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This study investigates how integrated reporting (IR) creates value for investors. It examines how providers of financial capital benefit from an improved firm information environment provided by IR. Specifically, this study investigates the effect of voluntary IR disclosure on analyst earnings forecast accuracy as well as on firm value. To do so, we use an international sample of 167 listed companies that voluntarily publish an integrated report. Our analysis shows no significant effect of a voluntary IR publication on analyst earnings forecast accuracy and no significant effect on firm value. We thus do not find evidence for the fulfillment of IR's promises regarding improved information environment and value creation of voluntary adopters. We conclude that such companies might already have a relatively high level of transparency leading to an absent additional effect of IR disclosure. Positive effects of IR appear to be more relevant in environments where IR is mandatory.
Process quality has reached a high level on mass production, utilizing well known methods like the DoE. The drawback of the unterlying statistical methods is the need for tests under real production conditions, which cause high costs due to the lost output. Research over the last decade let to methods for correcting a process by using in-situ data to correct the process parameters, but still a lot of pre-production is necessary to get this working. This paper presents a new approach in improving the product quality in process chains by using context data - which in part are gathered by using Industry 4.0 devices - to reduce the necessary pre-production.
Here, we study resin cure and network formation of solid melamine formaldehyde pre-polymer over a large temperature range viadynamic temperature curing profiles. Real-time infrared spectroscopy is used to analyze the chemical changes during network formation and network hardening. By applying chemometrics (multivariate curve resolution,MCR), the essential chemical functionalities that constitute the network at a given stage of curing are mathematically extracted and tracked over time. The three spectral components identified by MCR were methylol-rich, ether linkages-rich and methylene linkages-rich resin entities. Based on dynamic changes of their characteristic spectral patterns in dependence of temperature, curing is divided into five phases: (I) stationary phase with free methylols as main chemical feature, (II) formation of flexible network cross-linked by ether linkages, (III) formation of rigid, ether-cross-linked network, (IV) further hardening via transformation of methylols and ethers into methylene-cross-linkages, and (V) network consolidation via transformation of ether into methylene bridges. The presented spectroscopic/chemometric approach can be used as methodological basis for the functionality design of MF-based surface films at the stage of laminate pressing, i.e., for tailoring the technological property profile of cured MF films using a causal understanding of the underlying chemistry based on molecular markers and spectroscopic fingerprints.
Different types of raw cotton were investigated by a commercial ultraviolet-visible/near infrared (UV-Vis/NIR) spectrometer (210–2200 nm) as well as on a home-built setup for NIR hyperspectral imaging (NIR-HSI) in the range 1100–2200 nm. UV-Vis/NIR reflection spectroscopy reveals the dominant role proteins, hydrocarbons and hydroxyl groups play in the structure of cotton. NIR-HSI shows a similar result. Experimentally obtained data in combination with principal component analysis (PCA) provides a general differentiation of different cotton types. For UV-Vis/NIR spectroscopy, the first two principal components (PC) represent 82 % and 78 % of the total data variance for the UV-Vis and NIR regions, respectively. Whereas, for NIR-HSI, due to the large amount of data acquired, two methodologies for data processing were applied in low and high lateral resolution. In the first method, the average of the spectra from one sample was calculated and in the second method the spectra of each pixel were used. Both methods are able to explain ≥90 % of total variance by the first two PCs. The results show that it is possible to distinguish between different cotton types based on a few selected wavelength ranges. The combination of HSI and multivariate data analysis has a strong potential in industrial applications due to its short acquisition time and low-cost development. This study opens a novel possibility for a further development of this technique towards real large-scale processes.
Strong optical mode coupling between two adjacent λ/2 Fabry-Pérot microresonators consisting of three parallel silver mirrors is investigated experimentally and theoretically as a function of their detuning and coupling strength. Mode coupling can be precisely controlled by tuning the mirror spacing of one resonator with respect to the other by piezoelectric actuators. Mode splitting, anti-crossing and asymmetric modal damping are observed and theoretically discussed for the symmetric and antisymmetric supermodes of the coupled system. The spectral profile of the supermodes is obtained from the Fourier transform of the numerically calculated time evolution of the individual resonator modes, taking into account their resonance frequencies, damping and coupling constants, and is in excellent agreement with the experiments. Our microresonator design has potential applications for energy transfer between spatially separated quantum systems in micro optoelectronics and for the emerging field of polaritonic chemistry.
Customer orientation should be the core engine of every organisation while IT can be considered as the enabler to generate competitive advantages along customer processes in marketing, sales and service. Research shows that customer relationship management (CRM) enables organisations to perform better and experience indicates that organisations that focus on customer orientation are more successful. With marketplace organisations such as Amazon, Alibaba or Conrad shaping the future of customer centricity and information technology, German B2B organisations need to shift their value contribution from product-centric to customer-centric. While these organisations are currently attempting to implement CRM software and putting their customers more into focus, the question remains how organisations are approaching the implementation of CRM and whether these attempts are paying off in terms of business performance.
