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.