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This work is a report on practical experiences with the issue of interoperability in German practice management systems (PMSs) from an ongoing clinical trial on teledermatology, the TeleDerm project. A proprietary and established web-platform for store-and-forward telemedicine is integrated with the IT in the GPs’ offices for automatic exchange of basic patient data. Most of the 19 different PMSs included in the study sample lack support of modern health data exchange standards, therefore the relatively old but widely available German health data exchange interface “Gerätedatentransfer” (GDT) is used. Due to the lack of enforcement and regulation of the GDT standard, several obstacles to interoperability are encountered. As a partial, but reusable working solution to cope with these issues, we present a custom middleware which is used in conjunction with GDT. We describe the design, technical implementation and observed hindrances with the existing infrastructure. A discussion on health care interfacing standards and the current state of interoperability in German PMS software is given.
The diversity of energy prosumer types makes it difficult to create appropriate incentive mechanisms that satisfy both prosumers and energy system operators alike. Meanwhile, European energy suppliers buy guarantees of origin (GoO) which allow them to sell green energy at premium prices while in reality delivering grey energy to their customers. Blockchain technology has proven itself to be a robust paying system in which users transact money without the involvement of a third party. Blockchain tokens can be used to represent a unit of energy and, just as GoOs, be submitted to the market. This paper focuses on simulating marketplace using the ethereum blockchain and smart contracts, where prosumers can sell tokenized GoOs to consumers willing to subsidize renewable energy producers. Such markets bypass energy providers by allowing consumers to obtain tokenized GoOs directly from the producers, which in turn benefit directly from the earnings. Two market strategies where tokens are sold as GoOs have been simulated. In the Fix Price Strategy prosumers sell their tokens to the average GoO price of 2014. The Variable Price Strategy focuses on selling tokens at a price range defined by the difference between grey and green energy. The study finds that the ethereum blockchain is robust enough to functions as a platform for tokenized GoO trading. Simulation results have been compared and the results indicate that prosumers earn significantly more money by following the Variable Price
Strategy.
Context: Nowadays the market environment is characterized by high uncertainties due to high market dynamics, confronting companies with new challenges in creating and updating product roadmaps. Most companies are still using traditional approaches which typically fail in such environments. Therefore, companies are seeking opportunities for new product roadmapping approaches.
Objective: This paper presents good practices to support companies better understand what factors are required to conduct a successful product roadmapping in a dynamic and uncertain market environment.
Method: Based on a grey literature review, essential aspects for conducting product roadmapping in a dynamic and uncertain market environment were identified. Expert workshops were then held with two researchers and three practitioners to develop best practices and the proposed approach for an outcome-driven roadmap. These results were then given to another set of practitioners and their perceptions were gathered through interviews.
Results: The study results in the development of 9 good practices that provide practitioners with insights into what aspects are crucial for product roadmapping in a dynamic and uncertain market environment. Moreover, we propose an approach to product roadmapping that includes providing a flexible structure and focusing on delivering value to the customer and the business. To ensure the latter, this approach consists of the main items outcome hypothesis, validated outcomes, and discovered outputs.
In recent years, the parallel computing community has shown increasing interest in leveraging cloud resources for executing parallel applications. Clouds exhibit several fundamental features of economic value, like on-demand resource provisioning and a pay-per-use model. Additionally, several cloud providers offer their resources with significant discounts; however, possessing limited availability. Such volatile resources are an auspicious opportunity to reduce the costs arising from computations, thus achieving higher cost efficiency. In this paper, we propose a cost model for quantifying the monetary costs of executing parallel applications in cloud environments, leveraging volatile resources. Using this cost model, one is able to determine a configuration of a cloud-based parallel system that minimizes the total costs of executing an application.
Condition Monitoring for mechanical systems like bearings or transmissions is often done by analysing frequency spectra obtained from accelerometers mounted to the components under observation. Although this approach gives a high amount on information about the system behaviour, the interpretation of the resulting spectra requires expert knowledge, that is, a deep understanding of the effect on condition deterioration on the measured spectra. However, an increasing number of condition monitoring applications demands other representations of the measured signals that can be easily interpreted even by non–experts. Therefore, the objective of this paper is to develop an approach for processing measured process data in order to obtain an easy to interpret measure for assessing the component condition. The main idea is to evaluate the deterioration of a component condition by computing the correlation function of current measurements with past measurements in order to detect a component condition deterioration from a change in these correlation functions. Besides the simplicity of the obtained measure, this approach opens the opportunity for integrating a model based approach as well. The developed method is tested based on a condition monitoring application in a roller chain.
