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The current advancement of Artificial Intelligence (AI) combined with other digitalization efforts significantly impacts service ecosystems. Artificial intelligence has a substantial impact on new opportunities for the co-creation of value and the development of intelligent service ecosystems. Motivated by experiences and observations from digitalization projects, this paper presents new methodological perspectives and experiences from academia and practice on architecting intelligent service ecosystems and explores the impact of artificial intelligence through real cases supporting an ongoing validation. Digital enterprise architecture models serve as an integral representation of business, information, and technological perspectives of intelligent service-based enterprise systems to support management and development. This paper focuses on architectural models for intelligent service ecosystems, showing the fundamental business mechanism of AI-based value co-creation, the corresponding digital architecture, and management models. The focus of this paper presents the key architectural model perspectives for the development of intelligent service ecosystems.
Platforms and their surrounding ecosystems are becoming increasingly important components of many companies' strategies. Artificial Intelligence, in particular, has created new opportunities to create and develop ecosystems around the platform. However, there is not yet a methodology to systematically develop these new opportunities for enterprise development strategy. Therefore, this paper aims to lay a foundation for the conceptualization of Artificial Intelligence-based service ecosystems exploiting a Service-Dominant Logic. The basis for conceptualization is the study of value creation and particularly effective network effects. This research investigates the fundamental idea of extending specific digital concepts considering the influence of Artificial Intelligence on the design of intelligent services, along with their architecture of digital platforms and ecosystems, to enable a smooth evolutionary path and adaptability for human-centric collaborative systems and services. The paper explores an extended digital enterprise conceptual model through a combined, iterative, and permanent task of co-creating value between humans and intelligent systems as part of a new idea of cognitively adapted intelligent services.
This paper presents a permanent magnet tubular linear generator system for powering passive sensors using vertical vibration harvesting energy. The system consists of a permanent magnet tubular linear vibration generator and electric circuits. By using the design of mechanical resonant movers, the generator is capable of converting low frequencies small amplitude vertical vibration energy into more regular sinusoidal electrical energy. The distribution of the magnetic field and electromotive force are calculated by Finite Element Analysis. The characteristics of the linear vibration generator system are observed. The experimental results show the generator can produce about 0.4W~1.6W electrical power when the vibration source's amplitude is fixed on 2mm and the frequencies are between 13Hz and 22Hz.
Purpose
Computerized medical imaging processing assists neurosurgeons to localize tumours precisely. It plays a key role in recent image-guided neurosurgery. Hence, we developed a new open-source toolkit, namely Slicer-DeepSeg, for efficient and automatic brain tumour segmentation based on deep learning methodologies for aiding clinical brain research.
Methods
Our developed toolkit consists of three main components. First, Slicer-DeepSeg extends the 3D Slicer application and thus provides support for multiple data input/ output data formats and 3D visualization libraries. Second, Slicer core modules offer powerful image processing and analysis utilities. Third, the Slicer-DeepSeg extension provides a customized GUI for brain tumour segmentation using deep learning-based methods.
Results
The developed Slicer-DeepSeg was validated using a public dataset of high-grade glioma patients. The results showed that our proposed platform’s performance considerably outperforms other 3D Slicer cloud-based approaches.
Conclusions
Developed Slicer-DeepSeg allows the development of novel AI-assisted medical applications in neurosurgery. Moreover, it can enhance the outcomes of computer-aided diagnosis of brain tumours. Open-source Slicer-DeepSeg is available at github.com/razeineldin/Slicer-DeepSeg.
A hybrid deep registration of MR scans to interventional ultrasound for neurosurgical guidance
(2021)
Despite the recent advances in image-guided neurosurgery, reliable and accurate estimation of the brain shift still remains one of the key challenges. In this paper, we propose an automated multimodal deformable registration method using hybrid learning-based and classical approaches to improve neurosurgical procedures. Initially, the moving and fixed images are aligned using classical affine transformation (MINC toolkit), and then the result is provided to the convolutional neural network, which predicts the deformation field using backpropagation. Subsequently, the moving image is transformed using the resultant deformation into a moved image. Our model was evaluated on two publicly available datasets: the retrospective evaluation of cerebral tumors (RESECT) and brain images of tumors for evaluation (BITE). The mean target registration errors have been reduced from 5.35 ± 4.29 to 0.99 ± 0.22 mm in the RESECT and from 4.18 ± 1.91 to 1.68 ± 0.65 mm in the BITE. Experimental results showed that our method improved the state-of-the-art in terms of both accuracy and runtime speed (170 ms on average). Hence, the proposed method provides a fast runtime for 3D MRI to intra-operative US pair in a GPU-based implementation, which shows a promise for its applicability in assisting the neurosurgical procedures compensating for brain shift.
Accurate and safe neurosurgical intervention can be affected by intra-operative tissue deformation, known as brain-shift. In this study, we propose an automatic, fast, and accurate deformable method, called iRegNet, for registering pre-operative magnetic resonance images to intra-operative ultrasound volumes to compensate for brain-shift. iRegNet is a robust end-to-end deep learning approach for the non-linear registration of MRI-iUS images in the context of image-guided neurosurgery. Pre-operative MRI (as moving image) and iUS (as fixed image) are first appended to our convolutional neural network, after which a non-rigid transformation field is estimated. The MRI image is then transformed using the output displacement field to the iUS coordinate system. Extensive experiments have been conducted on two multi-location databases, which are the BITE and the RESECT. Quantitatively, iRegNet reduced the mean landmark errors from pre-registration value of (4.18 ± 1.84 and 5.35 ± 4.19 mm) to the lowest value of (1.47 ± 0.61 and 0.84 ± 0.16 mm) for the BITE and RESECT datasets, respectively. Additional qualitative validation of this study was conducted by two expert neurosurgeons through overlaying MRI-iUS pairs before and after the deformable registration. Experimental findings show that our proposed iRegNet is fast and achieves state-of-the-art accuracies outperforming state-of-the-art approaches. Furthermore, the proposed iRegNet can deliver competitive results, even in the case of non-trained images as proof of its generality and can therefore be valuable in intra-operative neurosurgical guidance.
Railway operators are being challenged by increasing complexity and safeguarding the availability of passenger rolling stock, bringing maintenance and especially emerging technologies into the focus. This paper presents a model for selection and implementation of Industry 4.0 technologies in rolling stock maintenance. The model consists of different stages and considers the main components of rolling stock, the related appropriate maintenance strategies and Industry 4.0 technologies considering the maturity level of the railway operators. Relevant criteria and main prerequisites of the technologies were identified. The model proposes relevant activities and was validated by industry experts.
Successful transitions to a sustainable bioeconomy require novel technologies, processes, and practices as well as a general agreement about the overarching normative direction of innovation. Both requirements necessarily involve collective action by those individuals who purchase, use, and co-produce novelties: the consumers. Based on theoretical considerations borrowed from evolutionary innovation economics and consumer social responsibility, we explore to what extent consumers’ scope of action is addressed in the scientific bioeconomy literature. We do so by systematically reviewing bioeconomy-related publications according to (i) the extent to which consumers are regarded as passive vs. active, and (ii) different domains of consumer responsibility (depending on their power to influence economic processes). We find all aspects of active consumption considered to varying degrees but observe little interconnection between domains. In sum, our paper contributes to the bioeconomy literature by developing a novel coding scheme that allows us to pinpoint different aspects of consumer activity, which have been considered in a rather isolated and undifferentiated manner. Combined with our theoretical considerations, the results of our review reveal a central research gap which should be taken up in future empirical and conceptual bioeconomy research. The system-spanning nature of a sustainable bioeconomy demands an equally holistic exploration of the consumers’ prospective and shared responsibility for contributing to its coming of age, ranging from the procurement of information on bio-based products and services to their disposal.
Die Annexion der Krim, die Kriegsführung in Syrien, das finanzielle Engagement in Zypern, das Tauziehen um die Ukraine und Weißrussland oder die Namensgebung Sputnik 5 für den Impfstoff gegen die Corona Epidemie sind eindeutige Belege für das aktuelle russische Machtstreben – und seine Expansionspolitik. Deshalb ist es nicht uninteressant zu fragen, welches Meinungsbild Friedrich List (1789–1846) von Russland hatte, zumal es heute noch so aktuell, wie vor 180 bis 190 Jahren erscheint und in seinen Schriften dargelegt ist. Dieses Meinungsbild wird in diesem Aufsatz erstmals untersucht und umfassend dargestellt.
Seit 5 Jahrzehnten steht die Erforschung von Leben, Werk und Wirkungsgeschichte von Friedrich List (1789–1846) im Zentrum der wissenschaftlichen Arbeit von Eugen Wendler. Im Laufe der Zeit sind ca. 30 Monographien und eine größere Anzahl von wissenschaftlichen Aufsätzen und journalistischen Artikeln entstanden. Dabei baute Eugen Wendler auf der unschätzbaren Vorarbeit der Herausgeber der Gesamtausgabe von Lists Werken von 1925 bis 1935 auf.
Der vorliegende Aufsatz vermittelt einen Überblick über die Buchpublikationen von Eugen Wendler zur List-Forschung. Mit seinem eindrucksvollen Oeuvre bekennt er sich zum letzten lebenden Fossil in der Nachfolge der FLG und erweist damit den Herausgebern die gebührende und längst überfällige Wertschätzung und Achtung.
Unter den widrigsten wirtschaftlichen und politischen Verhältnissen und Bedingungen wurde die Friedrich-List-Gesellschaft (FLG) 1925 gegründet und bis 1934 fortgeführt. Sie verfolgte vor allem den Zweck, die weit verstreuten, schwer zugänglichen und vielfach unbekannten Schriften, Reden und Briefe von Friedrich List (1789-1846) zusammenzutragen und in Form einer Gesamtausgabe zu publizieren.
Weder diese 10- bzw. 12-bändige Gesamtausgabe, noch die Namen ihrer Herausgeber haben in der Wirtschaftswissenschaft die gebührende Wertschätzung und Aufmerksamkeit erfahren. Die längst überfällige Dankesschuld wird in dem vorliegenden Beitrag nach nahezu 100 Jahren abgetragen. Ohne den engagierten und mutigen Einsatz der Herausgeber, insbesondere von Edgar Salin, wäre die List-Forschung undenkbar und die deutsche Wirtschaftswissenschaft um ein ruhmreiches Kapitel ärmer.
In buchstäblich letzter Minute haben sich die englische Regierung und die Europäische Union auf ein umfangreiches Abkommen geeinigt, um einen ungeregelten Brexit zu verhindern. Nach dem jahrelangen zähen Verhandlungsmarathon fällt der Jubel verhalten aus, dennoch herrscht auf beiden Seiten des Ärmelkanals Erleichterung, weil ein Modus Vivendi gefunden wurde, auf dem sich die künftigen Beziehungen aufbauen und fortführen lassen. Ob sich die englischen Blütenträume, die an den Brexit geknüpft wurden, erfüllen werden, wird die Zukunft erweisen.
