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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.
Context
Microservices as a lightweight and decentralized architectural style with fine-grained services promise several beneficial characteristics for sustainable long-term software evolution. Success stories from early adopters like Netflix, Amazon, or Spotify have demonstrated that it is possible to achieve a high degree of flexibility and evolvability with these systems. However, the described advantageous characteristics offer no concrete guidance and little is known about evolvability assurance processes for microservices in industry as well as challenges in this area. Insights into the current state of practice are a very important prerequisite for relevant research in this field.
Objective
We therefore wanted to explore how practitioners structure the evolvability assurance processes for microservices, what tools, metrics, and patterns they use, and what challenges they perceive for the evolvability of their systems.
Method
We first conducted 17 semi-structured interviews and discussed 14 different microservice-based systems and their assurance processes with software professionals from 10 companies. Afterwards, we performed a systematic grey literature review (GLR) and used the created interview coding system to analyze 295 practitioner online resources.
Results
The combined analysis revealed the importance of finding a sensible balance between decentralization and standardization. Guidelines like architectural principles were seen as valuable to ensure a base consistency for evolvability and specialized test automation was a prevalent theme. Source code quality was the primary target for the usage of tools and metrics for our interview participants, while testing tools and productivity metrics were the focus of our GLR resources. In both studies, practitioners did not mention architectural or service-oriented tools and metrics, even though the most crucial challenges like Service Cutting or Microservices Integration were of an architectural nature.
Conclusions
Practitioners relied on guidelines, standardization, or patterns like Event-Driven Messaging to partially address some reported evolvability challenges. However, specialized techniques, tools, and metrics are needed to support industry with the continuous evaluation of service granularity and dependencies. Future microservices research in the areas of maintenance, evolution, and technical debt should take our findings and the reported industry sentiments into account.
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.
Business opportunities for energy providers to utilize flexible industrial demand are platform-based, connecting small and medium-sized enterprises (SMEs) to a virtual power plant (VPP) in complex ecosystems. Unlike in other VPPs, the focus is on participation, data, and control sovereignty for the SMEs. An exemplary application for an existing cement mill demonstrates positive margins. Viable VPP business models for small and medium-sized utilities include the “orchestrator,” i.e., adding value by linking services of specialized providers, the “integrator,” i.e., incorporating internal and external processes and resources, as well as the “white label user,” i.e., using a turn-key VPP from an exclusive cooperation partner.
Background/Aim: The aim of this study was the conception, production, material analysis and cytocompatibility analysis of a new collagen foam for medical applications. Materials and Methods: After the innovative production of various collagen sponges from bovine sources, the foams were analyzed ex vivo in terms of their structure (including pore size) and in vitro in terms of cytocompatibility according to EN ISO 10993-5/-12. In vitro, the collagen foams were compared with the established soft and hard tissue materials cerabone and Jason membrane (both botiss biomaterials GmbH, Zossen, Germany). Results: Collagen foams with different compositions were successfully produced from bovine sources. Ex vivo, the foams showed a stable and long-lasting primary structure quality with a bubble area of 1,000 to 2,000 μm2. In vitro, all foams showed sufficient cytocompatibility. Conclusion: Collagen sponges represent a promising material for hard and soft tissue regeneration. Future studies could focus on integrating and investigating different additives in the foams.
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.
Identifikation von Schlaf- und Wachzuständen durch die Auswertung von Atem- und Bewegungssignalen
(2021)
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.
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.
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.
Highly viscous bioinks offer great advantages for the three-dimensional fabrication of cell-laden constructs by microextrusion printing. However, no standardised method of mixing a high viscosity biomaterial ink and a cell suspension has been established so far, leading to non-reproducible printing results. A novel method for the homogeneous and reproducible mixing of the two components using a mixing unit connecting two syringes is developed and investigated. Several static mixing units, based on established mixing designs, were adapted and their functionality was determined by analysing specific features of the resulting bioink. As a model system, we selected a highly viscous ink consisting of fresh frozen human blood plasma, alginate, and methylcellulose, and a cell suspension containing immortalized human mesenchymal stem cells. This bioink is crosslinked after fabrication. A pre-crosslinked gellan gum-based bioink providing a different extrusion behaviour was introduced to validate the conclusions drawn from the model system. For characterisation, bioink from different zones within the mixing device was analysed by measurement of its viscosity, shape fidelity after printing and visual homogeneity. When taking all three parameters into account, a comprehensive and reliable comparison of the mixing quality was possible. In comparison to the established method of manual mixing inside a beaker using a spatula, a significantly higher proportion of viable cells was detected directly after mixing and plotting for both bioinks when the mixing unit was used. A screw-like mixing unit, termed “HighVisc”, was found to result in a homogenous bioink after a low number of mixing cycles while achieving high cell viability rates.
This article studies the hidden blemishes of two benchmark rulings of the European Court of Justice (ECJ). In 2015 and 2018, the ECJ approved two unconventional monetary instruments, among others ‘Outright Monetary Transactions’ and the ‘Public Sector Purchase Program’. Yet, there is a vigorous debate about both monetary operations in law and economics. In this interdisciplinary article, we address law and economic arguments in order to elucidate insights to the legal community. In particular, we elaborate on the legal implications of a variety of concerning issues such as public policy interference, effect on wealth redistribution, erosion of democratic legitimacy and lack of effectiveness of monetary policy. These topics remain disregarded in the ECJ rulings. Consequently, the verdicts do not identify the economic boundaries of the European Central Bank’s mandate appropriately.
This paper studies the power of online search intensity metrics, measured by Google, for examining and forecasting exchange rates. We use panel data consisting of quarterly time series from 2004 to 2018 and ten international countries with the highest currency trading volume. Newly, we include various Google search intensity metrics to our panel data. We find that online search improves the overall econometric models and fits. First, four out of ten search variables are robustly significant at one percent and enhance the macroeconomic exchange rate models. Second, country regressions corroborate the panel results, yet the predictive power of search intensity with regard to exchange rates vary by country. Third, we find higher prediction performance for our exchange rate models with search intensity, particularly in regard to the direction of the exchange rate. Overall, our approach reveals a value-added of search intensity in exchange rate models.
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.
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.
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.
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.
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.
