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Rund 75 % des weltweiten Energieverbrauchs findet innerhalb urbaner Energiesysteme statt. Solche Systeme beinhalten mehrere Energiesektoren (Elektrizität, Wärme, Kälte, …), Verbrauchssektoren (Wohnen, Gewerbe, Industrie, Landwirtschaft, Mobilität, …) und Interessensgruppen und sind deshalb besonders komplex. Durch den Einsatz von Methoden der Energiesystemmodellierung können diese komplexen Systeme simuliert, analysiert und optimiert werden. Mit Simulationsmodellen können Kosten, Emissionen und verschiedene andere Systemparameter prognostiziert werden. Mithilfe von Optimierungsalgorithmen können Technologien miteinander verglichen, Anlagen dimensioniert und Betriebsweisen optimiert werden. Die Erkenntnisse aus Energiesystemmodellen können zur Einhaltung verschiedener politischer und sozialer Ziele, wie beispielsweise die Reduktion von Treibhausgasemissionen, der Bedarf nach kostengünstiger Energieversorgung oder auch die Stärkung der regionalen Wirtschaft, beitragen.
Im Projekt R2Q werden Ansätze der Energiesystemmodellierung für den Einsatz in der Planung urbaner Energiesysteme aufgearbeitet, angepasst und für städteplanerische Prozesse verfügbar gemacht. In ersten Modelldurchläufen für ein Testgebiet in Herne konnte durch die Kombination verschiedener Technologien eine rechnerische Minimierung der monetären Kosten um 19 % bei gleichzeitiger Reduktion der CO2-Emissionen um 36 % ermittelt werden. Durch ein emissionsoptimiertes Szenario können die CO2-Emissionen um 47 % reduziert werden, was jedoch mit einer Steigerung der Kosten um 29 % einhergeht.
Introduction: Many disease processes are accompanied and promoted
by increased inflammation in the body. Increased concentrations of high-sensitivity C-reactive protein (hs-CRP) in the blood are an indicator of subclinical inflammation, increased disease risk, and an increased risk of early death. A healthy plant-based diet and increased physical activity have been shown to reduce hs-CRP concentrations.
Objectives: Our objective was to test if a healthy lifestyle intervention program can improve hs-CRP levels and other risk factors.
Methodology: We are conducting a non-randomized, controlled intervention study with 6 times of measurement (baseline, after 2.5, 6, 12, 18 and 24 months). Participants in the intervention group (n = 104) took part in a 2.5-month intensive lifestyle program focusing on a plant-based diet (PBD), physical activity, stress management and group support. Currently they are in the less intensive phase (monthly seminars) which will be completed after 24 months. The control group (n = 62) did not take part in any program. In both groups hs-CRP was assessed, and participants with an infection/common cold at any of the times of measurement were excluded from the analyses.
Results: In the intervention group (n = 97) we observed a reduction in hsCRP from baseline to 2.5 months (p < 0.001). In the control group (n = 46) hs-CRP levels increased non-significantly. The changes from baseline to 2.5 months were significantly different between intervention and control (p < 0.01).
Conclusion: Our program led to a clinically relevant reduction in hs-CRP.
Continued follow-up will show if this improvement can be maintained in the intervention group. Our study confirms that a PBD and healthier lifestyle choices can lower hs-CRP.
In der Lebensgeschichte spielen bedeutsame Orte eine große Rolle, die auch in der Biografiearbeit zum Tragen kommt. Der Umgebungsradius älterer Menschen, die in Altenpflegeeinrichtungen leben, kann aufgrund gesundheitlicher und finanzieller Begrenzungen sehr eingeschränkt sein, Reisen können unter Umständen unmöglich sein. Aktuelle VR-Tech- nologie mit der Erfahrung der Immersion, des Eintauchens in die virtuelle Umgebung, könnte eine Möglichkeit sein, Orte (wieder) zu erleben, die schwierig oder unmöglich zu besuchen sind, und so in der Biografiearbeit genutzt werden. Die vorliegende Studie weist auf positive Effekte auf das Wohlbefinden hin und ermittelt Gratifikationseffekte der Nut- zung. Dabei sind besonders wichtige Kategorien Genuss, Hilfe beim Wiedererinnern und Erlebnis.
Laser shock peening is a new and important surface treatment technique that can enhance the mechanical properties of metal materials. Normally, the nanosecond laser with pulse-width between 5 ns and 20 ns is used to induce a high-pressure shock wave that can generate plastic deformation in the top layer of metals. The femtosecond laser shock peening in the air has been studied recently, which can induce higher pressure shock wave than that of traditional nanosecond laser shock peening in a very short time. The NiTi alloy is processed by femtosecond laser shock peening, then a nanoindentation device is used to measure its surface hardness and residual stress. The hardness results of NiTi alloy before and after treatment show that the femtosecond laser shock peening can increase the hardness of NiTi alloy, which also shows that the femtosecond laser can be used to perform laser shock peening on NiTi alloy without coating.
