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Nanocoatings based on sol–gel coatings are presented as suitable tool to modify materials based on polymers. The main focus is set onto textiles as the most common polymer materials. It presents which types of functionalization can be reached by modified sol–gel processes. Also a suitable categorization of functions is given and set into relation to common applications. A special focus is set on the functional properties, antimicrobial, UV protective, and flame retardant. The concept of bifunctional coatings is discussed and especially the combination of water-repellent and antistatic is presented.
Uncontrolled movements of laparoscopic instruments can lead to inadvertent injury of adjacent structures. The risk becomes evident when the dissecting instrument is located outside the field of view of the laparoscopic camera. Technical solutions to ensure patient safety are appreciated. The present work evaluated the feasibility of an automated binary classification of laparoscopic image data using Convolutional Neural Networks (CNN) to determine whether the dissecting instrument is located within the laparoscopic image section. A unique record of images was generated from six laparoscopic cholecystectomies in a surgical training environment to configure and train The CNN. By using a temporary version of the neural network, the annotation of the training image files could be automated and accelerated. A combination of oversampling and selective data augmentation was used to enlarge the fully labelled image data set and prevent loss of accuracy due to imbalanced class volumes. Subsequently the same approach was applied to the comprehensive, fully annotated Cholec80 database. The described process led to the generation of extensive and balanced training image data sets. The performance of the CNN-based binary classifiers was evaluated on separate test records from both databases. On our recorded data, an accuracy of 0.88 with regard to the safety-relevant classification was achieved. The subsequent evaluation on the Cholec80 data set yielded an accuracy of 0.84. The presented results demonstrate the feasibility of a binary classification of laparoscopic image data for the detection of adverse events in a surgical training environment using a specifically configured CNN architecture.
Im Lernzentrum der Hochschule Reutlingen werden die Dienste und Services der Hochschulbibliothek und des Rechen- und Medienzentrums unter einem Dach angeboten. Das Lernzentrum ist ein Ort zum Lernen und zum gemeinsamen Arbeiten in bestens ausgestatteten Räumen. Das Spektrum reicht von Einzelarbeitsplätzen, Gruppenräumen, Kabinen und Arbeitsinseln bis zu PC-Räumen, einer Lounge und einer Cafeteria.
Mit Auskunfts-, Beratungs- und Schulungsangeboten unterstützen SupportmitarbeiterInnen aus verschiedenen Abteilungen der Hochschule Studierende, Lehrende, Forschende, AnwärterInnen und MitarbeiterInnen dabei, ihren Studien- und Berufsalltag zu meistern.
Warum haben wir ausgerechnet einen Roboter für unseren Titel ausgesucht, der starke Ähnlichkeit hat mit Robbi aus „Robbi, Tobbi und das Fliewatüüt“. Ein Roboter aus den 80ern als Sinnbild für die Zukunft der Arbeit? Nicht ganz. Er steht vielmehr für die Anfänge der Automatisierung, mit der das Ende der Arbeit prophezeit wurde. Heute schaut sein moderner, agiler Nachfolger keck ums Eck. Der „Robbi“ von heute ist hervorgegangen aus einer ständigen technologischen Entwicklung, die unsere Arbeitswelt in erheblichem Maße verändert hat und noch verändern wird. Wie Sie sehen werden, war „Robbi“ sehr wandlungsfähig. Doch was bedeutet das für uns? Wie könnte sie aussehen, die Zukunft der Arbeit? Und was verändert sich dadurch für jeden Einzelnen von uns? Diese Fragen haben wir Professorinnen und Professoren aller Fakultäten gestellt. Sie beschäftigen sich in ihrer Forschung mit digitalen Arbeitsmodellen und zukunftsfähigen Bildungskonzepten, mit Krankenhäusern der Zukunft und Künstlicher Intelligenz. Vieles unterscheidet sich, doch vieles ist auch gleich: Es geht um Vertrauen. Was bedeutet die zunehmende Digitalisierung für unsere Arbeitskultur? Es geht um Verantwortung, uns selbst und anderen gegenüber. Es geht um Vielfalt. Wer sind sie, die Arbeitskräfte von morgen? Es geht um Vernetzung, denn die ist in einer digitalen Welt allgegenwärtig. Für uns als Hochschule ist die „Zukunft der Arbeit“ ein besonders wichtiges Thema, denn unsere Studierenden heute werden sich morgen in dieser neuen Arbeitswelt bewegen und sie gestalten. Welche Kompetenzen müssen wir Ihnen vermitteln? Das ist eine Frage, die wir uns immer wieder neu stellen.
Bio-Gütesiegel im B-to-B-Marketing – Teil 2/2: Bio ist bereits seit Langem kein Nischenprodukt mehr. Deutschland hat europaweit nicht nur den höchsten Verbrauch, sondern gleichzeitig auch den höchsten Umsatz an Bio-Produkten. Etwa jeder Vierte kauft hierzulande regelmäßig Bio-Lebensmittel. Damit haben es ökologische Produkte bereits jetzt in beinahe jeden deutschen Haushalt geschafft. Basierend auf der Relevanz von ökologischen Produkten im Markt, ergibt sich ein gesteigerter Fokus auf die Beziehungen zwischen Lebensmittelherstellern und Lebensmittelhändlern, der in den letzten Jahren angezogen ist. Unternehmerische Anforderungen konzentrieren sich daher mehr als je zuvor neben dem üblichen B-to-C-Marketing auf das B-to-B-Marketing. Mit den neuen Marktherausforderungen steigen somit die Erwartungen an die Marketingleistungen im B-to-B-Bereich.
Bio-Gütesiegel im B-to-B-Marketing – Teil 1/2: Bio ist bereits seit Langem kein Nischenprodukt mehr. Deutschland hat europaweit nicht nur den höchsten Verbrauch, sondern gleichzeitig auch den höchsten Umsatz an Bio-Produkten. Etwa jeder Vierte kauft hierzulande regelmäßig Bio-Lebensmittel. Damit haben es ökologische Produkte bereits jetzt in beinahe jeden deutschen Haushalt geschafft. Basierend auf der Relevanz von ökologischen Produkten im Markt, ergibt sich ein gesteigerter Fokus auf die Beziehungen zwischen Lebensmittelherstellern und Lebensmittelhändlern, der in den letzten Jahren angezogen ist. Unternehmerische Anforderungen konzentrieren sich daher mehr als je zuvor neben dem üblichen B-to-C-Marketing auf das B-to-B-Marketing. Mit den neuen Marktherausforderungen steigen somit die Erwartungen an die Marketingleistungen im B-to-B-Bereich.
