Flat Index Research Articles

WALSH: Write-Aggregating Log-Structured Hashing for Hybrid Memory

Persistent memory (PM) brings important opportunities for improving data storage including the widely used hash tables. However, PM is not friendly to small writes, which causes existing PM hashes to suffer from high hardware write amplification. Hybrid memory offers the performance and concurrency of DRAM and the durability and capacity of PM, but existing hybrid memory hashes cannot deliver high performance, low DRAM footprint, and fast recovery at the same time. This paper proposes WALSH, a flat hash with novel log-structured separate chaining designs to optimize the performance while ensuring low DRAM footprint and fast recovery. To address the overhead of hash resizing and garbage collection (GC), WALSH further proposes partial resizing/GC mechanisms and a 4-phase protocol for concurrent hash operations. As a result, WALSH is the first flat index for hybrid memory with embedded write aggregation ability. A comprehensive evaluation shows that WALSH substantially outperforms state-of-the-art hybrid memory hashes; e.g., its insert throughput is up to 2.4X that of related works while saving more than 87% of DRAM. WALSH also provides efficient recovery; e.g., it can recover a dataset with 1 billion objects in just a few seconds.

Distribution and Anisotropy of the Energy Transfer Rate in the Solar Wind Turbulence

The distribution of the energy transfer rate is critical for the interpretation of the intermittent energy cascade in the solar wind turbulence. However, the true observational distribution of the energy transfer rate in the solar wind and its anisotropy remain unknown. Here, we use a 7 day interval measured by Wind in the fast solar wind and investigate the distribution and anisotropy of the energy transfer rate based on the log-Poisson model. We find that the probability density distribution consists of two parts. The majority part locates at smaller values and is consistent with the log-normal distribution. The estimated mean value and standard deviation of the logarithmic energy transfer rate for the majority are both smaller in the direction parallel to the local mean magnetic field than in the perpendicular direction. The mean value displays a power-law shape with respect to the scale, with flatter index in the parallel direction and steeper index in the perpendicular direction. The minority part locates at larger values and expands as the scale decreases, indicating the growing intermittency toward smaller scales. The flatness for parallel logarithmic energy transfer rate is larger than that for perpendicular. And it rises as the scale decreases for all directions, demonstrating the relatively longer tail of the distribution with decreasing scale. Our results provide new insight to help interpret the intermittent energy cascade process in the solar wind turbulence.

Localizing Transitions via Interaction-Induced Flat Bands.

This Letter presents a theory of interaction-induced band-flattening in strongly correlated electron systems. We begin by illustrating an inherent connection between flat bands and index theorems, and presenting a generic prescription for constructing flat bands by periodically repeating local Hamiltonians with topological zero modes. Specifically, we demonstrate that a Dirac particle in an external, spatially periodic magnetic field can be cast in this form. We derive a condition on the field to produce perfectly flat bands and provide an exact analytical solution for the flat band wave functions. Furthermore, we explore an interacting model of Dirac fermions in a spatially inhomogeneous field. We show that certain Hubbard-Stratonovich configurations exist that "rectify" the field configuration, inducing band flattening. We present an explicit model where this localization scenario is energetically favorable-specifically in Dirac systems with nearly flat bands, where the energy cost of rectifying textures is quadratic in the order parameter, whereas the energy gain from flattening is linear. In conclusion, we discuss alternative symmetry-breaking channels, especially superconductivity, and propose that these interaction-induced band-flattening scenarios represent a generic nonperturbative mechanism for spontaneous symmetry breaking, pertinent to many strongly correlated electron systems.

