Coarse Scale Research Articles

Fusion Classification of HSI and MSI Using a Spatial-Spectral Vision Transformer for Wetland Biodiversity Estimation

The rapid development of remote sensing technology provides wealthy data for earth observation. Land-cover mapping indirectly achieves biodiversity estimation at a coarse scale. Therefore, accurate land-cover mapping is the precondition of biodiversity estimation. However, the environment of the wetlands is complex, and the vegetation is mixed and patchy, so the land-cover recognition based on remote sensing is full of challenges. This paper constructs a systematic framework for multisource remote sensing image processing. Firstly, the hyperspectral image (HSI) and multispectral image (MSI) are fused by the CNN-based method to obtain the fused image with high spatial-spectral resolution. Secondly, considering the sequentiality of spatial distribution and spectral response, the spatial-spectral vision transformer (SSViT) is designed to extract sequential relationships from the fused images. After that, an external attention module is utilized for feature integration, and then the pixel-wise prediction is achieved for land-cover mapping. Finally, land-cover mapping and benthos data at the sites are analyzed consistently to reveal the distribution rule of benthos. Experiments on ZiYuan1-02D data of the Yellow River estuary wetland are conducted to demonstrate the effectiveness of the proposed framework compared with several related methods.

Categorising cheetah behaviour using tri-axial accelerometer data loggers: a comparison of model resolution and data logger performance

BackgroundExtinction is one of the greatest threats to the living world, endangering organisms globally, advancing conservation to the forefront of species research. To maximise the efficacy of conservation efforts, understanding the ecological, physiological, and behavioural requirements of vulnerable species is vital. Technological advances, particularly in remote sensing, enable researchers to continuously monitor movement and behaviours of multiple individuals simultaneously with minimal human intervention. Cheetahs, Acinonyx jubatus, constitute a “vulnerable” species for which only coarse behaviours have been elucidated. The aims of this study were to use animal-attached accelerometers to (1) determine fine-scale behaviours in cheetahs, (2) compare the performances of different devices in behaviour categorisation, and (3) provide a behavioural categorisation framework.MethodsTwo different accelerometer devices (CEFAS, frequency: 30 Hz, maximum capacity: ~ 2 g; GCDC, frequency: 50 Hz, maximum capacity: ~ 8 g) were mounted onto collars, fitted to five individual captive cheetahs. The cheetahs chased a lure around a track, during which time their behaviours were videoed. Accelerometer data were temporally aligned with corresponding video footage and labelled with one of 17 behaviours. Six separate random forest models were run (three per device type) to determine the categorisation accuracy for behaviours at a fine, medium, and coarse resolution.ResultsFine- and medium-scale models had an overall categorisation accuracy of 83–86% and 84–88% respectively. Non-locomotory behaviours were best categorised on both loggers with GCDC outperforming CEFAS devices overall. On a coarse scale, both devices performed well when categorising activity (86.9% (CEFAS) vs. 89.3% (GCDC) accuracy) and inactivity (95.5% (CEFAS) vs. 95.0% (GCDC) accuracy). This study defined cheetah behaviour beyond three categories and accurately determined stalking behaviours by remote sensing. We also show that device specification and configuration may affect categorisation accuracy, so we recommend deploying several different loggers simultaneously on the same individual.ConclusionThe results of this study will be useful in determining wild cheetah behaviour. The methods used here allowed broad-scale (active/inactive) as well as fine-scale (e.g. stalking) behaviours to be categorised remotely. These findings and methodological approaches will be useful in monitoring the behaviour of wild cheetahs and other species of conservation interest.

