheitlich ressourceneffizienten Nachverdichtung urbaner Quartiere. Ein gründerzeitliches Industriegebäude und heutiges Tanzhaus wird energetisch saniert und um eine Wohnnutzung aufgestockt. In theoretischem Entwurf und folgender 1:1-Umsetzung wird die Weiternutzung und Revitalisierung des Gebäudebestands, kreislaufgerechte Konstruktionen und Materialverwendung, der Einsatz recycelter, ökologischer und wiederverwertbarer Materialien, sozialnachhaltige Aspekte im Sinne von Gemeinschaft und Teilhabe sowie die Nutzung lokaler, erneuerbarer Energien zum Ausgleich der Gebäudeenergie- und Ökobilanz adressiert.
Artificial intelligence methods based on machine learning or artificial neural networks have become indispensable in camera-based driver assistance systems, and also represent an essential building block for future autonomous driving. However, the great successes of these evaluation methods in environment perception and also driving planning are accompanied by equally great challenges in the validation and verification of these systems. One of the essential aspects for this is the required guaranteed safety of the functions under mass production conditions of the vehicles. This article explains this point of view using a detailed example from the field of camera-based driver assistance systems: the determination of inspection limits at the end of the production line. The camera is one of the most important sensor modalities for vehicle environment sensing and as such, the quality of the camera systems plays a key role in the safety argumentation of the overall system. Several illustrative application examples (role of simulations, calibration, influence of the windshield) will be presented. The basic ideas presented can be well transferred to the other sensor modalities (lidar, radar, ToF, etc.). The investigations/evidence show that doubts are allowed whether or how fast autonomous driving on level L4/5 will take hold as robotaxis or – even more challenging – in private ownership on a larger scale.
Der Beitrag entwickelt anhand zahlreicher Beispiele rechtsterroristischer Gewalt eine Systematisierung instrumenteller Zielsetzungen rechtsterroristischen Handelns. Dabei ruft er zunächst als zentrale weltanschauliche Referenz den völkischen Nationalismus auf, dem biopolitische Paradigmen inhärent sind, denen mittels rechtsterroristischer Gewalt Geltung verschafft werden soll. Dieses ist für eine Vielzahl rechtsterroristischer Gewalttaten zentral, denen es um die Aufrechterhaltung rassistischer, sexistischer und heteronormativer Gesellschaftsverhältnisse geht. Regelmäßig ist solche Gewalt auch als Botschaftsverbrechen zu verstehen, wobei ganz unterschiedliche Modi der Kommunikation beobachtbar sind. Rechtsterroristische Gewalt – sofern sie nicht parastaatlich verfasst ist – hat nicht die direkte Schwächung der Kampffähigkeit des Gegners zum Ziel, sondern zielt auf Bestrafung, Einschüchterung und Vertreibung ausgewählter sozialer Gruppen und Individuen sowie in manchen Fällen auf das strategische Moment der Zuspitzung gesellschaftlicher Konflikte, wie im abschließenden Teil des Beitrages an zahlreichen Beispielen gezeigt wird.
A swirling pulverized coal flame is computationally investigated. A Eulerian–Lagrangian formulation is used to describe the two-phase flow. Turbulence is modelled within a RANS (Reynolds averaged numerical simulation) framework. Four turbulence viscosity- (TV) based models, namely the standard k-ε model, realizable k-ε model, renormalization group theory k-ε model, and the shear stress transport k-ω model are used. In addition, a Reynolds stress transport model (RSM) is employed. The models are assessed by comparing the predicted velocity fields with the measurements of other authors. In terms of overall average values, the agreement of the predictions to the measurements is observed to be within the range 20–40%. A better performance of the RSM compared to the TV models is observed, with a nearly twice as better overall agreement to the experiments, particularly for the swirl velocity. In the second part of the investigation, the resolution of the discrete particle phase in modelling the turbulent particle dispersion (TPD) and particle size distribution (SD) is investigated. Using the discrete random walk model for the TPD, it is shown that even five random walks are sufficient for an accuracy that is quite high, with a less than 1% mean deviation from the solution obtained by thirty random walks. The approximation of the measured SD is determined by a continuous Rosin–Rammler distribution function, and inaccuracies that can occur in its subsequent discretization are demonstrated and discussed. An investigation on the resolution of the SD by discrete particle size classes (SC) indicates that 12 SC are required for an accuracy with a less than 1% mean deviation from the solution with 18 SC. Although these numbers may not necessarily be claimed to be sufficiently universal, they may serve as guidance, at least for SD with similar characteristics
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 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.
