620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
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Background: In general, the prevalence of work-related musculoskeletal disorders (WMSD) in dentistry is high, and dental assistants (DA) are even more affected than dentists (D). Furthermore, differentiations between the fields of dental specialization (e.g., general dentistry, endodontology, oral and maxillofacial surgery, or orthodontics) are rare. Therefore, this study aims to investigate the ergonomic risk of the aforementioned four fields of dental specialization for D and DA on the one hand, and to compare the ergonomic risk of D and DA within each individual field of dental specialization. Methods: In total, 60 dentists (33 male/27 female) and 60 dental assistants (11 male/49 female) volunteered in this study. The sample was composed of 15 dentists and 15 dental assistants from each of the dental field, in order to represent the fields of dental specialization. In a laboratory setting, all tasks were recorded using an inertial motion capture system. The kinematic data were applied to an automated version of the Rapid Upper Limb Assessment (RULA). Results: The results revealed significantly reduced ergonomic risks in endodontology and orthodontics compared to oral and maxillofacial surgery and general dentistry in DAs, while orthodontics showed a significantly reduced ergonomic risk compared to general dentistry in Ds. Further differences between the fields of dental specialization were found in the right wrist, right lower arm, and left lower arm in DAs and in the neck, right wrist, right lower arm, and left wrist in Ds. The differences between Ds and DAs within a specialist discipline were rather small. Discussion: Independent of whether one works as a D or DA, the percentage of time spent working in higher risk scores is reduced in endodontologists, and especially in orthodontics, compared to general dentists or oral and maxillofacial surgeons. In order to counteract the development of WMSD, early intervention should be made. Consequently, ergonomic training or strength training is recommended.
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.
Solar photovoltaics (PV) panels in combination with batteries are often proposed as a solution to provide stable power supply in rural areas. PV generation is mostly dominated by the solar diurnal cycle and has, in some countries, already started to have influence on the daily price distribution on the electricity market.
In this work, we study the performance and optimisation of rural PV-battery hybrid systems in a future renewable Polish power system. We use data on generation potentials to study PV and battery deployment. Together with a power system optimisation and dispatch model for the Polish power system, we study market values when selling at the national market for different CO2 price scenarios. We show that optimal orientations with respect to tilt/azimuth are subject to change as the PV share grows and that the benefit from batteries grows for higher shares of renewables.
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.
Sample-based longitudinal discrete choice experiments: preferences for electric vehicles over time
(2021)
Discrete choice experiments have emerged as the state-of-the-art method for measuring preferences, but they are mostly used in cross-sectional studies. In seeking to make them applicable for longitudinal studies, our study addresses two common challenges: working with different respondents and handling altering attributes. We propose a sample-based longitudinal discrete choice experiment in combination with a covariate-extended hierarchical Bayes logit estimator that allows one to test the statistical significance of changes. We showcase this method’s use in studies about preferences for electric vehicles over six years and empirically observe that preferences develop in an unpredictable, non-monotonous way. We also find that inspecting only the absolute differences in preferences between samples may result in misleading inferences. Moreover, surveying a new sample produced similar results as asking the same sample of respondents over time. Finally, we experimentally test how adding or removing an attribute affects preferences for the other attributes.
In this paper, we introduce an approach for future frames prediction based on a single input image. Our method is able to generate an entire video sequence based on the information contained in the input frame. We adopt an autoregressive approach in our generation process, i.e., the output from each time step is fed as the input to the next step. Unlike other video prediction methods that use “one shot” generation, our method is able to preserve much more details from the input image, while also capturing the critical pixel-level changes between the frames. We overcome the problem of generation quality degradation by introducing a “complementary mask” module in our architecture, and we show that this allows the model to only focus on the generation of the pixels that need to be changed, and to reuse those that should remain static from its previous frame. We empirically validate our methods against various video prediction models on the UT Dallas Dataset, and show that our approach is able to generate high quality realistic video sequences from one static input image. In addition, we also validate the robustness of our method by testing a pre-trained model on the unseen ADFES facial expression dataset. We also provide qualitative results of our model tested on a human action dataset: The Weizmann Action database.
The future of work has become a pressing matter of concern: Researchers, business consultancies, and industrial companies are intensively studying how new work models could be best implemented to increase workplace flexibility and creativity. In particular, the agile model has become one of the “must-have” elements for re-organizing work practices, especially for technology development work. However, the implementation of agile work often comes together with strong presumptions: it is regarded as an inevitable tool that can be universally integrated into different workplaces while having the same outcome of flexibility, transparency, and flattened hierarchies everywhere. This paper challenges such essentializing assumptions by turning agile work into a “matter of care.” We argue that care work occurs in contexts other than feminized reproductive work, namely, technology development. Drawing on concepts from feminist Science and Technology Studies and ethnographic research at agile technology development workplaces in Germany and Kenya, we examine what work it takes to actually keep up with the imperative of agile work. The analysis brings the often invisibilized care practices of human and nonhuman actors to the fore that are necessary to enact and stabilize the agile promises of flexibilization, co-working, and rapid prototyping. Revealing the caring sociotechnical relationships that are vital for working agile, we discuss the emergence of power asymmetries characterized by hierarchies of skills that are differently acknowledged in the daily work of technology development. The paper ends by speculating on the emancipatory potential of a care perspective, by which we seek to inspire careful Emancipatory Technology Studies.