Coarse-graining and the Haar wavelet transform for multiscale analysis

BackgroundMultiscale entropy (MSE) has become increasingly common as a quantitative tool for analysis of physiological signals. The MSE computation involves first decomposing a signal into multiple sub-signal ‘scales’ using a coarse-graining algorithm.MethodsThe coarse-graining algorithm averages adjacent values in a time series to produce a coarser scale time series. The Haar wavelet transform convolutes a time series with a scaled square wave function to produce an approximation which is equivalent to averaging points.ResultsCoarse-graining is mathematically identical to the Haar wavelet transform approximations. Thus, multiscale entropy is entropy computed on sub-signals derived from approximations of the Haar wavelet transform. By describing coarse-graining algorithms properly as Haar wavelet transforms, the meaning of ‘scales’ as wavelet approximations becomes transparent. The computed value of entropy is different with different wavelet basis functions, suggesting further research is needed to determine optimal methods for computing multiscale entropy.ConclusionCoarse-graining is mathematically identical to Haar wavelet approximations at power-of-two scales. Referring to coarse-graining as a Haar wavelet transform motivates research into the optimal approach to signal decomposition for entropy analysis.

Identifying core habitats and corridors for giant pandas by combining multiscale random forest and connectivity analysis.

Habitat loss and fragmentation are widely acknowledged as the main driver of the decline of giant panda populations. The Chinese government has made great efforts to protect this charming species and has made remarkable achievements, such as population growth and habitat expansion. However, habitat fragmentation has not been reversed. Protecting giant pandas in a large spatial extent needs to identify core habitat patches and corridors connecting them. This study used an equal‐sampling multiscale random forest habitat model to predict a habitat suitability map for the giant panda. Then, we applied the resistant kernel method and factorial least‐cost path analysis to identify core habitats connected by panda dispersal and corridors among panda occurrences, respectively. Finally, we evaluated the effectiveness of current protected areas in representing core habitats and corridors. Our results showed high scale dependence of giant panda habitat selection. Giant pandas strongly respond to bamboo percentage and elevation at a relatively fine scale (1 km), whereas they respond to anthropogenic factors at a coarse scale (≥2 km). Dispersal ability has significant effects on core habitats extent and population fragmentation evaluation. Under medium and high dispersal ability scenarios (12,000 and 20,000 cost units), most giant panda habitats in the Qionglai mountain are predicted to be well connected by dispersal. The proportion of core habitats covered by protected areas varied between 38% and 43% under different dispersal ability scenarios, highlighting significant gaps in the protected area network. Similarly, only 43% of corridors that connect giant panda occurrences were protected. Our results can provide crucial information for conservation managers to develop wise strategies to safeguard the long‐term viability of the giant panda population.

Facial Pityriasis Alba, Polymorphous Light Eruption, and Vitiligo in Children: A Dermoscopic Distinction

Background: Hypopigmentary skin disorders are the most worrisome complaints from Indian and Indian subcontinent people. Pityriasis alba (PA), polymorphic light eruption (PLE), and vitiligo are clinically look-alike conditions commonly seen in children. Objectives: We attempted to characterize the dermoscopic features of PA and PLE and differentiate them from vitiligo in the facial region in skin of color. Methods: Dermoscopic evaluation was done using a handheld dermoscope at 10X magnification on facial lesions of a total of 60 patients with PA, PLE, and vitiligo. Dermoscopic features of all three conditions were compared and correlated with histological features. Results: Out of 60 patients, 30 (50%), 20 (33.33%), and 10 (16.66%) patients were diagnosed with PA, PLE, and vitiligo clinically and histologically, respectively. Dermoscopy of PA showed white structureless areas (100%), ill-defined margins (86%), faint brownish pigmentation (70%), and fine scales (70%) (P value = 0.001). Besides, 70% of PLE cases showed white structureless areas and ill-defined margins, and 10% showed faint brownish pigmentation (P value = 0.0001). Coarse scales and clustered dots were the most common findings (75%) in PLE lesions, followed by light brown background (60%), crusts (40%), and yellow clods (30%) (P value = 0.0001). Also, 40% and 35% of the PLE cases showed white and brown course scales, respectively (P value = 0.0001). Moreover, 13.33% of PA cases showed coarse white scales, and 3.33% showed a light brown background. All vitiligo cases showed a white glow appearance (100%), followed by perifollicular pigmentation, leukotrichia, and koebnerization shown by 40% of the cases (P value = 0.0001). Finally, 20% of the cases showed perilesional pigmentation and satellite lesions. Conclusions: PA lesions are dermoscopically characterized by ill-defined white areas with fine branny scales, whereas PLE shows coarse brown scales, ring scales, crusts, and yellow clods along with the above findings. Vitiligo lesions are devoid of scales with various pigment network abnormalities, perilesional and perifollicular pigmentation, and leukotrichia.