Die Strategie und Taktik der englischen Regierungen zum Brexit und bei den Austrittsverhandlungen spiegeln sich in den Erfahrungen wider, die Friedrich List vor genau 175 Jahren bei seinen Bemühungen um eine deutsch-englische Allianz machen musste. Wegen der von England schon damals strikt befolgten Insular und Handelssuprematie musste er sich eingestehen, dass England diese Position hartnäckig verteidigt und deshalb frustriert und ernüchtert seine Pläne aufgeben. Deshalb setzte er seine Hoffnung auf eine "Kontinentalallianz" der europäischen Nationen, wie sie nun nach dem Austritt Großbritanniens aus der Europäischen Union entstanden ist. Vielleicht werden wir uns nun an den Begriff "Kontinentalallianz" gewöhnen müssen und dabei an die Weitsicht von Friedrich List erinnert.
Andererseits gilt auch für die englische Politik das Motto von Lists zweiter Pariser Preisschrift: "Le monde marche - Die Welt bewegt sich", allerdings mit völlig anderen Vorzeichen als vor 175 Jahren: Die Welthandelsachse hat sich von der westlichen auf die östliche Halbkugel verlagert; das britische Weltreich ist Geschichte, die Fließgeschwindigkeit des globalen Wandels hat sich dramatisch beschleunigt und trotz der Lingua Franca erscheint England, vor allem aus asiatischer Sicht, nur noch als kleiner Fleck auf der Weltkarte. Falls die schottische Regierung ihre Absicht durchsetzen und die Unabhängigkeit vom Vereinigten Königreich erreichen sollte, würde sich der Brexit als verhängnisvoller Bumerang erweisen.
The isothermal curing of melamine resin is investigated by in-line infrared spectroscopy at different temperatures. The infrared spectra are decomposed into time courses of characteristic spectral patterns using Multivariate Curve Resolution (MCR). It was found that depending on the applied curing temperature, melamine films with different spectral fingerprints and correspondingly different chemical network structures are formed. The network structures of fully cured resin films are specific for the applied curing temperatures used and cannot simply be compensated by changes in the curing time. For industrial curing processes, this means that cure temperature is the main system determining factor at constant M:F ratio. However, different MF resin networks can be specifically obtained from one and the same melamine resin by suitable selection of the curing time and temperatures profiles to design resin functionality. The spectral fingerprints after short curing time as well as after long curing time reflect the fundamental differences in the thermoset networks that can be obtained with industrial short-cycle and multi-daylight presses.
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.
Die vorliegende Studie zeigt, dass das Thema Smart Innovation (der Einsatz von KI-Systemen im Innovationsprozess) von hoher Relevanz ist und Zustimmung für den Einsatz von KI im Innovationsprozess besteht. Sowohl von den Unternehmen als auch von den Studierenden werden Effizienzsteigerung, schnellere Bearbeitung großer Datenmengen, die Steigerung der Wettbewerbsfähigkeit und Kosteneinsparungen als Gründe für den Einsatz von KI im Innovationsprozess gesehen. In Deutschland finden KI-Technologien bereits jetzt punktuell und branchenunabhängig Anwendung im Innovationsprozess. Einflussfaktoren, wie Hochschulkooperationen, Innovationsabteilungen und Open Innovation können den Einsatz fördern. Vor allem KMU aus den frühen Phasen der Industrialisierung sollten davon Gebrauch machen. In einem Zusammenspiel von menschlicher Expertise und der schnellen und präzisen Datenverarbeitung der KI liegt das Erfolgsgeheimnis eines möglichst effizienten Innovationsprozesses. Es wird deutlich, dass verschiedene Einflussfaktoren erforderlich sind, um die Anwendung von Smart Innovation praktikabel zu gestalten. So gilt es zunächst die technischen Voraussetzungen einer funktionierenden IT-Infrastruktur zu erfüllen. Gleichbedeutend sind offene Fragestellungen hinsichtlich der Datenverfügbarkeit, des Dateneigentums und der Datensicherheit. Ohne rechtlichen Rahmen sind kaum Akteure gewillt, ihre Daten zu teilen und zugänglich zu machen. Erschwert wird der Einsatz von KI durch den nationalen IT-Fachkräftemangel. So sehen sowohl Unternehmen als auch die Studierenden das größte Hindernis im Mangel von KI-relevantem Know-how. Dies hemmt einerseits die Forschung, andererseits fehlt es den Unternehmen an erforderlichen Fachkräften für eine Einführung von KI im Unternehmen. Es ist jedoch notwendig, den Unternehmen durch das Aufzeigen von Anwendungsbeispielen, die Potenziale und Chancen von Smart Innovation zu vermitteln. Es gilt, die anwendungsorientierte Forschung zu fördern und einen reibungslosen Transfer in die Wirtschaft sicherzustellen. Dieser Wissensaustausch erfordert zudem eine höhere unternehmerische Risikobereitschaft. Es wächst die Notwendigkeit, unternehmensspezifische KI-Strategien zu entwerfen. Die Technologien entwickeln sich schnell, es gilt daher auch für Unternehmen sich diesem Fortschritt anzupassen, um den Anschluss nicht zu verlieren und die Wettbewerbsfähigkeit zu sichern. So liegt die größte Herausforderung im grundlegenden Wandel der Geschäftsmodelle, denn die Wertschöpfung erfolgreicher Unternehmen basiert zunehmend auf "digitalen assets". Daten gelten generell als die neue Ressource, als Rohstoff, auch für Smarte Innovationen. Die Bedeutung von Smart Innovation wird in Zukunft weiterhin ansteigen. Kurz- und mittelfristig unterstützt die Schwache KI vor allem bei der Datensammlung und -analyse, bei der Prozessautomatisierung sowie bei der Bedürfnis- und Trendidentifikation. Weiter werden sich inkrementelle Veränderungen im Innovationsmanagement mithilfe von Simulationen und der zufälligen Kombination von Technologien erhofft. Langfristig wird eine stärkere KI den Einsatz der Menschen im Innovationsprozess in Teilen ersetzen können. Ob autonomes Innovieren zukünftig möglich sein wird, hängt zunächst von dem Ausmaß der Neuheit einer Innovation, aber vor allem auch von der Möglichkeit einer kreativen KI ab. Es ist davon auszugehen, dass die Fortschritte im Bereich der KI nicht nur radikale Innovationen ermöglichen werden, sondern auch zu einer strukturellen Veränderung unseres heutigen Verständnisses des Innovationsmanagements führen.
Massive data transfers in modern data-intensive systems resulting from low data-locality and data-to-code system design hurt their performance and scalability. Near-Data processing (NDP) and a shift to code-to-data designs may represent a viable solution as packaging combinations of storage and compute elements on the same device has become feasible. The shift towards NDP system architectures calls for revision of established principles. Abstractions such as data formats and layouts typically spread multiple layers in traditional DBMS, the way they are processed is encapsulated within these layers of abstraction. The NDP-style processing requires an explicit definition of cross-layer data formats and accessors to ensure in-situ executions optimally utilizing the properties of the underlying NDP storage and compute elements. In this paper, we make the case for such data format definitions and investigate the performance benefits under RocksDB and the COSMOS hardware platform.
Near-Data Processing is a promising approach to overcome the limitations of slow I/O interfaces in the quest to analyze the ever-growing amount of data stored in database systems. Next to CPUs, FPGAs will play an important role for the realization of functional units operating close to data stored in non-volatile memories such as Flash.It is essential that the NDP-device understands formats and layouts of the persistent data, to perform operations in-situ. To this end, carefully optimized format parsers and layout accessors are needed. However, designing such FPGA-based Near-Data Processing accelerators requires significant effort and expertise. To make FPGA-based Near-Data Processing accessible to non-FPGA experts, we will present a framework for the automatic generation of FPGA-based accelerators capable of data filtering and transformation for key-value stores based on simple data-format specifications.The evaluation shows that our framework is able to generate accelerators that are almost identical in performance compared to the manually optimized designs of prior work, while requiring little to no FPGA-specific knowledge and additionally providing improved flexibility and more powerful functionality.
This paper presents a generic method to enhance performance and incorporate temporal information for cardiorespiratory-based sleep stage classification with a limited feature set and limited data. The classification algorithm relies on random forests and a feature set extracted from long-time home monitoring for sleep analysis. Employing temporal feature stacking, the system could be significantly improved in terms of Cohen’s κ and accuracy. The detection performance could be improved for three classes of sleep stages (Wake, REM, Non-REM sleep), four classes (Wake, Non-REM-Light sleep, Non-REM Deep sleep, REM sleep), and five classes (Wake, N1, N2, N3/4, REM sleep) from a κ of 0.44 to 0.58, 0.33 to 0.51, and 0.28 to 0.44 respectively by stacking features before and after the epoch to be classified. Further analysis was done for the optimal length and combination method for this stacking approach. Overall, three methods and a variable duration between 30 s and 30 min have been analyzed. Overnight recordings of 36 healthy subjects from the Interdisciplinary Center for Sleep Medicine at Charité-Universitätsmedizin Berlin and Leave-One-Out-Cross-Validation on a patient-level have been used to validate the method.
The incudo-malleal joint (IMJ) in the human middle ear is a true diarthrodial joint and it has been known that the flexibility of this joint does not contribute to better middle-ear sound transmission. Previous studies have proposed that a gliding motion between the malleus and the incus at this joint prevents the transmission of large displacements of the malleus to the incus and stapes and thus contributes to the protection of the inner ear as an immediate response against large static pressure changes. However, dynamic behavior of this joint under static pressure changes has not been fully revealed. In this study, effects of the flexibility of the IMJ on middle-ear sound transmission under static pressure difference between the middle-ear cavity and the environment were investigated. Experiments were performed in human cadaveric temporal bones with static pressures in the range of +/- 2 kPa being applied to the ear canal (relative to middle-ear cavity). Vibrational motions of the umbo and the stapes footplate center in response to acoustic stimulation (0.2-8 kHz) were measured using a 3D-Laser Doppler vibrometer for (1) the natural IMJ and (2) the IMJ with experimentally-reduced flexibility. With the natural condition of the IMJ, vibrations of the umbo and the stapes footplate center under static pressure loads were attenuated at low frequencies below the middle-ear resonance frequency as observed in previous studies. After the flexibility of the IMJ was reduced, additional attenuations of vibrational motion were observed for the umbo under positive static pressures in the ear canal (EC) and the stapes footplate center under both positive and negative static EC pressures. The additional attenuation of vibration reached 4~7 dB for the umbo under positive static EC pressures and the stapes footplate center under negative EC pressures, and 7~11 dB for the stapes footplate center under positive EC pressures. The results of this study indicate an adaptive mechanism of the flexible IMJ in the human middle ear to changes of static EC pressure by reducing the attenuation of the middle-ear sound transmission. Such results are expected to be used for diagnosis of the IMJ stiffening and to be applied to design of middle-ear prostheses.
