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Disturbances of the cardiac conduction system causing reentry mechanisms above the atrioventricular (AV) node are induced by at least one accessory pathway with different conducting properties and refractory periods. This work aims to further develop the already existing and continuously expanding Offenburg heart rhythm model to visualise the most common supraventricular reentry tachycardias to provide a better understanding of the cause of the respective reentry mechanism.
The need for the logistics sector to timely respond to the increasing requirements of a globalised and digitalised world relies greatly on the com- petences and skills of its labour force. It becomes therefore essential to reinforce the cooperation between universities and business partners in the logistics and supply chain management fields across the European region and to build a logistics knowledge cluster supported by a communication and collaboration platform to foster continuous learning, skill acquisition and experience sharing anytime anywhere. In this paper we focus on designing the conceptual and technical framework for a communication and collaboration platform with the aim to establish the communication pipelines between the partner institutions, facilitating user interactions and exchange, leading to the creation of new knowledge and innovation in the logistics field. This framework is based on the requirements of the three main stakeholders: students, lecturers and companies, and consists of four functional areas defined according to the platform opera- tional requirements. A working prototype of the platform was developed using the Moodle learning management system and its core tools to determine its applicability and possible enhancement requirements. In the next stages of the project some additional tools like a knowledge base and the integration of the partners’ learning management systems to form the logistics knowledge cluster will be implemented.
With the increasing share of renewable energies and the nuclear phase-out, the energy transition is accelerating. From the perspective of building technology, there is great potential to support this transition given its large share in total energy consumption and the increasing number of flexible and controllable components and storages. However, a question often asked at the plant level is: "How do we use this flexibility to support the regional grid?". In this work, a grid-supportive controller of a real-world building energy plant was developed using mathematical optimisation methods and its technical feasibility was demonstrated. The results could convince actors from the energy industry and academia about the practicality of these methods and offer tools for their implementation.
The Go programming language is an increasingly popular language but some of its features lack a formal investigation. This article explains Go's resolution mechanism for overloaded methods and its support for structural subtyping by means of translation from Featherweight Go to a simple target language. The translation employs a form of dictionary passing known from type classes in Haskell and preserves the dynamic behavior of Featherweight Go programs.
A Hybrid Optoelectronic Sensor Platform with an Integrated Solution‐Processed Organic Photodiode
(2021)
Hybrid systems, unifying printed electronics with silicon‐based technology, can be seen as a driving force for future sensor development. Especially interesting are sensing elements based on printed devices in combination with silicon‐based high‐performance electronics for data acquisition and communication. In this work, a hybrid system integrating a solution‐processed organic photodiode in a silicon‐based system environment, which enables flexible device measurement and application‐driven development, is presented. For performance evaluation of the integrated organic photodiode, the measurements are compared to a silicon‐based counterpart. Therefore, the steady state response of the hybrid system is presented. Promising application scenarios are described, where a solution‐processed organic photodiode is fully integrated in a silicon system.
Cryptographic protection of messages requires frequent updates of the symmetric cipher key used for encryption and decryption, respectively. Protocols of legacy IT security, like TLS, SSH, or MACsec implement rekeying under the assumption that, first, application data exchange is allowed to stall occasionally and, second, dedicated control messages to orchestrate the process can be exchanged. In real-time automation applications, the first is generally prohibitive, while the second may induce problematic traffic patterns on the network. We present a novel seamless rekeying approach, which can be embedded into cyclic application data exchanges. Although, being agnostic to the underlying real-time communication system, we developed a demonstrator emulating the widespread industrial Ethernet system PROFINET IO and successfully use this rekeying mechanism.
Quantifying the midsole material characteristics of athletic footwear is a standard task in footwear research and development. Current material testing protocols primarily focus on the determination of cushioning properties of the heel region or the quantification of the midsole properties as one assembly. However, midsoles possess different spatial material properties that have not been quantified from previous methodologies. Therefore, new material testing methods are required to quantify the local material response of athletic footwear. We developed a cyclical force-controlled material testing protocol for the determination of non-homogeneously distributed material stiffness with a high spatial resolution. In five prototype shoes varying in their stiffness distribution, we found that the material properties can be reliably measured across the midsole. Furthermore, we observed a characteristic non-linear material response regardless of the midsole location. We found that the material stiffness increased with an increase of the applied force and that this effect is further intensified by higher testing cycles. Additionally, the obtained midsole stiffness depends on the geometry of the midsole. We explored different approaches to reduce the measurement time of the testing protocol and found that the number of measurements can be reduced by 70% using 2 D-interpolation procedures. Determining the spatial material properties of midsoles needs to be considered to understand foot-shoe interactions. Furthermore, this measurement protocol can be used for quality control within the footwear and can be adapted for considering the effects of different running styles or speeds on ground force application characteristics.
