Sampling Grid Research Articles

Spatial variability of chemical indicators of Amazon agricultural soils through geomultivariate statistics, Brazil.

This work focuses on evaluating the spatial variability of chemical attributes of soils under different agricultural use and native forest, indicating which are the possible indicator attributes of changes in environmental, through the use and management of the soil. The study was carried out in the southern region of the Amazonas state, in an Argissolo Vermelho-Amarelo (Ultisol). Sampling grids were established measuring: 90m × 70m with regular soil collection spacing of 10m for the guarana and forest areas; 90m × 56m spaced at 10m × 8m for annatto area; and 54m × 42m with spacing between points of 6m for the cupuaçu area, totaling 80 sampling points in each area, with soil samples collected at depths of 0.0-0.05; 0.05-0.10m and 0.10-0.20m. The following attributes were determined: pH, Al3+, K+, Ca2+, Mg2+, P, H + Al, CEC, V% and m%. Descriptive, geostatistical and multivariate statistical analyzes were performed. The results show that it is possible to state that the descriptive, geostatistical and multivariate statistical techniques were able to identify the difference between the spatial variability of the attributes according to each specific use of individual soils. The multivariate analysis made it possible to select the attributes that most contribute to the variability of these soils, and with that, it was found that the forest showed less spatial variability in the surface layer, with higher reach values by scaled semivariograms.

A quantitative CBCT pipeline based on 2D antiscatter grid and grid-based scatter sampling for image-guided radiation therapy.

Quantitative accuracy is critical for expanding the role of cone beam CT (CBCT) imaging from target localization to quantitative treatment monitoring and plan adaptations in radiation therapy. Despite advances in CBCT image quality improvement methods, quantitative accuracy gap between CBCT and multi-detector CT (MDCT) remains. In this work, a physics-driven approach was investigated that combined robust scatter rejection, raw data correction and iterative image reconstruction to further improve CBCT image quality and quantitative accuracy, referred to as quantitative CBCT (qCBCT). QCBCT approach includes tungsten 2D antiscatter grid hardware, residual scatter correction with grid-based scatter sampling, image lag, and beam hardening correction for offset detector geometry linac-mounted CBCT. Images were reconstructed with iterative image reconstruction to reduce image noise. qCBCT was evaluated using a variety of phantoms to investigate the effect of object size and its composition on image quality, and image quality was benchmarked against clinical CBCT and gold standard MDCT images used for treatment planning. QCBCT provided statistically significant improvement in CT number accuracy and reduced image artifacts when compared to clinical CBCT images. When compared to gold standard MDCT, mean HU errors in qCBCT and clinical CBCT were 17±9 and 38±29 HU, respectively. Magnitude of phantom size dependent HU variations were comparable between MDCT and qCBCT images. With iterative reconstruction, contrast-to-noise ratio improved by 25% when compared to clinical CBCT protocols. Combination of novel scatter suppression techniques and other data correction methods in qCBCT provided CT number accuracy comparable to gold standard MDCT used for treatment planning. This approach may potentially improve CBCT's promise in fulfilling the tasks that demand high quantitative accuracy, such as online dose calculations and treatment response assessment, in image guided radiation therapy.

List-mode quantitative joint reconstruction of activity and attenuation maps in Time-of-Flight PET

Quantitative PET imaging requires accurate determination of patient-specific attenuation maps, which remains challenging on hybrid PET/MRI systems owing to the lack of a direct relationship between MR image intensity and attenuation coefficients. The aim of the present study is to develop a list-mode based algorithm for accurate and robust attenuation correction of PET data using time-of-flight (TOF) emission information. We analyze and address the challenges of list-mode emission-based maximum-likelihood joint estimation of activity and attenuation (LM-MLAA) in state-of-the-art PET imaging. The proposed method exploits a rapid on-the-fly system matrix calculation algorithm based on elliptic integrals while updating the attenuation map from accumulating list-mode coincidences to achieve accelerated image reconstruction. The scattering compensation is incorporated inside it using an iterative approach in such that the current estimation of attenuation map used on a course grid sampling scattering points to make an estimate of scattering. The performance of the proposed LM-MLAA approach was evaluated on Monte Carlo simulations of a phantom at different time resolutions. The contrast and noise for hot and cold regions on reconstructed images at different time resolutions were analysed. The estimated attenuation map exhibits resilience against noise, effectively eliminates high-frequency cross-talk even in the absence of prior information on attenuation coefficients, and enables discrimination among different anatomical regions in the reconstructed image. The error in the mean estimated attenuation coefficients after 50 iterations was ∼ 2% in water and ∼ -14% in Teflon regions for TOF resolutions corresponding to those of most current commercial PET systems (∼ 500 ps). The proposed LM-MLAA framework can be used for joint reconstruction of activity and attenuation maps from list-mode emission data as standalone or a complementary approach to existing in multimodality imaging such as PET/MRI, where direct measurement attenuation maps is not possible.