Beyond Selling orientiert sich am komplexen, vornehmlich mittelständisch geprägten Multi-Kanal-Vertrieb. Beyond Selling hat den Anspruch, holistisch zu agieren und will aufzeigen, dass neben vielen bewährten Konzepten bestimmte Aspekte für die erfolgreiche Unternehmensführung zukünftig zunehmend wichtiger werden. So bleiben bspw. eine stringente Buying-Center-Analyse und auch eine treffsichere Formulierung des kundennutzenorientierten Leistungsversprechens im Rahmen des Value-Based-Selling-Konzeptes unabdingbar. Allerdings gilt es auch, den Fokus auf Themen zu legen, die zukünftig deutlich an Bedeutung gewinnen werden
This paper intends to give an insight on how to develop a customer loyalty-focused gamification concept, that will trigger intrinsic motivation and hence strengthen customer loyalty, using the mobility industry as an example. The authors conducted explorative expert interviews to create a cross-industry process chart that guides the generic development of a customer loyalty-focused gamification concept.
Durch das Verbot der ozonschädigenden Fluor-Chlorkohlenwasserstoffen als Kältemittel und der heute überwiegend eingesetzten Fluor-Kohlenwasserstoffe, welche sich negativ auf den Treibhauseffekt auswirken, gewinnt das umweltfreundlichere CO2 (Kohlendioxid) in der Verwendung als Kältemittel an Bedeutung. Ausgangspunkt dieser Arbeit sind ein Prototyp einer reversiblen CO2 Wärmepumpe und ein Simulationsmodell derselbigen. Ziel dieser Arbeit ist es das Simulationsmodell, anhand von realen Messergebnissen des Prototyps, zu verifizieren. Durch die Berechnung von Vergleichsparametern, das Festlegen von Randbedingungen und geeigneten Messpunkten am Prototyp wird die Simulation optimiert. Abschließend folgt die Bewertung der Ergebnisse im Hinblick auf die Funktionalität der Wärmepumpe und deren Abbild in der Simulation.
This paper describes the analysis of day-ahead power market data from the European Power Exchange (EPEX) SPOT over a period of 17 months till October 2020 and the forecasting model for electricity prices. High volatility of the DE-LU (Germany and Luxembourg) power market in order to improve the planning of the bidding strategy and maximize benefits was reflected. Forecasting models based on the Autoregressive Integrated Moving Average (ARIMA) approach and artificial neural networks are developed to predict Day-Ahead prices up to a week ahead. Models are built for a virtual power plant Neckar-Alb and will be used as a part of an optimization tool for the operationtimetable of connected distributed energy devices
Initial Coin Offering (ICO) und damit verbundene Token spielen bei der Unternehmensfinanzierung eine immer bedeutsamere Rolle. Dies gilt insbesondere im Fall von Start-ups, deren Geschäftsmodell auf der Blockchain-Technologie basiert. Dieser Beitrag stellt die verschiedenen Tokenvarianten im Rahmen eines ICO vor und gibt einen Überblick über den aktuellen rechtlichen Hintergrund.
Der hohe Stellenwert von Energiedienstleistungen steht für Energieversorger außer Zweifel. Der Handlungsbedarf bleibt aber nach wie vor immens, erforderliche Change-Prozesse werden erst in wenigen Fällen aktiv und gezielt gestaltet. Dies sind die zentralen Ergebnisse einer gemeinsamen empirischen Studie vom "Reutlinger Energiezentrum für Dezentrale Energiesysteme und Energieeffizienz (REZ) an der Hochschule Reutlingen und der international tätigen Unternehmensberatung kwp consulting group.
This paper explores why and how dominant international social standards used in the fashion industry are prone to implementation failures. A qualitative multiple-case study method was conducted, using purposive sampling to select 13 apparel supply chain actors. Data were collected through on-site semi-structured face-to-face interviews. The findings of the study are interpreted by using core tenets of agency theory. The case study findings clearly highlight why and how multi-tier apparel supply chains fail to implement social standards effectively. As a consequence of substantial goal conflicts and information asymmetries, sourcing agents and suppliers are driven to perform opportunistic behaviors in form of hidden characteristics, hidden intentions, and hidden actions, which significantly harm social standards. Fashion retailers need to empower their corporate social responsibility (CSR) departments by awarding an integrative role to sourcing decisions. Moreover, accurate calculation of orders, risk sharing, cost sharing, price premiums, and especially guaranteed order continuity for social compliance are critical to reduce opportunistic behaviors upstream of the supply chain. The development of social standards is highly suggested, e.g., by including novel metrics such as the assessment of buying practices or the evaluation of capacity planning at factories and the strict inclusion of subcontractors’ social performances. This paper presents evidence from multiple Vietnamese and Indonesian cases involving sourcing agents as well as Tier 1 and Tier 2 suppliers on a highly sensitive topic. With the development of the conceptual framework and the formulation of seven related novel propositions, this paper unveils the ineffectiveness of social standards, offers guidance for practitioners, and contributes to the neglected social dimension in sustainable supply chain management research and accountability literature.
Viele von uns saßen sicher schon einmal in einer Weiterbildungsveranstaltung, die gefühlt wenig brachte. Erfolgreiche Weiterbildung gelingt, indem Veranstaltungen gezielt an den Wünschen und Bedürfnissen der Nutzer ausgerichtet und geplant werden. Doch wie können persönliche Bedürfnisse der Nutzer von Weiterbildungsangeboten ermittelt und systematisch in die Gestaltung des Angebotes einbezogen werden?
Das Handbuch hilft dabei Weiterbildungsveranstaltungen zu entwickeln, die Teilnehmer motivieren und Wissens- und Kompetenzvermittlung sicherstellen. Die AutorInnen haben ein Methodenset entwickelt, dass die Teilnehmer schon bei der Entwicklung von Bildungsangeboten miteinbezieht und so eine effektive Umsetzung der Teilnehmerorientierung in der Weiterbildung ermöglicht. Im Buch beschreiben sie ihr Vorgehen und geben eine zusammenfassende Anleitung an die Hand.
