While soil erosion drives land degradation, the impact of erosion on soil microbial communities and multiple soil functions remains unclear. This hinders our ability to assess the true impact of ...erosion on soil ecosystem services and our ability to restore eroded environments. Here we examined the effect of erosion on microbial communities at two sites with contrasting soil texture and climates. Eroded plots had lower microbial network complexity, fewer microbial taxa, and fewer associations among microbial taxa, relative to non-eroded plots. Soil erosion also shifted microbial community composition, with decreased relative abundances of dominant phyla such as Proteobacteria, Bacteroidetes, and Gemmatimonadetes. In contrast, erosion led to an increase in the relative abundances of some bacterial families involved in N cycling, such as Acetobacteraceae and Beijerinckiaceae. Changes in microbiota characteristics were strongly related with erosion-induced changes in soil multifunctionality. Together, these results demonstrate that soil erosion has a significant negative impact on soil microbial diversity and functionality.
The aim of this research is to characterize the unique microstructural features of Al-matrix nanocomposites reinforced by graphene nano-platelets (GNPs), fabricated by multi-pass friction-stir ...processing (FSP). During this process, secondary phase GNPs were dispersed within the stir zone (SZ) of an AA5052 alloy matrix, with a homogenous distribution achieved after five cumulative passes. The microstructural characteristics and crystallographic textures of different regions in the FSPed nanocomposite, i.e., base metal (BM), heat affected zone (HAZ), thermo-mechanical affected zone (TMAZ), and SZ, were evaluated using electron back scattering diffraction (EBSD) and transmission electron microscopy (TEM) analyses. The annealed BM consisted of a nearly random crystal orientation distribution with an average grain size of 10.7μm. The SZ exhibited equiaxed recrystallized grains with a mean size of 2μm and a high fraction of high-angle grain boundaries (HAGBs) caused by a discontinuous dynamic recrystallization (DDRX) enhanced by pinning of grain boundaries by GNPs. The sub-grains and grain structure modification within the HAZ and TMAZ regions are governed by dislocation annihilation and reorganization in the grain interiors/within grains which convert low-angle to high-angle grain boundaries via dynamic recovery (DRV). The FSP process and incorporation of GNPs produced a pre-dominantly {100} cube texture component in the SZ induced by the stirring action of the rotating tool and hindering effect of nano-platelets. Although, a very strong {112} simple shear texture was found in the HAZ and TMAZ regions governed by additional heating and deformation imposed by the tool shoulder. These grain structure and texture features lead to a hardness and tensile strength increases of about 55% and 220%, respectively.
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•A new Al-matrix nanocomposite was prepared by friction stir processing.•Improved hardness and strength were attained by incorporation of graphene nano-platelets.•Microstructural changes, restoration mechanisms and textural developments were studied.•The correlation between the microstructural features and textural components was established.
In the present study, equiatomic CoCrFeMnNi high entropy alloy (HEA) was subjected to thickness reductions of 20, 40, and 60% during cold rolling in order to thoroughly investigate the evolutions of ...both the microstructure and the deformation texture. Important aspects of deformed microstructures such as the activation of multiple twin variants and the formation of shear bands in the matrix were captured using electron backscatter diffraction (EBSD) and electron channeling contrast imaging (ECCI) techniques. Twin trace analysis (TTA) was performed in conjunction with resolved shear stress (RSS) analysis for the identification of active twin variants. The RSS ratio, which is a ratio of the maximum RSS values for corresponding twin and slip systems, was used to reveal the orientation dependence of deformation twinning. Visco-plastic self-consistent (VPSC) simulations were carried out to predict the evolution of the crystallographic texture, the transition routes of ideal orientations subjected to multiple deformation twinning, and the role that deformation modes play in the rotation of orientation. Experimental and simulation results substantiated the key finding of the deformation twinning of a Brass orientation, which established new perspectives concerning the evolution of microstructure and texture. One twin variant of the Copper orientation was moved to a Goss orientation by dislocation slip while the other two variants were rotated towards Brass and S orientations. Meanwhile, twin variants of the S and Brass orientations primarily transitioned to a Brass orientation. The Goss orientation showed great resistance to the twinning mode. Furthermore, dislocation slip and the formation of shear bands contributed to the evolution of a strong texture while deformation twinning had the opposite effect.
