Local Spatial Gradients Research Articles

Turbulent boundary layer flow over a three-dimensional sinusoidal surface

The sinusoidal roughness effect is investigated using a direct numerical simulation (DNS) of a spatially developing turbulent boundary layer (TBL) over three-dimensional sinusoidal roughness. The validity of Townsend's outer-layer similarity hypothesis is assessed based on comparisons of mean and second-order flow statistics, with a DNS of smooth-wall TBL data set at a similar Reynolds number. The total, Reynolds and dispersive stress tensors are calculated using the double-averaging procedure. The mean and second-order statistical similarities in the outer layer between rough-wall and smooth-wall TBLs are generally observed. The transport between total, turbulent and dispersive kinetic energy is investigated utilising triple-decomposed kinetic energy transports equations. The transport behaviour of turbulent kinetic energy (TKE) is significantly affected by the local mean shear induced by the surface roughness. However, the TKE transport shows good collapse with the smooth-wall case in the outer region of the flow. On the other hand, the transport of dispersive kinetic energy, including local production, redistribution and dissipation, are confined within the roughness sublayer. The intercomponent transfer between TKE and dispersive kinetic energy is quantified from the triple-decomposed kinetic energy transport equations. The intercomponent energy transfer is associated with the local spatial gradients of the turbulent momentum fluxes generated near the roughness canopy.

Understanding why constant energy or constant temperature may affect nucleation behavior in MD simulations: A study of gas hydrate nucleation.

Molecular dynamics simulations have been widely used in exploring the nucleation behavior of many systems, including gas hydrates. Gas hydrates are ice-like solids in which gas molecules are trapped in water cages. During hydrate formation, a considerable amount of heat is released, and previous work has reported that the choice of temperature control scheme may affect the behavior of hydrate formation. The origins of this effect have remained an open question. To address this question, extensive NVE simulations and thermostatted (NPT and NVT) simulations with different temperature coupling strengths have been performed and compared for systems where a water nanodroplet is immersed in a H2S liquid. Detailed analysis of the hydrate structures and their mechanisms of formation has been carried out. Slower nucleation rates in NVE simulations in comparison to NPT simulations have been observed in agreement with previous studies. Probability distributions for various temperature measures along with their spatial distributions have been examined. Interestingly, a comparison of these temperature distributions reveals a small yet noticeable difference in the widths of the distributions for water. The somewhat reduced fluctuations in the temperature for the water species in the NVE simulations appear to be responsible for reducing the hydrate nucleation rate. We further conjecture that the NVE-impeded nucleation rate may be the result of the finite size of the surroundings (here the liquid H2S portion of the system). Additionally, a local spatial temperature gradient arising from the heat released during hydrate formation could not be detected.

Impact of gradient scheme and non-linear shimming on out-of-voxel echo artifacts in edited MRS.

Out-of-voxel (OOV) signals are common spurious echo artifacts in MRS. These signals often manifest in the spectrum as very strong "ripples," which interfere with spectral quantification by overlapping with targeted metabolite resonances. Dephasing optimization through coherence order pathway selection (DOTCOPS) gradient schemes are algorithmically optimized to suppress all potential alternative coherence transfer pathways (CTPs), and should suppress unwanted OOV echoes. In addition, second-order shimming uses non-linear gradient fields to maximize field homogeneity inside the voxel, which unfortunately increases the diversity of local gradient fields outside of the voxel. Given that strong local spatial B0 gradients can refocus unintended CTPs, it is possible that OOVs are less prevalent when only linear first-order shimming is applied. Here we compare the size of unwanted OOV signals in Hadamard-edited (HERMES) data acquired with either a local gradient scheme (which we refer to here as "Shared") or DOTCOPS, and with first- or second-order shimming. We collected data from 15 healthy volunteers in two brain regions (voxel size 30 × 26 × 26 mm3 ) from which it is challenging to acquire MRS data: medial prefrontal cortex and left temporal cortex. Characteristic OOV echoes were seen in both GABA- and GSH-edited spectra for both brain regions, gradient schemes, and shimming approaches. A linear mixed-effect model revealed a statistically significant difference in the average residual based on the gradient scheme in both GABA- (p< 0.001) and GSH-edited (p< 0.001) spectra: that is, the DOTCOPS gradient scheme resulted in smaller OOV artifacts compared with the Shared scheme. There were no significant differences in OOV artifacts associated with shimming method. Thus, these results suggest that the DOTCOPS gradient scheme for J-difference-edited PRESS acquisitions yields spectra with smaller OOV echo artifacts than the Shared gradient scheme implemented in a widely disseminated editing sequence.

