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The digitization of factories will be a significant issue for the 2020s. New scenarios are emerging to increase the efficiency of production lines inside the factory, based on a new generation of robots’ collaborative functions. Manufacturers are moving towards data-driven ecosystems by leveraging product lifecycle data from connected goods. Energy-efficient communication schemes, as well as scalable data analytics, will support these various data collection scenarios. With augmented reality, new remote services are emerging that facilitate the efficient sharing of knowledge in the factory. Future communication solutions should generally ensure connectivity between the various production sites spread worldwide and new players in the value chain (e.g., suppliers, logistics) transparent, real-time, and secure. Industry 4.0 brings more intelligence and flexibility to production. Resulting in more lightweight equipment and, thus, offering better ergonomics. 5G will guarantee real-time transmissions with latencies of less than 1 ms. This will provide manufacturers with new possibilities to collect data and trigger actions automatically.
Academic research is vital for innovation and industrial growth. However, a potential burden of processing ever more knowledge could be affecting research output and researchers’ careers. We look at a dataset of researchers who have published in journals in the field of economics during a period of 45 years. For a subset of these researchers, we amass data from journals listed in the EconLit database, supplemented with years of birth from public sources. Our results show an increase in the age of researchers at their first publication, in the number of articles referenced in debut articles, and in the number of co-authors. Simultaneously, we observe a decline in the probability of researchers changing research fields. Our findings extend earlier findings on patents and hint at a burden of knowledge pervading different areas of human progress. Moreover, our results indicate that researchers develop strategies of specialisation to deal with this challenge.
The main aim of presented in this manuscript research is to compare the results of objective and subjective measurement of sleep quality for older adults (65+) in the home environment. A total amount of 73 nights was evaluated in this study. Placing under the mattress device was used to obtain objective measurement data, and a common question on perceived sleep quality was asked to collect the subjective sleep quality level. The achieved results confirm the correlation between objective and subjective measurement of sleep quality with the average standard deviation equal to 2 of 10 possible quality points.
Assistant platforms are becoming a key element for the business model of many companies. They have evolved from assistance systems that provide support when using information (or other) systems to platforms in their own. Alexa, Cortana or Siri may be used with literally thousands of services. From this background, this paper develops the notion of assistant platforms and elaborates a conceptual model that supports businesses in developing appropriate strategies. The model consists of three main building blocks, an architecture that depicts the components as well as the possible layers of an assistant platform, the mechanism that determines the value creation on assistant platforms, and the ecosystem with its network effects, which emerge from the multi-sided nature of assistant platforms. The model has been derived from a literature review and is illustrated with examples of existing assistant platforms. Its main purpose is to advance the understanding of assistant platforms and to trigger future research.
Many modern DBMS architectures require transferring data from storage to process it afterwards. Given the continuously increasing amounts of data, data transfers quickly become a scalability limiting factor. Near-Data Processing and smart/computational storage emerge as promising trends allowing for decoupled in-situ operation execution, data transfer reduction and better bandwidth utilization. However, not every operation is suitable for an in-situ execution and a careful placement and optimization is needed.
In this paper we present an NDP-aware cost model. It has been implemented in MySQL and evaluated with nKV. We make several observations underscoring the need for optimization.
Context:
Test-driven development (TDD) is an agile software development approach that has been widely claimed to improve software quality. However, the extent to which TDD improves quality appears to be largely dependent upon the characteristics of the study in which it is evaluated (e.g., the research method, participant type, programming environment, etc.). The particularities of each study make the aggregation of results untenable.
Objectives:
The goal of this paper is to: increase the accuracy and generalizability of the results achieved in isolated experiments on TDD, provide joint conclusions on the performance of TDD across different industrial and academic settings, and assess the extent to which the characteristics of the experiments affect the quality-related performance of TDD.
Method:
We conduct a family of 12 experiments on TDD in academia and industry. We aggregate their results by means of meta-analysis. We perform exploratory analyses to identify variables impacting the quality-related performance of TDD.
Results:
TDD novices achieve a slightly higher code quality with iterative test-last development (i.e., ITL, the reverse approach of TDD) than with TDD. The task being developed largely determines quality. The programming environment, the order in which TDD and ITL are applied, or the learning effects from one development approach to another do not appear to affect quality. The quality-related performance of professionals using TDD drops more than for students. We hypothesize that this may be due to their being more resistant to change and potentially less motivated than students.
Conclusion:
Previous studies seem to provide conflicting results on TDD performance (i.e., positive vs. negative, respectively). We hypothesize that these conflicting results may be due to different study durations, experiment participants being unfamiliar with the TDD process, or case studies comparing the performance achieved by TDD vs. the control approach (e.g., the waterfall model), each applied to develop a different system. Further experiments with TDD experts are needed to validate these hypotheses.
This paper presents the concept of the system architecture of a flexible cyber-physical factory control system. The system allows the automation of process structures using cyber-physical fractal nodes. These nodes have a functional and independent form and can be clustered to larger structures. This makes it possible to equip the factory with a flexible, freely scalable, modular system. The description of this system architecture and the associated rules and conditions is outlined in the concept.
