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In order to maximize the possible travel distance of battery electric vehicles with one battery charge, it is mandatory to adjust all components of the powertrain carefully to each other. While current vehicle designs mostly simplify the powertrain rigorously and use an electric motor in combination with a gearbox with only one fixed transmission ratio, the use of multi-gear systems has great potential. First, a multi-speed system is able to improve the overall energy efficiency. Secondly, it is able to reduce the maximum momentum and therefore to reduce the maximum current provided by the traction battery, which results in a longer battery lifetime. In this paper, we present a systematic way to generate multi-gear gearbox designs that—combined with a certain electric motor—lead to the most efficient fulfillment of predefined load scenarios and are at the same time robust to uncertainties in the load. Therefore, we model the electric motor and the gearbox within a Mixed-Integer Nonlinear Program, and optimize the efficiency of the mechanical parts of the powertrain. By combining this mathematical optimization program with an unsupervised machine learning algorithm, we are able to derive global-optimal gearbox designs for practically relevant momentum and speed requirements.
The chemical industry is one of the most important industrial sectors in Germany in terms of manufacturing revenue. While thermodynamic boundary conditions often restrict the scope for reducing the energy consumption of core processes, secondary processes such as cooling offer scope for energy optimisation. In this contribution, we therefore model and optimise an existing cooling system. The technical boundary conditions of the model are provided by the operators, the German chemical company BASF SE. In order to systematically evaluate different degrees of freedom in topology and operation, we formulate and solve a Mixed-Integer Nonlinear Program (MINLP), and compare our optimisation results with the existing system.
Component failures within water supply systems can lead to significant performance losses. One way to address these losses is the explicit anticipation of failures within the design process. We consider a water supply system for high-rise buildings, where pump failures are the most likely failure scenarios. We explicitly consider these failures within an early design stage which leads to a more resilient system, i.e., a system which is able to operate under a predefined number of arbitrary pump failures. We use a mathematical optimization approach to compute such a resilient design. This is based on a multi-stage model for topology optimization, which can be described by a system of nonlinear inequalities and integrality constraints. Such a model has to be both computationally tractable and to represent the real-world system accurately. We therefore validate the algorithmic solutions using experiments on a scaled test rig for high-rise buildings. The test rig allows for an arbitrary connection of pumps to reproduce scaled versions of booster station designs for high-rise buildings. We experimentally verify the applicability of the presented optimization model and that the proposed resilience properties are also fulfilled in real systems.
This chapter describes three general strategies to master uncertainty in technical systems: robustness, flexibility and resilience. It builds on the previous chapters about methods to analyse and identify uncertainty and may rely on the availability of technologies for particular systems, such as active components. Robustness aims for the design of technical systems that are insensitive to anticipated uncertainties. Flexibility increases the ability of a system to work under different situations. Resilience extends this characteristic by requiring a given minimal functional performance, even after disturbances or failure of system components, and it may incorporate recovery. The three strategies are described and discussed in turn. Moreover, they are demonstrated on specific technical systems.
Algorithmic design and resilience assessment of energy efficient high-rise water supply systems
(2018)
High-rise water supply systems provide water flow and suitable pressure in all levels of tall buildings. To design such state-of-the-art systems, the consideration of energy efficiency and the anticipation of component failures are mandatory. In this paper, we use Mixed-Integer Nonlinear Programming to compute an optimal placement of pipes and pumps, as well as an optimal control strategy.Moreover, we consider the resilience of the system to pump failures. A resilient system is able to fulfill a predefined minimum functionality even though components fail or are restricted in their normal usage. We present models to measure and optimize the resilience. To demonstrate our approach, we design and analyze an optimal resilient decentralized water supply system inspired by a real-life hotel building.
