Variety Of Textures Research Articles

Spin-textured neutron beams with orbital angular momentum

We present a rigorous theoretical framework underpinning the technique of spin-echo modulated small-angle neutron scattering (SEMSANS), and show how the technique can be extended in order to generate spin-textured neutron beams with orbital angular momentum (OAM) via birefringent neutron spin-polarization devices known as magnetic Wollaston prisms. Neutron OAM beams are mathematically characterized by a ``cork-screw'' phase singularity ${e}^{i\ensuremath{\ell}\ensuremath{\phi}}$ about the propagation axis where $\ensuremath{\ell}$ is the OAM quantum number. To understand the precise relationship between the emergent OAM state and the variety of spin textures realized by various setups, we have developed a path-integral approach that in the interferometric limit makes a judicious use of magnetic Snell's law. We show that our proposed technique produces a complex two-dimensional pattern of spin-OAM entangled states which may be useful as a probe of quantum magnetic materials. We compare our path-integral approach to the well-known single-path Larmor precession model and present a pedagogical derivation of magnetic Snell's law of refraction for both massive and massless particles based on Maupertuis's action principle.

A Comparative View on the Degree of Realization of Urban Resilience based on Facts and Perceptions in Three Old, Middle and New Contexts of Qazvin City

Context and purpose: The discussion of urban resilience is considered the most important discussion in the organization of cities today. A resilient city is a city that evaluates, plans and takes action to prepare and respond to all kinds of natural and human hazards, sudden and slow, expected and unexpected. Now, this issue becomes more important when there is a variety of textures in a city, because according to the characteristics of each texture, special measures must be taken into account in terms of resilience. Also, paying attention to the opinion of the residents who are the main owners of the cities should be taken into account in making the cities resilient. The most important goal of the present research is to examine the differences in the new, middle and old contexts in Qazvin city and the existence of a relationship To determine the significance between facts (objective indicators) and perceptions (subjective indicators) in the field of urban resilience in Qazvin city. Method: The case study in this research is the city of Qazvin. This research is of applied type and its method is descriptive-analytical, and in order to examine the difference between new, middle and old contexts, Kruskal-Wallis test was used, and in order to measure the relationship between facts and perceptions, Pearson's correlation coefficient was used. Findings: The findings of this research indicate that in terms of institutional, physical, social and economic dimensions, except for some indicators, the new context is in a much better condition than other contexts, and other contexts need to be addressed in the field of urban resilience. are It was also studied that there is a significant relationship between people's opinions and objective facts in the field of resilience in Qazvin city. Conclusion: The chi-square results in the Kruskal-Wallis test showed that there is a significant difference in the field of urban resilience in the new, middle and old contexts of Qazvin city. Also, the positive and significant coefficients of the Spearman coefficient indicated that there is a significant relationship between objective and subjective indicators of urban resilience. The results of this research can be used in Qazvin Municipality and other institutions related to crisis management.

Liver segmentation using marker controlled watershed transform

<span lang="EN-US">The largest organ in the body is the liver and primarily helps in metabolism and detoxification. Liver segmentation is a crucial step in liver cancer detection in computer vision-based biomedical image analysis. Liver segmentation is a critical task and results in under-segmentation and over-segmentation due to the complex structure of abdominal computed tomography (CT) images, noise, and textural variations over the image. This paper presents liver segmentation in abdominal CT images using marker-based watershed transforms. In the pre-processing stage, a modified double stage gaussian filter (MDSGF) is used to enhance the contrast, and preserve the edge and texture information of liver CT images. Further, marker controlled watershed transform is utilized for the segmentation of liver images from the abdominal CT images. Liver segmentation using suggested MDSGF and marker-based watershed transform help to diminish the under-segmentation and over-segmentation of the liver object. The performance of the proposed system is evaluated on the LiTS dataset based on Dice score (DS), relative volume difference (RVD), volumetric overlapping error (VOE), and Jaccard index (JI). The proposed method gives (Dice score of 0.959, RVD of 0.09, VOE of 0.089, and JI of 0.921).</span>