Comparison of Sampling and Grid Methods for Regional Soil Erosion Assessment

To control soil erosion, the intensity, area, and distribution of regional soil erosion must be determined to accurately plan and implement corresponding soil conservation measures. Therefore, regional soil erosion assessment has received extensive attention worldwide. At present, a sampling survey approach and full-coverage grid-based calculation are mainly applied in regional soil erosion assessment. The quantitative evaluation of the entire region depends on the quality of the data source. Furthermore, owing to the greatness of the evaluation object, the difficulty of data acquisition, the high cost, and poor usability, the present approach is bound to be at the expense of data accuracy, spatial resolution, time resolution, etc. The sampling survey approach can obtain high-precision data of soil erosion factors. Therefore, it can accurately quantify soil erosion in a field investigation unit. However, the sampling method, sampling density, and extrapolation methods have a significant impact on regional soil erosion assessments. This study considers the case of Baiquan County in the rolling hills of Northeast China as an example. Regional soil erosion evaluation using sampling survey and grid computing were compared. The impact of the data source accuracy on the soil erosion assessment was also quantitatively evaluated. The results of grid method showed a phenomenon of large rates of soil erosion and the ratio of the soil erosion area (the share of areas above the mild level), which were overestimated by 20% and 6%, respectively. A digital elevation model (DEM) with a resolution of 30 m can be used for soil erosion evaluation in plain areas, but that with the same resolution in hilly areas has insufficient calculation accuracy and provides large errors. The grid method can be adopted when land use and soil conservation measures are accurate. Otherwise, the sampling method is recommended. Interpolation of the ratio of the soil erosion area in the survey unit based on land use can better evaluate regional soil erosion.

Estimating population size for California spotted owls and barred owls across the Sierra Nevada ecosystem with bioacoustics

Monitoring population size at ecosystem scales is difficult for most species of conservation concern. While assessing site occupancy at broad scales has proven feasible, rigorous tracking of changes in population size over time has not – even though it can provide a stronger basis for assessing population status and conservation-decision making. Therefore, we demonstrate how relatively low-intensity, ecosystem-scale passive acoustic monitoring (PAM) can be linked to local-density monitoring to estimate the population size of native California spotted owls (Strix occidentalis occidentalis) and invasive barred owls (S. varia) across the western Sierra Nevada, California. Based on a PAM sampling grid with 400 ha cells (the approximate home range size of these species), we estimated site occupancy to be between 0.42 (SE = 0.02) and 0.30 (SE = 0.02) for California spotted owls using relatively liberal and strict criteria, respectively, for considering a cell occupied. PAM-based site occupancy estimates within local-scale density monitoring study areas (range = 0.41–0.78 and 0.28–0.76 for liberal and strict criteria, respectively) were strongly and positively correlated with local density (range = 0.08–0.31 owl/km2) for this species. In contrast, ecosystem-wide site occupancy of barred owls was very low based on PAM (0.034, SE < 0.01), as were densities within local monitoring studies (range = 0–0.005 owls/km2). By scaling ecosystem-wide site occupancy estimates to densities estimated with local monitoring studies, we estimated that, depending on occupancy criteria, 2,218 (SE = 278) or 2,328 (SE = 489) California spotted owls occurred in the Sierra Nevada ecosystem in 2021. Thus, while California spotted owls are a rare subspecies, they were well-distributed across the Sierra Nevada. Because there were so few barred owl detections, we could not estimate ecosystem-scale abundance, which reflects the success of prior experimental removals in the region. In conclusion, our study provides a generalizable framework for estimating population size for territorial species with PAM at ecosystem scales when local-scale estimates of density are available. Thus, we demonstrate that this approach can provide novel and valuable insights into monitoring populations to aid species conservation.