Durch Design Thinking lassen sich radikale Innovationen schaffen. Es findet jedoch immer in einer bestimmten Unternehmenskultur statt, die wiederum in eine nationale Kultur eingebettet ist. Das hat Auswirkungen auf die Implementierung, da dieses Umfeld förderlich oder hinderlich sein kann. Umgekehrt kann die Unternehmenskultur vom Design Thinking beeinflusst werden.
In Deutschland gilt für Abschlussprüfer von Unternehmen des öffentlichen Interesses eine um sogenannte Key Audit Matters erweiterte Berichtspflicht im Bestätigungsvermerk, und zwar bei der Prüfung von Abschlüssen von Geschäftsjahren, die nach dem 16.06.2016 beginnen. Die vorliegende Untersuchung beschäftigt sich mit den Einflussfaktoren für die Anzahl an Key Audit Matters und fokussiert sich dabei auf die Berichtspraxis im Zeitverlauf, indem neben dem Erstanwendungsjahr auch das Folgejahr betrachtet wird. Im Mittelpunkt steht eine multivariate Analyse, sie zeigt, dass die Zugehörigkeit zu bestimmten Branchen, bestimmt beauftragte Abschlussprüfer. Die Unternehmensgröße, die Erstanwendung neuer IFRS-Regelungen und die im Vorjahr berichtete Anzahl an Key Audit Matters im Vergleich zum Branchendurchschnitt des Vorjahres signifikanten Einfluss auf die Veränderung der Anzahl der Key Audit Matters im Zeitablauf haben. Dabei werden die im DAX 30, MDAX und SDAX notierten Unternehmen betrachtet.
Innovation-HUBs sind aktuell in Mode. Allerdings beklagen viele Unternehmen, dass der nachhaltige Erfolg aus verschiedenen Gründen nicht ausreichend erzielt wird. Eine Tischtennisplatte und ein Basketballkorb sind eben keine Innovationsgaranten, sondern viel mehr die Mitarbeiter selbst, die ins Zentrum des Innovation-HUBs gestellt werden müssen. Es wird ein Qualifizierungsmodell für die Arbeit in Innovation-HUBs vorgestellt, das auf einem Innovation-HUB-Trainingscenter basiert, das an der Hochschule Reutlingen in der Ausbildung von Studierenden betrieben wird. Hier lernen die Studierenden, wie Sie durch Ihr Verhalten Innovationen treiben oder hemmen und wie sie nachhaltig den Erfolg eines Innovation-HUBs gestalten.
Supply chains have become increasingly complex, making it difficult to ensure transparency throughout the whole supply chain. In this context, first approaches came up, adopting the immutable, decentralised, and secure characteristics of the blockchain technology to increase the transparency, security, authenticity, and auditability of assets in supply chains. This paper investigates recent publications combining the blockchain technology and supply chain management and classifies them regarding the complexity to be mapped on the blockchain. As a result, the increase of supply chain transparency is identified as the main objective of recent blockchain projects in supply chain management. Thereby, most of the recent publications deal with simple supply chains and products. The few approaches dealing with complex parts only map sub-areas of supply chains. Currently no example exists which has the aim of increasing the transparency of complex manufacturing supply chains, and which enables the mapping of complex assembly processes, an efficient auditability of all assets, and an implementation of dynamic adjustments.
Monodisperse polystyrene spheres are functional materials with interesting properties, such as high cohesion strength, strong adsorptivity, and surface reactivity. They have shown a high application value in biomedicine, information engineering, chromatographic fillers, supercapacitor electrode materials, and other fields. To fully understand and tailor particle synthesis, the methods for characterization of their complex 3D morphological features need to be further explored. Here we present a chemical imaging study based on three-dimensional confocal Raman microscopy (3D-CRM), scanning electron microscopy (SEM), focused ion beam (FIB), diffuse reflectance infrared Fourier transform (DRIFT), and nuclear magnetic resonance (NMR) spectroscopy for individual porous swollen polystyrene/poly (glycidyl methacrylate-co-ethylene di-methacrylate) particles. Polystyrene particles were synthesized with different co-existing chemical entities, which could be identified and assigned to distinct regions of the same particle. The porosity was studied by a combination of SEM and FIB. Images of milled particles indicated a comparable porosity on the surface and in the bulk. The combination of standard analytical techniques such as DRIFT and NMR spectroscopies yielded new insights into the inner structure and chemical composition of these particles. This knowledge supports the further development of particle synthesis and the design of new strategies to prepare particles with complex hierarchical architectures.
Melamine-formaldehyde resins are widely used for decorative paper impregnation. Resin properties relevant for impregnation are mainly determined already at the stage of resin synthesis by the applied reaction conditions. Thus, understanding the relationship between reaction conditions and technological properties is important. Response surface methodology based on orthogonal parameter level variations is the most suitable tool to identify and quantify factor effects and deduce causal correlation patterns. Here, two major process factors of MF resin synthesis were systematically varied using such a statistical experimental design. To arrive at resins having a broad range of technological properties, initial pH and M:F ratio were varied in a wide range (pH: 7.9–12.1; M:F ratio: 1:1.5–1:4.5). The impregnation behavior of the resins was modeled using viscosity, penetration rate and residual curing capacity as technological responses. Based on the response surface models, nonlinear and synergistic action of process factors was quantified and a suitable process window for preparing resins with favorable impregnation performance was defined. It was found that low M:F ratios (~1:2–1:2.5) and comparatively high starting pHs (~pH 11) yield impregnation resins with rapid impregnation behavior and good residual curing capacity.
Deutschland, quo vadis?
(2020)
Shutdown in Deutschland im März 2020. Stillstand in Handel und Industrie. Der Börsenwert einer beachtlichen Anzahl von Unternehmen hat sich in kürzester Zeit halbiert. Anleger warfen alles auf den Markt. Und bei der hohen Unsicherheit verloren sämtliche Anlageklassen, zeitweise sogar Gold. Selbst Konzerne wie die Lufthansa werden es ohne Staatshilfe nicht mehr schaffen zu existieren.
