High-resolution Image Data Research Articles

Investigating the Impact of Two Major Programming Environments on the Accuracy of Deep Learning-Based Glioma Detection from MRI Images

Brain tumors have been the subject of research for many years. Brain tumors are typically classified into two main groups: benign and malignant tumors. The most common tumor type among malignant brain tumors is known as glioma. In the diagnosis of glioma, different imaging technologies could be used. Among these techniques, MRI is the most preferred imaging technology due to its high-resolution image data. However, the detection of gliomas from a huge set of MRI data could be challenging for the practitioners. In order to solve this concern, many Deep Learning (DL) models based on Convolutional Neural Networks (CNNs) have been proposed to be used in detecting glioma. However, understanding which CNN architecture would work efficiently under various conditions including development environment or programming aspects as well as performance analysis has not been studied so far. In this research work, therefore, the purpose is to investigate the impact of two major programming environments (namely, MATLAB and Python) on the accuracy of CNN-based glioma detection from Magnetic Resonance Imaging (MRI) images. To this end, experiments on the Brain Tumor Segmentation (BraTS) dataset (2016 and 2017) consisting of multiparametric magnetic MRI images are performed by implementing two popular CNN architectures, the three-dimensional (3D) U-Net and the V-Net in the programming environments. From the results, it is concluded that the use of Python with Google Colaboratory (Colab) might be highly useful in the implementation of CNN-based models for glioma detection. Moreover, the 3D U-Net model is found to perform better, attaining a high accuracy on the dataset. The authors believe that the results achieved from this study would provide useful information to the research community in their appropriate implementation of DL approaches for brain tumor detection.

Reorganization of brain networks in patients with temporal lobe epilepsy and comorbid headache

ObjectiveThe white matter structural network changes remain poorly understood in patients with temporal lobe epilepsy and comorbid headache (PWH). This study aimed at exploring topological changes in the structural network. MethodsTwenty-five PWH, 32 patients with temporal lobe epilepsy without headache, and 22 healthy controls were recruited in this study. High-resolution structural MRI and diffusion tensor imaging data were acquired from these participants. A graph theory-based approach was employed to characterize the topological properties of the structural network. A network-based statistical analysis was employed to explore abnormal connectivity alterations in PWH. ResultsCompared with healthy controls, PWH exhibited significantly decreased small-world index, shortest path length, increased clustering coefficient, global efficiency, and local efficiency. Patients with temporal lobe epilepsy and comorbid headache displayed a significantly reduced small-world index, shortest path length, and increased global efficiency when compared with patients with temporal lobe epilepsy without headache. In addition, PWH exhibited abnormal local network parameters, mainly located in the prefrontal, temporal, occipital, and parietal regions. Furthermore, network-based statistical analysis revealed that PWH had abnormal structural connections between the temporoparietal lobe, occipital lobe, insula, cingulate gyrus, and thalamus. ConclusionThis study reveals the abnormal white matter structural network alterations in PWH, allowing a better insight into the neuroanatomical mechanisms that predispose epileptic patients to comorbid headaches from the network levels.

Photometric and structural parameters of newly discovered nuclear star clusters in Local Volume galaxies

ABSTRACTWe use high-resolution Hubble Space Telescope imaging data of dwarf galaxies in the Local Volume ($\lesssim {11}\, \mathrm{Mpc}$) to parameterize 19 newly discovered nuclear star clusters (NSCs). Most of the clusters have stellar masses of $M_{\star }^{\mathrm{nsc}} \lesssim 10^{6}{\, {\rm M}_{\odot }}$ and compare to Galactic globular clusters in terms of ellipticity, effective radius, stellar mass, and surface density. The clusters are modelled with a Sérsic profile and their surface brightness evaluated at the effective radius reveals a tight positive correlation to the host galaxy stellar mass. Our data also indicate an increase in slope of the density profiles with increasing mass, perhaps indicating an increasing role for in situ star formation in more massive hosts. We evaluate the scaling relation between the clusters and their host galaxy stellar mass to find an environmental dependence: for NSCs in field galaxies, the slope of the relation is $\alpha = 0.82^{+0.08}_{-0.08}$ whereas $\alpha = 0.55^{+0.06}_{-0.05}$ for dwarfs in the core of the Virgo cluster. Restricting the fit for the cluster to $M_{\star }^{\mathrm{gal}} \ge 10^{6.5}{\, {\rm M}_{\odot }}$ yields $\alpha = 0.70^{+0.08}_{-0.07}$, in agreement with the field environment within the 1σ interval. The environmental dependence is due to the lowest-mass nucleated galaxies and we speculate that this is either due to an increased number of progenitor globular clusters merging to become an NSC, or due to the formation of more massive globular clusters in dense environments, depending on the initial globular cluster mass function. Our results clearly corroborate recent results in that there exists a tight connection between NSCs and globular clusters in dwarf galaxies.