Soil Fertility Status in Relation to Fallow Cycle in Shifting Cultivated Areas of DIMA HASAO

Aim: To understand the effect of fallow cycle on soil fertility status under shifting cultivation.
 Study Design: Latin square design (LSD).
 Place and Duration of Study: Dima Hasao District, Assam. Between September 2019- August 2021.
 Methodology: The present investigation was carried to study the effect of jhum fallow cycle on soil properties in the DIMA HASAO district of Assam. Total of 120 geo-referenced soil samples were collected from short jhum (1-3yrs.) fallow cycle, medium jhum (4-6yrs.) fallow cycle, long jhum (7-9 yrs.) fallow cycle and undisturbed soil. The collected soil samples were analyzed for parameters like pH, OC, N, P, K, S, Ca, Mg, CEC and soil texture.
 Results: The studied soil samples showed wide variations in texture varying from sandy loam to clay loam. Coarse texture sand was found to be dominant in short jhum and medium jhum while finer particles (silt and clay) were dominant in the long jhum and the undisturbed soil. The Soil pH was found lowest (mean value 4.55) in short jhum and highest (mean value 5.00) in the undisturbed soil. The OC content was minimum at initial short jhum fallow cycle and maximum was observed in the Undisturbed soil. The CEC of the study area was found highest in undisturbed soil (mean value 8.64 cmol (p+) kg-1) followed by long jhum, medium jhum and short jhum. The Avl. N content of the study area was found low to medium. Among the jhum fallow cycle Avl. N of short jhum was lowest (mean value 269.30kgha-1) followed by medium jhum, long jhum and highest in undisturbed soil (mean value 350.11 kgha-1). The Avl. P content of the study area was found low to medium, highest mean value 12.61 kg ha-1 was observed in long jhum fallow cycle and lowest in short jhum (mean value 9.33 kgha-1). The highest content of Avl. S was observed in long jhum fallow cycle and lowest in the short jhum. The highest of Ex. Ca, Mg was found in the undisturbed followed by long jhum, short jhum and lowest in the medium jhum. All the soil fertility parameters i.e., N, P, K, S, Ca and Mg exhibited significant positive correlation with pH, OC, CEC, and percent Clay.
 Conclusion: From the study it was observed that minimum of 7 to 10 years jhum fallow cycle is required in Dima Hasao are for the soil to restore its fertility and sustainable jhum cultivation.

Promoter Variation of the Key Apple Fruit Texture Related Gene MdPG1 and the Upstream Regulation Analysis

MdPG1 encoding polygalacturonase in apple (Malus × domestica) is a key gene associated with fruit firmness and texture variations among apple cultivars. However, the causative variants of MdPG1 are still not known. In this study, we identified a SNPA/C variant within an ERF-binding element located in the promoter region of MdPG1. The promoter containing the ERF-binding element with SNPA, rather than the SNPC, could be strongly bound and activated by MdCBF2, a member of the AP2/ERF transcription factor family, as determined by yeast-one-hybrid and dual-luciferase reporter assays. We also demonstrated that the presence of a novel long non-coding RNA, lncRNAPG1, in the promoter of MdPG1 was a causative variant. lncRNAPG1 was specifically expressed in fruit tissues postharvest. lncRNAPG1 could reduce promoter activity when it was fused to the promoter of MdPG1 and a tobacco gene encoding Mg-chelatase H subunit (NtCHLH) in transgenic tobacco cells but could not reduce promoter activity when it was supplied in a separate gene construct, indicating a cis-regulatory effect. Our results provide new insights into genetic regulation of MdPG1 allele expression and are also useful for the development of elite apple cultivars.

Blockchain-Based Deep CNN for Brain Tumor Prediction Using MRI Scans.