Despite its success against cancer, photothermal therapy (PTT) (>50 °C) suffers from several limitations such as triggering inflammation and facilitating immune escape and metastasis and also damage to the surrounding normal cells. Mild-temperature PTT has been proposed to override these shortcomings. We developed a nanosystem using HepG2 cancer cell membrane-cloaked zinc glutamate-modified Prussian blue nanoparticles with triphenylphosphine-conjugated lonidamine (HmPGTL NPs). This innovative approach achieved an efficient mild-temperature PTT effect by downregulating the production of intracellular ATP. This disrupts a section of heat shock proteins that cushion cancer cells against heat. The physicochemical properties, anti-tumor efficacy, and mechanisms of HmPGTL NPs both in vitro and in vivo were investigated. Moreover, the nanoparticles cloaked with the HepG2 cell membrane substantially prolonged the circulation time in vivo. Overall, the designed nanocomposites enhance the efficacy of mild-temperature PTT by disrupting the production of ATP in cancer cells. Thus, we anticipate that the mild-temperature PTT nanosystem will certainly present its enormous potential in various biomedical applications.
Metalworking fluids (MWFs) are widely used to cool and lubricate metal workpieces during processing to reduce heat and friction. Extending a MWF’s service life is of importance from both economical and ecological points of view. Knowledge about the effects of processing conditions on the aging behavior and reliable analytical procedures are required to properly characterize the aging phenomena. While so far no quantitative estimations of ageing effects on MWFs have been described in the literature other than univariate ones based on single parameter measurements, in the present study we present a simple spectroscopy-based set-up for the simultaneous monitoring of three quality parameters of MWF and a mathematical model relating them to the most influential process factors relevant during use. For this purpose, the effects of MWF concentration, pH and nitrite concentration on the droplet size during aging were investigated by means of a response surface modelling approach. Systematically varied model MWF fluids were characterized using simultaneous measurements of absorption coefficients µa and effective scattering coefficients µ’s. Droplet size was determined via dynamic light scattering (DLS) measurements. Droplet size showed non-linear dependence on MWF concentration and pH, but the nitrite concentration had no significant effect. pH and MWF concentration showed a strong synergistic effect, which indicates that MWF aging is a rather complex process. The observed effects were similar for the DLS and the µ’s values, which shows the comparability of the methodologies. The correlations of the methods were R2c = 0.928 and R2P = 0.927, as calculated by a partial least squares regression (PLS-R) model. Furthermore, using µa, it was possible to generate a predictive PLS-R model for MWF concentration (R2c = 0.890, R2P = 0.924). Simultaneous determination of the pH based on the µ’s is possible with good accuracy (R²c = 0.803, R²P = 0.732). With prior knowledge of the MWF concentration using the µa-PLS-R model, the predictive capability of the µ’s-PLS-R model for pH was refined (10 wt%: R²c = 0.998, R²p = 0.997). This highlights the relevance of the combined measurement of µa and µ’s. Recognizing the synergistic nature of the effects of MWF concentration and pH on the droplet size is an important prerequisite for extending the service life of an MWF in the metalworking industry. The presented method can be applied as an in-process analytical tool that allows one to compensate for ageing effects during use of the MWF by taking appropriate corrective measures, such as pH correction or adjustment of concentration.
Context: The manufacturing industry is facing a transformation with regard to Industry 4.0 (I4). A transformation towards full automation of production including a multitude of innovations is necessary. Startups and entrepreneurial processes can support such a transformation as has been shown in other industries. However, I4 has some specifics, so it is unclear how entrepreneurship can be adapted in I4. Understanding these specifics is important to develop suitable training programs for I4 startups and to accelerate the transformation.
Objective: This study identifies and outlines the essential characteristics and constraints of entrepreneurial processes in I4.
Method: 14 semi-structured interviews were conducted with experts in the field of I4 entrepreneurship. The interviews were analysed and categorized by qualitative analyses.
Results: The interviews revealed several characteristics of I4 that have a significant impact on the various phases of the entrepreneurial process. Examples of such specifics include the difficult access to customers, the necessary deep understanding of the customer and the domain, the difficulty of testing risky assumptions, and the complex development and productization of solutions. The complexity of hardware and software components, cost structures, and necessary customer-specific customizations affect the scalability of I4 startups. These essential characteristics also require specialised skills and resources from I4 startups.
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.
Product roadmaps in the new mobility domain: state of the practice and industrial experiences
(2021)
Context: The New Mobility industry is a young market that includes high market dynamics and is therefore associated with a high degree of uncertainty. Traditional product roadmapping approaches such a detailed planning of features over a long-time horizon typically fail in such environments. For this reason, companies that are active in the field of New Mobility are faced with the challenge of keeping their product roadmaps reliable for stakeholders while at the same time being able to react flexibly to changing market requirements.
Objective: The goal of this paper is to identify the state of practice regarding product roadmapping of New Mobility companies. In addition, the related challenges within the product roadmapping process as well as the success factors to overcome these challenges will be highlighted.
Method: We conducted semi-structured expert interviews with 8 experts (7 German company and one Finnish company) from the field of New Mobility and performed a content analysis.
Results: Overall the results of the study showed that the participating companies are aware of the requirements that the New Mobility sector entails. Therefore, they exhibit a high level of maturity in terms of product roadmapping. Nevertheless, some aspects were revealed that pose specific challenges for the participating companies. One major challenge, for example, is that New Mobility in terms of public clients is often a tender business with non-negotiable product requirements. Thus, the product roadmap can be significantly influenced from the outside. As factors for a successful product roadmapping mainly soft factors such as trust between all people involved in the product development process and transparency throughout the entire roadmapping process were mentioned.
Software is an integrated part of new features within the automotive sector, car manufacturers, the Hersteller Initiative Software (HIS) consortium defined metrics to determine software quality. Yet, problems with assigning metrics to quality attributes often occur in practice. The specified boundary values lead to discussions between contractors and clients as different standards and metric sets are used. This paper studies metrics used in the automotive sector and the quality attributes they address. The HIS, ISO/IEC 25010:2011, and ISO/IEC 26262:2018 are utilized to draw a big picture illustrating (i) which metrics and boundary values are reported in literature, (ii) how the metrics match the standards, (iii) which quality attributes are addressed, and (iv) how the metrics are supported by tools. Our findings from analyzing 38 papers include a catalog of 112 metrics of which 17 define boundary values and 48 are supported by tools. Most of the metrics are concerned with source code, are generic, and not specifically designed for automotive software development. We conclude that many metrics exist, but a clear definition of the metrics' context, notably regarding the construction of flexible and efficient measurement suites, is missing.
This paper presents the concept of the system architecture of a flexible cyber-physical factory control system. The system allows the automation of process structures using cyber-physical fractal nodes. These nodes have a functional and independent form and can be clustered to larger structures. This makes it possible to equip the factory with a flexible, freely scalable, modular system. The description of this system architecture and the associated rules and conditions is outlined in the concept.
Human bestrophin-1 protein (hBest1) is a transmembrane channel associated with the calcium-dependent transport of chloride ions in the retinal pigment epithelium as well as with the transport of glutamate and GABA in nerve cells. Interactions between hBest1, sphingomyelins, phosphatidylcholines and cholesterol are crucial for hBest1 association with cell membrane domains and its biological functions. As cholesterol plays a key role in the formation of lipid rafts, motional ordering of lipids and modeling/remodeling of the lateral membrane structure, we examined the effect of different cholesterol concentrations on the surface tension of hBest1/POPC (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine) and hBest1/SM Langmuir monolayers in the presence/absence of Ca2+ ions using surface pressure measurements and Brewster angle microscopy studies. Here, we report that cholesterol: (1) has negligible condensing effect on pure hBest1 monolayers detected mainly in the presence of Ca2+ ions, and; (2) induces a condensing effect on composite hBest1/POPC and hBest1/SM monolayers. These results offer evidence for the significance of intermolecular protein–lipid interactions for the conformational dynamics of hBest1 and its biological functions as multimeric ion channel.
The maintenance of railway infrastructure remains a challenge. Data acquisition technologies have evolved because of Industry 4.0, expanding the capabilities of predictive maintenance. Despite the advances, the potential of these emerging technologies has not been fully realised. This paper presents a technology selection framework in support of railway infrastructure predictive maintenance, which is based on qualitative methods. It consists of three stages, including the mapping of the infrastructure characteristics with the identified technologies, the evaluation of the most appropriate technologies, and the sourcing thereof. This presents the collective decision support output of the framework.
Since the beginning of the energy sector liberalization, the design of energy markets has become a prominent field of research. Markets nowadays facilitate efficient resource allocation in many fields of energy system operation, such as plant dispatch, control reserve provisioning, delimitation of related carbon emissions, grid congestion management, and, more recently, smart grid concepts and local energy trading. Therefore, good market designs play an important role in enabling the energy transition toward a more sustainable energy supply for all. In this chapter, we retrace how market engineering shaped the development of energy markets and how the research focus shifted from national wholesale markets to more decentralized and location-sensitive concepts.
Reacting to ever-changing business environments, in the last decade complex systems of systems accomplished giant leaps forward leading to great technological flexibility. However, this dimension of flexibility is often limited by the rigidity of super-ordinated planning systems. Especially when hybrid teams of automated and human resources are in place, the dynamic assignment of tasks taking into account ergonomics remains a challenge. After exposing a gap in the state of the art on the topic, this paper presents an approach to include ergonomics in dynamic resource allocation models. Combining and complementing existing approaches, the presented method monitors the actual ergonomic burden of the resources during a shift and it provides a linear optimization model to steer the resource allocation process.
Ambitious goals set by the European Union strategy towards the emission reduction of multimodal logistic chains and new requirements for intermodal terminals set by the evolution of customer needs, contribute to a shift in the driver for the infrastructure development: from economy of scale to economy of density. This paper aims to present an innovative method for designing a process oriented technology chain for intermodal terminals in order to fulfill these new demanding requirements. The results of the case study of the Zero Emission Logistic Terminal Reutlingen are presented, highlighting how this particular context enables the design and development of a modular concept, paving the way for the generalization of the findings towards the transfer to similar contexts of other European cities.