Perpetuating the malign legacy of colonialism? Traditional chiefs’ power and deforestation in Sierra Leone

Sierra Leone is affected by considerable deforestation. The chieftaincy as it exists today in Sierra Leone is a construct of colonialism with removed key aspects of bottom-up accountability that perpetuated itself after independence. In this setting, traditional chiefs have considerable power to shape multiple facets of everyday life in their local communities including land allocation that influences deforestation. There is an open question of whether keeping traditional chiefs as a construct from the colonial era perpetuates the malign legacy of colonialism. I explore from this perspective the role of traditional chiefs in deforestation in Sierra Leone. By combining high-resolution satellite image data with survey and historic data in a regression analysis setting, the central findings of this article are that keeping the institution of traditional chiefs in its state from colonial times, giving them power to define multiple aspects of everyday life at the local level, combined with chiefs’ and the broader state’s corruption accelerates deforestation. These results have important implications for the increasingly popular global trend toward forestry decentralization strategies. The article suggests that a reform of the traditional chiefs’ system in Sierra Leone and in countries with a similar structure of local traditional authorities is needed and that democratic decentralization of forest management with bottom-up accountability of traditional chiefs may be a viable solution.

A dataset of UAV multispectral images for the grassland-livestock balance

The grassland-livestock balance system plays an important role in reconciling the protection of grassland resources with the sustainable development of animal husbandry. The accurate estimation of the grassland-livestock balance is the basis for the implementation of the grassland-livestock balance system. In order to explore the accurate calculation method of the important parameter, i.e. livestock grass demand in the calculation system, in this study, we set up an experimental area in the natural grassland of Hulunbuir, Inner Mongolia, to simulate a real grazing scene, and conducted 4 grazing experiments controlling the sheep number and grazing time. We obtained multispectral images of the experimental area before and after grazing were with the help of the UAV remote sensing, and carried out a quadrat survey. After data sorting and preprocessing, we produced this dataset of high spatial resolution image data and accurate quadrat investigation data. It can be applied to the research on the daily grass demand of grazing sheep on Hulunbuir meadow steppe in autumn. This dataset provides data support for the accurate estimation of local grassland-livestock balance, and is of great significance for the adaptive dynamic management of grass and livestock.

Estimation of Gully Growth Rate and Erosion Amount Using UAV and Worldview-3 Images in Yimeng Mountain Area, China

Non-homogeneous soil’s high gravel content (also known as the “soil-rock dual structure”) may render it more prone to erosion and the significant development of gullies. In order to reveal the morphological characteristics and erosion rate of gullies in “soil-rock dual structure” areas, this study focused on the Shagou Reservoir basin in the Yimeng mountain area as the study area. Based on a complete digital orthophoto map (DOM, 0.03 m) and a digital elevation model (DEM, 0.03 m) acquired by an unmanned aerial vehicle (UAV), the researchers calculated the length (L), top width (TW), depth (D), area (A) and volume (V) of 19 gullies and built and optimized the volume estimation model. The DOM and the DEM were used to modify the morphological parameters of 43 gullies extracted from high-resolution remote sensing (RS) stereopair images (Worldview, 0.5 m), and the development and evolution of gully erosion were evaluated in large scale. The results showed that: (1) after correction, the average relative errors of parameters L, TW, D and A computed from the UAV data and the high-resolution RS stereopair image data fell below 0.005%; (2) the mean of TW/D was 5.20, i.e., the lateral erosion development of gullies far outweighed the downcutting erosion. The retrogressive erosion, lateral erosion and downcutting erosion rates of gullies were 0.01~0.83 m/a (averaged at 0.23 m/a), 0.01~0.68 m/a (averaged at 0.25 m/a) and 0.01~0.19 m/a (averaged at 0.09 m/a), respectively, between 2014 and 2021; (3) the volume-area (V-A) model for gullies is the optimal one (p < 0.01, R2 = 0.944).A total of 90.7% of the gully volume was growing at an erosion rate of 0.42~399.39 m³/a and the total erosion rate of the gullies was 3181.56 m3/a from 2014 to 2021. These research findings can serve as a basis for the quantitative modeling of gully erosion in water-eroded locations with a large-dimension “soil-rock dual structure”.