Brain tumors are nonlinear and present with variations in their size, form, and textural variation; this might make it difficult to diagnose them and perform surgical excision using magnetic resonance imaging (MRI) scans. The procedures that are currently available are conducted by radiologists, brain surgeons, and clinical specialists. Studying brain MRIs is laborious, error-prone, and time-consuming, but they nonetheless show high positional accuracy in the case of brain cells. The proposed convolutional neural network model, an existing blockchain-based method, is used to secure the network for the precise prediction of brain tumors, such as pituitary tumors, meningioma tumors, and glioma tumors. MRI scans of the brain are first put into pre-trained deep models after being normalized in a fixed dimension. These structures are altered at each layer, increasing their security and safety. To guard against potential layer deletions, modification attacks, and tempering, each layer has an additional block that stores specific information. Multiple blocks are used to store information, including blocks related to each layer, cloud ledger blocks kept in cloud storage, and ledger blocks connected to the network. Later, the features are retrieved, merged, and optimized utilizing a Genetic Algorithm and have attained a competitive performance compared with the state-of-the-art (SOTA) methods using different ML classifiers.

Field-induced magnetic phases in a qubit Penrose quasicrystal.

Unveiling the fundamental dynamics of naturally or artificially formed magnetic quasicrystals in the presence of an external magnetic field remains a difficult problem that may have implications for the design of information processing devices. By embedding a qubit magnetic Penrose quasicrystal into a quantum annealer, we were able to reproduce the formation of magnetic phases driven by specific physical parameter selections, allowing us to distinguish a wide range of frustrated magnetic configurations at the single-spin scale. In our experiments, we observe some spins dynamically activate, while others remain static, all within an average magnetization space defined by competing structural and magnetic degrees of freedom. Static spin structure factors reveal ferromagnetic and ferrimagnetic modulations that are compatible with a variety of spin textures. This research demonstrates that introducing structural aperiodicity in magnetic devices that exploit spin degeneracy in a single, richly intraconnected finite object can enable the engineering of quantum states in both the effective low-temperature and thermally excited regimes.

Hole doping induced ferromagnetism and Dzyaloshinskii–Moriya interaction in the two-dimensional group-IVA oxides

Based on the first-principles calculations, we examine the effect of hole doping on the ferromagnetism and Dzyaloshinskii–Moriya interaction (DMI) for PbSnO2, SnO2 and GeO2 monolayers. The nonmagnetic to ferromagnetic transition and the DMI can emerge simultaneously in the three two-dimensional IVA oxides. By increasing the hole doping concentration, we find the ferromagnetism can be strengthened for the three oxides. Due to different inversion symmetry breaking, isotropic DMI is found in PbSnO2, whereas anisotropic DMI presents in SnO2 and GeO2. More appealingly, for PbSnO2 with different hole concentrations, DMI can induce a variety of topological spin textures. Interestingly, a peculiar feature of synchronously switch of magnetic easy axis and DMI chirality upon hole doping is found in PbSnO2. Hence, Néel-type skyrmions can be tailored via changing hole density in PbSnO2. Furthermore, we demonstrate that both SnO2 and GeO2.with different hole concentrations can host antiskyrmions or antibimerons (in-plane antiskyrmions). Our findings demonstrate the presence and tunability of topological chiral structures in p-type magnets and open up new possibility for spintronics.

Effect of Al Concentration on Basal Texture Formation Behavior of AZ-Series Magnesium Alloys during High-Temperature Deformation

Magnesium and its alloys have been restricted in their industrial applications due to problems related to their formability. To overcome this issue, controlling the crystallographic texture is important, and the texture formation mechanism should be investigated in relation to factors including deformation conditions and solute atoms. In particular, the effects of solute atoms on the texture formation behavior should be further analyzed because they can considerably affect the deformation behavior. Thus, in this study, to clarify the effect of aluminum concentration on the texture formation behavior and microstructure, high-temperature uniaxial compression tests were conducted on three types of AZ-series magnesium alloys (AZ31, AZ61, and AZ91). Compression was conducted at 673 K and 723 K, with strain rates of 0.05 s-1 and 0.005 s-1, up to a true strain of -1.0. Cylindrical specimens were prepared from a rolled plate that had a (0001) basal texture and was compressed parallel to the c-axis of the grains. Consequently, work softening and fiber texture formation were observed in all the specimens. During the deformation, the development of grain boundaries, which is a typical characteristic of continuous dynamic recrystallization (CDRX), was observed, and the (0001) texture was highly developed with increasing Al content. Although each alloy was associated with the same deformation conditions and mechanisms, the AZ31 alloy exhibited a non-basal texture component. The stacking fault energy contributed to the generation of slip systems and gliding, and it was seen as the main reason for texture variation.