Technologische, sozioökonomische und kulturelle Entwicklungen der letzten Jahrzehnte sowie zuletzt die Covid-19-Pandemie führten zu einer erhöhten Nachfrage nach flexiblen Arbeitsformen wie Homeoffice. In der Forschung wird danach gefragt, welche Faktoren die Umsetzung von flexiblen Arbeitsmodellen in Unternehmen beeinflussen und wie die Faktoren miteinander zusammenhängen. Bisher bietet die Forschung allerdings zu wenige Ansatzpunkte, um relevante Handlungsempfehlungen für eine erfolgreiche Umsetzung von Homeoffice für die Praxis abzuleiten. In dieser Fallstudie können auf organisationaler Ebene technologische, formelle und informelle Rahmenbedingungen, auf interaktionaler Ebene das Team und die Kommunikation sowie auf individueller Ebene Präferenzen, Kompetenzen und personenbezogene Rahmenbedingungen der Beschäftigten als relevante Einflussfaktoren identifiziert werden. Die Themenfelder sind eng miteinander verbunden, wobei sich vor allem stark vernetzte und einflussreiche Faktoren, wie formelle und technologische Rahmenbedingungen sowie Kommunikation, als Ansatzpunkte für die Gestaltung von Homeoffice eignen.
Hearing contact lens (HCL) is a new type of hearing aid devices. One of its main components is a piezo-electric actuator (PEA). In order to evaluate and maximizethe HCL´s performance, a model of the HCL coupled to the middle ear was developed using finite element (FE)approach. To validate the model, vibrational measurements on the HCL and temporal bones were performed using a Laser-Doppler-Vibrometer (LDV). The model was validated step by step starting with HCL only. Then a silicone cap was fitted onto the HCL to provide an interface between the HCL and the tympanic membrane. The HCL was placed on the tympanic membrane and additional measurements were performed to validate the coupled model. The model was used to evaluate the sensitivity of geometrical and material parameters with respect to performance measures of the HCL. Moreover, deeper insight was gained into the feedback behavior, which causes whistling sounds, and the contact between the HCL and tympanic membrane.
Fast pyrolysis as a valorization mechanism for banana rachis and low-density polyethylene waste
(2021)
Banana rachis and low-density polyethylene (LDPE) were selected as secondary feedstocks for the study of fast pyrolysis in a free-fall reactor. The experiments were performed at 600 °C for banana rachis and 450 °C for LDPE, based on literature and thermogravimetric analysis. The gaseous products of both feedstocks present similar composition in the C1-C2 compounds, while C3 compounds are only found in LDPE. The liquid products from banana and LDPE correspond to functional groups and shorter hydrocarbons, respectively. Scanning electron microscopy (SEM) and Fourier transform infrared (FTIR) analyses of the char showed important morphological changes to spheres in LDPE and structural changes due to thermal decomposition in the biomass. The pyrolysis char has high potential as adsorbent, encapsulation, or catalyst.
The article analyzes experimentally and theoretically the influence of microscope parameters on the pinhole-assisted Raman depth profiles in uniform and composite refractive media. The main objective is the reliable mapping of deep sample regions. The easiest to interpret results are found with low magnification, low aperture, and small pinholes. Here, the intensities and shapes of the Raman signals are independent of the location of the emitter relative to the sample surface. Theoretically, the results can be well described with a simple analytical equation containing the axial depth resolution of the microscope and the position of the emitter. The lower determinable object size is limited to 2–4 μm. If sub-micrometer resolution is desired, high magnification, mostly combined with high aperture, becomes necessary. The signal intensities and shapes depend now in refractive media on the position relative to the sample surface. This aspect is investigated on a number of uniform and stacked polymer layers, 2–160 μm thick, with the best available transparency. The experimental depth profiles are numerically fitted with excellent accuracy by inserting a Gaussian excitation beam of variable waist and fill fraction through the focusing lens area, and by treating the Raman emission with geometric optics as spontaneous isotropic process through the lens and the variable pinhole, respectively. The intersectional area of these two solid angles yields the leading factor in understanding confocal (pinhole-assisted) Raman depth profiles.
Experimentelle Organisationsentwicklung : Perspektiven eines zukunftsfähigen Veränderungsansatzes
(2021)
Die Organisationsentwicklung selbst verändert sich und wird “zukunftsoffener”. In diesem Beitrag argumentieren die Autoren, dass die Idee des Experimentierens als zukunftsfähige Veränderungsmethode angewandt werden kann. Sie stellen zentrale Prämissen einer "experimentellen Organisationsentwicklung" und zeigen die Eckpunkte eines spezifischen Interventionsdesigns auf.
Die digitale Transformation und gesellschaftliche Entwicklungen verändern die Arbeitswelt nicht erst seit der Corona-Pandemie. Kommunikation, Kreativität und agile Vorgehensweisen in der Arbeitsorganisation rücken in den Vordergrund und werden gerade in Krisenzeiten zu wichtigen Stärken von Unternehmen. Der Grad der Selbstorganisation von Teams steigt und erfordert mehr individuelle Selbstorganisation der Beschäftigten. Dies birgt neben vielen Chancen auch Gesundheitsrisiken.
Der Report beleuchtet die agile Organisation und weitere moderne Organisationsmodelle wie die Soziokratie, die Holokratie und die evolutionäre Organisation unter dem Gesundheitsaspekt. All diese Organisationsmodelle sind gekennzeichnet durch die Abflachung von Hierarchien, eine stärkere Sinnorientierung, mehr Flexibilität sowie die Integration von Leistungspotentialen der Beschäftigten. Die Gemeinsamkeiten, aber auch Unterschiede und Konfliktpotenziale werden ausführlich erklärt.
Mit diesem Hintergrundwissen können Beratende Gesundheitsthemen besser in Phasen gesundheitlicher Belastungen einbringen und richtig adressieren. Der iga.Report 44 gibt einen Überblick zum noch jungen Stand der Forschung und liefert zahlreiche Ansatzpunkte für die Präventionsarbeit und die Betriebliche Gesundheitsförderung in einer neuen Arbeitswelt.
Today, many industrial tasks are not automated and still require human intervention. One of these tasks is the unloading of oversea containers. After the end of transportation to the sorting center, the containers must be unloaded manually for further sending the parcels to the recipients. A robot-based automatic unloading of containers was therefore researched. However, the promising results of the system developed in these projects could not be commercialized due to problems with its reliability. Mechanical, algorithmic or other limitations are possible causes of the observed errors. To analyze errors, it is necessary to evaluate the results of the robot’s work without complicating the existing system by adding new sensors to it. This paper presents a reference system based on machine learning to evaluate the robotics grasps of parcels. It analyzes two states of the container: before and after picking up one box. The states are represented as a point cloud received from a laser scanner. The proposed system evaluates the success of transferring a box from an overseas container to the sorting line by supervised learning using convolutional neural networks (CNN) and manual labeling of the data. The process of obtaining a working model using a hyperband model search with a maximum classification error of 3.9 % is also described.