The transition from college to university can have a variety of psychological effects on students who need to cope with daily obligations by themselves in a new setting, which can result in loneliness and social isolation. Mobile technology, specifically mental health apps (MHapps), have been seen as promising solutions to assist university students who are facing these problems, however, there is little evidence around this topic. My research investigates how a mobile app can be designed to reduce social isolation and loneliness among university students. The Noneliness app is being developed to this end; it aims to create social opportunities through a quest-based gamified system in a secure and collaborative network of local users. Initial evaluations with the target audience provided evidence on how an app should be designed for this purpose. These results are presented and how they helped me to plan the further steps to reach my research goals. The paper is presented at MobileHCI 2020 Doctoral Consortium.
Pure orbital blowout fractures occur within the confines of the internal orbital wall. Restoration of orbital form and volume is paramount to prevent functional and esthetic impairment. The anatomical peculiarity of the orbit has encouraged surgeons to develop implants with customized features to restore its architecture. This has resulted in worldwide clinical demand for patient-specific implants (PSIs) designed to fit precisely in the patient’s unique anatomy. Material extrusion or Fused filament fabrication (FFF) three-dimensional (3D) printing technology has enabled the fabrication of implant-grade polymers such as Polyetheretherketone (PEEK), paving the way for a more sophisticated generation of biomaterials. This study evaluates the FFF 3D printed PEEK orbital mesh customized implants with a metric considering the relevant design, biomechanical, and morphological parameters. The performance of the implants is studied as a function of varying thicknesses and porous design constructs through a finite element (FE) based computational model and a decision matrix based statistical approach. The maximum stress values achieved in our results predict the high durability of the implants, and the maximum deformation values were under one-tenth of a millimeter (mm) domain in all the implant profile configurations. The circular patterned implant (0.9 mm) had the best performance score. The study demonstrates that compounding multi-design computational analysis with 3D printing can be beneficial for the optimal restoration of the orbital floor.
The NaSiO Institute (Institute for Sustainable Silicate Research in Offenburg, https://inasio.hs-offenburg.de/) has been working for years on climate-friendly alternatives to insulation materials and inorganic binders, as well as the reasonable use of construction waste in the building industry. The aim of research is to realize the enormous CO 2 saving potential of the construction sector worldwide. A stopping of climate heating will only succeed if these climate-friendly alternatives are used in the construction industry. This is the only way to realize the enormous CO2 savings that will be needed in future to comply with the Paris Agreement.
Treadmills are essential to the study of human and animal locomotion as well as for applied diagnostics in both sports and medicine. The quantification of relevant biomechanical and physiological variables requires a precise regulation of treadmill belt velocity (TBV). Here, we present a novel method for time-efficient tracking of TBV using standard 3D motion capture technology. Further, we analyzed TBV fluctuations of four different treadmills as seven participants walked and ran at target speeds ranging from 1.0 to 4.5 m/s. Using the novel method, we show that TBV regulation differs between treadmill types, and that certain features of TBV regulation are affected by the subjects’ body mass and their locomotion speed. With higher body mass, the TBV reductions in the braking phase of stance became higher, even though this relationship differed between locomotion speeds and treadmill type (significant body mass × speed × treadmill type interaction). Average belt speeds varied between about 98 and 103% of the target speed. For three of the four treadmills, TBV reduction during the stance phase of running was more intense (> 5% target speed) and occurred earlier (before 50% of stance phase) unlike the typical overground center of mass velocity patterns reported in the literature. Overall, the results of this study emphasize the importance of monitoring TBV during locomotor research and applied diagnostics. We provide a novel method that is freely accessible on Matlab’s file exchange server (“getBeltVelocity.m”) allowing TBV tracking to become standard practice in locomotion research.
In recent years, physically unclonable functions (PUFs) have gained significant attraction in IoT security applications, such as cryptographic key generation and entity authentication. PUFs extract the uncontrollable production characteristics of different devices to generate unique fingerprints for security applications. When generating PUF-based secret keys, the reliability and entropy of the keys are vital factors. This study proposes a novel method for generating PUF-based keys from a set of measurements. Firstly, it formulates the group-based key generation problem as an optimization problem and solves it using integer linear programming (ILP), which guarantees finding the optimum solution. Then, a novel scheme for the extraction of keys from groups is proposed, which we call positioning syndrome coding (PSC). The use of ILP as well as the introduction of PSC facilitates the generation of high-entropy keys with low error correction costs. These new methods have been tested by applying them on the output of a capacitor network PUF. The results confirm the application of ILP and PSC in generating high-quality keys.