We investigate the toxicity of different types and sizes of microplastic particles (0.3–4 mm) under different conditions (new particles, aged particles with biofilm, and particles with adsorbed Tributyltin) on the freshwater amphipod Gammarus fossarum in 3-week exposures. All types of plastic particles, which were randomly taken up to a small extent, were mostly Polyphenylenoxide, Polybutylentherephthalate and Polypropylene, with particles < 1 mm in size. Plastic particles did not affect the feeding and locomotory behaviour of gammarids, and there was no strong difference between pristine plastic particles and aged particles with biofilm. Mortality tended to be higher compared with the control. Tributyltinhydride (TBTH) adsorbed to microplastic particles had no effect on uptake, survival, feeding and locomotory behaviour during the 3 weeks of exposure. Dissolved TBTH, however, was already very toxic after few days of exposure (LC50-96h < 1 ng l–1).
Hardly any software development process is used as prescribed by authors or standards. Regardless of company size or industry sector, a majority of project teams and companies use hybrid development methods (short: hybrid methods) that combine different development methods and practices. Even though such hybrid methods are highly individualized, a common understanding of how to systematically construct synergetic practices is missing. In this article, we make a first step towards a statistical construction procedure for hybrid methods. Grounded in 1467 data points from a large‐scale practitioner survey, we study the question: What are hybrid methods made of and how can they be systematically constructed? Our findings show that only eight methods and few practices build the core of modern software development. Using an 85% agreement level in the participants' selections, we provide examples illustrating how hybrid methods can be characterized by the practices they are made of. Furthermore, using this characterization, we develop an initial construction procedure, which allows for defining a method frame and enriching it incrementally to devise a hybrid method using ranked sets of practice.
Intraoperative brain deformation, so called brain shift, affects the applicability of preoperative magnetic resonance imaging (MRI) data to assist the procedures of intraoperative ultrasound (iUS) guidance during neurosurgery. This paper proposes a deep learning-based approach for fast and accurate deformable registration of preoperative MRI to iUS images to correct brain shift. Based on the architecture of 3D convolutional neural networks, the proposed deep MRI-iUS registration method has been successfully tested and evaluated on the retrospective evaluation of cerebral tumors (RESECT) dataset. This study showed that our proposed method outperforms other registration methods in previous studies with an average mean squared error (MSE) of 85. Moreover, this method can register three 3D MRI-US pair in less than a second, improving the expected outcomes of brain surgery.
With the expansion of cyber-physical systems (CPSs) across critical and regulated industries, systems must be continuously updated to remain resilient. At the same time, they should be extremely secure and safe to operate and use. The DevOps approach caters to business demands of more speed and smartness in production, but it is extremely challenging to implement DevOps due to the complexity of critical CPSs and requirements from regulatory authorities. In this study, expert opinions from 33 European companies expose the gap in the current state of practice on DevOps-oriented continuous development and maintenance. The study contributes to research and practice by identifying a set of needs. Subsequently, the authors propose a novel approach called Secure DevOps and provide several avenues for further research and development in this area. The study shows that, because security is a cross-cutting property in complex CPSs, its proficient management requires system-wide competencies and capabilities across the CPSs development and operation.
The development of new materials that mimic cartilage and its function is an unmet need that will allow replacing the damaged parts of the joints, instead of the whole joint. Polyvinyl alcohol (PVA) hydrogels have raised special interest for this application due to their biocompatibility, high swelling capacity and chemical stability. In this work, the effect of post-processing treatments (annealing, high hydrostatic pressure (HHP) and gamma-radiation) on the performance of PVA gels obtained by cast-drying was investigated and, their ability to be used as delivery vehicles of the anti-inflammatories diclofenac or ketorolac was evaluated. HHP damaged the hydrogels, breaking some bonds in the polymeric matrix, and therefore led to poor mechanical and tribological properties. The remaining treatments, in general, improved the performance of the materials, increasing their crystallinity. Annealing at 150 °C generated the best mechanical and tribological results: higher resistance to compressive and tensile loads, lower friction coefficients and ability to support higher loads in sliding movement. This material was loaded with the anti-inflammatories, both without and with vitamin E (Vit.E) or Vit.E + cetalkonium chloride (CKC). Vit.E + CKC helped to control the release of the drugs which occurred in 24 h. The material did not induce irritability or cytotoxicity and, therefore, shows high potential to be used in cartilage replacement with a therapeutic effect in the immediate postoperative period.
The data presented in this article characterize the thermomechanical and microhardness properties of a novel melamine-formaldehyde resin (MF) intended for the use as a self-healing surface coating. The investigated MF resin is able to undergo reversible crosslinking via Diels Alder reactive groups. The microhardness data were obtained from nanoindentation measurements performed on solid resin film samples at different stages of the self-healing cycle. Thermomechanical analysis was performed under dynamic load conditions. The data provide supplemental material to the manuscript published by Urdl et al. 2020 (https://doi.org/10.1016/j.eurpolymj.2020.109601) on the self-healing performance of this resin, where a more thorough discussion on the preparation, the properties of this coating material and its application in impregnated paper-based decorative laminates can be found.
