620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
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Surface plasmon polaritons on (silver) nanowires are promising components for future photonic technologies. Here, we study near-field patterns on silver nanowires with a scattering-type scanning near-field optical microscope that enables the direct mapping of surface waves. We analyze the spatial pattern of the plasmon signatures for different excitation geometries and polarization and observe a plasmon wave pattern that is canted relative to the nanowire axis, which we show is due to a superposition of two different plasmon modes, as supported by electromagnetic simulations including the influence of the substrate. These findings yield new insights into the excitation and propagation of plasmon polaritons for applications in nanoplasmonic devices.
In this roadmap article, we have focused on the most recent advances in terahertz (THz) imaging with particular attention paid to the optimization and miniaturization of the THz imaging systems. Such systems entail enhanced functionality, reduced power consumption, and increased convenience, thus being geared toward the implementation of THz imaging systems in real operational conditions. The article will touch upon the advanced solid-state-based THz imaging systems, including room temperature THz sensors and arrays, as well as their on-chip integration with diffractive THz optical components. We will cover the current-state of compact room temperature THz emission sources, both optolectronic and electrically driven; particular emphasis is attributed to the beam-forming role in THz imaging, THz holography and spatial filtering, THz nano-imaging, and computational imaging. A number of advanced THz techniques, such as light-field THz imaging, homodyne spectroscopy, and phase sensitive spectrometry, THz modulated continuous wave imaging, room temperature THz frequency combs, and passive THz imaging, as well as the use of artificial intelligence in THz data processing and optics development, will be reviewed. This roadmap presents a structured snapshot of current advances in THz imaging as of 2021 and provides an opinion on contemporary scientific and technological challenges in this field, as well as extrapolations of possible further evolution in THz imaging.
AttendAffectNet-emotion prediction of movie viewers using multimodal fusion with self-attention
(2021)
In this paper, we tackle the problem of predicting the affective responses of movie viewers, based on the content of the movies. Current studies on this topic focus on video representation learning and fusion techniques to combine the extracted features for predicting affect. Yet, these typically, while ignoring the correlation between multiple modality inputs, ignore the correlation between temporal inputs (i.e., sequential features). To explore these correlations, a neural network architecture—namely AttendAffectNet (AAN)—uses the self-attention mechanism for predicting the emotions of movie viewers from different input modalities. Particularly, visual, audio, and text features are considered for predicting emotions (and expressed in terms of valence and arousal). We analyze three variants of our proposed AAN: Feature AAN, Temporal AAN, and Mixed AAN. The Feature AAN applies the self-attention mechanism in an innovative way on the features extracted from the different modalities (including video, audio, and movie subtitles) of a whole movie to, thereby, capture the relationships between them. The Temporal AAN takes the time domain of the movies and the sequential dependency of affective responses into account. In the Temporal AAN, self-attention is applied on the concatenated (multimodal) feature vectors representing different subsequent movie segments. In the Mixed AAN, we combine the strong points of the Feature AAN and the Temporal AAN, by applying self-attention first on vectors of features obtained from different modalities in each movie segment and then on the feature representations of all subsequent (temporal) movie segments. We extensively trained and validated our proposed AAN on both the MediaEval 2016 dataset for the Emotional Impact of Movies Task and the extended COGNIMUSE dataset. Our experiments demonstrate that audio features play a more influential role than those extracted from video and movie subtitles when predicting the emotions of movie viewers on these datasets. The models that use all visual, audio, and text features simultaneously as their inputs performed better than those using features extracted from each modality separately. In addition, the Feature AAN outperformed other AAN variants on the above-mentioned datasets, highlighting the importance of taking different features as context to one another when fusing them. The Feature AAN also performed better than the baseline models when predicting the valence dimension.
Consequences of minimal length discretization on line element, metric tensor, and geodesic equation
(2021)
When minimal length uncertainty emerging from a generalized uncertainty principle (GUP) is thoughtfully implemented, it is of great interest to consider its impacts on gravitational Einstein field equations (gEFEs) and to try to assess consequential modifications in metric manifesting properties of quantum geometry due to quantum gravity. GUP takes into account the gravitational impacts on the noncommutation relations of length (distance) and momentum operators or time and energy operators and so on. On the other hand, gEFE relates classical geometry or general relativity gravity to the energy–momentum tensors, that is, proposing quantum equations of state. Despite the technical difficulties, we intend to insert GUP into the metric tensor so that the line element and the geodesic equation in flat and curved space are accordingly modified. The latter apparently encompasses acceleration, jerk, and snap (jounce) of a particle in the quasi-quantized gravitational field. Finite higher orders of acceleration apparently manifest phenomena such as accelerating expansion and transitions between different radii of curvature and so on.
High shares of intermittent renewable power generation in a European electricity system will require flexible backup power generation on the dominant diurnal, synoptic, and seasonal weather timescales. The same three timescales are already covered by today’s dispatchable electricity generation facilities, which are able to follow the typical load variations on the intra-day, intra-week, and seasonal timescales. This work aims to quantify the changing demand for those three backup flexibility classes in emerging large-scale electricity systems, as they transform from low to high shares of variable renewable power generation. A weather-driven modelling is used, which aggregates eight years of wind and solar power generation data as well as load data over Germany and Europe, and splits the backup system required to cover the residual load into three flexibility classes distinguished by their respective maximum rates of change of power output. This modelling shows that the slowly flexible backup system is dominant at low renewable shares, but its optimized capacity decreases and drops close to zero once the average renewable power generation exceeds 50% of the mean load. The medium flexible backup capacities increase for modest renewable shares, peak at around a 40% renewable share, and then continuously decrease to almost zero once the average renewable power generation becomes larger than 100% of the mean load. The dispatch capacity of the highly flexible backup system becomes dominant for renewable shares beyond 50%, and reach their maximum around a 70% renewable share. For renewable shares above 70% the highly flexible backup capacity in Germany remains at its maximum, whereas it decreases again for Europe. This indicates that for highly renewable large-scale electricity systems the total required backup capacity can only be reduced if countries share their excess generation and backup power.