Bayesian multiscale deep generative model for the solution of high-dimensional inverse problems

Estimation of spatially-varying parameters for computationally expensive forward models governed by partial differential equations (PDEs) is addressed. A novel multiscale Bayesian inference approach is introduced based on a multiscale deep generative model (MDGM). Such generative models provide a flexible representation and allow hierarchical parameter generation from coarse- to fine-scales. Combining the multiscale generative model with Markov Chain Monte Carlo (MCMC), inference across scales is achieved enabling us to obtain efficiently posterior parameter samples at various scales. To estimate the most salient features of parameters is essential in the proposed method. Inference of the fine-scale parameters is enabled by utilizing the posterior information in the immediate coarser scale. In this way, the global features are identified in the coarse-scale with the inference of low-dimensional variables and inexpensive forward computation, and the local features are refined and corrected in the fine-scale. The developed method is demonstrated with two types of permeability estimation for flow in heterogeneous media. One is a Gaussian random field (GRF) with uncertain length scales, and the other is channelized permeability with the two regions defined by different GRFs. The obtained results indicate that the method allows high-dimensional parameter estimation while exhibiting stability, efficiency, and accuracy.

A new exponentially decaying error correlation model for assimilating OCO-2 column-average CO<sub>2</sub> data using a length scale computed from airborne lidar measurements

Abstract. To check the accuracy of column-average dry air CO2 mole fractions (XCO2) retrieved from Orbiting Carbon Observatory (OCO-2) data, a similar quantity has been measured from the Multi-functional Fiber Laser Lidar (MFLL) aboard aircraft flying underneath OCO-2 as part of the Atmospheric Carbon and Transport (ACT) – America flight campaigns. Here we do a lagged correlation analysis of these MFLL–OCO-2 column CO2 differences and find that their correlation spectrum falls off rapidly at along-track separation distances under 10 km, with a correlation length scale of about 10 km, and less rapidly at longer separation distances, with a correlation length scale of about 20 km. The OCO-2 satellite takes many CO2 measurements with small (∼3 km2) fields of view (FOVs) in a thin (<10 km wide) swath running parallel to its orbit: up to 24 separate FOVs may be obtained per second (across a ∼6.75 km distance on the ground), though clouds, aerosols, and other factors cause considerable data dropout. Errors in the CO2 retrieval method have long been thought to be correlated at these fine scales, and methods to account for these when assimilating these data into top-down atmospheric CO2 flux inversions have been developed. A common approach has been to average the data at coarser scales (e.g., in 10 s long bins) along-track, then assign an uncertainty to the averaged value that accounts for the error correlations. Here we outline the methods used up to now for computing these 10 s averages and their uncertainties, including the constant-correlation-with-distance error model that was used to summarize the OCO-2 version 9 XCO2 retrievals as part of the OCO-2 flux inversion model intercomparison project. We then derive a new one-dimensional error model using correlations that decay exponentially with separation distance, apply this model to the OCO-2 data using the correlation length scales derived from the MFLL–OCO-2 differences, and compare the results (for both the average and its uncertainty) to those given by the current constant correlation error model. To implement this new model, the data are averaged first across 2 s spans to collapse the cross-track distribution of the real data onto the 1-D path assumed by the new model. Considering correlated errors can cause the average value to fall outside the range of the values averaged; two strategies for preventing this are presented. The correlation lengths over the ocean, which the land-based MFLL data do not clarify, are assumed to be twice those over the land. The new correlation model gives 10 s XCO2 averages that are only a few tenths of 1 ppm different from the constant correlation model. Over land, the uncertainties in the mean are also similar, suggesting that the +0.3 constant correlation coefficient currently used in the model there is accurate. Over the oceans, the twice-the-land correlation lengths that we assume here result in a significantly lower uncertainty on the mean than the +0.6 constant correlation currently gives – measurements similar to the MFLL ones are needed over the oceans to do better. Finally, we show how our 1-D exponential error correlation model may be used to account for correlations in inversion methods that choose to assimilate each XCO2 retrieval individually and also to account for correlations between separate 10 s averages when these are assimilated instead.