Proton magnetic resonance spectroscopy (MRS) in individuals with internet gaming.

Various comorbid psychiatric diagnoses, including attention deficit hyperactivity disorder (ADHD), have been reported in individuals with internet gaming disorder (IGD). Prior research has shown alterations in brain metabolites, including N-acetylaspartate (NAA), and combined glutamate and glutamine in patients with ADHD that were similar to those observed in patients with IGD. We hypothesized that the decreased NAA levels in the IGD group would be associated with a history of ADHD. Forty adults participated in this study. Participants were classified as having a high risk for IGD if they had a total score higher than 21 on the IGD Scale-short form. Proton magnetic resonance spectroscopy (1H-MRS) and high-resolution structural magnetic resonance imaging (MRI) data were acquired using a 3 Tesla Siemens Prisma scanner system. Levels of NAA within the right prefrontal cortex were lower in the IGD group than those observed in the control group. In a multiple linear regression analysis, internet addiction test scores and history of ADHD were shown to predict increased game play. In addition, history of ADHD predicted lower levels of NAA within the right prefrontal cortex. The preliminary results of current study suggest a mediating effect of ADHD on the severity of internet game play as well as the levels of NAA within the dorsolateral prefrontal cortex (DLPFC). The inclusion of ADHD in IGD research is important and deserving of further consideration.

Identification and Analysis of Landslides in the Ahai Reservoir Area of the Jinsha River Basin Using a Combination of DS-InSAR, Optical Images, and Field Surveys

We employed ascending and descending Sentinel-1A, optical image data, and field investigation methods to identify and monitor landslides in the Jinsha River Basin to overcome the difficulties associated with the use of a single method and its inaccuracies in identifying landslides in the alpine and canyon areas. Using distributed scatterer-synthetic aperture radar interferometry (DS-InSAR), Sentinel-1A ascending and descending data were integrated to obtain surface deformation information within the study area from July 2017 to May 2019. Thereafter, high-resolution optical image data were introduced to interpret landslides, and field investigations were conducted to validate landslides. These combined methods enabled the assessment of spatiotemporal evolutionary characteristics, and their accuracy in identifying typical landslides was verified. The results showed that the use of both ascending and descending data effectively avoided certain problems, such as the inability to identify certain landslide hazards or the retrieval of incomplete identification results due to geometric distortion associated with single-track SAR imaging. The combined use of these methods effectively improves the timeliness and verification of the accuracy of landslides. Fifteen landslides were identified in the study area, which had different degrees of tension cracks, vertical dislocations, and slip marks that were verified in the field. Of these, two landslides show serious deformation characteristics that currently pose a serious threat to lives and infrastructure. Follow-up monitoring of these landslides is essential. These findings will assist in obtaining comprehensive information about the distribution of landslides and their deformation developmental trends in the Ahai Reservoir area of the Jinsha River Basin and show that the combined methods can be employed to prevent and control landslides in this area.

Aberrant gray matter structure in neurosyphilis without conventional MRI abnormality: a pilot study with voxel and surface-based morphology.

