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User innovators follow multiple diffusion and adoption pathways for their self-developed innovations. Users may choose to commercialize their self-developed products on the marketplace by becoming entrepreneurs. Few studies exist that focus on understanding personal and interpersonal factors that affect some user innovators’ entrepreneurial decision-making. Hence, this paper focuses on how user innovators make key decisions relating to opportunity recognition and evaluation and when opportunity evaluation leads to subsequent entrepreneurial action in the entrepreneurial process. We conducted an exploratory study using a multi-grounded theory methodology as the user entrepreneurship phenomenon embodies complex social processes. We collected data through the netnography approach that targeted 18 entrepreneurs with potentially relevant differences through crowdfunding platforms. We integrated self-determination, human capital, and social capital theory to address the phenomena under study. This study’s significant findings posit that users’ motives are dissatisfaction with existing goods, interest in innovation, altruism, social recognition, desire for independence, and economic benefits. Besides, use-related experience, product-related knowledge, product diffusion, and iterative feedback positively impact innovative users’ entrepreneurial decision-making.
Due to its wide-ranging endocrine functions, adipose tissue influences the whole body’s metabolism. Engineering long-term stable and functional human adipose tissue is still challenging due to the limited availability of suitable biomaterials and adequate cell maturation. We used gellan gum (GG) to create manual and bioprinted adipose tissue models because of its similarities to the native extracellular matrix and its easily tunable properties. Gellan gum itself was neither toxic nor monocyte activating. The resulting hydrogels exhibited suitable viscoelastic properties for soft tissues and were stable for 98 days in vitro. Encapsulated human primary adipose-derived stem cells (ASCs) were adipogenically differentiated for 14 days and matured for an additional 84 days. Live-dead staining showed that encapsulated cells stayed viable until day 98, while intracellular lipid staining showed an increase over time and a differentiation rate of 76% between days 28 and 56. After 4 weeks of culture, adipocytes had a univacuolar morphology, expressed perilipin A, and secreted up to 73% more leptin. After bioprinting establishment, we demonstrated that the cells in printed hydrogels had high cell viability and exhibited an adipogenic phenotype and function. In summary, GG-based adipose tissue models show long-term stability and allow ASCs maturation into functional, univacuolar adipocytes.
Adipose tissue is related to the development and manifestation of multiple diseases, demonstrating the importance of suitable in vitro models for research purposes. In this study, adipose tissue lobuli were explanted, cultured, and used as an adipose tissue control to evaluate in vitro generated adipose tissue models. During culture, lobule exhibited a stable weight, lactate dehydrogenase, and glycerol release over 15 days. For building up in vitro adipose tissue models, we adapted the biomaterial gelatin methacryloyl (GelMA) composition and handling to homogeneously mix and bioprint human primary mature adipocytes (MA) and adipose-derived stem cells (ASCs), respectively. Accelerated cooling of the bioink turned out to be essential for the homogeneous distribution of lipid-filled MAs in the hydrogel. Last, we compared manual and bioprinted GelMA hydrogels with MA or ASCs and the explanted lobules to evaluate the impact of the printing process and rate the models concerning the physiological reference. The viability analyses demonstrated no significant difference between the groups due to additive manufacturing. The staining of intracellular lipids and perilipin A suggest that GelMA is well suited for ASCs and MA. Therefore, we successfully constructed physiological in vitro models by bioprinting MA-containing GelMA bioinks.
Characterization of low density polyethylene greenhouse films during the composting of rose residues
(2022)
This study presents an evaluation of a potential alternative to plastic degradation in the form of organic composting. It stems from the urgent need of finding solutions to the plastic residues and focuses on the compost-based degradation of greenhouse film covers in an important rose exporter company in Ecuador. Thus, this study analyzes the physical, chemical, and biological changes of rose wastes composting, and also evaluates the stability of new and aged agricultural plastic under these conditions. Interestingly, results of compost characterization show a slow degradation rate of organic matter and total organic carbon, along with a significant increase in pH and rise of bacterial populations. However, the results demonstrate that despite these findings, composting conditions had no significant influence on plastic degradation, and while deterioration of aged plastic samples was reported in some tests, it may be the result of environmental conditions and a prolonged exposure to solar radiation. Importantly, these factors could facilitate the adhesion of microorganisms and promote plastic biodegradation. Hence, it is encouraged for future studies to analyze the ecotoxicity of plastics in the compost, as well as isolate, identify, and evaluate the possible biodegradative potential of these microorganisms as an alternative to plastic waste management.
In times of climate change and growing urbanization, the way food is produced and consumed also changes. Meanwhile, digitization is transforming farming practices, which also applies to the domestic growing of crops. More and more so-called smart home farms (SHF) are finding their way into private households. This paper conceptualizes the unique nature of enabled smart services and their underlying technology. Following an inductive interpretive approach, this study explores the antecedents of smart home farming practices. Our sample consists of eleven actual smart home farmers. We found six constructs to be of salient importance: expected outcomes related to harvesting, positive feelings, and sustainability; a combination of one's affinity for green and novel technologies; and the smartness and visibility of the enabled services. In the outlook, we present some preliminary thoughts for testing our qualitative findings.
The proper selection of a demand forecasting method is directly linked to the success of supply chain management (SCM). However, today’s manufacturing companies are confronted with uncertain and dynamic markets. Consequently, classical statistical methods are not always appropriate for accurate and reliable forecasting. Algorithms of Artificial intelligence (AI) are currently used to improve statistical methods. Existing literature only gives a very general overview of the AI methods used in combination with demand forecasting. This paper provides an analysis of the AI methods published in the last five years (2017-2021). Furthermore, a classification is presented by clustering the AI methods in order to define the trend of the methods applied. Finally, a classification of the different AI methods according to the dimensionality of data, volume of data, and time horizon of the forecast is presented. The goal is to support the selection of the appropriate AI method to optimize demand forecasting.
The early involvement of experiences gained through intelligence and data analysis is becoming increasingly important in order to develop new products, leading to a completely different conception of product creation, development and engineering processes using the advantages that the dedication of the digital twin entails. Introducing a novel stage gate process in order to be holistically anchored in learning factories adopting idea generation and idea screening in an early stage, beta testing of first prototypes, technical implementation in real production scenarios, business analysis, market evaluation, pricing, service models as well as innovative social media portals. Corresponding product modelling in the sense of sustainability, circular economy, and data analytics forecasts the product on the market both before and after market launch with the interlinking of data interpretation nearby in real-time. The digital twin represents the link between the digital model and the digital shadow. Additionally, the connection of the digital twin with the product provides constantly updated operating status and process data as well as mapping of technical properties and real-world behaviours. A future-networking product, by embedded information technology with the ability to initiate and carry out one's own further development, is able to interact with people and environments and thus is relevant to the way of life of future generations. In today's development work for this new product creation approach, on one hand, "Werk150" is the object of the development itself and on the other hand the validation environment. In the next step, new learning modules and scenarios for trainings at master level will be derived from these findings.
We study whether compulsory religious education in schools affects students' religiosity as adults. We exploit the staggered termination of compulsory religious education across German states in models with state and cohort fixed effects. Using three different datasets, we find that abolishing compulsory religious education significantly reduced religiosity of affected students in adulthood. It also reduced the religious actions of personal prayer, church-going, and church membership. Beyond religious attitudes, the reform led to more equalized gender roles, fewer marriages and children, and higher labor-market participation and earnings. The reform did not affect ethical and political values or non-religious school outcomes.
We study whether compulsory religious education in schools affects students' religiosity as adults. We exploit the staggered termination of compulsory religious education across German states in models with state and cohort fixed effects. Using three different datasets, we find that abolishing compulsory religious education significantly reduced religiosity of affected students in adulthood. It also reduced the religious actions of personal prayer, church-going, and church membership. Beyond religious attitudes, the reform led to more equalized gender roles, fewer marriages and children, and higher labor-market participation and earnings. The reform did not affect ethical and political values or non-religious school outcomes.
Being exposed to compulsory religious education in school can have long-run consequences for students’ lives. At different points in time since the 1970s, German states terminated compulsory religious education in public schools and replaced it by a choice between ethics classes and religious education. This article shows that the reform not only led to reduced religiosity in students’ later life, but also eroded traditional attitudes towards gender roles and increased labor-market participation and earnings.
Nowadays, the importance of early active patient mobilization in the recovery and rehabilitation phase has increased significantly. One way to involve patients in the treatment is a gamification-like approach, which is one of the methods of motivation in various life processes. This article shows a system prototype for patients who require physical activity because of active early mobilization after medical interventions or during illness. Bedridden patients and people with a sedentary lifestyle (predominantly lying in bed) are also potential users. The main idea for the concept was non-contact system implementation for the patients making them feel effortless during its usage. The system consists of three related parts: hardware, software, and game application. To test the relevance and coherence of the system, it was used by 35 people. The participants were asked to play a video game requiring them to make body movements while lying down. Then they were asked to take part in a small survey to evaluate the system's usability. As a result, we offer a prototype consisting of hardware and software parts that can increase and diversify physical activity during active early mobilization of patients and prevent the occurrence of possible health problems due to predominantly low activity. The proposed design can be possibly implemented in hospitals, rehabilitation centers, and even at home.
Sleep analysis using a Polysomnography system is difficult and expensive. That is why we suggest a non-invasive and unobtrusive measurement. Very few people want the cables or devices attached to their bodies during sleep. The proposed approach is to implement a monitoring system, so the subject is not bothered. As a result, the idea is a non-invasive monitoring system based on detecting pressure distribution. This system should be able to measure the pressure differences that occur during a single heartbeat and during breathing through the mattress. The system consists of two blocks signal acquisition and signal processing. This whole technology should be economical to be affordable enough for every user. As a result, preprocessed data is obtained for further detailed analysis using different filters for heartbeat and respiration detection. In the initial stage of filtration, Butterworth filters are used.
