Texture Structure Research Articles

Ultralow Lattice Thermal Conductivity and High ZT Beyond 1 Realized in Bi2S3 Nanocomposites via Bottom-Up Structure Design.

Bismuth sulfides (Bi2S3), an n-type thermoelectric material, in expensive, and environmentally friendly. However, it has poor electrical conductivity due to its low electron concentration, and its thermoelectric properties need improvement. By adjusting the properties of microstructural units and then designing and preparing bulk materials, the transport properties are modulated to optimize thermoelectric properties. The textured structure and metal Bi dispersed in grain boundaries improved the carrier mobility, the Cl dopingimproved carrier concentration. The textured structure and metalBi dispersed in grain boundaries improved the carrier mobility, the Cl dopingimproved carrier concentration. The Bi2S3-Cl samples reduce using N2H4·H2O for 5 min at 673 K have a peak power factor (PF) reaching 676.6 µWm-1K-2, more than 6 times that of pristine Bi2S3. Furthermore, the point defects, dislocations, and the micropores caused by a part of Bivolatilization contribute to the strong phonon scattering, resulting in a low lattice thermal conductivity of 0.28 Wm-1K-1 at 623K. Due to the synergistically optimized electrical and thermal transport properties, the values of the maximum thermoelectric figure of merit (ZTmax) and the average thermoelectric figure of merit (ZTave) for the Bi2S3-Cl samples reduce using N2H4·H2O for 5 min are 1.01 at ≈673 K and 0.63 (323-673 K), respectively. This study demonstrates that bottom-up structure design can effectively strategize the improvement of thermoelectric performance.

Effects of exogenous selenium application on quality characteristics, selenium speciation, and in vitro bioaccessibility of rice pancakes

Selenium is an essential trace element for human health. To date, a hotspot of functional foods is strengthening the content of organic Se in food using biological Se enrichment. Herein, Se-enriched rice pancakes were produced by directly adding different sodium selenite concentrations into the fermentation process. The effects of sodium selenite addition on the texture properties, structure, and Se species of rice pancakes were investigated. Meanwhile, the bioaccessibility of Se and the changes of Se species in Se-enriched rice pancakes were determined by digestion experiments in vitro. The results showed significant differences in hardness, adhesiveness, chewiness, porosity, and flavor substances of Se-enriched rice pancakes after adding sodium selenite (p < 0.05). In Se-enriched rice pancakes, selenocystine (SeCys2) and methylselenocysteine (MeSeCys) are the main Se species. When sodium selenite was added at 3.3 μg/ml, the maximum values of SeCys2 and MeSeCys were 328.35 ± 33.43 and 311.11 ± 49.48 μg/kg, respectively. Se bioaccessibility was negatively correlated with sodium selenite content. The electronic nose results of Se-enriched rice pancakes showed that the sulfur compounds, nitrogen substances, alcohol substances, alkane substances, alcohols, aldehydes, and ketones in rice pancakes significantly increased following sodium selenite addition. The results can provide a significant basis for developing high efficiency Se-enriched fermented food and the processing of Se-enriched rice pancakes.

Lignin-derived porous carbons for efficient CO2 adsorption

Highly efficient and stable adsorbents play a crucial role in the adsorption treatment for CO2 capture. Lignin-derived porous carbons have gained increasing attention for CO2 adsorption application due to their abundant and cost-effective raw material sources. This study involves the synthesis of a series of porous carbons derived from sodium lignosulphonate using chemical activation and template methods. It was found that synthesis methods had significant influence on the properties of the obtained porous carbons, including texture structures and surface functional groups. The pore structure of the carbons obtained through the activation methods primarily consisted of etched channels, while that of the carbons acquired via the template methods predominantly originated from stacked sheet carbon. In comparison to the carbon samples obtained from template methods, chemically-activated porous carbons exhibited higher specific surface areas (1125 m2/g for the ZnCl2-activated sample and 1998.4 m2/g for the KOH-activated sample) and more micropores. The variations in the characteristics of the carbon samples derived from lignin with different synthesis methods also affected their CO2 adsorption performance. The CO2 adsorption capability of the chemically-activated porous carbons indicated their superior suitability for CO2 adsorption due to their higher specific surface area and abundant sulfur- and oxygen-containing functional groups. Notably, the porous carbons prepared with ZnCl2 as the activation agent (C-LS-ZnCl2) exhibited the most remarkable adsorption capacity of 4.45 mmol/g at 273 K and 100 kPa, and high CO2/N2 selectivity. The adsorption-desorption cycles confirmed the remarkable stability and regeneration ability of C-LS-ZnCl2. This study findings suggest that the utilization of the ZnCl2 activation method exhibits significant potential in transforming industrial lignin waste (sodium lignosulphonate) into a suitable adsorbent for CO2 adsorption.