The seamless fusion of the virtual world of information with the real physical world of things is considered the key for mastering the increasing complexity of production networks in the context of Industry 4.0. This fusion, widely referred to as the Internet of Things (IoT), is primarily enabled through the use of automatic identification (Auto-ID) technologies as an interface between the two worlds. Existing Auto-ID technologies almost exclusively rely on artificial features or identifiers that are attached to an object for the sole purpose of identification. In fact, using artificial features for the purpose of identification causes additional efforts and is not even always applicable. This paper, therefore, follows an approach of using multiple natural object features defined by the technical product information from computer-aided design (CAD) models for direct identification. By extending optical instance-level 3D-Object recognition by means of additional non-optical sensors, a multi-sensor automatic identification system (AIS) is realised, capable of identifying unpackaged piece goods without the need for artificial identifiers. While the implementation of a prototype confirms the feasibility of the approach, first experiments show improved accuracy and distinctiveness in identification compared to optical instance-level 3D-Object recognition. This paper aims to introduce the concept of multisensor identification and to present the prototype multi-sensor AIS.
So-called cloud-based management information systems are a fairly new phenomenon in management accounting in recent years. Quite a few companies (and especially their business managers and management accountants) do not always work via the cloud, but with hybrid solutions or on-premise solutions of ERP software such as SAP or Oracle, but often still with "manual" solutions such as Microsoft Excel.
This contribution presents a three-phase power stage for motor control with continuous output voltages using wide bandgap semiconductors and an asynchronous delta-sigma based switching signal generation. The focus of the paper is on an active damping approach for the LC output filter based on inductor current feedback.
This paper illustrates the implementation of series connected hardware modules as part of a scalable and modular power electronics device, which is ideally suited in the field of electric vehicles using wide bandgap semiconductor devices. The main benefit of the modular concept is that different current or voltage requirements can be satisfied based on the appropriate series or parallel connection of single modules. The particular design is based on the fact that the single modules generate a continuous and specified output voltage from a given dc voltage. The current work focuses on a brief classification of this work in different series connected concepts of power converters and in particular on an active damping approach for the series connected LC output filters based on inductor current feedback.
This paper presents a modular and scalable power electronics concept for motor control with continuous output voltage. In contrast to multilevel concepts, modules with continuous output voltage are connected in series. The continuous output voltage of each module is obtained by using gallium nitride (GaN) high electron motility transistor (HEMT)s as switches inside the modules with a switching frequency in the range between 500 kHz and 1 MHz. Due to this high switching frequency a LC filter is integrated into the module resulting in a continuous output voltage. A main topic of the paper is the active damping of this LC output filter for each module and the analysis of the series connection of the damping behaviour. The results are illustrated with simulations and measurements.
Escherichia coli (E. coli) is considered the most common life-threatening infectious bacteria in our daily life and poses a major challenge to human health. However, antibiotics frequently overused and misused has triggered increased multidrug resistance, hinders therapeutic outcomes, and causes higher mortalities. Herein, we addressed near-infrared (NIR) laser-excited human serum albumin (HSA) mediated graphene oxide loaded palladium nano-dots (HSA-GO-Pd) that can effectively combat Gram-negative E. coli in vitro. NIR laser-excited designed hybrid material highly generates singlet oxygen and hydroxyl radical by electron spin-resonance (ESR) analysis. Transmission electron microscope (TEM) images show small spherical sizes PdNPs on the surface of GO nano-sheets. The zeta (ζ) potential study indicates that in an aqueous medium, the average PdNPs size and surface capped charge comes from human body protein (HSA), HSA-GO-Pd is 5–8 nm, and +25 mV, respectively. The spectroscopic characterization reveals that in the synthesized HSA-GO-Pd nanocomposite, PdNPs successfully well-dispersed decorated on the surface of graphene oxide. The as-synthesized HSA-GO-Pd shows excellent antibacterial activity against gram-negative pathogen by killing 95% bacteria within 5 h. HSA-GO-Pd having very biocompatible and shows significant antibacterial activities. Owing to their intense photothermal conversation potential, low toxicity to normal cells, the as-addressed hybrid (HSA-GO-Pd) combined with NIR-irradiation will catch up valuable insight into the effective ablation of pathogenic bacteria.
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.
This paper presents a machine learning powered, procedural sizing methodology based on pre-computed look-up tables containing operating point characteristics of primitive devices. Several Neural Networks are trained for 90nm and 45nm technologies, mapping different electrical parameters to the corresponding dimensions of a primitive device. This transforms the geometric sizing problem into the domain of circuit design experts, where the desired electrical characteristics are now inputs to the model. Analog building blocks or entire circuits are expressed as a sequence of model evaluations, capturing the sizing strategy and intention of the designer in a procedure, which is reusable across different technology nodes. The methodology is employed for the sizing of two operational amplifiers, and evaluated for two technology nodes, showing the versatility and efficiency of this approach.
Lehre und Lernen unterliegt einem stetigen Wandel, wobei Interaktion als ein zentrales Element der Motivationssteigerung im Lernkontext angesehen wird. Der vorliegende Beitrag zeigt verschiedene Ansätze zur Gestaltung von interaktivem und kollaborativem Lehren und Lernen in einem virtuellen Klassenzimmer auf und stellt ein Beispiel für die Umsetzung und den Einsatz eines solchen Systems vor. Die Mehrwerte und Erfolgsfaktoren, die sich beim Einsatz virtueller Klassenzimmer und deren Gestaltung in Form einer interaktiven blended-learning Umgebung ergeben, werden dargestellt und diskutiert. Mit dem System Accelerator wird eine CSILT (Computer Supported Interactive Learning and Teaching)-Umgebung vorgestellt, in der diese Faktoren zum Einsatz kommen.
Respiratory diseases are leading causes of death and disability in the world. The recent COVID-19 pandemic is also affecting the respiratory system. Detecting and diagnosing respiratory diseases requires both medical professionals and the clinical environment. Most of the techniques used up to date were also invasive or expensive.
Some research groups are developing hardware devices and techniques to make possible a non-invasive or even remote respiratory sound acquisition. These sounds are then processed and analysed for clinical, scientific, or educational purposes.
We present the literature review of non-invasive sound acquisition devices and techniques.
The results are about a huge number of digital tools, like microphones, wearables, or Internet of Thing devices, that can be used in this scope.
Some interesting applications have been found. Some devices make easier the sound acquisition in a clinic environment, but others make possible daily monitoring outside that ambient. We aim to use some of these devices and include the non-invasive recorded respiratory sounds in a Digital Twin system for personalized health.
Context: Many companies are facing an increasingly dynamic and uncertain market environment, making traditional product roadmapping practices no longer sufficiently applicable. As a result, many companies need to adapt their product roadmapping practices for continuing to operate successfully in today’s dynamic market environment. However, transforming product roadmapping practices is a difficult process for organizations. Existing literature offers little help on how to accomplish such a process.
Objective: The objective of this paper is to present a product roadmap transformation approach for organizations to help them identify appropriate improvement actions for their roadmapping practices using an analysis of their current practices.
Method: Based on an existing assessment procedure for evaluating product roadmapping practices, the first version of a product roadmap transformation approach was developed in workshops with company experts. The approach was then given to eleven practitioners and their perceptions of the approach were gathered through interviews.
Results: The result of the study is a transformation approach consisting of a process describing what steps are necessary to adapt the currently applied product roadmapping practice to a dynamic and uncertain market environment. It also includes recommendations on how to select areas for improvement and two empirically based mapping tables. The interviews with the practitioners revealed that the product roadmap transformation approach was perceived as comprehensible, useful, and applicable. Nevertheless, we identified potential for improvements, such as a clearer presentation of some processes and the need for more improvement options in the mapping tables. In addition, minor usability issues were identified.
Context: The software-intensive business is characterized by increasing market dynamics, rapid technological changes, and fast-changing customer behaviors. Organizations face the challenge of moving away from traditional roadmap formats to an outcome-oriented approach that focuses on delivering value to the customer and the business. An important starting point and a prerequisite for creating such outcome-oriented roadmaps is the development of a product vision to which internal and external stakeholders can be aligned. However, the process of creating a product vision is little researched and understood.
Objective: The goal of this paper is to identify lessons-learned from product vision workshops, which were conducted to develop outcome-oriented product roadmaps.
Method: We conducted a multiple-case study consisting of two different product vision workshops in two different corporate contexts.
Results: Our results show that conducting product vision workshops helps to create a common understanding among all stakeholders about the future direction of the products. In addition, we identified key organizational aspects that contribute to the success of product vision workshops, including the participation of employees from functionally different departments.
Context: Nowadays, companies are challenged by increasing market dynamics, rapid changes and disruptive participants entering the market. To survive in such an environment, companies must be able to quickly discover product ideas that meet the needs of both customers and the company and deliver these products to customers. Dual-track agile is a new type of agile development that combines product discovery and delivery activities in parallel, iterative, and cyclical ways. At present, many companies have difficulties in finding and establishing suitable approaches for implementing dual-track agile in their business context.
Objective: In order to gain a better understanding of how product discovery and product delivery can interact with each other and how this interaction can be implemented in practice, this paper aims to identify suitable approaches to dual-track agile.
Method: We conducted a grey literature review (GLR) according to the guidelines to Garousi et al.
Results: Several approaches that support the integration of product discovery with product delivery were identified. This paper presents a selection of these approaches, i.e., the Discovery-Delivery Cycle model, Now-Next-Later Product Roadmaps, Lean Sprints, Product Kata, and Dual-Track Scrum. The approaches differ in their granularity but are similar in their underlying rationales. All approaches aim to ensure that only validated ideas turn into products and thus promise to lead to products that are better received by their users.
How to prioritize your product roadmap when everything feels important: a grey literature review
(2021)
Context: A key factor in achieving product success is to identify what and in which order outputs must be launched in order to deliver the most value to the customer and the business. Therefore, a well-established process to discover and prioritize the content of the product roadmap in the right way is crucial for the success of a company. However, most companies prioritize their product roadmap items based on opinions of experts or the management. Additionally, increasing market dynamics, rapidly evolving technologies and fast changing customer behavior complicate the conduction of the prioritization process. Therefore, many companies are struggling to finding and establishing suitable techniques for prioritizing their product roadmap.
Objective: In order to gain a better understanding of the prioritization process in a dynamic and uncertain market environment, this paper aims to identify suitable techniques for the prioritization in such environments.
Method: We conducted a Grey Literature Review according to the guidelines of Garousi et al.