With the progress of technology in modern hospitals, an intelligent perioperative situation recognition will gain more relevance due to its potential to substantially improve surgical workflows by providing situation knowledge in real-time. Such knowledge can be extracted from image data by machine learning techniques but poses a privacy threat to the staff’s and patients’ personal data. De-identification is a possible solution for removing visual sensitive information. In this work, we developed a YOLO v3 based prototype to detect sensitive areas in the image in real-time. These are then deidentified using common image obfuscation techniques. Our approach shows that it is principle suitable for de-identifying sensitive data in OR images and contributes to a privacyrespectful way of processing in the context of situation recognition in the OR.
Theoretical foundation, effectiveness, and design artefact for machine learning service repositories
(2022)
Machine learning (ML) has played an important role in research in recent years. For companies that want to use ML, finding the algorithms and models that fit for their business is tedious. A review of the available literature on this problem indicates only a few research papers. Given this gap, the aim of this paper is to design an effective and easy-to-use ML service repository. The corresponding research is based on a multi-vocal literature analysis combined with design science research, addressing three research questions: (1) How is current white and gray literature on ML services structured with respect to repositories? (2) Which features are relevant for an effective ML service repository? (3) How is a prototype for an effective ML service repository conceptualized? Findings are relevant for the explanation of user acceptance of ML repositories. This is essential for corporate practice in order to create and use ML repositories effectively.
In our initial DaMoN paper, we set out the goal to revisit the results of “Starring into the Abyss [...] of Concurrency Control with [1000] Cores” (Yu in Proc. VLDB Endow 8: 209-220, 2014). Against their assumption, today we do not see single-socket CPUs with 1000 cores. Instead, multi-socket hardware is prevalent today and in fact offers over 1000 cores. Hence, we evaluated concurrency control (CC) schemes on a real (Intel-based) multi-socket platform. To our surprise, we made interesting findings opposing results of the original analysis that we discussed in our initial DaMoN paper. In this paper, we further broaden our analysis, detailing the effect of hardware and workload characteristics via additional real hardware platforms (IBM Power8 and 9) and the full TPC-C transaction mix. Among others, we identified clear connections between the performance of the CC schemes and hardware characteristics, especially concerning NUMA and CPU cache. Overall, we conclude that no CC scheme can efficiently make use of large multi-socket hardware in a robust manner and suggest several directions on how CC schemes and overall OLTP DBMS should evolve in future.
Glioblastoma WHO IV belongs to a group of brain tumors that are still incurable. A promising treatment approach applies photodynamic therapy (PDT) with hypericin as a photosensitizer. To generate a comprehensive understanding of the photosensitizer-tumor interactions, the first part of our study is focused on investigating the distribution and penetration behavior of hypericin in glioma cell spheroids by fluorescence microscopy. In the second part, fluorescence lifetime imaging microscopy (FLIM) was used to correlate fluorescence lifetime (FLT) changes of hypericin to environmental effects inside the spheroids. In this context, 3D tumor spheroids are an excellent model system since they consider 3D cell–cell interactions and the extracellular matrix is similar to tumors in vivo. Our analytical approach considers hypericin as probe molecule for FLIM and as photosensitizer for PDT at the same time, making it possible to directly draw conclusions of the state and location of the drug in a biological system. The knowledge of both state and location of hypericin makes a fundamental understanding of the impact of hypericin PDT in brain tumors possible. Following different incubation conditions, the hypericin distribution in peripheral and central cryosections of the spheroids were analyzed. Both fluorescence microscopy and FLIM revealed a hypericin gradient towards the spheroid core for short incubation periods or small concentrations. On the other hand, a homogeneous hypericin distribution is observed for long incubation times and high concentrations. Especially, the observed FLT change is crucial for the PDT efficiency, since the triplet yield, and hence the O2 activation, is directly proportional to the FLT. Based on the FLT increase inside spheroids, an incubation time 30 min is required to achieve most suitable conditions for an effective PDT.
Even though near-data processing (NDP) can provably reduce data transfers and increase performance, current NDP is solely utilized in read-only settings. Slow or tedious to implement synchronization and invalidation mechanisms between host and smart storage make NDP support for data-intensive update operations difficult. In this paper, we introduce a low-latency cache-coherent shared lock table for update NDP settings in disaggregated memory environments. It utilizes the novel CCIX interconnect technology and is integrated in neoDBMS, a near-data processing DBMS for smart storage. Our evaluation indicates end-to-end lock latencies of ∼80-100ns and robust performance under contention.
Multi-versioning and MVCC are the foundations of many modern DBMSs. Under mixed workloads and large datasets, the creation of the transactional snapshot can become very expensive, as long-running analytical transactions may request old versions, residing on cold storage, for reasons of transactional consistency. Furthermore, analytical queries operate on cold data, stored on slow persistent storage. Due to the poor data locality, snapshot creation may cause massive data transfers and thus lower performance. Given the current trend towards computational storage and near-data processing, it has become viable to perform such operations in-storage to reduce data transfers and improve scalability. neoDBMS is a DBMS designed for near-data processing and computational storage. In this paper, we demonstrate how neoDBMS performs snapshot computation in-situ. We showcase different interactive scenarios, where neoDBMS outperforms PostgreSQL 12 by up to 5×.
The functionality of existing cyber-physical production systems generally focuses on mapping technologic specifications derived from production requirements. Consequently, such systems base their conception on a structurally mechanistic paradigm. Insofar as these approaches have considered humans, their conception likewise is based on the structurally identical paradigm. Due to the fundamental reorientation towards explicitly human-centered approaches, the fact that essential aspects of the dimension "human" remain unconsidered by the previous paradigm becomes more and more apparent. To overcome such limitations, mapping the "social" dimension requires a structurally different approach. In this paper, an anthropocentric approach is developed based on possible conceptions of the human being, enabling a structural integration of the human being in an extended dimension. Through the model, extending concepts for better integration of the human being in the sense of human-centered approaches, as envisioned in the Industrie 5.0 conception, is possible.
Artificial intelligence is a field of research that is seen as a means of realization regarding digitalization and industry 4.0. It is considered as the critical technology needed to drive the future evolution of manufacturing systems. At the same time, autonomous guided vehicles (AGV) developed as an essential part due to the flexibility they contribute to the whole manufacturing process within manufacturing systems. However, there are still open challenges in the intelligent control of these vehicles on the factory floor. Especially when considering dynamic environments where resources should be controlled in such a way, that they can be adjusted to turbulences efficiently. Therefore, this paper aimed to develop a conceptual framework for addressing a catalog of criteria that considers several machine learning algorithms to find the optimal algorithm for the intelligent control of AGVs. By applying the developed framework, an algorithm is automatically selected that is most suitable for the current operation of the AGV in order to enable efficient control within the factory environment. In future work, this decision-making framework can be transferred to even more scenarios with multiple AGV systems, including internal communication along with AGV fleets. With this study, the automatic selection of the optimal machine learning algorithm for the AGV improves the performance in such a way, that computational power is distributed within a hybrid system linking the AGV and cloud storage in an efficient manner.
Modern production systems are characterized by the increasingly use of CPS and IoT networks. However, processing the available information for adaptation and reconfiguration often occurs in relatively large time cycles. It thus does not take advantage of the optimization potential available in the short term. In this paper, a concept is presented that, considering the process information of the individual heterogeneous system elements, detects optimization potentials and performs or proposes adaptation or reconfiguration. The concept is evaluated utilizing a case study in a learning factory. The resulting system thus enables better exploitation of the potentials of the CPPS.
The paradigmatic shift of production systems towards Cyber-Physical Production Systems (CPPSs) requires the development of flexible and decentralized approaches. In this way, such systems enable manufacturers to respond quickly and accurately to changing requirements. However, domain-specific applications require the use of suitable conceptualizations. The issue at hand, when using various conceptualizations is the interoperability of different ontologies. To achieve flexibility and adaptability in CPPSs though requires overcoming interoperability issues within CPPSs. This paper presents an approach to increase flexibility and adaptability in CPPSs while addressing the interoperability issue. In this work, OWL ontologies conceptualize domain knowledge. The Intelligent Manufacturing Knowledge Ontology Repository (IMKOR) connects the domain knowledge in different ontologies. Testing if adaptions in one ontology within the IMKOR provide knowledge to the whole IMKOR. The tests showed, positive results and the repository makes the knowledge available to the whole CPPS. Furthermore, an increase in flexibility and adaptability was noticed.
The imparting of knowledge and skills in STEM education, especially under the influence of the Covid-19 pandemic, is increasingly taking place online and through digital formats. The partially asynchronous instruction eliminates, on the one hand, the social relation in the learning process and, on the other hand, the direct experience with physical objects. Here, the digital learning systems provide learning tools and controls to support the learning process on a general basis. Existing methods for simulating physical objects (digital twins) are also used to a minimal extent. The following approach presents a learning system framework that enables individualized learning, including all dimensions (social, physical). Implementing a concept that uses a personalized assistance system to orchestrate the individual learning steps enables efficient and effective learning. Applying the learning system framework exemplifies the STEM education at Reutlingen University in the logistics learning factory Werk150.
The strong demand for a transformation of the textile and fashion industry towards sustainability requires a continuous implementation of the guiding principle of Education for Sustainable Development (ESD) in education and industry [1, 2]. In a first step of the European research project "Sustainable fashion curriculum at textile Universities in Europe - Development, Implementation and Evaluation of a Teaching Module for Educators" (Fashion DIET) a continuing education module shall be created to implement ESD as a guiding principle in university teaching. The research-based teaching and learning materials are delivered through an e-learning portal.
Gamification is one of the recognized methods of motivating people in various life processes, and it has spread to many spheres of life, including healthcare. This article proposes a system design for long-term care patients using the method mentioned. The proposed system aims to increase patient engagement in the treatment and rehabilitation process via gamification. Literature research on available and earlier proposed systems was conducted to develop a suited system design. The primary target group includes bedridden patients and a sedentary lifestyle (predominantly lying in bed). One of the main criteria for selecting a suitable option was its contactless realization for the mentioned target groups in long-term care cases. As a result, we developed the system design for hardware and software that could prevent bedsores and other health problems from occurring because of low activity. The proposed design can be tested in hospitals, nursing homes, and rehabilitation centers.
