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Creators/Authors contains: "Yu, Xiao"

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  1. Free, publicly-accessible full text available February 17, 2024
  2. Free, publicly-accessible full text available September 1, 2023
  3. Free, publicly-accessible full text available January 1, 2024
  4. Observations on the lee of a topographic ridge show that the turbulence kinetic energy (TKE) dissipation rate due to shear instabilities is three orders of magnitude higher than the typical value in the open ocean. Laboratory-scale studies at low Reynolds number suggest that high turbulent dissipation occurs primarily within the core region of shear instabilities. However, field-scale studies indicate that high turbulence is mainly populated along the braids of shear instabilities. In this study, a high-resolution, resolving the Ozmidov-scale, non-hydrostatic model with Large Eddy Simulation (LES) turbulent closure is applied to investigate dominant mechanisms that control the spatial and temporal scales of shear instabilities and resulting mixing in stratified shear flow at high Reynolds number. The simulated density variance dissipation rate is elevated in the cusp-like bands of shear instabilities with a specific period, consistent with the acoustic backscatter taken by shipboard echo sounder. The vertical length scale of each cusp-like band is nearly half of the vertical length scale of the internal lee wave. However, it is consistent with instabilities originating from a shear layer based on linear stability theory. The model results indicate that the length scale and/or the period of shear instabilities are the key parameters tomore »the mixing enhancement that increases with lateral Froude number Fr L , i.e. stronger shear and/or steeper ridge.« less
  5. In the coastal ocean, interactions of waves and currents with large roughness elements, similar in size to wave orbital excursions, generate drag and dissipate energy. These boundary layer dynamics differ significantly from well-studied small-scale roughness. To address this problem, we derived spatially and phase-averaged momentum equations for combined wave–current flows over rough bottoms, including the canopy layer containing obstacles. These equations were decomposed into steady and oscillatory parts to investigate the effects of waves on currents, and currents on waves. We applied this framework to analyse large-eddy simulations of combined oscillatory and steady flows over hemisphere arrays (diameter $D$ ), in which current ( $U_c$ ), wave velocity ( $U_w$ ) and period ( $T$ ) were varied. In the steady momentum budget, waves increase drag on the current, and this is balanced by the total stress at the canopy top. Dispersive stresses from oscillatory flow around obstacles are increasingly important as $U_w/U_c$ increases. In the oscillatory momentum budget, acceleration in the canopy is balanced by pressure gradient, added-mass and form drag forces; stress gradients are small compared to other terms. Form drag is increasingly important as the Keulegan–Carpenter number $KC=U_wT/D$ and $U_c/U_w$ increase. Decomposing the drag term illustrates thatmore »a quadratic relationship predicts the observed dependences of steady and oscillatory drag on $U_c/U_w$ and $KC$ . For large roughness elements, bottom friction is well represented by a friction factor ( $f_w$ ) defined using combined wave and current velocities in the canopy layer, which is proportional to drag coefficient and frontal area per unit plan area, and increases with $KC$ and $U_c/U_w$ .« less
  6. Free, publicly-accessible full text available September 15, 2023
  7. Underwater robots, including Remote Operating Vehicles (ROV) and Autonomous Underwater Vehicles (AUV), are currently used to support underwater missions that are either impossible or too risky to be performed by manned systems. In recent years the academia and robotic industry have paved paths for tackling technical challenges for ROV/AUV operations. The level of intelligence of ROV/AUV has increased dramatically because of the recent advances in low-power-consumption embedded computing devices and machine intelligence (e.g., AI). Nonetheless, operating precisely underwater is still extremely challenging to minimize human intervention due to the inherent challenges and uncertainties associated with the underwater environments. Proximity operations, especially those requiring precise manipulation, are still carried out by ROV systems that are fully controlled by a human pilot. A workplace-ready and worker-friendly ROV interface that properly simplifies operator control and increases remote operation confidence is the central challenge for the wide adaptation of ROVs.

    This paper examines the recent advances of virtual telepresence technologies as a solution for lowering the barriers to the human-in-the-loop ROV teleoperation. Virtual telepresence refers to Virtual Reality (VR) related technologies that help a user to feel that they were in a hazardous situation without being present at the actual location. We present amore »pilot system of using a VR-based sensory simulator to convert ROV sensor data into human-perceivable sensations (e.g., haptics). Building on a cloud server for real-time rendering in VR, a less trained operator could possibly operate a remote ROV thousand miles away without losing the minimum situational awareness. The system is expected to enable an intensive human engagement on ROV teleoperation, augmenting abilities for maneuvering and navigating ROV in unknown and less explored subsea regions and works. This paper also discusses the opportunities and challenges of this technology for ad hoc training, workforce preparation, and safety in the future maritime industry. We expect that lessons learned from our work can help democratize human presence in future subsea engineering works, by accommodating human needs and limitations to lower the entrance barrier.

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  8. Free, publicly-accessible full text available October 1, 2023
  9. Abstract Background

    Systems-level analyses, such as differential gene expression analysis, co-expression analysis, and metabolic pathway reconstruction, depend on the accuracy of the transcriptome. Multiple tools exist to perform transcriptome assembly from RNAseq data. However, assembling high quality transcriptomes is still not a trivial problem. This is especially the case for non-model organisms where adequate reference genomes are often not available. Different methods produce different transcriptome models and there is no easy way to determine which are more accurate. Furthermore, having alternative-splicing events exacerbates such difficult assembly problems. While benchmarking transcriptome assemblies is critical, this is also not trivial due to the general lack of true reference transcriptomes.


    In this study, we first provide a pipeline to generate a set of the simulated benchmark transcriptome and corresponding RNAseq data. Using the simulated benchmarking datasets, we compared the performance of various transcriptome assembly approaches including both de novo and genome-guided methods. The results showed that the assembly performance deteriorates significantly when alternative transcripts (isoforms) exist or for genome-guided methods when the reference is not available from the same genome. To improve the transcriptome assembly performance, leveraging the overlapping predictions between different assemblies, we present a new consensus-based ensemble transcriptome assembly approach, ConSemble.


    Without usingmore »a reference genome, ConSemble using four de novo assemblers achieved an accuracy up to twice as high as any de novo assemblers we compared. When a reference genome is available, ConSemble using four genome-guided assemblies removed many incorrectly assembled contigs with minimal impact on correctly assembled contigs, achieving higher precision and accuracy than individual genome-guided methods. Furthermore, ConSemble using de novo assemblers matched or exceeded the best performing genome-guided assemblers even when the transcriptomes included isoforms. We thus demonstrated that the ConSemble consensus strategy both for de novo and genome-guided assemblers can improve transcriptome assembly. The RNAseq simulation pipeline, the benchmark transcriptome datasets, and the script to perform the ConSemble assembly are all freely available from:

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