This paper investigates the possibility to effectively monitor and control the respiratory action using a very simple and non invasive technique based on a single lightweight reduced-size wireless surface electromyography (sEMG) sensor placed below the sternum. The captured sEMG signal, due to the critical sensor position, is characterized by a low energy level and it is affected by motion artifacts and cardiac noise. In this work we present a preliminary study performed on adults for assessing the correlation of the spirometry signal and the sEMG signal after the removal of the superimposed heart signal. This study and the related findings could be useful in respiratory monitoring of preterm infants.
The presented research is dedicated to estimation of the correlation between the level of renewable energy sources and the costs of congestion management in electric networks in selected European countries. Data of six countries in North-West European area (Italy, Spain, Germany, France, Poland and Austria) were investigated. Factors considered included grid congestion costs including re-dispatching costs as well as countertrading costs, gross electricity generation, installed capacity of electric generating facilities, installed capacity of electric non-dispatchable renewable energy sources and total electricity consumption. Special attention is paid to the share of renewable energy sources. It is found that the grid congestion costs are not clearly affected by penetration of non-dispatchable renewables in all the analysed countries and therefore a clear mathematical correlation cannot no be extrapolated with the available data. The results of this research show in general a loose dependency of the grid congestion costs on the penetration of renewables and a strong dependency on the total electrical consumption of the country.
The automotive industry faces three major challenges – shortage of fossil fuels, politics of global warming and rising competition from new markets. In order to remain competitive companies have to develop more efficient and alternative fuel vehicles that meet the individual requirements of the customers. Functional Integration combined with new Technologies and materials are the key to stable success in this industry. The sustaining upward trend to system innovations within the last ten years confirms this. The development of complex products like automobiles claim skills of various disciplines e.g. engineering, chemistry. Furthermore, these skills are spread all over the supply chain. Hence the only way to stay successful in the automotive industry is cooperation and collaborative innovation. Interdisciplinary and interorganizational development has high demands on cooperation models especially in the automotive industry. In this case study cooperation models are analyzed and evaluated according to their applicability to interdisciplinary, interorganizational development projects in the automotive industry. Following, the research campus ARENA2036 is analyzed. ARENA2036 is an interdisciplinary, interorganizational development project housing automobile manufacturers, suppliers, research establishments and university institutes. Finally, based on interviews with the partners and the precede analyses of cooperation models, suggestions for implementation are given to ARENA2036.
Stellenausschreibungen sind ein wichtiges Mittel, um Rollen von Controllern auf dem Arbeitsmarkt zu kommunizieren. Stellenanzeigen öffnen ein Fenster zu dem, was Firmen als Rollen für ihre Controller wahrnehmen. Welche Rollen Stellenanzeigen kommunizieren, ist bisher nicht bekannt. Unter Verwendung einer großen Stichprobe von 889 Stellenanzeigen und eines Text-Mining-Ansatzes zeigen wir, dass es offenbar eine Mischung verschiedener Rollentypen mit einem starken Fokus auf einen eher klassischen Rollentyp gibt, die Watchdog-Rolle. Personen mit Business-Partner-Eigenschaften werden dagegen häufiger für Führungspositionen oder in Familienunternehmen und kleinen und mittleren Unternehmen (KMU) gesucht. Die Ergebnisse stellen die derzeitige Rollen-diskussion für Controller als Business Partner in der Praxis und in einigen Bereichen der Wissenschaft in Frage.