Facial beauty prediction (FBP) aims to develop a machine that automatically makes facial attractiveness assessment. In the past those results were highly correlated with human ratings, therefore also with their bias in annotating. As artificial intelligence can have racist and discriminatory tendencies, the cause of skews in the data must be identified. Development of training data and AI algorithms that are robust against biased information is a new challenge for scientists. As aesthetic judgement usually is biased, we want to take it one step further and propose an Unbiased Convolutional Neural Network for FBP. While it is possible to create network models that can rate attractiveness of faces on a high level, from an ethical point of view, it is equally important to make sure the model is unbiased. In this work, we introduce AestheticNet, a state-of-the-art attractiveness prediction network, which significantly outperforms competitors with a Pearson Correlation of 0.9601. Additionally, we propose a new approach for generating a bias-free CNN to improve fairness in machine learning.
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.
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.
Entrepreneurial software engineering: towards a hybrid development method for early-stage startups
(2021)
A considerable share of innovative software-intensive products is developed by startups. However, product development in an early-stage startup is not a sequential process. A business idea is usually based on a number of assumptions. The riskiest assumptions need to be tested. Depending on the test results, a product strategy may change several times. This raises the question of how to create sufficiently stable software using engineering principles despite a dynamic product strategy that is subject to many uncertainties. Hybrid development methods that combine agile aspects with classical engineering methods seem to be a good choice in such a start-up context. This paper proposes a lightweight hybrid development method that provides early-stage startups with a framework to support the development of single-feature minimum viable products. The method was derived from a start-up company's founding case and evaluated in expert interviews. The proposed method is intended to provide a basis for discussion between practitioners and scientists with the aim of better understanding the application of software engineering principles in software start-ups.
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.
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.
This paper describes the analysis of day-ahead power market data from the European Power Exchange (EPEX) SPOT over a period of 17 months till October 2020 and the forecasting model for electricity prices. High volatility of the DE-LU (Germany and Luxembourg) power market in order to improve the planning of the bidding strategy and maximize benefits was reflected. Forecasting models based on the Autoregressive Integrated Moving Average (ARIMA) approach and artificial neural networks are developed to predict Day-Ahead prices up to a week ahead. Models are built for a virtual power plant Neckar-Alb and will be used as a part of an optimization tool for the operationtimetable of connected distributed energy devices
Die zunehmende Technologie- und Produktkomplexität führen dazu, dass sich immer mehr Unternehmen für ihre F&E mit externen Organisationen vernetzen. So entstehen interorganisationale F&E-Projekte, welche temporäre Organisationen darstellen. Forschungsfragen zu diesen Projekten sind u.a. hinsichtlich der Praktiken und Verhaltensregeln offen. Über ein kulturbewusstes Projektmanagement können kooperations- und innovationsförderliche Praktiken und Verhaltensregeln aufgebaut werden, die für diese F&E-Projekte essenziell sind. So ist die Forschungsfrage dieses Beitrags, wie ein projektkulturbewusstes Management interorganisationaler F&E-Projekte erfolgen kann. Dafür wird auf Basis der theoretischen Grundlagen zum F&E-Projektmanagement, zu menschlichen Handlungssystemen und Ebenen der Zusammenarbeit, zu Kultur und Verhalten ein projektkulturbewusstes Management-Modell entwickelt. Das Modell umfasst zwei Teile. Im ersten Teil wird der Bereich aufgezeigt, in welchem sich die Projektkultur entwickelt. Im zweiten Teil wird aufgezeigt, wie die Faktoren für ein wahrscheinlich kooperatives und innovatives Verhalten innerhalb dieses Bereiches gestaltet werden sollten.
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.
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.
Energy efficiency optimization techniques for steady state operation of induction machines are the state-of-the-art, and many methods have already been developed. However, many real-world industrial and electric vehicle applications cannot be considered to be in steady state operation. The focus of this contribution is on the efficiency optimization of induction machines in dynamic operation. Online dynamic operation is challenging due to the computational complexity and the required low sample times in an inverter. An offline optimization is therefore conducted to gain knowledge. Based on this offline optimal solution, a simple and easy to implement template based solution is developed. This approach aims at replicating the solution found by the offline optimization by resembling the shape and anticipative characteristics of the optimal flux trajectory. The energy efficiency improvement of the template based solution is verified by simulations and measurements on a test bench and using a real-world drive cycle scenario. For comparison, a model predictive numerical online optimization is investigated too.
We examine the role of communication from users on dropout from digital learning systems to answer the following questions: (1) how does the sentiment within qualitative signals (user comments) affect dropout rates? (2) does the variance in the proportion of positive and negative sentiments affect dropout rates? (3) how do quantitative signals (e.g. likes) moderate the effect of the qualitative signals? and (4) how does the effect of qualitative signals on dropout rates change across early and late stages of learning? Our hypotheses draws from learning theory and self-regulation theory, and were tested using data of 447 learning videos across 32 series of online tutorials, spanning 12 different fields of learning. The findings indicate a main effect of negative sentiment on dropout rates but no effect of positive sentiment on preventing dropout behaviour. This main effect is stronger in the early stages of learning and weakens at later stages. We also observe an effect of the extent of variance of positive and negative sentiments on dropout behaviour. The effects are negatively moderated by quantitative signals. Overall, making commenting more broad-based rather than polarised can be a useful strategy in managing learning, transferring knowledge, and building consensus.
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.
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.
Direct observation of structural heterogeneity and tautomerization of single hypericin molecules
(2021)
Tautomerization is a fundamental chemical reaction which involves the relocation of a proton in the reactants. Studying the optical properties of tautomeric species is challenging because of ensemble averaging. Many molecules, such as porphines, porphycenes, or phenanthroperylene quinones, exhibit a reorientation of the transition dipole moment (TDM) during tautomerization, which can be directly observed in single-molecule experiments. Here, we study single hypericin molecules, which is a prominent phenanthroperylene quinone showing antiviral, antidepressive, and photodynamical properties. Observing abrupt flipping of the image pattern combined with time-dependent density functional theory calculations allows drawing conclusions about the coexistence of four tautomers and their conversion path. This approach allows the unambiguous assignment of a TDM orientation to a specific tautomer and enables the determination of the chemical structure in situ. Our approach can be applied to other molecules showing TDM reorientation during tautomerization, helping to gain a deeper understanding of this important process.