•The evolutions of both the microstructure and the deformation texture in HEA cold rolled up to 60% reduction in thickness were investigated.•RSS analysis was used with experimentally observed twin traces for the identification of active twin variants.•A preliminary analysis using the RSS ratio (max(RSSTwin)/max(RSSSlip)) predicted both the favorable (Copper and S) and unfavorable orientations (Brass and Goss) for twinning.•Twinning of the Brass orientation was found to delay the shifting of Copper-type texture to Brass-type texture leading to a stronger Goss component at 60% rolling reduction.•VPSC simulations predicted the twinning of the Brass orientation and role of multiple twin variants in the texture transitions.
•We propose a learnable residual pooling layer comprising of a residual encoding module and an aggregation module that retains spatial information and aggregates them to a feature with a lower ...dimension.•We propose an end-to-end learning framework that integrates the residual pooling layer into any pre-trained CNN model for efficient feature transfer for texture recognition.•We compare the performance of the proposed pooling layer with other residual encoding schemes to illustrate state-of-the-art performance on benchmark texture datasets, an industry dataset and a scene recognition dataset.
Current deep learning-based texture recognition methods extract spatial orderless features from pre-trained deep learning models that are trained on large-scale image datasets. These methods either produce high dimensional features or have multiple steps like dictionary learning, feature encoding and dimension reduction. In this paper, we propose a novel end-to-end learning framework that not only overcomes these limitations, but also demonstrates faster learning. The proposed framework incorporates a residual pooling layer consisting of a residual encoding module and an aggregation module. The residual encoder preserves the spatial information for improved feature learning and the aggregation module generates orderless feature for classification through a simple averaging. The feature has the lowest dimension among previous deep texture recognition approaches, yet it achieves state-of-the-art performance on benchmark texture recognition datasets such as FMD, DTD, 4D Light and one industry dataset used for metal surface anomaly detection. Additionally, the proposed method obtains comparable results on the MIT-Indoor scene recognition dataset. Our codes are available at https://github.com/maoshangbo/DRP-Texture-Recognition.
•We proposed a fruit-classification system that can recognize 18 types of fruits.•We used a hybrid feature set with color, texture, and shape information.•We used FNN as the classifier that is ...trained by FSCABC algorithm.•FSCABC–FNN obtained better classification accuracy than existing algorithms.
Fruit classification is a difficult challenge due to the numerous types of fruits. In order to recognize fruits more accurately, we proposed a hybrid classification method based on fitness-scaled chaotic artificial bee colony (FSCABC) algorithm and feedforward neural network (FNN). First, fruits images were acquired by a digital camera, and then the background of each image were removed by split-and-merge algorithm. We used a square window to capture the fruits, and download the square images to 256×256. Second, the color histogram, texture and shape features of each fruit image were extracted to compose a feature space. Third, principal component analysis was used to reduce the dimensions of the feature space. Finally, the reduced features were sent to the FNN, the weights/biases of which were trained by the FSCABC algorithm. We also used a stratified K-fold cross validation technique to enhance the generation ability of FNN. The experimental results of the 1653 color fruit images from the 18 categories demonstrated that the FSCABC–FNN achieved a classification accuracy of 89.1%. The classification accuracy was higher than Genetic Algorithm–FNN (GA–FNN) with 84.8%, Particle Swarm Optimization–FNN (PSO–FNN) with 87.9%, ABC–FNN with 85.4%, and kernel support vector machine with 88.2%. Therefore, the FSCABC–FNN was seen to be effective in classifying fruits.
This paper presents a novel haptic texture authoring algorithm. The main goal of this algorithm is to synthesize new virtual textures by manipulating the affective properties of already existing ...real-life textures. To this end, two different spaces are established: two-dimensional (2-D) "affective space" built from a series of psychophysical experiments where real textures are arranged according to affective properties (hard-soft, rough-smooth) and 2-D "haptic model space" where real textures are placed based on features from tool-surface contact acceleration patterns (movement-velocity, normal-force). Another space, called "authoring space" is formed to merge the two spaces; correlating changes in affective properties of real-life textures to changes in actual haptic signals in haptic space. The authoring space is constructed such that features of the haptic model space that were highly correlated with affective space become axes of the space. As a result, new texture signals corresponding to any point in authoring space can be synthesized based on weighted interpolation of three nearest real surfaces in perceptually correct manner. The whole procedure including the selection of nearest surfaces, finding weights, and weighted interpolation of multiple texture signals are evaluated through a psychophysical experiment, demonstrating the competence of the approach. The results of evaluation experiment show an average normalized realism score of 94<inline-formula><tex-math notation="LaTeX">\%</tex-math></inline-formula> for all authored textures.