We investigated the state of artificial intelligence (AI) in pharmaceutical research and development (R&D) and outline here a risk and reward perspective regarding digital R&D. Given the novelty of the research area, a combined qualitative and quantitative research method was chosen, including the analysis of annual company reports, investor relations information, patent applications, and scientific publications of 21 pharmaceutical companies for the years 2014 to 2019. As a result, we can confirm that the industry is in an ‘early mature’ phase of using AI in R&D. Furthermore, we can demonstrate that, despite the efforts that need to be managed, recent developments in the industry indicate that it is worthwhile to invest to become a ‘digital pharma player’.
Pharmaceutical companies are among the top investors into research and development (R&D) globally, as product innovation is still the main growth driver for the industry and because the related complexities necessitate enormous R&D investments. The market demand for new medicines to be more efficacious or to provide better safety than existing drugs and the regulatory need to prove superiority in clinical trials are reasons why drug R&D is increasingly expensive and pharmaceutical companies need to manage extraordinarily high costs per approved new compound.
Entrepreneurship education is becoming increasingly important in higher education and also drives the development of innovative teaching formats, which can increase student engagement. It does, however, need greater international focus to become more attractive for both domestic and international students. This paper presents the examination and course design of two case studies, which promote entrepreneurship education for domestic and international students. These examples show that entrepreneurship courses are attractive due to their focus on interdisciplinarity, experience-based learning, and project-based work. Following a design-based research approach, this paper provides a practical contribution by offering a detailed overview of course design principles, classroom practice and presents reflections and learnings from an iterative development process.
Globalization has increased the number of road trips and vehicles. The result has been an intensification of traffic accidents, which are becoming one of the most important causes of death worldwide. Traffic accidents are often due to human error, the probability of which increases when the cognitive ability of the driver decreases. Cognitive capacity is closely related to the driver’s mental state, as well as other external factors such as the CO2 concentration inside the vehicle. The objective of this work is to analyze how these elements affect driving. We have conducted an experiment with 50 drivers who have driven for 25 min using a driving simulator. These drivers completed a survey at the start and end of the experiment to obtain information about their mental state. In addition, during the test, their stress level was monitored using biometric sensors and the state of the environment (temperature, humidity and CO2 level) was recorded. The results of the experiment show that the initial level of stress and tiredness of the driver can have a strong impact on stress, driving behavior and fatigue produced by the driving test. Other elements such as sadness and the conditions of the interior of the vehicle also cause impaired driving and affect compliance with traffic regulations.
This study investigates empirically the development of working capital management and its impact on profitability and shareholder value in Germany. We analyse panel data of 115 firms listed on the German Prime Standard, covering the period from 2011 to 2017. The results provide evidence that efficient working capital management, indicated by a shorter cash conversion cycle, deteriorated over time, but that a shorter cash conversion has a positive impact on profitability and shareholder value. The findings highlight the need that managers should give greater priority to working capital optimization, even in a low-interest environment. The paper contributes to the literature by advancing this research area in Germany, and it is the first study investigating shareholder relationship with working capital management and all its determinants.
Customer foresight is a relatively new research field. We introduce the customer foresight territory by discussing it localization between customer research and foresight research. For this purposse, we look at a variety of methods that help to understand customers and future realities. On this basis we provide an overwiew of customer foresight methods and outline an ideal-typical research journey.
Customer foresight is a relatively new research field. We introduce the customer foresight territory by discussing its localization between customer research and foresight research. For this purpose, we look at a variety of methods that help to understand customers and future realities. On this basis we provide an overview of customer foresight methods and outline an ideal-typical research journey.
Background. We describe and provide an initial evaluation of the Climate Action Simulation, a simulation-based role playing game that enables participants to learn for themselves about the response of the climate-energy system to potential policies and actions. Participants gain an understanding of the scale and urgency of climate action, the impact of different policies and actions, and the dynamics and interactions of different policy choices.
Intervention. The Climate Action Simulation combines an interactive computer model, En-ROADS, with a role play in which participants make decisions about energy and climate policy. They learn about the dynamics of the climate and energy systems as they discover how En-ROADS responds to their own climate-energy decisions.
Methods. We evaluated learning outcomes from the Climate Action Simulation using pre- and post-simulation surveys as well as a focus group.
Results. Analysis of survey results showed that the Climate Action Simulation increases participants’ knowledge about the scale of emissions reductions and policies and actions needed to address climate change. Their personal and emotional engagement with climate change also grew. Focus group participants were overwhelmingly positive about the Climate Action Simulation, saying it left them feeling empowered to make a positive difference in addressing the climate challenge.
Polycaprolactone (PCL) was electrospun with the addition of arginine (Arg), an α-amino acid that accelerates the haeling process. The efficient needleless electrospinning technique was used for the fabrication of the nanofibrous layers. The materials produced consisted mainly of fibers with diameters of between 200 and 400 nm. Moreover, both microfibers and beads were present within the layers. Higher bead sized were observed with the increased addition of arginine.
In Germany, mobility is currently in a state of flux. Since June 2019, electric kick scooters (e-scooters) have been permitted on the roads, and this market is booming. This study employs a user survey to generate new data, supplemented by expert interviews to determine whether such e-scooters are a climate-friendly means of transport. The environmental impacts are quantified using a life cycle assessment. This results in a very accurate picture of e-scooters in Germany. The global warming potential of an e-scooter calculated in this study is 165 g CO2-eq./km, mostly due to material and production (that together account for 73% of the impact). By switching to e-scooters where the battery is swapped, the global warming potential can be reduced by 12%. The lowest value of 46 g CO2-eq./km is reached if all possibilities are exploited and the life span of e-scooters is increased to 15 months. Comparing these emissions with those of the replaced modal split, e-scooters are at best 8% above the modal split value of 39 g CO2-eq./km.