Die Blockchain-Technologie stellt einen vielversprechenden Ansatz für Transparenz und Resilienz in Lieferketten dar. In diesem Beitrag wird untersucht, welche Blockchain-Lösungen derzeit für die Supply-Chain zur Verfügung stehen, und die bislang umgesetzten Projekte in diesem Bereich analysiert. Die meisten der realisierten Projekte beziehen sich auf einfache Produkte und Supply-Chain-Strukturen. Der Grund ist, dass bislang Lösungen zur ganzheitlichen Abbildung von komplexen Produkten in dynamischen Supply-Chain-Strukturen gefehlt haben. Doch jetzt stehen erste vielversprechende Ansätze zur Verfügung.
Effektives Risiko-Management sollte neben quantifizierbaren, bekannten Risiken auch Ereignisse berücksichtigen, die entweder in ähnlicher Art bereits eingetreten oder grundsätzlich vorstellbar sind. Für eine Identifikation dieser "Grauen Schwäne" müssen institutionell-organisatorische Voraussetzungen geschaffen und analytisch-konzeptionelle Instrumente bereitgestellt werden.
We investigated the state of artificial intelligence (AI) in pharmaceutical research and development (R&D) and outline here a risk and reward perspective regarding digital R&D. Given the novelty of the research area, a combined qualitative and quantitative research method was chosen, including the analysis of annual company reports, investor relations information, patent applications, and scientific publications of 21 pharmaceutical companies for the years 2014 to 2019. As a result, we can confirm that the industry is in an ‘early mature’ phase of using AI in R&D. Furthermore, we can demonstrate that, despite the efforts that need to be managed, recent developments in the industry indicate that it is worthwhile to invest to become a ‘digital pharma player’.
We present the modification of ethylene-propylene rubber (EPM) with vinyltetra-methydisiloxane (VTMDS) via reactive extrusion to create a new silicone-based material with the potential for high-performance applications in the automotive, industrial and biomedical sectors. The radical-initiated modification is achieved with a peroxide catalyst starting the grafting reaction. The preparation process of the VTMDS-grafted EPM was systematically investigated using process analytical technology (in-line Raman spectroscopy) and the statistical design of experiments (DoE). By applying an orthogonal factorial array based on a face-centered central composite experimental design, the identification, quantification and mathematical modeling of the effects of the process factors on the grafting result were undertaken. Based on response surface models, process windows were defined that yield high grafting degrees and good grafting efficiency in terms of grafting agent utilization. To control the grafting process in terms of grafting degree and grafting efficiency, the chemical changes taking place during the modification procedure in the extruder were observed in real-time using a spectroscopic in-line Raman probe which was directly inserted into the extruder. Successful grafting of the EPM was validated in the final product by 1H-NMR and FTIR spectroscopy.
Im Projekt GalvanoFlex_BW sind verschiedene Möglichkeiten zur Verbesserung der Energieeffizienz von energieintensiven Industrieunternehmen aufgezeigt und untersucht worden. Die Einführung der KWK im stromoptimierten Betrieb stellte dabei einen besonders betrachteten Aspekt dar. Neben der technischen Untersuchung ist zudem eine sozialwissenschaftliche Betrachtung vorgenommen worden, um die Einführung und Umsetzung entsprechender Maßnahmen auch unter diesem Aspekt zu betrachten. Ein zusätzlicher wichtiger Schwerpunkt des Projektes war die Übertragung des erarbeiteten Wissens an weitere Unternehmen, Institutionen etc., die nicht direkt am Projekt beteiligt waren.
Durch die Zusammenarbeit von vier Forschungs- und drei Industriepartnern sind die Arbeiten praxisorientiert auf Basis realer Messdaten sowie im Zuge von Befragungen der handelnden Personen bei den Projektpartnern im Sinne eines Reallabors durchgeführt worden.
Die Notwendigkeit zur Einsparung von Energie und damit zur Umsetzung von Effizienzmaßnahmen ist ein wichtiger Schritt, um die von der EU geplante Reduktion von Treibhausgasen zu erreichen. Darüber hinaus ist zu erwarten, dass die Energiekosten auch zukünftig weiter ansteigen. Dadurch werden Maßnahmen zur Steigerung der Energieeffizienz zu einem wichtigen Element, eine wettbewerbsfähige Produktion zu gewährleisten. Im Rahmen des Projektes sind deshalb verschiedene Energieeffizienzmaßnahmen speziell für die Galvanotechnik recherchiert, analysiert und in einen Maßnahmenkatalog überführt worden. Des Weiteren wurde eine Bewertungsmethode entwickelt, die die Unternehmen der Galvanotechnik bei der Identifikation von sinnvollen Energieeffizienzmaßnahmen unterstützen soll.
Bei den Untersuchungen zur Umsetzung von Kraft-Wärme-Kopplungsanlagen konnte am Beispiel von zwei Unternehmen mit stark unterschiedlichen Strom- und Wärmebedarfswerten gezeigt werden, dass der Einsatz entsprechender Anlagen wirtschaftlich lohnenswert ist. Im günstigsten Fall ergeben sich Amortisationszeiten von etwa zwei Jahren. Es hat sich weiterhin gezeigt, dass die Auslegung des Block-heizkraftwerkes stark von den Strom- und Wärmebedarfswerten abhängt und dass der Pufferspeicher keinesfalls zu klein ausgelegt werden sollte. Eine intelligente stromoptimierte Steuerung mit Lastspitzenmanagement kann die Wirtschaftlichkeit jedoch häufig nur noch wenig gegenüber dem wärmegeführten Betrieb verbessern. Ursache dafür ist, dass das derzeitige Förder- und Vergütungssystem für Kraft-Wärme-Kopplungsanlagen nahezu keine Anreize für einen am Strombedarf und damit an der Deckung von Residuallast orientierten Betrieb bietet. Einzig bei Unternehmen mit ausgeprägten Spitzen im Strombezug, kann der gezielte Einsatz eines Blockheizkraftwerkes zu einer signifikanten Senkung des Leistungspreises führen.