Blade optimization design of Savonius hydraulic turbine based on radial basis function surrogate model and L-SHADE algorithm

The blade is the component responsible for capturing energy in the Savonius hydraulic turbine, and a good blade shape can extract more energy from the flow field. The shape of the turbine blade is optimized in this paper. First, the blade shape is expressed parametrically using a third-order quasi-uniform B-spline curve controlled by two parametric variables. Then, some initial design schemes are obtained through the grid sampling method from the design space and the torque coefficients of all initial design schemes are calculated using computerized fluid dynamics (CFD). Following this, a radial basis function (RBF) surrogate model is established by using initial design schemes and their corresponding torque coefficients. Then, the RBF surrogate model is solved by applying the L-SHADE. Finally, the resulting best design scheme is validated and further analyzed using CFD. By comparing the performance of the turbine before and after optimization, it is found that the power coefficient of the turbine with the optimized blades is increased by 7.07% at the tip speed ratio of 1, and the power coefficients at other tip speed ratios are also increased. In addition, the optimized blade has higher peak torque coefficients and its length is shortened by 15.34%.

A comparison of methods of analysis of heavy metals in soil samples of Mitrovica environment, Republic of Kosovo

In this work, the distribution of heavy metals in surface soil samples (0-5 cm) from the MitrovicaRegion, was studied. The investigated region (301.5 km2) is covered by a sampling grid of 1.4×1.4 km. Intotal 156 soil samples from 149 locations were collected. Digestion methods, including Aqua RegiaDigestion (mixture of HNO3 and HCl and water at 95ºC - the 1DX1 method) and acid digestion: use ofconcentrated acids such as hydrofluoric acid (HF), hydrochloric acid (HCl), nitric acid (HNO3) andperchloric acid (HClO4) (ISO 14869- 1:2001(E) method), are used to prepare samples for spectroscopicanalysis. High-sensitivity spectroscopy techniques such as inductively coupled plasma emissionspectrometry (ICP-AES) and inductively coupled plasma mass spectrometry (ICP-MS) were applied tomeasure the concentration of Ni and Co in soil samples. Data analysis and construction of the map wereperformed using the Statistica (ver. 9), AutoDesk Map (ver. 2008) and Surfer (ver. 9) software. It was foundthat the average content of Ni and Co in the surface soil for the entire study area is 96 mg/kg (with a rangeof 7.6-2600 mg/kg) and 22 mg/kg (with a range of 2.7-1600 mg/kg), respectively. The obtained averageand median values obtained by ICP-MS are very similar to those obtained by ICP-AES. Namely, thecorrelation factor for Co and Ni between the results from both methods are 0.92 (for normal distribution),0.93 (for logarithmic) and 0.94 (for rank), and 0.86 (for normal distribution), 0.94 (for logarithmic) and0.96 (for rank), respectively. The obtained results show that the high concentrations of Ni and Co in thesurface soil samples may originate from similar sources and their distribution follows the lithology of thestudy area.

Creating an Advanced Sensor Network to calculate real-time, mass-weighted flue gas composition and air heater leakage of a coal-fired utility boiler under dynamic operating conditions

Utilization of renewable energy sources to minimize the environmental impact of energy production has changed the way utility boilers operate, requiring frequent load cycling between full load and partial loads as low as 30%. Dynamic operation of coal-fired utility boilers significantly reduces boiler efficiency when compared to steady state at full load. Data-driven plant optimization has shown success with coal-fired utility boilers under dynamic operating conditions. The purpose of this work was to create an Advanced Sensor Network (ASN) to provide more extensive real-time data to inform dynamic plant optimization of Net Unit Heat Rate (NUHR). The ASN consists of gas sampling grids in the convective pass of the boiler and downstream of the air heater. These sampling grids allow for quantification of spatial variation of flue gas within the boiler and calculation of mass-weighted composition of flue gas through the combination of composition, velocity, and temperature measurements. The comparison of O2 between the inlet and outlet of the air heater is used to calculate air leakage in real time. Flue gas composition and air heater leakage are both important factors in boiler efficiency and NUHR.The results of this work support the value of mass-weighted averages for determining flue gas composition accurately. The measurements from the ASN show increased composition stratification during dynamic operation, with an average standard deviation 38% higher than observed during steady-state operation. Air heater leakage was also observed to increase from 2.8% to 5.1% following a load change. Prior to the installation of the ASN, these data would not have been available for dynamic control. These real-time data will be leveraged to calculate and optimize for NUHR during dynamic operation in future work.