Spatial Variation of False Map Turtle (Graptemys pseudogeographica) Bacterial Microbiota in the Lower Missouri River, United States

Turtle populations around the world are continually confronted with changing environments that affect their ecology and conservation status. Among freshwater turtles, population dynamics are thought to be mediated by complex yet often cryptic causes. One recent direction of focus in addressing these causes is the turtle-associated microbiota. In turtles, the gut-associated microbiota is of exceptional interest due to its continual association with host species under changing conditions. Diet-based fluctuations and changes in microbial diversity may correspond to varying external environments at both the individual and population level. Environmental responses are of particular interest due to the anthropogenic changes that may underlie them. Pollutants, disruption of climatic patterns, and habitat fragmentation all have the potential to affect turtle-associated microbiota and subsequent population and species conservation. To better understand potential human-induced changes, the diversity of turtle-associated microbiota over local spatial gradients must be better understood. We examined microbial community α- and β-diversity among 30 adult False Map Turtles (Graptemys pseudogeographica) at three sites within the lower Missouri River, United States. Our results indicate significant microbial community centroid differences among sites (β-diversity), which are likely mediated by various local environmental factors. Such factors will have to be carefully considered in any future attribution of anthropogenic determinants on turtle-associated microbiota as it relates to turtle population dynamics.

Scanning gradiometry with a single spin quantum magnetometer

Quantum sensors based on spin defects in diamond have recently enabled detailed imaging of nanoscale magnetic patterns, such as chiral spin textures, two-dimensional ferromagnets, or superconducting vortices, based on a measurement of the static magnetic stray field. Here, we demonstrate a gradiometry technique that significantly enhances the measurement sensitivity of such static fields, leading to new opportunities in the imaging of weakly magnetic systems. Our method relies on the mechanical oscillation of a single nitrogen-vacancy center at the tip of a scanning diamond probe, which up-converts the local spatial gradients into ac magnetic fields enabling the use of sensitive ac quantum protocols. We show that gradiometry provides important advantages over static field imaging: (i) an order-of-magnitude better sensitivity, (ii) a more localized and sharper image, and (iii) a strong suppression of field drifts. We demonstrate the capabilities of gradiometry by imaging the nanotesla fields appearing above topographic defects and atomic steps in an antiferromagnet, direct currents in a graphene device, and para- and diamagnetic metals.

Infrared Small Target Detection Algorithm Based on Local Spatial Gradient Peak

A sparse infrared small target detection algorithm based on local spatial gradient peaks is proposed to deal with the problem of slow running speed and edge sensitivity in low-rank decomposition methods.The detection steps are as follows. In the first step, the image expansion operation is used for preprocessing. We use the circular structure element to sharpen the edges of targets and smooth the background noise. Then, the saliency gradient features of the target local region are applied to calculate the overlapping gradient information of the image after expansion. The local area with a larger gradient peak is located in the original image, and the selected local area is considered to be the region of interest with candidate targets. Finally, we use the advanced accelerated proximal gradient algorithm to perform matrix decomposition in the extracted local regions of interest to extract sparse infrared small targets. Extensive experimental results under real scenarios illustrated that compared with the baseline low-rank sparse decomposition method, the proposed approach runs faster and shows superior detection performance in the comprehensive evaluation index.