Results: 18 techniques for the prioritization of the product roadmap could be identified. 15 techniques are primarily used to prioritize outputs by considering factors such as the expected impact or effort. Two technique are most suitable for prioritizing risky assumptions that need to be validated and one technique focuses on the prioritization of outcomes. All techniques have in common that they should be conducted as cross-functional team activity in order to include different perspectives in the prioritization process.
Public enterprises find themselves in increasingly competitive markets, a situation that makes having an entrepreneurial orientation (EO) an urgent need, given that EO is an indispensable driver of performance. Research describes politicians delaying the strategic change of public enterprises when serving as board members, but empirical evidence of the impact of board behavior on EO in public enterprises is lacking. We draw on stakeholder-agency theory (SAT) and resource dependence theory (RDT) and use structural equation modeling (SEM) to investigate survey data collected from 110 German energy suppliers that are majority government owned. Results indicate that board strategy control and board networking do not seem to predict EO on first sight. Closer analysis reveals a board networking–EO relationship depending on ownership structure. Remarkably, we find that it is not the usually suspected local municipal owner who hinders EO in our sample organizations but minority shareholders engaging in board networking activities. The results shed light on the intersection of governance and entrepreneurship with special reference to the fine-grained conceptualization of RDT.
Corporate entrepreneurship in the public sector: exploring the peculiarities of public enterprises
(2021)
Entrepreneurship is predominantly treated as a private-sector phenomenon and consequently its increasing importance in the public sector goes largely unremarked. That impedes the research field of entrepreneurship being capable of spanning multiple sectors. Accordingly, recent research calls for the study of corporate entrepreneurship (CE) as it manifests in the public sector where it can be labeled public entrepreneurship (PE). This dissertation considers government an essential entrepreneurial actor and is led by the central research question: What are the peculiarities of the public sector and how do they impact public enterprises’ entrepreneurial orientation (EO)?
Accordingly, this dissertation includes three studies focusing on public enterprises. Two of the studies set the scope of this thesis by investigating a specific type of organization in a specific context—German majority-government-owned energy suppliers. These enterprises operate in a liberalized market experiencing environmental uncertainties like competitiveness and business transformation.
The aims and results of the studies included in this dissertation can be summarized as follows: The systematic literature review illuminates the stimuli of and barriers to entrepreneurial activities in public enterprises and the potential outcomes of such activities discussed so far. The review reveals that research on EO has tended to focus on the private sector and consequently that barriers to and outcomes of entrepreneurial activities in the public sector remain under-researched. Building on these findings, the qualitative study focuses on the interrelated barriers affecting entrepreneurship in public enterprises and the outcomes of entrepreneurial activities being inhibited. The study adopts an explorative comparative causal mapping approach to address the above-mentioned research goal and the lack of clarity around how barriers identified in the public sphere are interrelated. Furthermore, the study bases its investigation on the different business segments of sales (competitive market) and the distribution grid (natural monopoly) to account for recent calls for fine-grained research on PE. Results were compared with prior findings in the public and private sector. That comparison indicates that the barriers revealed align with aspects discussed in prior research findings relating to both sectors. Examples include barriers associated with the external environment such as legal constraints and barriers originating from within the organization such as employee behavior linked to a value system that hampers entrepreneurial action. However, the most important finding is that a public enterprise’s supervisory board can hinder its progress, a finding running counter to those of previous private-sector research and one that underscores the widespread prejudice that the involvement of a public shareholder and its nominated board of directors has a negative effect on EO. The third study is quantitative (data collection via a questionnaire) and builds on both its predecessors to examine the little understood topic of board behavior and public enterprises’ social orientation as predictors of EO. The study’s results indicate that social orientation represses EO, whereas board strategy control (BSC) does not seem to predict EO. Regarding BSC, we find that the local government owners in our sample are less involved in BSC. The third study also examines board networking and finds its relationship with EO depends on the ownership structure of the public-sector organization. An important finding is that minority shareholders, such as majority privately-owned enterprises and hub firms, repress EO when engaging in board networking.
In summary, this doctoral thesis contributes to the under-researched topic of CE in the public sector. It investigates the peculiarities of this sector by focusing on the supervisory board and social oriented activities and their impact on the enterprise’s EO in the quantitative study. The thesis addresses institutional questions regarding ownership and the last study in particular contributes to expanding resource dependence theory, and invites a nuanced perspective: The original perspective suggests that interorganizational arrangements like interfirm network ties and equity holdings reduce external resource dependency and consequently improve firm performance. The findings within this thesis expose resource delivery to potential contrary effects to extend the understanding of interorganizational action with important implications for practice.
Flexible KWK – aber wie?
(2021)
Es ist mittlerweile unstrittig, dass Kraft-Wärme-Kopplungs-Anlagen (KWK-Anlagen) zunehmend flexible betrieben werden müssen. Nur so kann es gelingen, die Anlagen optimal in das elektrische Energiesystem einzubinden, beispielsweise zur Deckung der Residuallast oder zur Unterstützung der Verteilnetze, und damit zur Umsetzung der Energiewende beizutragen. Auch der Gesetzgeber fordert den flexiblen Betrieb durch die Absenkung der förderfähigen Betriebsstunden im KWK-Gesetz ein. Um vor diesem Hintergrund jedoch parallel die Deckung des erforderlichen Wärmebedarfs unter Gewährleistung der hohen Effizienz der KWK sicherzustellen, ist eine intelligente Steuerung der Geräte erforderlich. Zu diesem Zweck ist an der Hochschule Reutlingen ein vorausschauender Steuerungsalgorithmus zum „stromoptimierten“ und netzdienlichen“ Betrieb von KWK-Anlagen bei voller Nutzung der KWK-Wärme als Alternative zum standardmäßig anzutreffenden wärmegeführten Betrieb entwickelt worden.
Wenn Unternehmen den Schritt in die digitale Arbeitswelt gehen wollen, stehen sie vor der Herausforderung, konkrete Vorstellungen, Ziele und Maßnahmen zu entwickeln und umzusetzen. Häufig fehlt es Unternehmen an Wissen, ihre Transformation der Arbeitswelt strategisch zu gestalten und zu planen. Das Projekt DigiTraIn 4.0 setzt hier an und bietet mit dem Digitalisierungskompass ein Instrument, welches Unternehmen dabei unterstützt, eine Vision und spezifische Ziele für die Digitalisierung ihrer eigenen Arbeitswelt zu entwickeln. Im Anschluss daran unterstützt die Transformationsagenda Unternehmen dabei, konkrete Handlungsmaßnahmen zu entwickeln und deren Ablauf zu planen.
Das textile Bauen ist ein seit vielen Jahren wachsender Bereich der Textilindustrie. Durch die Verwendung textiler Materialien bieten sich nicht zuletzt für die Architektur neue gestalterische Möglichkeiten, die mit konventionellen Baumaterialien nicht realisierbar sind. Bekannte Beispiele für textile Bauwerke sind große Sportarenen, Bahnhöfe und Flughäfen. Dabei sind Leichtbauweisen und zumindest teilweise Transparenz der Bauwerke auf einer Seite herausragende Eigenschaften, auf der anderen Seite stellen diese Gebäude besondere Anforderungen an das Klima- und Energiemanagement. Der Innenraum kann sich bei Sonneneinstrahlung stark aufheizen, da neben dem sichtbaren Licht vor allem ein Großteil des Infrarotanteils der solaren Strahlung transmittieren kann. Im konventionellen Bauen existieren bereits hohe Anforderungen an die energietechnische Ausgestaltung von Bauwerken, die u.a. über eine effiziente Wärmedämmung erfüllt werden. Dies wird in der Regel mit Hilfe von voluminösen, offenporigen Dämmstoffen erreicht. Ziel ist es dabei vornehmlich, den Verlust von Wärme aus dem Innenraum zu verringern, gleichzeitig können schlecht wärmeleitende Stoffe bei hoher Masse und hoher spezifischer Wärmekapazität Temperaturspitzen im Sommer abpuffern. Auch für das textile Bauen ist die Energieeffizienz ein wichtiger Aspekt. Die Verwendung von schweren Dämmstoffen widerspricht dabei aber der Idee der flexiblen textilen Leichtbauweise.
Although spiral antennas have undergone continuous development and refinement since Edwin Turner conceived them in 1954, only a few compact planar arrays exist. The shortcoming is even more significant when it comes to spiral antenna arrays in mode M2 operation. The present work addresses this issue, among other things. It presents two planar arrays of spiral antennas operating in the same frequency band and radiating for the first one an axial mode M1 and a conical mode M2 for the second. Both arrays are modeled, simulated, and fed with a corporate feeding network embedded in a dielectric substrate. It is shown that keeping the same topology, the array for conical M1 mode can be obtained from the array for mode M2 by a simple introduction of a phase shift on one branch of the feed and vice versa, providing thus the possibility to obtain in the same structure a spiral antenna array operating in both modes in the same frequency band simultaneously. Comparison between simulated and measured data shows good agreement.
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.
The integration of renewable energy sources in single family homes is challenging. Advance knowledge of the demand of electrical energy, heat, and domestic hot water (DHW) is useful to schedule projectable devices like heat pumps. In this work, we consider demand time series for heat and DHW from 2018 for a single family home in Germany. We compare different forecasting methods to predict such demands for the next day. While the 1-day-back forecast method led to the prediction of heat demand, the N-day-average performed best for DHW demand when Unbiased Exponentially Moving Average (UEMA) is used with a memory of 2.5 days. This is surprising as these forecasting methods are very simple and do not leverage additional information sources such as weather forecasts.
Continuous monitoring of individual vital parameters can provide information for the assessment of one’s health and indications of medical problems in the context of personalized medicine. Correlations between parameters and health issues are to be evaluated. As one project in this topic area, a telemedicine platform is implemented to gather data of outpatients via wearables and accumulate them for physicians and researchers to review. This work extracts requirements, draws use case scenarios, and shows the current system architecture consisting of a patient application, a physician application with a web server, and a backend server application. In further work, the prototype will assist to develop a vendor-free and open monitoring solution. A conclusion on functionality and usability will be evaluated in an imminent first study.
As consumer awareness surrounding impacts of the climate crisis continues to be a notable threat, businesses are searching for new models to make their sustainability profile even better. As a result, the implementation of a company’s sustainability vision following the SDGs has to be linked closely to the integration of customers into strategic action. One success factor is the management of customers over their entire life cycle. The Customer Journey serves as a model to systematise this approach, by designing touchpoints throughout the purchasing process in order to motivate consumers to act sustainably. Based on behaviour models, the authors develop recommendations for the food industry to design a sustainable Customer Journey that helps to reduce the percentage of consumers reporting positive attitudes to sustainable products while not exhibiting corresponding behaviour.