Copper Oxide Spike Grids for Enhanced Solution Transfer in Cryogenic Electron Microscopy.

The formation of uniform vitreous ice is a crucial step in the preparation of samples for cryogenic electron microscopy (cryo-EM). Despite the rapid technological progress in EM, controlling the thickness of vitreous ice on sample grids with reproducibility remains a major obstacle to obtaining high-quality data in cryo-EM imaging. The commonly employed classical blotting process faces the problem of excess water that cannot be absorbed by the filter paper, resulting in the formation of thick and heterogeneous ice. In this study, we propose a novel approach that combines the recently developed nanowire self-wicking technique with the classical blotting method to effectively control the thickness and homogeneity of vitrified ice. With simple procedures, we generated a copper oxide spike (COS) grid by inducing COSs on commercially available copper grids, which can effectively remove excess water during the blotting procedure without damaging the holey carbon membrane. The ice thickness could be controlled with good reproducibility compared to non-oxidized grids. Incorporated into other EM techniques, our new modification method is an effective option for obtaining high-quality data during cryo-EM imaging.

Error Analysis of Sound Source Directivity Interpolation Based on Spherical Harmonics

Precise measurement of the sound source directivity not only requires special equipment, but also is time-consuming. Alternatively, one can reduce the number of measurement points and apply spatial interpolation to retrieve a high-resolution approximation of directivity function. This paper discusses the interpolation error for different algorithms with emphasis on the one based on spherical harmonics. The analysis is performed on raw directivity data for two loudspeaker systems. The directivity was measured using sampling schemes of different densities and point distributions (equiangular and equiareal). Then, the results were interpolated and compared with these obtained on the standard 5° regular grid. The application of the spherical harmonic approximation to sparse measurement data yields a mean error of less than 1 dB with the number of measurement points being reduced by 89%. The impact of the sparse grid type on the retrieval error is also discussed. The presented results facilitate optimal sampling grid choice for low-resolution directivity measurements.

ETER: Elastic Tessellation for Real-Time Pixel-Accurate Rendering of Large-Scale NURBS Models

We present ETER, an elastic tessellation framework for rendering large-scale NURBS models with pixel-accurate and crack-free quality at real-time frame rates. We propose a highly parallel adaptive tessellation algorithm to achieve pixel accuracy, measured by the screen space error between the exact surface and its triangulation. To resolve a bottleneck in NURBS rendering, we present a novel evaluation method based on uniform sampling grids and accelerated by GPU Tensor Cores. Compared to evaluation based on hardware tessellation, our method has achieved a significant speedup of 2.9 to 16.2 times depending on the degrees of the patches. We develop an efficient crack-filling algorithm based on conservative rasterization and visibility buffer to fill the tessellation-induced cracks while greatly reducing the jagged effect introduced by conservative rasterization. We integrate all our novel algorithms, implemented in CUDA, into a GPU NURBS rendering pipeline based on Mesh Shaders and hybrid software/hardware rasterization. Our performance data on a commodity GPU show that the rendering pipeline based on ETER is capable of rendering up to 3.7 million patches (0.25 billion tessellated triangles) in real-time (30FPS). With its advantages in performance, scalability, and visual quality in rendering large-scale NURBS models, a real-time tessellation solution based on ETER can be a powerful alternative or even a potential replacement for the existing pre-tessellation solution in CAD systems.

Efficient Table-based Function Approximation on FPGAs Using Interval Splitting and BRAM Instantiation

This article proposes a novel approach for the generation of memory-efficient table-based function approximation circuits for edge devices in general and FPGAs in particular. Given a function f(x) to be approximated in a given interval [ x 0 , x 0+a ) and a maximum approximation error E a , the goal is to determine a function table implementation with a minimized memory footprint, i.e., number of entries that need to be stored. Rather than state-of-the-art work performing an equidistant sampling of the given interval by so-called breakpoints and using linear interpolation between two adjacent breakpoints to determine f(x) at the maximum error bound, we propose and compare three algorithms for splitting the given interval into sub-intervals to reduce the required memory footprint drastically based on the observation that in sub-intervals of low gradient, a coarser sampling grid may be assumed while guaranteeing the maximum interpolation error bound E a . Experiments on elementary mathematical functions show that a large fraction in memory footprint may be saved. Second, a hardware architecture implementing the sub-interval selection, breakpoint lookup, and interpolation at a latency of just 9 clock cycles is introduced. Third, for each generated circuit design, BRAMs are automatically instantiated rather than synthesizing the reduced footprint function table using LUT primitives, providing an additional degree of resource efficiency. The approach presented here for FPGAs can equally be applied to other circuit technologies for fast and, at the same time, memory-optimized function approximation at the edge.