Influence of Vegetation Phenology on the Temporal Effect of Crop Fractional Vegetation Cover Derived from Moderate-Resolution Imaging Spectroradiometer Nadir Bidirectional Reflectance Distribution Function–Adjusted Reflectance

Moderate-Resolution Imaging Spectroradiometer (MODIS) Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) products are being increasingly used for the quantitative remote sensing of vegetation. However, the assumption underlying the MODIS NBAR product’s inversion model—that surface anisotropy remains unchanged over the 16-day retrieval period—may be unreliable, especially since the canopy structure of vegetation undergoes stark changes at the start of season (SOS) and the end of season (EOS). Therefore, to investigate the MODIS NBAR product’s temporal effect on the quantitative remote sensing of crops at different stages of the growing seasons, this study selected typical phenological parameters, namely SOS, EOS, and the intervening stable growth of season (SGOS). The PROBA-V bioGEOphysical product Version 3 (GEOV3) Fractional Vegetation Cover (FVC) served as verification data, and the Pearson correlation coefficient (PCC) was used to compare and analyze the retrieval accuracy of FVC derived from the MODIS NBAR product and MODIS Surface Reflectance product. The Anisotropic Flat Index (AFX) was further employed to explore the influence of vegetation type and mixed pixel distribution characteristics on the BRDF shape under different stages of the growing seasons and different FVC; that was then combined with an NDVI spatial distribution map to assess the feasibility of using the reflectance of other characteristic directions besides NBAR for FVC correction. The results revealed the following: (1) Generally, at the SOSs and EOSs, the differences in PCCs before vs. after the NBAR correction mainly ranged from 0 to 0.1. This implies that the accuracy of FVC derived from MODIS NBAR is lower than that derived from MODIS Surface Reflectance. Conversely, during the SGOSs, the differences in PCCs before vs. after the NBAR correction ranged between –0.2 and 0, suggesting the accuracy of FVC derived from MODIS NBAR surpasses that derived from MODIS Surface Reflectance. (2) As vegetation phenology shifts, the ensuing differences in NDVI patterning and AFX can offer auxiliary information for enhanced vegetation classification and interpretation of mixed pixel distribution characteristics, which, when combined with NDVI at characteristic directional reflectance, could enable the accurate retrieval of FVC. Our results provide data support for the BRDF correction timescale effect of various stages of the growing seasons, highlighting the potential importance of considering how they differentially influence the temporal effect of NBAR corrections prior to monitoring vegetation when using the MODIS NBAR product.

A Radio Study of Persistent Radio Sources in Nearby Dwarf Galaxies: Implications for Fast Radio Bursts

We present 1–12 GHz Karl G. Jansky Very Large Array observations of nine off-nuclear persistent radio sources (PRSs) in nearby (z ≲ 0.055) dwarf galaxies, along with high-resolution European VLBI Network observations for one of them at 1.7 GHz. We explore the plausibility that these PRSs are associated with fast radio burst (FRB) sources by examining their properties—physical sizes, host-normalized offsets, spectral energy distributions (SEDs), radio luminosities, and light curves—and compare them to those of the PRSs associated with FRB 20121102A and FRB 20190520B, two known active galactic nuclei (AGN), and one likely AGN in our sample with comparable data, as well as other radio transients exhibiting characteristics analogous to FRB-PRSs. We identify a single source in our sample, J1136+2643, as the most promising FRB-PRS, based on its compact physical size and host-normalized offset. We further identify two sources, J0019+1507 and J0909+5655, with physical sizes comparable to FRB-PRSs, but which exhibit large offsets and flat spectral indices potentially indicative of a background AGN origin. We test the viability of neutron star wind nebula and hypernebula models for J1136+2643 and find that the physical size, luminosity, and SED of J1136+2643 are broadly consistent with these models. Finally, we discuss the alternative interpretation that the radio sources are instead powered by accreting massive black holes, and we outline future prospects and follow-up observations for differentiating between these scenarios.

The Framework of Quantifying Biomarkers of OCT and OCTA Images in Retinal Diseases.

Despite the significant advancements facilitated by previous research in introducing a plethora of retinal biomarkers, there is a lack of research addressing the clinical need for quantifying different biomarkers and prioritizing their importance for guiding clinical decision making in the context of retinal diseases. To address this issue, our study introduces a novel framework for quantifying biomarkers derived from optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) images in retinal diseases. We extract 452 feature parameters from five feature types, including local binary patterns (LBP) features of OCT and OCTA, capillary and large vessel features, and the foveal avascular zone (FAZ) feature. Leveraging this extensive feature set, we construct a classification model using a statistically relevant p value for feature selection to predict retinal diseases. We obtain a high accuracy of 0.912 and F1-score of 0.906 in the task of disease classification using this framework. We find that OCT and OCTA's LBP features provide a significant contribution of 77.12% to the significance of biomarkers in predicting retinal diseases, suggesting their potential as latent indicators for clinical diagnosis. This study employs a quantitative analysis framework to identify potential biomarkers for retinal diseases in OCT and OCTA images. Our findings suggest that LBP parameters, skewness and kurtosis values of capillary, the maximum, mean, median, and standard deviation of large vessel, as well as the eccentricity, compactness, flatness, and anisotropy index of FAZ, may serve as significant indicators of retinal conditions.

