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Selten zuvor war das gesellschaftliche Interesse an wissenschaftlichem Wissen und daraus abzuleitenden Handlungsoptionen derart stark ausgeprägt wie in der Corona-Pandemie. Hat dieses Interesse zu einer partizipativen Öffnung der Wissenschaftskommunikation beigetragen, die darin Ausdruck findet, dass (wie häufig gefordert) die Teilnahme der Bevölkerung am Forschungsprozess und an Debatten über Forschung realisiert wird? Wir untersuchen auf Grundlage von drei Fallbeispielen, welche Einflüsse die Pandemie auf den Umfang multidirektional und partizipativ angelegter Wissenschaftskommunikation hatte. Dafür erfolgt eingangs eine Kategorisierung 127 bestehender Formate der Wissenschaftskommunikation nach ihrer Zielsetzung und Ausrichtung auf Partizipation und/oder Multidirektionalität. Diese Kategorisierung dient als Grundlage für die Analyse der Formate, über die im wissenschaftlichen Branchendienst Informationsdienst Wissenschaft (idw) und im Lokalteil von Regionalzeitungen vor sowie (zu unterschiedlichen Zeitpunkten) während der Pandemie berichtet wurde. Unsere Ergebnisse deuten darauf hin, dass partizipative oder multidirektionale Wissenschaftskommunikation in der Pandemie keinen Schub erhielt. In der akuten Krisenzeit 2020 setzte die Wissenschaftskommunikation in der Bearbeitung der Nachfrage nach wissenschaftlichem Wissen sowohl auf regionaler als auch auf überregionaler Ebene auf etablierte und unidirektional angelegte Formate.

The essay analyzes concepts of social justice, which were influential during the US-American Occupy protests of 2011. It discusses the recent genealogy of notions of social justice in the alter-globalization movements of the 1990s and argues that constitutive elements of Occupy’s tactics, like carnivalesque frivolity, recurred to protest forms of that decade. The essay investigates how the usage of the Guy Fawkes-mask, later associated with the comic superhero V, complicates binary logics of good and evil, arguing that such binary narratives helped in the organizational phase of the protests, but turned out to be inimical to further discussions of what constitutes social justice in the 21st century.

The essay focuses on the transnational entanglements between the USA and Nicaragua in the mid-nineteenth century, when U.S. adventurers (so-called ‘filibusters’) started private invasions to the American isthmus. One embodiment of these entanglements was El Nicaraguense“, a bilingual newspaper published by the invaders and distributed in the USA and Central America. The article analyzes how the paper contributed to the incorporation of Nicaragua into the U.S. colonial realm.

The use of artificial intelligence (AI) in sensor analytics is entering a new era based on the use of ubiquitous embedded connected devices. This transformation requires the adoption of design techniques that reconcile accurate results with sustainable system architectures. As such, improving the efficiency of AI hardware engines as well as backward compatibility must be considered. In this paper, we present the Hybrid-Float6 (HF6) quantization and its dedicated hardware design. We propose an optimized multiply-accumulate (MAC) hardware by reducing the mantissa multiplication to a multiplexor-adder operation. We exploit the intrinsic error tolerance of neural networks to further reduce the hardware design with approximation. To preserve model accuracy, we present a quantization-aware training (QAT) method, which in some cases improves accuracy. We demonstrate this concept in 2D convolution layers. We present a lightweight tensor processor (TP) implementing a pipelined vector dot-product. For compatibility and portability, the 6-bit floating-point (FP) is wrapped in the standard FP format, which is automatically extracted by the proposed hardware. The hardware/software architecture is compatible with TensorFlow (TF) Lite. We evaluate the applicability of our approach with a CNN-regression model for anomaly localization in a structural health monitoring (SHM) application based on acoustic emission (AE). The embedded hardware/software framework is demonstrated on XC7Z007S as the smallest Zynq-7000 SoC. The proposed implementation achieves a peak power efficiency and run-time acceleration of 5.7 GFLOPS/s/W and $48.3\times $ , respectively.

Due to the correlation between friction reduction and the road-covering water film height, knowledge about the current wetness level is of relevance for drivers and autonomous systems. A promising approach for wetness quantification is based on capacitive transducer arrays that enable to detect water spray ejected by the tires. Even though previous studies on this approach have shown the feasibility of wetness classification, optimization opportunities exist. While these previous investigations were limited to one feature selection algorithm, we study various algorithms and demonstrate the potential for optimization. Besides an application-specific algorithm that is capable of determining class-dependent features resulting in a performance improvement of more than 0.03 for the considered evaluation metrics, sequential forward floating selection in particular yields the most significant performance increase of more than 0.06. In addition, prior studies were limited to a test track with constrained conditions. Thus, in order to study the transferability of the preceding results, we present investigations on new experimental data acquired on public roads. The unknown and varying environmental conditions on public roads as well as a larger wheel speed range and steering angle effects are shown to significantly decrease classifier performance. We demonstrate that fusing two transducer arrays of different wheel arch liners increases performance by around 0.02. Here, a considerable information benefit can be attributed to the different transducer positions with design-related advantages. Furthermore, we show that the fusion with additional sensor data available in the vehicle results in a further performance improvement of more than 0.02.

Road surface wetness is a contributing factor in traffic accidents. As the amount of friction reduction correlates with the water film height covering the road surface, a quantification is of high relevance in order to improve traffic safety. Both drivers and autonomous vehicles would benefit from additional information. This paper presents a novel concept for road wetness quantification. It is based on a $2\times 4$ -planar capacitive transducer array, capable to detect water spray ejected by the tires and its wetness-related dependencies. The reliable assessment of these dependencies by a proposed capacitive sensor system is shown in an experimental study on an asphalt circuit for various wheel speeds. Besides the spray’s correlation with speed, the results reveal significant differences in transducer positions and designs confirming the array’s relevance regarding wetness quantification. In addition, a 1-nearest neighbor classifier capable of automatically distinguishing between eight wetness levels is proposed. The classifier is optimized by an extended version of balanced accuracy and reaches similar performance as binary classifiers from related research. A balanced ratio between capacitance increase-, standard deviation- and speed-related feature types is one key aspect of classifier performance. Furthermore, up to a certain extent, the array’s individual transducers can significantly contribute to classifier performance with design- and position-related advantages.

The present work investigates the size of gear damage required for significant recognisable change in the vibration signal and presents a method to determine digital filter limits in order to emphasise the vibration behaviour in the time domain. For this purpose, two gears are artificially damaged to four different degrees. The damage levels are determined by a tactile gear measurement and the gears are inserted into two intact gearboxes. Measurements at different speeds are used to generate a representative dataset. On the one hand, the recorded signals are examined via cross-correlation in the time domain. On the other hand, the occurring frequency components are examined using a windowed fast Fourier transformation. Based on the two observations, a statement is made about the recognisability of the damage levels of the two gears in the vibration signal. Furthermore, smoothed spectra are calculated via linear prediction coefficients (LPC) and an appropriate number of required coefficients is estimated via the Akaike information criterion. Subsequently, the calculated prediction coefficients are used as coefficients of an all-pole filter to calculate difference spectra. Based on the difference spectra, filter limits for a digital filter are derived to emphasise the damaged tooth meshing in the time domain.