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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.