Darüber hinaus ist die Einführung einer Kraft-Wärme-Kopplungsanlage generell als komplexe Energieeffizienzmaßnahme anzusehen, die deshalb neben den wirtschaftlichen Aspekten erhöhte Anforderungen an die Unternehmen und das professionelle Umfeld stellt, die im Rahmen der sozialwissenschaftlichen Begleitforschung untersucht worden sind. Dabei konnten Treiber aber auch Hemmnisse zur Umsetzung der Technologie sowohl innerhalb der Unternehmen als auch außerhalb identifiziert werden. Die internen Hemmnisse sind dabei auf unterschiedliche Ursachen zurückzuführen, wie die hohe Komplexität der KWK-Technologie, die schwierige Bewertung des Gesamtnutzens im Unternehmen, die mangelnde personelle Ausstattung und fehlende Unternehmerentscheidungen. Abhilfe schaffen, und damit als Treiber wirken, können hier ein verbessertes Beratungsangebot insbesondere seitens neutraler Stellen sowie der Anlagenbetrieb im Contracting.
Die Übertragung der im Projekt erarbeiteten Ergebnisse ist bereits während der Projektlaufzeit über die Branchenplattform im Zuge verschiedener Workshops, die speziell auf Unternehmen und Institutionen außerhalb des Projektkonsortiums ausgerichtet waren, erfolgt. Zur Verstärkung der Verbreitung des erarbeiteten Wissens ist zum Projektende eine Serie aus vier Fachartikeln in einem namhaften Branchenmagazin erschienen, und es ist eine Internetseite zum Projekt erstellt worden (www.galvanoflex_bw.de). Letztere hat dabei nicht nur die Aufgabe der Wissensverbreitung, sondern sie soll auch über das Projektende hinaus als Kontaktplattform dienen, um die Umsetzung von Effizienzmaßnahmen mit dem im Projekt generierten Wissen zu unterstützen.
The cloud evolved into an attractive execution environment for parallel applications, which make use of compute resources to speed up the computation of large problems in science and industry. Whereas Infrastructure as a Service (IaaS) offerings have been commonly employed, more recently, serverless computing emerged as a novel cloud computing paradigm with the goal of freeing developers from resource management issues. However, as of today, serverless computing platforms are mainly used to process computations triggered by events or user requests that can be executed independently of each other and benefit from on-demand and elastic compute resources as well as per-function billing. In this work, we discuss how to employ serverless computing platforms to operate parallel applications. We specifically focus on the class of parallel task farming applications and introduce a novel approach to free developers from both parallelism and resource management issues. Our approach includes a proactive elasticity controller that adapts the physical parallelism per application run according to user-defined goals. Specifically, we show how to consider a user-defined execution time limit after which the result of the computation needs to be present while minimizing the associated monetary costs. To evaluate our concepts, we present a prototypical elastic parallel system architecture for self-tuning serverless task farming and implement two applications based on our framework. Moreover, we report on performance measurements for both applications as well as the prediction accuracy of the proposed proactive elasticity control mechanism and discuss our key findings.
Unternehmen wenden insbesondere bei IT-nahen Projekten seit einigen Jahren auch im Controlling verstärkt ein agiles Vorgehen an. Erfahrungen zeigen jedoch, dass dies nicht bei allen Projekten in jedem Unternehmen funktioniert. Hybride Ansätze, die agile mit klassischen Projekt-Management-Methoden verbinden, bieten eine Lösung.
Direct observation of structural heterogeneity and tautomerization of single hypericin molecules
(2021)
Tautomerization is a fundamental chemical reaction which involves the relocation of a proton in the reactants. Studying the optical properties of tautomeric species is challenging because of ensemble averaging. Many molecules, such as porphines, porphycenes, or phenanthroperylene quinones, exhibit a reorientation of the transition dipole moment (TDM) during tautomerization, which can be directly observed in single-molecule experiments. Here, we study single hypericin molecules, which is a prominent phenanthroperylene quinone showing antiviral, antidepressive, and photodynamical properties. Observing abrupt flipping of the image pattern combined with time-dependent density functional theory calculations allows drawing conclusions about the coexistence of four tautomers and their conversion path. This approach allows the unambiguous assignment of a TDM orientation to a specific tautomer and enables the determination of the chemical structure in situ. Our approach can be applied to other molecules showing TDM reorientation during tautomerization, helping to gain a deeper understanding of this important process.
The effect of hard segment content and diisocyanate structure on the transparency and mechanical properties of soft poly(dimethylsiloxane) (PDMS)-based urea elastomers (PSUs) was investigated. A series of PSU elastomers were synthesized from an aminopropyl-terminated PDMS (M¯n: 16,300 g·mol−1), which was prepared by ring chain equilibration of the monomers octamethylcyclotetrasiloxane (D4) and 1,3-bis(3-aminopropyl)-tetramethyldisiloxane (APTMDS). The hard segments (HSs) comprised diisocyanates of different symmetry, i.e., 4,4′-methylenebis(cyclohexyl isocyanate) (H12MDI), 4,4′-methylenebis(phenyl isocyanate) (MDI), isophorone diisocyanate (IPDI), and trans-1,4-cyclohexane diisocyanate (CHDI). The HS contents of the PSU elastomers based on H12MDI and IPDI were systematically varied between 5% and 20% by increasing the ratio of the diisocyanate and the chain extender APTMDS. PSU copolymers of very low urea HS contents (1.0–1.6%) were prepared without the chain extender. All PSU elastomers and copolymers exhibited good elastomeric properties and displayed elongation at break values between 600% and 1100%. The PSUs with HS contents below 10% were transparent and became increasingly translucent at HS contents of 15% and higher. The Young’s modulus (YM) and ultimate tensile strength values of the elastomers increased linearly with increasing HS content. The YM values differed significantly among the PSU copolymers depending on the symmetry of the diisocyanate. The softest elastomer was that based on the asymmetric IPDI. The elastomers synthesized from H12MDI and MDI both exhibited an intermediate YM, while the stiffest elastomer, i.e., that comprising the symmetric CHDI, had a YM three-times higher than that prepared with IPDI. The PSUs were subjected to load–unload cycles at 100% and 300% strain to study the influence of HS morphology on 10-cycle hysteresis behavior. At 100% strain, the first-cycle hysteresis values of the IPDI- and H12MDI-based elastomers first decreased to a minimum of approximately 9–10% at an HS content of 10% and increased again to 22–28% at an HS content of 20%. A similar, though less pronounced, trend was observed at 300% strain. First-cycle hysteresis among the PSU copolymers at 100% strain was lowest in the case of CHDI and highest in the IPDI-based elastomer. However, this effect was reversed at 300% strain, with CHDI displaying the highest hysteresis in the first cycle. In vitro cytotoxicity tests performed using HaCaT cells did not show any adverse effects, revealing their potential suitability for biomedical applications.