The underlying mechanism of neurosyphilis was not fully understood. To assess gray matter (GM) microstructure in patients with early-stage neurosyphilis without overt conventional magnetic resonance imaging (MRI) abnormality using voxel-based morphometry (VBM) and surface-based morphometry (SBM) analyses. Three-dimensional high-resolution T1-weighted imaging data from 19 individuals with neurosyphilis and 19 healthy controls were analyzed. A battery of neuropsychological tests was performed before each MRI examination. The differences of GM volume and cerebral cortical morphological data between the two groups were compared. The correlations between MRI metrics and neuropsychology/laboratory tests in the patient group were investigated. Regional decreased GM volumes in patients with neurosyphilis were found in the left frontal cortices (Rolandic operculum, middle frontal, and precentral) and bilateral temporal/occipital cortices (bilateral middle temporal, left lingual, and right middle occipital) (P < 0.05, FDR correction). SBM analysis showed significant cortical thickness reduction in the right medial orbitofrontal lobe, and reduced gyrification index in the left insula in patients with neurosyphilis (P < 0.05, FDR correction). Additionally, in the patient group, the GM volume in the middle frontal gyrus, the cortical thickness of right medial orbitofrontal lobe, and the gyrification index in the left insula were negatively correlated to the number connection test-A scores. The gyrification index was also negatively correlated to cerebrospinal fluid white blood cell count. Early-stage neurosyphilis without conventional MRI abnormality presented regional GM volume reduction and cortical morphological changes, which might be related to cognitive impairment and intra-cranial infection. VBM and SBM analyses might be useful for understanding the underlying neural trait of neurosyphilis.

Morphology and distribution of Martian wedge dunes and their terrestrial analogs

Wedge dunes are widespread on Mars and contain important information about Mars surface processes and environmental characteristics. These dunes are wedge-shaped in plan-view, similar in scale to a barchan dune, with two slip faces intersecting at an obtuse angle and extending outward toward the main ridge downwind. And, the length of its main ridge does not exceed its width. At present, our understanding of wedge dunes and their development in nature is limited. The type, morphology, distribution and spatial composition of wedge dunes on Mars and Earth were investigated using high-resolution satellite image data. The results reveal that wedge dunes are simple in type and similar in size to barchan dunes. Martian wedge dunes are similar in shape to terrestrial wedge dunes but larger in size. The average angles between the sub-ridges of Martian and terrestrial wedge dunes are similar, at about 116°. Martian wedge dunes are mainly located in Abalos Undae, Siton Undae, and Aspledon Undae in the North Polar region, accounting for about 5% of the total area covered by dune fields. Wedge dunes are rare on Earth and can only be found on the edge of a few contiguous dune fields. These dunes indicate acute bimodal or obtuse bimodal wind regimes. Wedge dunes have distinct transitional characteristics and may be the initial stage of the development of various dune patterns. The implications of these findings are discussed, in particular the periodic changes in the regional wind direction reflected by the wedge dunes, as well as the significance of the distinctive transitional features of the wedge dunes for improving our understanding of the genesis of complex dune patterns.

Extraction Method of Stand Density Based on High-Resolution Remote Sensing Imagery

In this paper, a stand density extraction method is proposed based on QuickBird high-resolution satellite image data of Baisha artificial forest farm in Minhou, Fujian. Firstly, through the analysis band canopy image of QuickBird panchromatic, it is known that there is a maximum reflectivity point in the canopy. Then, the vegetation-covered area was extracted by the normalized difference vegetation index ( NDVI ). The canopy image of the forest was extracted through the decision tree classification algorithm. The texture information and spectral information were comprehensively utilized to classify the images. Then, the number of stand trees was extracted by the maximum filtering method, and the stand density was calculated to produce the stand density map. The experiment verified that this method can meet the needs of a stand density survey.

