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The discrete element method (DEM) is the most widely applied numerical tool to simulate triaxial test, a common geotechnical test to measure the shear strength of soil. However, the typical DEM model uses sphere clusters to approximate soil particles, which is not sufficiently accurate to simulate realistic soil particles. This paper shows the potential of using a physics engine technique as a promising alternative to typical DEM method. Originally developed for simulating realistic physical and mechanical processes in video games and computer-animated films, physics engines have developed quickly and are being applied in scientific computing. Physics engines use triangular face tesselations to represent realistic objectives, which provides higher accuracy to model realistic soil particle geometries. In this paper, physics engine is applied to simulate true triaxial tests ofMonterey No. 0 sand. The numerical results agree well with experimental results. This study provides DEM modelers with the physics engine technique as another promising option to simulate realistic soil particles in geotechnical tests. 

The structural design of self-healing materials determines the ultimate performance of the product that can be used in a wide range of applications. Incorporating intrinsic self-healing moieties into puncture-resistant materials could significantly improve the failure resistance and product longevity, since their rapidly rebuilt bonds will provide additional recovery force to resist the external force. Herein, we present a series of tailored urea-modified poly(dimethylsiloxane)-based self-healing polymers (U-PDMS-SPs) that exhibit excellent puncture-resistant properties, fast autonomous self-healing, multi-cycle adhesion capabilities, and well-tunable mechanical properties. Controlling the composition of chemical and physical cross-links enables the U-PDMS-SPs to have an extensibility of 528% and a toughness of 0.6 MJ m −3 . U-PDMS-SPs exhibit fast autonomous self-healability with 25% strain recovery within 2 minutes of healing, and over 90% toughness recovery after 16 hours. We further demonstrate its puncture-resistant properties under the ASTM D5748 standard with an unbreakable feature. Furthermore, the multi-cycle adhesive properties of U-PDMS-SPs are also revealed. High puncture resistance (>327 mJ) and facile adhesion with rapid autonomous self-healability will have a broad impact on the design of adhesives, roofing materials, and many other functional materials with enhanced longevity. 

Technological advances in psychological research have enabled large-scale studies of human behavior and streamlined pipelines for automatic processing of data. However, studies of infants and children have not fully reaped these benefits because the behaviors of interest, such as gaze duration and direction, still have to be extracted from video through a laborious process of manual annotation, even when these data are collected online. Recent advances in computer vision raise the possibility of automated annotation of these video data. In this article, we built on a system for automatic gaze annotation in young children, iCatcher, by engineering improvements and then training and testing the system (referred to hereafter as iCatcher+) on three data sets with substantial video and participant variability (214 videos collected in U.S. lab and field sites, 143 videos collected in Senegal field sites, and 265 videos collected via webcams in homes; participant age range = 4 months–3.5 years). When trained on each of these data sets, iCatcher+ performed with near human-level accuracy on held-out videos on distinguishing “LEFT” versus “RIGHT” and “ON” versus “OFF” looking behavior across all data sets. This high performance was achieved at the level of individual frames, experimental trials, and study videos; held across participant demographics (e.g., age, race/ethnicity), participant behavior (e.g., movement, head position), and video characteristics (e.g., luminance); and generalized to a fourth, entirely held-out online data set. We close by discussing next steps required to fully automate the life cycle of online infant and child behavioral studies, representing a key step toward enabling robust and high-throughput developmental research.