This article studies the renewed interest surrounding sustainable public finance and the topic of tax evasion as well as the new theory of information inattention. Extending a model of tax evasion with the notion of inattention reveals novel findings about policy instruments that can be used to mitigate tax evasion. We show that the attention parameters regarding tax rates, financial penalty schemes and income levels are as important as the level of the detection probability and the financial penalty incurred. Thus, our theory recommends the enhancement of sustainability in public policy, particularly in tax policy. Consequently, the paper contributes both to the academic and public policy debate.
During curing of thermosetting resins the technologically relevant properties of binders and coatings develop. However, curing is difficult to monitor due to the multitude of chemical and physical processes taking place. Precise prediction of specific technological properties based on molecular properties is very difficult. In this study, the potential of principal component analysis (PCA) and principal component regression (PCR) in the analysis of Fourier transform infrared (FTIR) spectra is demonstrated using the example of melamine-formaldehyde (MF) resin curing in solid state. FTIR/PCA-based reaction trajectories are used to visualize the influence of temperature on isothermal cure. An FTIR/PCR model for predicting the hydrolysis resistance of cured MF resin from their spectral fingerprints is presented which illustrates the advantages of FTIR/PCR compared to the combination differential scanning calorimetry/isoconversional kinetic analysis. The presented methodology is transferable to the curing reactions of any thermosetting resin and can be applied to model other technologically relevant final properties as well.
Mit diesem Strategiepapier formulieren die Universitäts-, Landes- und Hochschulbibliotheken des Landes Baden-Württemberg die aus ihrer Sicht zentralen Entwicklungsfelder und Herausforderungen der kommenden Jahre. Die Bibliotheken und das Bibliotheksservice-Zentrum Baden-Württemberg (BSZ) sorgen als Wissenschafts- und Kultureinrichtungen gemeinsam für die akademische Informationsinfrastruktur. Sie nehmen die Herausforderungen der Digitalisierung an und gestalten den Wandel im Dialog mit Forschenden, Lehrenden und Studierenden aktiv mit.
Automatic anode rod inspection in aluminum smelters using deep-learning techniques: a case study
(2020)
Automatic fault detection using machine learning has become an exciting and promising area of research. This because it accurate and timely way to manage and classify with minimal human effort. In the computer vision community, deep-learning methods have become the most suitable approaches for this task. Anodes are large carbon blocks that are used to conduct electricity during the aluminum reduction process. The most basic function of anode rod inspection is to prevent a situation where the anode rod will not fit into the stub-holes of a new anode. It would be the case for a rod containing either severe toe-in, missing stubs, or a retained thimble on one or more stubs. In this work, to improve the accuracy of shape defect inspection for an anode rod, we use the Fast Region-based Convolutional Network method (Fast R-CNN), model. To train the detection model, we collect an image dataset composed of multi-class of anode rod defects with annotated labels. Our model is trained using a small number of samples, an essential requirement in the industry where the number of available defective samples is limited. It can simultaneously detect multi-class of defects of the anode rod in nearly real-time.
Ever since the 1980s, researchers in computer science and robotics have been working on making autonomous cars. Due to recent breakthroughs in research and devel- opment, such as the Bertha Benz Project [ZBS+14], the goal of fully autonomous vehicles seems closer than ever before. Yet a lot of questions remain unanswered. Especially now that the automotive industry moves towards autonomous systems in series production vehicles, the task of precise localization has to be solved with automotive grade sensors and keep memory and processing consumption at a mini- mum. This thesis investigates the Simultaneous Localization and Mapping (SLAM) prob- lem for autonomous driving scenarios on a parking lot using low cost automotive sensors. The main focus is herby devoted to the RAdio Detection And Ranging (RADAR) sensor, which has not been widely analyzed in an autonomous driving scenario so far, even though they are abundant in the automotive industry for ap- plications such as Adaptive Cruise Control (ACC). Due to the high noise floor, the radar sensor has widely been disregarded in the Intelligent Transportation Systems and Robotics communities with regards to SLAM applications. However in this thesis, it is shown that the RADAR sensor proves to be an affordable, robust and precise sensor, when modeling its physical properties correctly. In this regard, a GraphSLAM based framework is introduced, which extracts features from the RADAR sensor and generates an optimized map of the surroundings using the RADAR sensor alone. This framework is used to enable crowd based localization, which is not limited to the RADAR sensor alone. By integrating an automotive Light Detection and Ranging (LiDAR) and stereo camera sensor, a robust and precise localization system can be built that that is suitable for autonomous driving even in complex parking lot scenarios. It it is thereby shown that the RADAR sensor is strongly contributing to obtaining good results in a sensor fusion setup. These results were obtained on an extensive dataset on a parking lot, which has been recorded over the course of several months. It contains different weather conditions, different configurations of parked cars and a multitude of different trajectories to validate the approaches described in this thesis and to come to the conclusion that the RADAR sensor is a reliable sensor in series autonomous driving systems, both in a multi sensor framework and as a single component for localization.
Enhancing data-driven algorithms for human pose estimation and action recognition through simulation
(2020)
Recognizing human actions, reliably inferring their meaning and being able to potentially exchange mutual social information are core challenges for autonomous systems when they directly share the same space with humans. Intelligent transport systems in particular face this challenge, as interactions with people are often required. The development and testing of technical perception solutions is done mostly on standard vision benchmark datasets for which manual labelling of sensory ground truth has been a tedious but necessary task. Furthermore, rarely occurring human activities are underrepresented in these datasets, leading to algorithms not recognizing such activities. For this purpose, we introduce a modular simulation framework, which offers to train and validate algorithms on various human-centred scenarios. We describe the usage of simulation data to train a state-of-the-art human pose estimation algorithm to recognize unusual human activities in urban areas. Since the recognition of human actions can be an important component of intelligent transport systems, we investigated how simulations can be applied for his purpose. Laboratory experiments show that we can train a recurrent neural network with only simulated data based on motion capture data and 3D avatars, which achieves an almost perfect performance in the classification of those human actions on real data.