Potential engineering applications of volcanic scoria from Mount Cameroon area, Central Africa: evidence from petrography and geochemistry

This study was initiated to explore the suitability of volcanic scoria from Mount Cameroon along the Cameroon Volcanic Line (CVL) in engineering applications. The mineral composition has been obtained through thin sections observation and XRD method. The XRF analytical methods have been used to determine major element composition. The studied rocks are dark gray to reddish in color with a vacuolar structure and porphyritic microlithic texture recalling the Strombolian dynamism. They are mainly made up of pyroxene, olivine, plagioclase, opaque minerals, quartz, magnetite and iddingsite. The presence of phenocrysts of olivine and clinopyroxene is due to mantle contamination. The main chemical components of scoria of Mount Cameroon are silica, alumina, iron and calcium oxides. The abundant vitreous phases are responsible for the acidic character revealed by high SiO2 + Al2O3 contents. Fe2O3, MnO and MgO are hosted by ferromagnesian minerals while SiO2, Al2O3, K2O, NaO and P2O5 are found in acidic vitreous phases. The mineral and chemical composition of the scoria of Mount Cameroun is similar to those of scoria previously studied around the world. They can be used as absorbents for metals because of their high adsorption capacity and affinity for metals. The high SiO2 + Al2O3 + Fe2O3 contents and low LOI values show that the scoria from Mount Cameroon are suitable to the use in cement and concretes. They can improve the resistance to sulfate, and chlorite and alkali silica reaction. According to the chemical composition, the rocks are conforming to French standard NFP18310. Their high CaO contents are favorable to improve the quality of scoria blended cements.

UNet-Att: a self-supervised denoising and recovery model for two-photon microscopic image

Two-photon microscopy is indispensable in cell and molecular biology for its high-resolution visualization of cellular and molecular dynamics. However, the inevitable low signal-to-noise conditions significantly degrade image quality, obscuring essential details and complicating morphological analysis. While existing denoising methods such as CNNs, Noise2Noise, and DeepCAD serve broad applications in imaging, they still have limitations in preserving texture structures and fine details in two-photon microscopic images affected by complex noise, particularly in sophisticated structures like neuronal synapses. To improve two-photon microscopy image denoising effectiveness, by experimenting on real two-photon microscopy images, we propose a novel deep learning framework, the UNet-Att model, which integrates a specifically tailored UNet++ architecture with attention mechanisms. Specifically, this approach consists of a sophisticated downsampling module for extracting hierarchical features at varied scales, and an innovative attention module that strategically emphasizes salient features during the integration process. The architecture is completed by an ingenious upsampling pathway that reconstructs the image with high fidelity, ensuring the retention of textural integrity. Additionally, the model supports end-to-end training, optimizing its denoising efficacy. The UNet-Att model proves to surpass mainstream algorithms in the dual objectives of denoising and preserving the textural intricacies of original images, which is evidenced by an increase of 9.42 dB in the high Peak Signal-to-Noise Ratio (PSNR) coupled with an improvement of 0.1131 in the Structural Similarity Index Measurement (SSIM). The ablation experiments reveal the effectiveness of each module. The associated Python packages and datasets of UNet-Att are freely available at https://github.com/ZjjDh/UNet-Att.

