Structure of turbulence and sediment stratification in wave-supported mud layers

We present results from laboratory experiments in a wave flume with and without a sediment bed to investigate the turbulent structure and sediment dynamics of wave-supported mud layers. The presence of sediment on the bed significantly alters the structure of the wave boundary layer relative to that observed in the absence of sediment, increasing the TKE by more than a factor of 3 at low wave orbital velocities and suppressing it at the highest velocities. The transition between the low and high-velocity regimes occurs when ReΔ ≃ 450, where ReΔ is the Stokes Reynolds number. In the low-velocity regime (ReΔ 450) the ripples are significantly smaller, the near-bed sediment concentrations are significantly higher and density stratification due to sediment becomes important. In this regime the TKE and Reynolds stress are lower in the sediment bed runs than in comparable runs with no sediment. The regime transition at ReΔ = 450 appears to result from washout of the ripples and increased concentrations of fine sand suspended in the boundary layer, which increases the settling flux and the stratification near the bed. The increased stratification damps turbulence, especially near the top of the high-concentration layer, reducing the layer thickness. We anticipate that these effects will influence the transport capacity of wave-supported gravity currents on the continental shelf

Real-time classification of multivariate olfaction data using spiking neural networks

Recent studies in bioinspired artificial olfaction, especially those detailing the application of spike-based neuromorphic methods, have led to promising developments towards overcoming the limitations of traditional approaches, such as complexity in handling multivariate data, computational and power requirements, poor accuracy, and substantial delay for processing and classification of odors. Rank-order-based olfactory systems provide an interesting approach for detection of target gases by encoding multi-variate data generated by artificial olfactory systems into temporal signatures. However, the utilization of traditional pattern-matching methods and unpredictable shuffling of spikes in the rank-order impedes the performance of the system. In this paper, we present an SNN-based solution for the classification of rank-order spiking patterns to provide continuous recognition results in real-time. The SNN classifier is deployed on a neuromorphic hardware system that enables massively parallel and low-power processing on incoming rank-order patterns. Offline learning is used to store the reference rank-order patterns, and an inbuilt nearest neighbor classification logic is applied by the neurons to provide recognition results. The proposed system was evaluated using two different datasets including rank-order spiking data from previously established olfactory systems. The continuous classification that was achieved required a maximum of 12.82% of the total pattern frame to provide 96.5% accuracy in identifying corresponding target gases. Recognition results were obtained at a nominal processing latency of 16ms for each incoming spike. In addition to the clear advantages in terms of real-time operation and robustness to inconsistent rank-orders, the SNN classifier can also detect anomalies in rank-order patterns arising due to drift in sensing arrays

Vertical Boil Propagation from a Submerged Estuarine Sill

Surface disruptions by boils during strong tidal flows over a rocky sill were observed in thermal infrared imagery collected at the Snohomish River estuary in Washington State. Locations of boil disruptions and boil diameters at the surface were quantified and are used to test an idealized model of vertical boil propagation. The model is developed as a two-dimensional approximation of a three-dimensional vortex loop, and boil vorticity is derived from the flow shear over the sill. Predictions of boil disruption locations were determined from the modeled vertical velocity, the sill depth, and the over-sill velocity. Predictions by the vertical velocity model agree well with measured locations (rms difference 3.0 m) and improve by using measured velocity and shear (rms difference 1.8 m). In comparison, a boil-surfacing model derived from laboratory turbulent mixed-layer wakes agrees with the measurements only when stratification is insignificant

Turbulent Kinetic Energy and Coherent Structures in a Tidal River

We investigate the relationship between turbulence statistics and coherent structures (CS) in an unstratified reach of the Snohomish River estuary using in situ velocity measurements and surface infrared (IR) imaging. Sequential IR images are used to estimate surface flow characteristics via a particle-image-velocimetry (PIV) technique, and are conditionally sampled to delineate the surface statistics of bottom-generated CS, or boils. In the water column, we find that turbulent kinetic energy (TKE) production exceeds dissipation near the bed but is less than dissipation in the midwater column and that TKE flux divergence closes a significant portion of the measured imbalance. The surface boundary leads to divergence in upwelling CS, and leads to the redistribution of vertical TKE to the horizontal. Very near the surface, statistical anisotropy is observed at length scales larger than the depth H (3–5 m), while boil-scale motions of O(1)m are nearly isotropic and exhibit a 25/3 turbulent cascade to smaller scales. Conditional sampling suggests that TKE dissipation in boils is approximately 2 times greater on average than dissipation in ambient flow. Similarly, surface boils are marked by significantly greater velocity variance, upwelling, divergence, and TKE flux divergence than ambient flow regions. Coherent structures and their surface manifestation, therefore, play an important role in the vertical transport of TKE and the water column distribution of dissipation, and are an important component of the TKE budget

What Children’s Imagined Uses of the BBC micro:bit Tells Us About Designing for their IoT Privacy, Security and Safety

Ensuring that young people reap the benefits of the Internet of Things requires proactively attending to the risks they may encounter in entering the world this new technology affords. The e-safety guidelines currently taught in UK schools may not sufficiently prepare children for navigating the risks that come with connected devices. In this paper we describe initial results from the PETRAS project IoT4Kids, exploring the privacy and security implications of children programming the BBC micro:bit, an IoT-ready device designed for children. We report on children’s (ages 9–10) likely uses of the micro:bit and discuss their implications, highlighting shortcomings of e-safety education and policy guidelines for such uses

Linking a Multi-Compartment T2 Model to Diffusion Microstructure in Prostate Cancer

This work develops a multi-compartment T2 model for prostate imaging. We investigate whether this model can provide information about differences in tissue microstructure, such as those between normal prostate tissue and tumour, by comparing it to the VERDICT diffusion model6. The high correlations found between a number of the parameters suggest that the proposed model is capable of detecting some microstructural differences. In the future this method may be able to provide different and complementary microstructural information to current diffusion models

The impact of storms and stratification on sediment transport in the Rhine region of freshwater influence

We present measurements of along and across-shore sediment transport in a region of the Dutch coast 10 kilometers north of the Rhine River mouth. This section of the coast is characterized by strong vertical density stratification because it is within the mid-field region of the Rhine region of freshwater influence, where processes typical of the far-field, such as tidal straining, are modified by the passage of distinct freshwater lenses at the surface. The experiment captured two storms, and a wide range of wind, wave, tidal and stratification conditions. We focus primarily on the mechanisms leading to cross-shore sediment flux at a mooring location in 12m of water, which are responsible for the exchange of sediment between the near-shore and the inner shelf. Net transport during storms was directed offshore and influenced by cross-shelf winds, while net transport during spring tides was determined by the mean state of stratification. Tidal straining dominated during neap tides; however, cross-shore transport was negligible due to small sediment concentrations. The passage of freshwater lenses manifested as strong pulses of offshore transport primarily during spring tides. We observe that both barotropic and baroclinic processes are relevant for cross-shore transport at depth and, since transport rates due to these competing processes were similar, the net transport direction will be determined by the frequency and sequencing of these modes of transport. Based on our observations, we find that wind- and wave-driven transport during storms tends move fine sediment offshore, while calmer, more stratified conditions move it back onshore