Successful optimization requires an appropriate model of the system under consideration. When selecting a suitable level of detail, one has to consider solution quality as well as the computational and implementation effort. In this paper, we present a MINLP for a pumping system for the drinking water supply of high-rise buildings. We investigate the influence of the granularity of the underlying physical models on the solution quality. Therefore, we model the system with a varying level of detail regarding the friction losses, and conduct an experimental validation of our model on a modular test rig. Furthermore, we investigate the computational effort and show that it can be reduced by the integration of domain-specific knowledge.
The application of mathematical optimization methods for water supply system design and operation provides the capacity to increase the energy efficiency and to lower the investment costs considerably. We present a system approach for the optimal design and operation of pumping systems in real-world high-rise buildings that is based on the usage of mixed-integer nonlinear and mixed-integer linear modeling approaches. In addition, we consider different booster station topologies, i.e. parallel and series-parallel central booster stations as well as decentral booster stations. To confirm the validity of the underlying optimization models with real-world system behavior, we additionally present validation results based on experiments conducted on a modularly constructed pumping test rig. Within the models we consider layout and control decisions for different load scenarios, leading to a Deterministic Equivalent of a two-stage stochastic optimization program. We use a piecewise linearization as well as a piecewise relaxation of the pumps’ characteristics to derive mixed-integer linear models. Besides the solution with off-the-shelf solvers, we present a problem specific exact solving algorithm to improve the computation time. Focusing on the efficient exploration of the solution space, we divide the problem into smaller subproblems, which partly can be cut off in the solution process. Furthermore, we discuss the performance and applicability of the solution approaches for real buildings and analyze the technical aspects of the solutions from an engineer’s point of view, keeping in mind the economically important trade-off between investment and operation costs.
The recently discovered first hyperbolic objects passing through the Solar System, 1I/’Oumuamua and 2I/Borisov, have raised the question about near term missions to Interstellar Objects. In situ spacecraft exploration of these objects will allow the direct determination of both their structure and their chemical and isotopic composition, enabling an entirely new way of studying small bodies from outside our solar system. In this paper, we map various Interstellar Object classes to mission types, demonstrating that missions to a range of Interstellar Object classes are feasible, using existing or near-term technology. We describe flyby, rendezvous and sample return missions to interstellar objects, showing various ways to explore these bodies characterizing their surface, dynamics, structure and composition. Their direct exploration will constrain their formation and history, situating them within the dynamical and chemical evolution of the Galaxy. These mission types also provide the opportunity to explore solar system bodies and perform measurements in the far outer solar system.
Water distribution systems are an essential supply infrastructure for cities. Given that climatic and demographic influences will pose further challenges for these infrastructures in the future, the resilience of water supply systems, i.e. their ability to withstand and recover from disruptions, has recently become a subject of research. To assess the resilience of a WDS, different graph-theoretical approaches exist. Next to general metrics characterizing the network topology, also hydraulic and technical restrictions have to be taken into account. In this work, the resilience of an exemplary water distribution network of a major German city is assessed, and a Mixed-Integer Program is presented which allows to assess the impact of capacity adaptations on its resilience.
Geochemical characterisation of hypersaline waters is difficult as high concentrations of salts hinder the analysis of constituents at low concentrations, such as trace metals, and the collection of samples for trace metal analysis in natural waters can be easily contaminated. This is particularly the case if samples are collected by non-conventional techniques such as those required for aquatic subglacial environments. In this paper we present the first analysis of a subglacial brine from Taylor Valley, (~ 78°S), Antarctica for the trace metals: Ba, Co, Mo, Rb, Sr, V, and U. Samples were collected englacially using an electrothermal melting probe called the IceMole. This probe uses differential heating of a copper head as well as the probe’s sidewalls and an ice screw at the melting head to move through glacier ice. Detailed blanks, meltwater, and subglacial brine samples were collected to evaluate the impact of the IceMole and the borehole pump, the melting and collection process, filtration, and storage on the geochemistry of the samples collected by this device. Comparisons between melt water profiles through the glacier ice and blank analysis, with published studies on ice geochemistry, suggest the potential for minor contributions of some species Rb, As, Co, Mn, Ni, NH4+, and NO2−+NO3− from the IceMole. The ability to conduct detailed chemical analyses of subglacial fluids collected with melting probes is critical for the future exploration of the hundreds of deep subglacial lakes in Antarctica.