Image Scale-Space Filtering Using Directional Local Variance Controlled Anisotropic Diffusion

The purpose of this paper is to develop an effective edge indicator and propose an image scale-space filter based on anisotropic diffusion equation for image denoising. We first develop an effective edge indicator named directional local variance (DLV) for detecting image features, which is anisotropic and robust and able to indicate the orientations of image features. We then combine two edge indicators (i.e., DLV and local spatial gradient) to formulate the desired image scale-space filter and incorporate the modulus of noise magnitude into the filter to trigger time-varying selective filtering. Moreover, we theoretically show that the proposed filter is robust to the outliers inherently. A series of experiments are conducted to demonstrate that the DLV metric is effective for detecting image features and the proposed filter yields promising results with higher quantitative indexes and better visual performance, which surpass those of some benchmark models.

Total-internal-reflection deflectometry for measuring small deflections of a fluid surface

We describe a method that uses total internal reflection at the water–air interface inside a large, transparent tank, to measure the interface’s deflections. Using this configuration, we obtain an optical set-up where the liquid surface acts as a deformable mirror. The set-up is shown to be extremely sensitive to very small disturbances of the reflecting water surface, which are detected by means of visualising the reflections of a reference pattern. When the water surface is deformed, it reflects a distorted image of the reference pattern, similar to a synthetic Schlieren set-up. The distortions of the pattern are analysed using a suitable image correlation method. The displacement fields thus obtained correlate to the local spatial gradients of the water surface. The gradient fields are integrated in a least-squares sense to obtain a full instantaneous reconstruction of the water surface. This method is particularly useful when a solid object is placed just above water surface, whose presence makes the liquid surface otherwise optically inaccessible.Graphical abstract

Face Recognition on a Smart Image Sensor Using Local Gradients.

In this paper, we present the architecture of a smart imaging sensor (SIS) for face recognition, based on a custom-design smart pixel capable of computing local spatial gradients in the analog domain, and a digital coprocessor that performs image classification. The SIS uses spatial gradients to compute a lightweight version of local binary patterns (LBP), which we term ringed LBP (RLBP). Our face recognition method, which is based on Ahonen’s algorithm, operates in three stages: (1) it extracts local image features using RLBP, (2) it computes a feature vector using RLBP histograms, (3) it projects the vector onto a subspace that maximizes class separation and classifies the image using a nearest neighbor criterion. We designed the smart pixel using the TSMC 0.35 μm mixed-signal CMOS process, and evaluated its performance using postlayout parasitic extraction. We also designed and implemented the digital coprocessor on a Xilinx XC7Z020 field-programmable gate array. The smart pixel achieves a fill factor of 34% on the 0.35 μm process and 76% on a 0.18 μm process with 32 μm × 32 μm pixels. The pixel array operates at up to 556 frames per second. The digital coprocessor achieves 96.5% classification accuracy on a database of infrared face images, can classify a -pixel image in 94 μs, and consumes 71 mW of power.

Adaptive fourth-order diffusion smoothing via bilateral kernel

This paper presents a flexible and adaptive fourth-order diffusion model for image noise removal. A flexible fourth-order diffusion equation is first presented. Then, an efficient and robust contextual discontinuity indicator based on the bilateral kernel is constructed and combined with the local discontinuity indicator (local spatial gradient) to get the desired adaptive fourth-order diffusion model. The proposed model degenerates to an anisotropic equation which performs the tangent smoothing for sharpening the inter-object boundaries and reduces to an isotropic equation which implements the fast smoothing in the homogenous regions for noise removal. Moreover, it is able to overcome the staircase artifacts arisen by most of second-order diffusion models. Experimental results are provided to validate the effectiveness of the proposed model with regard to objective evaluation metrics and visual performance which outperform those of some benchmark models.