Multilayer landscape classification based on potential vegetation

Vegetation-based landscape classifications reflecting combinations of different types of vegetation promote the understanding of landscape patterns and ecological restoration. However, widespread landscape classifications containing a single thematic resolution may oversimplify landscape patterns. This study aimed at providing a solution for and testing formalized landscape classification, relying on the landscape’s full vegetation potential, i.e. on multiple potential vegetation (MPV). Two areas were studied: the territory of Hungary at a coarse scale and an agriculture-dominated landscape, the Körös-Maros Interfluve (south-eastern Hungary), at a fine scale. Hierarchical clustering and ordination were used to determine landscape types based on potential vegetation type composition of landscapes at each spatial scale. After cutting the resulting dendrogram at several levels and plotting the results on maps and ordination plots, the most relevant thematic resolutions were selected based on the plots and the separation of the groups was tested statistically. The vegetation-based landscape units were reasonably well aligned with biogeographical knowledge at both thematic resolution levels when the study included the whole country. Landscape unit delineation and interpretation based on the typical potential habitats linked to them benefitted from the use of a series of thematic resolutions. For example, in the case of the Körös-Maros Interfluve, apart from well-known grassland vegetation, theMPV-based approach highlighted the distribution of different types of landscape and the potential for woodland in a currently non-wooded area. Furthermore, the finer thematic resolution indicated the possibility of a new type of landscape along temporary small streams. The combined application of clustering and ordination enhanced the interpretation of types of landscape. The use of potential vegetation as an input also enables the classification of currently transformed landscapes. The series of maps with different thematic resolutions allows a flexible choice for specific uses.

Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo.

In this paper, we propose some efficient multi-view stereo methods for accurate and complete depth map estimation. We first present our basic methods with Adaptive Checkerboard sampling and Multi-Hypothesis joint view selection (ACMH & ACMH+). Based on our basic models, we develop two frameworks to deal with the depth estimation of ambiguous regions (especially low-textured areas) from two different perspectives: multi-scale information fusion and planar geometric clue assistance. For the former one, we propose a multi-scale geometric consistency guidance framework (ACMM) to obtain the reliable depth estimates for low-textured areas at coarser scales and guarantee that they can be propagated to finer scales. For the latter one, we propose a planar prior assisted framework (ACMP). We utilize a probabilistic graphical model to contribute a novel multi-view aggregated matching cost. At last, by taking advantage of the above frameworks, we further design a multi-scale geometric consistency guided and planar prior assisted multi-view stereo (ACMMP). This greatly enhances the discrimination of ambiguous regions and helps their depth sensing. Experiments on extensive datasets show our methods achieve state-of-the-art performance, recovering the depth estimation not only in low-textured areas but also in details. Related codes are available at https://github.com/GhiXu.

Identifying the drivers of structural complexity on Hawaiian coral reefs

Habitat structural complexity is created by biotic and abiotic processes that operate over a range of scales. This can be seen clearly on coral reefs, where corals and reef geomorphology create structure from mm to km scales. Here, we quantified the relative contribution of biotic and abiotic structures to habitat complexity using ‘Structure from Motion’, a technology that allows accurate 3D models of environments to be reconstructed from overlapping photographs. We calculated the linear fractal dimension of these models using a virtual analogue of a profile gauge. By adjusting the spacing between profile gauge rods, we partitioned structural complexity into a series of scale intervals. We identified scales that were most indicative of coral cover (0.5-16 cm) and reef geomorphology (16-256 cm). We found that reefs in the Main Hawaiian Islands have more complexity at finer scales than reefs in the Northwest Hawaiian Islands, which we attribute to the latitudinal gradient in coral cover along the archipelago. At coarser scales, islands at each end of the archipelago have sites with high structural complexity, with less complexity in the center of the archipelago. These differences are consistent with geologic factors shaping island uplift, subsidence, and reef formation. In addition, we found that different coral genera and morphologies display unique patterns of fractal dimension, with branchingPoritescorals creating the greatest amount of habitat structure at nearly all scales. This study demonstrates how multi-scale approaches can be used to identify the processes responsible for reef structural complexity and changes in structure over time.