Spatial-Temporal evolvement and the contributing factors for the economic potential of ecosystem services in counties situated along a river

Counties situated along a river are located at the intersection of water and land transportation that are rich in ecological resources, provide crucial support for economic development. Local economic growth is dependent on ecosystem services. During recent years, with the over-extraction of local ecological resources, the normal supply of ecosystem services has been seriously affected, and the economic expansion of counties situated along rivers has gradually been restricted by their ecosystem services. This forms the basis for realizing regional sustainable development to measure the economic development potential of a county ecosystem along a river and thoroughly explore the reasons for changes in this potential. Therefore, this study measures the economic potential of ecosystem services by the perspective of land use transformation in Zhijiang County during the years 2010, 2015 and 2020 and analyses the spatial and temporal characteristics of ecosystem service value and its economic potential by using small-scale grid sampling method. Next, the paper employs the geographical detector to determine the contributing elements in the regional discrepancy in the economic potential of ecosystem services. Finally, the functional zoning of Zhijiang County is carried out in accordance with the analysis results. The findings show what follows: (1) The value of ecosystem services generated by water in Zhijiang County constitutes the highest, up to 69.70–75.94 % of the total value. (2) Except for woodland, the economic potential of the ecosystem services supplied by other land-use types is negative. (3) Population density, vegetation coverage and urbanisation rate are the most important elements influencing the regional differentiation of economic potential of ecosystem services in Zhijiang County. (4) Among the three types of functional zoning in Zhijiang County, the unit services that ecosystems that exist in agricultural-ecological conservation zones have the highest value and economic potential. The results of this research are essential towards advancing a comprehensive green transformation of societal and economic advancement in counties located along a river.

Spatial Variability of Soil Resistance to Penetration in Fruit Cultivation in Eastern Amazonia

The application of precision agriculture in cocoa and papaya cultivation in Brazil is still incipient. This study aimed to evaluate the spatial variability of the physical attributes of soil cultivated with a consortium of papaya and cocoa. The study was conducted in two sampling grids of 50 points, in two areas cultivated with papaya and cocoa with different planting times (three and eleven months). The soil attributes soil resistance to penetration (RP) and soil gravimetric moisture (UG) were determined at soil depths of 0–20, 20–40 and 40–60 cm. The data were submitted to an exploratory and descriptive analysis. Subsequently, a geostatistical analysis was performed to quantify spatial dependence and then interpolation of the data through kriging. The maps showed weak spatial variability for the UG and RP. In the two areas, it was observed that the depth of 0–20 cm had a lower RP (1.7 Mpa) and a higher UG (40 g g−1), and as the depth was higher, had a higher RP (4.4 Mpa) and a lower UG (38 g g−1). Area 1 presented higher RP values in depth, showing greater susceptibility to compaction. The area characterized by the consortium of papaya and cocoa presented more susceptible to compaction. The mechanical resistance of the soil to penetration was more critical in the 40–60 cm layer for the two consortia evaluated, evidencing areas with possible restriction to plant growth.

DynIceData: a gridded ice–water classification dataset at short-time intervals based on observations from multiple satellites over the marginal ice zone