Strain rate effects on fragment morphology of ceramic alumina: A synchrotron-based study

Dynamic (600–1000 s−1) and quasi-static (0.001–0.01 s−1) compression experiments are conducted on a high-purity alumina ceramic using a split Hopkinson pressure bar and a material test system, respectively. The postmortem fragments of ceramic samples at different strain rates are then characterized via synchrotron micro computed tomography (CT). The three-dimensional (3D) morphologies of fragments are quantified using the gyration tensor analysis after proper segmentation of CT images. The mean fragment size decreases in a power-law form while the mean shape indices (sphericity, elongation index and flatness index) increase in a logarithmic-linear form, with increasing strain rates. In addition, the fragment size and shape distributions are all found to follow the Weibull probability distribution. To reveal the underlying mechanisms of such strain rate effects, high-speed optical and X-ray imaging are implemented to capture the fracture process of ceramic samples under quasi-static and dynamic compression. Primary and secondary wing cracks (PWCs/SWCs) control the fracture and fragmentation of the ceramic under both loading conditions. Compared to quasi-static loading, a considerably larger number of SWCs are produced under dynamic loading, and pronounced branching and bridging occur among the PWCs, which prevent the PWCs from coalescing into axial splitting cracks. Consequently, crack networks composed of high-density wing cracks break the sample into finer and more isotropic fragments, consistent with CT characterizations. Scanning electron microscopy is utilized to analyze the micro damage modes of ceramic samples. Transgranular fracture dominates the grain-scale damage and contributes to the higher dynamic fracture resistance of the alumina under dynamic loading, as a result of more homogeneous nucleation and growth of micro cracks.

An Angle Effect Correction Method for High-Resolution Satellite Side-View Imaging Data to Improve Crop Monitoring Accuracy

In recent years, the advancement of CubeSat technology has led to the emergence of high-resolution, flexible imaging satellites as a pivotal source of information for the efficient and precise monitoring of crops. However, the dynamic geometry inherent in flexible side-view imaging poses challenges in acquiring the high-precision reflectance data necessary to accurately retrieve crop parameters. This study aimed to develop an angular correction method designed to generate nadir reflectance from high-resolution satellite side-swing imaging data. The method utilized the Anisotropic Flat Index (AFX) in conjunction with a fixed set of Bidirectional Reflectance Distribution Function (BRDF) parameters to compute the nadir reflectance for the Jilin-1 GP01/02 multispectral imager (PMS). Crop parameter retrieval was executed using regression models based on vegetation indices, the leaf area index (LAI), fractional vegetation cover (FVC), and chlorophyll (T850 nm/T720 nm) values estimated based on angle corrected reflectance compared with field measurements taken in the Inner Mongolia Autonomous Region. The findings demonstrate that the proposed angular correction method significantly enhances the retrieval accuracy of the LAI, FVC, and chlorophyll from Jilin-1 GP01/02 PMS data. Notably, the retrieval accuracy for the LAI and FVC improved by over 25%. We expect that this approach will exhibit considerable potential to improve crop monitoring accuracy from high-resolution satellite side-view imaging data.

