620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
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Water bodies are a highly abundant feature of Arctic permafrost ecosystems and strongly influence their hydrology, ecology and biogeochemical cycling. While very high resolution satellite images enable detailed mapping of these water bodies, the increasing availability and abundance of this imagery calls for fast, reliable and automatized monitoring. This technical work presents a largely automated and scalable workflow that removes image noise, detects water bodies, removes potential misclassifications from infrastructural features, derives lake shoreline geometries and retrieves their movement rate and direction on the basis of ortho-ready very high resolution satellite imagery from Arctic permafrost lowlands. We applied this workflow to typical Arctic lake areas on the Alaska North Slope and achieved a successful and fast detection of water bodies. We derived representative values for shoreline movement rates ranging from 0.40-0.56 m yr(-1) for lake sizes of 0.10 ha-23.04 ha. The approach also gives an insight into seasonal water level changes. Based on an extensive quantification of error sources, we discuss how the results of the automated workflow can be further enhanced by incorporating additional information on weather conditions and image metadata and by improving the input database. The workflow is suitable for the seasonal to annual monitoring of lake changes on a sub-meter scale in the study areas in northern Alaska and can readily be scaled for application across larger regions within certain accuracy limitations.
Simple and robust
(2021)
A spectrum of 7562 publications on Molecularly Imprinted Polymers (MIPs) has been presented in literature within the last ten years (Scopus, September 7, 2020). Around 10 % of the papers published on MIPs describe the recognition of proteins. The straightforward synthesis of MIPs is a significant advantage as compared with the preparation of enzymes or antibodies. MIPs have been synthesized from only one up to six functional monomers while proteins are made up of 20 natural amino acids. Furthermore, they can be synthesized against structures of low immunogenicity and allow multi-analyte measurements via multi-target synthesis. Electrochemical methods allow simple polymer synthesis, removal of the template and readout. Among the different sensor configurations electrochemical MIP-sensors provide the broadest spectrum of protein analytes. The sensitivity of MIP-sensors is sufficiently high for biomarkers in the sub-nanomolar region, nevertheless the cross-reactivity of highly abundant proteins in human serum is still a challenge. MIPs for proteins offer innovative tools not only for clinical and environmental analysis, but also for bioimaging, therapy and protein engineering.
The nanoscale combination of a conductive carbon and a carbon-based material with abundant heteroatoms for battery electrodes is a method to overcome the limitation that the latter has high affinity to alkali metal ions but low electronic conductivity. The synthetic protocol and the individual ratios and structures are important aspects influencing the properties of such multifunctional compounds. Their interplay is, herein, investigated by infiltration of a porous ZnO-templated carbon (ZTC) with nitrogen-rich carbon obtained by condensation of hexaazatriphenylene-hexacarbonitrile (HAT-CN) at 550-1000 degrees C. The density of lithiophilic sites can be controlled by HAT-CN content and condensation temperature. Lithium storage properties are significantly improved in comparison with those of the individual compounds and their physical mixtures. Depending on the uniformity of the formed composite, loading ratio and condensation temperature have different influence. Most stable operation at high capacity per used monomer is achieved with a slowly dried composite with an HAT-CN:ZTC mass ratio of 4:1, condensed at 550 degrees C, providing more than 400 mAh g(-1) discharge capacity at 0.1 A g(-1) and a capacity retention of 72% after 100 cycles of operation at 0.5 A g(-1) due to the homogeneity of the composite and high content of lithiophilic sites.
ControlCenter 4.0
(2021)
Machine learning for improvement of thermal conditions inside a hybrid ventilated animal building
(2021)
In buildings with hybrid ventilation, natural ventilation opening positions (windows), mechanical ventilation rates, heating, and cooling are manipulated to maintain desired thermal conditions. The indoor temperature is regulated solely by ventilation (natural and mechanical) when the external conditions are favorable to save external heating and cooling energy. The ventilation parameters are determined by a rule-based control scheme, which is not optimal. This study proposes a methodology to enable real-time optimum control of ventilation parameters. We developed offline prediction models to estimate future thermal conditions from the data collected from building in operation. The developed offline model is then used to find the optimal controllable ventilation parameters in real-time to minimize the setpoint deviation in the building. With the proposed methodology, the experimental building's setpoint deviation improved for 87% of time, on average, by 0.53 degrees C compared to the current deviations.
The evaluation of how (human) individuals perceive robots is a central issue to better understand human-robot interaction (HRI). On this topic, promising proposals have emerged. However, present tools are not able to assess a sufficient part of the composite psychological dimensions involved in the evaluation of HRI. Indeed, the percentage of variance explained is often under the recommended threshold for a construct to be valid. In this article, we consolidate the lessons learned from three different studies and propose a further developed questionnaire based on a multicomponent approach of anthropomorphism by adding traits from psychosocial theory about the perception of others and the attribution and deprivation of human characteristics: the de-humanization theory. Among these characteristics, the attribution of agency is of main interest in the field of social robotics as it has been argued that robots could be considered as intentional agents. Factor analyses reveal a four sub-dimensions scale including Sociability, Agency, Animacy, and the Disturbance. We discuss the implication(s) of these dimensions on future perception of and attitudes towards robots.
