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The shape and the actuation capability of state of the art robotic devices typically relies on multimaterial systems from a combination of geometry determining materials and actuation components. Here, we present multifunctional 4D-actuators processable by 3D-printing, in which the actuator functionality is integrated into the shaped body. The materials are based on crosslinked poly(carbonate-urea-urethane) networks (PCUU), synthesized in an integrated process, applying reactive extrusion and subsequent water-based curing. Actuation capability could be added to the PCUU, prepared from aliphatic oligocarbonate diol, isophorone diisocyanate (IPDI) and water, in a thermomechanical programming process. When programmed with a strain of epsilon(prog) = 1400% the PCUU networks exhibited actuation apparent by reversible elongation epsilon'(rev) of up to 22%. In a gripper a reversible bending epsilon'(rev)((be)(nd)()) in the range of 37-60% was achieved when the actuation temperature (T-high) was varied between 45 degrees C and 49 degrees C. The integration of actuation and shape formation could be impressively demonstrated in two PCUU-based reversible fastening systems, which were able to hold weights of up to 1.1 kg. In this way, the multifunctional materials are interesting candidate materials for robotic applications where a freedom in shape design and actuation is required as well as for sustainable fastening systems.
The prehistory of electrets is not known yet, but it is quite likely that the electrostatic charging behavior of amber (Greek: τò ηλεκτρoν, i.e., “electron”) already was familiar to people in ancient cultures (China, Egypt, Greece, etc.), before the Greek philosopher and scientist Thales of Miletus (6th century BCE)-or rather his disciples and followers-reported it in writing (cf. Figure 1). More than two millennia later, William Gilbert (1544–1603), the physician of Queen Elizabeth I, coined the term “electric” in his book De Magnete, Magneticisque Corporibus, et de Magno Magnete Tellure (1600) for dielectric materials that attract like amber and that included sulfur and glass [1]. The second half of the 18th century saw the invention of the electrophorus or electrophore [2], a capacitive electret device, in 1762 by Johan Carl Wilcke (1732–1796).
A comprehensive workflow to analyze ensembles of globally inverted 2D electrical resistivity models
(2022)
Electrical resistivity tomography (ERT) aims at imaging the subsurface resistivity distribution and provides valuable information for different geological, engineering, and hydrological applications. To obtain a subsurface resistivity model from measured apparent resistivities, stochastic or deterministic inversion procedures may be employed. Typically, the inversion of ERT data results in non-unique solutions; i.e., an ensemble of different models explains the measured data equally well. In this study, we perform inference analysis of model ensembles generated using a well-established global inversion approach to assess uncertainties related to the nonuniqueness of the inverse problem. Our interpretation strategy starts by establishing model selection criteria based on different statistical descriptors calculated from the data residuals. Then, we perform cluster analysis considering the inverted resistivity models and the corresponding data residuals. Finally, we evaluate model uncertainties and residual distributions for each cluster. To illustrate the potential of our approach, we use a particle swarm optimization (PSO) algorithm to obtain an ensemble of 2D layer-based resistivity models from a synthetic data example and a field data set collected in Loon-Plage, France. Our strategy performs well for both synthetic and field data and allows us to extract different plausible model scenarios with their associated uncertainties and data residual distributions. Although we demonstrate our workflow using 2D ERT data and a PSObased inversion approach, the proposed strategy is general and can be adapted to analyze model ensembles generated from other kinds of geophysical data and using different global inversion approaches.
There is an increasing interest in fusing data from heterogeneous sources. Combining data sources increases the utility of existing datasets, generating new information and creating services of higher quality. A central issue in working with heterogeneous sources is data migration: In order to share and process data in different engines, resource intensive and complex movements and transformations between computing engines, services, and stores are necessary.