The use of learning factories for education in maintenance concepts is limited, despite the important role maintenance plays in the effective operation of organizational assets. A training programme in a learning factory environment is presented where a combination of gamification, classroom training and learning factory applications is used to introduce students to the concepts of maintenance plan development, asset failure characteristics and the costs associated with maintenance decision-making. The programme included a practical task to develop a maintenance plan for different advanced manufacturing machines in a learning factory setting. The programme stretched over a four-day period and demonstrated how learning factories can be effectively utilized to teach management related concepts in an interdisciplinary team context, where participants had no, or very limited, previous exposure to these concepts.
It has been widely shown that biomaterial surface topography can modulate host immune response, but a fundamental understanding of how different topographies contribute to pro-inflammatory or anti-inflammatory responses is still lacking. To investigate the impact of surface topography on immune response, we undertook a systematic approach by analyzing immune response to eight grades of medical grade polyurethane of increasing surface roughness in three in vitro models of the human immune system. Polyurethane specimens were produced with defined roughness values by injection molding according to the VDI 3400 industrial standard. Specimens ranged from 0.1 μm to 18 μm in average roughness (Ra), which was confirmed by confocal scanning microscopy. Immunological responses were assessed with THP-1-derived macrophages, human peripheral blood mononuclear cells (PBMCs), and whole blood following culture on polyurethane specimens. As shown by the release of pro-inflammatory and anti-inflammatory cytokines in all three models, a mild immune response to polyurethane was observed, however, this was not associated with the degree of surface roughness. Likewise, the cell morphology (cell spreading, circularity, and elongation) in THP-1-derived macrophages and the expression of CD molecules in the PBMC model on T cells (HLA-DR and CD16), NK cells (HLA-DR), and monocytes (HLA-DR, CD16, CD86, and CD163) showed no influence of surface roughness. In summary, this study shows that modifying surface roughness in the micrometer range on polyurethane has no impact on the pro-inflammatory immune response. Therefore, we propose that such modifications do not affect the immunocompatibility of polyurethane, thereby supporting the notion of polyurethane as a biocompatible material.
Zero or plus energy office buildings must have very high building standards and require highly efficient energy supply systems due to space limitations for renewable installations. Conventional solar cooling systems use photovoltaic electricity or thermal energy to run either a compression cooling machine or an absorption-cooling machine in order to produce cooling energy during daytime, while they use electricity from the grid for the nightly cooling energy demand. With a hybrid photovoltaic-thermal collector, electricity as well as thermal energy can be produced at the same time. These collectors can produce also cooling energy at nighttime by longwave radiation exchange with the night sky and convection losses to the ambient air. Such a renewable trigeneration system offers new fields of applications. However, the technical, ecological and economical aspects of such systems are still largely unexplored.
In this work, the potential of a PVT system to heat and cool office buildings in three different climate zones is investigated. In the investigated system, PVT collectors act as a heat source and heat sink for a reversible heat pump. Due to the reduced electricity consumption (from the grid) for heat rejection, the overall efficiency and economics improve compared to a conventional solar cooling system using a reversible air-to-water heat pump as heat and cold source.
A parametric simulation study was carried out to evaluate the system design with different PVT surface areas and storage tank volumes to optimize the system for three different climate zones and for two different building standards. It is shown such systems are technically feasible today. With a maximum utilization of PV electricity for heating, ventilation, air conditioning and other electricity demand such as lighting and plug loads, high solar fractions and primary energy savings can be achieved.
Annual costs for such a system are comparable to conventional solar thermal and solar electrical cooling systems. Nevertheless, the economic feasibility strongly depends on country specific energy prices and energy policy. However, even in countries without compensation schemes for energy produced by renewables, this system can still be economically viable today. It could be shown, that a specific system dimensioning can be found at each of the investigated locations worldwide for a valuable economic and ecological operation of an office building with PVT technologies in different system designs.
In an increasingly competitive environment, suppliers are now seen as an important source of innovation. Long term partnerships enable companies to access the knowledge of suppliers to optimize their business. "Procurement 4.0" is one of the concepts that come to the fore when talking about digitalization of business processes. The major aim of this research is to discuss a conceptual model of "Procurement 4.0" and its potential to rethink the management of supplier relationships, which will be one of the main future tasks of procurement. The paper is based on a factual-analytical research approach that serves to continuously specify and supplement the elements of the frame of reference: Two challenging concepts, "Procurement 4.0" and Supplier Relationship Management, are merged to contribute to the fact that purchasing is indispensable as an "interface" within a global supply chain to reap the benefits of digitization. The factors that prove to be obstacles to digital supplier relationship management along the digital supplier journey - e.g. lack of guidelines, approaches or tools and a lack of understanding of the importance of long-term relationships - are reflected within the identified technologies of digital transformation. A comprehensive analysis of the given situation within digital supplier relationship management in Germany is provided. The most important digital supplier touchpoints are discussed in order to develop a traditional supplier relationship towards a digital relationship management. Thus, this paper succeeds in illustrating how the innovative concept of a supplier journey can be implemented in practice to counteract the future, entrepreneurial challenges.