Aboveground biomass estimation in dry forest in northeastern Brazil using metrics extracted from sentinel-2 data: Comparing parametric and non-parametric estimation methods

Caatinga is a dry forest, which occupies almost one million square kilometers in the Brazilian territory, representing more than 11% of the territory. However because of the extensive areas to be inventoried and the difficulties in collecting data from this natural vegetation using traditional techniques, the operation becomes very costly, often resulting in low sampling intensity owing to cost and time constraints. Therefore, remote sensing tools have become very useful for minimizing the difficulties caused by traditional methods. Given this context, our study aims to evaluate the performance of spectral and texture variables extracted from an MSI/Sentinel-2 image in the estimation and mapping of AGB in a fragment (natural vegetation area) of Caatinga vegetation in northeastern Brazil. The original reflectance values, the ratio between spectral bands, vegetation indices, texture metrics (extracted from the 3 × 3, 5 × 5, 7 × 7, and 9 × 9-pixel windows), and fraction images (proportion of the vegetation, soil, and shadow/water components) were associated with AGB. These variables were derived from a forest inventory with systematic sampling composed of 40 plots of 900 m2 each. We evaluated the estimates using a parametric model of multiple linear regression (MLR) and a non-parametric model of an artificial neural network (ANN). The texture metrics (angular second moment, correlation, entropy, and contrast), extracted in a 7 × 7-pixel window, were the best predictors. The parametric model was the most accurate for AGB estimation. The MLR and ANN techniques showed bias validation of 2.58% and 2.79%, and relative root mean square errors of 12.02% and 16.60%, respectively. AGB estimates ranged from 1.49 Mg ha−1 to approximately 73 Mg ha−1 on the MLR and ANN maps. The textural metrics showed the potential to accurately predict AGB, providing valuable information for the use of open access data. Such information is very useful in the management of natural resources in large areas, such as the Caatinga vegetation. The use of optical data for AGB estimation in the region is relatively unexplored and this study contributes to filling that gap or better understand their application for deriving AGB in a dry forest in the semiarid region of Sergipe.

Ecohydrogeochemistry of the Slikken van Flakkee, a former tidal wetland in the Netherlands

The Slikken van Flakkee, a former tidal wetland in the Grevelingen, the Netherlands, has been changing under freshening conditions since 1971; in that year, the salty Grevelingen estuary was closed from the North Sea as part of the Dutch Delta Works. Our research aimed to analyse vegetation development over 1972–2016 and relates succession to quantified ecohydrogeochemical processes in the Slikken van Flakkee. We analysed succession and physico-chemical conditions at 63 monitoring locations. Based on extensive fieldwork in 2016 and 2017, we quantified the identified geochemical processes via inverse modelling and applied Canonical Correspondence Analysis (CCA) to search for interrelations with vegetation. Nine per cent of the 229 observed plant species are rare and threatened or vulnerable, according to the Dutch Red List. Succession series showed a development from saline pioneer communities towards grassland and shrub vegetation dependent on management measures (grazing and mowing versus spontaneous development), desalinisation and geochemical processes, of which dissolution of carbonates appeared to be the dominant one. During desalinisation, the water-type shifted from NaCl towards CaHCO3. The CCA confirmed the impact of geochemical and texture variables, elevation and management on vegetation. Grazing and mowing led to species-rich grasslands and forb vegetation harbouring twice as many species than the spontaneously developed area after 50 years, demonstrating the importance of active nature management. We argue that hydrological-geochemical modelling is necessary to support policy-making before deciding on the partial re-establishment of the tide in Lake Grevelingen.