Radiofrequency ablation is an ablation technique to treat tumors with focused heat. Computer tomography, ultrasound and magnetic resonance imaging (MRI) are imaging modalities which can be used for image-guided procedures. MRI offers several advantages in comparison to the other imaging modalities, such as radiation-free fluoroscopic imaging, temperature mapping, a high-soft-tissue contrast and free selection of imaging planes. This work addresses the application of 3Dcontrollers for controlling interventional, fluoroscopic MR sequences at the scenario of MR guided radiofrequency ablation of hepatic malignancies. During this procedure, the interventionalist can monitor the targeting of the tumor with near-real time fluoroscopic sequences. In general, adjustments of the imaging planes are necessary during tumor targeting, which is performed by an assistant in the control room. Therefore, communication between the interventionalist in the scanner room and the assistant in the control room is essential. However, verbal communication is impaired due to the loud scanning noises. Alternatively, non-verbal communication between the two persons is possible, however limited to a few gestures and susceptible to misunderstandings. This work is analyzing different 3D-controllers to enable control of interventional MR sequences during MR-guided procedures directly by the interventionalist. Leap Motion, Wii Remote, SpaceNavigator, Phantom Omni and Foot Switch were selected. For that a simulation was built in C++ with VTK to feign the real scenario for test purposes. Previous results showed that Leap Motion is not suitable for the application while Wii Remote and Foot Switch are possible input devices. Final evaluation showed a generally time reduction with the use of 3D-controllers. Best results were reached with Wii Remote in 34 seconds. Handholding input devices like Wii Remote have further potential to integrate them in real environment to reduce intervention time.
In networked operating room environments, there is an emerging trend towards standardized non-proprietary communication protocols which allow to build new integration solutions and flexible human-machine interaction concepts. The most prominent endeavor is the IEEE 11073 SDC protocol. For some uses cases, it would be helpful if not just medical devices could be controlled based on SDC, but also building automation systems like light, shutters, air condition, etc. For those systems, the KNX protocol is widely used. We build an SDC-to-KNX gateway which allows to use the SDC protocol for sending commands to connected KNX devices. The first prototype system was successfully implemented at the demonstration operating room at Reutlingen University. This is a first step toward the integration of a broader variety of KNX devices.
Rapid value delivery requires a company to utilize empirical evaluation of new features and products in order to avoid unnecessary product risks. This helps to make data-driven decisions and to ensure that the development is focused on features that provide real value for customers. Short feedback loops are a prerequisite as they allow for fast learning and reduced reaction times. Continuous experimentation is a development practice where the entire R&D process is guided by constantly conducting experiments and collecting feedback. Although principles of continuous experimentation have been successfully applied in domains such as game software or SAAS, it is not obvious how to transfer continuous experimentation to the business to-business domain. In this article, a case study from a medium-sized software company in the B2B domain is presented. The study objective is to analyze the challenges, benefits and organizational aspects of continuous experimentation in the B2B domain. The results suggest that technical challenges are only one part of the challenges a company encounters in this transition. The company also has to address challenges related to the customer and organizational culture. Unique properties in each customers business play a major role and need to be considered when designing experiments. Additionally, the speed by which experiments can be conducted is relative to the speed by which production deployments can be made. Finally, the article shows how the study results can be used to modify the development in the case company in a way that more feedback and data is used instead of opinions.
Due to frequently changing requirements, the internal structure of cloud services is highly dynamic. To ensure flexibility, adaptability, and maintainability for dynamically evolving services, modular software development has become the dominating paradigm. By following this approach, services can be rapidly constructed by composing existing, newly developed and publicly available third-party modules. However, newly added modules might be unstable, resource-intensive, or untrustworthy. Thus, satisfying non-functional requirements such as reliability, efficiency, and security while ensuring rapid release cycles is a challenging task. In this paper, we discuss how to tackle these issues by employing container virtualization to isolate modules from each other according to a specification of isolation constraints. We satisfy non-functional requirements for cloud services by automatically transforming the modules comprised into a container-based system. To deal with the increased overhead that is caused by isolating modules from each other, we calculate the minimum set of containers required to satisfy the isolation constraints specified. Moreover, we present and report on a prototypical transformation pipeline that automatically transforms cloud services developed based on the Java Platform Module System into container-based systems.
Besides the optimisation of the car, energy-efficiency and safety can also be increased by optimising the driving behaviour. Based on this fact, a driving system is in development whose goal is to educate the driver in energy-efficient and safe driving. It monitors the driver, the car and the environment and gives energy-efficiency and safety relevant recommendations. However, the driving system tries not to distract or bother the driver by giving recommendations for example during stressful driving situations or when the driver is not interested in that recommendation. Therefore, the driving system monitors the stress level of the driver as well as the reaction of the driver to a given recommendation and decides whether to give a recommendation or not. This allows to suppress recommendations when needed and, thus, to increase the road safety and the user acceptance of the driving system.