Determination of accelerometer sensor position for respiration rate detection: initial research
(2022)
Continuous monitoring of a patient's vital signs is essential in many chronic illnesses. The respiratory rate (RR) is one of the vital signs indicating breathing diseases. This article proposes the initial investigation for determining the accelerometric sensor position of a non-invasive and unobtrusive respiratory rate monitoring system. This research aims to determine the sensor position in relation to the patient, which can provide the most accurate values of the mentioned physiological parameter. In order to achieve the result, the particular system setup, including a mechanical sensor holder construction was used. The breathing signals from 5 participants were analyzed corresponding to the relaxed state. The main criterion for selecting a suitable sensor position was each patient's average acceleration amplitude excursion, which corresponds to the respiratory signal. As a result, we provided one more defined important parameter for the considered system, which was not determined before.
The respiratory rate is a vital sign indicating breathing illness. It is necessary to analyze the mechanical oscillations of the patient's body arising from chest movements. An inappropriate holder on which the sensor is mounted, or an inappropriate sensor position is some of the external factors which should be minimized during signal registration. This paper considers using a non-invasive device placed under the bed mattress and evaluates the respiratory rate. The aim of the work is the development of an accelerometer sensor holder for this system. The normal and deep breathing signals were analyzed, corresponding to the relaxed state and when taking deep breaths. The evaluation criterion for the holder's model is its influence on the patient's respiratory signal amplitude for each state. As a result, we offer a non-invasive system of respiratory rate detection, including the mechanical component providing the most accurate values of mentioned respiratory rate.
Gender pay gaps are commonly studied in populations with already completed educational careers. We focus on an earlier stage by investigating the gender pay gap among university students working alongside their studies. With data from five cohorts of a large-scale student survey from Germany, we use regression and wage decomposition techniques to describe gender pay gaps and potential explanations. We find that female students earn about 6% less on average than male students, which reduces to 4.1% when accounting for a rich set of explanatory variables. The largest explanatory factor is the type of jobs male and female students pursue.
Compared to the automotive sector, where automation is the rule, in many other less standardized sectors automation is still the exception. This could soon hurt the productivity of industrialized countries, where the unemployment is low and the population is aging. Phenomena like the recent downfall in productivity, due to lockdowns and social distancing for prevention of health hazards during the COVID19 pandemic, only add to the problem. For these reasons, the relevance, motivation and intention for more automation in less standardized sectors has probably never been higher. However, available statistics say that providers and users of technologies struggle to bring more automation into action in automation-unfriendly sectors. In this paper, we present a decision support method for investment in automation that tackles the problem: the STIC analysis. The method takes a holistic and quantitative approach tying together technological, context-related and economic input parameters and synthetizing them in a final economic indicator. Thanks to the modelling of such parameters, it is possible to gain sensibility on the technological and/or process adjustments that would have the highest impact on the efficiency of the automation, thereby delivering value for both technology users and technology providers.
Literature reviews are essential for any scientific work, both as part of a dissertation or as a stand-alone work. Scientists benefit from the fact that more and more literature is available in electronic form, and finding and accessing relevant literature has become more accessible through scientific databases. However, a traditional literature review method is characterized by a highly manual process, while technologies and methods in big data, machine learning, and text mining have advanced. Especially in areas where research streams are rapidly evolving, and topics are becoming more comprehensive, complex, and heterogeneous, it is challenging to provide a holistic overview and identify research gaps manually. Therefore, we have developed a framework that supports the traditional approach of conducting a literature review using machine learning and text mining methods. The framework is particularly suitable in cases where a large amount of literature is available, and a holistic understanding of the research area is needed. The framework consists of several steps in which the critical mind of the scientist is supported by machine learning. The unstructured text data is transformed into a structured form through data preparation realized with text mining, making it applicable for various machine learning techniques. A concrete example in the field of smart cities makes the framework tangible.
The rapid development and growth of knowledge has resulted in a rich stream of literature on various topics. Information systems (IS) research is becoming increasingly extensive, complex, and heterogeneous. Therefore, a proper understanding and timely analysis of the existing body of knowledge are important to identify emerging topics and research gaps. Despite the advances of information technology in the context of big data, machine learning, and text mining, the implementation of systematic literature reviews (SLRs) is in most cases still a purely manual task. This might lead to serious shortcomings of SLRs in terms of quality and time. The outlined approach in this paper supports the process of SLRs with machine learning techniques. For this purpose, we develop a framework with embedded steps of text mining, cluster analysis, and network analysis to analyze and structure a large amount of research literature. Although the framework is presented using IS research as an example, it is not limited to the IS field but can also be applied to other research areas.
Data governance have been relevant for companies for a long time. Yet, in the broad discussion on smart cities, research on data governance in particular is scant, even though data governance plays an essential role in an environment with multiple stakeholders, complex IT structures and heterogeneous processes. Indeed, not only can a city benefit from the existing body of knowledge on data governance, but it can also make the appropriate adjustments for its digital transformation. Therefore, this literature review aims to spark research on urban data governance by providing an initial perspective for future studies. It provides a comprehensive overview of data governance and the relevant facets embedded in this strand of research. Furthermore, it provides a fundamental basis for future research on the development of an urban data governance framework.
For a holistic assessment of the interaction between the human body and tight fitted clothing, it is necessary to consider the mechanical properties of the body. Default avatars in CAD software are usually solid and do not take this interaction into account. For this purpose, a solid avatar is converted to a deformable one by using the soft body physics implementation in the simulation program Blender. The fit of a 3D garment on both avatars are compared, which allows a first evaluation of the differences between these approaches.
When wearing compressive garments, the tissue of the human body is altered in relation to its natural shape by the properties of the applied material and by the pattern construction used.
To check the fit of garments, both construction and selected materials can be virtually simulated in 3D on avatars in corresponding CAD programs before fabrication.
The software Blender allows the modelling of an avatar and to generate in respective to the different tissue zones with their specific properties to adjust them with soft body physics according to the testing of real soft tissue but the models in Blender are mainly using linear springs.
The implementation of human resource (HR) policies often proves troublesome due to the appearance, and stubborn persistence, of gaps in the process. Human resource management (HRM) scholars problematise these gaps and advocate tight implementation to reduce gaps and to ensure the desired impact of policies on organisational performance. Drawing on organisational institutionalism, we contend that gaps in implementing HR policies can actually be productive, as they secure organisational legitimacy, and thus enable organisations to operate viably within several institutional environments. We suggest that different approaches to implementation are needed, some of them premised on accepting sustained implementation gaps. We introduce minimum and moderate implementation approaches, rooted in the notion of decoupling, to complement approaches aimed at tight implementation. Our aim is to support the further development of research based on a richer interpretation of HRM implementation challenges and choices they present for HR managers.
The global demand for resources such as energy, land, or water is constantly increasing. It is therefore not sur- prising that research on the Food-Energy-Water (FEW) nexus has become a scientific as well as a general focus in recent years. A significant increase in publications since 2015 can be observed, and it can be expected that this trend will continue. A multilevel (macro, meso, and micro) perspective is essential, as the FEW nexus has cross- sectoral interdependencies. Several review studies on the FEW nexus can be found in the literature, in general, it can be concluded that the FEW nexus is a multi-disciplinary and complex topic. The studies examined identify essential fields of action for research, policy, and society. However, questions such as what are the main research fields at each level? Is it possible to divide the research into specific clusters? and do the clusters correlate with the levels, and what are the methods of modeling used in the clusters and levels? are still not fully discussed in the literature. An extensive literature review was conducted to get insight into the existing research areas. Especially in such fields as the FEW nexus, the amount of literature can get huge, and a human could get lost analyzing the literature manually. For that, we created word clouds and performed a cluster- and network-analysis to support the selection of most relevant papers for a detailed reading. In 2021, the most publications were published, with 173 publications, which corresponds to a share of 26.6 %. There has been a significant increase since 2015, and it can be expected that this trend will continue in the coming years. Most of the first authors come from the USA (25.4 %), followed by China with 22.4 %. From the word cloud and the top 20 words, which appear in the title and abstract, it can be deduced that the topic water is the most represented. However, the terms system, resource, model, study, change, development, and management also appear to be very important, which indi- cates the importance of a holistic approach to the topic. In total 9 clusters could be identified at the different levels. It can be seen that three clusters form well. For the others, a rather diffuse picture can be observed. In order to find out which topics are hidden behind the individual clusters, 6 publications from each cluster were subjected to a more detailed examination. With these steps, a number of 54 publications were identified for de- tailed consideration. The modeling approaches that are currently being applied in research can be classified into domain-specific tools (e. g. global water models, crop models or global climate models) and into more general tools to perform for example a life cycle analysis, spatial analysis using geographic information system, or system dynamics for a general understanding of the links between the domains. With the domain-specific tools, detailed research questions can be addressed to answer questions for a specific domain. However, these tools have the disadvantage that especially the links between the sectors food, energy, and water are not fully considered. Many implementations that are made today are at lowest level (micro) relate to bounded spatial areas and are derived from macro and meso level goals.
Purpose: Interpretive research in management accounting and control provides rich insights from empirically based studies, but it has been criticised for lacking generalisability and potential subjectivity. On the latter, triangulation is useful, and this paper aims to offer some insights on a triangulation technique thus far not commonly reported in management accounting/control research.
Design/methodology/approach: Drawing on a study of the roles of management accountants in performance management systems, this paper offers some insights from empirical experiences on the use of concept maps as a tool to assist triangulation and improve understanding of complex empirical phenomena.
Findings: The concept maps as utilised revealed additional insights which were not recounted by interviewees during the normal interview time. This is a potentially important finding for consideration of future researchers.
Practical implications: In this paper, how concept maps were used is detailed, and it is hoped that future researchers will find their use beneficial in interview settings.
Originality/value: Thus far, concept maps seem under-utilised in management accounting and control research. This paper gives some initial insights on how they may be used in case study settings.
