Texture Structure Research Articles

Effect of Different Polysaccharides on the Texture and Fibrous Structure of High-Moisture Extruded Pea Protein Isolate

Effect of Different Polysaccharides on the Texture and Fibrous Structure of High-Moisture Extruded Pea Protein Isolate

The application of bionics in optimizing ship resistance reduction

Since humans and nature have a shared destiny, harmony is the way for the two to get along. Learning from nature can lead to long-term and steady development for humans. Bionics is an emerging discipline that learns from nature modestly. The structure and products of the best organisms in species evolution work as a beacon for scientific and technological progress. In the field of ship fluid mechanics, the combination of bionics and ship and ocean engineering provides many simple and effective design schemes for the fluid performance optimization of ship materials and structures. This paper mainly summarizes and discusses the research and application status of bionics in the field of ship fluid drag reduction from four aspects, namely the imitation of biological shape, biological epidermal texture, biological function, and biological structure. Conclusions can be drawn that bionics can used to improve a ships shape, structure, surface materials, and marine coatings to optimize ship fluid performance and effectively reduce ship drag.

Super-resolution image visual quality assessment based on structure–texture features

Assessing the visual quality of super-resolution images (SRIs) is crucial for advancing algorithm development, but it remains an unsolved problem. In this paper, we present a novel reduced-reference image quality assessment (RR-IQA) method specifically suited for evaluating SRIs. Our approach leverages information from the input low-resolution (LR) image as a reference signal to extract features that are most relevant to modeling the visual quality of SRIs. We analyze the artifact characteristics of SRIs and demonstrate that features describing edge orientations, high frequency components, and textures are the most important for this task. To extract these features, we first perform structure–texture decompositions (STD) on both the SRI and its LR input, then obtain the edge orientation feature through a traditional hand-crafted approach, and deep neural networks to extract features related to high frequency components and textures. We employ a shallow multilayer perceptron (MLP) to predict an image quality score based on these quality-relevant features. To improve feature representation ability and prevent overfitting, we pretrain the feature extraction module using a large number of unlabeled samples, which accounts for over 99.8% of the total model parameters. We use precious samples with mean opinion score (MOS) labels to train a high-quality shallow MLP predictor. Our experimental results show that the proposed method outperforms classical and state-of-the-art models.

POTENTIAL OF METAL MINERAL RESOURCES IN MOUNT GOGOREA WAHLUA FORMATION BURU ISLAND

Geologically, Buru Island is composed of quite varied rocks, both in the form of sedimentary rock, igneous rock and metamorphic rock produced by subduction and magmatism and sedimentation activities. This is why Buru Island has quite a variety of potential metal and non-metallic mineral resources. The Wahlua Formation is the most widely distributed rock formation on Buru Island, but until now the petrographic, geochemical characteristics, and potential geological and mineral resources in this formation have not been studied in detail. The aims of this study were (1) to obtain data on the petrographic and geochemical characteristics of the rocks at Mount Gogorea, and (2) to determine the potential of metal minerals at the Gogorea location. The research was carried out at 4 observation points and 4 rock sampling points. Identification of petrographic and mineral characteristics was carried out megascopically and microscopically. Rock geochemical data were obtained using the AAS and ICP OES methods. The main rocks that make up the Geology of Mount Gogorea in the Wahlua formation are light-colored quartzite, granuloblastic texture, and non-foliated structure, various colored schist (reddish-brown-fawn and dark gray), lepidoblastic texture, foliated-schistose structure with mica as constituent minerals, phyllite greenish-gray, brownish to light in color, crystalloblastic texture with a smooth surface, and foliated-phyllitic structure, minerals making up mica, quartz, chlorite, and dark gneic rocks (greenish gray, dark gray, dark green), granoblastic texture and foliated-gneissic structure, with minerals in quartz, mica and hornblende rocks. The levels of precious metal Au were 16.7 ppm and 16.4 ppm, Hg was 2.49 ppm, As was 17 ppm while the highest base metal content was Mn at 925 ppm. Potential metal minerals at the Mount Gogorea location include precious metals (Au, Hg and As) and base metals such as manganese, zinc, barium, lead, copper and nickel. The presence of Au elements in rocks is associated with the levels of other elements, namely Ba, Co, Cr, Cu, Fe, La, Mn, Ni, Pb, Sr, and Zn.