We present our latest results on a refined unimorph deformable mirror which was developed in the frame of the ESA GSTP activity ”Enabling Technologies for Piezo-Based Deformable Mirrors in Active Optics Correction Chains”. The identified baseline concept with the soft piezoceramic material PIC151 successfully sustained all vibration requirements (17.8 gRMS random and 20 g sine) and shock testing (300 g SRS). We cover the mirror design development which reduces the stress in the brittle piezo-ceramic by 90 % compared to the design from
a former GSTP activity. We briefly address the optical characterization of the deformable mirror, namely the achieved Zernike amplitudes as well as the unpowered surface deformation (1.7 µm) and active flattening (12.3 nmRMS). The mirror produces low-order Zernike modes with a stroke of several tens of micrometer over a correction aperture of 50 mm, which makes the mirror a versatile tool for space telescopes.
This paper presents the results of the technology development project “Enabling Technologies for Piezo-Based Deformable Mirrors in Active Optics Correction Chains” conducted by OHB System AG together with its partner Münster University of Applied Sciences (MUAS). The project was funded by ESA within their General Support Technology Programme
(GSTP).
We address in this paper mainly the definition, flow-down and verification of the requirements for the Deformable Mirror (DM). The requirements were derived from a set of real space mission applications. The deformation of the mirror is performed by piezo-ceramic actuators in an unimorph configuration. The finally developed DM is able produce Zernike modes with a stroke of several tens of µm over a clear optical aperture of 50 mm in diameter. It underwent successfully a full environmental qualification campaign including thermal cycling, shock- and vibration testing, as well as exposure to
proton and γ–ray radiation. Thermal and performance tests were performed in the temperature range from 100 K to 300 K.
Furthermore, the DM sustained all vibration (random 17.8 g RMS and sinus) and shock (300 g) testing. Thereby all criticalities which were identified a previous study have been overcome successfully.
A Technology Readiness Level (TRL) of 5 is reached, as the component has been validated in relevant environment. Based on the high level of maturity, this deformable mirror is now ready for the incorporation in future flight instruments. The achieved TRL of 5 is sufficient for the status of a PDR at payload level and gives thus a very good basis for all kinds of potential B2, C/D payload developments.
The effects of different unsteady ventilation strategies on flow-structures in a room are investigated and compared to steady ventilation with the same mean exchange rate. For this, whole-field optical flow measurements were executed by means of a particle image velocimetry system (PIV) in a Reynolds-scaled room model in water. In a first series of experiments, sinusoidal varied supply flows with different frequencies were analysed; two equally supplied simple nozzles in the ceiling were used as inlets. The setup was validated by comparing jet velocities with literature values.
Typically, room airflows are investigated with punctual measurement techniques (e.g.
anemometers), which have an impact on the flow field, or with smoke gas experiments. By using PIV, the flow can be analysed without any influence of sensors or stands/traverses and whole-field measurement data with high spatial resolution and detailed information on the flow field can be collected.
Local and time-averaged velocities and standard deviations were calculated for all scenarios. Unsteady conditions were created by a sinusoidal variation of the supply flow rate with frequencies between 0.025 1/s and 0.050 1/s, an offset of about 1.1 m3/h and an amplitude of about ±1.0 m3/h, which leads to a mean exchange rate of 3.5 1/h. Although averaged velocity fields only show slight differences between steady and unsteady conditions, single pictures vary widely. First effects of unsteady ventilation on flow structures can be recognized. Steady structures are destroyed, and velocities change rapidly.
The inlets will be changed to small-scale ceiling-diffusors in future experiments to create more realistic room ventilation conditions. Other types of unsteady supply flows will be implemented, and parameters will be varied. The results of the PIV-measurements can be used to validate CFD simulations and to derive dimensioning rules and application recommendations.
Process-Driven Applications flourish through the interaction between an executable BPMN process model, human tasks, and external software services. All these components operate on shared process data, so it is even more important to check the correct data flow. However, data flow is in most cases not explicitly defined but hidden in model elements, form declarations, and program code. This paper elaborates on data-flow anomalies acting as indicators for potential errors and how such anomalies can be uncovered despite implicit and hidden data-flow definitions. By considering an integrated view, it goes beyond other approaches which are restricted to separate data-flow analysis of either process model or source code. The main idea is to merge call graphs representing programmed services into a control-flow representation of the process model, to label the resulting graph with associated data operations, and to detect anomalies in that labeled graph using a dedicated data-flow analysis. The applicability of the solution is demonstrated by a prototype designed for the Camunda BPM platform.