Public transport maps are typically designed in a way to support route finding tasks for passengers, while they also provide an overview about stations, metro lines, and city-specific attractions. Most of those maps are designed as a static representation, maybe placed in a metro station or printed in a travel guide. In this paper, we describe a dynamic, interactive public transport map visualization enhanced by additional views for the dynamic passenger data on different levels of temporal granularity. Moreover, we also allow extra statistical information in form of density plots, calendar-based visualizations, and line graphs. All this information is linked to the contextual metro map to give a viewer insights into the relations between time points and typical routes taken by the passengers. We also integrated a graph-based view on user-selected routes, a way to interactively compare those routes, an attribute- and property-driven automatic computation of specific routes for one map as well as for all available maps in our repertoire, and finally, also the most important sights in each city are included as extra information to include in a user-selected route. We illustrate the usefulness of our interactive visualization and map navigation system by applying it to the railway system of Hamburg in Germany while also taking into account the extra passenger data. As another indication for the usefulness of the interactively enhanced metro maps we conducted a controlled user experiment with 20 participants.
We present a multitask network that supports various deep neural network based pedestrian detection functions. Besides 2D and 3D human pose, it also supports body and head orientation estimation based on full body bounding box input. This eliminates the need for explicit face recognition. We show that the performance of 3D human pose estimation and orientation estimation is comparable to the state-of-the-art. Since very few data sets exist for 3D human pose and in particular body and head orientation estimation based on full body data, we further show the benefit of particular simulation data to train the network. The network architecture is relatively simple, yet powerful, and easily adaptable for further research and applications.
The hearing contact lens® (HCL) is a new type of hearing aid devices. One of its main components is a piezo-electric actuator. In order to evaluate and maximize the HCL's performance, a model of the HCL coupled to a middle-ear model was developed using finite element approach. The model was validated step by step starting with the HCL only. To validate the HCL model, vibrational measurements on the HCL were performed using a laser-doppler-vibrometer (LDV). Then, a silicone cap was placed onto the HCL to provide an interface between the HCL and the tympanic membrane of the middle-ear model, and additional LDV measurements on temporal bones were performed to validate the coupled model that was used to evaluate the equivalent sound pressure of the HCL. Moreover, a de-eper insight was gained into the contact between the HCL and tympanic membrane and its effects on the HCL performance. The model can be used to investigate the sensitivity of geometrical and material parameters with respect to performance measures of the HCL and evaluate the feedback behavior.
In order to evaluate the performance of different stapes prosthesis types, a coupled finite element (FE) model of human ear was developed. First, the middle-ear FE model was developed and validated using the middle-ear transfer function measurements available in literature including pathological cases. Then, the inner-ear FE model was developed and validated using tonotopy, impedance, and level of cochlea amplification curves from literature. Both models are based on pre-existing research with some improvements and were combined into one coupled FE model. The stapes in the coupled FE ear model was replaced with a model of a stapes prosthesis to create a reconstructed ear model that can be used to estimate how different types of protheses perform relative to each other as well as to the natural ear. This will help in designing of new innovative types of stapes prostheses or any other type of middle-ear prostheses as well as to improve the ones that are already available on the market.
Motivation: Aim of this project is the automatic classification of total hip endoprosthesis (THEP) components in 2D Xray images. Revision surgeries of total hip arthroplasty (THA) are common procedures in orthopedics and trauma surgery. Currently, around 400.000 procedures per year are performed in the United States (US) alone. To achieve the best possible result, preoperative planning is crucial. Especially if parts of the current THEP system are to be retained.
Methods: First, a ground truth based on 76 X-ray images was created: We used an image processing pipeline consisting of a segmentation step performed by a convolutional neural network and a classification step performed by a support vector machine (SVM). In total, 11 classes (5 pans and 6 shafts) shall be classified.
Results: The ground truth generated was of good quality even though the initial segmentation was performed by technicians. The best segmentation results were achieved using a U-net architecture. For classification, SVM architectures performed much better than additional neural networks.
Conclusions: The overall image processing pipeline performed well, but the ground truth needs to be extended to include a broader variability of implant types and more examples per training class.
From the perspective of manufacturing companies, the political, media and economic discourse on decarbonisation in the recent years manifests itself as an increasing social expectation of action. In Germany, in particular, this discourse is also being driven forward by powerful companies, respectively sectors, most notably the automotive industry. Against this background, the present paper examines how German manufacturing companies react to rising societal pressure and emerging policies. It examines which measures the companies have taken or plan to take to reduce their carbon footprint, which aspirations are associated with this and the structural characteristics (company size, energy intensity, and sector) by which these are influenced. A mix methods approach is applied, utilising data gathered from approx. 900 companies in context of the Energy Efficiency Index of German Industry (EEI), along with media research focusing on the announced decarbonisation plans and initiatives. We demonstrate that one-size-serves-all approaches are not suitable to decarbonise industry, as the situation and ambitions differ considerably depending on size, energy intensity and sector. Even though the levels of ambition and urgency are high, micro and energy intensive companies, in particular, are challenged. The present research uncovers a series of questions that call for attention to materialise the ambitions and address the challenges outlined.
An autonomous vehicle is a robotic vehicle with decision and action capability capable of performing assigned tasks without or with minimal human intervention. Autonomous cars have been in development for many years. The Society of Automotive Engineers (SAE International) published in 2014 a classification in five levels of driving automation, with level 0 corresponding to completely manual driving, and level 5 to an ideal dream where the vehicle would be able to navigate entirely autonomously for all missions and in all environments. This work addressed the navigation of an autonomous vehicle in general. We focus on one of the most complex scenarios of the road network and crossing of road intersections. In this paper, the critical features of autonomous intelligent vehicles are reviewed. Furthermore, the associated problems are presented, and the most advanced solutions are derived. This article aims to allow a novice in this field to understand the different facets of localization and perception problems for autonomous vehicles.
Personalized remote healthcare monitoring is in continuous development due to the technology improvements of sensors and wearable electronic systems. A state of the art of research works on wearable sensors for healthcare applications is presented in this work. Furthermore, a state of the art of wearable devices, chest and wrist band and smartwatches available on the market for health and sport monitoring is presented in this paper. Many activity trackers are commercially available. The prices are continuously reducing and the performances are improving, but commercial devices do not provide raw data and are therefore not useful for research purposes.
This paper reviews suggestions for changes to database technology coming from the work of many researchers, particularly those working with evolving big data. We discuss new approaches to remote data access and standards that better provide for durability and auditability in settings including business and scientific computing. We propose ways in which the language standards could evolve, with proof-of-concept implementations on Github.
We address the problem of 3D face recognition based on either 3D sensor data, or on a 3D face reconstructed from a 2D face image. We focus on 3D shape representation in terms of a mesh of surface normal vectors. The first contribution of this work is an evaluation of eight different 3D face representations and their multiple combinations. An important contribution of the study is the proposed implementation, which allows these representations to be computed directly from 3D meshes, instead of point clouds. This enhances their computational efficiency. Motivated by the results of the comparative evaluation, we propose a 3D face shape descriptor, named Evolutional Normal Maps, that assimilates and optimises a subset of six of these approaches. The proposed shape descriptor can be modified and tuned to suit different tasks. It is used as input for a deep convolutional network for 3D face recognition. An extensive experimental evaluation using the Bosphorus 3D Face, CASIA 3D Face and JNU-3D Face datasets shows that, compared to the state of the art methods, the proposed approach is better in terms of both computational cost and recognition accuracy.
A closed-loop control for a cooperative innovation culture in interorganizational R&D projects
(2022)
Since project managers only have a limited authority in interorganizational R&D projects a cooperative innovation culture is essential for team cohesion and thus for achieving project scope in time and cost. For its development different factors depending on underlying values are essential. These factors must be learned iteratively by the project members so that they are living the values of a cooperative innovation culture. Hence, this paper raises the following research question: “How to control living the values of a cooperative innovation culture in interorganizational R&D projects?” To answer this question, a closed-loop control for a cooperative innovation culture is developed. The developed closed-loop control system includes several different functional units which show essential roles and several different variables which show what to consider and design in the control system. In addition, the developed closed-loop control system is generalized for other types of projects such as intraorganizational projects.
Since project managers still face problems in managing interorganizational R&D projects, it is a promising approach to manage these projects project-culturally-aware. However, an important prerequisite for a project-culture-aware management is that the involved individual organizations pursue a collaborative strategy. Therefore, our article provides a conceptual approach including a new tool, the Collaborative Iron Triangle, which supports both project sponsors and managers in different phases of the collaboration process to pursue a collaborative strategy in interorganizational R&D projects.
Towards a model for holistic mapping of supply chains by means of tracking and tracing technologies
(2022)
The usage of tracking and tracing technologies not only enables transparency and visibility of supply chains but also offers far-reaching advantages for companies, such as ensuring product quality or reducing supplier risks. Increasing the amount of shared information supports both internal and external planning processes as well as the stability and resilience of globally operating value chains. This paper aims to differentiate and define the functionalities of tracking and tracing technologies that are frequently used interchangeably in literature. Furthermore, this paper incorporates influencing factors impacting a sequencing of the connected world in Industry4.0 supply chain networks. This includes legal influences, the embedment of supply chain-related standards, and new possibilities of emerging technologies. Finally, the results are summarized in a model for the holistic mapping of supply chains by means of tracking and tracing technologies. The resulting technological solutions that can be derived from the model enable companies to address missing elements in order to enable the holistic mapping of supply chain events as well as the transparent representation of a digital shadow throughout the entire supply chain.
Recently, blockchain-based tokens have earned an important role in fields such as the art market or online gaming. First approaches exist, which adopt the potentials of blockchain tokens in supply chain management to increase transparency, visibility, automation, and disintermediation of supply chains. In context, the tokenization of assets in supply chains refers to the practice of creating virtual representations of physical assets on the blockchain. Solutions in supply chain management based on the tokenization of assets vary in terms of application objectives, token types, asset characteristics, as well as the complexities of supply chain events to be mapped on the blockchain. Currently, however, no review exists that summarizes the characteristics of blockchain-based tokens and their scope of applications. This paper provides a clear terminological distinction of existing blockchain token types and therefore distinguishes between fungible tokens, non-fungible tokens, smart non-fungible tokens, and dynamic smart non-fungible tokens. Subsequently, the token types are classified regarding their traceability, modifiability, and authorization to evaluate suitability for mapping assets in supply chains. Given the potential of blockchain in supply chain management, the results of the review serve as a foundation for a practical guide supporting the selection process of suitable token types for industrial applications.