Governments have restricted public life during the COVID-19 pandemic, inter alia closing sports facilities and gyms. As regular exercise is essential for health, this study examined the effect of pandemic-related confinements on physical activity (PA) levels. A multinational survey was performed in 14 countries. Times spent in moderate-to-vigorous physical activity (MVPA) as well as in vigorous physical activity only (VPA) were assessed using the Nordic Physical Activity Questionnaire (short form). Data were obtained for leisure and occupational PA pre- and during restrictions. Compliance with PA guidelines was calculated based on the recommendations of the World Health Organization (WHO). In total, n = 13,503 respondents (39 ± 15 years, 59% females) were surveyed. Compared to pre-restrictions, overall self-reported PA declined by 41% (MVPA) and 42.2% (VPA). Reductions were higher for occupational vs. leisure time, young and old vs. middle-aged persons, previously more active vs. less active individuals, but similar between men and women. Compared to pre-pandemic, compliance with WHO guidelines decreased from 80.9% (95% CI: 80.3–81.7) to 62.5% (95% CI: 61.6–63.3). Results suggest PA levels have substantially decreased globally during the COVID-19 pandemic. Key stakeholders should consider strategies to mitigate loss in PA in order to preserve health during the pandemic.
The following describes a new method for estimating the parameters of an interior permanent magnet synchronous machine (IPMSM). For the estimation of the parameters the current slopes caused by the switching of the inverter are used to determine the unknowns of the system equations of the electrical machine. The angle and current dependence of the machine parameters are linearized within a PWM cycle. By considering the different switching states of the inverter, several system equations can be derived and a solution can be found within one PWM cycle. The use of test signals and filter-based approaches is avoided. The derived algorithm is explained and validated with measurements on a test bench.
Time-Sensitive Networking (TSN) is the most promising time-deterministic wired communication approach for industrial applications. To extend TSN to "IEEE 802.11" wireless networks two challenging problems must be solved: synchronization and scheduling. This paper is focused on the first one. Even though a few solutions already meet the required synchronization accuracies, they are built on expensive hardware that is not suited for mass market products. While next Wi-Fi generation might support the required functionalities, this paper proposes a novel method that makes possible high-precision wireless synchronization using commercial low-cost components. With the proposed solution, a standard deviation of synchronization error of less than 500 ns can be achieved for many use cases and system loads on both CPU and network. This performance is comparable to modern wired real-time field busses, which makes the developed method a significant contribution for the extension of the TSN protocol to the wireless domain.
Most eCommerce applications, like web-shops have millions of products. In this context, the identification of similar products is a common sub-task, which can be utilized in the implementation of recommendation systems, product search engines and internal supply logistics. Providing this data set, our goal is to boost the evaluation of machine learning methods for the prediction of the category of the retail products from tuples of images and descriptions.
A Review on Kinetic Energy Harvesting with Focus on 3D Printed Electromagnetic Vibration Harvesters
(2021)
The increasing amount of Internet of Things (IoT) devices and wearables require a reliable energy source. Energy harvesting can power these devices without changing batteries. Three-dimensional printing allows us to manufacture tailored harvesting devices in an easy and fast way. This paper presents the development of hybrid and non-hybrid 3D printed electromagnetic vibration energy harvesters. Various harvesting approaches, their utilised geometry, functional principle, power output and the applied printing processes are shown. The gathered harvesters are analysed, challenges examined and research gaps in the field identified. The advantages and challenges of 3D printing harvesters are discussed. Reported applications and strategies to improve the performance of printed harvesting devices are presented.
It is important to minimize the unscheduled downtime of machines caused by outages of machine components in highly automated production lines. Considering machine tools such as, grinding machines, the bearing inside of spindles is one of the most critical components. In the last decade, research has increasingly focused on fault detection of bearings. In addition, the rise of machine learning concepts has also intensified interest in this area. However, up to date, there is no single one-fits-all solution for predictive maintenance of bearings. Most research so far has only looked at individual bearing types at a time.
This paper gives an overview of the most important approaches for bearing-fault analysis in grinding machines. There are two main parts of the analysis presented in this paper. The first part presents the classification of bearing faults, which includes the detection of unhealthy conditions, the position of the error (e.g. at the inner or at the outer ring of the bearing) and the severity, which detects the size of the fault. The second part presents the prediction of remaining useful life, which is important for estimating the productive use of a component before a potential failure, optimizing the replacement costs and minimizing downtime.
Multiple Object Tracking (MOT) is a long-standing task in computer vision. Current approaches based on the tracking by detection paradigm either require some sort of domain knowledge or supervision to associate data correctly into tracks. In this work, we present a self-supervised multiple object tracking approach based on visual features and minimum cost lifted multicuts. Our method is based on straight-forward spatio-temporal cues that can be extracted from neighboring frames in an image sequences without supervision. Clustering based on these cues enables us to learn the required appearance invariances for the tracking task at hand and train an AutoEncoder to generate suitable latent representations. Thus, the resulting latent representations can serve as robust appearance cues for tracking even over large temporal distances where no reliable spatio-temporal features can be extracted. We show that, despite being trained without using the provided annotations, our model provides competitive results on the challenging MOT Benchmark for pedestrian tracking.