Dieser Beitrag entwickelt ein Managementmodell, das Unternehmen dabei unterstützt, relevante Aktionsfelder zur nachhaltigen Steuerung von Konsumenten entlang der eigenen Customer Journey zu identifizieren. Aufbauend auf dem SHIFT-Modell, als strukturelle Abbildung des nachhaltigen Käuferverhaltens, wird die Customer Journey entlang der owned, paid und earned Touchpoints aufgezogen. Mithilfe des faktisch analytischen Ansatzes, der die Integration neuer Erkenntnisse in die Forschungsstrategie unterstützt, werden Aktionsfelder identifiziert, die als grundlegende Logik Unternehmen dazu anleiten sollen, bei der Ausgestaltung der eigenen nachhaltigen Customer Journey dieses Strukturraster anzunehmen.
Silicon photonic micro-ring resonators (MRR) developed on the silicon-on-insulator (SOI) platform, owing to their high sensitivity and small footprint, show great potential for many chemical and biological sensing applications such as label-free detection in environmental monitoring, biomedical engineering, and food analysis. In this tutorial, we provide the theoretical background and give design guidelines for SOI-based MRR as well as examples of surface functionalization procedures for label-free detection of molecules. After introducing the advantages and perspectives of MRR, fundamentals of MRR are described in detail, followed by an introduction to the fabrication methods, which are based on a complementary metal-oxide semiconductor (CMOS) technology. Optimization of MRR for chemical and biological sensing is provided, with special emphasis on the optimization of waveguide geometry. At this point, the difference between chemical bulk sensing and label-free surface sensing is explained, and definitions like waveguide sensitivity, ring sensitivity, overall sensitivity as well as the limit of detection (LoD) of MRR are introduced. Further, we show and explain chemical bulk sensing of sodium chloride (NaCl) in water and provide a recipe for label-free surface sensing.
Characterization of brain tumours requires neuropathological expertise and is generally performed by histological evaluation and molecular analysis. One emerging technique to assist pathologists in future tumour diagnostics is multimodal optical spectroscopy. In the current clinical routine, tissue preprocessing with formalin is widely established and suitable for spectroscopic investigations since degradation processes impede the measurement of native tissue. However, formalin fixation results in alterations of the tissue chemistry and morphology for example by protein cross-linking. As optical spectroscopy is sensitive to these variations, we evaluate the effects of formalin fixation on multimodal brain tumour data in this proof-of-concept study. Nonfixed and formalin-fixed cross sections of different common human brain tumours were subjected to analysis of chemical variations using ultraviolet and Fourier-transform infrared microspectroscopy. Morphological changes were assessed by elastic light scattering microspectroscopy in the visible wavelength range. Data were analysed with multivariate data analysis and compared with histopathology. Tissue type classifications deduced by optical spectroscopy are highly comparable and independent from the preparation and the fixation protocol. However, formalin fixation leads to slightly better classification models due to improved stability of the tissue. As a consequence, spectroscopic methods represent an appropriate additional contrast for chemical and morphological information in neuropathological diagnosis and should be investigated to a greater extent. Furthermore, they can be included in the clinical workflow even after formalin fixation.
Increasing complexity in manufacturing processes poses new challenges for industrial maintenance. In addition, advanced machine monitoring and lifetime forecasting options expand the tools and maintenance strategies available. Today, maintenance strategy selection is performed sequentially usually based on prioritised machines and components. These selections are optimized locally for each machine isolated, not considering the context of other machines within the value-adding network. To overcome these challenges, this paper presents an approach for an integrated maintenance strategy selection in one-step by an integrated model considering possible machine failures and the context of other machines within the value-adding network in parallel.
Omnichannel retailing and sustainability are two important challenges for the fast fashion industry. However, the sustainable behavior of fast fashion consumers in an omnichannel environment has not received much attention from researchers. This paper aims to examine the factors that determine consumers’ willingness to participate in fast fashion brands’ used clothes recycling plans in an omnichannel retail environment. In particular, we examine the impact of individual consumer characteristics (environmental attitudes, consumer satisfaction), organizational arrangements constitutive for omnichannel retailing (channel integration), and their interplay (brand identification, impulsive consumption). A conceptual model was developed based on findings from previous research and tested on data that were collected online from Chinese fast fashion consumers. Findings suggest that consumers’ intentions for clothes recycling are mainly determined by individual factors, such as environmental attitudes and consumer satisfaction. Organizational arrangements (perceived channel integration) showed smaller effects. This study contributes to the literature on omnichannel (clothing) retail, as well as on sustainability in the clothing industry, by elucidating individual and organizational determinants of consumers’ recycling intentions for used clothes in an omnichannel environment. It helps retailers to organize used clothes recycling plans in an omnichannel environment and to motivate consumers to participate in them.
High moisture permeability, excellent mechanical properties in a wet state, high water-holding capability, and high exudate absorption make bacterial nanocellulose (BNC) a favorable candidate for biomedical device production, especially wound dressings. The lack of antibacterial activity and healing-promoting ability are the main drawbacks that limit its wide application. Pullulan (Pul) is a nontoxic polymer that can promote wound healing. Zinc oxide nanoparticles (ZnO-NPs) are well-known as a safe antibacterial agent. In this study, aminoalkylsilane was chemically grafted on a BNC membrane (A-g-BNC) and used as a bridge to combine BNC with Pul-ZnO-NPs hybrid electrospun nanofibers. FTIR results confirmed the successful production of A-g-BNC/Pul-ZnO. The obtained dressing demonstrated blood clotting performance better than that of BNC. The dressing showed an ability to release ZnO, and its antibacterial activity was up to 5 log values higher than that of BNC. The cytotoxicity of the dressing toward L929 fibroblast cells clearly showed safety due to the proliferation of fibroblast cells. The animal test in a rat model indicated faster healing and re-epithelialization, small blood vessel formation, and collagen synthesis in the wounds covered by A-g-BNC/Pul-ZnO. The new functional dressing, fabricated with a cost-effective and easy method, not only showed excellent antibacterial activity but could also accelerate wound healing.
Introduction
Despite its high accuracy, polysomnography (PSG) has several drawbacks for diagnosing obstructive sleep apnea (OSA). Consequently, multiple portable monitors (PMs) have been proposed.
Objective
This systematic review aims to investigate the current literature to analyze the sets of physiological parameters captured by a PM to select the minimum number of such physiological signals while maintaining accurate results in OSA detection.
Methods
Inclusion and exclusion criteria for the selection of publications were established prior to the search. The evaluation of the publications was made based on one central question and several specific questions.
Results
The abilities to detect hypopneas, sleep time, or awakenings were some of the features studied to investigate the full functionality of the PMs to select the most relevant set of physiological signals. Based on the physiological parameters collected (one to six), the PMs were classified into sets according to the level of evidence. The advantages and the disadvantages of each possible set of signals were explained by answering the research questions proposed in the methods.
Conclusions
The minimum number of physiological signals detected by PMs for the detection of OSA depends mainly on the purpose and context of the sleep study. The set of three physiological signals showed the best results in the detection of OSA.
Melamine-formaldehyde (MF) resins are widely used as surface finishes for engineered wood-based panels in decorative laminates. Since no additional glue is applied in lamination, the overall residual curing capacity of MF resins is of great technological importance. Residual curing capacity is measured by differential scanning calorimetry (DSC) as the exothermic curing enthalpy integral of the liquid resin. After resin synthesis is completed, the resulting pre-polymer has a defined chemical structure with a corresponding residual curing capacity. Predicting the residual curing capacity of a resin batch already at an early stage during synthesis would enable corrective measures to be taken by making adjustments while synthesis is still in progress. Thereby, discarding faulty batches could be avoided. Here, by using a batch modelling approach, it is demonstrated how quantitative predictions of MF residual curing capacity can be derived from inline Fourier Transform infrared (FTIR) spectra recorded during resin synthesis using partial least squares regression. Not only is there a strong correlation (R2 = 0.89) between the infrared spectra measured at the end of MF resin synthesis and the residual curing capacity. The inline reaction spectra obtained already at the point of complete dissolution of melamine upon methylolation during the initial stage of resin synthesis are also well suited for predicting final curing performance of the resin. Based on these IR spectra, a valid regression model (R2 = 0.85) can be established using information obtained at a very early stage of MF resin synthesis.
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.
Covid-19 und die Maßnahmen zu Eindämmung der Pandemie wirkten für viele Menschen lebensverändernd und zwangen Unternehmen zu teilweise substantiellen Anpassungen ihrer gewohnten Praktiken. Sie führten jedoch auch zu Veränderungen, die sich weitgehend außerhalb der öffentlichen Wahrnehmung vollzogen haben. Ein Beispiel hierfür ist die deutliche Verschiebung der Kraftverhältnisse im Markt für Bannerwerbung, auf dem sich sowohl für Werbetreibende, als auch für Vermarkter zu deutlichen Veränderungen kam. Gleichzeitig verändert sich der Markt strukturell. Es kommt derzeit zu einer Professionalisierung, bei der Werbetreibende heute die richtigen Weichen stellen müssen, um in Zukunft zu den Gewinnern in diesem Markt zu zählen. Dieser Report fasst die wichtigen strukturellen und Corona-bedingten Veränderungen zusammen und erklärt die Implikationen für Werbetreibende.
Bausparverträge sind kombinierte Spar- und Finanzierungsinstrumente, die für die breite Bevölkerung ausgelegt sind. Im Jahr 2020 umfasste der Bestand an Bausparverträgen in Deutschland ca. 25 Mio. Verträge. Ein wesentlicher Teil der Attraktivität des Bausparvertrags für Kunden liegt in der hohen Flexibilität dieser Finanzprodukte, die im Vertragsablauf eine flexible Anpassung an individuelle Finanzierungsbedingungen ermöglicht. In der Sparphase sind das insbesondere Möglichkeiten zur Erhöhung, Ermäßigung und Teilung der Verträge sowie zur relativ flexiblen Anpassung der Sparrate. Bei einem zuteilungsreifen Vertrag kann die Sparphase innerhalb bestimmter zeitlicher Grenzen fortgesetzt werden. In der Darlehensphase sind flexible Sondertilgungen jederzeit und ohne Vorfälligkeitsentschädigung möglich.
Die Vielzahl eingebetteter Optionen beeinflussen sich wechselseitig und müssen in ihrer Wirkungsweise immer gesamthaft betrachtet und gesteuert werden. Die empirische Erfahrung der letzten Jahrzehnte zeigt bezüglich der Optionsausübung ein Kundenverhalten, das sich zwar an finanzmathematischen Überlegungen orientiert, aber nicht vollständig finanzrational abläuft.