Effect and mechanism of DC magnetic field combined with calcium chloride on saltiness and flavor enhancement of low salt pork gel

This study intended to investigate the synergistic effect of direct current magnetic field (DC-MF) and calcium chloride (CaCl2) on improving the saltiness and quality of pork gel and to explore the potential adjustment mechanisms involved. Pork mince was subjected to low-salt treatment of DC-MF for 3 h (T1), CaCl2 substitution (T2) or DC-MF combined with CaCl2 (T3) respectively under high and low salt controls (HC and LC). Heat-induced gels of pork mince were prepared and analyzed in terms of gel texture, saltiness perception, moisture status, salt release and protein structure. Results indicated that DC-MF combined with CaCl2 treatment could reduce the addition of sodium chloride by 5% while maintaining the saltiness and overall acceptability of mince as well as improving its texture and yield. Further studies revealed that DC-MF in concert with CaCl2 treatment could not only increase the moisture content of mince gel, but also enhance saltiness perception by causing gel matrix to release salt-bearing juices under external forces. The energy spectrum analysis also illustrated that co-treatment technique could avoid the salt loss of minced meat during cooking process. Finally, a possible potential regulatory mechanism was speculated that the synergistic action of DC-MF and CaCl2 could alter the gel texture and sodium salt availability by modifying protein conformation and inducing a competitive binding reaction site for calcium and sodium ions. In conclusion, synergistic treatment by DC-MF and CaCl2 was a potential strategy in meat salt reduction.

A CSR-based visible and infrared image fusion method in low illumination conditions for sense and avoid

AbstractMachine vision has been extensively researched in the field of unmanned aerial vehicles (UAV) recently. However, the ability of Sense and Avoid (SAA) largely limited by environmental visibility, which brings hazards to flight safety in low illumination or nighttime conditions. In order to solve this critical problem, an approach of image enhancement is proposed in this paper to improve image qualities in low illumination conditions. Considering the complementarity of visible and infrared images, a visible and infrared image fusion method based on convolutional sparse representation (CSR) is a promising solution to improve the SAA ability of UAVs. Firstly, the source image is decomposed into a texture layer and structure layer since infrared images are good at characterising structural information, and visible images have richer texture information. Both the structure and the texture layers are transformed into the sparse convolutional domain through the CSR mechanism, and then CSR coefficient mapping are fused via activity level assessment. Finally, the image is synthesised through the reconstruction results of the fusion texture and structure layers. In the experimental simulation section, a series of visible and infrared registered images including aerial targets are adopted to evaluate the proposed algorithm. Experimental results demonstrates that the proposed method increases image qualities in low illumination conditions effectively and can enhance the object details, which has better performance than traditional methods.

Image Structure-Induced Semantic Pyramid Network for Inpainting

The existing deep-learning-based image inpainting algorithms often suffer from local structure disconnections and blurring when dealing with large irregular defective images. To solve these problems, an image structure-induced semantic pyramid network for inpainting is proposed. The model consists of two parts: the edge inpainting network and the content-filling network. U-Net-based edge inpainting network restores the edge of the image defect with residual blocks. The edge inpainting map is input into the pyramid content-filling network together with the image in the prior condition. In the content-filling network, the attention transfer module (ATM) is designed to reconfigure the encoding features of each scale step by step, and the recovered feature map is linked to the decoding layer and the corresponding potential feature fusion decoding to improve the global consistency of the image and finally obtain the restored image. The quantitative analysis shows that the average L1 loss is reduced by about 1.14%, the peak signal-to-noise ratio (PSNR) is improved by about 3.51, and the structural similarity (SSIM) is improved by about 0.163 on the CelebA-HQ and Places2 datasets compared with the current mainstream algorithms. The qualitative analysis shows that this model not only generates semantically sound content as a whole but also better matches the human visual perception in terms of local structural connectivity and texture synthesis.

Spatial analysis of Texture and Soil Structure Characteristics and their effect on wheat and barley producation in Ranya District

Spatial analysis of Texture and Soil Structure Characteristics and their effect on wheat and barley producation in Ranya District

DMVC: Decomposed Motion Modeling for Learned Video Compression

Inter prediction is the critical component in hybrid coding framework to deal with the temporal redundancy. Most of the neural video coding methods typically follow the motion compensation based inter coding scheme, establishing motion vector (MV) as the central role. In this paper, we innovatively propose an efficient motion modeling approach by inherently decomposing it into two components, the intrinsic motion and the compensatory motion. The intrinsic motion originates from the implicit spatiotemporal context hidden in the historical sequence, which can be intuitively captured free of bits. On the top of it, the compensatory motion acts a role of structural refinement and texture enhancement as a form of side information. In particular, the inter prediction is performed in the feature space as a manner of progressive temporal transition, conditioned on the decomposed motion. By the motion decomposition paradigm, we innovatively answer the question of motion representation, compensation and coding in the learned video compression framework. With the temporal prediction, the remaining pixel residue is signaled to obtain the reconstruction. Extensive experimental results demonstrate that the proposed method achieves state-of-the-art coding performance on par with other end-to-end coding methods, and outperforms versatile video coding (VVC) under low-delay P (LDP) configuration in terms of MS-SSIM metric.