Das Projekt DigiTraIn 4.0 hat ein Beratungskonzept entwickelt und erprobt, das Unternehmen bei der erfolgreichen Digitalisierung ihrer Arbeitswelt unterstützt. Das Beratungskonzept basiert auf vier anwendungsorientierten Instrumenten: Der Digitalisierungsatlas bildet die Digitalisierung der Arbeitswelt in all ihren Dimensionen ab und ermöglicht es, die Notwendigkeit sowie Chancen und Risiken der Veränderungen zu verstehen. Hierauf aufbauend können Unternehmen mit dem Digitalisierungsindex ihren aktuellen Ist-Digitalisierungsgrad der Arbeitswelt individuell bestimmen. Der individuelle Digitalisierungsgrad dient als Ausgangspunkt für den Digitalisierungskompass, der es dem Unternehmen ermöglicht, die Soll-Vorstellung der digitalen Arbeitswelt zu illustrieren und eine unternehmensspezifische Transformationsagenda abzuleiten. Der Beratungsprozess und die Entwicklung der zentralen Instrumente werden in diesem Beitrag dargestellt.
Digitalisierung und Mediatisierung prägen die Gesellschaft und auch die Erwachsenenbildung/Weiterbildung. Der Beitrag geht der Frage nach, wie Digitalisierung in Angeboten der Erwachsenenbildung/Weiterbildung gelingt. Damit wird ein Fokus auf den Einsatz digitaler Medien gelegt. Dazu werden die Angebotsentwicklung für Adressatinnen und Adressaten sowie Teilnehmende, medienbezogene Inhalte, Lehr- und Lernarrangements mit digitalen Medien, der Einsatz digitaler Medien und die Zugänglichkeit von Lehr- und Lernmaterialien als relevante Merkmale identifiziert. Insgesamt zeigen die analysierten Interviewdaten, dass der Einsatz digitaler Medien in Angeboten eine Erweiterung der didaktischen Aufgaben darstellt, da Angebote mit digitalen Medien zielgenau auf die Bedarfe und Möglichkeiten von Adressatinnen und Adressaten sowie Teilnehmenden abgestimmt werden müssen.
The disruptive potential of digital transformation (DT) has been widely discussed in scholarly literature and practitioner-oriented discourses. The management control (MC) function is an important corporate function, as it provides transparency on the economic situation of a firm. DT challenges MC in a two-fold and reciprocal nature as it (i) changes the MC function itself as well as (ii) the entire firm and its business models, which needs to be accompanied by the MC function. Given the complexity and variety of phenomena within the developments in the context of DT, a comprehensive management approach is essential. Surprisingly, there exist few convincing approaches, which support a comprehensive management of the DT. The objectives of this paper are therefore to discuss the impact of DT on MC, as well as, to develop a framework to control DT of an organization from a MC perspective. Based on a literature review and conceptual research, our study contributes to knowledge by proposing an initial, preliminary conceptual framework to manage DT, from a MC perspective. The framework highlights important dimensions that should be considered in the management of DT, for example related to processes and MC instruments.
Prior to the introduction of AI-based forecast models in the procurement department of an industrial retail company, we assessed the digital skills of the procurement employees and surveyed their attitudes toward a new digital technology. The aim of the survey was to ascertain important contextual factors which are likely to influence the acceptance and the successful use of the new forecast tool. What we find is that the digital skills of the employees show an intermediate level and that their attitudes toward key aspects of new digital technologies are largely positive. Thus, the conditions for high acceptance and the successful use of the models are good, as evidenced by the high intention of the procurement staff to use the models. In line with previous research, we find that the perceived usefulness of a new technology and the perceived ease of use are significant drivers of the willingness to use the new forecast tool.
Digitalization increases the pressure for companies to innovate. While current research on digital transformation mostly focuses on technological and management aspects, less attention has been paid to organizational culture and its influence on digital innovations. The purpose of this paper is to identify the characteristics of organizational culture that foster digital innovations. Based on a systematic literature review on three scholarly databases, we initially found 778 articles that were then narrowed down to a total number of 23 relevant articles through a methodical approach. After analyzing these articles, we determine nine characteristics of organizational culture that foster digital innovations: corporate entrepreneurship, digital awareness and necessity of innovations, digital skills and resources, ecosystem orientation, employee participation, agility and organizational structures, error culture and risk-taking, internal knowledge sharing and collaboration, customer and market orientation as well as open-mindedness and willingness to learn.
Sustainable technologies are being increasingly used in various areas of human life. While they have a multitude of benefits, they are especially useful in health monitoring, especially for certain groups of people, such as the elderly. However, there are still several issues that need to be addressed before its use becomes widespread. This work aims to clarify the aspects that are of great importance for increasing the acceptance of the use of this type of technology in the elderly. In addition, we aim to clarify whether the technologies that are already available are able to ensure acceptable accuracy and whether they could replace some of the manual approaches that are currently being used. A two-week study with people 65 years of age and over was conducted to address the questions posed here, and the results were evaluated. It was demonstrated that simplicity of use and automatic functioning play a crucial role. It was also concluded that technology cannot yet completely replace traditional methods such as questionnaires in some areas. Although the technologies that were tested were classified as being “easy to use”, the elderly population in the current study indicated that they were not sure that they would use these technologies regularly in the long term because the added value is not always clear, among other issues. Therefore, awareness-raising must take place in parallel with the development of technologies and services.
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.
Forschungsfrage: Was sind Motivationen, Bedenken und Karriereerwartungen von Hochschulabsolventen, die sich für eine selbstinitiierte Auslandsentsendung direkt nach dem Studienabschluss entscheiden?
Methodik: Quantitative Befragung unter Absolventen deutscher Hochschulen
Praktische Implikationen: Aus den Ergebnissen lassen sich Maßnahmen zur Gewinnung und Bindung von Hochschulabsolventen als selbstinitiierte Entsandte und damit zur Nutzung des Potenzials der selbstinitiierten Entsendung ableiten.
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.
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.
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.
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.