To maximize the travel distances of battery electric vehicles such as cars or buses for a given amount of stored energy, their powertrains are optimized energetically. One key part within optimization models for electric powertrains is the efficiency map of the electric motor. The underlying function is usually highly nonlinear and nonconvex and leads to major challenges within a global optimization process. To enable faster solution times, one possibility is the usage of piecewise linearization techniques to approximate the nonlinear efficiency map with linear constraints. Therefore, we evaluate the influence of different piecewise linearization modeling techniques on the overall solution process and compare the solution time and accuracy for methods with and without explicitly used binary variables.
Concentrating Solar Power
(2021)
The focus of this chapter is the production of power and the use of the heat produced from concentrated solar thermal power (CSP) systems.
The chapter starts with the general theoretical principles of concentrating systems including the description of the concentration ratio, the energy and mass balance. The power conversion systems is the main part where solar-only operation and the increase in operational hours.
Solar-only operation include the use of steam turbines, gas turbines, organic Rankine cycles and solar dishes. The operational hours can be increased with hybridization and with storage.
Another important topic is the cogeneration where solar cooling, desalination and of heat usage is described.
Many examples of commercial CSP power plants as well as research facilities from the past as well as current installed and in operation are described in detail.
The chapter closes with economic and environmental aspects and with the future potential of the development of CSP around the world.
Test-retest reliability of the internal shoulder rotator muscles' stretch reflex in healthy men
(2021)
Until now the reproducibility of the short latency stretch reflex of the internal rotator muscles of the glenohumeral joint has not been identified. Twenty-three healthy male participants performed three sets of external shoulder rotation stretches with various pre-activation levels on two different dates of measurement to assess test-retest reliability. All stretches were applied with a dynamometer acceleration of 104°/s2 and a velocity of 150°/s. Electromyographical response was measured via surface EMG. Reflex latencies showed a pre-activation effect (ƞ2 = 0,355). ICC ranged from 0,735 to 0,909 indicating an overall “good” relative reliability. SRD 95% lay between ±7,0 to ±12,3 ms.. The reflex gain showed overall poor test-retest reproducibility. The chosen methodological approach presented a suitable test protocol for shoulder muscles stretch reflex latency evaluation. A proof-of-concept study to validate the presented methodical approach in shoulder involvement including subjects with clinically relevant conditions is recommended.
One central challenge for self-driving cars is a proper path-planning. Once a trajectory has been found, the next challenge is to accurately and safely follow the precalculated path. The model-predictive controller (MPC) is a common approach for the lateral control of autonomous vehicles. The MPC uses a vehicle dynamics model to predict the future states of the vehicle for a given prediction horizon. However, in order to achieve real-time path control, the computational load is usually large, which leads to short prediction horizons. To deal with the computational load, the control algorithm can be parallelized on the graphics processing unit (GPU). In contrast to the widely used stochastic methods, in this paper we propose a deterministic approach based on grid search. Our approach focuses on systematically discovering the search area with different levels of granularity. To achieve this, we split the optimization algorithm into multiple iterations. The best sequence of each iteration is then used as an initial solution to the next iteration. The granularity increases, resulting in smooth and predictable steering angle sequences. We present a novel GPU-based algorithm and show its accuracy and realtime abilities with a number of real-world experiments.