In recent years, artificial intelligence (AI) has increasingly become a relevant technology for many companies. While there are a number of studies that highlight challenges and success factors in the adoption of AI, there is a lack of guidance for firms on how to approach the topic in a holistic and strategic way. The aim of this study is therefore to develop a conceptual framework for corporate AI strategy. To address this aim, a systematic literature review of a wide spectrum of AI-related research is conducted, and the results are analyzed based on an inductive coding approach. An important conclusion is that companies should consider diverse aspects when formulating an AI strategy, ranging from technological questions to corporate culture and human resources. This study contributes to knowledge by proposing a novel, comprehensive framework to foster the understanding of crucial aspects that need to be considered when using the emerging technology of AI in a corporate context.
The maintenance of railway infrastructure remains a challenge. Data acquisition technologies have evolved because of Industry 4.0, expanding the capabilities of predictive maintenance. Despite the advances, the potential of these emerging technologies has not been fully realised. This paper presents a technology selection framework in support of railway infrastructure predictive maintenance, which is based on qualitative methods. It consists of three stages, including the mapping of the infrastructure characteristics with the identified technologies, the evaluation of the most appropriate technologies, and the sourcing thereof. This presents the collective decision support output of the framework.
Intralogistics operations in automotive OEMs increasingly confront problems of overcomplexity caused by a customer-centred production that requires customisation and, thus, high product variability, short-notice changes in orders and the handling of an overwhelming number of parts. To alleviate the pressure on intralogistics without sacrificing performance objectives, the speed and flexibility of logistical operations have to be increased. One approach to this is to utilise three-dimensional space through drone technology. This doctoral thesis aims at establishing a framework for implementing aerial drones in automotive OEM logistic operations.
As of yet, there is no research on implementing drones in automotive OEM logistic operations. To contribute to filling this gap, this thesis develops a framework for Drone Implementation in Automotive Logistics Operations (DIALOOP) that allows for a close interaction between the strategic and the operative level and can lead automotive companies through a decision and selection process regarding drone technology.
A preliminary version of the framework was developed on a theoretical basis and was then revised using qualitative-empirical data from semi-structured interviews with two groups of experts, i.e. drone experts and automotive experts. The drone expert interviews contributed a current overview of drone capabilities. The automotive experts interview were used to identify intralogistics operations in which drones can be implemented along with the performance measures that can be improved by drone usage.
Furthermore, all interviews explored developments and changes with a foreseeable influence on drone implementation.
The revised framework was then validated using participant validation interviews with automotive experts.
The finalised framework defines a step-by-step process leading from strategic decisions and considerations over the identification of logistics processes suitable for drone implementation and the relevant performance measures to the choice of appropriate drone types based on a drone classification specifically developed in this thesis for an automotive context.
Maintenance is an increasingly complex and knowledge-intensive field. In order to address these challenges, assistance systems based on augmented, mixed, or virtual reality can be applied. Therefore, the objective of this paper is to present a framework that can be used to identify, select, and implement an assistance system based on reality technology in the maintenance environment. The development of the framework is based on a systematic literature review and subject matter expert interviews. The framework provides the best technological and economic solution in several steps. The validation of the framework is carried out through a case study.
A hybrid deep registration of MR scans to interventional ultrasound for neurosurgical guidance
(2021)
Despite the recent advances in image-guided neurosurgery, reliable and accurate estimation of the brain shift still remains one of the key challenges. In this paper, we propose an automated multimodal deformable registration method using hybrid learning-based and classical approaches to improve neurosurgical procedures. Initially, the moving and fixed images are aligned using classical affine transformation (MINC toolkit), and then the result is provided to the convolutional neural network, which predicts the deformation field using backpropagation. Subsequently, the moving image is transformed using the resultant deformation into a moved image. Our model was evaluated on two publicly available datasets: the retrospective evaluation of cerebral tumors (RESECT) and brain images of tumors for evaluation (BITE). The mean target registration errors have been reduced from 5.35 ± 4.29 to 0.99 ± 0.22 mm in the RESECT and from 4.18 ± 1.91 to 1.68 ± 0.65 mm in the BITE. Experimental results showed that our method improved the state-of-the-art in terms of both accuracy and runtime speed (170 ms on average). Hence, the proposed method provides a fast runtime for 3D MRI to intra-operative US pair in a GPU-based implementation, which shows a promise for its applicability in assisting the neurosurgical procedures compensating for brain shift.
Introduction
Despite its high accuracy, polysomnography (PSG) has several drawbacks for diagnosing obstructive sleep apnea (OSA). Consequently, multiple portable monitors (PMs) have been proposed.
Objective
This systematic review aims to investigate the current literature to analyze the sets of physiological parameters captured by a PM to select the minimum number of such physiological signals while maintaining accurate results in OSA detection.
Methods
Inclusion and exclusion criteria for the selection of publications were established prior to the search. The evaluation of the publications was made based on one central question and several specific questions.
Results
The abilities to detect hypopneas, sleep time, or awakenings were some of the features studied to investigate the full functionality of the PMs to select the most relevant set of physiological signals. Based on the physiological parameters collected (one to six), the PMs were classified into sets according to the level of evidence. The advantages and the disadvantages of each possible set of signals were explained by answering the research questions proposed in the methods.