Thermoplastic polycarbonate urethane elastomers (TPCU) are potential implant materials for treating degenerative joint diseases thanks to their adjustable rubber-like properties, their toughness, and their durability. We developed a water-containing high-molecular-weight sulfated hyaluronic acid-coating to improve the interaction of TPCU with the synovial fluid. It is suggested that trapped synovial fluid can act as a lubricant that reduces the friction forces and thus provides an enhanced abrasion resistance of TPCU implants. Aims of this work were (i) the development of a coating method for novel soft TPCU with high-molecular sulfated hyaluronic acid to increase the biocompatibility and (ii) the in vitro validation of the functionalized TPCUs in cell culture experiments.
Artificial Intelligence-based Assistants AIAs are spreading quickly both in homes and offices. They already have left their original habitats of "intelligent speakers" providing easy access to music collections. The initiated a multitude of new devices and are already populating devices such as TV sets. Characteristic for the intelligent digital assistants is the formation of platforms around their core functionality. Thus, AIS capabilities of the assistants are used to offer new services and create new interfaces for business processes. There are positive network effects between the assistants and the services as well as within the services. Therefore, many companies see the need to get involved in the field of digital assistants but lack a framework to align their initiatives with their corporate strategies. In order to lay the foundation for a comprehensive method, we are therefore investigating intelligent digital assistants. Based on this analysis, we are developing a framework of strategic opportunities and challenges.
Globalisation, shorter product life cycles, and increasing product varieties have led to complex supply chains. At the same time, there is a growing interest of customers and governments in having a greater transparency of brands, manufacturers, and producers throughout the supply chain. Due to the complex structure of collaborative manufacturing networks, the increase of supply chain transparency is a challenge for manufacturing companies. The blockchain technology offers an innovative solution to increase the transparency, security, authenticity, and auditability of products. However, there are still uncertainties when applying the blockchain technology to manufacturing scenarios and thus enable all stakeholders to trace back each component of an assembled product. This paper proposes a framework design to increase the transparency and auditability of products in collaborative manufacturing networks by adopting the blockchain technology. In this context, each component of a product is marked with a unique identification number generated by blockchain-based smart contracts. In this way, a transparent auditability of assembled products and their components can be achieved for all stakeholders, including the custome.
The shift of populations to cities is creating challenges in many respects, thus leading to increasing demand for smart solutions of urbanization problems. Smart city applications range from technical and social to economic and ecological. The main focus of this work is to provide a systematic literature review of smart city research to answer two main questions: (1) How is current research on smart cities structured? and (2) What directions are relevant for future research on smart cities? To answer these research questions, a text-mining approach is applied to a large number of publications. This provides an overview and gives insights into relevant dimensions of smart city research. Although the main dimensions of research are already described in the literature, an evaluation of the relevance of such dimensions is missing. Findings suggest that the dimensions of environment and governance are popular, while the dimension of economy has received only limited attention.
Monday is unique for its reputation as a “bad” day—one that is characterized by pessimism and reluctance as noted by Rystrom and Benson (Financ Anal J 45(5):75–78, 1989). But the extent to which this applies to stock markets is still in dispute. While early evidence points to a Monday effect leading to negative returns, recent studies tend to suggest its disappearance or reversal.As a replication study, this paper searches for new evidence of this effect in the German stock market.We use data on the German blue-chip index DAX between 2000 and 2017 to test for the presence of a Monday effect by applying regression and controlling with GARCH analysis. The observation period provides a detailed insight into different market phases in one of the most liquid and information efficient international stock markets. Our results contribute no evidence to the persistent existence of a Monday effect on the German stock market. Our analysis is robust against the background of different market sentiments before, during and after the financial crisis.
The key aim of Open Strategy is to open up the process of strategy development to larger groups within and even outside an organization. Furthermore, Open Strategy aims to include broad groups of stakeholders in the various steps of the strategy process. The question at hand is how can Open Strategy be achieved? What approaches can be used? Scenario planning and business wargaming are approaches perceived as relevant tools in the field of strategy and strategic foresight and in the context of Open Strategy because of their participative nature. The aim of this article is to assess to what degree scenario planning and business wargaming can be used in the context of Open Strategy. While these approaches are suitable, their current application limits the number of potential participants. Further research and experimentation in practice with larger groups and/or online approaches, or a combination of both, are needed to explore the potential of scenario planning and business wargaming as tools for Open Strategy.
Melamine-formaldehyde resins are widely used for decorative paper impregnation. Resin properties relevant for impregnation are mainly determined already at the stage of resin synthesis by the applied reaction conditions. Thus, understanding the relationship between reaction conditions and technological properties is important. Response surface methodology based on orthogonal parameter level variations is the most suitable tool to identify and quantify factor effects and deduce causal correlation patterns. Here, two major process factors of MF resin synthesis were systematically varied using such a statistical experimental design. To arrive at resins having a broad range of technological properties, initial pH and M:F ratio were varied in a wide range (pH: 7.9–12.1; M:F ratio: 1:1.5–1:4.5). The impregnation behavior of the resins was modeled using viscosity, penetration rate and residual curing capacity as technological responses. Based on the response surface models, nonlinear and synergistic action of process factors was quantified and a suitable process window for preparing resins with favorable impregnation performance was defined. It was found that low M:F ratios (~1:2–1:2.5) and comparatively high starting pHs (~pH 11) yield impregnation resins with rapid impregnation behavior and good residual curing capacity.
