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Radar data may potentially provide valuable information for precipitation quantification, especially in regions with a sparse network of in situ observations or in regions with complex topography. Therefore, our aim is to conduct a feasibility study to quantify precipitation intensities based on radar measurements and additional meteorological variables. Beyond the well-established Z–R relationship for the quantification, this study employs Artificial Neural Networks (ANNs) in different settings and analyses their performance. For this purpose, the radar data of a station in Upper Bavaria (Germany) is used and analysed for its performance in quantifying in situ observations. More specifically, the effects of time resolution, time offsets in the input data, and meteorological factors on the performance of the ANNs are investigated. It is found that ANNs that use actual reflectivity as only input are outperforming the standard Z–R relationship in reproducing ground precipitation.
This is reflected by an increase in correlation between modelled and observed data from 0.67 (Z–R) to 0.78 (ANN) for hourly and 0.61 to 0.86, respectively, for 10 min time resolution. However, the focus of this study was to investigate if model accuracy benefits from additional input features. It is shown that an expansion of the input feature space by using time-lagged reflectivity with lags up to two and additional meteorological variables such as temperature, relative humidity, and sunshine duration significantly increases model performance. Thus, overall, it is shown that a systematic predictor screening and the correspondent extension of the input feature space substantially improves the performance of a simple Neural Network model. For instance, air temperature and relative humidity provide valuable additional input information. It is concluded that model performance is dependent on all three ingredients: time resolution, time lagged information, and additional meteorological input features. Taking all of these into account, the model performance can be optimized to a correlation of 0.9 and minimum model bias of 0.002 between observed and modelled precipitation data even with a simple ANN architecture.
Thermal cyclers are used to perform polymerase chain reaction runs (PCR runs) and Peltier modules are the key components in these instruments. The demand for thermal cyclers has strongly increased during the COVID-19 pandemic due to the fact that they are important tools used in the research, identification, and diagnosis of the virus. Even though Peltier modules are quite durable, their failure poses a serious threat to the integrity of the instrument, which can lead to plant shutdowns and sample loss. Therefore, it is highly desirable to be able to predict the state of health of Peltier modules and thus reduce downtime. In this paper methods from three sub-categories of supervised machine learning, namely classical methods, ensemble methods and convolutional neural networks, were compared with respect to their ability to detect the state of health of Peltier modules integrated in thermal cyclers. Device-specific data from on-deck thermal cyclers (ODTC®) supplied by INHECO Industrial Heating & Cooling GmbH (Fig 1), Martinsried, Germany were used as a database for training the models. The purpose of this study was to investigate methods for data-driven condition monitoring with the aim of integrating predictive analytics into future product platforms. The results show that information about the state of health can be extracted from operational data - most importantly current readings - and that convolutional neural networks were the best at producing a generalized model for fault classification.
The performance of nanoelectronic and molecular electronic devices relies strongly on the employed functional units and their addressability, which is often a matter of appropriate interfaces and device design. Here, we compare two promising designs to build solid-state electronic devices utilizing the same functional unit. Optically addressable Ru-terpyridine complexes were incorporated in supramolecular wires or employed as ligands of gold nanoparticles and contacted by nanoelectrodes. The resulting small-area nanodevices were thoroughly electrically characterized as a function of temperature and light exposure. Differences in the resulting device conductance could be attributed to the device design and the respective transport mechanism, that is, thermally activated hopping conduction in the case of Ru-terpyridine wire devices or sequential tunneling in nanoparticle-based devices. Furthermore, the conductance switching of nanoparticle-based devices upon 530 nm irradiation was attributed to plasmon-induced metal-to-ligand charge transfer in the Ru-terpyridine complexes used as switching ligands. Finally, our results reveal a superior device performance of nanoparticle-based devices compared to molecular wire devices based on Ru-terpyridine complexes as functional units.