The design process for a single phase, smart, universal charger for light electric vehicles, is presented. With a step up, power factor correction circuit, followed by a phase shifted, full bridge converter, with synchronous rectification on the secondary side. Due to the resistor-capacitor-diode snubber on the secondary side, the current peak at the start of power transfer, leads to false triggering during light load control with peak current mode control. The solution developed for light loads, is to change from peak current control to voltage control. This is achieved by limiting the maximum phase shift, instead of changing the reference value. For the power factor correction stage, measured and calculated efficiencies are compared as a function of the output power. The voltage and current waveforms are shown for the power factor correction circuit, and for the phase shifted bridge, the measured current waveform is compared with simulation.
Urgent action is needed to keep the chance of limiting global warming to 1.5°C or even 2.0°C. Current outlooks by IPCC, and many other organisations forecast that this will be impossible at current pace of emission 'reductions' – Germany has already hit 1.5° warming this year. Across 2019, particularly during the UN New York Climate summit, numerous organisations declared their ambition to become net carbon neutral. Amongst these were investors and companies, including quite a number of German ones.
We apply a mixed methods approach, utilising data gathered from approx. 900 companies after Climate Week in context of the Energy Efficiency Index of German Industry (EEI), along with media research focusing on decarbonisation plans announced and initiatives pledging climate action.
With this, we analyse how German companies in the manufacturing sectors react to rising societal pressure and emerging policies, particularly what measures they have taken or plan to implement to reduce the footprint of their company, their products and their supply chain. In this, we particularly analyse whether and in what way energy- and resource consumption, as well as carbon emissions are considered in the development and lifecycle of goods manufactured. This is of huge relevance as these goods determine the future footprint of buildings, vehicles and industry.
Regarding the supply chain, current articles indicate that small and medium-sized enterprises (SME) are particularly challenged by increasing demands from their large corporate clients and an alleged lack of preparedness to be able to take and afford prompt decarbonisation action themselves (Buchenau et. al. 2019). Notably the automotive industry recently announced new models that will be 100% carbon neutral all the way through (ibid). We thus analyse if and how factors such as company size, energy intensity and sector affiliation influence a company’s plan to fully decarbonize. Ownership structure and corporate culture, it appears, significantly impact on the degree of decarbonisation action underway.
Despite strong political efforts across Europe, small and medium- sized enterprises (SMEs) seem to neglect adopting effective measures for energy efficiency. Adopting a cultural perspective and based on a study among industrial SMEs in Southern Germany, we investigate what drives decisions for energy efficiency in SMEs and how energy management contributes to closing the energy efficiency gap. The study follows a mixed-methods approach and combines eleven ethnographic case studies and a quantitative survey among 500 manufacturing SMEs in Southern Germany.
The main contribution of the paper is to offer a perspective on energy efficiency in SMEs beyond the diffusion of energyefficient technology. By contrast, our results strongly suggest that the diffusion of energy efficiency in industrial companies should not be solely reduced to decisions for technical measures. We shed light on how energy efficiency is established and the importance of energy management in SMEs.
Our study shows that energy efficiency is well established in the investigated SMEs. At the same time, establishment cannot be explained by company size or energy demand. By contrast, the contextual environment of the company and the individual leadership of the company appear to have a more substantial influence. The embedding of energy efficiency in corporate strategy, a broad spectrum of different practices, the involvement of the employees, actions for raising awareness in everyday work life, and distributing attention by organizational measures constitute the driving forces in establishing energy efficiency, and these drivers can be subsumed under the label of energy management.
Sol-Gel basierte Flammschutzmittel stellen einen vielversprechenden Ansatz für Textilien dar, gerade im Bereich des Ersatzes von derzeit etablierten halogenhaltigen Flammschutzmitteln. Letztere sind aufgrund ihrer toxikologisch Bedenklichkeit sowie ihrer mitunter bioakkumulierenden Eigenschaften in die Kritik geraten. In diesem Forschungsvorhaben wurde daher untersucht wie aus Phosphor- und stickstoffhaltige Silane halogenfreie Flammschutzmittel verwirklicht werden können. Die Sol-Gel-Schicht fungierte dabei zum einen als nicht brennbarer Binder, zum anderen konnten über das Anbinden von Phosphorgruppen in an kommerziell verfügbare Silane Flammschutz aktive Gruppen direkt mit eingebunden werden. Verschiedene Syntheseansätze wurden dabei verfolgt, wobei durch alle hergestellten N-P-Silane ein Flammschutz nach DIN EN ISO 15025 (Schutzkleidung – Schutz gegen Hitze und Flammen) erhalten wurden. Dabei hängt die Flammschutzwirkung stark von den funktionellen Gruppen und der Oxidationsstufe des Phosphors ab, dabei konnte ein entsprechender Flammschutz bei Auflagen von 5 % erzielt werden. Es konnte gezeigt werden, dass ein Mechanismus auf Basis der Bildung einer Schutzschicht hauptsächlich verantwortlich für den Flammschutz ist. Dieses Ergebnis ist für eine zukünftige, weitere Optimierung entsprechender Ausrüstungen nicht zu unterschätzen. Durch Ausrüstungsversuche im semi-industriellen Maßstab konnte weiterhin gezeigt werden, dass einer großtechnischen Umsetzung der angewandten Ausrüstungen prinzipiell nichts im Wege steht. Je nach funktioneller Gruppe am Phosphor konnte die Wasserlöslichkeit und die Waschstabilität kontrolliert werden. Dabei konnte zum einen gezeigt werden, dass hydrophobes N-P-Silane eine bessere Waschbeständigkeit aufweisen, hydrophile N-P-Silane erhalten diese erst bei Fixierungstemperaturen von 180 °C. Ausgehend von den Ergebnissen konnten sticktstoffgenerierende und cyanursäure-basierte N-P-Silane entwickelt werden, welche sich besonders in einer guten Flammschutzwirkung bei Mischgeweben auszeichnen. Insgesamt konnte innerhalb des Forschungsvorhabens gezeigt werden, dass N-P-Silane hervorragende permanente Flammschutzmittel für Textilien sind und auf welchem Mechanismus dieser Flammschutz begründet ist.