The increasing urban population growth leads to challenges in cities in many aspects: Urbanisation problems such as excessive environmental pollution or increasing urban traffic demand new and innovative solutions. In this context, the concept of smart cities is discussed. An enabling element of the smart city concept is applying information technology (IT) to improve administrative efficiency and quality of life while reducing costs and resource consumption and ensuring greater citizen participation in administrative and urban development issues. While these smart city services are technologically studied and implemented, government officials, citizens or businesses are often unaware of the large variety of smart city service solutions. Therefore, this work deals with developing a smart city services catalogue that documents best practice services to create a platform that brings citizens, city government, and businesses together. Although the concept of IT service catalogues is not new and guidelines and recommendations for the design and development of service catalogues already exist in the corporate context, there is little work on smart city service catalogues. Therefore, approaches from agile software development and pattern research were adapted to develop the smart city service catalogue platform in this work.
This study determines the correlation between industry-specific success patterns of Germany’s engineering industry and the business models applied within. In order to identify this correlation, the following objectives are addressed within the framework of this paper: (1) identification and description of business models used by Germany’s engineering industry; (2) analysis of industry-specific success patterns of Germany’s engineering industry by the usage of Key-Performance-Indicators (KPIs); and (3) determination of correlation between the KPIs and Germany’s engineering industry’s business models’ effectiveness. These objectives are mainly achieved by literature research and expert surveys. The findings highlight the KPIs (overall 41) that are relevant for the respective business models. This enables a better understanding of the interrelationships of the business model, in order to derive relevant conclusions. The paper contributes to the literature as it advances this field of research in Germany, and it is one of the first studies to examine the relationship between business models and industry-specific success patterns with relevant KPIs.
Purpose – This paper aims to determine the affecting factors of the brand authenticity of startups in social media.
Design/methodology/approach – Using a qualitative method based on a grounded theory approach, this research specifies and classifies the affecting factors of brand authenticity of startups in social media through in-depth semi-structured interviews.
Findings – Multiple factors affecting the brand authenticity of startups in social media are determined and categorized as indexical, iconic and existential cues through this research. Connection to heritage and having credible support are determined as indexical cues. Founder intellectuality, brand intellectuality, commitment toward customers and proactive clear and interesting communications are identified as iconic cues. Having self-confidence and self-satisfaction, having intimacy with the brand and a joyful feeling for interactions with the community around the brand are determined as existential cues in this research. This research furthers previous arguments on a multiplicity of brand authenticity by shedding light on the relationship between the different aspects of authenticity and the form that different affecting factors can be organized together. Consumers eventually evaluate a strengthened perception of brand authenticity through existential cues that reflect the cues of other aspects (iconic and indexical) which passed through the goal-based assessment and self-authentication filter.
Research limitations/implications – The research sampling population can be more diversified in terms of sociodemographic attributes. Due to the qualitative methodology of this research, assessment of the findings through quantitative methods can be considered in future research. Practical implications – Using the findings of this research, startup managers can properly build a perception of authenticity in their consumers’ minds by using alternate factors while lacking major indexical cues such as heritage. This research helps startup businesses to design their brand communications better to convey their authenticity to their audiences.
Originality/value – This research determines the factors affecting the authenticity of startup brands in social media. It also defines the process of authenticity perception through different aspects of brand authenticity.
Selecting a suitable development method for a specific project context is one of the most challenging activities in process design. To extend the so far statistical construction of hybrid development methods, we analyze 829 data points to investigate which context factors influence the choice of methods or practices. Using exploratory factor analysis, we derive five base clusters consisting of up to 10 methods. Logistic regression analysis then reveals which context factors have an influence on the integration of methods from these clusters in the development process. Our results indicate that only a few context factors including project/product size and target application domain significantly influence the choice. This summary refers to the paper “Determining Context Factors for Hybrid Development Methods with Trained Models”. This paper was published in the proceedings of the International Conference on Software and System Process in 2020.
A lens-based Raman spectrometer is characterized by studying the optical elements in the optical path and we study the measure of aberration–diffraction effects. This is achieved by measuring the spectral resolution (SR) thus encompassing almost all optical elements of a spectrometer that are mostly responsible for such effects. An equation for SR is used to determine the quality factor Q which measures aberration/diffraction effects occurring in a spectrometer. We show how the quality factor changes with different spectrometer parameters such as grating groove density, the wavelength of excitation, pinhole width, charge-coupled device (CCD) pixel density, etc. This work provides an insight into the quality of a spectrometer and helps to monitor the performance of the spectrometer over a certain period. Commercially available spectrometers or home-built spectrometers are prone to misalignment in optical elements and can benefit from this work that allows maintaining the overall quality of the setup. Performing such experiments over a period helps to minimize the aberration/ diffraction effects occurring as a result of time and maintaining the quality of measurements.
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.
Normal breathing during sleep is essential for people’s health and well-being. Therefore, it is crucial to diagnose apnoea events at an early stage and apply appropriate therapy. Detection of sleep apnoea is a central goal of the system design described in this article. To develop a correctly functioning system, it is first necessary to define the requirements outlined in this manuscript clearly. Furthermore, the selection of appropriate technology for the measurement of respiration is of great importance. Therefore, after performing initial literature research, we have analysed in detail three different methods and made a selection of a proper one according to determined requirements. After considering all the advantages and disadvantages of the three approaches, we decided to use the impedance measurement-based one. As a next step, an initial conceptual design of the algorithm for detecting apnoea events was created. As a result, we developed an activity diagram on which the main system components and data flows are visually represented.
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.
Forecasting intermittent and lumpy demand is challenging. Demand occurs only sporadically and, when it does, it can vary considerably. Forecast errors are costly, resulting in obsolescent stock or unmet demand. Methods from statistics, machine learning and deep learning have been used to predict such demand patterns. Traditional accuracy metrics are often employed to evaluate the forecasts, however these come with major drawbacks such as not taking horizontal and vertical shifts over the forecasting horizon into account, or indeed stock-keeping or opportunity costs. This results in a disadvantageous selection of methods in the context of intermittent and lumpy demand forecasts. In our study, we compare methods from statistics, machine learning and deep learning by applying a novel metric called Stock-keeping-oriented Prediction Error Costs (SPEC), which overcomes the drawbacks associated with traditional metrics. Taking the SPEC metric into account, the Croston algorithm achieves the best result, just ahead of a Long Short-Term Memory Neural Network.