Already successfully used products or designs, past projects or our own experiences can be the basis for the development of new products. As reference products or existing knowledge, it is reused in the development process and across generations of products. Since further, products are developed in cooperation, the development of new product generations is characterized by knowledge-intensive processes in which information and knowledge are exchanged between different kinds of knowledge carriers. The particular knowledge transfer here describes the identification of knowledge, its transmission from the knowledge carrier to the knowledge receiver, and its application by the knowledge receiver, which includes embodied knowledge of physical products. Initial empirical findings of the quantitative effects regarding the speed of knowledge transfers already have been examined. However, the factors influencing the quality of knowledge transfer to increase the efficiency and effectiveness of knowledge transfer in product development have not yet been examined empirically. Therefore, this paper prepares an experimental setting for the empirical investigation of the quality of knowledge transfers.
Background Boredom during learning activities has the potential of impeding attention, motivation, learning and eventually achievement. Yet, research focusing on its possible antecedents seems to have received less attention especially within the physics domain. Based on assumptions of the Control Value Theory of Achievement Emotions (CVTAE), this study aimed at examining gender differences and structural relationships between students' reported perceived teacher autonomy support (PTAS), cognitive appraisals (self-efficacy and task value) and learning-related boredom in physics. A sample of 375 (56% females) randomly selected 9(th) grade students (mean age = 15.03 years; SD = 1.02) from five secondary schools in Masaka district of Uganda took part in the study. Results Data collected from students' self-reports using standardised instruments revealed that higher levels of PTAS, self-efficacy, and task value were significantly associated with lower levels of boredom during physics learning. Females reported significantly greater task value for learning physics than the males. Self-efficacy (beta = - .10, p < .05) and task value (beta = - .09, p < .01) partially mediated the relationship between PTAS and boredom. PTAS showed significant direct negative contributions to boredom (beta = - .34, p < .001). Conclusion These findings provide support for theory and practice about the importance of promoting autonomy among students by adjusting instructional behaviours among teachers of physics. Teacher autonomy supportive behaviours influence formation of students' beliefs about ability, subjective value and learning-related boredom in physics. Implications and suggestions for further research are also discussed in this paper.
Optimized deep learning model as a basis for fast UAV mapping of weed species in winter wheat crops
(2021)
Weed maps should be available quickly, reliably, and with high detail to be useful for site-specific management in crop protection and to promote more sustainable agriculture by reducing pesticide use. Here, the optimization of a deep residual convolutional neural network (ResNet-18) for the classification of weed and crop plants in UAV imagery is proposed. The target was to reach sufficient performance on an embedded system by maintaining the same features of the ResNet-18 model as a basis for fast UAV mapping. This would enable online recognition and subsequent mapping of weeds during UAV flying operation. Optimization was achieved mainly by avoiding redundant computations that arise when a classification model is applied on overlapping tiles in a larger input image. The model was trained and tested with imagery obtained from a UAV flight campaign at low altitude over a winter wheat field, and classification was performed on species level with the weed species Matricaria chamomilla L., Papaver rhoeas L., Veronica hederifolia L., and Viola arvensis ssp. arvensis observed in that field. The ResNet-18 model with the optimized image-level prediction pipeline reached a performance of 2.2 frames per second with an NVIDIA Jetson AGX Xavier on the full resolution UAV image, which would amount to about 1.78 ha h(-1) area output for continuous field mapping. The overall accuracy for determining crop, soil, and weed species was 94%. There were some limitations in the detection of species unknown to the model. When shifting from 16-bit to 32-bit model precision, no improvement in classification accuracy was observed, but a strong decline in speed performance, especially when a higher number of filters was used in the ResNet-18 model. Future work should be directed towards the integration of the mapping process on UAV platforms, guiding UAVs autonomously for mapping purpose, and ensuring the transferability of the models to other crop fields.
In clinical practice, only a few reliable measurement instruments are available for monitoring knee joint rehabilitation. Advances to replace motion capturing with sensor data measurement have been made in the last years. Thus, a systematic review of the literature was performed, focusing on the implementation, diagnostic accuracy, and facilitators and barriers of integrating wearable sensor technology in clinical practices based on a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. For critical appraisal, the COSMIN Risk of Bias tool for reliability and measurement of error was used. PUBMED, Prospero, Cochrane database, and EMBASE were searched for eligible studies. Six studies reporting reliability aspects in using wearable sensor technology at any point after knee surgery in humans were included. All studies reported excellent results with high reliability coefficients, high limits of agreement, or a few detectable errors. They used different or partly inappropriate methods for estimating reliability or missed reporting essential information. Therefore, a moderate risk of bias must be considered. Further quality criterion studies in clinical settings are needed to synthesize the evidence for providing transparent recommendations for the clinical use of wearable movement sensors in knee joint rehabilitation.