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 manufacturing processes 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 when applying these token-based approaches to dynamic manufacturing processes. As a first step, this paper investigates existing mapping approaches and exemplifies weaknesses regarding their suitability for products with changeable configurations. Secondly, a concept is proposed to overcome these weaknesses by introducing logically coupled tokens embedded into a flexible smart contract structure. Finally, a concept for a token-based architecture is introduced to map manufacturing processes of products with changeable configurations.
Blockchain is a technology for the secure processing and verification of data transactions based on a distributed peer-to-peer network that uses cryptographic processes, consensus algorithms, and backward-linked blocks to make transactions virtually immutable. Within supply chain management, blockchain technology offer potentials in increasing supply chain transparency, visibility, automation, and efficiency. However, its complexity requires future employees to have comprehensive knowledge regarding the functionality of blockchain-based applications in order to be able to apply their benefits to scenarios in supply chain and production. Learning factories represent a suitable environment allowing learners to experience new technologies and to apply them to virtual and physical processes throughout value chains. This paper presents a concept to practically transfer knowledge about the technical functionality of blockchain technology to future engineers and software developers working within supply chains and production operations to sensitize them regarding the advantages of decentralized applications. First, the concept proposes methods to playfully convey immutable backward-linked blocks and the embedment of blockchain smart contracts. Subsequently, the students use this knowledge to develop blockchain-based application scenarios by means of an exemplary product in a learning factory environment. Finally, the developed solutions are implemented with the help of a prototypical decentralized application, which enables a holistic mapping of supply chain events.
The transmembrane Ca2+ − activated Cl− channel - human bestrophin-1 (hBest1) is expressed in retinal pigment epithelium and mutations of BEST1 gene cause ocular degenerative diseases colectivelly referred to as “bestrophinopathies”. A large number of genetical, biochemical, biophysical and molecular biological studies have been performed to understand the relationship between structure and function of the hBest1 protein and its pathophysiological significance. Here, we review the current understanding of hBest1 surface organization, interactions with membrane lipids in model membranes, and its association with microdomains of cellular membranes. These highlights are significant for modulation of channel activity in cells.
Public transport causes in rural areas high costs per passenger and kilometer as the frequency of scheduled busses is low and therefore, many people avoid using public transport. With the trend of moving from urban regions to countryside individual traffic will further increase. To tackle issues of emissions, mobility for young and elderly people and provide economically meaningful public transport a new concept was elaborated in Germany. This consists of (partly) autonomous shuttle busses which are remote controlled. For implementation rural districts of Germany have worked together and set up a three-phase plan consisting of a project with public funding, a highly frequent used pilot region and industrial partners with the commitment and possibilities for necessary investments. The concept promises economical value with respect to installation, service and maintaining costs, it leads to lower barriers for public transport of young and elderly people and ultimately reduces emissions and congestions.
Process risks are omnipresent in the corporate world and repeatedly present organizations with the challenge of how to deal with these risks. Efforts in trying to analyze and prevent these risks are costly and require many resources, which do not always bring the desired added value. The goal of this work is to determine how a benefit-oriented resource allocation can be made for risk-oriented process management. For this purpose, the following research question is posed: "How can systematic prioritization decisions regarding risk-oriented process management be made?” To answer it, an evaluation procedure is developed which assesses processes based on their characteristics regarding potential risk disposition as well as entrepreneurial relevance. For this purpose, requirements for such a procedure are first collected and used to define selection criteria for it. After the detailed analysis of known selection and evaluation procedures, one of them is selected and used for further development. Next steps include the definition of relevant criteria for the evaluation of the processes by examining process characteristics regarding their suitability for process evaluation. The focus here lies on characteristics that provide indications of the risk disposition and business relevance of processes. The result of this approach is a scoring model with a criteria catalog consisting of 15 criteria according to which a process is evaluated. The evaluation result is presented both numerically and in a matrix. This enables the comparison of several processes and a derived prioritization of those for a more in-depth risk analysis. The application of this approach will ensure a benefit-oriented allocation of resources in the management of process risks and increased process reliability.
Perforations of the tympanic membrane (TM) can occur as a result of injury or inflammation of the middle ear. These perforations can lead to conductive hearing loss (HL), where in some cases the magnitude of HL exceeds that attributable to the observed TM perforation alone. We aim with this study to better understand the effects of location and size of TM perforations on the sound transmitting properties of the middle ear.
The middle ear transfer function (METF) of six human temporal bones (TB; freshly frozen specimen of body donors) were compared before and after perforation of the TM at different locations (anterior or posterior lower quadrant) and of different sizes (1mm, ¼ of the TM, ½ of the TM, and full ablation). The
METF were correlated with a Finite Element (FE) model of the middle ear, in which similar alterations were simulated.
The measured and simulated FE model METFs exhibited frequency and perforation size dependent amplitude losses at all locations and severities. In direct comparison, posterior TM perforations affected the transmission properties to a larger degree than perforations of the anterior quadrant. This could possibly be caused by an asymmetry of the TM, where the malleus-incus complex rotates and results in larger deflections in the posterior TM half than in the anterior TM half. The FE model of the TM with a sealed cavity suggest that small perforations result in a decrease of TM rigidity and thus to an increase in oscillation amplitude of the TM, mostly above 1 kHz.
The location and size of TM perforations influence the METF in a reproducible way. Correlating our data with the FE model could help to better understand the pathologic mechanisms of middle-ear diseases. If small TM perforations with uncharacteristically significant HL are observed in daily clinical practice, additional middle ear pathologies should be considered. Further investigations on the loss of TM pretension due to perforations may be informative.
The requirements for textiles differ greatly depending on the area of application, whereby it often does not remain with only one required functionality. For example, in the field of functional clothing or protective clothing/PPE, it is necessary to protect the textile’s wearers from UV radiation. At the same time, self-cleaning effects offer certain advantages in that field. In addition, an antimicrobial effect in functional clothing can reduce the formation of unpleasant odors, and in PPE – especially in the healthcare sector – can contribute to the interruption of the chain of infection. One way to achieve these 3 desired functions in just one finishing step is to immobilize titanium dioxide (TiO2). However, TiO2 is viewed critically for application in the textile sector due to a REACH listing. Another disadvantage is that it only takes effect under UV radiation and is therefore not suitable for indoor use. Alternatively, photocatalysts such as doped zinc oxides (ZnO) can be used, which also exhibit catalytic activity through activation by visible light, which can lead to the killing of microorganisms and the degradation of organic soiling.
Bioactive cations, including calcium, copper and magnesium, have shown the potential to become the alternative to protein growth factor-based therapeutics for bone healing. Ion substitutions are less costly, more stable, and more effective at low concentrations. Although they have been shown to be effective in providing bone grafts with more biological functions, the precise control of ion release kinetics is still a challenge. Moreover, the synergistic effect of three or more metal ions on bone regeneration has rarely been studied. In this study, vaterite-calcite CaCO3 particles were loaded with copper (Cu2+) and magnesium (Mg2+). The polyelectrolyte multilayer (PEM) was deposited on CaCuMg-CO3 particles via layer-by-layer technique to further improve the stability and biocompatibility of the particles and to enable controlled release of multiple metal ions. The PEM coated microcapsules were successfully combined with collagen at the outmost layer, providing a further stimulating microenvironment for bone regeneration. The in vitro release studies showed remarkably stable release of Cu2+ in 2 months without initial burst release. Mg2+ was released in relatively low concentration in the first 7 days. Cell culture studies showed that CaCuMg-PEM-Col microcapsules stimulated cell proliferation, extracellular maturation and mineralization more effectively than blank control and other microcapsules without collagen adsorption (Ca-PEM, CaCu-PEM, CaMg-PEM, CaCuMg-PEM). In addition, the CaCuMg-PEM-Col microcapsules showed positive effects on osteogenesis and angiogenesis in gene expression studies. The results indicate that such a functional and controllable delivery system of multiple bioactive ions might be a safer, simpler and more efficient alternative of protein growth factor-based therapeutics for bone regeneration. It also provides an effective method for functionalizing bone grafts for bone tissue engineering.
In this paper we presented the results of the workshop with the topic: Co-creation in citizen science (CS) for the development of climate adaptation measurements - Which success factors promote, and which barriers hinder a fruitful collaboration and co-creation process between scientists and volunteers? Under consideration of social, motivational, technical/technological and legal factors., which took place at the CitSci2022. We underlined the mentioned factors in the work with scientific literature. Our findings suggest that a clear communication strategy of goals and how citizen scientists can contribute to the project are important. In addition, they have to feel include and that the contribution makes a difference. To achieve this, it is critical to present the results to the citizen scientists. Also, the relationship between scientist and citizen scientists are essential to keep the citizen scientists engaged. Notification of meetings and events needs to be made well in advance and should be scheduled on the attendees' leisure time. The citizen scientists should be especially supported in technical questions. As a result, they feel appreciated and remain part of the project. For legal factors the current General Data Protection Regulation was considered important by the participants of the workshop. For the further research we try to address the individual points and first of all to improve our communication with the citizen scientist about the project goals and how they can contribute. In addition, we should better share the achieved results.
The energy turnaround, digitalization and decreasing revenues forces enterprises in the energy domain to develop new business models. Following a Design Science Research approach, we showed in two action research projects that businesses models in the energy domain result in complex ecosystems with multiple actors. Additionally, we identified that municipal utilities have problems with the systematic development of business models. In order to solve the problem, we captured together with the partners of the enterprises the requirements in a second phase. Further we developed a method which consist of the following components: Method for the creative development of a new business model in form of a Business Model Canvas (BMC). A mapping between the e3Value ontology and the BMC for modelling a business ecosystem. The Business Model Configurator (BMConfig) prototype for modelling and simulating the e3Value-Ontology. The Business model can be quantified and analyzed for its viability. We demonstrate the feasibility of our approach in business model of a power community.