We present a densitometric quantification method for triclosan in toothpaste, separated by high-performance thin-layer chromatography (HPTLC) and using a 48-bit flatbed scanner as the detection system. The sample was band-wise applied to HPTLC plates (10 × 20 cm), with fluorescent dye, Merck, Germany (1.05554). The plates were developed in a vertical developing chamber with 20 min of chamber saturation over 70 mm, using n-heptane–methyl tert-butyl ether–acetic acid (92:8:0.1, V/V) as solvent. The RF value of triclosan is hRF = 22.4, and quantification is based on direct measurements using an inexpensive 48-bit flatbed scanner for color measurements (in red, green, and blue) after plate staining with 2,6-dichloroquinone-4-chloroimide (Gibbs' reagent). Evaluation of the red channel makes the measurements of triclosan very specific. For linearization, an extended Kubelka–Munk expression was used for data transformation. The range of linearity covers more than two orders of magnitude and is between 91 and 1000 ng. The separation method is inexpensive, fast and reliable.
Most recently, the federal government in Germany published new climate goals in order reach climate neutrality by 2045. This paper demonstrates a path to a cost optimal energy supply system for the German power grid until the year 2050. With special regard to regionality, the system is based on yearly myopic optimization with the required energy system transformation measures and the associated system costs. The results point out, that energy storage systems (ESS) are fundamental for renewables integration in order to have a feasible energy transition. Moreover, the investment in storage technologies increased the usage of the solar and wind technologies. Solar energy investments were highly accompanied with the installation of short-term battery storage. Longer-term storage technologies, such as H2, were accompanied with high installations of wind technologies. The results pointed out that hydrogen investments are expected to overrule short-term batteries if their cost continues to decrease sharply. Moreover, with a strong presence of ESS in the energy system, biomass energy is expected to be completely ruled out from the energy mix. With the current emission reduction strategy and without a strong presence of large scale ESS into the system, it is unlikely that the Paris agreement 2° C target by 2050 will be achieved, let alone the 1.5° C.
Achieving Positive Hospitality Experiences through Technology: Findings from Singapore and Malaysia
(2021)
Customers’ experience is one of the most impactful factors in the tourism industry. Only by offering customers an excellent experience is it possible to build and ensure long-term customer loyalty. In today’s world, technology plays a key role in providing customers with an excellent customer experience. This study has the objective of analyzing how a positive customer experience can be achieved, and which technologies are necessary to ensure this. Results were collected through a literature review, and qualitative interviews with managers of selected hotels, as well as of attractions in Malaysia and Singapore. The analysis of these hotels and attractions is based on a set of criteria to determine the extent of the adoption of the new standards that contribute to positive online customer experiences. As a conclusion, different perspectives are compared, and positive and negative aspects of the use of modern technologies in the tourism industry are specified and discussed.
Active participation of industrial enterprises in electricity markets - a generic modeling approach
(2021)
Industrial enterprises represent a significant portion of electricity consumers with the potential of providing demand-side energy flexibility from their production processes and on-site energy assets. Methods are needed for the active and profitable participation of such enterprises in the electricity markets especially with variable prices, where the energy flexibility available in their manufacturing, utility and energy systems can be assessed and quantified. This paper presents a generic model library equipped with optimal control for energy flexibility purposes. The components in the model library represent the different technical units of an industrial enterprise on material, media, and energy flow levels with their process constraints. The paper also presents a case study simulation of a steel-powder manufacturing plant using the model library. Its energy flexibility was assessed when the plant procured its electrical energy at fixed and variable electricity prices. In the simulated case study, flexibility use at dynamic prices resulted in a 6% cost reduction compared to a fixed-price scenario, with battery storage and the manufacturing system making the largest contributions to flexibility.
Additive manufacturing is a rapidly growing manufacturing process for which many new processes and materials are currently being developed. The biggest advantage is that almost any shape can be produced, while conventional manufacturing methods reach their limits. Furthermore, a lot of material is saved because the part is created in layers and only as much material is used as necessary. In contrast, in the case of machining processes, it is not uncommon for more than half of the material to be removed and disposed of. Recently, new additive manufacturing processes have been on the market that enables the manufacturing of components using the FDM process with fiber reinforcement. This opens up new possibilities for optimizing components in terms of their strength and at the same time increasing sustainability by reducing materials consumption and waste. Within the scope of this work, different types of test specimens are to be designed, manufactured and examined. The test specimens are tensile specimens, which are used both for standardized tensile tests and for examining a practical component from automotive engineering used in student project. This project is a vehicle designed to compete in the Shell Eco-marathon, one of the world’s largest energy efficiency competitions. The aim is to design a vehicle that covers a certain distance with as little fuel as possible. Accordingly, it is desirable to manufacture the components with the lowest possible weight, while still ensuring the required rigidity. To achieve this, the use of fiber-reinforced 3D-printed parts is particularly suitable due to the high rigidity. In particular, the joining technology for connecting conventionally and additively manufactured components is developed. As a result, the economic efficiency was assessed, and guidelines for the design of components and joining elements were created. In addition, it could be shown that the additive manufacturing of the component could be implemented faster and more sustainably than the previous conventional manufacturing.