Conclusions
The minimum number of physiological signals detected by PMs for the detection of OSA depends mainly on the purpose and context of the sleep study. The set of three physiological signals showed the best results in the detection of OSA.
In the upcoming years, huge benefits are expected from Artificial Intelligence (AI). However, there are also risks involved in the technology, such as accidents of autonomous vehicles or discrimination by AI-based recruitment systems. This study aims to investigate public perception of these risks, focusing on realistic risks of Narrow AI, i.e., the type of AI that is already productive today. Based on perceived risk theory, several risk scenarios are examined using data from an exploratory survey. This research shows that AI is perceived positively overall. The participants, however, do evaluate AI critically when being confronted with specific risk scenarios. Furthermore, a strong positive relationship between knowledge about AI and perceived risk could be shown. This study contributes to knowledge by advancing our understanding of the awareness and evaluation of the risks by consumers and has important implications for product development, marketing and society.
Context: Many companies are facing an increasingly dynamic and uncertain market environment, making traditional product roadmapping practices no longer sufficiently applicable. As a result, many companies need to adapt their product roadmapping practices for continuing to operate successfully in today’s dynamic market environment. However, transforming product roadmapping practices is a difficult process for organizations. Existing literature offers little help on how to accomplish such a process.
Objective: The objective of this paper is to present a product roadmap transformation approach for organizations to help them identify appropriate improvement actions for their roadmapping practices using an analysis of their current practices.
Method: Based on an existing assessment procedure for evaluating product roadmapping practices, the first version of a product roadmap transformation approach was developed in workshops with company experts. The approach was then given to eleven practitioners and their perceptions of the approach were gathered through interviews.
Results: The result of the study is a transformation approach consisting of a process describing what steps are necessary to adapt the currently applied product roadmapping practice to a dynamic and uncertain market environment. It also includes recommendations on how to select areas for improvement and two empirically based mapping tables. The interviews with the practitioners revealed that the product roadmap transformation approach was perceived as comprehensible, useful, and applicable. Nevertheless, we identified potential for improvements, such as a clearer presentation of some processes and the need for more improvement options in the mapping tables. In addition, minor usability issues were identified.
In this paper we describe an interactive web-based tool for visual analysis of Formula 1 data. A calendar-like representation provides an overview of all races on a yearly basis, either in absolute or normalized time. After selecting a dedicated race more details about this race can be explored. Furthermore it is possible to compare up to three different races. Beside visualizing details on dedicated races it is also possible to analyse driver and team performance over time. A user study was applied to get feedback about the usage of the application and decide between different visualization options.
This paper illustrates the implementation of series connected hardware modules as part of a scalable and modular power electronics device, which is ideally suited in the field of electric vehicles using wide bandgap semiconductor devices. The main benefit of the modular concept is that different current or voltage requirements can be satisfied based on the appropriate series or parallel connection of single modules. The particular design is based on the fact that the single modules generate a continuous and specified output voltage from a given dc voltage. The current work focuses on a brief classification of this work in different series connected concepts of power converters and in particular on an active damping approach for the series connected LC output filters based on inductor current feedback.
This paper presents a modular and scalable power electronics concept for motor control with continuous output voltage. In contrast to multilevel concepts, modules with continuous output voltage are connected in series. The continuous output voltage of each module is obtained by using gallium nitride (GaN) high electron motility transistor (HEMT)s as switches inside the modules with a switching frequency in the range between 500 kHz and 1 MHz. Due to this high switching frequency a LC filter is integrated into the module resulting in a continuous output voltage. A main topic of the paper is the active damping of this LC output filter for each module and the analysis of the series connection of the damping behaviour. The results are illustrated with simulations and measurements.
This contribution presents a three-phase power stage for motor control with continuous output voltages using wide bandgap semiconductors and an asynchronous delta-sigma based switching signal generation. The focus of the paper is on an active damping approach for the LC output filter based on inductor current feedback.
Adaptation of the business model canvas template to develop business models for the circular economy
(2021)
The Business Model Canvas as a template for strategic management serves the development of new or the documentation of existing linear business models. However, the change towards a Circular Economy requires new value creation structures and thus changed business models. To develop business models for circular economies, it is necessary to adapt the existing template, since the actors involved along the value chain take on changed roles. In the context of this paper, a template is presented, based on the existing Business Model Canvas, which allows to develop and document business models for a Circular Economy.
Enterprise architecture (EA) is useful for effectively structuring digital platforms with digital transformation in information societies. Moreover, digital platforms in the healthcare industry accelerate and increase the efficiency of drug discovery and development processes. However, there is the lack of knowledge concerning relationships between EA and digital platforms, in spite of the needs of it. In this paper, we investigated and analyzed the process of drug design and development within the healthcare industry, together with related work in using an enterprise architecture framework for the digital era named the Adaptive Integrated Digital Architecture Framework (AIDAF), specifically supporting the design of digital platforms there. Based on this analysis, we evaluate a method and propose a new reference architecture for promoting digital platforms in the healthcare industry, with future specific aspects of them making effective use of Artificial Intelligence (AI). The practical and theoretical contributions include: (1) Streamlined processes through digital platforms in organizations. (2) Informal knowledge supply and sharing among organizational members through digital platforms. (3) Efficiency and effectiveness in planning production and business for drug development. The findings indicate that EA with digital platforms using the AIDAF contribute to digital transformation with effectiveness for new drugs in the healthcare industry.