Towards enhanced performance of fertilizer-drawn forward osmosis process coupled with sludge thickening using a thin-film nanocomposite membrane interlayered with Mxene scaffolded alginate hydrogel

Forward osmosis (FO) offers the potential for sustainable wastewater reuse and enhance water resource sustainability and resiliency. However, low performance of FO membranes due to the high structural parameter and poor fouling resistance limits their widespread implementation. Structural parameter minimization of substrates and modulation of the surface structures of polyamide nanofilms are crucial to achieve enhanced FO performance. Herein, a novel thin-film nanocomposite (TFN) FO membrane supported with Mxene scaffolded alginate hydrogel interlayer is fabricated. It is found that the incorporation of the hydrophilic and high surface energy alginate hydrogel@Mxene interlayer sandwiched between polyamide nanofilms and microporous substrate minimizes the structural parameter by creating abundant tortuous paths leading to minimized internal concentration polarization. Meanwhile, the nanoconfinement effect induced by the interlayer enabled the formation of lumpy network of bubble wrap-like textured polyamide structures on the membranes. Subsequently, the resulting membranes exhibited enhanced water flux of up to 45.6 LMH in PRO mode using 1.0 M NaCl as the draw solution, while the lower surface roughness bestowed the membranes with the minimal fouling propensity which resulted in more than 80% of the water recovery. Additionally, the integration of the fertilizer-drawn forward osmosis process with sludge thickening successfully enabled the dilution of a 2 M KCl fertilizer solution by 2.4 times after 12 h operation, while simultaneously concentrating the sludge from the MLSS of 2000 mg L−1 to 5183 mg L−1. This work provides important insightful concepts to inspire the development of advanced TFN-FO membranes with good overall performance suitable for sustainable water reuse using osmotically driven membrane processes.

Wetting model of rough solid surfaces and the effect of aggregate properties on epoxy resin binder-aggregate interface wetting

In order to study the effect of aggregate characteristics on the wetting of epoxy resin binder-aggregate interface, based on the Wenzel model and Cassie model, this paper established a new wetting model of the irregular rough surface. The model regularizes the rough solid surface and can calculate the wetting depth and apparent contact angle between different irregular rough solids and liquids. The texture parameters of aggregates, the surface tension of test liquids and the contact angles of different liquids-aggregates were tested by laser confocal tester and optical contact angle measuring instrument. It was found that the surface texture parameters of different aggregates were quite different. The relative error between the apparent contact angle calculated by the prediction model and the contact angle measured by the test was less than 15 %, indicating that the prediction model had excellent prediction effect. Through the analysis of the wetting model, it is found that the surface tension of the test liquid, the lithology of the solid aggregate, the surface roughness and the texture structure are the important control parameters affecting the wetting. The surface morphology of rough solid can be designed by changing the resolution k and depth H of micropores, so as to control the wettability of solid aggregate surface and epoxy resin aggregate.

Fabrication of Textured Ni-Coated Carbon Tubes for a Flexible Strain Sensor: Effect of the Device Elastic Modulus on Sensor Performance.

The exploration of flexible resistive sensors with excellent performance remains a challenge. In this paper, a nickel-coated carbon tube with a textured structure was prepared as a conductive sensitive material and inserted into the poly(dimethylsiloxane) (PDMS) polymer; interestingly, the sensor performance was controlled by the elastic modulus of the matrix resin. The results show that Pd2+ may be adsorbed by the active groups on the surface of a plant fiber as a catalytic center for the reduction of Ni2+. After 300 °C annealing, the inner plant fiber would be carbonized and attached to the outside of the nickel tube; to be precise, the textured Ni-encapsulated C tube was fabricated successfully. It is worth noting that the C tube serves as a layer of support for the external Ni coating, providing sufficient mechanical strength. In addition, resistance sensors with different properties were prepared by controlling the elasticity modulus of the PDMS polymer by introducing different contents of curing agents. The limit uniaxial tensile strain was enhanced from 42 to 49% and sensitivity reduced from 0.2 to 2.0% with the elasticity modulus of the matrix resin increasing from 0.32 to 2.2 MPa. As expected, the sensor is obviously appropriate for the detection of elbow joints, human speaking, and human joints with the reduction of the elasticity modulus of the matrix resin. To be precise, the optimal elastic modulus of the sensor matrix resin would facilitate the improvement of its sensitivity to monitor different human behaviors.