In bimodal cochlear implant (CI) / hearing aid (HA) users a constant interaural time delay in the order of several milliseconds occurs due to differences in signal processing of the devices. For MED-EL CI systems in combination with different HA types, we have quantified the respective device delay mismatch (Zirn et al. 2015). In the current study, we investigate the effect of the device delay mismatch in simulated and actual bimodal listeners on sound localization accuracy.
To deal with the device delay mismatch in actual bimodal listeners we delayed the CI stimulation according to the measured HA processing delay and two other values. With all delay values highly significant improvements of the rms error in the localization task were observed compared to the test without the delay. The results help to narrow down the optimal patient-specific delay value.
Wer als Pädagoge und Wissenschaftler das Thema „Digitalisierung und Unterricht“ kritisch reflektiert, stellt fest, dass nur Wenige die Tragweite der schon lange beabsichtigten Transformation von Bildungseinrichtungen zu IT-konformen, algorithmisch gesteuerten Lernfabriken realisieren. Die Corona-Pandemie ist nur der aktuelle Anlass, seit langem bekannte Digitalisierungsstrategien nur schneller umzusetzen. Dabei ist der Wechsel von ursprünglich pädagogischen Prämissen als Basis von Lehr- und Lernprozessen hin zum Paradigma der datengestützte Schulentwicklung und der empirischer Bildungsforschung wesentlich. Daten und Statistik dominieren das Individuum wie das Unterrichtsgeschehen. Es bedeutet sachlogisch, möglichst viele Daten der Schülerinnen und Schüler zu sammeln, auszuwerten und zur Grundlage von Entscheidungen über Lerninhalte und -prozessen zu machen. Lehren und Lernen wird wieder einmal als, heute digital, steuerbarer Prozess behauptet, wie schon beim programmierten Lernen in den 1950er Jahren. Was sind mögliche Alternativen?
IoT networks are increasingly used as entry points for cyberattacks, as often they offer low-security levels, as they may allow the control of physical systems and as they potentially also open the access to other IT networks and infrastructures. Existing intrusion detection systems (IDS) and intrusion prevention systems (IPS) mostly concentrate on legacy IT networks. Nowadays, they come with a high degree of complexity and adaptivity, including the use of artificial intelligence. It is only recently that these techniques are also applied to IoT networks. In this paper, we present a survey of machine learning and deep learning methods for intrusion detection, and we investigate how previous works used federated learning for IoT cybersecurity. For this, we present an overview of IoT protocols and potential security risks. We also report the techniques and the datasets used in the studied works, discuss the challenges of using ML, DL and FL for IoT cybersecurity and provide future insights.
Soziale Roboter, die mit uns kommunizieren und menschliche Verhaltensmuster imitieren, sind ein wichtiges Zukunftsthema. Während viele Arbeiten ihr Design und ihre Akzeptanz erforschen, gibt es bislang nur wenige Untersuchungen zu ihrer Marktfähigkeit. Der Schwerpunkt dieser Arbeit liegt auf dem Einsatz sozialer Roboter in den Bereichen Gesundheit und Pflege, wo die zukünftige Integration sozialer Roboter ein enormes Potenzial hat. Eine Studie mit 197 Personen aus Italien und Deutschland untersucht gewünschte Funktionalitäten und Kaufpräferenzen und berücksichtigt hierbei kulturelle Unterschiede. Dabei bestätigte sich die Wichtigkeit mehrerer Dimensionen des ALMERE-Modells (z. B. wahrgenommene Freude, Nützlichkeit und Vertrauenswürdigkeit). Die Akzeptanz korreliert stark mit der Investitionsbereitschaft. Viele ältere Personen betrachten soziale Roboter als „assistierende technische Geräte“ und erwarten, dass diese von Versicherungen und der öffentlichen Hand bezuschusst werden. Um ihren zukünftigen Einsatz zu erleichtern, sollten soziale Roboter in die Datenbanken medizinischer Hilfsmittel integriert werden.
An Empirical Investigation of Model-to-Model Distribution Shifts in Trained Convolutional Filters
(2021)
We present first empirical results from our ongoing investigation of distribution shifts in image data used for various computer vision tasks. Instead of analyzing the original training and test data, we propose to study shifts in the learned weights of trained models. In this work, we focus on the properties of the distributions of dominantly used 3x3 convolution filter kernels. We collected and publicly provide a data set with over half a billion filters from hundreds of trained CNNs, using a wide range of data sets, architectures, and vision tasks. Our analysis shows interesting distribution shifts (or the lack thereof) between trained filters along different axes of meta-parameters, like data type, task, architecture, or layer depth. We argue, that the observed properties are a valuable source for further investigation into a better understanding of the impact of shifts in the input data to the generalization abilities of CNN models and novel methods for more robust transfer-learning in this domain.