The usage of webinars is witnessing a resurgence in popularity, especially since the COVID-19 pandemic. It is now revealing itself to be an actual shift in how buyers and sellers do business in the future. Therefore, this study aims to investigate whether webinars will continue to be a valuable tool in B2B sales in the future. Specifically, it aims to gain a deeper understanding of how the role of webinars evolved in recent years and analyze its future potential.
Webinars are an essential tool for a wide variety of different use cases. While they have been around for over a decade, webinars recently have seen a resurgence in popularity. As the COVID-19 pandemic strictly limited contact between people and made them work from home as an only option, hosting and participating in webinars has become more common than ever - whether in business, education, or leisure.
Webinars can be effective for various purposes as they are held in real-time and allow multiple engagement opportunities between attendees and hosts. Moreover, because of their remote nature, webinars are more cost-effective and time-saving in organizing and supervising. Consequently, it is cheaper and more convenient to reach your desired target group as a webinar host than to hold a seminar in physical form. Among other reasons, convenience and interaction seem to be the most potent aspects of cementing webinars as a tool in the digital world. Nevertheless, where exactly are they used, and how do they create value in their respective usage fields?
Social commerce
(2022)
The concept of “social commerce” refers to a synthesis of e-commerce and social media. Despite the rapid growth in users of and revenues from social commerce, so far little in-depth research has been conducted in Germany on this topic. The goal here, therefore, is to conduct schematic research of the literature and an online survey of German consumers to analyze the potential opportunities for companies.
This article aims to give an overview of what German business needs in current times. By illustrating the Made in Germany label as a perceived image in sales, specific attributes are being evaluated to explain better the challenges German businesses are currently facing: Digitization, Education, Environment, and Quality & China.
Due to the growing importance of videos for B2B sales outreach, this study proposes an extended model based on the Corporate-Video Model by Büsching and Meidel (2016) and a qualitative survey with high-ranking company representatives. The findings comprise seven complementary categories: structure, communication, product display, information content, unique selling proposition, value-based selling, and dramaturgy of product videos. The model extension aids practitioners in analyzing and conceptualizing compelling B2B product videos.
Modifying the natural characteristics of PLA 3D-printed models is of interest in various research areas in which 3D-printing is applied. Thus, in this study, we describe the simple impregnation of FDM 3D-printed PLA samples with well-defined silver nanoparticles and an iron metal salt. Quasi-spherical and dodecahedra silver particles were strongly attached at the channels of 3D-printed milli-fluidic reactors to demonstrate their attachment and interaction with the flow, as an example. Furthermore, Fenton-like reactions were successfully developed by an iron catalyst impregnated in 3D-printed stirrer caps to induce the degradation of a dye and showed excellent reproducibility.
To generate greater value faster from digital innovation, many companies are increasing how much they learn from their own innovation efforts. However, in many companies, these changes are limited to one stakeholder group: innovation teams. Two other stakeholder groups, senior executives and experts from corporate functions, also need to learn from digital innovation initiatives. We have defined three learning imperatives that address a company’s needs to learn continually about building (1) a successful innovation, (2) a portfolio of initiatives that realizes strategic objectives faster, and (3) shared resources that propel multiple initiatives. All three imperatives involve collecting data regularly from digital innovation initiatives. In this research briefing we outline the three learning imperatives and provide examples of how companies are pursuing them to achieve strategic objectives more effectively and efficiently.
This research briefing describes the organizational capability of scaling at scale, which we define as enabling multiple digital innovation initiatives to realize bottom-line value from their innovation by leveraging shared resources. We illustrate this concept with a case study from global multi-energy company Repsol, which implemented scaling at scale to cultivate a portfolio of more than 450 initiatives and helped over seventy percent of initiatives to reach the scale-up stage. As a result, over five years Repsol realized €800 million of bottom-line value from digital innovations.
Home health applications have evolved over the last few decades. Assistive systems such as a data platform in connection with health devices can allow for health-related data to be automatically transmitted to a database. However, there remain significant challenges concerning intermodular communication. Central among them is the challenge of achieving interoperability, the ability of devices to communicate and share data with each other. A major goal of this project was to extend an existing data platform (COMES®) and establish working interoperability by connecting assistive devices with differing approaches. We describe this process for a sleep monitoring and a physical exercise device. Furthermore, we aimed to test this setup and the implementation with a data platform in both a laboratory and an in-home setting with 11 elderly participants. The platform modification was realized, and the relevant changes were made so that the incoming data could be processed by the data platform, as well as visually displayed in real-time. Data was recorded by the respective device and transmitted into the data server with minor disruptions. Our observations affirmed that difficulties and data loss are far more likely to occur with increasing technical complexity, in the event of instable internet connection, or when the device setup requires (elderly) subjects to take specific steps for proper functioning. We emphasize the importance for tests and evaluations of home health technologies in real-life circumstances.
The euphoria around microservices has decreased over the years, but the trend of modernizing legacy systems to this novel architectural style is unbroken to date. A variety of approaches have been proposed in academia and industry, aiming to structure and automate the often long-lasting and cost-intensive migration journey. However, our research shows that there is still a need for more systematic guidance. While grey literature is dominant for knowledge exchange among practitioners, academia has contributed a significant body of knowledge as well, catching up on its initial neglect. A vast number of studies on the topic yielded novel techniques, often backed by industry evaluations. However, practitioners hardly leverage these resources. In this paper, we report on our efforts to design an architecture-centric methodology for migrating to microservices. As its main contribution, a framework provides guidance for architects during the three phases of a migration. We refer to methods, techniques, and approaches based on a variety of scientific studies that have not been made available in a similarly comprehensible manner before. Through an accompanying tool to be developed, architects will be in a position to systematically plan their migration, make better informed decisions, and use the most appropriate techniques and tools to transition their systems to microservices.
The majority of people in sub-Saharan Africa (SSA) rely on so-called “paratransit” for their mobility needs. The term refers to a large informal transport sector that runs independent of government, of which 83% comprises minibus taxis (MBT). MBT technology is often old and contribute significantly to climate change with their high carbon dioxide (CO2) emissions. Issues related to sustainability and climate change are becoming more important world-wide and hardly any attention is given to MBTs. Converting the MBTs from internal combustion engines (ICEs) to electric motors could be a possible solution. The existing power grid in SSA is largely based on fossil power plants and is unstable. This can be seen by frequent local power blackouts. To avoid further strain on the existing power grid, it would therefore make sense to charge the electric minibus taxis (eMBTs) through a grid consisting of renewable energies. A mobility map is created via simulations with collected data points of the MBTs. By using this mobility map, the energy demand of the eMBTs is calculated. Furthermore, a region-specific photovoltaic (PV) and wind simulation can be realised based on existing weather data, and a tool to size the supply system to charge the eMBTs is developed after all data has been collected. With the help of this work, it can be determined to what extent renewable energies such as PV and wind power can be used to support the transition from ICEs to electric engines in the MBT sector.
In many cases continuous monitoring of vital signals is required and low intrusiveness is an important requirement. Incorporating monitoring systems in the hospital or home bed could have benefits for patients and caregivers. The objective of this work is the definition of a measurement protocol and the creation of a data set of measurements using commercial and low-cost prototypes devices to estimate heart rate and breathing rate. The experimental data will be used to compare results achieved by the devices and to develop algorithms for feature extraction of vital signals.
The scoring of sleep stages is an essential part of sleep studies. The main objective of this research is to provide an algorithm for the automatic classification of sleep stages using signals that may be obtained in a non-obtrusive way. After reviewing the relevant research, the authors selected a multinomial logistic regression as the basis for their approach. Several parameters were derived from movement and breathing signals, and their combinations were investigated to develop an accurate and stable algorithm. The algorithm was implemented to produce successful results: the accuracy of the recognition of Wake/NREM/REM stages is equal to 73%, with Cohen's kappa of 0.44 for the analyzed 19324 sleep epochs of 30 seconds each. This approach has the advantage of using the only movement and breathing signals, which can be recorded with less effort than heart or brainwave signals, and requiring only four derived parameters for the calculations. Therefore, the new system is a significant improvement for non-obtrusive sleep stage identification compared to existing approaches.
The importance of sleep for human life is enormous. It affects physical, mental, and psychological health. Therefore, it is vital to recognise sleep disorders in a timely manner in order to be able to initiate therapy. There are two methods for measuring sleep-related parameters - objective and subjective. Whether the substitution of a subjective method for an objective one is possible is investigated in this paper. Such replacement may bring several advantages, including increased comfort for the user. To answer this research question, a study was conducted in which 75 overnight recordings were evaluated. The primary purpose of this study was to compare both ways of measurement for total sleep time and sleep efficiency, which are essential parameters for, e.g., insomnia diagnosis and treatment. The evaluation results demonstrated that, on average, there are 32 minutes of difference between the two measurement methods when total sleep time is analysed. In contrast, on average, both measurement methods differ by 7.5% for sleep efficiency measurement. It should also be noted that people typically overestimate total sleep time and efficiency with the subjective method, where the perceived values are measured.
Healthy sleep is required for sufficient restoration of the human body and brain. Therefore, in the case of sleep disorders, appropriate therapy should be applied timely, which requires a prompt diagnosis. Traditionally, a sleep diary is a part of diagnosis and therapy monitoring for some sleep disorders, such as cognitive behaviour therapy for insomnia. To automatise sleep monitoring and make it more comfortable for users, substituting a sleep diary with a smartwatch measurement could be considered. With the aim of providing accurate results, a study with a total of 30 night recordings was conducted. Objective sleep measurement with a Samsung Galaxy Watch 4 was compared with a subjective approach (sleep diary), evaluating the four relevant sleep characteristics: time of getting asleep, wake up time, sleep efficiency (SE), and total sleep time (TST). The performed analysis has demonstrated that the median difference between both measurement approaches was equal to 7 and 3 minutes for a time of getting asleep and wake up time correspondingly, which allows substituting a subjective measurement with a smartwatch. The SE was determined with a median difference between the two measurement methods of 5.22%. This result also implicates a possibility of substitution. Some single recordings have indicated a higher variance between the two approaches. Therefore, the conclusion can be made that a substitution provides reliable results primarily in the case of long-term monitoring. The results of the evaluation of the TST measurement do not allow to recommend substitution of the measurement method.