Influence of Structure and Texture Feature on Retrieval of Ramie Leaf Area Index

Leaf area index (LAI), as one of the important crop field phenotypes, plays a crucial role in evaluating crop growth and yield. The remote sensing method for crop parameters monitoring makes it possible to obtain crop LAI in a large area, efficiently and non-destructively. Fusion of spectral features, structure features, and texture features obtained by remote sensing images can effectively improve LAI estimation performance, but the impact of crop structure and texture feature differences on LAI remote sensing monitoring remains unclear. In this study, ramie was classified according to the differences of canopy structure, and then the LAI inversion performance of ramie was evaluated under different datasets to determine the contribution of structure features (canopy coverage, plant height) and texture features derived from remote sensing images to LAI estimation. Finally, multi-feature remote sensing indexes were integrated to construct the optimal ramie LAI estimation model. The results showed that structure features and texture features had an effect on ramie LAI inversion performance, and there were significant differences in LAI estimation accuracy among different data sets. A single structure feature or texture feature could not significantly optimize LAI inversion performance, while multi-feature fusion could effectively improve LAI estimation accuracy (R2 = 0.776, RMSE = 0.740). This study provides a robust, practical, and low-cost method for ramie LAI estimation, and provides a reference for crop LAI inversion research based on structural features and texture features.

A reinterpreted-texture strategy for rendering point symbols based on graphics processing unit

ABSTRACT The increasing demands of presenting large numbers of points in maps have promoted the progress of rendering point symbols in GPUs. Although the drawing efficiency issue can be handled with texture mapping methods, the rendering quality problem due to the fixed resolution that affects map renders’ visual experiences remains. The method of directly drawing vector paths of a point symbol can be used to satisfy the sharper effect of point symbols. However, it requires high memory cost and affects the drawing efficiency. This paper proposes a point symbol rendering method using the idea of reinterpreted textures. The rendering data used in this method are based on vectors to achieve refined results. Vector properties of symbols are encoded and organized into the texture structure with specific layout schemes. In the rendering phase, an instanced pipeline is launched to accept the texture and decode the required attributes. The proposed method takes advantage of fast access and continuity of textures while retaining geometric transformations. These features allow all symbols to be drawn in one single draw call and rotated or scaled arbitrarily. Experiments on drawing quality and efficiency demonstrate that the proposed method achieves fast and stable performance while maintaining the rendering quality.

Migration of Framework Aluminum and Tunable Acidity in Y Zeolite by Post-treatment

Two industrial Y zeolites were treated sequentially by steaming and citric acid to investigate the Al migration and acidity of the modified Y zeolites. Even though the two parent Y zeolites displayed similar properties including crystallinity, Si/Al ratio, and textural structure, they showed different acidic and textural properties upon identical post-treatments by steaming and citric acid due to the different Al migration rules, resulting in their different activity and cracking ability in the methylcyclohexane transformation. Steaming treatment performed the prominent dealumination, leading to the increase of the framework Si/Al ratio from ca. 2.5 to ca. 5.0 and the formation of extra-framework Al species. During steaming treatment, the dealumination is related with the transformation from Al(IVb) to Al(V). The following citric acid leaching hardly removed the framework Al but removed the extra-framework Al species that existed in the form of Al(V) readily and some residual Na+ ions. The remaining framework-associated Al species are related to the generation of Lewis acid sites.

Lightweight Infrared and Visible Image Fusion via Adaptive DenseNet with Knowledge Distillation

The fusion of infrared and visible images produces a complementary image that captures both infrared radiation information and visible texture structure details using the respective sensors. However, the current deep-learning-based fusion approaches mainly tend to prioritize visual quality and statistical metrics, leading to an increased model complexity and weight parameter sizes. To address these challenges, we propose a novel dual-light fusion approach using adaptive DenseNet with knowledge distillation to learn and compress from pre-existing fusion models, which achieves the goals of model compression through the use of hyperparameters such as the width and depth of the model network. The effectiveness of our proposed approach is evaluated on a new dataset comprising three public datasets (MSRS, M3FD, and LLVIP), and both qualitative and quantitative experimental results show that the distillated adaptive DenseNet model effectively matches the original fusion models’ performance with smaller model weight parameters and shorter inference times.