Impact of Incidence Angle Diversity on SMOS and Sentinel-1 Soil Moisture Retrievals at Coarse and Fine Scales

Incidence angle diversity of space-borne radiometer and radar systems operating at low microwave frequencies needs to be taken into consideration to accurately estimate soil moisture (<i>SM</i>) across spatial scales. In this study, the Single Channel Algorithm (SCA) is first applied to SMOS brightness temperatures at vertical polarization (<i>TB_V</i>) to estimate <i>SM</i> at coarse-resolution (25 km) and develop a land cover-specific and incidence angle (32.5&#x00B0;, 42.5&#x00B0; and 52.5&#x00B0;)-adaptive calibration of single scattering albedo (&#x03C9;) and soil roughness (<i>h_s</i>) parameters. These effective parameters are used together with fine-scale multi-angular Sentinel-1 backscatter in a single-pass active-passive downscaling approach to estimate <i>TB_V</i> at fine-scale (1 km) for each SMOS incidence angle. These <i>TB_V</i> are finally inverted to obtain the corresponding high-resolution <i>SM</i> maps. Results over the Iberian Peninsula for year 2018 show an increasing trend of &#x03C9; and a decreasing trend of <i>h_s</i> with SMOS incidence angle, with almost no variability of &#x03C9; across land cover types. The active-passive covariation parameter is shown to increase with SMOS incidence angle and decrease with Sentinel-1 incidence angle. Coarse and fine <i>TB_V</i> maps from the three SMOS incidence angles show similar distributions (mean differences below 0.38 K). Resulting high-resolution <i>SM</i> maps have maximum differences in mean and standard deviation of 0.016 and 0.015 m³/m³, respectively, and compare well with <i>in situ</i> measurements. Our results indicate that model-based microwave approaches to estimate <i>SM</i> can be adequately adapted to account for the incidence angle diversity of planned missions such as CIMR, ROSE-L and Sentinel-1 next generation.

A deep learning based nonlinear upscaling method for transport equations

We will develop a nonlinear upscaling method for nonlinear transport equations. The proposed scheme gives a coarse scale equation for the cell average of the solution. In order to compute the parameters in the coarse scale equation, a local downscaling operator is constructed. This downscaling operation recovers fine scale properties using cell averages. This is achieved by solving the equation on an oversampling region with the given cell average as constraint. Due to the nonlinearity, one needs to compute these downscaling operations on the fly and cannot pre-compute these quantities. In order to give an efficient downscaling operation, we apply a deep learning approach. We will use a deep neural network to approximate the downscaling operation. Our numerical results show that the proposed scheme can achieve good accuracy and efficiency.

Opportunities for agent based modeling of retinal stem cell transplantation.

Opportunities for agent based modeling of retinal stem cell transplantation.

Insights from the quantitative calibration of an elasto-plastic model from a Lennard-Jones atomic glass

We compare the macroscopic and the local plastic behavior of a model amorphous solid based on two radically different numerical descriptions. On the one hand, we simulate glass samples by atomistic simulations. On the other, we implement a mesoscale elasto-plastic model based on a solid-mechanics description. The latter is extended to consider the anisotropy of the yield surface via statistically distributed local and discrete weak planes on which shear transformations can be activated. To make the comparison as quantitative as possible, we consider the simple case of a quasistatically driven two-dimensional system in the stationary flow state and compare mechanical observables measured on both models over the same length scales. We show that the macroscale response, including its fluctuations, can be quantitatively recovered for a range of elasto-plastic mesoscale parameters. Using a newly developed method that makes it possible to probe the local yield stresses in atomistic simulations, we calibrate the local mechanical response of the elasto-plastic model at different coarse-graining scales. In this case, the calibration shows a qualitative agreement only for an optimized subset of mesoscale parameters and for sufficiently coarse probing length scales. This calibration allows us to establish a length scale for the mesoscopic elements that corresponds to an upper bound of the shear transformation size, a key physical parameter in elasto-plastic models. We find that certain properties naturally emerge from the elasto-plastic model. In particular, we show that the elasto-plastic model reproduces the Bauschinger effect, namely the plasticity-induced anisotropy in the stress-strain response. We discuss the successes and failures of our approach, the impact of different model ingredients and propose future research directions for quantitative multi-scale models of amorphous plasticity.