Academic research is vital for innovation and industrial growth. However, a potential burden of processing ever more knowledge could be affecting research output and researchers’ careers. We look at a dataset of researchers who have published in journals in the field of economics during a period of 45 years. For a subset of these researchers, we amass data from journals listed in the EconLit database, supplemented with years of birth from public sources. Our results show an increase in the age of researchers at their first publication, in the number of articles referenced in debut articles, and in the number of co-authors. Simultaneously, we observe a decline in the probability of researchers changing research fields. Our findings extend earlier findings on patents and hint at a burden of knowledge pervading different areas of human progress. Moreover, our results indicate that researchers develop strategies of specialisation to deal with this challenge.
This paper presents an improvement in usability and integrity of simulation-based analog circuit sizing. Instead of using geometrical sizing parameters (width, length), a transformed design-space, consisting exclusively of electrical parameters (branch currents, efficiencies and speed) is utilized. This design-space is explored more efficiently by optimizers. Moreover, this design-space can be reduced without affecting the quality of the result. The method is illustrated on two application examples, a symmetrical and a miller operational amplifier. Sizing the circuits using the transformed design-space showed significant reduction in required circuit simulations (up to 11x faster), better convergence, without loss in quality.
In recent years, artificial intelligence (AI) has increasingly become a relevant technology for many companies. While there are a number of studies that highlight challenges and success factors in the adoption of AI, there is a lack of guidance for firms on how to approach the topic in a holistic and strategic way. The aim of this study is therefore to develop a conceptual framework for corporate AI strategy. To address this aim, a systematic literature review of a wide spectrum of AI-related research is conducted, and the results are analyzed based on an inductive coding approach. An important conclusion is that companies should consider diverse aspects when formulating an AI strategy, ranging from technological questions to corporate culture and human resources. This study contributes to knowledge by proposing a novel, comprehensive framework to foster the understanding of crucial aspects that need to be considered when using the emerging technology of AI in a corporate context.
Manufacturing companies are confronted with external (e.g. short-term change of product configuration by the customer) and internal (e.g. production process deviations) turbulences which are affecting the performance of production. Predefined, centrally controlled logistics processes are limiting the possibilities of production to initiate countermeasures to react in an optimized way to these turbulences. The autonomous control of intralogistics offers a great potential to cope with these turbulences by using the respective flexibility corridors of production systems and applying intelligent logistic objects with decentralized decision and process execution capabilities to maintain a target-optimized production. A method for AI-based storage-location- and material-handling-optimization to achieve performance-optimized intralogistics system through continuous monitoring of performance-relevant parameters and influencing factors by using AI (e.g. for pattern recognition) has been developed. To provide the basis to investigate and demonstrate the potentials of autonomously controlled intralogistics in connection with turbulences of production and in combination with AI, an intelligent warehouse involving an indoor localization system, smart bins, manual, semi-automated/collaborative and autonomous transport systems has been developed and implemented at Werk150, the factory on campus of ESB Business School (Reutlingen University). This scenario, which has been integrated into graduate training modules, allows the analysis and demonstration of different measures of intralogistics to cope with turbulences in production involving amongst others storage and material provision processes. The target fulfilment of the applied intralogistics measures to master arising turbulences is assessed based on the overall performance of production considering lead times and adherence to delivery dates. By applying artificial intelligence (AI) algorithms the intelligent logistical objects (smart bin, transport systems, etc.) as well as the entire logistics system should be enabled to improve their decision and process execution capabilities to master short-term turbulences in the production system autonomously.
Comparative analysis of the R&D efficiency of 14 leading pharmaceutical companies for the years 1999–2018 shows that there is a close positive correlation between R&D spending and the two investigated R&D output parameters, approved NMEs and the cumulative impact factor of their publications. In other words, higher R&D investments (input) were associated with higher R&D output. Second, our analyses indicate that there are "economies of scale" (size) in pharmaceutical R&D.
In today’s marketplace, the consumption of luxury goods is at a peak due to increasing global wealth and low interest rates, resulting in a vast supply of goods and services to which customer experiences are more relevant than ever before. One of the most recent developments in this field shows that consumers no longer simply purchase a product or service based on the fact sheet; they are also interested in the experience around the product. Successful brands must develop and maintain individual images to sustain their competitive advantage and build brand equity that is beneficial for customers and firms. Ideally, these will lead to satisfaction and loyalty between a brand, its products, and its customers. Existing research about brand experience and brand equity has mainly focused on functional aspects, which seem to differ for high-value luxury goods. Most studies have focused on industries like retail and fashion brands, sampling university students or visitors to shopping malls, and some have even mixed different types of industries together. This underpins the need for research within a single luxury industry with actual luxury customers who have a solid background with brand experiences.
The purpose of this study was to explore the brand experience spectrum within the automotive industry in Germany, particularly in the affordable luxury sport car sector. Identifying the factors and components that constitute, influence, or leverage/drive a brand experience from their perspective was a clear aim of the study. To achieve this, the study collected data from indepth interviews with German (n=60) respondents who had experience with affordable and luxury sport cars. The conceptual framework was based on two empirically tested models guiding this exploratory consumer research. The first model to build on was the consumerbased brand equity model, empirically tested by Çifci et al. (2016) and Nam et al. (2011). The second conceptual framework was Lemon and Verhoef’s (2016) customer journey model consisting of relevant touchpoints along the following three stages: pre-purchase, purchase, and post-purchase.
The findings of the research demonstrate that, although the six brand equity concepts – brand awareness, physical quality, staff behaviour, self-congruence, brand identification, and lifestyle – are broadly applicable in understanding customer experience in the affordable luxury car industry, the content of these dimensions differs from that suggested by the previous authors. The research established that cognitive and affective (or symbolic) components build the foundation of customer brand experience and supports Çifci et al.’s (2016) and Nam et al.’s (2011) study results. The study also identified brand trust as an important and highly relevant concept for customer brand experience in the luxury automotive car industry. Brand trust influences customer satisfaction and loyalty, therefore improving and complementing the existing model. Furthermore, the study confirmed Lemon and Verhoef’s (2016) process model of the customer journey and experience; however, it suggested two different customer journeys depending on the customers’ previous experience (first-time and experienced buyers). The differences between the two groups and the relevance of the journey touchpoints within the three purchase stages vary significantly in terms and are distinct. Identified key touchpoints for both groups are the contact to a dealer as well as information gathering online. Differences have been found in the length of purchase stages and across the customer journey. The study highlights the importance of trust, identification, and product quality for customer brand experience. Moreover, the findings of this study complement the brand equity model of Çifci et al. (2016) by adding the new concept of trust, which is highly relevant. The current knowledge is complemented by a new understanding and mapping of the customer journey for luxury sports cars in Germany. This study can assist practitioners and managers by providing a compass indicating which touchpoints are relevant to which customer group. Social value can be achieved by encouraging interactions between brand and consumer (e.g. central product launch events) and through brand-oriented interactions among consumers (e.g. dealer events, clubs, or communities). Customers are motivated to express their distinctiveness through product experience and brand identification (belonging/distinction) and to develop a loyal link to brands.
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.
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
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.
Assistant platforms are becoming a key element for the business model of many companies. They have evolved from assistance systems that provide support when using information (or other) systems to platforms in their own. Alexa, Cortana or Siri may be used with literally thousands of services. From this background, this paper develops the notion of assistant platforms and elaborates a conceptual model that supports businesses in developing appropriate strategies. The model consists of three main building blocks, an architecture that depicts the components as well as the possible layers of an assistant platform, the mechanism that determines the value creation on assistant platforms, and the ecosystem with its network effects, which emerge from the multi-sided nature of assistant platforms. The model has been derived from a literature review and is illustrated with examples of existing assistant platforms. Its main purpose is to advance the understanding of assistant platforms and to trigger future research.
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.
In the upcoming years, huge benefits are expected from Artificial Intelligence (AI). However, there are also risks involved in the technology, such as accidents of autonomous vehicles or discrimination by AI-based recruitment systems. This study aims to investigate public perception of these risks, focusing on realistic risks of Narrow AI, i.e., the type of AI that is already productive today. Based on perceived risk theory, several risk scenarios are examined using data from an exploratory survey. This research shows that AI is perceived positively overall. The participants, however, do evaluate AI critically when being confronted with specific risk scenarios. Furthermore, a strong positive relationship between knowledge about AI and perceived risk could be shown. This study contributes to knowledge by advancing our understanding of the awareness and evaluation of the risks by consumers and has important implications for product development, marketing and society.
Context:
Test-driven development (TDD) is an agile software development approach that has been widely claimed to improve software quality. However, the extent to which TDD improves quality appears to be largely dependent upon the characteristics of the study in which it is evaluated (e.g., the research method, participant type, programming environment, etc.). The particularities of each study make the aggregation of results untenable.
Objectives:
The goal of this paper is to: increase the accuracy and generalizability of the results achieved in isolated experiments on TDD, provide joint conclusions on the performance of TDD across different industrial and academic settings, and assess the extent to which the characteristics of the experiments affect the quality-related performance of TDD.
Method:
We conduct a family of 12 experiments on TDD in academia and industry. We aggregate their results by means of meta-analysis. We perform exploratory analyses to identify variables impacting the quality-related performance of TDD.
Results:
TDD novices achieve a slightly higher code quality with iterative test-last development (i.e., ITL, the reverse approach of TDD) than with TDD. The task being developed largely determines quality. The programming environment, the order in which TDD and ITL are applied, or the learning effects from one development approach to another do not appear to affect quality. The quality-related performance of professionals using TDD drops more than for students. We hypothesize that this may be due to their being more resistant to change and potentially less motivated than students.
Conclusion:
Previous studies seem to provide conflicting results on TDD performance (i.e., positive vs. negative, respectively). We hypothesize that these conflicting results may be due to different study durations, experiment participants being unfamiliar with the TDD process, or case studies comparing the performance achieved by TDD vs. the control approach (e.g., the waterfall model), each applied to develop a different system. Further experiments with TDD experts are needed to validate these hypotheses.
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.
Controlled adhesion of HUVEC on polyelectrolyte multilayers by regulation of coating conditions
(2021)
Adhesion of host cells on the surface of implants is necessary for a healthy ingrowth of the implanted material. One possibility of surface modification is the coating of the implant with a second material with advantageous physical–chemical surface properties for the biological system. The coverage with blood proteins takes place immediately after implantation. It is followed by host–cell interaction on the surface. In this work, the effect of polyelectrolyte multilayer coatings (PEMs) on adhesion and activity of human umbilical vein endothelial cells (HUVECs) was studied. The PEMs were formed from poly(styrenesulfonate) (PSS) and poly(allylamine hydrochloride) (PAH) from solutions with different concentrations of NaCl varying between 0 and 1.0 M. The adhesion of HUVEC and their viability on the PEM is related to the amount of adsorbed proteins from the applied cell growth medium. The amount of adsorbed proteins is controlled not only by the surface charge but also by the internal excess charge of the PEM. The internal excess charge of the PEM was controlled by changing the electrolyte concentration in the deposition solutions.