Guided Filtered Sparse Auto-Encoder for Accurate Crop Mapping from Multitemporal and Multispectral Imagery

Accurate crop mapping is a fundamental requirement in various agricultural applications, such as inventory, yield modeling, and resource management. However, it is challenging due to crop fields’ high spectral, spatial, and temporal variabilities. New technology in space-borne Earth observation systems has provided high spatial and temporal resolution image data as a valuable source of information, which can produce accurate crop maps through efficient analytical approaches. Spatial information has high importance in accurate crop mapping; a Window-based strategy is a common way to extract spatial information by considering neighbourhood information. However, crop field boundaries implicitly exist in image data and can be more helpful in identifying different crop types. This study proposes Guided Filtered Sparse Auto-Encoder (GFSAE) as a deep learning framework guided implicitly with field boundary information to produce accurate crop maps. The proposed GFSAE was evaluated over two time-series datasets of high-resolution PlanetScope (3 m) and RapidEye (5 m) imagery, and the results were compared against the usual Sparse Auto Encoder (SAE). The results show impressive improvements in terms of all performance metrics for both datasets (namely 3.69% in Overal Accuracy, 0.04 in Kappa, and 4.15% in F-score for the PlanetScope dataset, and 3.71% in OA, 0.05 in K, and 1.61% in F-score for RapidEye dataset). Comparing accuracy metrics in field boundary areas has also proved the superiority of GFSAE over the original classifier in classifying these areas. It is also appropriate to be used in field boundary delineation applications.

FFT-based homogenization at finite strains using composite boxels (ComBo)

Computational homogenization is the gold standard for concurrent multi-scale simulations (e.g., FE2) in scale-bridging applications. Often the simulations are based on experimental and synthetic material microstructures represented by high-resolution 3D image data. The computational complexity of simulations operating on such voxel data is distinct. The inability of voxelized 3D geometries to capture smooth material interfaces accurately, along with the necessity for complexity reduction, has motivated a special local coarse-graining technique called composite voxels (Kabel et al. Comput Methods Appl Mech Eng 294: 168–188, 2015). They condense multiple fine-scale voxels into a single voxel, whose constitutive model is derived from the laminate theory. Our contribution generalizes composite voxels towards composite boxels (ComBo) that are non-equiaxed, a feature that can pay off for materials with a preferred direction such as pseudo-uni-directional fiber composites. A novel image-based normal detection algorithm is devised which (i) allows for boxels in the firsts place and (ii) reduces the error in the phase-averaged stresses by around 30% against the orientation cf. Kabel et al. (Comput Methods Appl Mech Eng 294: 168–188, 2015) even for equiaxed voxels. Further, the use of ComBo for finite strain simulations is studied in detail. An efficient and robust implementation is proposed, featuring an essential selective back-projection algorithm preventing physically inadmissible states. Various examples show the efficiency of ComBo against the original proposal by Kabel et al. (Comput Methods Appl Mech Eng 294: 168–188, 2015) and the proposed algorithmic enhancements for nonlinear mechanical problems. The general usability is emphasized by examining various Fast Fourier Transform (FFT) based solvers, including a detailed description of the Doubly-Fine Material Grid (DFMG) for finite strains. All of the studied schemes benefit from the ComBo discretization.

GST Data-processing Workflow: Image Registration and Alignment

Multiple solar instrument observation campaigns are increasingly popular among the solar physics and space science communities. Scientists organize high-resolution ground-based telescopes and spacecraft to study the evolution of the complex solar atmosphere and the origin of space weather. Image registration and coalignment between different instruments are vital for accurate data product comparison. We developed a Python language package for registration of ground-based high-resolution imaging data acquired by the Goode Solar Telescope (GST) to space-based full-disk continuum intensity data provided by the Solar Dynamics Observatory (SDO) with the scale-invariant feature transform method. The package also includes tools to align data sets obtained in different wavelengths and at different times utilizing the optical flow method. We present the image registration and coalignment workflow. The aliment accuracy of each alignment method is tested with the aid of radiative magnetohydrodynamics simulation data. We update the pointing information in GST data fits headers and generate GST and SDO imaging data products as science-ready four-dimensional (x, y, λ, t) data cubes.