An Empirical Study of Explainable AI Techniques on Deep Learning Models For Time Series Tasks
(2021)
Decision explanations of machine learning black-box models are often generated by applying Explainable AI (XAI) techniques. However, many proposed XAI methods produce unverified outputs. Evaluation and verification are usually achieved with a visual interpretation by humans on individual images or text. In this preregistration, we propose an empirical study and benchmark framework to apply attribution methods for neural networks developed for images and text data on time series. We present a methodology to automatically evaluate and rank attribution techniques on time series using perturbation methods to identify reliable approaches.
A coordinated operation of decentralised micro-scale hybrid energy systems within a locally managed network such as a district or neighbourhood will play a significant role in the sector-coupled energy grid of the future. A quantitative analysis of the effects of the primary energy factors, energy conversion efficiencies, load profiles, and control strategies on their energy-economic balance can aid in identifying important trends concerning their deployment within such a network. In this contribution, an analysis of the operational data from five energy laboratories in the trinational Upper-Rhine region is evaluated and a comparison to a conventional reference system is presented. Ten exemplary data-sets representing typical operation conditions for the laboratories in different seasons and the latest information on their national energy strategies are used to evaluate the primary energy consumption, CO2 emissions, and demand-related costs. Various conclusions on the ecologic and economic feasibility of hybrid building energy systems are drawn to provide a toe-hold to the engineering community in their planning and development.
We present a video-densitometric high-performance thin-layer chromatography (HPTLC) quantification method for patulin in apple juice, developed in a vertical chamber from the starting point to a distance of 50 mm, using MTBE, n-pentane (9 + 5, v/v) as mobile phase. After separation the plate is sprayed with methyl-benzothiazolinone hydrazone hydrochloride monohydrate (MBTH) solution (40 mg in 20 mL methanol) and heated at 105 °C for 15 min. Patulin zones are transformed into yellow spots. The quantification is based on direct measurements using an inexpensive 48-bit flatbed scanner for color measurements (in red, green, and blue). Evaluation of the blue channel makes the measurements very specific. Quantification in fluorescence was also done by use of a 16-bit CCD-camera and UV-366 nm illumination as well as using a HPTLC DAD-scanner. For linearization the extended Kubelka–Munk expression for data transformation was used. The range of linearity covers more than two magnitudes and lies between 5 and 800 ng patulin. The extraction of 20 g apple juice and an extract application on plate up to 50 µL allows statistically defined checking the limit of detection (LOD) of 50 ng patulin per track, which is equivalent to 50 µg patulin per kg apple juice.
Ein tiefgreifendes Verständnis des zyklischen Plastizitätsverhaltens metallischer Werkstoffe ist sowohl für die Optimierung der Materialeigenschaften als auch für die industrielle Auslegung und Fertigung von Bauteilen von hoher Relevanz. Insbesondere moderne Legierungen wie Duplex-Stähle zeigen unter Lastumkehr aufgrund des komplexen mehrphasigen Gefüges sowie der Neigung zu verschiedenen Ausscheidungsreaktionen einen ausgeprägten Bauschinger-Effekt, welcher bei technischen Umformvorgängen berücksichtigt werden muss. Der Bauschinger-Effekt begründet sich maßgeblich in der Entstehung von Rückspannungen, welche aus dem unterschiedlichen Plastizitätsverhalten der austenitischen und ferritischen Phase resultieren. Instrumentierte Mikroindenter-Versuche in ausgewählten Ferrit- und Austenitkörnern haben gezeigt, dass austenitische Gefügebestandteile durch einen deutlich früheren Fließbeginn sowie eine stärkere Rückplastifizierung während der Entlastung charakterisiert sind. Zudem wurde nachgewiesen, dass Ausscheidungen im Rahmen einer 475°C-Versprödung diesen Phasenunterschied verstärken und somit in einem höheren Bauschinger-Effekt resultieren.
Das vermutlich wichtigste Tatbestandsmerkmal der Business Judgment Rule ist das Vorliegen einer angemessenen Informationsgrundlage. Sie gilt dann als erreicht, wenn ein Geschäftsleiter vernünftigerweise annehmen darf, dass die Verbesserung einer gegebenen Informationsqualität den dafür erforderlichen Aufwand an Zeit bzw. Geld nicht rechtfertigt. Implizit wird hierbei vorausgesetzt, dass man verschiedene Ausmaße an Zeit, Geld und Informationsqualität unterscheiden kann. Für den Zeit- und Geldaufwand stimmt das auch, aber wie stuft man die Informationsqualität ab? Im Beitrag wird für prognosebezogene Informationen ein entsprechender Vorschlag gemacht.