ABSTRACT High-resolution observations of short-term changes in sea ice are critical to understanding ice dynamics and also provide important information used in advice to shipping, especially in the Arctic. Although individual satellite sensors provide periodic sea ice observations with spatial resolutions of tens of meters, information regarding changes that occur over short time intervals of minutes or hours is limited. In this study, a gridded ice–water classification dataset with a high temporal resolution was developed based on observations acquired by multiple satellite sensors in the Marginal Ice Zone (MIZ). This dataset – DynIceData – which combines Sentinel-1 Synthetic Aperture Radar (SAR) data with Gaofen-3 (GF-3) SAR and SDGSAT-1 thermal infrared imagery was used to obtain observations of the MIZ with a range of temporal resolutions ranging from minutes to tens of hours. The areas of the Arctic covered include the Kara Sea, Beaufort Sea, and Greenland Sea during the period from August 2021 to August 2022. Object-oriented segmentation and thresholding were used to obtain the ice–water classification map from Sentinel-1 and GF-3 SAR image pairs and Sentinel-1 SAR and SDGSAT-1 thermal image pairs. The time interval between the images in each pair ranged from 1 minute to 68 hours. Ten-kilometer grid sample granules with a spatial resolution of 25 m for the GF-3 SAR data and 30 m for the SDGSAT-1 thermal data were used. The classification was verified as having an overall accuracy of at least 95.58%. The DynIceData dataset consists of 7338 samples, which could be used as reference data for further research on rapid changes in sea ice patterns at different short time scales and provide support for research on thermodynamic and dynamic models of sea ice in combination with other environmental data, thus potentially improving the accuracy of sea ice forecasting using Artificial Intelligence. The dataset can be accessed at https://doi.org/10.57760/sciencedb.j00001.00784.

Shock excitation of H2 in the James Webb Space Telescope era

Context. Molecular hydrogen, H2, is the most abundant molecule in the Universe. Thanks to its widely spaced energy levels, it predominantly lights up in warm gas, T ≳ 102 K, such as shocked regions externally irradiated or not by interstellar UV photons, and it is one of the prime targets of James Webb Space Telescope (JWST) observations. These may include shocks from protostellar outflows, supernova remnants impinging on molecular clouds, all the way up to starburst galaxies and active galactic nuclei. Aims. Sophisticated shock models are able to simulate H2 emission from such shocked regions. We aim to explore H2 excitation using shock models, and to test over which parameter space distinct signatures are produced in H2 emission. Methods. We here present simulated H2 emission using the Paris-Durham shock code over an extensive grid of ~14 000 plane-parallel stationary shock models, a large subset of which are exposed to a semi-isotropic external UV radiation field. The grid samples six input parameters: the preshock density, shock velocity, transverse magnetic field strength, UV radiation field strength, the cosmic-ray-ionization rate, and the abundance of polycyclic aromatic hydrocarbons, PAHs. Physical quantities resulting from our self-consistent calculations, such as temperature, density, and width, have been extracted along with H2 integrated line intensities. These simulations and results are publicly available on the Interstellar Medium Services platform. Results. The strength of the transverse magnetic field, as quantified by the magnetic scaling factor, b, plays a key role in the excitation of H2. At low values of b (≲0.3, J-type shocks), H2 excitation is dominated by vibrationally excited lines; whereas, at higher values (b ≳ 1, C-type shocks), rotational lines dominate the spectrum for shocks with an external radiation field comparable to (or lower than) the solar neighborhood. Shocks with b ≥ 1 can potentially be spatially resolved with JWST for nearby objects. H2 is typically the dominant coolant at lower densities (≲104 cm−3); at higher densities, other molecules such as CO, OH, and H2O take over at velocities ≲20 km s−1 and atoms, for example, H, O, and S, dominate at higher velocities. Together, the velocity and density set the input kinetic energy flux. When this increases, the excitation and integrated intensity of H2 increases similarly. An external UV field mainly serves to increase the excitation, particularly for shocks where the input radiation energy is comparable to the input kinetic energy flux. These results provide an overview of the energetic reprocessing of input kinetic energy flux and the resulting H2 line emission.

Crafting the MPEG metrics for objective and perceptual quality assessment of volumetric videos.

Efficient objective and perceptual metrics are valuable tools to evaluate the visual impact of compression artifacts on the visual quality of volumetric videos (VVs). In this paper, we present some of the MPEG group efforts to create, benchmark and calibrate objective quality assessment metrics for volumetric videos represented as textured meshes. We created a challenging dataset of 176 volumetric videos impaired with various distortions and conducted a subjective experiment to gather human opinions (more than 5896 subjective scores were collected). We adapted two state-of-the-art model-based metrics for point cloud evaluation to our context of textured mesh evaluation by selecting efficient sampling methods. We also present a new image-based metric for the evaluation of such VVs whose purpose is to reduce the cumbersome computation times inherent to the point-based metrics due to their use of multiple kd-tree searches. Each metric presented above is calibrated (i.e., selection of best values for parameters such as the number of views or grid sampling density) and evaluated on our new ground-truth subjective dataset. For each metric, the optimal selection and combination of features is determined by logistic regression through cross-validation. This performance analysis, combined with MPEG experts' requirements, lead to the validation of two selected metrics and recommendations on the features of most importance through learned feature weights.