Soil clay minerals abundance mapping using AVIRIS-NG data

Advanced hyperspectral remote sensing technology can identify various clay minerals in the soil. The improved spectral and spatial resolution of the airborne hyperspectral sensor can be used for a better interpretation of clay mineral abundance in the soil. This research utilizes the high-resolution image data acquired from Advanced Visible Infrared Imaging Spectrometer Next Generation (AVIRIS-NG) on an airborne platform to map the abundance of clay minerals in the Udaipur region of Rajasthan (24° 34′ 16.5720′' N, 73° 41′ 29.5584′' E). The representative soil samples for various soil clay minerals, e.g., kaolinite, montmorillonite, and illite, were classified from AVIRIS-NG data using a spectral feature fitting algorithm. A total of thirty sites were selected from agricultural and wastelands for soil sample collection. X-ray diffraction (XRD) analysis of representative soil samples was carried out to find the percentage and type of clay minerals present. Regression analysis between absorption peak depth values estimated from the hyperspectral image of the sampling sites in the spectral region from 2205 to 2214 nm and corresponding clay content value showed a significant relationship. The regression line obtained for the known pixels was used to prepare the mineral abundance map over the study area. This study significantly indicates the dominance of montmorillonite clay mineral over kaolinite and illite in the soils of the Udaipur region.

Applicability of Artificial Neural Network for Automatic Crop Type Classification on UAV-Based Images

Recent advances in optical remote sensing, especially with the development of machine learning models have made it possible to automatically classify different crop types based on their unique spectral characteristics. In this article, a simple feed-forward artificial neural network (ANN) was implemented for the automatic classification of various crop types. A DJI Mavic air drone was used to simultaneously collect about 549 images of a mixed-crop farmland belonging to Federal University of Technology Minna, Nigeria. The images were annotated and the ANN algorithm was implemented using custom-designed Python programming scripts with libraries such as NumPy, Label box, and Segmentation Mask, for the classification. The algorithm was designed to automatically classify maize, rice, soya beans, groundnut, yam and a non-crop feature into different land spectral classes. The model training performance, using 70% of the dataset, shows that the loss curve flattened down with minimal over-fitting, showing that the model was improving as it trained. Finally, the accuracy of the automatic crop-type classification was evaluated with the aid of the recorded loss function and confusion matrix, and the result shows that the implemented ANN gave an overall training classification accuracy of 87.7% from the model and an overall accuracy of 0.9393 as computed from the confusion matrix, which attests to the robustness of ANN when implemented on high-resolution image data for automatic classification of crop types in a mixed farmland. The overall accuracy, including the user accuracy, proved that only a few images were incorrectly classified, which demonstrated that the errors of omission and commission were minimal.

Validation of TESS exoplanet candidates orbiting solar analogues in the all-sky PLATO input catalogue

ABSTRACT The Transiting Exoplanet Survey Satellite (TESS) is focusing on relatively bright stars and has found thousands of planet candidates. However, mainly because of the low spatial resolution of its cameras (≈ 21 arcsec/pixel), TESS is expected to detect several false positives (FPs); hence, vetting needs to be done. Here, we present a follow-up program of TESS candidates orbiting solar-analogue stars that are in the all-sky PLATO input catalogue. Using Gaia photometry and astrometry we built an absolute colour–magnitude diagram and isolated solar-analogue candidates’ hosts. We performed a probabilistic validation of each candidate using the vespa software and produced a prioritized list of objects that have the highest probability of being genuine transiting planets. Following this procedure, we eliminated the majority of FPs and statistically vetted 23 candidates. For this remaining set, we performed a stellar neighbourhood analysis using Gaia Early Data Release 3 and centroid motion tests, greatly enhancing the on-target probability of 12 of them. We then used publicly available high-resolution imaging data to confirm their transit source and found five new, fully validated planets. For the remaining candidates, we propose on–off photometry to further refine the list of genuine candidates and prepare for the subsequent radial velocity follow-up.