An Automatic Digital Camouflage Pattern Generation Method based on Texture Structure

This article investigates the problem of scattered burst signal detection based on multiple sensors to obtain overall decisions. In the explosion detection system studied in the article, sensors independently transmit their decisions on measuring explosion information to the data fusion processing terminal, which provides overall decisions based on fusion rules. The researchers focus on the data fusion theory of the distributed parallel detection burst point data fusion system based on the Bayesian rule. This paper has obtained the data fusion rule and sensor decision criteria that make the overall system optimal, and proposed a nonlinear Gauss Seidel mathematical variable algorithm that optimizes the data fusion rule and multi-sensor decision criteria The data fusion problem when detecting burst point signals with two different and three identical types of sensors. The data fusion algorithm proposed in this article is validated and simulated through computer experiments on the detection of three types of sensors. The relevant experimental data show that the performance of a data fusion system based on Bayesian detection is significantly improved compared with the sensor acquisition of burst point information. In the experiment, the risk of Bayesian missing detection of burst point signal coefficient of the data fusion system using three sensors with the same performance is reduced by 32.7%.

Investigating build orientation-induced mechanical anisotropy in additive manufacturing 316L stainless steel

Build orientation-induced mechanical anisotropy is critical for additive manufacturing metals. To leverage the existing research on anisotropy between Z and X/Y build orientations, this work focuses on the anisotropic behavior within 2D planes. Therefore, tensile coupons of 316L stainless steel (316L-SS) were fabricated in five different build orientations (X, Y, XY45°, Z, and ZX45°) using the laser powder bed fusion process with a pulsed laser. Different characterization techniques at various length scales were employed to investigate the mechanisms of mechanical anisotropy. According to the electron back-scattered diffraction (EBSD) study, X and Y samples had a larger fraction of <001> and <110> grains, whereas XY45° coupons had a larger fraction of <111> grains along the tensile axis. In contrast, Z samples exhibited a dominant <110> texture parallel to the tensile axis. X-ray diffraction analysis revealed that all the samples had a single austenitic phase but different dislocation densities. The tensile results of all samples showed higher yield strength and comparable ductility with those in the literature. Among all the build orientations, XY45° coupons had the highest yield strength due to the large fraction of <111> grains oriented along the tensile axis and the highest dislocation density of 2.2 × 1015 m−2. In contrast, the highest ductility of Z samples was caused due to twinning-favored <110> crystallographic texture along the tensile axis. The onset and termination of different stages of strain hardening were mainly affected by the cellular sub-grain structure and crystallographic texture, as well as their interactions with dislocation generation and evolution. These findings indicated that crystallographic texture and dislocation density were the dominant contributions to the mechanical anisotropy followed by grain morphology.

Color Processing System of Art Color Based on Fractional Differential Equation

Abstract This paper mainly studies a high-precision correction method for warp knitting seamless images. The color factor conversion method is used to transform the gray level of the center point in the frequency domain. Then this paper proposes an image-denoising method based on an average way to improve image quality. The multichannel scalar channel is solved using the gradient domain method, and the fractional differential equations of two images are given. A structure tensor matrix is constructed to describe the geometric properties of images. The regional contrast of the image is calculated by using the characteristics of adjacent layers of pixels. The fusion of multiple images is based on the limitations of object functionality. This can make the texture structure of artistic color pictures more obvious. The experiment of color restoration shows that this method has a good interpretation effect. The color image processing technique has obtained an excellent restoration effect.

A Transformer-Based Cross-Window Aggregated Attentional Image Inpainting Model

To overcome the fault of convolutional networks, which can be over-smooth, blurred, or discontinuous, a novel transformer network with cross-window aggregated attention is proposed. Our network as a whole is constructed as a generative adversarial network model, and by embedding the Window Aggregation Transformer (WAT) module, we improve the information aggregation between windows without increasing the computational complexity and effectively obtain the image long-range dependencies to solve the problem that convolutional operations are limited by local feature extraction. First, the encoder extracts the multi-scale features of the image with convolution kernels of different scales; second, the feature maps of different scales are input into a WAT module to realize the aggregation between feature information and finally, these features are reconstructed by the decoder, and then, the generated image is input into the global discriminator, in which the discrimination between real and fake images is completed. It is experimentally verified that our designed Transformer window attention network is able to make the structured texture of the restored images richer and more natural when performing the restoration task of large broken or structurally complex images.