Muses is a distributed, high-performance data migration engine that is able to interconnect distributed data stores by forwarding, transforming, repartitioning, or broadcasting data among distributed engines' instances in a resource-, cost-, and performance-adaptive manner. As such, it performs seamless information sharing across all participating resources in a standard, modular manner. We show an overall improvement of 30 % for pipelining jobs across multiple engines, even when we count the overhead of Muses in the execution time. This performance gain implies that Muses can be used to optimise large pipelines that leverage multiple engines.
Additive Manufacturing (AM) in terms of laser powder-bed fusion (L-PBF) offers new prospects regarding the design of parts and enables therefore the production of lattice structures. These lattice structures shall be implemented in various industrial applications (e.g. gas turbines) for reasons of material savings or cooling channels. However, internal defects, residual stress, and structural deviations from the nominal geometry are unavoidable.
In this work, the structural integrity of lattice structures manufactured by means of L-PBF was non-destructively investigated on a multiscale approach.
A workflow for quantitative 3D powder analysis in terms of particle size, particle shape, particle porosity, inter-particle distance and packing density was established. Synchrotron computed tomography (CT) was used to correlate the packing density with the particle size and particle shape. It was also observed that at least about 50% of the powder porosity was released during production of the struts.
Struts are the component of lattice structures and were investigated by means of laboratory CT. The focus was on the influence of the build angle on part porosity and surface quality. The surface topography analysis was advanced by the quantitative characterisation of re-entrant surface features. This characterisation was compared with conventional surface parameters showing their complementary information, but also the need for AM specific surface parameters.
The mechanical behaviour of the lattice structure was investigated with in-situ CT under compression and successive digital volume correlation (DVC). The deformation was found to be knot-dominated, and therefore the lattice folds unit cell layer wise.
The residual stress was determined experimentally for the first time in such lattice structures. Neutron diffraction was used for the non-destructive 3D stress investigation. The principal stress directions and values were determined in dependence of the number of measured directions. While a significant uni-axial stress state was found in the strut, a more hydrostatic stress state was found in the knot. In both cases, strut and knot, seven directions were at least needed to find reliable principal stress directions.
Remote sensing plays an increasingly key role in the determination of soil organic carbon (SOC) stored in agriculturally managed topsoils at the regional and field scales. Contemporary Unmanned Aerial Systems (UAS) carrying low-cost and lightweight multispectral sensors provide high spatial resolution imagery (<10 cm). These capabilities allow integrate of UAS-derived soil data and maps into digitalized workflows for sustainable agriculture. However, the common situation of scarce soil data at field scale might be an obstacle for accurate digital soil mapping. In our case study we tested a fixed-wing UAS equipped with visible and near infrared (VIS-NIR) sensors to estimate topsoil SOC distribution at two fields under the constraint of limited sampling points, which were selected by pedological knowledge. They represent all releva nt soil types along an erosion-deposition gradient; hence, the full feature space in terms of topsoils' SOC status. We included the Topographic Position Index (TPI) as a co-variate for SOC prediction. Our study was performed in a soil landscape of hummocky ground moraines, which represent a significant of global arable land. Herein, small scale soil variability is mainly driven by tillage erosion which, in turn, is strongly dependent on topography. Relationships between SOC, TPI and spectral information were tested by Multiple Linear Regression (MLR) using: (i) single field data (local approach) and (ii) data from both fields (pooled approach). The highest prediction performance determined by a leave-one-out-cross-validation (LOOCV) was obtained for the models using the reflectance at 570 nm in conjunction with the TPI as explanatory variables for the local approach (coefficient of determination (R-2) = 0.91; root mean square error (RMSE) = 0.11% and R-2 = 0.48; RMSE = 0.33, respectively). The local MLR models developed with both reflectance and TPI using values from all points showed high correlations and low prediction errors for SOC content (R-2 = 0.88, RMSE = 0.07%; R-2 = 0.79, RMSE = 0.06%, respectively). The comparison with an enlarged dataset consisting of all points from both fields (pooled approach) showed no improvement of the prediction accuracy but yielded decreased prediction errors. Lastly, the local MLR models were applied to the data of the respective other field to evaluate the cross-field prediction ability. The spatial SOC pattern generally remains unaffected on both fields; differences, however, occur concerning the predicted SOC level. Our results indicate a high potential of the combination of UAS-based remote sensing and environmental covariates, such as terrain attributes, for the prediction of topsoil SOC content at the field scale. The temporal flexibility of UAS offer the opportunity to optimize flight conditions including weather and soil surface status (plant cover or residuals, moisture and roughness) which, otherwise, might obscure the relationship between spectral data and SOC content. Pedologically targeted selection of soil samples for model development appears to be the key for an efficient and effective prediction even with a small dataset.