Conventional EEG devices cannot be used in everyday life and hence, past decade research has been focused on Ear-EEG for mobile, at-home monitoring for various applications ranging from emotion detection to sleep monitoring. As the area available for electrode contact in the ear is limited, the electrode size and location play a vital role for an Ear-EEG system. In this investigation, we present a quantitative study of ear-electrodes with two electrode sizes at different locations in a wet and dry configuration. Electrode impedance scales inversely with size and ranges from 450 kΩ to 1.29 MΩ for dry and from 22 kΩ to 42 kΩ for wet contact at 10 Hz. For any size, the location in the ear canal with the lowest impedance is ELE (Left Ear Superior), presumably due to increased contact pressure caused by the outer-ear anatomy. The results can be used to optimize signal pickup and SNR for specific applications. We demonstrate this by recording sleep spindles during sleep onset with high quality (5.27 μVrms).
Multi-attribute relation extraction (MARE): simplifying the application of relation extraction
(2021)
Natural language understanding’s relation extraction makes innovative and encouraging novel business concepts possible and facilitates new digitilized decision-making processes. Current approaches allow the extraction of relations with a fixed number of entities as attributes. Extracting relations with an arbitrary amount of attributes requires complex systems and costly relation-trigger annotations to assist these systems. We introduce multi-attribute relation extraction (MARE) as an assumption-less problem formulation with two approaches, facilitating an explicit mapping from business use cases to the data annotations. Avoiding elaborated annotation constraints simplifies the application of relation extraction approaches. The evaluation compares our models to current state-of-the-art event extraction and binary relation extraction methods. Our approaches show improvement compared to these on the extraction of general multi-attribute relations.
We introduce a new way to measure the forecast effort that analysts devote to their earnings forecasts by measuring the analyst's general effort for all covered firms. While the commonly applied effort measure is based on analyst behaviour for one firm, our measure considers analyst behaviour for all covered firms. Our general effort measure captures additional information about analyst effort and thus can identify accurate forecasts. We emphasise the importance of investigating analyst behaviour in a larger context and argue that analysts who generally devote substantial forecast effort are also likely to devote substantial effort to a specific firm, even if this effort might not be captured by a firm-specific measure. Empirical results reveal that analysts who devote higher general forecast effort issue more accurate forecasts. Additional investigations show that analysts' career prospects improve with higher general forecast effort. Our measure improves on existing methods as it has higher explanatory power regarding differences in forecast accuracy than the commonly applied effort measure. Additionally, it can address research questions that cannot be examined with a firm-specific measure. It provides a simple but comprehensive way to identify accurate analysts.
Determinants of earnings forecast error, earnings forecast revision and earnings forecast accuracy
(2012)
Earnings forecasts are ubiquitous in today’s financial markets. They are essential indicators of future firm performance and a starting point for firm valuation. Extremely inaccurate and overoptimistic forecasts during the most recent financial crisis have raised serious doubts regarding the reliability of such forecasts. This thesis therefore investigates new determinants of forecast errors and accuracy. In addition, new determinants of forecast revisions are examined. More specifically, the thesis answers the following questions: 1) How do analyst incentives lead to forecast errors? 2) How do changes in analyst incentives lead to forecast revisions?, and 3) What factors drive differences in forecast accuracy?
Communication via serial bus systems, like CAN, plays an important role for all kinds of embedded electronic and mechatronic systems. To cope up with the requirements for functional safety of safety-critical applications, there is a need to enhance the safety features of the communication systems. One measure to achieve a more robust communication is to add redundant data transmission path to the applications. In general, the communication of real-time embedded systems like automotive applications is tethered, and the redundant data transmission lines are also tethered, increasing the size of the wiring harness and the weight of the system. A radio link is preferred as a redundant transmission line as it uses a complementary transmission medium compared to the wired solution and in addition reduces wiring harness size and weight. Standard wireless links like Wi-Fi or Bluetooth cannot meet the requirements for real-time capability with regard to bus communication. Using the new dual-mode radio enables a redundant transmission line meeting all requirements with regard to real-time capability, robustness and transparency for the data bus. In addition, it provides a complementary transmission medium with regard to commonly used tethered links. A CAN bus system is used to demonstrate the redundant data transfer via tethered and wireless CAN.