Im Verlauf der Covid-19-Pandemie war in Gesellschaft, Politik und Wissenschaft immer wieder von einer "Krise der Demokratie" die Rede. Diese "Krise der Demokratie" ist auch eine "Krise der Repräsentation": Reale Politik wird hinter verschlossenen Türen gemacht - und das im Eiltempo -, die Mehrheit der Bürger*innen spielt eine passive Rolle. Politische Erwachsenenbildung hat die Aufgabe, diese Krise zu diskutieren. Allerdings befindet sie sich selbst schon seit Längerem in einer Krise: In ihrer Heterogenität fehlt ihr eine Standortbestimmung, die Übergänge zur non-formalen Bildung und schulischen politischen Bildung, aber auch zu anderen Professionen sind fließend. Darüber hinaus gibt es noch kein einheitliches Profil hinsichtlich Professionalität in der politischen Erwachsenenbildung, kein einheitliches Berufsbild, keine einheitliche Aus-, Fort- oder Weiterbildung und auch keine einheitliche Didaktik. Nicht zuletzt fehlt es an stetiger Finanzierung politischer Bildung und die Teilnahmequoten liegen deutlich hinter anderen Weiterbildungsbereichen zurück. An Ansatzpunkten zur Krisenbewältigung fehlt es also nicht.
In this paper, the results of Thermally Stimulated Depolarization Currents (TSDC)-measurements of a complex system of stacked dielectric layers, like typically appearing in semiconductor packages, is presented. Characteristics of the individual isolation layers are presented solely and the results are compared to the stack. The investigated layers are comprising polyimide film, alkali-free glass, polyolefin film and epoxy based mold compound. Measurements have been carried out in the temperature range from 19 - 195°C at varying polarization fields for every material. Various relaxation peaks, originated by different polarization mechanisms within the materials could be observed. A comparison between the TSDC-peaks of the layer stack and the individual materials is shown. Our results demonstrate, that the super positioned spectrum is getting dominated by a single portion, the mold compound.
As use of digital fabrication increases in architecture, engineering and construction, the industry seeks appropriate management and processes to enable the adoption during the design/planning phase. Many enablers have been identified across various studies; however, a comprehensive synthesis defining the enablers of design for digital fabrication does not yet exist. This work conducts a systematic literature review of 59 journal articles published in the past decade and identifies 140 enablers under eight categories: actors, resources, conditions, attributes, processes, artefacts, values and risks. The enablers' frequency network is illustrated using an adjacency matrix. Through the lens of actor-network theory, the work creates a relational ontology to demonstrate the linkages between different enablers. Three examples are presented using onion diagrams: circular construction focus, business model focus and digital twin in industrialisation focus. Finally, this work discusses the intersection of relational ontology with process modelling to design future digital fabrication work routines.
Steps on Steps
(2022)
The present paper provides an overview on current developments of cash usage and issue of central bank digital currency (CBDC) in the euro area and proposes a possible design of a digital euro that allows for instant offline payments. Cash usage at the point-of-sale decreased perceptibly in the past years mainly due to the ongoing trend towards digitalization. However, we show that there are also indications that consumers were somewhat pushed into cashless payments by government regulations and supply-side restrictions by commercial banks. Nonetheless, overall demand for euro cash remained strong and even increased relative to GDP since the financial crisis in 2008. In this process, however, we observe a supply-driven shift towards lower banknote denominations. Central banks all over the world are intensively thinking about the potential issue of CBDC as a substitute or complement to cash. Based on some of its preferred characteristics, we propose a double pre-paid scheme combining central elements of TARGET Instant Payment Settlement and electronic money features enabling offline and online instant payments. Since anonymity is categorically discarded by the ECB and as cash has some special advantages from a consumer perspective, the digital euro will rather co-circulate with cash than replace it in transactions.
Wie können Unternehmen die Bindung, Rückkehrintention und Weiterempfehlungsbereitschaft ehemaliger Praktikant:innen und Auszubildender positiv beeinflussen? Mithilfe eines Onlinefragebogens wurden hierfür Studierende befragt. Die Ergebnisse geben Hinweise, welche Bindungsmaßnahmen Unternehmen nutzen können, um den Kontakt zu ehemaligen Praktikant:innen und Auszubildenden zu halten und durch ein verbleibendes Commitment eine höhere Rückkehrintention und Weiterempfehlungsbereitschaft zu erreichen.