Spatiotemporal estimation of satellite-borne and ground-level NO2 using full residual deep networks

Compared with the limited capability of ground-level monitoring, remote sensing provides useful image data at a moderate or high spatial or temporal resolution with global coverage for monitoring of air pollutants, e.g., aerosol optical depth (AOD) observations from the MODIS for fine particulate matter (PM2.5) and Ozone Monitoring Instrument (OMI) nitrogen dioxide (NO2) vertical columns for ground-level NO2 concentration. However, the extensive nonrandom missingness of OMI-NO2 data (e.g., an approximate per-pixel missing proportion of 59% for mainland China in 2015) due to cloud contamination or high reflectance limits applicability of these data in estimation of ground-level NO2. This paper proposes the use of a full residual deep learning method to impute missing satellite-borne NO2 data (OMI-NO2) and to estimate (map) ground-level NO2 with uncertainty (coefficient of variation) at a high spatial (1 × 1 km2) and temporal (daily) resolution. For the large study region (mainland China except Hainan Province), the presented method achieved robust performance with a stable learning efficiency (mean test R2: 0.98 with a small standard deviation of 0.01; mean test RMSE: 0.42 × 1015 molecules/cm2) for imputation of OMI-NO2. In the model, the coordinates and elevation were used to capture the spatial variability of the OMI-NO2 columns, and fused meteorological grid data and planetary boundary layer height and ozone data from GEOS-FP were used to capture spatiotemporal variability of OMI-NO2. The evaluation with ground in situ NO2 measurements showed considerable contribution of the complete (raw observed and imputed) OMI-NO2 columns, meteorology and traffic variables to inference of ground-level NO2 (test R2: 0.82; test RMSE: 8.80 μg/m3). The complete grids of OMI-NO2 columns showed natural and smooth spatial transitions between the raw observed and imputed values. The surfaces of predicted NO2 concentration not only showed consistent distributions with OMI-NO2 at a regional and temporal scale, but also presented local spatial gradients of ground-level NO2. OMI-NO2 can be downscaled and imputed to be used as an important predictor to improve the estimation of high-resolution ground-level NO2. The reliable estimates of ground-level NO2 concentration with uncertainty can reduce the bias in estimates of NO2 exposure and subsequently evaluations of its health effects.

A basic/helix–loop–helix transcription factor controls leaf shape by regulating auxin signaling in apple

Climate-driven phenological change across local spatial gradients leads to leaf shape variation. At higher elevations, leaves of broadleaf species tend to become narrower, but the underlying molecular mechanism is largely unknown. In this study, a series of morphometric analyses and biochemical assays, combined with functional identification in apple, were performed. We show that the decrease in apple leaf width with increasing altitude is controlled by a basic/helix-loop-helix transcription factor (bHLH TF), MdbHLH3. The MdbHLH3-overexpressing lines have a lower transcript abundance of MdPIN1 encoding an auxin efflux carrier but a higher transcript abundance of MdGH3-2 encoding a putative auxin amido conjugate synthase, resulting in a lower free auxin concentration; feeding the transgenic leaves with exogenous auxin partially restores leaf width. MdbHLH3 transcriptionally suppresses and activates MdPIN1 and MdGH3-2, respectively, by specifically binding to their promoters. This alters auxin homeostasis and transport, consequently leading to changes in leaf shape. These findings suggest that the bHLH TF MdbHLH3 directly modulates auxin signaling in controlling leaf shape in response to local spatial gradients in apple.

Contribution of beta diversity in shaping stream macroinvertebrate communities among hydro-ecoregions