This book provides a compact introduction to the bootstrap method. In addition to classical results on point estimation and test theory, multivariate linear regression models and generalized linear models are covered in detail. Special attention is given to the use of bootstrap procedures to perform goodness-of-fit tests to validate model or distributional assumptions. In some cases, new methods are presented here for the first time.
The text is motivated by practical examples and the implementations of the corresponding algorithms are always given directly in R in a comprehensible form. Overall, R is given great importance throughout. Each chapter includes a section of exercises and, for the more mathematically inclined readers, concludes with rigorous proofs. The intended audience is graduate students who already have a prior knowledge of probability theory and mathematical statistics.
The integration of frequently changing, volatile product data from different manufacturers into a single catalog is a significant challenge for small and medium-sized e-commerce companies. They rely on timely integrating product data to present them aggregated in an online shop without knowing format specifications, concept understanding of manufacturers, and data quality. Furthermore, format, concepts, and data quality may change at any time. Consequently, integrating product catalogs into a single standardized catalog is often a laborious manual task. Current strategies to streamline or automate catalog integration use techniques based on machine learning, word vectorization, or semantic similarity. However, most approaches struggle with low-quality or real-world data. We propose Attribute Label Ranking (ALR) as a recommendation engine to simplify the integration process of previously unknown, proprietary tabular format into a standardized catalog for practitioners. We evaluate ALR by focusing on the impact of different neural network architectures, language features, and semantic similarity. Additionally, we consider metrics for industrial application and present the impact of ALR in production and its limitations.
The progress in natural language processing (NLP) research over the last years, offers novel business opportunities for companies, as automated user interaction or improved data analysis. Building sophisticated NLP applications requires dealing with modern machine learning (ML) technologies, which impedes enterprises from establishing successful NLP projects. Our experience in applied NLP research projects shows that the continuous integration of research prototypes in production-like environments with quality assurance builds trust in the software and shows convenience and usefulness regarding the business goal. We introduce STAMP 4 NLP as an iterative and incremental process model for developing NLP applications. With STAMP 4 NLP, we merge software engineering principles with best practices from data science. Instantiating our process model allows efficiently creating prototypes by utilizing templates, conventions, and implementations, enabling developers and data scientists to focus on the business goals. Due to our iterative-incremental approach, businesses can deploy an enhanced version of the prototype to their software environment after every iteration, maximizing potential business value and trust early and avoiding the cost of successful yet never deployed experiments.
The fourth industrial revolution introduces disruptive technologies to production environments. One of these technologies are multi-agent systems (MASs), where agents virtualize machines. However, the agent's actual performances in production environments can hardly be estimated as most research has been focusing on isolated projects and specific scenarios. We address this gap by implementing a highly connected and configurable reference model with quantifiable key performance indicators (KPIs) for production scheduling and routing in single-piece workflows. Furthermore, we propose an algorithm to optimize the search of extrema in highly connected distributed systems. The benefits, limits, and drawbacks of MASs and their performances are evaluated extensively by event-based simulations against the introduced model, which acts as a benchmark. Even though the performance of the proposed MAS is, on average, slightly lower than the reference system, the increased flexibility allows it to find new solutions and deliver improved factory-planning outcomes. Our MAS shows an emerging behavior by using flexible production techniques to correct errors and compensate for bottlenecks. This increased flexibility offers substantial improvement potential. The general model in this paper allows the transfer of the results to estimate real systems or other models.
Magnetic nanoparticle relaxation in biomedical application: focus on simulating nanoparticle heating
(2021)
Background:
Additional stabilization of the “comma sign” in anterosuperior rotator cuff repair has been proposed to provide biomechanical benefits regarding stability of the repair.
Purpose:
This in vitro investigation aimed to investigate the influence of a comma sign–directed reconstruction technique for anterosuperior rotator cuff tears on the primary stability of the subscapularis tendon repair.