Convolutional neural networks for expediting the determination of minimum volume requirements for studies of microstructurally small cracks, part II: Model interpretation

A significant question in the study of microstructurally small cracks (MSCs) is: What is the minimum microstructural volume that should be included in studies involving MSCs? To answer this, representative volume elements for microstructurally small cracks (RVEMSC), or the minimum volume of microstructure required around an MSC to achieve convergence of crack-front parameters with respect to volume size, were previously determined using finite element (FE) simulations. The large computational expense of determining RVEMSC via FE simulations motivated the implementation of convolutional neural networks (CNNs) to expedite the determination of RVEMSC (Part I). In addition to expediting the determination of RVEMSC, trained CNNs provide the opportunity to gain insights about RVEMSC predictions through various interpretation methods, which we investigate in the current work. First, an inspection of CNN predictions reveals trends learned by the CNN. Second, an input sampling grid study offers insights into the volume of microstructure around an MSC that most influences predictions of RVEMSC. Third, an input feature sensitivity analysis compares the influence of microstructural and geometrical features on RVEMSC predictions. Fourth, visual inspections of saliency maps reveal the local microstructure that is most important to the CNN when predicting RVEMSC. The CNN interpretation results show that microstructural features are more critical than geometrical features to the CNN predictions. Despite inherent limitations in interpreting saliency maps, the results demonstrate that the CNN can learn to identify various microstructural arrangements at individual crack-front points. Overall, this study highlights the importance of considering a variety of microstructural instantiations when determining RVEMSC, as RVEMSC should be a conservative minimum volume requirement that applies across a wide range of microstructural instantiations.

Adding random points to sampling grids to improve the quality of soil fertility maps

Adding random points to sampling grids to improve the quality of soil fertility maps

Regularized SUPER-CAIPIRINHA: Accelerating 3D variable flip-angle T1 mapping with accurate andefficient reconstruction.

To propose an acceleration method for 3D variable flip-angle (VFA) T1 mapping based on a technique called shift undersampling improves parametric mapping efficiency and resolution (SUPER). The proposed method incorporates strategies of SUPER, controlled aliasing in volumetric parallel imaging (CAIPIRINHA), and total variation-based regularization to accelerate 3D VFA T1 mapping. The k-space sampling grid of CAIPIRINHA is internally undersampled with SUPER along the contrast dimension. A proximal algorithm was developed to preserve the computational efficiency of SUPER in the presence of regularization. The regularized SUPER-CAIPIRINHA (rSUPER-CAIPIRINHA) was compared with lowrank plus sparsity (L + S), reconstruction of principal component coefficient maps (REPCOM), and other SUPER-based methods via simulations and in vivo brain T1 mapping. The results were assessed quantitatively with NRMSE and structural similarity index measure (SSIM), and qualitatively by two experienced reviewers. rSUPER-CAIPIRINHA achieved a lower NRMSE and higher SSIM than L + S (0.11 ± 0.01 vs. 0.19 ± 0.03, p < 0.001; 0.66 ± 0.05 vs. 0.37 ± 0.03, p < 0.001) and REPCOM (0.16 ± 0.02, p < 0.001; 0.46 ± 0.04, p < 0.001). The reconstruction time of rSUPER-CAIPIRINHA was 6% of L + S and 2% of REPCOM. For the qualitative comparison, rSUPER-CAIPIRINHA showed improvement of overall image quality and reductions of artifacts and blurring, although with a lower apparent SNR. Compared with 2D SUPER-SENSE, rSUPER-CAIPIRINHA significantly reduced NRMSE (0.11 ± 0.01 vs. 0.23 ± 0.04, p < 0.001) and generated less noisy reconstructions. By incorporating SUPER, CAIPIRINHA, and regularization, rSUPER-CAIPIRINHA mitigated noise amplification, reduced artifacts and blurring, and achieved faster reconstructions compared with L + S and REPCOM. These advantages render 3D rSUPER-CAIPIRINHA VFA T1 mapping potentially useful for clinical applications.