High-temperature tolerant enzymes offer multiple advantages over enzymes from mesophilic organisms for the industrial production of sustainable chemicals due to high specific activities and stabilities towards fluctuations in pH, heat, and organic solvents. The production of molecular hydrogen (H2) is of particular interest because of the multiple uses of hydrogen in energy and chemicals applications, and the ability of hydrogenase enzymes to reduce protons to H2 at a cathode. We examined the activity of Hydrogen-Dependent CO2 Reductase (HDCR) from the thermophilic bacterium Thermoanaerobacter kivui when immobilized in a redox polymer, cobaltocene-functionalized polyallylamine (Cc-PAA), on a cathode for enzyme-mediated H2 formation from electricity. The presence of Cc-PAA increased reductive current density 340-fold when used on an electrode with HDCR at 40 °C, reaching unprecedented current densities of up to 3 mA·cm−2 with minimal overpotential and high faradaic efficiency. In contrast to other hydrogenases, T. kivui HDCR showed substantial reversibility of CO-dependent inactivation, revealing an opportunity for usage in gas mixtures containing CO, such as syngas. This study highlights the important potential of combining redox polymers with novel enzymes from thermophiles for enhanced electrosynthesis.
Organ-on-a-chip technology has the potential to accelerate pharmaceutical drug development, improve the clinical translation of basic research, and provide personalized intervention strategies. In the last decade, big pharma has engaged in many academic research cooperations to develop organ-on-a-chip systems for future drug discoveries. Although most organ-on-a-chip systems present proof-of-concept studies, miniaturized organ systems still need to demonstrate translational relevance and predictive power in clinical and pharmaceutical settings. This review explores whether microfluidic technology succeeded in paving the way for developing physiologically relevant human in vitro models for pharmacology and toxicology in biomedical research within the last decade. Individual organ-on-a-chip systems are discussed, focusing on relevant applications and highlighting their ability to tackle current challenges in pharmacological research.
Collaboration is an important 21st Century skill. Co-located (or face-to-face) collaboration (CC) analytics gained momentum with the advent of sensor technology. Most of these works have used the audio modality to detect the quality of CC. The CC quality can be detected from simple indicators of collaboration such as total speaking time or complex indicators like synchrony in the rise and fall of the average pitch. Most studies in the past focused on “how group members talk” (i.e., spectral, temporal features of audio like pitch) and not “what they talk”. The “what” of the conversations is more overt contrary to the “how” of the conversations. Very few studies studied “what” group members talk about, and these studies were lab based showing a representative overview of specific words as topic clusters instead of analysing the richness of the content of the conversations by understanding the linkage between these words. To overcome this, we made a starting step in this technical paper based on field trials to prototype a tool to move towards automatic collaboration analytics. We designed a technical setup to collect, process and visualize audio data automatically. The data collection took place while a board game was played among the university staff with pre-assigned roles to create awareness of the connection between learning analytics and learning design. We not only did a word-level analysis of the conversations, but also analysed the richness of these conversations by visualizing the strength of the linkage between these words and phrases interactively. In this visualization, we used a network graph to visualize turn taking exchange between different roles along with the word-level and phrase-level analysis. We also used centrality measures to understand the network graph further based on how much words have hold over the network of words and how influential are certain words. Finally, we found that this approach had certain limitations in terms of automation in speaker diarization (i.e., who spoke when) and text data pre-processing. Therefore, we concluded that even though the technical setup was partially automated, it is a way forward to understand the richness of the conversations between different roles and makes a significant step towards automatic collaboration analytics.
This study presents an ultra-wideband, elliptical slot, planar monopole antenna for early breast cancer microwave imaging. The on-body antenna's operation is optimised by direct contact with the patient's skin. With a compact size of 9 × 7 mm, the antenna covers a wide bandwidth from 16 to 24 GHz for reflection coefficients lower than –10 dB. Besides, it also features an electrode for electrical impedance tomography applications. Verification on a volunteer's breast gives an excellent agreement with the simulation for the defined bandwidth. Furthermore, as the first stage of the system's characterisation, pork fat is also used to demonstrate the possibility to enhance the transmission between the antennas within the high loss environment. Those results propose the feasibility of implementing a high-frequency radar system for breast cancer detection.
We present experimental results and theoretical simulations of the adsorption behavior of the metal–organic precursor Co2(CO)8 on SiO2 surfaces after application of two different pretreatment steps, namely by air plasma cleaning or a focused electron beam pre-irradiation. We observe a spontaneous dissociation of the precursor molecules as well as autodeposition of cobalt on the pretreated SiO2 surfaces. We also find that the differences in metal content and relative stability of these deposits depend on the pretreatment conditions of the substrate. Transport measurements of these deposits are also presented. We are led to assume that the degree of passivation of the SiO2 surface by hydroxyl groups is an important controlling factor in the dissociation process. Our calculations of various slab settings, using dispersion-corrected density functional theory, support this assumption. We observe physisorption of the precursor molecule on a fully hydroxylated SiO2 surface (untreated surface) and chemisorption on a partially hydroxylated SiO2 surface (pretreated surface) with a spontaneous dissociation of the precursor molecule. In view of these calculations, we discuss the origin of this dissociation and the subsequent autocatalysis.