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Abstract: A volume-penalization immersed boundary (VPIB) method was developed to study flow interactions with aquatic vegetation. The model has been validated with data from laboratory experiments and previous high-fidelity models with satisfactory results. Sensitivity analyzes on both penalty parameter and thickness parameter were conducted, and optimal values for these parameters are recommended. The validated model has been applied to study the effects of swaying motion of vegetation stems on the flow dynamics at both vegetate-stem scale and patch scale. The swaying motion of the vegetation stem is prescribed following a cubic law that peaks at the top and decreases to zero at the bottom. At stem-scale, the hydrodynamics depend on the Keulegan Carpenter number (KC), which is defined as the maximum excursion of the vegetation stem to the diameter of the stem. Simulations with three KC values were carried out. For KC≥1, the flow turbulence is significantly enhanced by the swaying motion of the stem, and turbulence becomes more isotropic in the wake. The swaying motion of vegetation stems caused a 5% increase of the bottom shear stress at the shoulders of the stem, and the effect is negligible in the wake. At patch-scale, the hydrodynamics depend on the effective Keulegan Carpenter number based on the patch size of the vegetation patch, and the solid volume fraction for dense vegetation canopy. Solid volume fraction was varied while maintaining the same effective Keulegan Carpenter in the simulations. When the effective Keulgen Carpenter number is small (KC<1), effects of the swaying motion of vegetation stems on the large patch-scale dynamics are not significant, including both the turbulence statistics and the bottom stress. 

We propose a novel technique to register sparse 3D scans in the absence of texture. While existing methods such as KinectFusion or Iterative Closest Points (ICP) heavily rely on dense point clouds, this task is particularly challenging under sparse conditions without RGB data. Sparse texture-less data does not come with high-quality boundary signal, and this prohibits the use of correspondences from corners, junctions, or boundary lines. Moreover, in the case of sparse data, it is incorrect to assume that the same point will be captured in two consecutive scans. We take a different approach and first re-parameterize the point-cloud using a large number of line segments. In this re-parameterized data, there exists a large number of line intersection (and not correspondence) constraints that allow us to solve the registration task. We propose the use of a two-step alternating projection algorithm by formulating the registration as the simultaneous satisfaction of intersection and rigidity constraints. The proposed approach outperforms other top-scoring algorithms on both Kinect and LiDAR datasets. In Kinect, we can use 100X downsampled sparse data and still outperform competing methods operating on full-resolution data. 

This proceeding was published in a special issue of J. Laser Appl. as: H. Cheng, C. Xia, S. M. Kuebler, P. Golvari, M. Sun, M. Zhang, X. Yu*. "Generation of Bessel-beam arrays for parallel fabrication in two-photon polymerization." J. Laser Appl. 2021, 33, 012040-1 - 012040-6; https://doi.org/10.2351/7.0000313. 

Legumes are the second most important family of crop plants. One defining feature of legumes is their unique ability to establish a nitrogen-fixing root nodule symbiosis with soil bacteria known as rhizobia. Since domestication from their wild relatives, crop legumes have been under intensive breeding to improve yield and other agronomic traits but with little attention paid to the belowground symbiosis traits. Theoretical models predict that domestication and breeding processes, coupled with high‐input agricultural practices, might have reduced the capacity of crop legumes to achieve their full potential of nitrogen fixation symbiosis. Testing this prediction requires characterizing symbiosis traits in wild and breeding populations under both natural and cultivated environments using genetic, genomic, and ecological approaches. However, very few experimental studies have been dedicated to this area of research. Here, we review how legumes regulate their interactions with soil rhizobia and how domestication, breeding and agricultural practices might have affected nodulation capacity, nitrogen fixation efficiency, and the composition and function of rhizobial community. We also provide a perspective on how to improve legume-rhizobial symbiosis in sustainable agricultural systems.