A Statistical Approach to Identify Appropriate Sampling Scheme Capable of Geographical Identification Analysis of the Protected Origin Pulse Crops in Greece

In this study, we aimed to develop a sampling method that could be used in geographical discrimination studies of Protected Geographical Indication (PGI) dry beans (Phaseolus vulgaris L.) by considering the geoclimatic variability within the cultivation zone of the analyzed product. The Regional Unit of Kastoria in Greece, a major area of protected designation origin of pulse crops, was selected for detailed investigation. Meteorological data were collected from five weather stations in different subregions of Kastoria (Argos Orestiko, Kalochori, Lakkomata, Lithia, and Polykarpi), over a period of six years (2015 to 2020), along with data of soil texture. The collected data were analyzed in order to determine statistically significant differences among the subregions with regard to the aforementioned parameters. A seasonality pattern was observed for all subregions concerning the microclimate, which splits the data into two clusters. Moreover, a significant variation of the soil textures was revealed, frequently affecting farmers’ decision regarding agronomic practices, leading to the unique stable-isotope ratios and multi-elemental composition. This study guides the dry bean sample collection and will enable the designation of the boundaries of protected origin regions and enable future sampling schemes for stable-isotope and multi-elemental analysis.

A multi-resolution unet algorithm based on data augmentation and multi-center training for polyp automatic segmentation

In clinical practice, segmenting polyps from colonoscopy images plays an important role in the diagnosis and treatment of colorectal cancer since it provides valuable information. However, accurate polyp segmentation is full of changes due to the following reasons: (1) the small training datasets with a limited number of samples and the lack of data variability; (2) the same type of polyps with a variation in texture, size, and color; (3) the weak boundary between a polyp and its surrounding mucosa. To address these challenges, we propose a novel robust deep neural network based on data augmentation, called Robust Multi-center Multi-resolution Unet (RMMSUNet), for the polyp segmentation task. Data augmentation and Multi-center training are both utilized to increase the amount and diversity of training dataset. The new multi-resolution blocks make up for the lack of fine-grained information in U-Net, and ensures the generation of more accurate pixel-level segmentation prediction graphs. Region-based refinement is added as the post-processing for the network output, to correct some wrongly predicted pixels and further refine the segmentation results. Quantitative and qualitative evaluations on the challenging polyp dataset show that our RMMSUNet improves the segmentation accuracy significantly, when comparing to other SOTA algorithms.

Towards the discovery of goat meat quality biomarkers using label-free proteomics

This study aimed to identify for the first time protein biomarkers of meat quality traits from Longissimus thoracis (LT) muscle of goats (Capra hircus). Male goats of similar age and weight reared under extensive rearing conditions were used to relate the LT muscle proteome with multiple meat quality traits. The early post-mortem muscle proteome analyzed using label-free proteomics was compared among three texture clusters built using hierarchical clustering analysis. Twenty-five proteins were differentially abundant and their mining using bioinformatics revealed three major biological pathways to be involved: 10 muscle structure proteins (MYL1, MYL4, MYLPF, MYL6B, MYH1, MYH2, ACTA1, ACTBL2, FHL1 and MYOZ1); 6 energy metabolism proteins (ALDOA, PGAM2, ATP5F1A, GAPDH, PGM1 and ATP5IF1), and two heat shock proteins: HSPB1 (small) and HSPA8 (large). Seven other miscellaneous proteins belonging to pathways such as regulation, proteolysis, apoptosis, transport and binding, tRNA processing or calmodulin-binding were further identified to play a role in the variability of goat meat quality. The differentially abundant proteins were correlated with the goat meat quality traits in addition to multivariate regression models built to propose the first regression equations of each quality trait. This study is the first to highlight in a multi-trait quality comparison the early post-mortem changes in the goat LT muscle proteome. It also evidenced the mechanisms underpinning the development of several quality traits of interest in goat meat production along the major biochemical pathways at interplay. SignificanceThe discovery of protein biomarkers in the field of meat research is an emerging topic. In the case of goat meat quality, very few studies using proteomics have been conducted with the aim of proposing biomarkers. Therefore, this study is the first to quest for biomarkers of goat meat quality using label-free shotgun proteomics with a focus on multiple quality traits. We identified the molecular signatures underlying goat meat texture variation, which were found to belong to muscle structure and related proteins, energy metabolism and heat shock proteins along with other proteins involved in regulation, proteolysis, apoptosis, transport and binding, tRNA processing or calmodulin-binding. We further evaluated the potential of the candidate biomarkers to explain meat quality using the differentially abundant proteins by means of correlation and regression analyses. The results allowed the explanation of the variation in multiple traits such as pH, color, water-holding capacity, drip and cook losses traits and texture.