In the context of production and factory planning, the expansion of the factory must already be taken into account during initial planning. This results in an increase in planning complexity, as the involved planners have to know the expansion stages of the factory in the different time periods and have to evaluate concept modifications across all time periods. This paper presents an idea for a planning tool, which takes expansion stages into consideration. The data model contains all relevant information to generate a simulation model of the factory in an almost automated way. The aim is to enable factory planners to quickly investigate concept changes with the help of simulation, for example, to identify bottlenecks.
One major goal of green-field factory planning is to decide on space requirements in the plant. In this phase, detailed information about the supply chain network (e.g. which suppliers deliver which parts) is often unavailable. Nevertheless, typical decisions in supply chain management, like the definition of replenishment processes and quantities or make-or-buy decisions, impact space requirements in the factory and should therefore be considered in the factory planning. This research article proposes a simulation approach for a factory simulation in which raw material replenishment is integrated to evaluate the space requirements for raw materials.
To evaluate the performance of a ropeway in an urban environment, simulations of the dynamic passenger transport characteristics are required. Therefore, a modular simulation model for urban ropeway networks was developed, which can be flexibly adapted to any city and passenger volume. This simulation model was used to analyze the ropeway network concept of the German city Regensburg and to determine the expected operating conditions. The passenger volume, different types of persons, their occurrence probability and their destination distribution is depending on the location and daytime and can be defined for each individual station. In an initial analysis, the number of passengers currently occurring in bus traffic were projected onto the ropeway network. To enable climate-friendly and efficient operation, different strategies were developed to significantly reduce the number of gondolas. The best fitting strategies resulted in significant cost savings while passenger comfort, as represented by queue time, remained unchanged.
Simulation Based Approach for Reconfiguration and Ramp up Scenario Analysis in Factory Planning
(2022)
Structural changes in production entail a potential economic risk for manufacturing companies. It is necessary to identify a suitable strategy for the reconfiguration process and to continue to meet the demand during the change in the factory structure and ramp-up phase. A simulation offers the possibility to analyze different ramp-up scenarios for the factory structure and to select a suitable concept for the reconfiguration process. A discrete event simulation approach is presented that can be used to evaluate variants of structural changes and serves as a basis for deciding on a reconfiguration strategy. This approach is demonstrated using a specific production step of a plant producing hydrogen electrolyzers, the results and generalized conclusions are discussed.
Due to the various demands for lecturers, there is only a limited time to prepare lectures and lab courses. Therefore, it is necessary to invest the time target-oriented for optimal student learning success. A theoretic model is developed to structure course preparation work regarding scientific content, didactic preparation, and course presentation. Model parameters have to be chosen for each course depending on topic complexity, the lecturer’s prior knowledge, and the already available preparation from the prior semesters. With these parameters, a course preparation model for a complete semester is developed. Analytic models for different optimization strategies are introduced according to the overall goal of the lecturer. Numerical optimization is done to find the appropriate course preparation times to reach an optimal course preparation for high student learning success. It is seen that due to the different optimization strategies, the preparation time results vary and no single truth is given. But this optimization system gives hints on how to invest preparation time target-oriented for high student learning success.
Since conventional generation plants provide a considerable share of reactive power but are gradually being shut down due to energy transition, leading to power deficits at the transmission grid level, new concepts for the supply of reactive power must be developed. As one possibility for the latter, industrial plants in the distribution grid are considered suitable, as their reactive power potentials can contribute to voltage maintenance and thus to a grid- or system-serving behavior. In this paper, the determination of reactive power potentials provided by industrial compensation systems (ICS) is presented. An industrial plant in the medium voltage grid, with three installed ICSs, is investigated. The potentials are determined and described based on their time characteristics, which provide information about the occurrence and distribution of inductive and capacitive potentials. A grid simulation of the investigated industrial grid is used to analyze the effects of retrieving the reactive power potentials regarding equipment utilizations and voltage limits.