'THIS ISN'T ME!': the role of age-related self- and user images for robot acceptance by elders
(2020)
Although companion-type robots are already commercially available, little interest has been taken in identifying reasons for inter-individual differences in their acceptance. Elders’ age-related perceptions of both their own self (self-image) and of the general older robot user (user image) could play a relevant role in this context. Since little is known to date about elders’ companion-type robot user image, it is one aim of this study to investigate its age-related facets, concentrating on possibly stigmatizing perceptions of elder robot users. The study also addresses the association between elders’ age-related self-image and robot acceptance: Is the association independent of the user image or not? To investigate these research questions, N = 28 adults aged 63 years and older were introduced to the companion-type robot Pleo. Afterwards, several markers of robot acceptance were assessed. Actual and ideal self- and subjective robot user image were assessed by a study-specific semantic differential on the stereotype dimensions of warmth and competence. Results show that participants tended to stigmatize elder robot users. The self-images were not directly related to robot acceptance, but affected it in the context of the user image. A higher fit between self- and user image was associated with higher perceived usefulness, social acceptance, and intention to use the robot. To conclude, elders’ subjective interpretations of new technologies play a relevant role for their acceptance. Together with elders’ individual self-images, they need to be considered in both robot development and implementation. Future research should consider that associations between user characteristics and robot acceptance by elders can be complex and easily overlooked.
This work aims at radar sensors in the frequency band from 57 to 64 GHz that can be embedded in wind turbine blades during manufacturing, enabling non-destructive quality inspection directly after production and structural health monitoring (SHM) during the complete service life of the blade. In this paper, we show the fundamental damage detection capability of this sensor technology during fatigue testing of typical rotor blade materials. Therefore, a frequency modulated continuous wave (FMCW) radar sensor is used for damage diagnostics, and the results are validated by simultaneous camera recordings. Here, we focus on the failure modes delamination, fiber waviness (ondulation), and inter-fiber failure. For each failure mode, three samples have been designed and experimentally investigated during fatigue testing. A damage index has been proposed based on residual, that is, differential, signals exploiting measurements from pristine structural conditions. This study shows that the proposed innovative radar approach is able to detect continuous structural degradation for all failure modes by means of gradual signal changes.
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.
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.
Objectives: The four-dimensional ultrasound (4D-US) enables imaging of the aortic segment and simultaneous determination of the wall expansion. The method shows a high spatial and temporal resolution, but its in vivo reliability is so far unknown for low-measure values. The present study determines the intraobserver repeatability and interobserver reproducibility of 4D-US in the atherosclerotic and non-atherosclerotic infrarenal aorta. Methods: In all, 22 patients with non-aneurysmal aorta were examined by an experienced examiner and a medical student. After registration of 4D images, both the examiners marked the aortic wall manually before the commercially implemented speckle tracking algorithm was applied. The cyclic changes of the aortic diameter and circumferential strain were determined with the help of custom-made software. The reliability of 4D-US was tested by the intraclass correlation coefficient (ICC). Results: The 4D-US measurements showed very good reliability for the maximum aortic diameter and the circumferential strain for all patients and for the non-atherosclerotic aortae (ICC >0.7), but low reliability for circumferential strain in calcified aortae (ICC = 0.29). The observer- and masking-related variances for both maximum diameter and circumferential strain were close to zero. Conclusions: Despite the low-measured values, the high spatial and temporal resolution of the 4D-US enables a reliable evaluation of cyclic diameter changes and circumferential strain in non-aneurysmal aortae independent from the observer experience but with some limitations for calcified aortae. The 4D-US opens up a new perspective with regard to noninvasive, in vivo assessment of kinematic properties of the vessel wall in the abdominal aorta.
This paper explores the many interesting implications for oscillator design, with optimized phase-noise performance, deriving from a newly proposed model based on the concept of oscillator conjugacy. For the case of 2-D (planar) oscillators, the model prominently predicts that only circuits producing a perfectly symmetric steady-state can have zero amplitude-to-phase (AM-PM) noise conversion, a so-called zero-state. Simulations on standard industry oscillator circuits verify all model predictions and, however, also show that these circuit classes cannot attain zero-states except in special limit-cases which are not practically relevant. Guided by the newly acquired design rules, we describe the synthesis of a novel 2-D reduced-order LC oscillator circuit which achieves several zero-states while operating at realistic output power levels. The potential future application of this developed theoretical framework for implementation of numerical algorithms aimed at optimizing oscillator phase-noise performance is briefly discussed.
Nano-granular metals are materials that fall into the general class of granular electronic systems in which the interplay of electronic correlations, disorder and finite size effects can be studied. The charge transport in nano-granular metals is dominated by thermally-assisted, sequential and correlated tunneling over a temperature-dependent number of metallic grains. Here we study the frequency-dependent conductivity (AC conductivity) of nano-granular Platinum with Pt nano-grains embedded into amorphous carbon (C). We focus on the transport regime on the insulating side of the insulator metal transition reflected by a set of samples covering a range of tunnel-coupling strengths. In this transport regime polarization contributions to the AC conductivity are small and correlation effects in the transport of free charges are expected to be particularly pronounced. We find a universal behavior in the frequency dependence that can be traced back to the temperature-dependent zero-frequency conductivity (DC conductivity) of Pt/C within a simple lumped-circuit analysis. Our results are in contradistinction to previous work on nano-granular Pd/ZrO2ZrO2 in the very weak coupling regime where polarization contributions to the AC conductivity dominated. We describe possible future applications of nano-granular metals in proximity impedance spectroscopy of dielectric materials.
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.
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.