Relationship between a high-performance work system and employee outcomes: a multilevel analysis
(2020)
Although research on high-performance work systems (HPWS) is increasing, there are few studies in which the focus is on whether and how firm-level HPWS affect individual-level employee outcomes. Using social identity theory, we examined the relationship between HPWS and employee outcomes, and the role organizational identification plays as a mediator in this relationship. We used a multilevel research design and collected data at the organizational and individual levels from a sample of 485 employees of 32 companies in Guangdong Province, China. We used Amos 17.0 and hierarchical linear modeling 6.08 software to examine our hypotheses and the theoretical model. Results showed that organizational identification fully mediated the relationship between HPWS and employees’ job performance as well as that between HPWS and their turnover intention. Our findings provide new insights into the relationship between firm-level human resource management and individual-level employee outcomes, and highlight the importance of considering the implementation of HPWS practices to strengthen employees’ identification with the organization and improve their performance.
Predictive maintenance information systems: the underlying conditions and technological aspects
(2020)
Predictive maintenance has the potential to improve the reliability of production and service provisioning. However, there is little knowledge about the proper implementation of predictive maintenance in research and practice. Therefore, we conducted a multi-case study and investigated underlying conditions and technological aspects for implementing a predictive maintenance system and where it leads to. We found that predictive maintenance initiatives are triggered by severe impacts of failures on revenue and profit. Furthermore, successful predictive maintenance initiatives require that pre-conditions are fulfilled: Data must be available and accessible. Very important is also the support by the management. We identified four factors important for the implementation of predictive maintenance. The integration of data is highly facilitated by Cloud-based mechanisms. The detection of events is enabled by advanced analytics. The execution of predictive maintenance operations is supported by data-driven process automation and visualization.
With the continuous development of economy, consumers pay more attention to the demand for personalization clothing. However, the recommendation quality of the existing clothing recommendation system is not enough to meet the user’s needs. When browsing online clothing, facial expression is the salient information to understand the user’s preference. In this paper, we propose a novel method to automatically personalize clothing recommendation based on user emotional analysis. Firstly, the facial expression is classified by multiclass SVM. Next, the user’s multi-interest value is calculated using expression intensity that is obtained by hybrid RCNN. Finally, the multi-interest value is fused to carry out personalized recommendation. The experimental results show that the proposed method achieves a significant improvement over other algorithms.
Participation in fast fashion brands’ clothes recycling plans in an omnichannel retail environment
(2020)
The rise of the fast fashion industry allows more and more people to participate in fashion consumption, but goes along with negative consequences on the environment. To reduce wastage, fast fashion retailers have begun to offer used clothes recycling plans to which customers can submit clothes they no longer wear. Since these recycling plans have mainly been operated in offline stores so far, the rise of omnichannel retailing poses new challenges on retailers with regard to organizing the plan and motivating consumers to participate. On a sample of N=370 Chinese fast fashion consumers, this paper investigates, which factors determine consumers’ willingness to participate in fast fashion brands’ used clothes recycling plans in an omnichannel retailing environment. It finds that consumers’ clothes recycling intention is determined by individual predispositions (environmental attitude, impulsive consumption), as well as by organizational arrangements (channel integration quality), as well as by the outcomes of their interaction (consumer satisfaction, brand identification). Conclusions are drawn, implications for omnichannel fast fashion retailing practice, as well as for further research, derived, and limitations discussed.
Thermoplastic polymers like ethylene-octene copolymer (EOC) may be grafted with silanes via reactive extrusion to enable subsequent crosslinking for advanced biomaterials manufacture. However, this reactive extrusion process is difficult to control and it is still challenging to reproducibly arrive at well-defined products. Moreover, high grafting degrees require a considerable excess of grafting reagent. A large proportion of the silane passes through the process without reacting and needs to be removed at great expense by subsequent purification. This results in unnecessarily high consumption of chemicals and a rather resource-inefficient process. It is thus desired to be able to define desired grafting degrees with optimum grafting efficiency by means of suitable process control. In this study, the continuous grafting of vinyltrimethoxysilane (VTMS) on ethylene-octene copolymer (EOC) via reactive extrusion was investigated. Successful grafting was verified and quantified by 1H-NMR spectroscopy. The effects of five process parameters and their synergistic interactions on grafting degree and grafting efficiency were determined using a face-centered experimental design (FCD). Response surface methodology (RSM) was applied to derive a causal process model and define process windows yielding arbitrary grafting degrees between <2 and >5% at a minimum waste of grafting agent. It was found that the reactive extrusion process was strongly influenced by several second-order interaction effects making this process difficult to control. Grafting efficiencies between 75 and 80% can be realized as long as grafting degrees <2% are admitted.