The Internet of Things (IoT) is coined by many different standards, protocols, and data formats that are often not compatible to each other. Thus, the integration of different heterogeneous (IoT) components into a uniform IoT setup can be a time-consuming manual task. This lacking interoperability between IoT components has been addressed with different approaches in the past. However, only very few of these approaches rely on Machine Learning techniques. In this work, we present a new way towards IoT interoperability based on Deep Reinforcement Learning (DRL). In detail, we demonstrate that DRL algorithms, which use network architectures inspired by Natural Language Processing (NLP), can be applied to learn to control an environment by merely taking raw JSON or XML structures, which reflect the current state of the environment, as input. Applied to IoT setups, where the current state of a component is often reflected by features embedded into JSON or XML structures and exchanged via messages, our NLP DRL approach eliminates the need for feature engineering and manually written code for pre-processing of data, feature extraction, and decision making.
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.
Fault diagnosis of rolling bearings is an essential process for improving the reliability and safety of the rotating machinery. It is always a major challenge to ensure fault diag- nosis accuracy in particular under severe working conditions. In this article, a deep adversarial domain adaptation (DADA) model is proposed for rolling bearing fault diagnosis. This model con- structs an adversarial adaptation network to solve the commonly encountered problem in numerous real applications: the source domain and the target domain are inconsistent in their distribution. First, a deep stack autoencoder (DSAE) is combined with representative feature learning for dimensionality reduction, and such a combination provides an unsupervised learning method to effectively acquire fault features. Meanwhile, domain adaptation and recognition classification are implemented using a Softmax classifier to augment classification accuracy. Second, the effects of the number of hidden layers in the stack autoencoder network, the number of neurons in each hidden layer, and the hyperparameters of the proposed fault diagnosis algorithm are analyzed. Third, comprehensive analysis is performed on real data to vali- date the performance of the proposed method; the experimental results demonstrate that the new method outperforms the existing machine learning and deep learning methods, in terms of classification accuracy and generalization ability.
The Thirteenth International Conference on Advances in Databases, Knowledge, and Data Applications (DBKDA 2021), held between May 30 – June 3rd, 2021, continued a series of international events covering a large spectrum of topics related to advances in fundamentals on databases, evolution of relation between databases and other domains, data base technologies and content processing, as well as specifics in applications domains databases.
Advances in different technologies and domains related to databases triggered substantial improvements for content processing, information indexing, and data, process and knowledge mining. The push came from Web services, artificial intelligence, and agent technologies, as well as from the generalization of the XML adoption.
High-speed communications and computations, large storage capacities, and load-balancing for distributed databases access allow new approaches for content processing with incomplete patterns, advanced ranking algorithms and advanced indexing methods.
Evolution on e-business, ehealth and telemedicine, bioinformatics, finance and marketing, geographical positioning systems put pressure on database communities to push the ‘de facto’ methods to support new requirements in terms of scalability, privacy, performance, indexing, and heterogeneity of both content and technology.
In various German cities free-floating e-scooter sharing is an upcoming trend in e-mobility. Trends such as climate change, urbanization, demographic change, amongst others are arising and forces the society to develop new mobility solutions. Contrasting the more scientifically explored car sharing, the usage patterns and behaviors of e-scooter sharing customers still need to be analyzed. This presumably enables a better addressing of customers as well as adaptions of the business model to increase scooter utilization and therefore the profit of the e-scooter providers. The customer journey is digitally traceable from registration to scooter reservation and the ride itself. These data enable to identifies customer needs and motivations. We analyzed a dataset from 2017 to 2019 of an e-scooter sharing provider operating in a big German city. Based on the datasets we propose a customer clustering that identifies three different customer segments, enabling to draw multiple conclusions for the business development and improving the problem-solution fit of the e-scooter sharing model.
In a networked world, companies depend on fast and smart decisions, especially when it comes to reacting to external change. With the wealth of data available today, smart decisions can increasingly be based on data analysis and be supported by IT systems that leverage AI. A global pandemic brings external change to an unprecedented level of unpredictability and severity of impact. Resilience therefore becomes an essential factor in most decisions when aiming at making and keeping them smart. In this chapter, we study the characteristics of resilient systems and test them with four use cases in a wide-ranging set of application areas. In all use cases, we highlight how AI can be used for data analysis to make smart decisions and contribute to the resilience of systems.
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.
We report on the cure characterization, based on inline monitoring of the dielectric parameters, of a commercially available epoxy phenol resin molding compound with a high glass transition temperature (>195 °C), which is suitable for the direct packaging of electronic components. The resin was cured under isothermal temperatures close to general process conditions (165–185 °C). The material conversion was determined by measuring the ion viscosity. The change of the ion viscosity as a function of time and temperature was used to characterize the cross-linking behavior, following two separate approaches (model based and isoconversional). The determined kinetic parameters are in good agreement with those reported in the literature for EMCs and lead to accurate cure predictions under process-near conditions. Furthermore, the kinetic models based on dielectric analysis (DEA) were compared with standard offline differential scanning calorimetry (DSC) models, which were based on dynamic measurements. Many of the determined kinetic parameters had similar values for the different approaches. Major deviations were found for the parameters linked to the end of the reaction where vitrification phenomena occur under process-related conditions. The glass transition temperature of the inline molded parts was determined via thermomechanical analysis (TMA) to confirm the vitrification effect. The similarities and differences between the resulting kinetics models of the two different measurement techniques are presented and it is shown how dielectric analysis can be of high relevance for the characterization of the curing reaction under conditions close to series production.
The situation in the markets is changing rapidly and competition in the business sector is increasing rapidly. As a result, corporate marketing decisions are based on creating greater value for the consumer, which creates competitiveness and provides an advantage in competing for future customer loyalty. The purpose of this study is to determine whether there is a link between marketing communication tools and consumer perceived value in pursuit of consumer loyalty. Qualitative (observational research) and quantitative (a questionnaire survey) research methods were used to investigate the problem empirically. The observational research elucidated the value provided to the consumer by the research objects through marketing communication tools, supplementing the key questions for the quantitative study. Correlation and regression analysis were used in the study, with the results showing a statistically significant relationship between marketing communication tools and consumer perceived value in terms of user loyalty. It has also been determined that the greatest and strongest relationship in consumer value creation through marketing communication tools is the appropriate, mutually coordinated and complementary use of a package of marketing communication tools to achieve synergies that create the preconditions for increasing consumer loyalty in a competitive market.