Textural landscapes of VOC-sensitive chiral liquid crystal-based materials

Liquid crystal-based materials, in which liquid crystal molecules are confined and ordered in compartments, are dynamic materials yielding a variety of optical textures that can be tuned as a response to physical and chemical stimuli. While nematic and smectic-based gel materials have been reported as dynamic optical sensors to report volatile organic compounds (VOCs), chiral systems are less explored despite having the potential to yield extremely rich optical landscapes. Here, we report for the first time the confinement of chiral liquid crystal formulations by an interface formed by ionic liquid molecules. The resultant self-assembled ionic liquid/liquid crystal droplets are simultaneously immobilized on a gelatin matrix. The droplets feature a rich variety of unique topological states. We explored, by means of polarizing optical microscopy, the various droplet optical textures and categorized them with regard to their relative chirality parameter. We further investigated their optical response in the presence of gas analytes and discussed their potential utilization as dynamic liquid crystal-based optical VOC sensors. The newly generated soft materials with semi-selective VOC sensing capabilities can be further utilized in arrays of liquid crystal-based gas sensors for the analysis of complex gas samples using artificial olfaction approaches.

Unraveling Textural and Chemical Features in Volcanic Rocks Through Advanced Image Processing: A Case Study From the 2019 Paroxysmal Eruptions of Stromboli

AbstractThe Quantitative X‐Ray Map Analyzer software, a new tool for image processing, has been tested on intertwined pumices and scoriae emitted during the two paroxysmal eruptions of Stromboli of 2019, whose textural and compositional heterogeneities reflect the coexistence of low porphyritic shoshonitic‐basalts and high porphyritic shoshonites. The procedure applied was designed to quantitatively document the complex variations in texture and composition of these products, allowing substantial time reduction of analytical and data processing. The procedure utilizes Principal Components Analysis and supervised Maximum Likelihood Classification for multivariate statistical data handling of an array of X‐ray elemental maps acquired at the millimeter scale in thin‐sections. This technique permits the production of high‐contrast colored images, which allow the classification of rock constituents, extrapolating the associated modal abundances and imaging chemical variations within the glass. Results highlight the close interconnection at the microscale of two types of magma in the erupted products, manifested in each processed image by the presence of contiguous areas preserving textural bulk properties typical of the pumice or scoria. The proportion of the two magmas feeding the eruptions is not simply represented by the proportions of scoria and pumice in individual clasts, as both scoria and pumice contain glass with the composition of shoshonitic‐basalts and shoshonites. This method also allows the recognition of important discordances between the textural and chemical features of the two fractions involved, as well as discernment of the compositions of the two interacting magmas at the microscopic scale, even in those micro‐domains showing evidence of intense interaction processes.

Decoupling build orientation-induced geometric and texture effects on the mechanical response of additively manufactured IN625 thin-walled elements

When additively manufactured (AM) metallic components have highly anisotropic microstructures, the relationship between microstructure and mechanical properties becomes more complicated with the introduction of additional design parameters, such as build orientation and geometric variations. This study serves to decouple the contribution of build orientation-induced texture and geometric effects on the mechanical response of thin-walled Inconel 625 (IN625) T-elements fabricated by laser-based powder bed fusion and subjected to a combination of heat treatments. T-elements were printed at 40° and 90° inclinations from the build plate, which led to variation in crystallographic texture as well as a 10% ligament width difference. T-elements from both build orientations underwent a standard stress-relief heat treatment of 870 °C for 1 h, and subsets of these were homogenized at 1150 °C for 90 min. Milli-scale tests indicated that the 40° stress-relieved T-elements were 30% less stiff than their 90° counterparts due to both geometric and texture differences. Effective elastic tensor estimations from EBSD maps allowed the determination of the relative contributions of texture and geometry. Combined results for stress-relieved and homogenized samples indicate that the (+)10% change in ligament width led to a (+)38% change in the elastic response, while in-plane texture differences affected the elastic response by (−)8%. These findings highlight the importance of build orientation-induced geometric variation for the performance of printed-AM thin-walled structures in complex loading conditions.