Study Design:
Controlled laboratory study.
Methods:
A total of 18 fresh-frozen cadaveric shoulders were used in this study. Anterosuperior rotator cuff tears (complete full-thickness tear of the supraspinatus and subscapularis tendons) were created, and supraspinatus repair was performed with a standard suture bridge technique. The subscapularis was repaired with either a (1) single-row or (2) comma sign technique. A high-resolution 3D camera system was used to analyze 3-mm and 5-mm gap formation at the subscapularis tendon-bone interface upon incremental cyclic loading. Moreover, the ultimate failure load of the repair was recorded. A Mann-Whitney test was used to assess significant differences between the 2 groups.
Results:
The comma sign repair withstood significantly more loading cycles than the single-row repair until 3-mm and 5-mm gap formation occurred (P≤ .047). The ultimate failure load did not reveal any significant differences when the 2 techniques were compared (P = .596).
Conclusion:
The results of this study show that additional stabilization of the comma sign enhanced the primary stability of subscapularis tendon repair in anterosuperior rotator cuff tears. Although this stabilization did not seem to influence the ultimate failure load, it effectively decreased the micromotion at the tendon-bone interface during cyclic loading.
Clinical Relevance:
The proposed technique for stabilization of the comma sign has shown superior biomechanical properties in comparison with a single-row repair and might thus improve tendon healing. Further clinical research will be necessary to determine its influence on the functional outcome.
In positron emission tomography improving time, energy and spatial detector resolutions and using Compton kinematics introduces the possibility to reconstruct a radioactivity distribution image from scatter coincidences, thereby enhancing image quality. The number of single scattered coincidences alone is in the same order of magnitude as true coincidences. In this work, a compact Compton camera module based on monolithic scintillation material is investigated as a detector ring module. The detector interactions are simulated with Monte Carlo package GATE. The scattering angle inside the tissue is derived from the energy of the scattered photon, which results in a set of possible scattering trajectories or broken line of response. The Compton kinematics collimation reduces the number of solutions. Additionally, the time of flight information helps localize the position of the annihilation. One of the questions of this investigation is related to how the energy, spatial and temporal resolutions help confine the possible annihilation volume. A comparison of currently technically feasible detector resolutions (under laboratory conditions) demonstrates the influence on this annihilation volume and shows that energy and coincidence time resolution have a significant impact. An enhancement of the latter from 400 ps to 100 ps leads to a smaller annihilation volume of around 50%, while a change of the energy resolution in the absorber layer from 12% to 4.5% results in a reduction of 60%. The inclusion of single tissue-scattered data has the potential to increase the sensitivity of a scanner by a factor of 2 to 3 times. The concept can be further optimized and extended for multiple scatter coincidences and subsequently validated by a reconstruction algorithm.
Thrombogenic complications are a main issue in mechanical circulatory support (MCS). There is no validated in vitro method available to quantitatively assess the thrombogenic performance of pulsatile MCS devices under realistic hemodynamic conditions. The aim of this study is to propose a method to evaluate the thrombogenic potential of new designs without the use of complex in-vivo trials. This study presents a novel in vitro method for reproducible thrombogenicity testing of pulsatile MCS systems using low molecular weight heparinized porcine blood. Blood parameters are continuously measured with full blood thromboelastometry (ROTEM; EXTEM, FIBTEM and a custom-made analysis HEPNATEM). Thrombus formation is optically observed after four hours of testing. The results of three experiments are presented each with two parallel loops. The area of thrombus formation inside the MCS device was reproducible. The implantation of a filter inside the loop catches embolizing thrombi without a measurable increase of platelet activation, allowing conclusions of the place of origin of thrombi inside the device. EXTEM and FIBTEM parameters such as clotting velocity (α) and maximum clot firmness (MCF) show a total decrease by around 6% with a characteristic kink after 180 minutes. HEPNATEM α and MCF rise within the first 180 minutes indicate a continuously increasing activation level of coagulation. After 180 minutes, the consumption of clotting factors prevails, resulting in a decrease of α and MCF. With the designed mock loop and the presented protocol we are able to identify thrombogenic hot spots inside a pulsatile pump and characterize their thrombogenic potential.