Recognition of sleep and wake states is one of the relevant parts of sleep analysis. Performing this measurement in a contactless way increases comfort for the users. We present an approach evaluating only movement and respiratory signals to achieve recognition, which can be measured non-obtrusively. The algorithm is based on multinomial logistic regression and analyses features extracted out of mentioned above signals. These features were identified and developed after performing fundamental research on characteristics of vital signals during sleep. The achieved accuracy of 87% with the Cohen’s kappa of 0.40 demonstrates the appropriateness of a chosen method and encourages continuing research on this topic.
In recent decades, it can be observed that a steady increase in the volume of tourism is a stable trend. To offer travel opportunities to all groups, it is also necessary to prepare offers for people in need of long-term care or people with disabilities. One of the ways to improve accessibility could be digital technologies, which could help in planning as well as in carrying out trips. In the work presented, a study of barriers was first conducted, which led to selecting technologies for a test setup after analysis. The main focus was on a mobile app with travel information and 360° tours. The evaluation results showed that both technologies could increase accessibility, but some essential aspects (such as usability, completeness, relevance, etc.) need to be considered when implementing them.
Highly active MgP catalyst for biodiesel production and polyethylene terephthalate depolymerization
(2022)
A highly active heterogeneous catalyst was designed and employed for two relevant transesterification reactions. i. e. biodiesel production and depolymerization of polyethylene terephthalate (PET). The material was prepared in the presence of pectin by the co-precipitation method followed by calcination at 600°C (MgP). MgP is efficient for biodiesel production, with a yield of ≈99% in 6 h/65°C, and with a molar ratio methanol: oil of 21:1. The reference material (MgR, prepared in absence of pectin) showed a poor catalytic performance in the same experimental conditions. For the methanolysis of PET, 100% PET conversion was obtained with 3 wt% catalyst, 200:1 methanol: PET molar ratio at milder conditions 160°C/4 h, compared to a 33% conversion without the presence of a catalyst. The catalyst showed remarkable stability and negligible deactivation after five consecutive runs. Materials were characterized by SEM, XRD, IR, TGA, and BET.
Continuous manufacturing is becoming more important in the biopharmaceutical industry. This processing strategy is favorable, as it is more efficient, flexible, and has the potential to produce higher and more consistent product quality. At the same time, it faces some challenges, especially in cell culture. As a steady state has to be maintained over a prolonged time, it is unavoidable to implement advanced process analytical technologies to control the relevant process parameters in a fast and precise manner. One such analytical technology is Raman spectroscopy, which has proven its advantages for process monitoring and control mostly in (fed-) batch cultivations. In this study, an in-line flow cell for Raman spectroscopy is included in the cell-free harvest stream of a perfusion process. Quantitative models for glucose and lactate were generated based on five cultivations originating from varying bioreactor scales. After successfully validating the glucose model (Root Mean Square Error of Prediction (RMSEP) of ∼0.2 g/L), it was employed for control of an external glucose feed in cultivation with a glucose-free perfusion medium. The generated model was successfully applied to perform process control at 4 g/L and 1.5 g/L glucose over several days, respectively, with variability of ±0.4 g/L. The results demonstrate the high potential of Raman spectroscopy for advanced process monitoring and control of a perfusion process with a bioreactor and scale-independent measurement method.
This article explores the question of how sustainability and labour law are interrelated. The modern world of work is characterised by the growing social and environmental responsibility of companies. Especially in the post-COVID era, sustainability also plays an increasingly important role in the corporate context, which is also noticeable in the so-called ‘war for talent’. Achieving personal career goals is no longer enough for employees today. Corporate values and in particular the so-called ESG criteria (Environment, Social, Governance) are thus also becoming increasingly important in the employment relationship and in corporate reporting requirements. In terms of social sustainability, labour law instruments can, for example, promote the creation of a discrimination-free working environment, the introduction of flexible working time models or the protection of whistleblowers. From an ecological perspective, labour regulations are also suitable for implementing ‘green mobility’ and other measures to reduce companies’ ecological footprints. Working from home, which experienced a huge boom during the COVID-19 pandemic, is also sustainable, especially from an ecological point of view. Appropriate consideration of these sustainable work tools in future corporate social responsibility (CSR) strategies not only creates a competitive advantage but can also be beneficial in recruitment.
Purpose
The purpose of this study is to examine private households’ preferences for service bundles in the German energy market.
Design/methodology/approach
This investigation is based on survey data collected from 3,663 customers of seven mainly municipal energy suppliers in the German energy market. The data set was analyzed via a binary logistic regression model to identify the most prospective customers and their preferences regarding bundles of energy services.
Findings
The results indicate that potential adopters of energy-related service bundles have greater prior knowledge about service bundles; place higher importance on simplified handling, flat rates and long price guarantees; prefer to purchase a service bundle from an energy supplier; live in urban areas and have a gas tariff; are both less likely to have a green electricity tariff and to support the German energy transition; have a greater intention to purchase a smart home product; are less likely to already be prosumers; and prefer customer centers and social media as communication channels with energy providers.
Practical implications
This paper offers several implications for decision-makers in developing marketing strategies for bundled offerings in a highly competitive energy market.
Originality/value
This paper contributes to the sparse research on service bundles in the energy sector, despite the growing interest of energy suppliers and consumers in this topic. It expands the research focusing on the telecommunications sector.
Sleep is essential to existence, much like air, water, and food, as we spend nearly one-third of our time sleeping. Poor sleep quality or disturbed sleep causes daytime solemnity, which worsens daytime activities' mental and physical qualities and raises the risk of accidents. With advancements in sensor and communication technology, sleep monitoring is moving out of specialized clinics and into our everyday homes. It is possible to extract data from traditional overnight polysomnographic recordings using more basic tools and straightforward techniques. Ballistocardiogram is an unobtrusive, non-invasive, simple, and low-cost technique for measuring cardiorespiratory parameters. In this work, we present a sensor board interface to facilitate the communication between force sensitive resistor sensor and an embedded system to provide a high-performing prototype with an efficient signal-to-noise ratio. We have utilized a multi-physical-layer approach to locate each layer on top of another, yet supporting a low-cost, compact design with easy deployment under the bed frame.
Global, competitive markets which are characterised by mass customisation and rapidly changing customer requirements force major changes in production styles and the configuration of manufacturing systems. As a result, factories may need to be regularly adapted and optimised to meet short-term requirements. One way to optimise the production process is the adaptation of the plant layout to the current or expected order situation. To determine whether a layout change is reasonable, a model of the current layout is needed. It is used to perform simulations and in the case of a layout change it serves as a basis for the reconfiguration process. To aid the selection of possible measurement systems, a requirements analysis was done to identify the important parameters for the creation of a digital shadow of a plant layout. Based on these parameters, a method is proposed for defining limit values and specifying exclusion criteria. The paper thus contributes to the development and application of systems that enable an automatic synchronisation of the real layout with the digital layout.
This article explores the determinants of people’s growth prospects in survey data as well as the impact of the European recovery fund to future growth. The focus is on the aftermath of the Corona pandemic, which is a natural limit to the sample size. We use Eurobarometer survey data and macroeconomic variables, such as GDP, unemployment, public deficit, inflation, bond yields, and fiscal spending data. We estimate a variety of panel regression models and develop a new simulation-regression methodology due to limitation of the sample size. We find the major determinant of people’s growth prospect is domestic GDP per capita, while European fiscal aid does not significantly matter. In addition, we exhibit with the simulation-regression method novel scientific insights, significant outcomes, and a policy conclusion alike.
The aim of this work is to establish and generalize a relationship between fractional partial differential equations (fPDEs) and stochastic differential equations (SDEs) to a wider class of stochastic processes, including fractional Brownian motions and sub-fractional Brownian motions with Hurst parameter H ∈ (1/2,1). We start by establishing the connection between a fPDE and SDE via the Feynman-Kac Theorem, which provides a stochastic representation of a general Cauchy problem. In hindsight, we extend this connection by assuming SDEs with fractional and sub-fractional Brownian motions and prove the generalized Feynman-Kac formulas under a (sub-)fractional Brownian motion. An application of the theorem demonstrates, as a by-product, the solution of a fractional integral, which has relevance in probability theory.
Rational behavior is a standard assumption in science. Indeed, rationality is required for environmental action towards net-zero emissions or public health interventions during the SARS-CoV-2 pandemic. Yet, little is known about the elements of rationality. This paper explores a dualism of rationality comprised of optimality and consistency. By designing a new guessing game, we experimentally uncover and disentangle two building blocks of human rationality: the notions of optimality and consistency. We find evidence that rationality is largely associated to optimality and weakly to consistency. Remarkably, under uncertainty, rationality gradually shifts to a heuristic notion. Our findings provide insights to better understand human decision making.
Geometry of music perception
(2022)
Prevalent neuroscientific theories are combined with acoustic observations from various studies to create a consistent geometric model for music perception in order to rationalize, explain and predict psycho-acoustic phenomena. The space of all chords is shown to be a Whitney stratified space. Each stratum is a Riemannian manifold which naturally yields a geodesic distance across strata. The resulting metric is compatible with voice-leading satisfying the triangle inequality. The geometric model allows for rigorous studies of psychoacoustic quantities such as roughness and harmonicity as height functions. In order to show how to use the geometric framework in psychoacoustic studies, concepts for the perception of chord resolutions are introduced and analyzed.