Models of monitoring of self-like traffic of information and communication networks for attack detection systems

Autoregressive, fractal and multifractal models of network self-similar traffic are con-sidered, which allow to form an adequate reference model (template) of "normal" traffic and to detect traffic anomalies in attack detection and prevention systems. Models of fractal Brownian motion and fractal Gaussian noise were considered as models of fractal motions, because they have self-similarity and long-term dependence properties that correspond to the properties of experimental data, as well as the possibility of their analytical interpretation. When evaluating and identifying processes for the implementation of autoregressive models use adaptive filters-approximators, among which there are neural network and neuro-wavelet. The following were used as multifractal models: a multifractal wavelet model with a beta distribution and a hybrid multifractal wavelet model in which the beta distribution is used on a coarse scale and the dis-tribution of point masses on an accurate scale By modeling as a result of adaptation and learning of models, autocorrelation functions, spectra and variances of model signals qualitatively correspond to the graphs of the experimental signal. In addition, the qualitative and numerical values of the characteristics of the model signals generally correspond to the characteristics of the experimental signal. In this case, beta multifractal wavelet models have a smaller error of determination of characteristics than hybrid multifractal wavelet models, and the relative root mean square error of approximation of the experimental signal using a neural network adaptive filter approximator does not exceed 0.046. Statistical verification by non-parametric criterion of signs allowed to establish the adequacy of experimental and model signals with a significance level of 0.01. Further research should be aimed at developing and using predictive models of self-similar traffic in attack detection and prevention systems, which will increase the efficiency of attack detection.

Variational based effective models for inelastic materials

AbstractMechanical systems with inelastic materials and given boundary conditions can be generally defined and solved. However, for large systems, this problem becomes non trivial and computationally costly, especially when microstructures occure. Therefore, multi‐scale modeling is used to capture the behavior of the system.In this study, we present a methodology to provide such models for inelastic materials starting from the variational scheme. Using the variational principle we define the material strain energy and dissipation potential at the coarse scale (macro scale) starting from their counterparts on the fine scale, through the homogenization scheme. Then we solve the material model at the macro level instead of solving the fine scale model or the whole combination of scales. This multi‐scale model offers a reduced number of degrees of freedom and can generally provide the behavior of the system. Moreover, the macro scale model preserves the mathematical structure of the micro scale one. Nevertheless, for this effective model, special care needs to be given to the selection of the inelastic variables solved on the macro scale. This model is illustrated by the application to problems in classical plasticity.

Generalized adaptive refinement for grid-based hexahedral meshing

Due to their nice numerical properties, conforming hexahedral meshes are considered a prominent computational domain for simulation tasks. However, the automatic decomposition of a general 3D volume into a small number of hexahedral elements is very challenging. Methods that create an adaptive Cartesian grid and convert it into a conforming mesh offer superior robustness and are the only ones concretely used in the industry. Topological schemes that permit this conversion can be applied only if precise compatibility conditions among grid elements are observed. Some of these conditions are local, hence easy to formulate; others are not and are much harder to satisfy. State-of-the-art approaches fulfill these conditions by prescribing additional refinement based on special building rules for octrees. These methods operate in a restricted space of solutions and are prone to severely over-refine the input grids, creating a bottleneck in the simulation pipeline. In this article, we introduce a novel approach to transform a general adaptive grid into a new grid meeting hexmeshing criteria, without resorting to tree rules. Our key insight is that we can formulate all compatibility conditions as linear constraints in an integer programming problem by choosing the proper set of unknowns. Since we operate in a broader solution space, we are able to meet topological hexmeshing criteria at a much coarser scale than methods using octrees, also supporting generalized grids of any shape or topology. We demonstrate the superiority of our approach for both traditional grid-based hexmeshing and adaptive polycube-based hexmeshing. In all our experiments, our method never prescribed more refinement than the prior art and, in the average case, it introduced close to half the number of extra cells.