Rivers are heterogeneous and patchy-structured systems in which regional biodiversity of aquatic communities typically varies as a function of local habitat conditions and spatial gradients. Understanding which environmental and spatial constraints shape the diversity and composition of benthic communities is therefore a pivotal challenge for basic and applied research in river ecology. In this study, benthic invertebrates were collected from 27 sites across three hydro-ecoregions with the aim of investigating patterns in α- and β diversity. We first assessed the contribution to regional biodiversity of different and nested spatial scales, ranging from micro-habitat to hydro-ecoregion. Then, we tested differences in α diversity, taxonomic composition and ecological uniqueness among hydro-ecoregions. Variance partitioning analysis was used to evaluate the mechanistic effects of environmental and spatial variables on the composition of macroinvertebrate communities. Macroinvertebrate diversity was significantly affected by all the spatial scales, with a differential contribution according to the type of metric. Sampling site was the spatial scale that mostly contributed to the total richness, while the micro-habitat level explained the largest proportion of variance in Shannon–Wiener index. We found significant differences in the taxonomic composition, with 39 invertebrate families significantly associated with one or two hydro-ecoregions. However, effects of environmental and spatial controls were context dependent, indicating that the mechanisms that promote beta diversity probably differ among hydro-ecoregions. Evidence for species sorting, due to natural areas and stream order, was observed for macroinvertebrate communities in alpine streams, while spatial and land-use variables played a weak role in other geographical contexts.

Contextual adaptive fourth-order smoothing

This paper aims to propose a novel contextual adaptive fourth-order smoothing method for noise removal. The classical Laplacian operator is first modified into a multi-scale and robust form, which combines two distinct discontinuity measures simultaneously, i.e., inhomogeneity and local spatial gradient. Then the contextual adaptive fourth-order smoothing method is proposed based on the new Laplacian operator. Moreover, a gain control function is employed for the gain control of smoothing in terms of contextual discontinuities to better preserve the important features. Experimental results support that the proposed method achieves the best performance among the comparative methods with respect to some objective evaluation metrics and visual effects.

Climate Change: Flowering Time May Be Shifting in Surprising Ways.

A new study examined how flowering phenology has changed over the past three decades along an elevational gradient. These findings indicate that climate change is shifting flowering time in complex ways, even across local spatial gradients.

Changing Climate Drives Divergent and Nonlinear Shifts in Flowering Phenology across Elevations

Climate change is known to affect regional weather patterns and phenology; however, we lack understanding of how climate drives phenological change across local spatial gradients. This spatial variation is critical for determining whether subpopulations andmetacommunities are changing in unison or diverging in phenology. Divergent responses could reduce synchrony both within species (disrupting gene flow among subpopulations) and among species (disrupting interspecific interactions in communities). We also lack understanding of phenological change in environments where life history events are frequently aseasonal, such as the tropical, arid, and semi-arid ecosystems that cover vast areas. Using a 33-year-long dataset spanning a 1,267-m semi-arid elevational gradient in the southwestern United States, we test whether flowering phenology diverged among subpopulations within species andamong five communities comprising 590 species. Applying circular statistics to test for changes in year-round flowering, we show flowering has become earlier for all communities except at the highest elevations. However, flowering times shifted at different rates across elevations likely because ofelevation-specific changes in temperature and precipitation, indicating diverging phenologies of neighboring communities. Subpopulations of individual species also diverged at mid-elevation but converged in phenology at high elevation. These changes in flowering phenology among communities and subpopulations are undetectable when data are pooled across the gradient. Furthermore, we show that nonlinear changes in flowering times over the 33-year record are obscured by traditional calculations of long-term trends. These findings reveal greater spatiotemporal complexity in phenological responses than previously recognized and indicate climate is driving phenological reshuffling across local spatial gradients.

Color image enhancement based on local spatial homomorphic filtering and gradient domain variance guided image filtering

Image quality is affected by various factors, especially low illumination. We propose an image color enhancement method to improve the brightness and quality of images. First, we apply local spatial homomorphic filtering to images to improve the brightness and avoid edge block effect as well as image size limitation at the same time. Second, gradient domain variance guided image filtering is proposed for restrain noise amplification in image enhancement, which has better denoising and edge preservation. The above two algorithms are applied to the V-channel in the hue, saturation, value space to form a color image enhancement algorithm. This method not only improves the brightness and quality of low-illumination images but also avoids noise amplification after image enhancement. The experimental results prove that our proposed algorithm is superior to other methods in improving brightness and noise removal of low-illumination images.

