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Homeoffice und Führung
(2021)
Poor young people more often face health difficulties, (learning) disabilities, and are overrepresented in special schools. Consequently, youth from poor households disproportionately frequently participate in disability‐specific programs aiming to improve their educational levels and labor market opportunities. They face a double burden of disability and poverty. In our study, we look at poor and non‐poor youth with disabilities (YPWD) who participate in vocational rehabilitation (VR) and whether VR helps them (a) in transitioning into employment and (b) in leaving poverty. We examine the association between the receipt of initial basic income support (BIS) as a poverty indicator, later labor market outcomes, and earned vocational qualification using administrative data. We make use of a sample of all persons accepted for VR in 2010 (N = 36,645). We employ logit models on VR attendees’ labor market outcomes three and five years after being accepted for VR as well as on their earned vocational qualifications. Beside initial poverty status, we control for educational level, type, and degree of disability and program pattern during the VR process. Our findings show that YPWD from poor households have a decreased likelihood of a vocational certificate and employment. Additionally, they are more likely to receive BIS than young people not from poor households and thus more likely to remain poor. In conclusion, VR seems to support poor YPWD less in their school‐to‐work transitions. Thus, disability‐specific programs should be more tailored to the social situations of participants, and counsellors should be more sensitive to their social backgrounds.
Community Music
(2021)
Musik im Strafvollzug
(2021)
Management des Innovationsportfolios: Portfolio-Matrizen zumManagement von Innovationsprojekten
(2021)
SOLAR DECATHLON EUROPE - TEAM MIMO DER HSD STELLT SICH DER AUFSTOCKUNG SAMT SANIERUNG DES CAFÉS ADA
(2021)
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.
Development of open educational resources for renewable energy and the energy transition process
(2021)
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.
Vorwort Gender Report
Liebe Leserinnen und Leser,
wie bereits in den vergangenen Jahren haben wir auch in diesem Jahr erneut einen eigenständigen Gender Report als wichtige Ergänzung zum Zahlenspiegel der Hochschule Düsseldorf erstellt, der einen besonderen Fokus auf die Entwicklung und die aktuellen Zahlen der weiblichen Studierenden, Absolventinnen und Beschäftigten legt.
Insbesondere in der jüngsten Vergangenheit ist enorm viel Bewegung in die Debatte rund um das Thema „Gender (und) Diversity“ gekommen – eine wie wir finden äußerst erfreuliche Entwicklung, um immer mehr Menschen für eine offene Diskussion und paritätische Teilhabe zu sensibilisieren. Auch wenn die HSD mit der zielorientierten Umsetzung ihres „gender diversity action plan“ mit gutem Beispiel vorangeht und in der Vergangenheit bereits beachtliche Erfolge in Hinblick auf die Herstellung, Förderung und Wahrung von (Geschlechter-)Gerechtigkeit und verstärkter (Gender)Diversity erreicht hat, sind wir noch nicht am Ziel angelangt.
Vorwort
Liebe Leserinnen und Leser, ich freue mich, Ihnen auch in diesem besonderen Jahr unser statistisches Zahlenwerk vorstellen zu können. Die jüngste Gesamtausgabe – der Zahlenspiegel 2020 – ermöglicht Ihnen detaillierte Einblicke in die Studierenden- und Absolvent*innendaten sowie eine ausführliche Betrachtung der Beschäftigungsverhältnisse und Kennzahlen der Hochschule Düsseldorf.
Zur Abbildung der Zahlen der weiblichen Studierenden, Absolventinnen und Beschäftigten wurde im Rahmen der Umsetzung des „gender diversity action plan“ erneut ein eigenständiger Gender Report erstellt. ...
In dieser Arbeit werden die definierenden Eigenschaften von Open Educational Resources (OER) recherchiert. Wie können die Anforderungen an Inhalten, Medien-wahl, didaktischen Konzept mit einem hohen Grad an Interaktivität erfüllt werden? – Die Literatur zu diesen Fragestellungen liefert den aktuellen Forschungsstand.
Aufgrund der §§ 2 Abs. 4 S. 1, 26 Abs. 3 S. 2 des Gesetzes über die Hochschulen des Landes Nord-rhein-Westfalen (Hochschulgesetz - HG) vom 16.09.2014 (GV.NRW. S. 547) in der aktuell gültigen Fassung und der Grundordnung der Hochschule Düsseldorf vom 08.10.2015 (Verkündungsblatt der Hochschule Düsseldorf, Amtliche Mitteilung Nr. 414) in der aktuell gültigen Fassung hat der Fachbereichsrat des Fachbereichs Design der Hochschule Düsseldorf die folgende Ordnung als Satzung erlassen.
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.
Aufgrund des § 82a Absatz 1 Satz 1 des Gesetzes über die Hochschulen des Landes Nordrhein-Westfalen (Hochschulgesetz - HG) vom 16.09.2014 (GV. NRW. S. 547) in der aktuell gültigen Fassung in Verbindung mit der Verordnung zur Bewältigung der durch die Coronavirus SARS-CoV-2- Epidemie an den Hochschulbetrieb gestellten Herausforderungen (Corona-Epidemie-Hochschulverordnung) vom 01.12.2021 (GV. NRW. S. 81) in der aktuell gültigen Fassung hat das Präsidium der Hochschule Düsseldorf die folgende Ordnung als Satzung erlassen.
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.
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.
Als Einführung in die Benutzung der Software SQL Developer Command Line (SQLcl) von Oracle werden folgende Aktionen erklärt: Anmelden, Ausgeben von Tabellen und ihren Metadaten, Verwenden der Befehlshistorie, Formatieren der Ausgabe, Speichern, Bearbeiten und Wiederverwenden von Befehlen sowie das Anlegen von Logdateien. Vorausgesetzt werden geringe Vorkenntnisse von relationalen Datenbankmanagementsystemen mit SQL. Das Video wurde im Rahmen des Projektes EILD.nrw entwickelt, gefördert durch das Ministerium für Kultur und Wissenschaft gemeinsam mit der Digitalen Hochschule NRW (DH.NRW) in der Förderlinie „OERContent.nrw“ (Open Education Resources).
Methanol Synthesis with Steel Mill Gases: Performance Investigations in an On‐Site Technical Center
(2021)
Hochschulreport 2020
(2021)