The provision of reactive power is one option for maintaining the grid voltage, that is defined as an ancillary service in Germany. This paper presents an approach for determining deficient voltage deviation demands within an electrical grid for long term reactive power planning investigations. In contrast to previous approaches, which evaluate the Q-behavior of extended ward elements or grid assets, voltage deviations are analyzed bus-specifically. So further reactive power planning investigations are able to scale and optimize additional reactive power sources directly on planning voltage limits using load flow sensitivity techniques. The focus lies on the analysis of the steady state demand at base case conditions. Therefore, a grid planning process is conceptualized. An optimal power flow algorithm based on Differential Evolution is used for an optimal reactive power dispatch of installed reactive power sources, e. g. reactive power compensation systems or (renewable) energy sources to minimize the total voltage deviation according to voltage limits of Transmission System Operators planning principles. Methodological and processuals specifications as well as an application use case with an exemplary transmission system are presented in this paper.
Ascertaining reproducibility of scientific experiments is receiving increased attention across disciplines. We argue that the necessary skills are important beyond pure scientific utility, and that they should be taught as part of software engineering (SWE) education. They serve a dual purpose: Apart from acquiring the coveted badges assigned to reproducible research, reproducibility engineering is a lifetime skill for a professional industrial career in computer science.
SWE curricula seem an ideal fit for conveying such capabilities, yet they require some extensions, especially given that even at flagship conferences like ICSE, only slightly more than one-third of the technical papers (at the 2021 edition) receive recognition for artefact reusability. Knowledge and capabilities in setting up engineering environments that allow for reproducing artefacts and results over decades (a standard requirement in many traditional engineering disciplines), writing semi-literate commit messages that document crucial steps of a decision-making process and that are tightly coupled with code, or sustainably taming dynamic, quickly changing software dependencies, to name a few: They all contribute to solving the scientific reproducibility crisis, and enable software engineers to build sustainable, long-term maintainable, software-intensive, industrial systems. We propose to teach these skills at the undergraduate level, on par with traditional SWE topics.
This work aims at investigating the kinetic mechanisms of the reduction/oxidation (redox) reactions of iron oxide/iron pellets under different operating conditions. The reaction principle is the basis of a thermochemical hydrogen storage system. To simulate the charging phase, a single pellet consisting of iron oxide (90% Fe2O3, 10% stabilising cement) is reduced with different hydrogen (H2) concentrations at temperatures between 600 and 800 °C. The discharge phase is initiated by the oxidation of the previously reduced pellet by water vapour (H2O) at different concentrations in the same temperature range. In both reactions, nitrogen (N2) is used as a carrier gas. The redox reactions have been experimentally measured in a thermogravimetric analyser (TGA) at a flow rate of 250 mL/min. An extensive literature review has been conducted on the existing reactions’ kinetic mechanisms along with their applicability to describe the obtained results. It turned out that the measured kinetic results can be excellently described with the so-called shrinking core model. Using the geometrical contracting sphere reaction mechanism model, the concentration- and temperature-dependent reduction and oxidation rates can be reproduced with a maximum deviation of less than 5%. In contrast to the reduction process, the temperature has a smaller effect on the oxidation reaction kinetics, which is attributed to 71% less activation energy (Ea,Re=56.9 kJ/mol versus Ea,Ox=16.0 kJ/mol). The concentration of the reacting gas showed, however, an opposite trend: namely, to have an almost twofold impact on the oxidation reaction rate constant compared to the reduction rate constant.
The article presents a fabrication process and characterization of silicon emitters designed for MEMS X-ray source. The emitters, made of p-type and n-type silicon, were prepared by a modified laser micromachining process. Both types of emitters worked without any carbon nanotube coverage, which was the case in the previous realization of electron emitters. The p-type emitter gave smaller electron beam currents (<50 nA) with stable emission in a saturation region. The n-type emitter provides higher emission currents (>1 µA) not limited by saturation region, but with higher fluctuations. The final choice of emitter will be adjusted for different applications.