Systematic investigation into the role of particle multi-level morphology in determining the shear behavior of granular materials via DEM simulation

This study aims to numerically investigate the dependence of shear behaviors of granular materials on particle multi-level morphology, encompassing form (mean of elongation and flatness), angularity, and roughness. To this end, four series of particles—ellipsoidal particles with varying elongation indices (EI) and flatness indices (FI), and equiaxed concave particles with varying angularity indices (AI) and roughness (RG)—are adopted to isolate the effects of particle morphology at each level. Through discrete element method (DEM) simulations, granular assemblies with varying values of EI, FI, AI, and RG are consolidated to achieve the maximum packing density under the same confining pressure. Subsequently, they are subjected to triaxial drained compression tests until reaching a critical state. Based on numerical results, the independent effects of EI, FI, AI, and RG on the macro- and micro-mechanical response of granular materials are examined and compared in detail. Furthermore, using the ‘Stress-force-fabric’ (SFF) relationship, the anisotropic origins of the peak- and critical-state shear strengths induced by EI, FI, AI, and RG are also analyzed.

3D shape and size characterization of micron-sized coal particle with XRCT and SH

The shape and size of solid fuel particles are significant parameters in combustion process, and have attracted broad attentions in simulation and experiment studies. In this study, X-ray computer tomography (XRCT) and spherical harmonics (SH) were introduced and combined to conduct accurate morphology characterization of micron-sized coal powder in three dimensions (3D) with purpose of providing references for morphology characterization, size measurement, and modeling of solid fuel particles. The shape parameters including volume, surface area, sphericity index, specific surface area, enlongation index, and flatness index, and the size parameters including diameter of volume equivalent sphere, three principal dimensions, and sieve size, were obtained with SH expansion. Validation on standard geometries demonstrated the accuracy of the method under the SH degree N ≥ 20, with errors for volume, surface area, and sieve size within 1 %, 3 %, and 2.5 %, respectively. These parameters were analyzed statistically to reveal the morphological characteristics of micron-sized coal powder. The Zingg diagram indicated that nearly 60 % of the coal powder particles are nearly spherical. Comparison among 3D size, 2D size, and sieve size implied that characterizing with 2D size may overestimate the fineness of coal powder. Although there was a variance in the particle size distribution, it was demonstrated that there exists a strong linear relationship between the 3D size, 2D size, and sieve size. The method could serve as a functional auxiliary tool for fuel particle research.

The Impact of Water Potential and Temperature on Native Species' Capability for Seed Germination in the Loess Plateau Region, China.

Global warming is increasing the frequency and intensity of heat waves and droughts. One important phase in the life cycle of plants is seed germination. To date, the association of the temperature and water potential thresholds of germination with seed traits has not been explored in much detail. Therefore, we set up different temperature gradients (5-35 °C), water potential gradients (-1.2-0 MPa), and temperature × water potential combinations for nine native plants in the Loess Plateau region to clarify the temperature and water combinations suitable for their germination. Meanwhile, we elucidated the temperature and water potential thresholds of the plants and their correlations with the mean seed mass and flatness index by using the thermal time and hydrotime models. According to our findings, the germination rate was positively correlated with the germination percentage and water potential, with the former rising and the latter decreasing as the temperature increased. Using the thermal time and hydrotime models, the seed germination thresholds could be predicted accurately, and the germination thresholds of the studied species varied with an increase in germination percentage. Moreover, temperature altered the impact of water potential on the germination rate. Overall, the base water potential for germination, but not the temperature threshold, was negatively correlated with mean seed mass and was lower for rounder seeds than for longer seeds. This study contributes to improving our understanding of the seed germination characteristics of typical plants and has important implications for the management and vegetation restoration of degraded grasslands.

Soil Classification Mapping Using a Combination of Semi-Supervised Classification and Stacking Learning (SSC-SL)

In digital soil mapping, machine learning models have been widely applied. However, the accuracy of machine learning models can be limited by the use of a single model and a small number of soil samples. This study introduces a novel method, semi-supervised classification combined with stacking learning (SSC-SL), to enhance soil classification mapping in hilly and low-mountain areas of Northern Jurong City, Jiangsu Province, China. This study incorporated Gaofen-2 (GF-2) remote sensing imagery along with its associated remote sensing indices, the ALOS Digital Elevation Model (DEM) and their derived topographic factors, and soil parent material data in its modelling process. We first used three base learners, Ranger, Rpart, and XGBoost, to construct the SL model. In addition, we employed the fuzzy c-means clustering algorithm (FCM) to construct a clustering map. To fully leverage the information from a multitude of environmental variables, understand the distribution of data, and enhance the effectiveness of the classification, we selected unlabelled samples near the boundaries of the patches on the clustering map. The SSC-SL model demonstrated superior stability and performance, with optimal accuracy at a 0.9 confidence level, achieving an overall accuracy of 0.77 and a kappa coefficient of 0.73. These metrics exceeded those of the highest performing base learner (Ranger model) by 10.4% and 12.3%, respectively, and they outperformed the least effective base learner (Rpart model) by 27.3% and 32.9%. It notably improves the spatial distribution accuracy of soil types. Key environmental variables influencing soil type distribution include soil parent material (SPM), land use (LU), the multi-resolution valley bottom flatness index (MRVBF), and Elevation (Ele). In conclusion, the SSC-SL model offers a novel and effective approach for enhancing the predictive accuracy of soil classification mapping.