Fitting 3D Morphable Face Models (3DMM) to a 2D face image allows the separation of face shape from skin texture, as well as correction for face expression. However, the recovered 3D face representation is not readily amenable to processing by convolutional neural networks (CNN). We propose a conformal mapping from a 3D mesh to a 2D image, which makes these machine learning tools accessible by 3D face data. Experiments with a CNN based face recognition system designed using the proposed representation have been carried out to validate the advocated approach. The results obtained on standard benchmarking data sets show its promise.
Ein wesentliches Ziel der unter dem Schlagwort Industrie 4.0 gebündelten neuen Entwicklungen ist die Vernetzung intelligenter Komponenten in industriellen Anlagen, um Prozesse transparenter und effizienter zu gestalten. Ein weiteres Ziel ist das Condition Monitoring, d.h. die Überwachung des Zustands der Komponenten während der Laufzeit und die Abschätzung der Restlebensdauer, damit die gesamte Lebensdauer der Komponente ausgenutzt und Wartungsintervalle besser geplant werden können. Die Bewertung des Komponentenzustands erfolgt anhand von Messgrößen, die entweder durch zusätzlich in den Prozess eingebrachte Sensoren erfasst werden oder durch Prozessdaten, die in den Regel- und Steuereinrichtungen verfügbar sind. Diese Messdaten werden ausgewertet und das Ergebnis wird dem Anwender angezeigt.
Der vorliegende Beitrag gibt einen kurzen Überblick über verwendete Messgrößen sowie verwendete Auswerteverfahren. Darüber hinaus wird ein Verfahren erläutert, das die Schwierigkeiten bei der Beurteilung der üblicherweise verwendeten Frequenzspektren vermeidet.
This paper describes a new method for condition monitoring of a roller chain. In contrast to conventional methods, no additional accelerometers are used to measure and interpret frequency spectra but the chain condition is evaluated using an easy to interpret similarity measure based on correlation functions using the driving motor torque. An additional clustering of current data and reference measurements yields an easy to understand representation of the chain condition.
Der vorliegende Beitrag zeigte die Anwendung eines Extended Kalman Filters für die Beurteilung des Verschleißzustandes von Rollenketten. Anders als in den üblicherweise eingesetzten signalbasierten Verfahren wurde damit ein modellbasierter Ansatz gewählt. Der Einsatz des Extended Kalman Filters ermöglicht die Schätzung von Parametern eines reduzierten Kettenmodells, das die Dynamik der einzigen Messgröße, nämlich des Drehmoments des antreibenden Motors näherungsweise nachbildet. Im Beitrag wurde dieses Verfahren auf Messdaten aus vier Dauerversuchen an Rollenketten eingesetzt und gezeigt, dass mit steigendem Verschleiß eine Änderung ausgewählter Modellparameter erfolgt.
Diese Vorgehensweise ist ein erster Ansatz, der durch weitere Forschungsarbeiten noch verbessert werden muss. In zukünftigen Forschungsarbeiten wird zusätzlich zur Parameterschätzung eine Prädiktion durchgeführt, um einen Schätzwert für die Restlebensdauer zu erhalten. Hierzu gibt es Ansätze in der Literatur, die auf das konkrete Problem angepasst werden müssen. Zudem muss die Modellierungssystematik so erweitert werden, dass Wissen über das Prozessverhalten in die Modellierung mit eingebracht wird, um die Aussagekraft der Ergebnisse sowie die Robustheit des Verfahrens bezüglich Betriebsparametern, Umgebungsbedingungen und Exemplarstreuungen zu verbessern.
Platforms and their surrounding ecosystems are becoming increasingly important components of many companies' strategies. Artificial Intelligence, in particular, has created new opportunities to create and develop ecosystems around the platform. However, there is not yet a methodology to systematically develop these new opportunities for enterprise development strategy. Therefore, this paper aims to lay a foundation for the conceptualization of Artificial Intelligence-based service ecosystems exploiting a Service-Dominant Logic. The basis for conceptualization is the study of value creation and particularly effective network effects. This research investigates the fundamental idea of extending specific digital concepts considering the influence of Artificial Intelligence on the design of intelligent services, along with their architecture of digital platforms and ecosystems, to enable a smooth evolutionary path and adaptability for human-centric collaborative systems and services. The paper explores an extended digital enterprise conceptual model through a combined, iterative, and permanent task of co-creating value between humans and intelligent systems as part of a new idea of cognitively adapted intelligent services.