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.
The purpose of the present study was to investigate the role of gender and gender stereotype traits (masculinity, femininity) in cyber victimization behaviors (cyber relational victimization, cyber verbal victimization, hacking) through different technologies (mobile phones, gaming consoles, social networking sites). There were 456 8th graders (226 females; M age = 13.66, SD = 0.41) from two midwestern middle schools in the United States included in this study. They completed questionnaires on their endorsement of masculine and feminine traits, and self-reported cyber victimization through different technologies. The findings revealed main effects of types of cyber victimization for boys and of technology for girls. In particular, boys with feminine traits experienced the most victimization by cyber verbal aggression, cyber relational aggression, and hacking when compared to the other groups of boys. Girls with feminine traits experienced the most cyber victimization through social networking sites, gaming consoles, and mobile phones in comparison to the other groups of girls. For girls with feminine traits, they reported more cyber relational victimization and cyber verbal victimization through mobile phones and social networking sites, as well as more hacking via social networking sites. Such findings underscore the importance of considering gender stereotype traits, types of victimization, and technologies when examining cyber victimization.
The purpose of the present study was to investigate the role of gender and gender stereotype traits (masculinity, femininity) in cyber victimization behaviors (cyber relational victimization, cyber verbal victimization, hacking) through different technologies (mobile phones, gaming consoles, social networking sites). There were 456 8th graders (226 females; M age = 13.66, SD = 0.41) from two midwestern middle schools in the United States included in this study. They completed questionnaires on their endorsement of masculine and feminine traits, and self-reported cyber victimization through different technologies. The findings revealed main effects of types of cyber victimization for boys and of technology for girls. In particular, boys with feminine traits experienced the most victimization by cyber verbal aggression, cyber relational aggression, and hacking when compared to the other groups of boys. Girls with feminine traits experienced the most cyber victimization through social networking sites, gaming consoles, and mobile phones in comparison to the other groups of girls. For girls with feminine traits, they reported more cyber relational victimization and cyber verbal victimization through mobile phones and social networking sites, as well as more hacking via social networking sites. Such findings underscore the importance of considering gender stereotype traits, types of victimization, and technologies when examining cyber victimization.
Cyber victimization research reveals various personal and contextual correlations and negative consequences associated with this experience. Despite increasing attention on cyber victimization, few studies have examined such experiences among ethnic minority adolescents. The purpose of the present study was to examine the moderating effect of ethnicity in the longitudinal associations among cyber victimization, school-belongingness, and psychological consequences (i.e., depression, loneliness, anxiety). These associations were investigated among 416 Latinx and white adolescents (46% female; M age = 13.89, SD = 0.41) from one middle school in the United States. They answered questionnaires on cyber victimization, school belongingness, depression, loneliness, and anxiety in the 7th grade (Time 1). One year later, in the 8th grade (Time 2), they completed questionnaires on depression, loneliness, and anxiety. Low levels of school-belongingness strengthened the positive relationships between cyber victimization and Time 2 depression and anxiety, especially among Latinx adolescents. The positive association between cyber victimization and Time 2 loneliness was strengthened for low levels of school-belongingness for all adolescents. These findings may indicate that cyber victimization threatens adolescents’ school-belongingness, which has implications for their emotional adjustment. Such findings underscore the importance of considering diverse populations when examining cyber victimization.