A Development of a Robust Machine for Removing Irregular Noise with the Intelligent System of Auto-Encoder for Image Classification of Coastal Waste

Currently, the seashore is threatened by the environment of climate change and increasing coastal waste. The past environmental groups used a large amount of manpower to manage the coast to maintain the seashore environment. The computational time cost and efficiency are not ideal for the vast area of the seashore. With the progress of GIS (Geographic Information System) technology, the ability of remote sensing technology can capture a wide range of data in a short period. This research is based on the application of remote sensing technology combined with machine learning to display the observation of our seashore. However, in the process of image classification, the seashore wastes are small, which required the use of high-resolution image data. Thus, how to remove the noise becomes a crucial issue in developing an image classifier machine. The difficulties include how to adjust the value of parameters for removing/avoiding noises. First, the texture information and vegetation indices were employed as ancillary information in our image classification. On the other hand, auto-encoder is a very good tool to denoise a given image; hence, it is used to transform high-resolution images by considering ancillary information to extract attributes. Multi-layer perceptron (MLP) and support vector machine (SVM) were compared for classifier performance in a parallel study. The overall accuracy is about 85.5% and 83.9% for MLP and SVM, respectively. If the AE is applied for preprocessing, the overall accuracy is increased by about 10–12%.

Displacement-length scaling relations of nearside graben: Evidence of restricted normal faults on the Moon

Evidence of significant extension on the Moon is associated with nearside mare. The load induced by the mare basalts resulted in lithospheric flexure and graben forming extension. We use high-resolution topographic and image data from the Lunar Reconnaissance Orbiter (LRO) and the SELenological and ENngineering Explorer (SELENE) to generate detailed fault displacement-length (D/L) scaling observations for graben bounding normal faults. Specifically, we focus on the Dmax/L relations of graben within three geologic settings: lunar highlands, mare, and mixed highlands-mare terrains. The population scaling factor γ for the measured lunar graben is 4.5 × 10−3 (n = 50), falling within the range for terrestrial normal faults. The scatter in the Dmax/L data for mare graben (γ = 4.9 × 10–3,n = 25) is significantly higher than that for graben in highlands (γ = 6.3 × 10−3, n = 16) and mixed terrains (γ = 3.0 × 10−3, n = 9). The shape of a displacement profiles for faults is indicative of the mode of growth (i.e., restricted or unrestricted). Displacement plots show 52% of mare graben exhibit plateau-shaped profiles, indicating restricted fault growth, compared to 19% and 33% for highlands and mixed terrain graben, respectively. We attribute the large percentage of plateau-shaped profiles and the scatter in mare graben Dmax/L data to restricted fault growth controlled by the local thickness of the mare basalts. Finally, we consider whether observationally based estimates of the distribution of mare basalt thicknesses is consistent with the width-depth relation of the inferred restricted graben.

High performance multi-platform computing for large-scale image-based finite element modeling of bone

BackgroundImage-based finite element (FE) modeling of bone is a non-invasive method to estimate bone stiffness and strength. High-resolution imaging data as input allows for inclusion of bone microarchitecture but results in large amounts of data unsuitable for traditional FE solvers. Bone-specific mesh-free solvers have been developed over the past 20 years to improve on memory efficiency in simulated bone loading applications. The objective of this study was to provide linear performance benchmarking for a bone-specific, mesh-free solver (FAIM) using µCT and HR-pQCT image data on Mac, Linux, and Windows operating systems using both single- and multi-thread CPU and GPU processing. MethodsThe focus is on the linear gradient-descent solver using standardized uniaxial loading of bone models from µCT, and first- and second-generation HR-pQCT scans of the radius and tibia. Convergence, speedup, memory, and batch performance tests were completed using CPUs and GPUs on three laboratory-based systems with Windows, Linux, and Mac operating systems. ResultsAlthough varying by system and model size, time-per-iteration was as low as 0.03 s when an HR-pQCT-based radius model (6.45 million DOF) was solved with 3 GPUs. Strong scaling was achieved with GPU and CPU parallel processing, with strong parallel efficiencies when models were solved using 3 GPUs or ≤ 10 CPU threads. Errors in force, strain energy density, and Von Mises stress were as low as 0.1% when a convergence tolerance of 10−3 or smaller was used. ConclusionThe results of this study indicate that to maximize computational efficiency and minimize model solution times using FAIM software under the standardized tested conditions using µCT, XCT1 and XCT2 HR-pQCT image data, convergence tolerance set to 10−4, and 10 threads or 2 GPUs are sufficient for efficient solution times. Less strict convergence tolerances will improve solution times but will introduce more error in the outcome measures.