Deep learning-based EEG detection of mental alertness states from drivers under ethical aspects
(2021)
One of the most critical factors for a successful road trip is a high degree of alertness while driving. Even a split second of inattention or sleepiness in a crucial moment, will make the difference between life and death. Several prestigious car manufacturers are currently pursuing the aim of automated drowsiness identification to resolve this problem. The path between neuro-scientific research in connection with artificial intelligence and the preservation of the dignity of human individual’s and its inviolability, is very narrow. The key contribution of this work is a system of data analysis for EEGs during a driving session, which draws on previous studies analyzing heart rate (ECG), brain waves (EEG), and eye function (EOG). The gathered data is hereby treated as sensitive as possible, taking ethical regulations into consideration. Obtaining evaluable signs of evolving exhaustion includes techniques that obtain sleeping stage frequencies, problematic are hereby the correlated interference’s in the signal. This research focuses on a processing chain for EEG band splitting that involves band-pass filtering, principal component analysis (PCA), independent component analysis (ICA) with automatic artefact severance, and fast fourier transformation (FFT). The classification is based on a step-by-step adaptive deep learning analysis that detects theta rhythms as a drowsiness predictor in the pre-processed data. It was possible to obtain an offline detection rate of 89% and an online detection rate of 73%. The method is linked to the simulated driving scenario for which it was developed. This leaves space for more optimization on laboratory methods and data collection during wakefulness-dependent operations.
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.
Today's logistics systems are characterized by uncertainty and constantly changing requirements. Rising demand for customized products, short product life cycles and a large number of variants increases the complexity of these systems enormously. In particular, intralogistics material flow systems must be able to adapt to changing conditions at short notice, with little effort and at low cost. To fulfil these requirements, the material flow system needs to be flexible in three important parameters, namely layout, throughput and product. While the scope of the flexibility parameters is described in literature, the respective effects on an intralogistics material flow system and the influencing factors are mostly unknown. This paper describes how flexibility parameters of an intralogistics system can be determined using a multi-method simulation. The study was conducted in the learning factory “Werk150” on the campus of Reutlingen University with its different means of transport and processes and validated in terms of practical experiments.
Die leistungsfähigen Verfahren des maschinellen Lernens halten unaufhaltsam Einzug in die verschiedensten Anwendungsbereiche im Finanzsektor. Während sie von einer großen Gemeinschaft von Forschern und Anwendern laufend weiterentwickelt werden, nimmt sich auch die Bankenaufsicht dieses Themas aktiv an und bezieht in Richtlinien und Diskussionspapieren Stellung.
Die digitale Arbeitswelt in ihrer Mehrdimensionalität verstehen: Digitalisierungsatlas und -index
(2021)
Die digitale Transformation der Arbeitswelt ist deshalb so herausfordernd, da die Arbeitswelt für sich bereits ein komplexes mehrdimensionales System ist, das sich kaum überblicken lässt. Für Unternehmen ist es deshalb wichtig, die Mehrdimensionalität und Komplexität der digitalen Arbeitswelt zu verstehen, hierfür ein gemeinsames Sprachspiel zu entwickeln und auf dieser Basis eine gemeinsame Einschätzung des Status quo der eigenen Arbeitswelt zu beschreiben. Mithilfe von zwei Instrumenten, dem Digitalisierungsatlas und dem Digitalisierungsindex, kann dies gelingen. In diesem Beitrag werden diese Instrumente im Detail dargestellt und es wird erklärt, wie sie Organisationen dabei helfen, zu beschreiben und zu verstehen, wo sie selbst in der digitalen Transformation der Arbeitswelt stehen.
In this work, a comparison between different brushless harmonic-excited wound-rotor synchronous machines is performed. The general idea of all topologies is the elimination of the slip rings and auxiliary windings by using the already existing stator and rotor winding for field excitation. This is achieved by injecting a harmonic airgap field with the help of power electronics. This harmonic field does not interact with the fundamental field, it just transfers the excitation power across the airgap. Alternative methods with varying number of phases, different pole-pair combinations, and winding layouts are covered and compared with a detailed Finite-Element-parameterized model. Parasitic effects due to saturation and coupling between the harmonic and main windings are considered.
Avatars are in use when interacting in virtual environments in different contexts, in collaborative work, as well as in gaming and also in virtual meetings with friends. Therefore it is important to understand how the relationship between user and avatar works. In this study, an online survey is used to determine how the perception of an avatar changes in different contexts by relating it to existing avatar relationship typologies. Additionally, it is determined whether in each context a realistic, abstract or comic-like representation is preferred by the participants. One result was a preference of low poly representations in the work context, which are associated with the perception of the avatar as a tool. In the context of meeting friends, a realistic representation is perceived as more appropriate, which is perceived as an accurate self-representation. In the gaming context, the results are less clear, which can be attributed to different gaming preferences. Here, unlike in the other contexts, a comic-like representation is also perceived as appropriate, which is associated with the perception of the avatar as a friend. A symbiotic user-avatar relationship is not directly related to any form of representation, but always lies in the midfield, which is attributed to the fact that it represents a whole spectrum between other categories.
Focal adhesion clusters (FAC) are dynamic and complex structures that help cells to sense physicochemical properties of their environment. Research in biomaterials, cell adhesion or cell migration often involves the visualization of FAC by fluorescence staining and microscopy, which necessitates quantitative analysis of FAC and other cell features in microscopy images using image processing. Fluorescence microscopy images of human umbilical vein endothelial cells (HUVEC) obtained at 63x magnification were quantitatively analysed using ImageJ software. A generalised algorithm for selective segmentation and morphological analysis of FAC, nucleus and cell morphology is implemented. Further, a method for discrimination of FACnear the nucleus and around the periphery is implemented using masks. Our algorithm is able to effectively quantify different morphological characteristics of cell components and shows a high sensitivity and specificity while providing a modular software implementation.
Preliminary results of homomorphic deconvolution application to surface EMG signals during walking
(2021)
Homomorphic deconvolution is applied to sEMG signals recorded during walking. Gastrocnemius lateralis and tibialis anterior signals were acquired according to SENIAM recommendation. MUAP parameters like amplitude and scale were estimated, whilst the MUAP shape parameter was fixed. This features a useful time-frequency representation of sEMG signal. Estimation of scale MUAP parameter was verified extracting the mean frequency of filtered EMG signal, extracted from the scale parameter estimated with two different MUAP shape values.
The present work proposes the use of modern ICT technologies such as smartphones, NFCs, internet, and web technologies, to help patients in carrying out their therapies. The implemented system provides a calendar with a reminder of the assumptions, ensures the drug identification through NFC, allows remote assistance from healthcare staff and family members to check and manage the therapy in real-time. The system also provides centralized information on the patient's therapeutic situation, helpful in choosing new compatible therapies.
Imagine a world in which the search for tomorrow's trends is not subject to a long and laborious data search but is possible with a single mouse click. Through the use of artificial intelligence (AI), this reality is made possible and is to be further advanced through research. The study therefore aims to provide an initial overview of the young research field. Based on research, expert interviews, company and student surveys, current application possibilities of AI in the innovation process (defined as Smart Innovation), existing challenges that slow down the further development are discussed in more detail and future application possibilities are presented. Finally, a recommendation for action is made for business, politics and science to help overcome the current obstacles together and thus drive the future of Smart Innovation.
Guerrilla marketing is the selection of atypical and non-dogmatic marketing activities that aim to achieve the greatest possible impact – in the ideal case with a comparable minimum investment. Guerrilla marketing has developed into a basic strategy overarching the marketing mix, a basic marketing policy attitude for market development that goes off the beaten track to consciously seek new, unconventional, previously disregarded, possibly even frown-upon possibilities for the deployment of tools. Digital marketing tools such as social media provide new ways and promising opportunities for innovative guerrilla marketing. This article provides an overview of innovative digital guerrilla marketing. It describes and structures guerrilla marketing in a novel form and shows illustrating examples as well as developmental trends.
The halo effect is a cognitive bias known from social psychology. A halo effect occurs when a global impression or information about a salient characteristic shapes the evaluation of other characteristics. In a sports-related context, the halo effect has hardly been researched so far, although this could contribute significantly to understanding the thinking and behavior of sports fans. In this research paper, the following questions are investigated: Is there a halo effect in soccer? Does the sporting success or failure of a club outshine other sporting aspects? Does sporting success or failure possibly even distort fans' perception of non-sporting aspects? The research paper reflects the current state of halo research and presents the results of an empirical study in which fans of soccer clubs from the German Bundesliga were interviewed. The results of the analyses substantiate the distortion of the fans’ perception with regard to a very diverse range of aspects that is triggered by the sporting success or failure of their favorite club.
It has always been interesting for scientists to look at economic indicators in order to explain current economic developments and to forecast a boom or a recession. In addition to classic, hard economic indicators such as the Gross National Product or the Ifo index, there are also a number of psychological and soft indicators that economists consult. The lipstick effect is one of these psychological indicators. The paper introduces the lipstick effect, explains the causes behind the phenomenon, shows the connection to brand management and provides references to the current Corona pandemic.
An advanced ‘clickECM’ that can be modified by the inverse-electron demand Diels-Alder reaction
(2021)
The extracellular matrix (ECM) represents the natural environment of cells in tissue and therefore is a promising biomaterial in a variety of applications. Depending on the purpose, it is necessary to equip the ECM with specific addressable functional groups for further modification with bioactive molecules, for controllable cross-linking and/or covalent binding to surfaces. Metabolic glycoengineering (MGE) enables the specific modification of the ECM with such functional groups without affecting the native structure of the ECM. In a previous approach (S. M. Ruff, S. Keller, D. E. Wieland, V. Wittmann, G. E. M. Tovar, M. Bach, P. J. Kluger, Acta Biomater. 2017, 52, 159–170), we demonstrated the modification of an ECM with azido groups, which can be addressed by bioorthogonal copper-catalyzed azide-alkyne cycloaddition (CuAAC). Here, we demonstrate the modification of an ECM with dienophiles (terminal alkenes, cyclopropene), which can be addressed by an inverse-electron-demand Diels-Alder (IEDDA) reaction. This reaction is cell friendly as there are no cytotoxic catalysts needed. We show the equipment of the ECM with a bioactive molecule (enzyme) and prove that the functional groups do not influence cellular behavior. Thus, this new material has great potential for use as a biomaterial, which can be individually modified in a wide range of applications.