Laboratory investigation on the single particle crushing strength of carbonate gravel incorporating size and shape effects

Particle strength is one of the critical factors that causes the degradation of the mechanical behaviour of crushable soil. Because of the complexity of particle shape, its effect on particle strength has not been completely understood. In this study, single particle crushing tests were performed on carbonate gravel particles with three size fractions (i.e. 5–10 mm, 10–15 mm and 15–20 mm) and three shapes (i.e. blocky, rodlike and flaky), using a PartAn3D Maxi large particle size and shape analyser to obtain the shape characteristics of the particles. The determination of crushing strength for particles with complex shapes was discussed, and it was found that it is appropriate to use the area equivalent circle diameter to obtain the crushing strength. The influence of particle size and shape parameters, including the elongation index, flatness index, intercept sphericity, circularity and convexity, on the crushing strength were identified, and an index termed the form factor considering dimensions in three directions was proposed to describe the effect of particle shape. Based on this form factor, a probabilistic model was established to predict the distribution of crushing strength of carbonate gravel particles with various sizes and shapes. The proposed model could be used in the discrete-element method to consider the influence of shape in addition when evaluating particle breakage.

5–12 pc resolution ALMA imaging of gas and dust in the obscured compact nucleus of IRAS 17578-0400

AbstractWe here present 0.02–0.04″ resolution ALMA observation of the compact obscured nucleus (CON) of IRAS 17578-0400. A dusty torus within the nucleus, approximately 4 pc in radius, has been uncovered, exhibiting a usually flat spectral index at ALMA band 3, likely due to the millimeter corona emission from the central supermassive black hole (SMBH). The dense gas disk, traced by 13CO(1-0), spans 7 pc in radius and suggests an outflow driven by a disk wind due to its asymmetrical structure along the minor axis. Collimated molecular outflows (CMO), traced by the low-velocity components of the HCN(3-2) and HCO+(3-2) lines, align with the minor axis gas disk. Examination of position-velocity plots of HCN(3-2) and HCO+(3-2) reveals a flared dense gas disk extended a radius of ∼60 pc, infalling and rotating at speeds of about 200 km s−1 and 300 km s−1 respectively. A centrifugal barrier, located around 4 pc from the dynamical center, implies an SMBH mass of approximately 108Mȯ, consistent with millimeter corona emission estimates. The CMO maintains a steady rotation speed of 200 km s−1 over the 100 pc scale along the minor axis. The projected speed of the CMO is about 80 km s−1, corresponding to around ∼500 km s−1, assuming an inclination angle of 80°. Such a kinematics structure of disk-driven collimated rotating molecular outflow with gas supplies from a falling rotating disk indicates that the feedback of the compact obscured nucleus is likely regulated by the momentum transfer of the molecular gas that connects to both the feeding of the nuclear starburst and supermassive black hole.