Enterprise architecture (EA) is useful for promoting digital transformation in global companies and information societies. In this paper, the authors investigated and analyzed the process for digital transformation in global companies, together with related work in using and applying an enterprise architecture framework for the digital era named the adaptive integrated digital architecture framework (AIDAF). Moreover, they position the AIDAF framework for processing digital transformation in global companies. Based on this analysis, the authors propose and describe a new enterprise architecture process for promoting digital transformation in global companies. Furthermore, the authors propose an adaptive EA cycle-based architecture board framework on digital platforms, while verifying them with case studies in global companies. Finally, the authors clarify the challenges and critical success factors of the process and framework for digital transformation with architecture board reviews in the adaptive EA cycle to assist EA practitioners with its implementation.
The production environment experiences copious challenges, but likewise discovers many new potential opportunities. To meet the new requirements, caused by the developments towards mass-customization, human-robot-cooperation (HRC) was identified as a key piece of technology and is becoming more and more important. HRC combines the strengths of robots, such as reliability, endurance and repeatability, with the strengths of humans, for instance flexibility and decision-making skills. Notwithstanding the high potential of HRC applications, the technology has not achieved a breakthrough in production so far. Studies have shown that one of the biggest obstacles for implementing HRC is the allocation of tasks. Another key technology that offers various opportunities to improve the production environment is Artificial Intelligence (AI). Therefore, this paper describes an AI supported method to improve the work organization in HRC in regards to the task-allocation. The aim of this method is to build a dynamic, semi-autonomous group work environment which keeps not just employee motivation at a high level, but also the product quality due to a decreased failure rate. The AI helps to detect the perfect condition in which the employee delivers the best performance and also supports at identifying the time when the worker leaves this optimal state. As soon as the employee reaches this trigger event, the allocation of the tasks adapts based on the identified stress. This adaptation aims to return the employee to the state of the optimal performance. In order to realize such a dynamic allocation, this method describes the creation of a pool with various interaction scenarios, as well as the AI supported recognition of the defined trigger event.
A full understanding of the relationship between surface properties, protein adsorption, and immune responses is lacking but is of great interest for the design of biomaterials with desired biological profiles. In this study, polyelectrolyte multilayer (PEM) coatings with gradient changes in surface wettability were developed to shed light on how this impacts protein adsorption and immune response in the context of material biocompatibility. The analysis of immune responses by peripheral blood mononuclear cells to PEM coatings revealed an increased expression of proinflammatory cytokines tumor necrosis factor (TNF)-α, macrophage inflammatory protein (MIP)-1β, monocyte chemoattractant protein (MCP)-1, and interleukin (IL)-6 and the surface marker CD86 in response to the most hydrophobic coating, whereas the most hydrophilic coating resulted in a comparatively mild immune response. These findings were subsequently confirmed in a cohort of 24 donors. Cytokines were produced predominantly by monocytes with a peak after 24 h. Experiments conducted in the absence of serum indicated a contributing role of the adsorbed protein layer in the observed immune response. Mass spectrometry analysis revealed distinct protein adsorption patterns, with more inflammation-related proteins (e.g., apolipoprotein A-II) present on the most hydrophobic PEM surface, while the most abundant protein on the hydrophilic PEM (apolipoprotein A-I) was related to anti-inflammatory roles. The pathway analysis revealed alterations in the mitogen-activated protein kinase (MAPK)-signaling pathway between the most hydrophilic and the most hydrophobic coating. The results show that the acute proinflammatory response to the more hydrophobic PEM surface is associated with the adsorption of inflammation-related proteins. Thus, this study provides insights into the interplay between material wettability, protein adsorption, and inflammatory response and may act as a basis for the rational design of biomaterials.
Enterprises and information societies confront crucial challenges currently, while Industry 4.0 becomes important in the global manufacturing industry and Society 5.0 should contribute to a supersmart society, especially for healthcare. Physical activity monitoring digital platforms are architected to improve the healthcare status of patients with diabetes and other lifestyle-related diseases. Furthermore, digital platforms are expected to generate profits for health technology companies and help control costs in the healthcare ecosystem. However, current digital enterprise architecture approaches are not well-established, and the potentials have not yet been realized. Design thinking approach and agile software development methodologies can overcome these limitations, beginning with proof of concept and pilot projects and then scaling to the production environment. In this paper, we describe how that the adaptive integrated digital architecture framework (AIDAF) for Design Thinking approach is proposed and verified in a case of a university hospital in the Americas. In addition, challenges and future activities for this area are discussed that cover the directions for Society 5.0.