A robust image descriptor-local radial grouped invariant order pattern

Sorted-based LBP variants have been validated as effective grayscale inverse image classification methods. However, most of these methods encode the order of sampling points at the same scale and thus suffer from two problems: 1) Ignoring inter-scale correlation leads to descriptors that are not resistant to real scene changes. 2) The inherent flaws of sorted encoding cause descriptors to discriminate complex texture structures, showing low discriminability. To address these problems, we design the new scale-structure model and region encoding to realize a more robust and discriminative representation called Local Radial Grouped Invariant Order Pattern (LRGIOP). LRGIOP can effectively distinguish texture details in real scenes while resisting various complex imaging conditions. Experiments on several image databases show that the LRGIOP descriptor achieves state-of-the-art classification results under linear or even nonlinear grayscale-inversion transformations.

Joint Classification of Hyperspectral and LiDAR Data via Multiprobability Decision Fusion Method

With the development of sensor technology, the sources of remotely sensed image data for the same region are becoming increasingly diverse. Unlike single-source remote sensing image data, multisource remote sensing image data can provide complementary information for the same feature, promoting its recognition. The effective utilization of remote sensing image data from various sources can enhance the extraction of image features and improve the accuracy of feature recognition. Hyperspectral remote sensing (HSI) data and light detection and ranging (LiDAR) data can provide complementary information from different perspectives and are frequently combined in feature identification tasks. However, the process of joint use suffers from data redundancy, low classification accuracy and high time complexity. To address the aforementioned issues and improve feature recognition in classification tasks, this paper introduces a multiprobability decision fusion (PRDRMF) method for the combined classification of HSI and LiDAR data. First, the original HSI data and LiDAR data are downscaled via the principal component–relative total variation (PRTV) method to remove redundant information. In the multifeature extraction module, the local texture features and spatial features of the image are extracted to consider the local texture and spatial structure of the image data. This is achieved by utilizing the local binary pattern (LBP) and extended multiattribute profile (EMAP) for the two types of data after dimensionality reduction. The four extracted features are subsequently input into the corresponding kernel–extreme learning machine (KELM), which has a simple structure and good classification performance, to obtain four classification probability matrices (CPMs). Finally, the four CPMs are fused via a multiprobability decision fusion method to obtain the optimal classification results. Comparison experiments on four classical HSI and LiDAR datasets demonstrate that the method proposed in this paper achieves high classification performance while reducing the overall time complexity of the method.

Luminance decomposition and Transformer based no-reference tone-mapped image quality assessment

Tone-Mapping Operators (TMOs) play a crucial role in converting High Dynamic Range (HDR) images into Tone-Mapped Images (TMIs) with standard dynamic range for optimal display on standard monitors. Nevertheless, TMIs generated by distinct TMOs may exhibit diverse visual artifacts, highlighting the significance of TMI Quality Assessment (TMIQA) methods in predicting perceptual quality and guiding advancements in TMOs. Inspired by luminance decomposition and Transformer, a new no-reference TMIQA method based on deep learning is proposed in this paper, named LDT-TMIQA. Specifically, a TMI will change under the influence of different TMOs, potentially resulting in either over-exposure or under-exposure, leading to structure distortion and changes in texture details. Therefore, we first decompose the luminance channel of a TMI into a base layer and a detail layer that capture structure information and texture information, respectively. Then, they are employed with the TMI collectively as inputs to the Feature Extraction Module (FEM) to enhance the availability of prior information on luminance, structure, and texture. Additionally, the FEM incorporates the Cross Attention Prior Module (CAPM) to model the interdependencies among the base layer, detail layer, and TMI while employing the Iterative Attention Prior Module (IAPM) to extract multi-scale and multi-level visual features. Finally, a Feature Selection Fusion Module (FSFM) is proposed to obtain final effective features for predicting the quality scores of TMIs by reducing the weight of unnecessary features and fusing the features of different levels with equal importance. Extensive experiments on the publicly available TMI benchmark database indicate that the proposed LDT-TMIQA reaches the state-of-the-art level.