Modern implantable cardiologic devices communicate via radio frequency techniques and nearby gateways to a backend server on the internet. Those implanted devices, gateways, and servers form an ecosystem of proprietary hardware and protocols that process sensitive medical data and is often vital for patients’ health.
This paper analyzes the security of this Ecosystem, from technical gateway aspects, via the programmer, to configure the implanted device, up to the processing of personal medical data from large cardiological device producers. Based on a real-world attacker model, we evaluated different devices and found several severe vulnerabilities. Furthermore, we could purchase a fully functional programmer for implantable cardiological devices, allowing us to re-program such devices or even induce electric shocks on untampered implanted devices.
Additionally, we sent several Art. 15 and Art. 20 GDPR inquiries to manufacturers of implantable cardiologic devices, revealing non-conforming processes and a lack of awareness about patients’ rights and companies’ obligations. This, and the fact that many vulnerabilities are still to be found after many vulnerability disclosures in recent years, present a worrying security state of the whole ecosystem.
Due to the increasing connectivity of modern vehicles, collected data is no longer only stored in the vehicle itself but also transmitted to car manufacturers and vehicle assistant apps. This development opens up new possibilities for digital forensics in criminal investigations involving modern vehicles. This paper deals with the digital forensic analysis of vehicle assistant apps of eight car manufacturers. We reconstruct the driver’s activities based on the data stored on the smartphones and in the manufacturer’s backend.
For this purpose, data of the Android and iOS apps of the car manufacturers Audi, BMW, Ford, Mercedes, Opel, Seat, Tesla, and Volkswagen were extracted from the smartphone and examined using digital forensic methods following forensics guidelines. Additionally, manufacturer data was retrieved using Subject Access Requests. Using the extensive data gathered, we reconstruct trips and refueling processes, determine parking positions and duration, and track the locking and unlocking of the vehicle.
Our findings show that the digital forensic investigation of smartphone applications is a useful addition to vehicle forensics and should therefore be taken into account in the strategic preparation of future digital forensic investigations.
TLS is one of today's most widely used and best-analyzed encryption technologies. However, for historical reasons, TLS for email protocols is often not used directly but negotiated via STARTTLS. This additional negotiation adds complexity and was prone to security vulnerabilities such as naive STARTTLS stripping or command injection attacks in the past.
We perform the first structured analysis of STARTTLS in SMTP, POP3, and IMAP and introduce EAST, a semi-automatic testing toolkit with more than 100 test cases covering a wide range of variants of STARTTLS stripping, command and response injections, tampering attacks, and UI spoofing attacks for email protocols. Our analysis focuses on the confidentiality and integrity of email submission (email client to SMTP server) and email retrieval (email client to POP3 or IMAP server). While some of our findings are also relevant for email transport (from one SMTP server to another), the security implications in email submission and retrieval are more critical because these connections involve not only individual email messages but also user credentials that allow access to a user's email archive.
We used EAST to analyze 28 email clients and 23 servers. In total, we reported over 40 STARTTLS issues, some of which allow mailbox spoofing, credential stealing, and even the hosting of HTTPS with a cross-protocol attack on IMAP. We conducted an Internet-wide scan for the particularly dangerous command injection attack and found that 320.000 email servers (2% of all email servers) are affected. Surprisingly, several clients were vulnerable to STARTTLS stripping attacks. In total, only 3 out of 28 clients did not show any STARTTLS-specific security issues. Even though the command injection attack received multiple CVEs in the past, EAST detected eight new instances of this problem. In total, only 7 out of 23 tested servers were never affected by this issue. We conclude that STARTTLS is error-prone to implement, under-specified in the standards, and should be avoided.
Africa recognised the potential of digital transformation before the COVID-19 pandemic. With the Digital Transformation Strategy, the continent committed itself to support this change in line with the Agenda 2063: The Africa We Want with focus on investment in Information and Communication Technology (ICT) , the promotion of the digital economy and the adoption of open and distance learning in tertiary education. However, the pandemic has given renewed impetus for digital change in higher education. The shutdowns of educational institutions brought about by COVID-19 have demonstrated that teaching and learning can be re-designed and educational institutions developed further, with the sprouting of collaborations in and across countries and continents. The drive for digital transformation, which is now gaining momentum throughout higher education institutions worldwide, is of major significance in giving renewed stimulus to one of the boldest agendas that have been adopted by mankind, the SDGs, the United Nations' Sustainable Development Goals.