Surface acoustic waves are propagated toward the edge of an anisotropic elastic medium (a silicon crystal), which supports leaky waves with a high degree of localization at the tip of the edge. At an angle of incidence corresponding to phase matching with this leaky wedge wave, a sharp peak in the reflection coefficient of the surface wave was found. This anomalous reflection is associated with efficient excitation of the leaky wedge wave. In laser ultrasound experiments, surface acoustic wave pulses were excited and their reflection from the edge of the sample and their partial conversion into leaky wedge wave pulses was observed by optical probe-beam deflection. The reflection scenario and the pulse shapes of the surface and wedge-localized guided waves, including the evolution of the acoustic pulse traveling along the edge, have been confirmed in detail by numerical simulations.
The applicability of characteristics of local magnetic fields for more precise determination of localization of subjects and/or objects in indoor environments, such as railway stations, airports, exhibition halls, showrooms, or shopping centers, is considered. An investigation has been carried out to find out whether and how low-cost magnetic field sensors and mobile robot platforms can be used to create maps that improve the accuracy and robustness of later navigation with smartphones or other devices.
IoT-Plattformen stellen ein zentrales Element für die Vernetzung von physischen Objekten und die Bereitstellung deren Daten für digitale Zwillinge dar. Der Markt für solche Plattformen ist in den vergangenen Jahren stark gewachsen. Bei inzwischen über 600 Anbietern ist die Wahl der „richtigen“ Plattform für das eigene Unternehmen keine triviale Aufgabe mehr. Dieser Beitrag soll Unternehmen im Auswahlprozess unterstützen, indem gängige Funktionen von IoT-Plattformen und Kriterien für die Auswahl von IoT-Plattformen aufgezeigt werden.
Ziel des Pilotprojektes EnMa-HAW ist die Erarbeitung und Erprobung technisch und organisatorisch übertragbarer Konzepte für ein automationsgestütztes Energiemanagement an allen Hochschulen für angewandte Wissenschaften im Land Baden-Württemberg. Das Energiemanagement wird technisch mittels Messtechnik, Datenerfassung, Datenspeicherung und Visualisierung umgesetzt und organisatorisch mit einem Energiezirkel in den Hochschulen verankert.
Digital- und Medientechnik sind heute typische Bestandteile des Unterrichts. Um Lern- und Verstehensprozesse zu ermöglichen, braucht es aber vor allem das Gespräch und den Diskurs. Lernen ist ein individueller und sozialer Prozess, der nicht digital kompensiert werden kann, wenn Verstehen und nicht nur Repetition das Ziel ist. Medien und Medientechnik können Lernprozesse unterstützen, aber wir lernen im Miteinander.
Dieser Band betrachtet die beabsichtigte digitale Transformation von Schule und Unterricht aus sowohl pädagogischer wie philosophischer, aus bildungstheoretischer wie kognitionswissenschaftlicher Perspektive. Es werden praxisnah die beabsichtigte digitale Steuerung und Quantifizierung von Lernprozessen transparent gemacht und Alternativen für einen verantwortungsvollen und pädagogisch sinnvollen Einsatz von Medien- wie Digitaltechnik im Unterricht aufgezeigt. Das Ziel ist Emanzipation und Mündigkeit durch konstruktive und produktive Medienarbeit.
Cyclic micro-bending tests on fcc single crystal Ni-base Alloy 718 cantilevers with different crystal orientations were performed to analyze the influence of activated slip systems on dislocation plasticity, latent hardening and the Bauschinger effect. The investigations indicate that plasticity in single crystal micro-cantilevers is significantly influenced by two phenomena - dislocation interaction and dislocation pile-up at the neutral plane. Both phenomena occur at the same time. Their ratio seems to be determined by the activated slip systems. Slip trace analysis indicates that the activation of only one slip system leads to a strong localization of plasticity to a limited number of parallel slip bands. This results in low dislocation interaction and consequently pronounced pile-ups at the neutral plane. In multi slip orientation, the second slip system leads to activation of significantly more dislocation sources, causing a much earlier and more homogeneous elastic-plastic transition zone. In stress-strain hysteresis loops during bending, pronounced dislocation interaction in multi slip orientation leads to a more pronounced latent hardening. The results suggest that on a microstructural length scale, plasticity behavior is strongly affected by activated slip systems, which determine local dislocation phenomena. Based on the results presented in this paper, a finite element analysis of latent hardening and the Bauschinger effect using a single crystal plasticity model with latent kinematic hardening is presented in Part II.
In the last decade, deep learning models for condition monitoring of mechanical systems increasingly gained importance. Most of the previous works use data of the same domain (e.g., bearing type) or of a large amount of (labeled) samples. This approach is not valid for many real-world scenarios from industrial use-cases where only a small amount of data, often unlabeled, is available.
In this paper, we propose, evaluate, and compare a novel technique based on an intermediate domain, which creates a new representation of the features in the data and abstracts the defects of rotating elements such as bearings. The results based on an intermediate domain related to characteristic frequencies show an improved accuracy of up to 32 % on small labeled datasets compared to the current state-of-the-art in the time-frequency domain.