Aneurysmal subarachnoid hemorrhage (aSAH) is associated with early and delayed brain injury due to several underlying and interrelated processes, which include inflammation, oxidative stress, endothelial, and neuronal apoptosis. Treatment with melatonin, a cytoprotective neurohormone with anti-inflammatory, anti-oxidant and anti-apoptotic effects, has been shown to attenuate early brain injury (EBI) and to prevent delayed cerebral vasospasm in experimental aSAH models. Less is known about the role of endogenous melatonin for aSAH outcome and how its production is altered by the pathophysiological cascades initiated during EBI. In the present observational study, we analyzed changes in melatonin levels during the first three weeks after aSAH.
Cardiopulmonary bypass (CPB) is a standard technique for cardiac surgery, but comes with the risk of severe neurological complications (e.g. stroke) caused by embolisms and/or reduced cerebral perfusion. We report on an aortic cannula prototype design (optiCAN) with helical outflow and jet-splitting dispersion tip that could reduce the risk of embolic events and restores cerebral perfusion to 97.5% of physiological flow during CPB in vivo, whereas a commercial curved-tip cannula yields 74.6%. In further in vitro comparison, pressure loss and hemolysis parameters of optiCAN remain unaffected. Results are reproducibly confirmed in silico for an exemplary human aortic anatomy via computational fluid dynamics (CFD) simulations. Based on CFD simulations, we firstly show that optiCAN design improves aortic root washout, which reduces the risk of thromboembolism. Secondly, we identify regions of the aortic intima with increased risk of plaque release by correlating areas of enhanced plaque growth and high wall shear stresses (WSS). From this we propose another easy-to-manufacture cannula design (opti2CAN) that decreases areas burdened by high WSS, while preserving physiological cerebral flow and favorable hemodynamics. With this novel cannula design, we propose a cannulation option to reduce neurological complications and the prevalence of stroke in high-risk patients after CPB.
Biologically sensitive field-effect devices (BioFEDs) advantageously combine the electronic field-effect functionality with the (bio)chemical receptor’s recognition ability for (bio)chemical sensing. In this review, basic and widely applied device concepts of silicon-based BioFEDs (ion-sensitive field-effect transistor, silicon nanowire transistor, electrolyte-insulator-semiconductor capacitor, light-addressable potentiometric sensor) are presented and recent progress (from 2019 to early 2021) is discussed. One of the main advantages of BioFEDs is the label-free sensing principle enabling to detect a large variety of biomolecules and bioparticles by their intrinsic charge. The review encompasses applications of BioFEDs for the label-free electrical detection of clinically relevant protein biomarkers, deoxyribonucleic acid molecules and viruses, enzyme-substrate reactions as well as recording of the cell acidification rate (as an indicator of cellular metabolism) and the extracellular potential.
In the study, the process chain of additive manufacturing by means of powder bed fusion will be presented based on the material glass. In order to reliably process components additively, new concepts with different solutions were developed and investigated.
Compared to established metallic materials, the properties of glass materials differ significantly. Therefore, the process control was adapted to the material glass in the investigations. With extensive parameter studies based on various glass powders such as borosilicate glass and quartz glass, scientifically proven results on powder bed fusion of glass are presented. Based on the determination of the particle properties with different methods, extensive investigations are made regarding the melting behavior of glass by means of laser beams. Furthermore, the experimental setup was steadily expanded. In addition to the integration of coaxial temperature measurement and regulation, preheating of the building platform is of major importance. This offers the possibility to perform 3D printing at the transformation temperatures of the glass materials. To improve the component’s properties, the influence of a subsequent heat treatment was also investigated.