Development of a tidal flat recognition index based on multispectral images for mapping tidal flats

Tidal flats worldwide are facing the dual challenges of tidal reclamation and climatic changes, with China being particularly affected. The distinctive and vital ecosystem functions and services offered by tidal flats in coastal regions necessitate the accurate and precise mapping of this specific land cover type. Current methods for mapping tidal flats rely on a large number of sample points and intricate, time-consuming classification algorithms. However, these methods are inadequate for large-scale tidal flat mapping due to their low computational efficiency and limited general applicability. This study proposes a Tidal Flat Recognition Index (TFRI) based on multispectral images. This index is designed to enhance the distinction in water content between tidal flats and other land cover types by incorporating near-infrared (NIR) bands. Four representative tidal flat areas in China (i.e., Beibu Gulf, Guangdong Province, Zhangpu County, Fujian Province, Jiaozhou Bay, Shandong Province and Seaside new area, Tianjin City) were chosen as the study areas, and sample datasets were generated through field surveys and high-resolution images obtained from Google Earth. The separability of the TFRI for tidal flats was discussed, and TFRI exhibited better ability to distinguish tidal flats. TFRI was applied to tidal flat extraction in the four study areas using Sentinel-2 images, and the extraction results were compared with existing tidal flat maps and the results of the object-oriented method. The results demonstrated that TFRI exhibited superior overall accuracy and Kappa coefficient compared to the reference tidal flat maps. Further tests indicated that TFRI was also applicable to other remotely-sensed images, including NIR bands from Gaofen-2, Landsat, and Zhuhai-1 satellites. These results suggest that TFRI is adaptable to various remotely-sensed images and various types of tidal flat extraction. Furthermore, this index exhibits remarkable potential for effective application in tidal flat mapping.

Energy management of buildings with energy storage and solar photovoltaic: A diversity in experience approach for deep reinforcement learning agents

Deep reinforcement learning (DRL) is a suitable approach to handle uncertainty in managing the energy consumption of buildings with energy storage systems. Conventionally, DRL agents are trained by randomly selecting samples from a data set, which can result in overexposure to some data categories and under/no exposure to other data categories. Thus, the trained model may be biased towards some data groups and underperform (provide suboptimal results) for data groups to which it was less exposed. To address this issue, diversity in experience-based DRL agent training framework is proposed in this study. This approach ensures the exposure of agents to all types of data. The proposed framework is implemented in two steps. In the first step, raw data are grouped into different clusters using the K-means clustering method. The clustered data is then arranged by stacking the data of one cluster on top of another. In the second step, a selection algorithm is proposed to select data from each cluster to train the DRL agent. The frequency of selection from each cluster is in proportion to the number of data points in that cluster and therefore named the proportional selection method. To analyze the performance of the proposed approach and compare the results with the conventional random selection method, two indices are proposed in this study: the flatness index and the divergence index. The model is trained using different data sets (1-year, 3-year, and 5-year) and also with the inclusion of solar photovoltaics. The simulation results confirmed the superior performance of the proposed approach to flatten the building’s load curve by optimally operating the energy storage system.

New Insights on 30 Dor B Revealed by High-quality Multiwavelength Observations

The supernova remnant (SNR) 30 Dor B is associated with the H ii region ionized by the OB association LH99. The complex interstellar environment has made it difficult to study the physical structure of this SNR. We have used Hubble Space Telescope Hα images to identify SNR shocks and deep Chandra X-ray observations to detect faint diffuse emission. We find that 30 Dor B hosts three zones with very different X-ray surface brightnesses and nebular kinematics that are characteristic of SNRs in different interstellar environments and/or evolutionary stages. The ASKAP 888 MHz map of 30 Dor B shows counterparts to all X-ray emission features except the faint halo. The ASKAP 888 and 1420 MHz observations are used to produce a spectral index map, but its interpretation is complicated by the background thermal emission and the pulsar PSR J0537−6910's flat spectral index. The stellar population in the vicinity of 30 Dor B indicates a continuous star formation in the past 8–10 Myr. The observed very massive stars in LH99 cannot be coeval with the progenitor of 30 Dor B’s pulsar. Adopting the pulsar’s spin-down timescale, 5000 yr, as the age of the SNR, the X-ray shell would be expanding at ∼4000 km s−1 and the post-shock temperature would be 1–2 orders of magnitude higher than that indicated by the X-ray spectra. Thus, the bright central region of 30 Dor B and the X-ray shell requires two separate SN events, and the faint diffuse X-ray halo perhaps other older SN events.