The links between innovativeness as a driver of economic performance, and the determinants of innovativeness have been investigated by management scholars and economists for decades, focusing mostly on “hard factors” as investment in research and development, or education. Focusing on a relatively neglected, but in times of globalization even more important aspect, the infuence of cultural characteristics on innovativeness, we apply diferent econometric models to test for links between cultural tightness and looseness on the one hand, and national innovativeness on the other hand. We fnd that cultural tightness—in the sense of homogenous and intolerant societies—has a negative link to national innovativeness, while cultural looseness—in the sense of tolerant and diverse societies—displays a positive link to national innovativeness.
Gender in the making: An empirical approach to understand gender relations in the maker movement
(2021)
Differentiable Compound Optics and Processing Pipeline Optimization for End-to-end Camera Design
(2021)
Most modern commodity imaging systems we use directly for photography‐-or indirectly rely on for downstream applications‐-employ optical systems of multiple lenses that must balance deviations from perfect optics, manufacturing constraints, tolerances, cost, and footprint. Although optical designs often have complex interactions with downstream image processing or analysis tasks, today’s compound optics are designed in isolation from these interactions. Existing optical design tools aim to minimize optical aberrations, such as deviations from Gauss’ linear model of optics, instead of application-specific losses, precluding joint optimization with hardware image signal processing (ISP) and highly parameterized neural network processing. In this article, we propose an optimization method for compound optics that lifts these limitations. We optimize entire lens systems jointly with hardware and software image processing pipelines, downstream neural network processing, and application-specific end-to-end losses. To this end, we propose a learned, differentiable forward model for compound optics and an alternating proximal optimization method that handles function compositions with highly varying parameter dimensions for optics, hardware ISP, and neural nets. Our method integrates seamlessly atop existing optical design tools, such as Zemax. We can thus assess our method across many camera system designs and end-to-end applications. We validate our approach in an automotive camera optics setting‐-together with hardware ISP post processing and detection‐-outperforming classical optics designs for automotive object detection and traffic light state detection. For human viewing tasks, we optimize optics and processing pipelines for dynamic outdoor scenarios and dynamic low-light imaging. We outperform existing compartmentalized design or fine-tuning methods qualitatively and quantitatively, across all domain-specific applications tested.
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.
Individual addressing of trapped ions and coupling of motional and spin states using RF radiation
(2009)
Individual electrodynamically trapped and laser cooled ions are addressed in frequency space using radio-frequency radiation in the presence of a static magnetic field gradient. In addition, an interaction between motional and spin states induced by an rf field is demonstrated employing rf optical double resonance spectroscopy. These are two essential experimental steps towards realizing a novel concept for implementing quantum simulations and quantum computing with trapped ions.
We present an experimental technique that allows the simultaneous spatial imaging and spectral analysis of falling droplets that exhibit lasing. Single droplet investigations serve as, among other purposes, a preliminary study for spray and combustion researchers. The described setup provides a valuable tool for the evaluation of microdroplet investigations with laser-spectroscopic techniques that rely on laser-induced fluorescence (LIF) or similar spectroscopical phenomena. The emphasis is that both spatial and spectral information are obtained from single-shot images of a falling droplet. Furthermore, combining spatial imaging and a spatially resolving optical multichannel analyzer makes a pointwise rastering of the droplets spectrum possible. This allows for the (almost) unambiguous determination of sources of influence on the spectrum of these droplets-such as geometrical distortion and lasing, nondissolved tracer lumps, and similar phenomena. Although the focus is on the experimental technique itself, we supplement detailed studies of lasing in falling microdroplets. These results were obtained with the aim of developing a system for measuring temperature distributions in droplets and sprays. In the light of these results the practice of calibrating a droplets spectrum by use of a bulk liquid sample needs to be critically reviewed.
Musicians and music professionals are often considered to be expert listeners for listening tests on room acoustics. However, these tests often target acoustic parameters other than those typically relevant in music such as pitch, rhythm, amplitude, or timbre. To assess the expertise in perceiving and understanding room acoustical phenomena, a listening test battery was constructed to measure the perceptual sensitivity and cognitive abilities in the identification of rooms with different reverberation times and different spectral envelopes. Performance in these tests was related to data from the Goldsmiths Musical Sophistication Index, self-reported previous experience in music recording and acoustics, and academic knowledge on acoustics. The data from 102 participants show that sensory and cognitive abilities are both correlated significantly with musical training, analytic listening skills, recording experience, and academic knowledge on acoustics, whereas general interest in and engagement with music do not show any significant correlations. The regression models, using only significantly correlated criteria of musicality and professional expertise, explain only small to moderate amounts (11%–28%) of the variance in the “room acoustic listening expertise” across the different tasks of the battery. Thus, the results suggest that the traditional criteria for selecting expert listeners in room acoustics are only weak predictors of their actual performances.