An advanced ‘clickECM’ that can be modified by the inverse-electron demand Diels-Alder reaction
(2021)
The extracellular matrix (ECM) represents the natural environment of cells in tissue and therefore is a promising biomaterial in a variety of applications. Depending on the purpose, it is necessary to equip the ECM with specific addressable functional groups for further modification with bioactive molecules, for controllable cross-linking and/or covalent binding to surfaces. Metabolic glycoengineering (MGE) enables the specific modification of the ECM with such functional groups without affecting the native structure of the ECM. In a previous approach (S. M. Ruff, S. Keller, D. E. Wieland, V. Wittmann, G. E. M. Tovar, M. Bach, P. J. Kluger, Acta Biomater. 2017, 52, 159–170), we demonstrated the modification of an ECM with azido groups, which can be addressed by bioorthogonal copper-catalyzed azide-alkyne cycloaddition (CuAAC). Here, we demonstrate the modification of an ECM with dienophiles (terminal alkenes, cyclopropene), which can be addressed by an inverse-electron-demand Diels-Alder (IEDDA) reaction. This reaction is cell friendly as there are no cytotoxic catalysts needed. We show the equipment of the ECM with a bioactive molecule (enzyme) and prove that the functional groups do not influence cellular behavior. Thus, this new material has great potential for use as a biomaterial, which can be individually modified in a wide range of applications.
Increasing complexity in manufacturing processes poses new challenges for industrial maintenance. In addition, advanced machine monitoring and lifetime forecasting options expand the tools and maintenance strategies available. Today, maintenance strategy selection is performed sequentially usually based on prioritised machines and components. These selections are optimized locally for each machine isolated, not considering the context of other machines within the value-adding network. To overcome these challenges, this paper presents an approach for an integrated maintenance strategy selection in one-step by an integrated model considering possible machine failures and the context of other machines within the value-adding network in parallel.
Railway operators are being challenged by increasing complexity and safeguarding the availability of passenger rolling stock, bringing maintenance and especially emerging technologies into the focus. This paper presents a model for selection and implementation of Industry 4.0 technologies in rolling stock maintenance. The model consists of different stages and considers the main components of rolling stock, the related appropriate maintenance strategies and Industry 4.0 technologies considering the maturity level of the railway operators. Relevant criteria and main prerequisites of the technologies were identified. The model proposes relevant activities and was validated by industry experts.
Today's logistics systems are characterized by uncertainty and constantly changing requirements. Rising demand for customized products, short product life cycles and a large number of variants increases the complexity of these systems enormously. In particular, intralogistics material flow systems must be able to adapt to changing conditions at short notice, with little effort and at low cost. To fulfil these requirements, the material flow system needs to be flexible in three important parameters, namely layout, throughput and product. While the scope of the flexibility parameters is described in literature, the respective effects on an intralogistics material flow system and the influencing factors are mostly unknown. This paper describes how flexibility parameters of an intralogistics system can be determined using a multi-method simulation. The study was conducted in the learning factory “Werk150” on the campus of Reutlingen University with its different means of transport and processes and validated in terms of practical experiments.
Porous silica materials are often used for drug delivery. However, systems for simultaneous delivery of multiple drugs are scarce. Here we show that anisotropic and amphiphilic dumbbell core–shell silica microparticles with chemically selective environments can entrap and release two drugs simultaneously. The dumbbells consist of a large dense lobe and a smaller hollow hemisphere. Electron microscopy images show that the shells of both parts have mesoporous channels. In a simple etching process, the properly adjusted stirring speed and the application of ammonium fluoride as etching agent determine the shape and the surface anisotropy of the particles. The surface of the dense lobe and the small hemisphere differ in their zeta potentials consistent with differences in dye and drug entrapment. Confocal Raman microscopy and spectroscopy show that the two polyphenols curcumin (Cur) and quercetin (QT) accumulate in different compartments of the particles. The overall drug entrapment efficiency of Cur plus QT is high for the amphiphilic particles but differs widely between Cur and QT compared to controls of core–shell silica microspheres and uniformly charged dumbbell microparticles. Furthermore, Cur and QT loaded microparticles show different cancer cell inhibitory activities. The highest activity is detected for the dual drug loaded amphiphilic microparticles in comparison to the controls. In the long term, amphiphilic particles may open up new strategies for drug delivery.
In this paper, we propose a radical new approach for scale-out distributed DBMSs. Instead of hard-baking an architectural model, such as a shared-nothing architecture, into the distributed DBMS design, we aim for a new class of so-called architecture-less DBMSs. The main idea is that an architecture-less DBMS can mimic any architecture on a per-query basis on-the-fly without any additional overhead for reconfiguration. Our initial results show that our architecture-less DBMS AnyDB can provide significant speedup across varying workloads compared to a traditional DBMS implementing a static architecture.
Arbeitswelten strategisch entwicklen: mit den DigiTraIn-Instrumenten zur digitalen Transformation
(2021)
Der Weg in die digitale Arbeitswelt ist für viele Unternehmen eine herausfordernde und komplexe Transformation. Um diesen Weg erfolgreich zu beschreiten, benötigen Unternehmen funktionierende Managementinstrumente. Im Projekt DigiTraIn 4.0 wurden vier Instrumente für eine gelingende Transformation in das digitale Arbeiten entwickelt und in der Unternehmenspraxis erprobt. Diese Instrumente werden im vorliegenden Beitrag, ausgehend von der Zielsetzung des Projekts, einführend dargestellt. Zudem wird ein Überblick über die weiteren Beiträge in diesem Buch gegeben, in denen die Instrumente im Detail erläutert werden und spezifische Aspekte des Wandels in die digitale Arbeitswelt im Fokus stehen.