Specifying roles in purchasing and supply management in the era of Industry 4.0: A Delphi study
(2021)
New technologies and systems within the field of purchasing and supply management (PSM) call forth responsibilities and require expertise. Moving towards Industry 4.0 in purchasing, increasing attention on specialization within talent and skills, where human capital is needed to exploit the full potential of technologies. Based on an internet-based real-time Delhi study with 47 experts within the PSM field, six future purchasing roles have been defined and elaborated. These future roles connect to the maturing and emerging technologies within the purchasing field and provide a guideline to further develop towards Industry 4.0 in purchasing based on a human-centered evolutionary approach.
To increase maturity within purchasing and supply management (PSM), future purchasing skills are needed based on the technological development towards Industry 4.0. Past research, eg, the work of Bals, Schulze, Kelly, and Stek (2019), started to address this issue based on literature review and interview studies. However, a detailed description of these skills is missing. Utilizing a real-time Delhi study with 45 experts within the PSM field, nine future purchasing skills have been elaborated. Identified skills connect to the maturing and emerging technologies within purchasing and provide a guideline towards Industry 4.0 in purchasing based on a human-centric perspective.
This paper uses the findings from a literature review and series of expert interviews to develop a richer and Purchasing and Supply Management (PSM) context-specific perspective of the different key techniques, tools and principles that can be used to develop gamified learning to enhance the skills required by PSM professionals in dealing with current and future challenges, such as the transformation to Industry 4.0. It also provides further details of the different stages of implementing gamified learning, which can enhance the success of any such provision.
Professional roles, including specific skills for each role, are a step towards higher professionalism and maturity within purchasing and supply management (PSM). The global development towards increasing digitalization, Industry 4.0, globalization, and increasing attention for corporate social responsibility force change within the purchasing organizations. Here, PSM's professional roles and skills are a good starting point to manage these changes by redefining professional roles organized by specific skills and responsibilities. For this reason, based on a systematic literature review and three World Cafés with 29 purchasing professionals, this study compiles a list of Industry 4.0 professional roles and skills in PSM.
Piston pumps play a key role in factory automation and their availability is very critical for the smooth running of production processes. Modern installations, such as production plants and machines, are becoming increasingly complex. Therefore, the probability of a complete system failure due to a single critical component also increases. Maintenance processes with intelligent devices are therefore very important to achieve maximum economic efficiency and safety. Periodic or continuous monitoring of system components provides key information about the current physical state of the system, enabling early detection of emerging failures. Knowledge of future failures makes it possible to move from the concept of preventive maintenance to intelligent predictive maintenance. In this way, consequential damage and complete system failure can be avoided, maximizing system availability and safety. This paper reflects the development and implementation of a neural network system for abnormal state prediction of piston pumps. After a short introduction into piston pumps and their potential abnormal states, statistical and periodical analysis are presented. Then the design and implementation of suitable neural networks are discussed. Finally, a conclusion is drawn and the observed accuracies as well as potential next steps are discussed.
State of the art classifiers split Alzheimer’s disease progression into a limited number of stages and use a comparatively small database. For the best treatment, it is desirable to have the highest resolution from the progression of the disease. This paper proposes a reliable deep convolutional neural network for the classification of six different Alzheimer’s disease stages based on Magnetic Resonance Imaging (MRI). The peculiarity of this paper is the introduction of a new, sixth, disease stage, and the large amount of data that has been taken into account. Additionally, not only the testing accuracy is analyzed, but also the robustness of the classifier to have feedback on how certain the neural network makes its predictions.
Introduction: Moving towards a more plant-based dietary pattern would likely be beneficial in terms of a variety of sustainability dimensions.
Methodology: We conducted a 2-year intervention study with six measurement time points (baseline, 10 weeks, 6 months, 1 year, 1½ years, 2 years) in rural northwest Germany. The intervention consisted of a lifestyle programme, and dietary recommendations were to move towards a healthy, plant-based diet. The control group received no intervention. Diet quality was assessed with the healthful plant-based diet index (hPDI).
Results: In the intervention group (n = 67), the 2-year trajectory of hPDI was significantly higher compared to control (n = 39; p 0.001; between-group difference: 5.7 (95% CI 4.0, 7.3) food portions/day; adjusted for baseline). The 2-year trajectory of meat intake was significantly lower in the intervention group (n = 79) compared to control (n = 40; p 0.001; between-group difference: -0.7 (95% CI -0.9, -0.5) portions/day; adjusted for baseline).