Simple and robust depth-wise cascaded network for polyp segmentation

The segmentation of the polyp region in colonoscopy images is considered difficult due to size, texture, and color variation. To segment polyps successfully, models based on convolutional neural networks (CNN), transformers, and their combinations have been developed. However, these methods are limited in that they can only model the local appearance of polyps or lack multi-level feature representation for spatial dependency in the decoding process. In this paper, we propose a simple, efficient yet powerful polyp segmentation framework that unifies the network with a multiscale cascaded path. The proposed MMS-Net utilizes multiscale and multipath convolutional operations in conjunction with multiple deep feature aggregation. The overall dense empowered features are sufficient for pixel-by-pixel detection of the polyp region. Extensive experiments on two popular benchmark datasets for polyp segmentation (Kvasir and CVC-Clinic DB) and two datasets of other medical applications (DRIVE and MC) are presented. The results show that our MMS-Net performs comparably to or better than other state-of-the-art methods despite having two or even three orders of magnitude fewer trainable parameters.

Mechanical Anisotropy of Selective Laser Melted Ti-6Al-4V Using a Reduced-order Crystal Plasticity Finite Element Model

In this study, a reduced-order crystal plasticity finite element (CPFE) model was developed to study the effects of the microstructural morphology and crystallographic texture on the mechanical anisotropy of selective laser melted (SLMed) Ti-6Al-4V. First, both hierarchical and equiaxed microstructures in columnar prior grains were modeled to examine the influence of the microstructural morphology on mechanical anisotropy. Second, the effects of crystallographic anisotropy and textural variability on mechanical anisotropy were investigated at the granular and representative volume element (RVE) scales, respectively. The results show that hierarchical and equiaxed CPFE models with the same crystallographic texture exhibit the same mechanical anisotropy. At the granular scale, the significance of crystallographic anisotropy varies with different crystal orientations. This indicates that the present SLMed Ti-6Al-4V sample with weak mechanical anisotropy resulted from the synthetic effect of crystallographic anisotropies at the granular scale. Therefore, combinations of various crystallographic textures were applied to the reduced-order CPFE model to design SLMed Ti-6Al-4V with different mechanical anisotropies. Thus, the crystallographic texture is considered the main controlling variable for the mechanical anisotropy of SLMed Ti-6Al-4V in this study.

Impact of crop types and irrigation on soil moisture downscaling in water-stressed cropland regions

Soil moisture (SM) is one of the determining variables of natural ecosystems and human managed regions. The downscaling of coarse spatial-resolution SM data is an important way to obtain high-spatial-resolution data and conduct precise agricultural management. However, the spatial heterogeneity of agricultural management makes it challenging to improve the accuracy of SM data downscaling in croplands. Previously, natural index was considered, such as meteorology, crop growth, topography and soil texture variables, but less so have human agricultural management index. We integrated important agricultural management index (crop type and irrigation) into random forest (RF) model for a downscaling study involving Global Land Data Assimilation System (GLDAS) SM products (from 0.25° to 1 km). The integration of crop type and irrigation improved the downscaling accuracy (R-values of 0.724 and 0.749, respectively) and increased the time series consistency (the Euclidean distance (ED) were reduced by 51% and 20%, respectively) for different soil layers (0–10 cm and 10–40 cm). The best correlations were found for winter wheat and summer maize (both had R-values >0.780). The relative importance of the crop type and irrigation in SM downscaling reached 15% ∼ 18%, behind only to the effects of topography (35%) and meteorology (19% ∼ 22%). This study emphasizes the important impact of human activities on soil moisture in croplands.