Especially, if the potential of technical and organizational measures for ergonomic workplace design is limited, exoskeletons can be considered as innovative ergonomic aids to reduce the physical workload of workers. Recent scientific findings from ergonomic analyses with and without exoskeletons are indicating that strain reduction can be achieved, particularly at workplaces with lifting, holding, and carrying processes. Currently, a work system design method is under development incorporating criteria and characteristics for the design of work systems in which a human worker is supported by an exoskeleton. Based on the properties of common passive and active exoskeletons, factors influencing the human on which an exoskeleton can have a positive or negative effect (e.g. additional weight) were derived. The method will be validated by the conceptualization and setup of several work system demonstrators at Werk150, the factory of ESB Business School on campus of Reutlingen University, to prove the positive ergonomic effect on humans and the supporting process to choose the suitable exoskeleton. The developed method and demonstrators enable the user to experience the positive ergonomic effects of exoskeletal support in lifting, holding and carrying processes in logistics and production. The new work system design method will contribute to the fact that employees can pursue their professional activity longer without substantial injuries or can be used more flexibly at different work stations. Also new work concepts, strategies and scenarios are opened up to reduce the risk of occupational accidents and to promote the compatibility of work for employees. A training module is being developed and evaluated with participants from industry and master students to build up competence.
Purpose
Context awareness in the operating room (OR) is important to realize targeted assistance to support actors during surgery. A situation recognition system (SRS) is used to interpret intraoperative events and derive an intraoperative situation from these. To achieve a modular system architecture, it is desirable to de-couple the SRS from other system components. This leads to the need of an interface between such an SRS and context-aware systems (CAS). This work aims to provide an open standardized interface to enable loose coupling of the SRS with varying CAS to allow vendor-independent device orchestrations.
Methods
A requirements analysis investigated limiting factors that currently prevent the integration of CAS in today's ORs. These elicited requirements enabled the selection of a suitable base architecture. We examined how to specify this architecture with the constraints of an interoperability standard. The resulting middleware was integrated into a prototypic SRS and our system for intraoperative support, the OR-Pad, as exemplary CAS for evaluating whether our solution can enable context-aware assistance during simulated orthopedical interventions.
Results
The emerging Service-oriented Device Connectivity (SDC) standard series was selected to specify and implement a middleware for providing the interpreted contextual information while the SRS and CAS are loosely coupled. The results were verified within a proof of concept study using the OR-Pad demonstration scenario. The fulfillment of the CAS’ requirements to act context-aware, conformity to the SDC standard series, and the effort for integrating the middleware in individual systems were evaluated. The semantically unambiguous encoding of contextual information depends on the further standardization process of the SDC nomenclature. The discussion of the validity of these results proved the applicability and transferability of the middleware.
Conclusion
The specified and implemented SDC-based middleware shows the feasibility of loose coupling an SRS with unknown CAS to realize context-aware assistance in the OR.
One of the key challenges for automatic assistance is the support of actors in the operating room depending on the status of the procedure. Therefore, context information collected in the operating room is used to gain knowledge about the current situation. In literature, solutions already exist for specific use cases, but it is doubtful to what extent these approaches can be transferred to other conditions. We conducted a comprehensive literature research on existing situation recognition systems for the intraoperative area, covering 274 articles and 95 cross-references published between 2010 and 2019. We contrasted and compared 58 identified approaches based on defined aspects such as used sensor data or application area. In addition, we discussed applicability and transferability. Most of the papers focus on video data for recognizing situations within laparoscopic and cataract surgeries. Not all of the approaches can be used online for real-time recognition. Using different methods, good results with recognition accuracies above 90% could be achieved. Overall, transferability is less addressed. The applicability of approaches to other circumstances seems to be possible to a limited extent. Future research should place a stronger focus on adaptability. The literature review shows differences within existing approaches for situation recognition and outlines research trends. Applicability and transferability to other conditions are less addressed in current work.
Class Phi2 amplifier using GaN HEMTs at 13.56MHz with tuned transformer for wireless power transfer
(2022)
This paper discusses a design procedure of a wireless power transfer system at a RF switching frequency of 13.56MHz. The wireless power transfer amplifier uses GaN HEMTs in aClass phi2 topology and is designed in order to achieve high efficiency and high power density. A design method for the load over a certain bandwidth is presented for a transformer with its tuning network.
Protective welding clothing must meet various requirements. Among other things, it must be flame-resistant, protect against splashes of metal or sparks and also ensure protection against radiant heat and UV light caused by exposure to the welding arc. The protection against molten metal splashes is directly related to the fabric weight per unit area of the protective welding clothing and the level of protection is normally determined by the number of molten metal droplets that fall on the fabric. The higher the weight per unit area, the greater the protection against welding spatter. However, increasing the fabric weight per unit area also leads to psychologically uncomfortable wearing and thus increasing the physical strain on the wearer. The required basis weight per unit area of protective welding clothing can be reduced by applying nanoparticles as a protective layer while preserving other indispensable properties.
Protective welding clothing must meet various requirements. Among other things, it must be flame-resistant, protect against splashes of metal or sparks and also ensure protection against radiant heat and UV light caused by exposure to the welding arc. The protection against molten metal splashes is directly related to the fabric weight per unit area of the protective welding clothing and the level of protection is normally determined by the number of molten metal droplets that fall on the fabric. The higher the weight per unit area, the greater the protection against welding spatter. However, increasing the fabric weight per unit area also leads to psychologically uncomfortable wearing and thus increasing the physical strain on the wearer. The required basis weight per unit area of protective welding clothing can be reduced by applying nanoparticles as a protective layer while preserving other indispensable properties.
Cyanate ester resins
(2022)
Cyanate ester resins are an important class of thermosetting compounds that experience an ever-increasing interest as matrix systems for advanced polymer composite materials, which among other application fields are especially suitable for highly demanding applications in the aerospace or microelectronics industries. Other names for cyanate ester resins are cyanate resins, cyanic esters, or triazine resins. The various types of cyanate ester monomers share the –OCN functional group that trimerizes in the course of resin formation to yield a highly branched heterocyclic polymeric network based on the substituted triazine core structure.
Process analysis and process control have attracted increasing interest in recent years. The development and application of process analytical methods are a prerequisite for the knowledge-based manufacturing of industrial goods and allow for the production of high-value products of defined, constantly good quality. Discussed in this chapter are the measurement principle and some relevant aspects and illustrative examples of online monitoring tools as the basis for process control in the manufacturing and processing of thermosetting resins. Optical spectroscopy is featured as one of the main process analytical methods applicable to, among other applications, online monitoring of resin synthesis. In combination with chemometric methods for multivariate data analysis, powerful process models can be generated within the framework of feedback and feed-forward control concepts. Other analytical methods covered in this chapter are those frequently used to control further processing of thermosets to the final parts, including dielectric analysis, ultrasonics, fiber optics, and Fiber Bragg Grating sensors.
Unsaturated polyester resins (UPR) and vinyl ester resins (VER) are among the most commercially important thermosetting matrix materials for composites. Although comparatively low cost, their technological performance is suitable for a wide range of applications, such as fiber-reinforced plastics, artificial marble or onyx, polymer concrete, or gel coats. The main areas of UPR consumption include the wind energy, marine, pipe and tank, transportation, and construction industries.
This chapter discusses basic UPR and VER chemistry and technology of manufacturing, and consequent applications. Some important properties and performance characteristics are discussed, such as shrinkage behavior, flame retardance, and property modification by nanoparticles. Also briefly introduced and described are the practical aspects of UPR and VER processing, with special emphasis on the most widely used technological approaches, such as hand and spray layup, resin infusion, resin transfer molding, sheet and bulk molding, pultrusion, winding, and centrifugal casting.
Cell migration plays an essential role in wound healing and inflammatory processes inside the human body. Peripheral blood neutrophils, a type of polymorphonuclear leukocyte (PMN), are the first cells to be activated during inflammation and subsequently migrate toward an injured tissue or infection site. This response is dependent on both biochemical signaling and the extracellular environment, one aspect of which includes increased temperature in the tissues surrounding the inflammation site. In our study, we analyzed temperature-dependent neutrophil migration using differentiated HL-60 cells. The migration speed of differentiated HL-60 cells was found to correlate positively with temperature from 30 to 42 °C, with higher temperatures inducing a concomitant increase in cell detachment. The migration persistence time of differentiated HL-60 cells was higher at lower temperatures (30–33 °C), while the migration persistence length stayed constant throughout the temperature range. Coupled with the increased speed observed at high temperatures, this suggests that neutrophils are primed to migrate more effectively at the elevated temperatures characteristic of inflammation. Temperature gradients exist on both cell and tissue scales. Taking this into consideration, we also investigated the ability of differentiated HL-60 cells to sense and react to the presence of temperature gradients, a process known as thermotaxis. Using a two-dimensional temperature gradient chamber with a range of 27–43 °C, we observed a migration bias parallel to the gradient, resulting in both positive and negative thermotaxis. To better mimic the extracellular matrix (ECM) environment in vivo, a three-dimensional collagen temperature gradient chamber was constructed, allowing observation of biased neutrophil-like differentiated HL-60 migration toward the heat source.
Demand forecasting intermittent time series is a challenging business problem. Companies have difficulties in forecasting this particular form of demand pattern. On the one hand, it is characterized by many non-demand periods and therefore classical statistical forecasting algorithms, such as ARIMA, only work to a limited extent. On the other hand, companies often cannot meet the requirements for good forecasting models, such as providing sufficient training data. The recent major advances of artificial intelligence in applications are largely based on transfer learning. In this paper, we investigate whether this method, originating from computer vision, can improve the forecasting quality of intermittent demand time series using deep learning models. Our empirical results show that, in total, transfer learning can reduce the mean square error by 65 percent. We also show that especially short (65 percent reduction) and medium long (91 percent reduction) time series benefit from this approach.
The time has come : application of artificial intelligence in small- and medium-sized enterprises
(2022)
Artificial intelligence (AI) is not yet widely used in small- and medium-sized industrial enterprises (SME). The reasons for this are manifold and range from not understanding use cases, not enough trained employees, to too little data. This article presents a successful design-oriented case study at a medium-sized company, where the described reasons are present. In this study, future demand forecasts are generated based on historical demand data for products at a material number level using a gradient boosting machine (GBM). An improvement of 15% on the status quo (i.e. based on the root mean squared error) could be achieved with rather simple techniques. Hence, the motivation, the method, and the first results are presented. Concluding challenges, from which practical users should derive learning experiences and impulses for their own projects, are addressed.