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.
To power dynamic processes in cells, the actin and microtubule cytoskeletons organize into complex structures. Although it is known that cytoskeletal coordination is vital for cell function, the mechanisms by which cross-linking proteins coordinate actin and microtubule activities remain poorly understood. In particular, it is unknown how the distinct mechanical properties of different actin architectures modulate the outcome of actin-microtubule interactions. To address this question, we engineered the protein TipAct, which links growing microtubule ends via end-binding proteins to actin filaments. We show that growing microtubules can be captured and guided by stiff actin bundles, leading to global actin-microtubule alignment. Conversely, growing microtubule ends can transport, stretch and bundle individual actin filaments, thereby globally defining actin filament organization. Our results provide a physical basis to understand actin-microtubule cross-talk, and reveal that a simple cross-linker can enable a mechanical feedback between actin and microtubule organization that is relevant to diverse biological contexts.
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.
Despite their overwhelming complexity, living cells display a high degree of internal mechanical and functional organization which can largely be attributed to the intracellular biopolymer scaffold, the cytoskeleton. Being a very complex system far from thermodynamic equilibrium, the cytoskeleton's ability to organize is at the same time challenging and fascinating. The extensive amounts of frequently interacting cellular building blocks and their inherent multifunctionality permits highly adaptive behavior and obstructs a purely reductionist approach. Nevertheless (and despite the field's relative novelty), the physics approach has already proved to be extremely successful in revealing very fundamental concepts of cytoskeleton organization and behavior. This review aims at introducing the physics of the cytoskeleton ranging from single biopolymer filaments to multicellular organisms. Throughout this wide range of phenomena, the focus is set on the intertwined nature of the different physical scales (levels of complexity) that give rise to numerous emergent properties by means of self-organization or self-assembly.
Many different cell types are able to migrate by formation of a thin actin-based cytoskeletal extension. Recently, it became evident that this extension consists of two distinct substructures, designated lamellipodium and lamellum, which differ significantly in their kinetic and kinematic properties as well as their biochemical composition. We developed a stochastic two-dimensional computer simulation that includes chemical reaction kinetics, G-actin diffusion, and filament transport to investigate the formation of growing actin networks in migrating cells. Model parameters were chosen based on experimental data or theoretical considerations. In this work, we demonstrate the system's ability to form two distinct networks by self-organization. We found a characteristic transition in mean filament length as well as a distinct maximum in depolymerization flux, both within the first 1-2 microm. The separation into two distinct substructures was found to be extremely robust with respect to initial conditions and variation of model parameters. We quantitatively investigated the complex interplay between ADF/cofilin and tropomyosin and propose a plausible mechanism that leads to spatial separation of, respectively, ADF/cofilin- or tropomyosin-dominated compartments. Tropomyosin was found to play an important role in stabilizing the lamellar actin network. Furthermore, the influence of filament severing and annealing on the network properties is explored, and simulation data are compared to existing experimental data.
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.
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.
Actin droplet machine
(2019)
The actin droplet machine is a computer model of a three-dimensional network of actin bundles developed in a droplet of a physiological solution, which implements mappings of sets of binary strings. The actin bundle network is conductive to travelling excitations, i.e. impulses. The machine is interfaced with an arbitrary selected set of k electrodes through which stimuli, binary strings of length k represented by impulses generated on the electrodes, are applied and responses are recorded. The responses are recorded in a form of impulses and then converted to binary strings. The machine's state is a binary string of length k: if there is an impulse recorded on the ith electrode, there is a '1' in the ith position of the string, and '0' otherwise. We present a design of the machine and analyse its state transition graphs. We envisage that actin droplet machines could form an elementary processor of future massive parallel computers made from biopolymers.
Actin filaments are conductive to ionic currents, mechanical and voltage solitons. These travelling localisations can be utilised to generate computing circuits from actin networks. The propagation of localisations on a single actin filament is experimentally unfeasible to control. Therefore, we consider excitation waves propagating on bundles of actin filaments. In computational experiments with a two-dimensional slice of an actin bundle network we show that by using an arbitrary arrangement of electrodes, it is possible to implement two-inputs-one-output circuits.
The synthesis of Nb-doped TiO2 nanoparticles by spray drying: an efficient and scalable method
(2011)
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.
Methanol Synthesis with Steel Mill Gases: Performance Investigations in an On‐Site Technical Center
(2021)
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.
Designing core ontologies
(2011)