The current advancement of Artificial Intelligence (AI) combined with other digitalization efforts significantly impacts service ecosystems. Artificial intelligence has a substantial impact on new opportunities for the co-creation of value and the development of intelligent service ecosystems. Motivated by experiences and observations from digitalization projects, this paper presents new methodological perspectives and experiences from academia and practice on architecting intelligent service ecosystems and explores the impact of artificial intelligence through real cases supporting an ongoing validation. Digital enterprise architecture models serve as an integral representation of business, information, and technological perspectives of intelligent service-based enterprise systems to support management and development. This paper focuses on architectural models for intelligent service ecosystems, showing the fundamental business mechanism of AI-based value co-creation, the corresponding digital architecture, and management models. The focus of this paper presents the key architectural model perspectives for the development of intelligent service ecosystems.
Annotations of character IDs in news images are critical as ground truth for news retrieval and recommendation system. Universality and accuracy optimization of deep neural network models constitutes the key technology to improve the precision and computing efficiency of automatic news character identification, which is attracting increased attention globally. This paper explores the optimized deep neural network model for automatic focus personage identification in multi-lingual news. First, the face model of the focus personage is trained by using the corresponding face images from German news as positive samples. Next, the scheme of Recurrent Convolutional Neural Network (RCNN) + Bi-directional Long-Short Term Memory (Bi-LSTM) + Conditional Random Field (CRF) is utilized to label the focus name, and the RCNN-RCNN encoder–decoder is applied to translate names of people into multiple languages. Third, face features are described by combining the advantages of Local Gabor Binary Pattern Histogram Sequence (LGBPHS) and RCNN, and iterative quantization (ITQ) is used to binarize codes. Finally, a name semantic network is built for different domains. Experiments are performed on a dataset which comprises approximately 100,000 news images. The experimental results demonstrate that the proposed method achieves a significant improvement over other algorithms.
Purpose: To develop a method for synthesizing a fuzzy automatic control system for a shearer drum in terms of coal seam hypsometry basing on the information criterion of the beginning of rock cutting-off by the drum to reduce ash content of the extracted coal.
Methodology: Taking into consideration peculiarities of determining a distinct information criterion of the beginning of rock cutting-off by the drum and regularities of its variations during the shearer operation, a fuzzy inference algorithm is developed for a system of fuzzy automatic drum control in terms of seam hypsometry. In this context, rules of fuzzy productions, parameters of the membership functions of terms of the output linguistic variable system, and fuzzy operations are substantiated according to the recommendations of a classic Mamdani fuzzy inference algorithm. Studies are carried out to analyze the effi¬ciency of the proposed fuzzy inference algorithm basing on the introduced relative parameter of the number of effective control actions formed by the fuzzy control system. Simulation modeling makes it possible to perform comparative analysis of the efficiency of the drum control.
Findings: In the course of research, an algorithm of fuzzy control of the shearer’s upper drum in terms of coal seam hypsometry has been developed basing on the determination of direct and inverse transfer from coal breaking near the seam roof by the shearer drum to rock breaking with the help of statistical analysis of the stator power of a cutting drive motor.
Originality: For the first time, a method of synthesis of fuzzy automatic control of the drum in terms of seam hypsometry has been proposed.
Practical value: The proposed method is the theoretical basis to solve important scientific and applied problem of the automation of the coal shearer drum in terms of seam hypsometry to reduce ash content of the produced coal.
Autonomisierung von Shopfloor Management : Der Weg vom analogen zum autonomen Shopfloor Management
(2021)
Neue Technologien der Digitalisierung, Vernetzung und künstlichen Intelligenz werden zunehmend auch im Shopfloor Management (SFM) Einzug halten. Dieser Beitrag beschreibt in vier Stufen, wie sich das klassische SFM über das digitale SFM hin zu einem smarten und autonomen SFM entwickeln könnte. Darauf aufbauend wird diskutiert, welche Auswirkungen der Einsatz dieser neuen Technologien auf die operative Gestaltung der Durchführung eines SFM hätte und welche Konsequenzen somit auf Mitarbeiter und Führungskräfte zukommen würden.*)
Gelatin is one of the most prominent biopolymers in biomedical material research and development. It is frequently used in hybrid hydrogels, which combine the advantageous properties of bio‐based and synthetic polymers. To prevent the biological component from leaching out of the hydrogel, the biomolecules can be equipped with azides. Those groups can be used to immobilize gelatin covalently in hydrogels by the highly selective and specific azide–alkyne cycloaddition. In this contribution, we functionalized gelatin with azides at its lysine residues by diazo transfer, which offers the great advantage of only minimal side‐chain extension. Approximately 84–90% of the amino groups are modified as shown by 1H‐NMR spectroscopy, 2,4,6‐trinitrobenzenesulfonic acid assay as well as Fourier‐transform infrared spectroscopy, rheology, and the determination of the isoelectric point. Furthermore, the azido‐functional gelatin is incorporated into hydrogels based on poly(ethylene glycol) diacrylate (PEG‐DA) at different concentrations (0.6, 3.0, and 5.5%). All hydrogels were classified as noncyctotoxic with significantly enhanced cell adhesion of human fibroblasts on their surfaces compared to pure PEG‐DA hydrogels. Thus, the new gelatin derivative is found to be a very promising building block for tailoring the bioactivity of materials.