The demand for clean labels has increased the importance of natural texture modifying ingredients. Proteins are unique compounds that can impart unique textural and structural changes in food. ...However, lack of solubility and extensive aggregability of proteins have increased the demand for enzymatically hydrolyzed proteins, to impart functional and structural modifications to food products. The review elaborates the recent application of various proteins, protein hydrolysates, and their role in texture modification. The impact of protein hydrolysates interaction with other food macromolecules, the effect of pretreatments, and dependence of various protein functionalities on textural and structural modification of food products with controlled enzymatic hydrolysis are explained in detail. Many researchers have acknowledged the positive effect of enzymatically hydrolyzed proteins on texture modification over natural protein. With enzymatic hydrolysis, various textural properties including foaming, gelling, emulsifying, water holding capacity have been effectively improved. It is evident that each protein is unique and imparts exceptional structural changes to different food products. Thus, selection of protein requires a fundamental understanding of its structure-substrate property relation. For wider applicability in the industrial sector, more studies on interactions at the molecular level, dosage, functionality changes, and sensorial attributes of protein hydrolysates in food systems are required.
Deep generative approaches have recently made considerable progress in image inpainting by introducing structure priors. Due to the lack of proper interaction with image texture during structure ...reconstruction, however, current solutions are incompetent in handling the cases with large corruptions, and they generally suffer from distorted results. In this paper, we propose a novel two-stream network for image inpainting, which models the structure-constrained texture synthesis and texture-guided structure reconstruction in a coupled manner so that they better leverage each other for more plausible generation. Furthermore, to enhance the global consistency, a Bi-directional Gated Feature Fusion (Bi-GFF) module is designed to exchange and combine the structure and texture information and a Contextual Feature Aggregation (CFA) module is developed to refine the generated contents by region affinity learning and multi-scale feature aggregation. Qualitative and quantitative experiments on the CelebA, Paris StreetView and Places2 datasets demonstrate the superiority of the proposed method. Our code is available at https://github.com/Xiefan-Guo/CTSDG.
The separation of coal and gangue is an important aspect of coal production, which can improve coal quality, save energy, reduce consumption, and enable the rational use of resources. At present, ...manual selection remains widely implemented, but the working environment is harsh, labour intensity is high, and efficiency is low. With the continual developments in artificial intelligence and image technology, in this paper, a new method is proposed to separate gangue from coal based on image processing, the visual differences between the coal and gangue are analysed, and the supplementary texture is extracted based on morphology. Pre-treatments of coal and gangue images, including filtering, sharpening, and segmentation, are also introduced. Owing to the differences in physicochemical properties, coal and gangue exhibit different visual traces in the process of mining and transportation, but the traces appear to be irregular. Thus, the morphological method is applied to seek the region of interest where the coal or gangue body is exposed, and then the supplementary texture is extracted to establish the coal and gangue classifier. The effect of this classifier is evaluated and compared to the traditional texture classifier, and it is found that the recognition accuracy is significantly improved without special treatment (wash or blow).
Additive manufacturing offers a unique way of anisotropic microstructure control with a high degree of design freedom. This study demonstrates that application of suitable process parameters and ...laser sources in selective laser melting may favour either one sharp single component texture, more uniformly distributed crystal orientation, or a combination of the above in a preferred gradient, which influence the mechanical properties. It is shown that transitions in microstructure, texture, and properties in fabricated Inconel 718 functionally graded components can be obtained at relatively small or large length scales, depending upon the functional gradient desired in a particular application. Results obtained by electron backscatter diffraction showed distinct regions of coarse elongated grains with a strong (001) orientation uniformly embedded in randomly distributed fine grained matrix. Mechanical tests in the form of hardness, tensile and in-situ digital image correlation tests showed steep transitions in the developed Inconel gradients. The observed mechanical properties were found to be primarily dependent on the grain size and texture and are superior to the cast samples for both laser sources. The developed process strategy can be further applied to design functional gradients with selected tailored properties and to account for directional anisotropy of solidified components.
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•The functionally graded Inconel 718 was produced with different regions of fine and coarse grained microstructure.•Areas processed with high power energy showed a strongly textured microstructure and grains elongated in (001) direction.•There is a sharp transition in mechanical properties and microstructure for processed Inconel gradients.•The developed processing strategy shows the feasibility of creating materials with user-defined functional performance.
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