Furthermore, a Convolutional Neural Network (CNN) architecture is proposed for transfer learning. We also propose and evaluate a new approach for transfer learning, which we call Layered Maximum Mean Discrepancy (LMMD). This approach is based on the Maximum Mean Discrepancy (MMD) but extends it by considering the special characteristics of the proposed intermediate domain. The presented approach outperforms the traditional combination of Hilbert–Huang Transform (HHT) and S-Transform with MMD on all datasets for unsupervised as well as for semi-supervised learning. In most of our test cases, it also outperforms other state-of-the-art techniques.
This approach is capable of using different types of bearings in the source and target domain under a wide variation of the rotation speed.
Disruptive innovations can solve major global challenges. However, the system in Germany does not sufficiently favor the development of such innovations. The disruptive output of leading nations like the United States puts increasing pressure on Germany’s innovation leadership. The German innovation agency SPRIND was founded in 2019 and is a suitable instrument to promote disruptive innovations. The SPRIND itself cites the American innovation agency DARPA, which has been promoting disruptive innovations since 1958, a role model. Therefore, the aim of this paper is to conduct a comparative analysis of DARPA and SPRIND. To answer the research question, secondary sources were used. In addition, two expert interviews were conducted with employees of SPRIND. The result of this paper is a systematic comparison that identifies the key differences and similarities between the two agencies. SPRIND is based on DARPA in key success factors, such as the person-centered approach, funding instruments or risk management. However, compared to DARPA, SPRIND has a major disadvantage; namely several administrative hurdles which inhibit agile action.
There is a strong interaction between the urban atmospheric canopy layer and the building energy balance. The urban atmospheric conditions affect the heat transfer through exterior walls, the long-wave heat transfer between the building surfaces and the surroundings, the short-wave solar heat gains, and the heat transport by ventilation. Considering also the internal heat gains and the heat capacity of the building structure, the energy demand for heating and cooling and the indoor thermal environment can be calculated based on the urban microclimatic conditions. According to the building energy concept, the energy demand results in an (anthropogenic) waste heat; this is directly transferred to the urban environment. Furthermore, the indoor temperature is re-coupled via the building envelope to the urban environment and affects indirectly the urban microclimate with a temporally lagged and damped temperature fluctuation. We developed a holistic building model for the combined calculation of indoor climate and energy demand based on an analytic solution of Fourier's equation and implemented this model into the PALM model.
Datenanalysen gibt es schon immer, auch wenn statistische Verfahren, Optimierungsmodelle, Regressionsanalysen oder Zeitreihenmodelle in allen möglichen Publikationen neu aufbereitet und unter dem Titel Business Analytics verkauft werden. Der entscheidende Unterschied liegt jedoch darin, dass heute größere und vor allem andere Datenmengen sowie leistungsfähigere Verarbeitungs- und Speicherkapazitäten zur Verfügung stehen, die zusammen mit bestehenden und neu entwickelten Methoden erhebliche Einsichten ermöglichen. Erfolgversprechend sind Ansätze, die eine komplette Einbettung von Business Analytics in den strategischen Managementprozess garantieren und die optimale Ausschöpfung operationaler Exzellenz über ein Alignment ermöglichen. Die funktionale Perspektive bildet hier das Marketing mit einer konkreten Betrachtung der Kundenloyalität und deren Profitabilität. Dieser Zusammenhang wird über ein Monte-Carlo Simulationsmodell hergestellt. Das darf aber nicht darüber hinwegtäuschen, dass die Breite und die Tiefe der Methoden erheblich sind, vermehrt zu Spezialisierungen führen und andere Analyse-Optionen zur Wahl stehen. Letztendlich müsste es aber deutlich werden, dass das Thema Business Analytics technologische, Domain-spezifische, methodologische und methodische Kenntnisse erfordert, die selten nur durch eine Person erbracht werden können.
Um die im Pariser Klimaschutzabkommen vereinbarte Begrenzung der Erderwärmung auf 1,5 Grad Celsius zu begrenzen, muss die Energiewende deutlich stärker vorangetrieben werden als bisher. Das Schaufenster C/sells in der größten der SINTEG-Modellregionen hat sich dieser Herausforderung gestellt. Über vier Jahre haben 56 Partner aus Energiewirtschaft, Wissenschaft und Politik in Baden-Württemberg, Bayern und Hessen daran gearbeitet, ein zelluläres Energiesystem zu etablieren. Sie haben Musterlösungen für eine erfolgreiche Energiewende entwickelt. In mehr als 30 Demonstrationszellen sowie in neun Partizipationszellen, den sogenannten C/sells-Citys, wurde demonstriert, wie ein Informationssystem die intelligente Organisation von Stromversorgungsnetzen und den regionalisierten Handel mit Energie und Flexibilitäten ermöglicht.