On the design of an urban data and modeling platform and its application to urban district analyses
(2020)
An integrated urban platform is the essential software infrastructure for smart, sustainable and resilitent city planning, operation and maintenance. Today such platforms are mostly designed to handle and analyze large and heterogeneous urban data sets from very different domains. Modeling and optimization functionalities are usually not part of the software concepts. However, such functionalities are considered crucial by the authors to develop transformation scenarios and to optimized smart city operation. An urban platform needs to handle multiple scales in the time and spatial domain, ranging from long term population and land use change to hourly or sub-hourly matching of renewable energy supply and urban energy demand.
nKV in action: accelerating KVstores on native computational storage with NearData processing
(2020)
Massive data transfers in modern data intensive systems resulting from low data-locality and data-to-code system design hurt their performance and scalability. Near-data processing (NDP) designs represent a feasible solution, which although not new, has yet to see widespread use.
In this paper we demonstrate various NDP alternatives in nKV, which is a key/value store utilizing native computational storage and near-data processing. We showcase the execution of classical operations (GET, SCAN) and complex graph-processing algorithms (Betweenness Centrality) in-situ, with 1.4x-2.7x better performance due to NDP. nKV runs on real hardware - the COSMOS+ platform.
Controlling the surface properties and structure of thin nanosized coatings is of primary importance in diverse engineering and medical applications. Here we report on how the nanostructure, growth mechanism, thickness, roughness, and hydrophilicity of nanocomposites composed of weak natural or strong synthetic polyelectrolytes (PE) can be tailored by graphene oxide (GO) doping. GO reverses the build‐up mechanism affecting the internal structure and the hydrophilicity in a way depending on the type of the PE‐matrix. The extent of GO‐adsorption and its impact on the surface morphology was found to be independent on the type of the underlying PE‐matrix. The nanostructure of the hybrid films is not significantly altered when a single surface‐exposed GO‐layer is deposited, while increasing the number of embedded GO‐layers leads to pronounced surface heterogeneity. These results are expected to have valuable impact on the construction strategies of coatings with tunable surface properties.
This paper studies the impact of financial liquidity on the macro-economy. We extend a classic macroeconomic modeland compute numerical simulations. The model confirms that persistently low inflation can occur despite a high degreeof financial liquidity due to a reallocation of cash, normal and risk-free bonds. In that regard, our model uncovers anexplanation of a flat Phillips curve. Overall, our approach contributes to a rather disregarded matter in macroeconomictheory.
Deep learning-based fabric defect detection methods have been widely investigated to improve production efficiency and product quality. Although deep learning-based methods have proved to be powerful tools for classification and segmentation, some key issues remain to be addressed when applied to real applications. Firstly, the actual fabric production conditions of factories necessitate higher real-time performance of methods. Moreover, fabric defects as abnormal samples are very rare compared with normal samples, which results in data imbalance. It makes model training based on deep learning challenging. To solve these problems, an extremely efficient convolutional neural network, Mobile-Unet, is proposed to achieve the end-to-end defect segmentation. The median frequency balancing loss function is used to overcome the challenge of sample imbalance. Additionally, Mobile-Unet introduces depth-wise separable convolution, which dramatically reduces the complexity cost and model size of the network. It comprises two parts: encoder and decoder. The MobileNetV2 feature extractor is used as the encoder, and then five deconvolution layers are added as the decoder. Finally, the softmax layer is used to generate the segmentation mask. The performance of the proposed model has been evaluated by public fabric datasets and self-built fabric datasets. In comparison with other methods, the experimental results demonstrate that segmentation accuracy and detection speed in the proposed method achieve state-of-the-art performance.
Human bestrophin-1 (hBest1) is a transmembrane Ca2+- dependent anion channel, associated with the transport of Cl−, HCO3- ions, γ-aminobutiric acid (GABA), glutamate (Glu), and regulation of retinal homeostasis. Its mutant forms cause retinal degenerative diseases, defined as Bestrophinopathies. Using both physicochemical - surface pressure/mean molecular area (π/A) isotherms, hysteresis, compressibility moduli of hBest1/sphingomyelin (SM) monolayers, Brewster angle microscopy (BAM) studies, and biological approaches - detergent membrane fractionation, Laurdan (6-dodecanoyl-N,N-dimethyl-2-naphthylamine) and immunofluorescence staining of stably transfected MDCK-hBest1 and MDCK II cells, we report:
1) Ca2+, Glu and GABA interact with binary hBest1/SM monolayers at 35 °C, resulting in changes in hBest1 surface conformation, structure, self-organization and surface dynamics. The process of mixing in hBest1/SM monolayers is spontaneous and the effect of protein on binary films was defined as “fluidizing”, hindering the phase-transition of monolayer from liquid-expanded to intermediate (LE-M) state;
2) in stably transfected MDCK-hBest1 cells, bestrophin-1 was distributed between detergent resistant (DRM) and detergent-soluble membranes (DSM) - up to 30 % and 70 %, respectively; in alive cells, hBest1 was visualized in both liquid-ordered (Lo) and liquid-disordered (Ld) fractions, quantifying protein association up to 35 % and 65 % with Lo and Ld. Our results indicate that the spontaneous miscibility of hBest1 and SM is a prerequisite to diverse protein interactions with membrane domains, different structural conformations and biological functions.