Conclusion: Our study confirms that plant-based nutrition education in the general population is likely to result in at least modest dietary improvements in terms of general healthfulness and meat reduction.
In the so-called ecosystem economy, new platform-based business models evolve rap-idly based on the prospects of digital technology. In the B2B context especially, data-driven platforms are highly relevant. Thus far, little research has been conducted on service providers, the so-called complementors of data-driven platforms. Therefore, this paper represents just a starting point for gaining deeper insights into the different facets of complementor management. For empirical evidence, we draw on semi-structured expert interviews with platform managers. The findings outline the distinct characteristics of open and closed platforms which need to be taken into account for complementor management. Moreover, the paper reveals a number of differences in managing suppliers compared to managing complementors. In addition, our study shows that the key factors influencing complementor management include platform openness, partnership intensity, strategic fit, and market structure respectively poten-tial.
Innovative business models for data-driven B2B platforms evolve rapidly based on the prospects of digital technology. In addition to the platform provider, service providers on the supply side of the digital platform - the so-called complementors - play an important role in the process of value creation. This paper highlights the complementors’ perspective on the different facets of complementor relationship management (CoRM) and answers the following research questions: From the perspective of a complementor, what are the main fields of CoRM for data-driven B2B platforms? What factors of influence comprise the reason complementors join a platform?
Complementor relationship management for Data-driven B2B platforms: Towards a Holistic approach
(2021)
In the so-called ecosystem economy, new platform-based business models evolve rapidly based on the prospects of digital technology. Especially in the B2B context, data-driven platforms are highly relevant. Thus far, little research has been conducted on the supply side of data-driven platforms and especially on service providers, the so-called complementors. Therefore, this paper offers insights into the various facets of complementor relationship management (CoRM). The paper aims to develop a framework for the management of complementors of data-driven B2B platforms. For empirical evidence, we draw on 14 semi-structured expert interviews with platform managers and complementors. The findings outline two big areas of CoRM and discuss distinct characteristics of partner management and technology management. For partner management the differentiation into open and closed platform needs to be taken into account for complementor relationship management. Moreover, our study reveals the key factors of technology management which lead from platform infrastructure to digital applications like digital twins or predictive maintenance.
BPMN-based Process-Driven Applications (PDA) require less coding since they are not only based on source code, but also on executable process models. Automated testing of such model-driven applications gains growing relevance, and it becomes a key enabler if we want to found their development on continuous integration (CI) techniques.While process analysts are typically responsible for test case specifications from a business perspective, technically skilled process engineers take the responsibility for implementing the required test code. This is time-consuming and, due to their often different skills and backgrounds, might result in communication problems such as information losses and misunderstandings. This paper presents a new approach which enables an analyst to generate executable tests for PDAs without the need for manual coding. It consists of a sophisticated model analysis, a wizard-based specification of test cases, and a subsequent code generation. The resulting tests can easily be integrated into CI pipelines.The concept is underpinned by a user-friendly tool which has been evaluated in case studies and in real-world implementation projects from different industry sectors. During the evaluation, the prototype proved a more efficient test creation process and a higher test quality.
A data sender in an IP based network is only capable to efficiently use a network path if it knows the packet size limit of the path, i.e., the Path Maximum Transmission Unit (PMTU). The IETF recently specified a PMTU discovery framework for transport protocols like QUIC. This paper complements this specification by presenting a search algorithm. In addition, it defines several metrics and shows results of analyses for the algorithm with various PMTU candidate sequences using these metrics. We integrated the PMTU discovery with our algorithm into a QUIC simulation model. This paper describes the integration and presents measurements obtained by simulations.
A user-friendly Pitot probe data reduction Excel-Refprop-Routine for non-ideal gas flow applications
(2021)
The Gaulwerk hydropower plant (HPP) has a design discharge of 3.5 m3/s and generates about 6.5 GWh per year. The HPP has been in operation since 1963 and uses the flow of two alpine streams. The HPP impounds a 300 m long reservoir with a 6.50 m high weir. The storage is completely filled with sediments and is classified as a valuable habitat for fauna and flora. Due to the sedimentation, the area upstream of the reservoir head inundates about two to three times per year during small flood events, leading to complaints from affected landowners and adjacent municipalities. To investigate sustainable solutions, a study of alternatives has been carried out in which three alternatives to im-prove both the sediment and flood situation are being investigated. In addition, the residual flow release will be adjusted and fish facilities realized in all alternatives. The paper will summarize the analysis of the alternatives encompassing the (1)
flood situation, (2) sediment management, (3) reha-bilitation measures of the hydraulic structures and their costs and (4) the environmental impact.