Zonal Concept: Landscape Level Parameters and Application

Abstract Zonal concept is a traditional approach in land assessment. Although its principles have been known for over a hundred years, they have not yet been thoroughly evaluated using modern analytical approaches. Assessing the empirically established parameters for characterising a zonal site, establishing threshold values of significant environmental factors, along with assessing the applicability of the zonal concept, were the goals of this study. The data analysed were obtained from the robust and objective Czech National Forest Inventory database. Regression, indirect ordination, hierarchical clustering and spatial analyses of geo-information systems were used. The study revealed seven crucial environmental factors: Slope, Slope Height, Terrain Surface Texture, Negative Openness, Multi-Resolution Index of Valley Bottom Flatness, Soil Type and Soil Subtype. A graphical model of zonal/azonal sites was constructed based on calculated threshold values of the factors. This methodic approach introduces significant geomorphological information that are otherwise problematically detectable in field mapping. We suggest it is possible to use the zonal concept as a base layer for general landscape assessment. Zonal site classification can become a part of a precise land management practice, consisting of valuable empiricism of traditional landscape ecological classifications enriched by modelling in disturbance ecology and prediction of climate change effects.

Effects of Particle Shape and Packing Density on the Mechanical Performance of Recycled Aggregates for Construction Purposes

This paper employs the discrete element method (DEM) to study the mechanical properties of artificial crushed stone. Different grain shapes and gradations are considered, and three types of 3D artificial stone models are generated based on the statistical conclusions in the relevant literature and the observed data. Concurrently, the 3D models of the artificial stones are divided into three groups by their shape parameters (elongation index and flatness index). Furthermore, three types of gradation with different Cu (coefficient of uniformity) and Cc (coefficient of curvature) are also considered. Then, several 3D triaxial compression tests are conducted with the numerical methods to determine the relationship between the grain shapes and their mechanical characteristics. The test results showed that there was a positive correlation between a particles’ angularities and the maximum deviatoric stress in the triaxial compression tests when there were obvious distinctions between the particles. In addition, gradations had a conspicuous impact on the stiffness of the sample. The stress–strain curve possessed a larger slope when the coefficient of curvature was bigger. In terms of shear strength, the results in this paper align well with the traditional shear strength envelope which are convincing for the dependability of the methods used in this paper. The radial deformation capacity and volume strain of the specimen during the triaxial compression tests are also examined. It is believed that there were great differences in deformability between different samples. At the mesoscopic level, the change in coordination number is identified as the fundamental reason for the change in volume strain trend.

SRGeJ045359.9+622444: A 55 Minute Period Eclipsing AM Canum Venaticorum Star Discovered from a Joint SRG/eROSITA + ZTF Search

Abstract AM Canum Venaticorum (AM CVn) systems are ultracompact binaries where a white dwarf accretes from a helium-rich degenerate or semidegenerate donor. Some AM CVn systems will be among the loudest sources of gravitational waves for the upcoming Laser Interferometer Space Antenna; yet the formation channel of AM CVns remains uncertain. We report the study and characterization of a new eclipsing AM CVn, SRGeJ045359.9+622444 (hereafter, SRGeJ0453), discovered from a joint Spektrum-Roentgen-Gamma (SRG) Extended Roentgen Survey with an Imaging Telescope Array (eROSITA) mission and Zwicky Transient Facility program to identify cataclysmic variables (CVs). We obtained optical photometry to confirm the eclipse of SRGeJ0453 and determine the orbital period to be P orb = 55.0802 ± 0.0003 min . We constrain the binary parameters by modeling the high-speed photometry and radial-velocity curves and find M donor = 0.044 ± 0.024M ⊙ and R donor = 0.078 ± 0.012R ⊙. The X-ray spectrum is approximated by a power-law model with an unusually flat photon index of Γ ∼ 1 previously seen in magnetic CVs with SRG/eROSITA, but verifying that the magnetic nature of SRGeJ0453 requires further investigation. Optical spectroscopy suggests that the donor star of SRGeJ0453 could have initially been a He star or a He white dwarf. SRGeJ0453 is the ninth eclipsing AM CVn system published to date, and its lack of optical outbursts have made it elusive in previous surveys. The discovery of SRGeJ0453 using joint X-ray and optical surveys highlights the potential for discovering similar systems in the near future.