COVID-19 and educational inequality: How school closures affect low- and high-achieving students
(2021)
In spring 2020, governments around the globe shut down schools to mitigate the spread of the novel coronavirus. We argue that low-achieving students may be particularly affected by the lack of educator support during school closures. We collect detailed time-use information on students before and during the school closures in a survey of 1099 parents in Germany. We find that while students on average reduced their daily learning time of 7.4 h by about half, the reduction was significantly larger for low-achievers (4.1 h) than for high-achievers (3.7 h). Low-achievers disproportionately replaced learning time with detrimental activities such as TV or computer games rather than with activities more conducive to child development. The learning gap was not compensated by parents or schools who provided less support for low-achieving students.
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.
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.
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.
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.
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.
Context-aware systems to support actors in the operating room depending on the status of the intervention require knowledge about the current situation in the intra-operative area. In literature, solutions to achieve situation awareness already exist for specific use cases, but applicability and transferability to other conditions are less addressed. It is assumed that a unified solution that can be adapted to different processes and sensors would allow for greater flexibility, applicability, and thus transferability to different applications. To enable a flexible and intervention-independent system, this work proposes a concept for an adaptable situation recognition system. The system consists of four layers with several modular components for different functionalities. The feasibility is demonstrated via prototypical implementation and functional evaluation of a first basic framework prototype. Further development goal is the stepwise extension of the prototype.
The early detection of head and neck cancer is a prolonged challenging task. It requires a precise and accurate identification of tissue alterations as well as a distinct discrimination of cancerous from healthy tissue areas. A novel approach for this purpose uses microspectroscopic techniques with special focus on hyperspectral imaging (HSI) methods. Our proof-of-principle study presents the implementation and application of darkfield elastic light scattering spectroscopy (DF ELSS) as a non-destructive, high-resolution, and fast imaging modality to distinguish lingual healthy from altered tissue regions in a mouse model. The main aspect of our study deals with the comparison of two varying HSI detection principles, which are a point-by-point and line scanning imaging, and whether one might be more appropriate in differentiating several tissue types. Statistical models are formed by deploying a principal component analysis (PCA) with the Bayesian discriminant analysis (DA) on the elastic light scattering (ELS) spectra. Overall accuracy, sensitivity, and precision values of 98% are achieved for both models whereas the overall specificity results in 99%. An additional classification of model-unknown ELS spectra is performed. The predictions are verified with histopathological evaluations of identical HE-stained tissue areas to prove the model’s capability of tissue distinction. In the context of our proof-of-principle study, we assess the Pushbroom PCA-DA model to be more suitable for tissue type differentiations and thus tissue classification. In addition to the HE-examination in head and neck cancer diagnosis, the usage of HSI-based statistical models might be conceivable in a daily clinical routine.
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.
Sichtprüfungen von Produktoberflächen werden überwiegend von Mitarbeitern ausgeführt, wobei Automatisierungsansätze mit Kamera- und Bildverarbeitungssystemen großes Potenzial zeigen. Auch Cobots werden in Qualitätssicherungsprozesse einbezogen.Im Folgenden werden die Integrationsmöglichkeiten von Cobots in die Sichtprüfung diskutiert und ein Entscheidungsmodell dargestellt, mit dem Sichtprüfungsprozesse auf ihre Cobot-Tauglichkeit überprüft werden können. Das Entscheidungsmodell ist für die direkte Integration in bereits existierende Cobot-Eignungsuntersuchungsverfahren konzipiert und dient als erste strategische Entscheidungshilfe.
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.
Classification model of supply chain events regarding their transferability to blockchain technology
(2021)
The blockchain technology represents a decentralized database that stores information securely in immutable data blocks. Regarding supply chain management, these characteristics offer potentials in increasing supply chain transparency, visibility, automation, and efficiency. In this context, first token-based mapping approaches exist to transfer certain supply chain events to the blockchain, such as the creation or assembly of parts as well as their transfer of ownership. However, the decentralized and immutable structure of blockchain technology also creates challenges. In particular, the scalability, storage capacity, and the special requirements for storage formats make it currently impossible to map all supply chain events unrestrictedly on the blockchain. As a first step, this paper identifies important supply chain events for different use cases combining blockchain technology and supply chain management. Secondly, the supply chain events are classified in terms of their expected technical properties and their relevance for the respective use case. Finally, the identified supply chain events are evaluated regarding their transferability to blockchain technology and a classification model is introduced.
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.
A laboratory prototype for hyperspectral imaging in ultra-violet (UV) region from 225 to 400 nm was developed and used to rapidly characterize active pharmaceutical ingredients (API) in tablets. The APIs are ibuprofen (IBU), acetylsalicylic acid (ASA) and paracetamol (PAR). Two sample sets were used for a comparison purpose. Sample set one comprises tablets of 100% API and sample set two consists of commercially available painkiller tablets. Reference measurements were performed on the pure APIs in liquid solutions (transmission) and in solid phase (reflection) using a commercial UV spectrometer. The spectroscopic part of the prototype is based on a pushbroom imager that contains a spectrograph and charge-coupled device (CCD) camera. The tablets were scanned on a conveyor belt that is positioned inside a tunnel made of polytetrafluoroethylene (PTFE) in order to increase the homogeneity of illumination at the sample position. Principal component analysis (PCA) was used to differentiate the hyperspectral data of the drug samples. The first two PCs are sufficient to completely separate all samples. The rugged design of the prototype opens new possibilities for further development of this technique towards real large-scale application.
Energy consumption by air-conditioning is expansive and leads to the emission of millions of tons of CO2 every year. A promising approach to circumvent this problem is the reflection of solar radiation: Rooms that would not heat up by irradiation will not need to be cooled down. Especially, transparent conductive metal oxides exhibit high infrared (IR) reflectivity and are commonly applied as low-emissivity coatings (low-e coatings). Indium tin oxide (ITO) coatings are the state-of-the-art application, though indium is a rare and expensive resource. This work demonstrates that aluminum-doped zinc oxide (AZO) can be a suitable alternative to ITO for IR-reflection applications. AZO synthesized here exhibits better emissivity to be used as roofing membrane coatings for buildings in comparison to commercially available ITO coatings. AZO particles forming the reflective coating are generated via solvothermal synthesis routes and obtain high conductivity and IR reflectivity without the need of any further post-thermal treatment. Different synthesis parameters were studied, and their effects on both conductive and optical properties of the AZO nanoparticles were evaluated. To this end, a series of characterization methods, especially 27Al-nuclear magnetic resonance spectroscopy (27Al-NMR) analysis, have been conducted for a deeper insight into the particles’ structure to understand the differences in conductivity and optical properties. The optimized AZO nanoparticles were coated on flexible transparent textile-based roofing membranes and tested as low-e coatings. The membranes demonstrated higher thermal reflectance compared with commercial ITO materials with an emissivity value lowered by 16%.
