Dynamics of Aeolian Sand Streamers and Airflow Turbulence over a Beach Surface

<p>Sand transport by wind over granular beds displays dynamic structure and organisation in the form of streamers (aka ‘sand snakes’) that appear, meander and intertwine, and then dissipate as they are advected downwind. These patterns of saltating grain populations are thought to be initiated and controlled by coherent flow structures in the turbulent boundary layer wind that scrape over the bed surface raking up sand into entrainment. Streamer behaviour is thus fundamental to understanding sand transport dynamics, in particular its strong spatio-temporal variability, and is equally relevant to granular transport in other geophysical flows (fluvial, submarine).</p><p>This paper presents findings on streamer dynamics and associated wind turbulence observed in a field experiment on a beach, with measurements from 30Hz video-imagery using Large-Scale Particle Image Velocimetry (LS-PIV), combined with 50Hz wind measurements from 3D sonic anemometry and co-located sand transport rate monitoring using an array of laser particle counters (‘Wenglors’), all taking place over an area of ~10 m<sup>2</sup> and over periods of several minutes. The video imagery was used to identify when and where streamers advected past the sonic anemometer and laser sensors so that relationships could be detected between the passage of turbulence structures in the airflow and the length- and time-scales, propagation speeds, and sand transport intensities of associated streamers. The findings form the basis for a phenomenological model of streamer dynamics under turbulent boundary layer flows that predicts the impact of spatio-temporal variability on local measurement of sand transport.</p>

The Effect of Video Imagery Training on Soccer Skills Performance of High School Players

This study evaluated the effect of PETTLEP video imagery onreactive motor skill test (Total RMST time, sprint time, reactive agility time, passing time, and passing accuracy) among 32 Malaysian high school soccer players (mean age of 15.31±1.83) who were randomly assigned into an experimental group (N:16) and a control group (N:16). A pre-test-post-test design was used to evaluate the effect of the intervention on the high schoolplayers' soccer skill performance. The experimental group received 10-minute PETTLEP video imagery trainingbefore their regular soccer training for eight weeks in addition to their regular soccer training, while thecontrol group only attended their regular soccer training. The data analysis revealed a significant effect of video imagery training on players' performance in the experimental group. The results showed that total RMST time, reactive agility time, passing time, and passing accuracy were statistically different within the experimental group F(15, 154.993)= 14.440, p = < .001, Wilks' Λ = .050 and between groups F(5, 56.00)=379.774, p = < .001, Wilks' Λ = .050 after receiving the training, except for the sprint time which was not significant. The findings of this study provide evidence that eight weeks ofPETTLEPvideo imagery training interventioncan significantly improve soccer players' total RMST time, reactive agility time, passing time, and passing accuracy. These findings could be integrated into training programs by coaches and players in order to improve the performance of different soccer skills among high school players.

The potential of video imagery from worldwide cabled observatory networks to provide information supporting fish-stock and biodiversity assessment

Seafloor multiparametric fibre-optic-cabled video observatories are emerging tools for standardized monitoring programmes, dedicated to the production of real-time fishery-independent stock assessment data. Here, we propose that a network of cabled cameras can be set up and optimized to ensure representative long-term monitoring of target commercial species and their surrounding habitats. We highlight the importance of adding the spatial dimension to fixed-point-cabled monitoring networks, and the need for close integration with Artificial Intelligence pipelines, that are necessary for fast and reliable biological data processing. We then describe two pilot studies, exemplary of using video imagery and environmental monitoring to derive robust data as a foundation for future ecosystem-based fish-stock and biodiversity management. The first example is from the NE Pacific Ocean where the deep-water sablefish (Anoplopoma fimbria) has been monitored since 2010 by the NEPTUNE cabled observatory operated by Ocean Networks Canada. The second example is from the NE Atlantic Ocean where the Norway lobster (Nephrops norvegicus) is being monitored using the SmartBay observatory developed for the European Multidisciplinary Seafloor and water column Observatories. Drawing from these two examples, we provide insights into the technological challenges and future steps required to develop full-scale fishery-independent stock assessments.

Wave-Tracking in the Surf Zone Using Coastal Video Imagery with Deep Neural Networks

In this paper, we propose a series of procedures for coastal wave-tracking using coastal video imagery with deep neural networks. It consists of three stages: video enhancement, hydrodynamic scene separation and wave-tracking. First, a generative adversarial network, trained using paired raindrop and clean videos, is applied to remove image distortions by raindrops and to restore background information of coastal waves. Next, a hydrodynamic scene of propagated wave information is separated from surrounding environmental information in the enhanced coastal video imagery using a deep autoencoder network. Finally, propagating waves are tracked by registering consecutive images in the quality-enhanced and scene-separated coastal video imagery using a spatial transformer network. The instantaneous wave speed of each individual wave crest and breaker in the video domain is successfully estimated through learning the behavior of transformed and propagated waves in the surf zone using deep neural networks. Since it enables the acquisition of spatio-temporal information of the surf zone though the characterization of wave breakers inclusively wave run-up, we expect that the proposed framework with the deep neural networks leads to improve understanding of nearshore wave dynamics.