This article examines centralised and decentralised approaches towards managing internationalisation by means of a case study. Reutlingen University (Hochschule Reutlingen), a university of applied sciences in Southern Germany, has three decades of experience in managing internationalisation. Its strongly integrated and hybrid approach combines centralised and decentralised strategies with the aim of achieving responsiveness, innovation, transparency, quality, and goal alignment. Centralisation and decentralisation are manifested on two levels: university versus schools, and school versus individual programmes. Since internationalisation is embedded in virtually all areas of the university’s operations, examples will be provided ranging from administration and marketing to research, international programme management and curricula.
Purpose: Despite growing interest in the intersection of supply chain management (SCM) and management accounting (MA) in the academic debate, there is a lack of understanding regarding both the content and the delimitation of this topic. As of today, no common conceptualization of supply chain management accounting (SCMA) exists. The purpose of this study is to provide an overview of the research foci of SCMA in the scholarly debate of the past two decades. Additionally, it analyzes whether and to what extent the academic discourse of MA in SCs has already found its way into both SCM and MA higher education, respectively.
Design/methodology/approach: A content analysis is conducted including 114 higher education textbooks written in English or in German language.
Findings: The study finds that SC-specific concepts of MA are seldom covered in current textbooks of both disciplines. The authors conclude that although there is an extensive body of scholarly research about SCMA concepts, there is a significant discrepancy with what is taught in higher education textbooks.
Practical implications: There is a large discrepancy between the extensive knowledge available in scholarly research and what we teach in both disciplines. This implies that graduates of both disciplines lack important knowledge and skills in controlling and accounting for SCs. To bring about the necessary change, MA and SCM in higher education must be more integrative.
Originality/value: To the best of the authors knowledge, this study is first of its kind comprising a large textbook sample in both English and German languages. It is the first substantiated assessment of the current state of integration between SCM and MA in higher education.
The evaluation of the effectiveness of different machine learning algorithms on a publicly available database of signals derived from wearable devices is presented with the goal of optimizing human activity recognition and classification. Among the wide number of body signals we choose a couple of signals, namely photoplethysmographic (optically detected subcutaneous blood volume) and tri-axis acceleration signals that are easy to be simultaneously acquired using commercial widespread devices (e.g. smartwatches) as well as custom wearable wireless devices designed for sport, healthcare, or clinical purposes. To this end, two widely used algorithms (decision tree and k-nearest neighbor) were tested, and their performance were compared to two new recent algorithms (particle Bernstein and a Monte Carlo-based regression) both in terms of accuracy and processing time. A data preprocessing phase was also considered to improve the performance of the machine learning procedures, in order to reduce the problem size and a detailed analysis of the compression strategy and results is also presented.
Human adipose-derived stem cells (hASCs) have become an important cell source for the use in tissue engineering and other medical applications. Not every biomaterial is suitable for human cell culture and requires surface modifications to enable cell adhesion and proliferation. Our hypothesis is that chemical surface modifications introduced by low-discharge plasma enhance the adhesion and proliferation of hASCs. Polystyrene (PS) surfaces were modified either by ammonia (NH3), carbon dioxide (CO2) or acrylic acid (AAc) plasma. The results show that the initial cell adhesion is significantly higher on all modified surfaces than on unmodified material as evaluated by bright field microscopy, live/dead staining, total DNA amount and scanning electron microscopy. The formation of focal adhesions was well pronounced on the Tissue Culture PS, NH3-, and CO2 plasma modified samples. The number of matured fibrillar adhesions was significantly higher on NH3 plasmamodified surfaces than on all other surfaces. Our study validates the suitability of chemical plasma activation and represents a method to enhance hASCs adhesion and improved cell expansion. All chemical modification promoted hASCs adhesion and can therefore be used for the modification of different scaffold materials whereby NH3-plasma modified surfaces resulted in the best outcome concerning hASCs adhesion and proliferation.
Improvement of a three-layered in vitro skin model for topical application of irritating substances
(2020)
In the field of skin tissue engineering, the development of physiologically relevant in vitro skin models comprising all skin layers, namely epidermis, dermis, and subcutis, is a great challenge. Increasing regulatory requirements and the ban on animal experiments for substance testing demand the development of reliable and in vivo-like test systems, which enable high-throughput screening of substances. However, the reproducibility and applicability of in vitro testing has so far been insufficient due to fibroblast-mediated contraction. To overcome this pitfall, an advanced 3-layered skin model was developed. While the epidermis of standard skin models showed an 80% contraction, the initial epidermal area of our advanced skin models was maintained. The improved barrier function of the advanced models was quantified by an indirect barrier function test and a permeability assay. Histochemical and immunofluorescence staining of the advanced model showed well-defined epidermal layers, a dermal part with distributed human dermal fibroblasts and a subcutis with round-shaped adipocytes. The successful response of these advanced 3-layered models for skin irritation testing demonstrated the suitability as an in vitro model for these clinical tests: only the advanced model classified irritative and non-irritative substances correctly. These results indicate that the advanced set up of the 3-layered in vitro skin model maintains skin barrier function and therefore makes them more suitable for irritation testing.