MIXED EVIDENCE FOR NICHE CONSERVATISM IN MOUNTAIN BEAVER (APLODONTIA RUFA) LINEAGES

Range shifts in response to past climatic changes have been documented in a variety of species and have often resulted in the isolation of relict populations. Understanding how these isolated populations develop local adaptations or maintain their historic climatic niche is crucial to creating effective management plans in the face of current climate change. While Mountain Beavers (Aplodontia rufa) have endured through major climatic shifts in the past, they maintain physiological constraints that limit their distribution to cool, humid climates. Increasing temperatures since the last glacial maximum likely had a strong influence in reducing their range. The species now persists as 5 genetically distinct clades, but it is not clear to what extent climatic differences have driven genetic isolation compared to other factors like topography. We compared species-distribution models (SDMs) for the 5 clades of Mountain Beaver to understand whether this species tends towards niche conservatism or adapts to local climates. Presence points from the Global Biodiversity Information Facility were divided into clades and combined with climatic layers from BioClim to develop SDMs for each clade. Niche overlap was then compared to genetic relatedness between all pairings of clades. High temperatures were a limiting factor in distribution for all clades and, despite a low level of niche overlap at broad scales, Mountain Beavers appeared to display some level of niche conservatism. These landscape level SDMs showed that some clades do exist in a warmer climate than other Mountain Beavers; however, fine-scale models for the Point Arena subspecies suggested they persist by selecting the coolest places within that range. This suggests that niche overlap between clades may be higher than what is detected at the coarser scale. Further research is needed to understand the mechanisms limiting the distribution of these subspecies.

Investigation on the Thermodynamic Stability of Nanocrystalline W-Based Alloys: A Combined Theoretical and Experimental Approach.

The stability of nanostructured metal alloys is currently being extensively investigated, and several mathematical models have been developed to describe the thermodynamics of these systems. However, model capability in terms of thermal stability predictions strongly relies on grain boundary-related parameters that are difficult to measure or estimate accurately. To overcome this limitation, a novel theoretical approach is proposed and adopted in this work to identify W-based nanocrystalline alloys which are potentially able to show thermodynamic stability. A comparison between model outcomes and experimental findings is reported for two selected alloys, namely W-Ag and W-Al. Experimental results clearly highlight that W-Ag mixtures retain a segregated structure on relatively coarse length scales even after prolonged mechanical treatments. Moreover, annealing at moderate temperatures readily induces demixing of the constituent elements. In contrast, homogeneous nanostructured W-Al solid solutions are obtained by ball milling of elemental powders. These alloys show enhanced thermal stability with respect to pure W even at high homologous temperatures. Experimental evidences agree with model predictions for both the investigated systems.

Fast and Accurate Initialization for Monocular Vision/INS/GNSS Integrated System on Land Vehicle

Initialization is a fundamental task for the state estimation of multi-sensor integration. In this paper, we propose a novel approach to achieve a fast and accurate initialization for the integrated system. With the outputs from GNSS, the rough roll, pitch, yaw, and direction of gravity are calculated. Based on these initial states, the GNSS/INS fusion and Visual-Inertial Odometry (VIO) are simultaneously launched whenever the observations are available. Subsequently, a coarse scale is retrieved by assigning the initial states of GNSS/INS fusion to the VIO procedure. The scale can be utilized to get poses and points very close to the real world. Once the VIO estimation converges, the transformation parameters between the poses of VIO and GNSS/INS fusion are estimated via non-linear optimization. Thus, we can align these two trajectories and evaluate the scale error. Through six vehicular tests, we analyze the performance of the proposed method with different illumination, obstructions, and motion patterns, and compare the results of the proposed approach with the state-of-the-art algorithms. The results indicated that the Visual-Inertial parameters converge within 9 seconds in our method. The scale errors of both our method are mostly within 10%. The alignment errors are mostly at centimeter-level in our method, which is comparable to the existing approaches.