Due to decreased mobility or families living apart, older adults are especially vulnerable to the issue of social isolation. Literature suggests that technology can help to prevent this isolation. The present work addresses an approach to participate in society by sharing knowledge that is cherished. We propose the cooking recipe exchange application PrecRec for older adults to make them feel precious and valued. PrecRec has been developed and evaluated in an iterative process with eleven older adults. The results show that a broad perspective has to be taken into account when designing such systems.
Traditional communication of research on climate change fails to encourage individual, corporate, and political leaders to take appropriate action. We argue that this problem is based on an overly simplistic unidirectional model of science communication. Conversely, theory shows that active learning processes are better suited to initiate and mobilize engagement among all stakeholders. Here, we integrate theoretical insights on active learning with empirical evidence from serious gaming: communication should be understood as an integral design feature that relates active learning on climate change to tangible action.
This article examines centralised and decentralised approaches towards managing internationalisation by means of a case study. Reutlingen University (Hochschule Reutlingen), a university of applied sciences in Southern Germany, has three decades of experience in managing internationalisation. Its strongly integrated and hybrid approach combines centralised and decentralised strategies with the aim of achieving responsiveness, innovation, transparency, quality, and goal alignment. Centralisation and decentralisation are manifested on two levels: university versus schools, and school versus individual programmes. Since internationalisation is embedded in virtually all areas of the university’s operations, examples will be provided ranging from administration and marketing to research, international programme management and curricula.
Hypermedia as the Engine of Application State (HATEOAS) is one of the core constraints of REST. It refers to the concept of embedding hyperlinks into the response of a queried or manipulated resource to show a client possible follow-up actions and transitions to related resources. Thus, this concept aims to provide a client with a navigational support when interacting with a Web-based application. Although HATEOAS should be implemented by any Web-based API claiming to be RESTful, API providers tend to offer service descriptions in place of embedding hyperlinks into responses. Instead of relying on a navigational support, a client developer has to read the service description and has to identify resources and their URIs that are relevant for the interaction with the API. In this paper, we introduce an approach that aims to identify transitions between resources of a Web-based API by systematically analyzing the service description only. We devise an algorithm that automatically derives a URI Model from the service description and then analyzes the payload schemas to identify feasible values for the substitution of path parameters in URI Templates. We implement this approach as a proxy application, which injects hyperlinks representing transitions into the response payload of a queried or manipulated resource. The result is a HATEOAS-like navigational support through an API. Our first prototype operates on service descriptions in the OpenAPI format. We evaluate our approach using ten real-world APIs from different domains. Furthermore, we discuss the results as well as the observations captured in these tests.
Annotations of character IDs in news images are critical as ground truth for news retrieval and recommendation system. Universality and accuracy optimization of deep neural network models constitutes the key technology to improve the precision and computing efficiency of automatic news character identification, which is attracting increased attention globally. This paper explores the optimized deep neural network model for automatic focus personage identification in multi-lingual news. First, the face model of the focus personage is trained by using the corresponding face images from German news as positive samples. Next, the scheme of Recurrent Convolutional Neural Network (RCNN) + Bi-directional Long-Short Term Memory (Bi-LSTM) + Conditional Random Field (CRF) is utilized to label the focus name, and the RCNN-RCNN encoder–decoder is applied to translate names of people into multiple languages. Third, face features are described by combining the advantages of Local Gabor Binary Pattern Histogram Sequence (LGBPHS) and RCNN, and iterative quantization (ITQ) is used to binarize codes. Finally, a name semantic network is built for different domains. Experiments are performed on a dataset which comprises approximately 100,000 news images. The experimental results demonstrate that the proposed method achieves a significant improvement over other algorithms.
With the continuous development of economy, consumers pay more attention to the demand for personalization clothing. However, the recommendation quality of the existing clothing recommendation system is not enough to meet the user’s needs. When browsing online clothing, facial expression is the salient information to understand the user’s preference. In this paper, we propose a novel method to automatically personalize clothing recommendation based on user emotional analysis. Firstly, the facial expression is classified by multiclass SVM. Next, the user’s multi-interest value is calculated using expression intensity that is obtained by hybrid RCNN. Finally, the multi-interest value is fused to carry out personalized recommendation. The experimental results show that the proposed method achieves a significant improvement over other algorithms.
Deep learning–based fabric defect detection methods have been widely investigated to improve production efficiency and product quality. Although deep learning–based methods have proved to be powerful tools for classification and segmentation, some key issues remain to be addressed when applied to real applications. Firstly, the actual fabric production conditions of factories necessitate higher real-time performance of methods. Moreover, fabric defects as abnormal samples are very rare compared with normal samples, which results in data imbalance. It makes model training based on deep learning challenging. To solve these problems, an extremely efficient convolutional neural network, Mobile-Unet, is proposed to achieve the end-to-end defect segmentation. The median frequency balancing loss function is used to overcome the challenge of sample imbalance. Additionally, Mobile-Unet introduces depth-wise separable convolution, which dramatically reduces the complexity cost and model size of the network. It comprises two parts: encoder and decoder. The MobileNetV2 feature extractor is used as the encoder, and then five deconvolution layers are added as the decoder. Finally, the softmax layer is used to generate the segmentation mask. The performance of the proposed model has been evaluated by public fabric datasets and self-built fabric datasets. In comparison with other methods, the experimental results demonstrate that segmentation accuracy and detection speed in the proposed method achieve state-of-the-art performance.
”I have never seen one who loves virtue as much as he loves beauty,” Confucius once said. If beauty is more important as goodness, it becomes clear why people invest so much effort in their first impression. The aesthetic of faces has many aspects and there is a strong correlation to all characteristics of humans, like age and gender. Often, research on aesthetics by social and ethic scientists lacks sufficient labelled data and the support of machine vision tools. In this position paper we propose the Aesthetic-Faces dataset, containing training data which is labelled by Chinese and German annotators. As a combination of three image subsets, the AF-dataset consists of European, Asian and African people. The research communities in machine learning, aesthetics and social ethics can benefit from our dataset and our toolbox. The toolbox provides many functions for machine learning with state-of-the-art CNNs and an Extreme-Gradient-Boosting regressor, but also 3D Morphable Model technolo gies for face shape evaluation and we discuss how to train an aesthetic estimator considering culture and ethics.