Bausparverträge sind kombinierte Spar- und Finanzierungsinstrumente, die für die breite Bevölkerung ausgelegt sind. Im Jahr 2020 umfasste der Bestand an Bausparverträgen in Deutschland ca. 25 Mio. Verträge. Ein wesentlicher Teil der Attraktivität des Bausparvertrags für Kunden liegt in der hohen Flexibilität dieser Finanzprodukte, die im Vertragsablauf eine flexible Anpassung an individuelle Finanzierungsbedingungen ermöglicht. In der Sparphase sind das insbesondere Möglichkeiten zur Erhöhung, Ermäßigung und Teilung der Verträge sowie zur relativ flexiblen Anpassung der Sparrate. Bei einem zuteilungsreifen Vertrag kann die Sparphase innerhalb bestimmter zeitlicher Grenzen fortgesetzt werden. In der Darlehensphase sind flexible Sondertilgungen jederzeit und ohne Vorfälligkeitsentschädigung möglich.
Die Vielzahl eingebetteter Optionen beeinflussen sich wechselseitig und müssen in ihrer Wirkungsweise immer gesamthaft betrachtet und gesteuert werden. Die empirische Erfahrung der letzten Jahrzehnte zeigt bezüglich der Optionsausübung ein Kundenverhalten, das sich zwar an finanzmathematischen Überlegungen orientiert, aber nicht vollständig finanzrational abläuft.
Bedarfe gezielt erheben
(2021)
Für die digitale 3D-VR-Fabrikplanung sind unterschiedliche Soft- und Hardwaresysteme am Markt verfügbar, die teilweise erhebliche Kompatibilitätsprobleme aufweisen. Für die Bewertung der Hardwareeignung für die 3D-VR-Fabrikplanung wird ein Bewertungssystem vorgestellt, das anhand konkreter Softwareapplikationen und einem passiven 3D-Stereo-Monitor mit Head-Tracking erläutert wird. Es wird dazu auch die Notwendigkeit des Einsatzes von Software-Middleware zur Nutzungssteigerung diskutiert.
Beyond Selling orientiert sich am komplexen, vornehmlich mittelständisch geprägten Multi-Kanal-Vertrieb. Beyond Selling hat den Anspruch, holistisch zu agieren und will aufzeigen, dass neben vielen bewährten Konzepten bestimmte Aspekte für die erfolgreiche Unternehmensführung zukünftig zunehmend wichtiger werden. So bleiben bspw. eine stringente Buying-Center-Analyse und auch eine treffsichere Formulierung des kundennutzenorientierten Leistungsversprechens im Rahmen des Value-Based-Selling-Konzeptes unabdingbar. Allerdings gilt es auch, den Fokus auf Themen zu legen, die zukünftig deutlich an Bedeutung gewinnen werden
Background: One of the most promising health care development areas is introducing telemedicine services and creating solutions based on blockchain technology. The study of systems combining both these domains indicates the ongoing expansion of digital technologies in this market segment.
Objective: This paper aims to review the feasibility of blockchain technology for telemedicine.
Methods: The authors identified relevant studies via systematic searches of databases including PubMed, Scopus, Web of Science, IEEE Xplore, and Google Scholar. The suitability of each for inclusion in this review was assessed independently. Owing to the lack of publications, available blockchain-based tokens were discovered via conventional web search engines (Google, Yahoo, and Yandex).
Results: Of the 40 discovered projects, only 18 met the selection criteria. The 5 most prevalent features of the available solutions (N=18) were medical data access (14/18, 78%), medical service processing (14/18, 78%), diagnostic support (10/18, 56%), payment transactions (10/18, 56%), and fundraising for telemedical instrument development (5/18, 28%).
Conclusions: These different features (eg, medical data access, medical service processing, epidemiology reporting, diagnostic support, and treatment support) allow us to discuss the possibilities for integration of blockchain technology into telemedicine and health care on different levels. In this area, a wide range of tasks can be identified that could be accomplished based on digital technologies using blockchains.
Public enterprises find themselves in increasingly competitive markets, a situation that makes having an entrepreneurial orientation (EO) an urgent need, given that EO is an indispensable driver of performance. Research describes politicians delaying the strategic change of public enterprises when serving as board members, but empirical evidence of the impact of board behavior on EO in public enterprises is lacking. We draw on stakeholder-agency theory (SAT) and resource dependence theory (RDT) and use structural equation modeling (SEM) to investigate survey data collected from 110 German energy suppliers that are majority government owned. Results indicate that board strategy control and board networking do not seem to predict EO on first sight. Closer analysis reveals a board networking–EO relationship depending on ownership structure. Remarkably, we find that it is not the usually suspected local municipal owner who hinders EO in our sample organizations but minority shareholders engaging in board networking activities. The results shed light on the intersection of governance and entrepreneurship with special reference to the fine-grained conceptualization of RDT.