Towards a novel stellar opacity measurement scheme using stability properties of double ablation front structures

A novel method for bringing sample elements to hydrodynamic conditions relevant to the base of the solar convection zone is investigated. The method is designed in the framework of opacity measurements and exploits the temporal and spatial stability of hydrodynamic parameters in counter-propagating Double Ablation Front (DAF) structures. The physics of symmetric DAF structures is first studied in 1D geometries to assess the influence of tracer layers in the target. These results are used to propose an experimental design compatible with the OMEGA [Boehly et al., Opt. Commun. 133(1-6), 495–506 (1997)] laser. Radiative-hydrodynamic simulations conducted using the Chic code [Breil et al., Comput. Fluids 46, 161–167 (2011).] in 2D-axisymmetric geometries suggest that a Fe sample can be brought to an electron temperature of ∼160 eV and electron number density of ∼1.35 × 1023 cm−3. These parameters are reached during a 500 ps window with temporal variations of the order of 10 eV and 1022 cm−3, respectively. This allows for potential time-integrated spectral measurements. During that time, the sample is almost at local thermal equilibrium and 2D spatial gradients in the sample are less than 5% in a 360 μm diameter cylindrical volume, including the potential effects of Hot Electrons (HE) and typical uncertainties related to target fabrication and laser performances. The effects of HEs are assessed using an inline model in Chic. The HEs are found to deposit most of their energy in the cold and dense ablator between the two fronts, leading to a small efficiency loss on the DAF parameters. The calculations also suggest that negligible amounts of unabsorbed HEs are present in the probed volume, thus not affecting the atomic properties of the sample. Potential extensions of the current design to higher sample temperatures within the OMEGA capabilities are briefly discussed.

Three-Dimensional Printed Insulation For Dynamic Thermoelectric Harvesters With Encapsulated Phase Change Materials

Energy harvesting devices have demonstrated their ability to provide power autonomy to wireless sensor networks. However, the adoption of such powering solutions by the industry is challenging due to their reliance on very specific environmental conditions such as vibration at a specific frequency, direct sunlight, or a local temperature difference. Dynamic thermoelectric harvesting has been shown to expand the applicability of thermoelectric generators by creating a local spatial temperature gradient from a temporal temperature fluctuation. Here, a simple method for prototyping or short-run production of such devices is introduced. It is based on the design and 3-D printing of an insulating container, insertion of a phase change material in encapsulated form, and use of commercial thermoelectric generators. The simplicity of this dry assembly method is demonstrated. Two prototype devices with double-wall insulation structures are fabricated, using a stainless-steel and a plastic phase change material encapsulation and a commercial TEG. Performance tests under a temperature cycle between 25C show energy output of 43.6 and 32.1J from total device masses of 69 and 50g, respectively. Tests under multiple temperature cycles demonstrate the reliability and performance repeatability of such devices. The proposed method addresses the complication of requiring a wet stage during the final assembly of dynamic thermoelectric harvesters. It allows design and customization to particular size, energy, and insulation geometry requirements. This is important because it makes dynamic harvesting prototyping widely available and easy to reproduce, test, and integrate into systems with various energy requirements and size restrictions.

Void waves propagating in the bubbly two-phase turbulent boundary layer beneath a flat-bottom model ship during drag reduction

The injection of bubbles into a turbulent boundary layer can reduce the skin friction of a wall. Conventionally, the drag reduction rate is evaluated using time-averaged quantities of the mean gas flow rate or mean void fraction. Actually, as bubbles are subject to strong shear stresses near the wall, void waves and local bubble clusters appear. For pipe and channel flows, such wave-like behavior of the dispersed phase has been investigated intensely as an internal two-phase flow problem. We investigate how this wavy structure forms within the boundary layer as an external spatially developing two-phase flow along a horizontal flat plate. We describe how our model ship is designed to meet that purpose and report bubble-traveling behavior that accompanies unexpectedly strong wavy oscillations in the streamwise direction. A theoretical explanation based on a simplified two-fluid model is given to support this experimental fact, which suggests that void waves naturally stand out when drag reduction is enhanced through the local spatial gradient of the void fraction.