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.
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.
The energy sector in Germany, as in many other countries, is undergoing a major transformation. To achieve the climate targets, numerous measures to implement smart energy and resource efficiency are necessary. Therefore, energy companies are experiencing increasing pressure from politics and society to transform their business areas in a sustainable manner and implement smart and sustainable business models. Consequently, numerous resources are expected to flow into the development and implementation of new business models. But often these efforts remain unsuccessful in practice. There is a large amount of literature on barriers and drivers of smart and sustainable business models in the energy sector. But what are the factors that companies struggle with most when developing and implementing new business models in practice? To answer this question, the results of a systematic literature review were evaluated by conducting semi-structured interviews with experts of the German energy sector. Six categories of transformation barriers were identified: Organizational, Financial, Legal, Partner-Network, Societal and Technological barriers. To overcome these barriers, recommendations for action and key success factors are outlined by the experts interviewed. The interview study validates key barriers and drivers in terms of their significance in practice in the German energy sector and makes recommendations to advance the smart and sustainable transformation of the energy sector.
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.
Background: One of the most promising health care development areas is introducing telemedicine services and creating solutions based on blockchain technology. The study of systems combining both these domains indicates the ongoing expansion of digital technologies in this market segment.
Objective: This paper aims to review the feasibility of blockchain technology for telemedicine.
Methods: The authors identified relevant studies via systematic searches of databases including PubMed, Scopus, Web of Science, IEEE Xplore, and Google Scholar. The suitability of each for inclusion in this review was assessed independently. Owing to the lack of publications, available blockchain-based tokens were discovered via conventional web search engines (Google, Yahoo, and Yandex).
Results: Of the 40 discovered projects, only 18 met the selection criteria. The 5 most prevalent features of the available solutions (N=18) were medical data access (14/18, 78%), medical service processing (14/18, 78%), diagnostic support (10/18, 56%), payment transactions (10/18, 56%), and fundraising for telemedical instrument development (5/18, 28%).
Conclusions: These different features (eg, medical data access, medical service processing, epidemiology reporting, diagnostic support, and treatment support) allow us to discuss the possibilities for integration of blockchain technology into telemedicine and health care on different levels. In this area, a wide range of tasks can be identified that could be accomplished based on digital technologies using blockchains.
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
Für die digitale 3D-VR-Fabrikplanung sind unterschiedliche Soft- und Hardwaresysteme am Markt verfügbar, die teilweise erhebliche Kompatibilitätsprobleme aufweisen. Für die Bewertung der Hardwareeignung für die 3D-VR-Fabrikplanung wird ein Bewertungssystem vorgestellt, das anhand konkreter Softwareapplikationen und einem passiven 3D-Stereo-Monitor mit Head-Tracking erläutert wird. Es wird dazu auch die Notwendigkeit des Einsatzes von Software-Middleware zur Nutzungssteigerung diskutiert.
Bedarfe gezielt erheben
(2021)
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.
Gelatin is one of the most prominent biopolymers in biomedical material research and development. It is frequently used in hybrid hydrogels, which combine the advantageous properties of bio‐based and synthetic polymers. To prevent the biological component from leaching out of the hydrogel, the biomolecules can be equipped with azides. Those groups can be used to immobilize gelatin covalently in hydrogels by the highly selective and specific azide–alkyne cycloaddition. In this contribution, we functionalized gelatin with azides at its lysine residues by diazo transfer, which offers the great advantage of only minimal side‐chain extension. Approximately 84–90% of the amino groups are modified as shown by 1H‐NMR spectroscopy, 2,4,6‐trinitrobenzenesulfonic acid assay as well as Fourier‐transform infrared spectroscopy, rheology, and the determination of the isoelectric point. Furthermore, the azido‐functional gelatin is incorporated into hydrogels based on poly(ethylene glycol) diacrylate (PEG‐DA) at different concentrations (0.6, 3.0, and 5.5%). All hydrogels were classified as noncyctotoxic with significantly enhanced cell adhesion of human fibroblasts on their surfaces compared to pure PEG‐DA hydrogels. Thus, the new gelatin derivative is found to be a very promising building block for tailoring the bioactivity of materials.
Autonomisierung von Shopfloor Management : Der Weg vom analogen zum autonomen Shopfloor Management
(2021)
Neue Technologien der Digitalisierung, Vernetzung und künstlichen Intelligenz werden zunehmend auch im Shopfloor Management (SFM) Einzug halten. Dieser Beitrag beschreibt in vier Stufen, wie sich das klassische SFM über das digitale SFM hin zu einem smarten und autonomen SFM entwickeln könnte. Darauf aufbauend wird diskutiert, welche Auswirkungen der Einsatz dieser neuen Technologien auf die operative Gestaltung der Durchführung eines SFM hätte und welche Konsequenzen somit auf Mitarbeiter und Führungskräfte zukommen würden.*)
Purpose: To develop a method for synthesizing a fuzzy automatic control system for a shearer drum in terms of coal seam hypsometry basing on the information criterion of the beginning of rock cutting-off by the drum to reduce ash content of the extracted coal.
Methodology: Taking into consideration peculiarities of determining a distinct information criterion of the beginning of rock cutting-off by the drum and regularities of its variations during the shearer operation, a fuzzy inference algorithm is developed for a system of fuzzy automatic drum control in terms of seam hypsometry. In this context, rules of fuzzy productions, parameters of the membership functions of terms of the output linguistic variable system, and fuzzy operations are substantiated according to the recommendations of a classic Mamdani fuzzy inference algorithm. Studies are carried out to analyze the effi¬ciency of the proposed fuzzy inference algorithm basing on the introduced relative parameter of the number of effective control actions formed by the fuzzy control system. Simulation modeling makes it possible to perform comparative analysis of the efficiency of the drum control.
Findings: In the course of research, an algorithm of fuzzy control of the shearer’s upper drum in terms of coal seam hypsometry has been developed basing on the determination of direct and inverse transfer from coal breaking near the seam roof by the shearer drum to rock breaking with the help of statistical analysis of the stator power of a cutting drive motor.
Originality: For the first time, a method of synthesis of fuzzy automatic control of the drum in terms of seam hypsometry has been proposed.
Practical value: The proposed method is the theoretical basis to solve important scientific and applied problem of the automation of the coal shearer drum in terms of seam hypsometry to reduce ash content of the produced coal.
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.