The energy sector in Germany, as in many other countries, is undergoing a major transformation. To achieve the climate targets, numerous measures to implement smart energy and resource efficiency are necessary. Therefore, energy companies are experiencing increasing pressure from politics and society to transform their business areas in a sustainable manner and implement smart and sustainable business models. Consequently, numerous resources are expected to flow into the development and implementation of new business models. But often these efforts remain unsuccessful in practice. There is a large amount of literature on barriers and drivers of smart and sustainable business models in the energy sector. But what are the factors that companies struggle with most when developing and implementing new business models in practice? To answer this question, the results of a systematic literature review were evaluated by conducting semi-structured interviews with experts of the German energy sector. Six categories of transformation barriers were identified: Organizational, Financial, Legal, Partner-Network, Societal and Technological barriers. To overcome these barriers, recommendations for action and key success factors are outlined by the experts interviewed. The interview study validates key barriers and drivers in terms of their significance in practice in the German energy sector and makes recommendations to advance the smart and sustainable transformation of the energy sector.
Continuous monitoring of individual vital parameters can provide information for the assessment of one’s health and indications of medical problems in the context of personalized medicine. Correlations between parameters and health issues are to be evaluated. As one project in this topic area, a telemedicine platform is implemented to gather data of outpatients via wearables and accumulate them for physicians and researchers to review. This work extracts requirements, draws use case scenarios, and shows the current system architecture consisting of a patient application, a physician application with a web server, and a backend server application. In further work, the prototype will assist to develop a vendor-free and open monitoring solution. A conclusion on functionality and usability will be evaluated in an imminent first study.
Despite its success against cancer, photothermal therapy (PTT) (>50 °C) suffers from several limitations such as triggering inflammation and facilitating immune escape and metastasis and also damage to the surrounding normal cells. Mild-temperature PTT has been proposed to override these shortcomings. We developed a nanosystem using HepG2 cancer cell membrane-cloaked zinc glutamate-modified Prussian blue nanoparticles with triphenylphosphine-conjugated lonidamine (HmPGTL NPs). This innovative approach achieved an efficient mild-temperature PTT effect by downregulating the production of intracellular ATP. This disrupts a section of heat shock proteins that cushion cancer cells against heat. The physicochemical properties, anti-tumor efficacy, and mechanisms of HmPGTL NPs both in vitro and in vivo were investigated. Moreover, the nanoparticles cloaked with the HepG2 cell membrane substantially prolonged the circulation time in vivo. Overall, the designed nanocomposites enhance the efficacy of mild-temperature PTT by disrupting the production of ATP in cancer cells. Thus, we anticipate that the mild-temperature PTT nanosystem will certainly present its enormous potential in various biomedical applications.
Energy consumption by air-conditioning is expansive and leads to the emission of millions of tons of CO2 every year. A promising approach to circumvent this problem is the reflection of solar radiation: Rooms that would not heat up by irradiation will not need to be cooled down. Especially, transparent conductive metal oxides exhibit high infrared (IR) reflectivity and are commonly applied as low-emissivity coatings (low-e coatings). Indium tin oxide (ITO) coatings are the state-of-the-art application, though indium is a rare and expensive resource. This work demonstrates that aluminum-doped zinc oxide (AZO) can be a suitable alternative to ITO for IR-reflection applications. AZO synthesized here exhibits better emissivity to be used as roofing membrane coatings for buildings in comparison to commercially available ITO coatings. AZO particles forming the reflective coating are generated via solvothermal synthesis routes and obtain high conductivity and IR reflectivity without the need of any further post-thermal treatment. Different synthesis parameters were studied, and their effects on both conductive and optical properties of the AZO nanoparticles were evaluated. To this end, a series of characterization methods, especially 27Al-nuclear magnetic resonance spectroscopy (27Al-NMR) analysis, have been conducted for a deeper insight into the particles’ structure to understand the differences in conductivity and optical properties. The optimized AZO nanoparticles were coated on flexible transparent textile-based roofing membranes and tested as low-e coatings. The membranes demonstrated higher thermal reflectance compared with commercial ITO materials with an emissivity value lowered by 16%.
A laboratory prototype for hyperspectral imaging in ultra-violet (UV) region from 225 to 400 nm was developed and used to rapidly characterize active pharmaceutical ingredients (API) in tablets. The APIs are ibuprofen (IBU), acetylsalicylic acid (ASA) and paracetamol (PAR). Two sample sets were used for a comparison purpose. Sample set one comprises tablets of 100% API and sample set two consists of commercially available painkiller tablets. Reference measurements were performed on the pure APIs in liquid solutions (transmission) and in solid phase (reflection) using a commercial UV spectrometer. The spectroscopic part of the prototype is based on a pushbroom imager that contains a spectrograph and charge-coupled device (CCD) camera. The tablets were scanned on a conveyor belt that is positioned inside a tunnel made of polytetrafluoroethylene (PTFE) in order to increase the homogeneity of illumination at the sample position. Principal component analysis (PCA) was used to differentiate the hyperspectral data of the drug samples. The first two PCs are sufficient to completely separate all samples. The rugged design of the prototype opens new possibilities for further development of this technique towards real large-scale application.