The experience gained was incorporated into a new experimental system, which allows a much better exploration of the 3D printing of glass. Currently, studies are being conducted to improve surface texture, building accuracy, and geometrical capabilities using three-dimensional specimen.
The contribution shows the development of research in the field of 3D printing of glass, gives an insight into the machine and process engineering as well as an outlook on the possibilities and applications.
A new formulation to calculate the shakedown limit load of Kirchhoff plates under stochastic conditions of strength is developed. Direct structural reliability design by chance con-strained programming is based on the prescribed failure probabilities, which is an effective approach of stochastic programming if it can be formulated as an equivalent deterministic optimization problem. We restrict uncertainty to strength, the loading is still deterministic. A new formulation is derived in case of random strength with lognormal distribution. Upper bound and lower bound shakedown load factors are calculated simultaneously by a dual algorithm.
Project work and inter disciplinarity are integral parts of today's engineering work. It is therefore important to incorporate these aspects into the curriculum of academic studies of engineering. At the faculty of Electrical Engineering and Information Technology an interdisciplinary project is part of the bachelor program to address these topics. Since the summer term 2020 most courses changed to online mode during the Covid-19 crisis including the interdisciplinary projects. This online mode introduces additional challenges to the execution of the projects, both for the students as well as for the lecture. The challenges, but also the risks and chances of this kind of project courses are subject of this paper, based on five different interdisciplinary projects
During the Covid-19 pandemic, vocational colleges, universities of applied science and technical universities often had to cancel laboratory sessions requiring students’ attendance. These above of all are of decisive importance in order to give learners an understanding of theory through practical work.This paper is a contribution to the implementation of distance learning for laboratory work applicable for several upper secondary educational facilities. Its aim is to provide a paradigm for hybrid teaching to analyze and control a non-linear system depicted by a tank model. For this reason, we redesign a full series of laboratory sessions on the basis of various challenges. Thus, it is suitable to serve different reference levels of the European Qualifications Framework (EQF).We present problem-based learning through online platforms to compensate the lack of a laboratory learning environment. With a task deduced from their future profession, we give students the opportunity to develop own solutions in self-defined time intervals. A requirements specification provides the framework conditions in terms of time and content for students having to deal with the challenges of the project in a self-organized manner with regard to inhomogeneous previous knowledge. If the concept of Complete Action is introduced in classes before, they will automatically apply it while executing the project.The goal is to combine students’ scientific understanding with a procedural knowledge. We suggest a series of remote laboratory sessions that combine a problem formulation from the subject area of Measurement, Control and Automation Technology with a project assignment that is common in industry by providing extracts from a requirements specification.
Dual frequency magnetic excitation of magnetic nanoparticles (MNP) enables enhanced biosensing applications. This was studied from an experimental and theoretical perspective: nonlinear sum-frequency components of MNP exposed to dual-frequency magnetic excitation were measured as a function of static magnetic offset field. The Langevin model in thermodynamic equilibrium was fitted to the experimental data to derive parameters of the lognormal core size distribution. These parameters were subsequently used as inputs for micromagnetic Monte-Carlo (MC)-simulations. From the hysteresis loops obtained from MC-simulations, sum-frequency components were numerically demodulated and compared with both experiment and Langevin model predictions. From the latter, we derived that approximately 90% of the frequency mixing magnetic response signal is generated by the largest 10% of MNP. We therefore suggest that small particles do not contribute to the frequency mixing signal, which is supported by MC-simulation results. Both theoretical approaches describe the experimental signal shapes well, but with notable differences between experiment and micromagnetic simulations. These deviations could result from Brownian relaxations which are, albeit experimentally inhibited, included in MC-simulation, or (yet unconsidered) cluster-effects of MNP, or inaccurately derived input for MC-simulations, because the largest particles dominate the experimental signal but concurrently do not fulfill the precondition of thermodynamic equilibrium required by Langevin theory.