Relationship Between Salt Accumulation and Soil Structure Fractals in Cotton Fields in an Arid Inland Basin

The relationship between soil structure and salt accumulation is unclear; thus, experiments on salt accumulation under different soil structures were conducted in cotton fields in arid areas of northwest China. Thirty-nine sets of soil samples were collected from the 0 to 180 cm profile of three experimental areas. The total salt content of the soil extracts and the particle size distribution of the soil samples were determined using a JENCO TDS and a laser particle size analyzer, respectively, and the fractal dimension of the soil structure was obtained using fractal theory. Pearson’s correlation analysis and Tukey’s test (p &lt; 0.01) were used to analyze the correlation between soil salinity, soil particle size distribution, and fractal dimensions in the three profiles. The results showed soil salinity accumulation was affected mutually by soil texture and soil structure, and soil salinity tended to accumulate in fine-grained soil. The soil fractal dimension (D) could indicate soil texture and quantify soil salinity content. When the sand content was more than 50%, there was a significant positive correlation between the soil fractal dimension and soil salinity (correlation coefficient R = 0.943). The results provide valuable insights into cotton production in arid areas.

Dietary Malondialdehyde Impair Intestinal Health and Fillet Quality of Hybrid Grouper (Epinephelus fuscoguttatus♀ × E. lanceolatus♂).

Aquafeed kept at elevated temperatures and humidity can result in malondialdehyde (MDA) formation, adversely affecting aquafeed quality and triggering negative reactions in fish. To investigate the detrimental effects of dietary MDA on fish, six experimental diets with varying MDA levels (ranging from 0.03 to 17.72 mg/kg, on dietary crude lipid basis) were administered to three replicates of hybrid grouper for 8 weeks. Dietary inclusion of 4.43 mg/kg MDA significantly decreased serum complement 4 content and lysozyme activity, along with intestinal complement 3, complement 4, and immunoglobulin M contents. Furthermore, dietary inclusion of 8.86 mg/kg MDA significantly increased the activities of interleukin-1 receptor-associated kinase, ubiquitin-protein ligase, p38 mitogen-activated protein kinase, and tumor necrosis factor-α, downregulated the relative expression of Occludin but upregulated the relative expression of HSP70 in the hindgut. Additionally, the highest inclusion of MDA (17.72 mg/kg) significantly upregulated the relative levels of pro-inflammatory cytokines (IL-1β and TNF-α), caused intestinal inflammation, and damaged the intestinal microbial structure and fish fillet texture. This study demonstrated a dose-dependent response of MDA on hybrid grouper. A low dietary dose of MDA (<2.21 mg/kg) exhibited minimal impact on immune response and fillet quality. However, higher inclusion levels (≥4.43 mg/kg) impaired the intestinal health and fillet quality. Consequently, the safety limit for MDA content in the diet for hybrid grouper has been established at 4.43 mg/kg based on dietary crude lipid basis.

Fluorescence microscopic image enhancement method based on multi-saliency guided filtering fusion

Abstract Aiming at addressing the issues of colour deviation and clarity reduction in digital pathological imaging systems caused by complex environmental interferences such as uneven slice thickness, sample jitter, and background noise, this paper proposes a multi-saliency-guided filtering fusion enhancement method that combines energy saliency and Gaussian saliency. Firstly, a series of Gamma correction and spatial linear saturation adjustments are applied to enhance the contrast and colour saturation of blurred images, resulting in a sequence of pseudo-exposure images with improved contrast and saturation. Then, the pseudo-exposure images are decomposed into base and detail layers, and the fusion rules of energy saliency and Gaussian saliency-guided filtering are applied to each layer separately. Finally, clear microscopic images are obtained through two-scale reconstruction. This method overcomes the limitations of conventional deblurring algorithms that rely on prior estimation of scene depth and complex depth mapping processes. Experimental results on a dataset of cytological bright-field microscopic imaging samples show that the clarity evaluation metrics, including peak signal-to-noise ratio, root mean square error, and structural similarity index, have significantly improved, indicating that the proposed method can effectively improve the colour fidelity of the samples while eliminating noise interference and enhancing the clarity of cellular texture structures.

Differences in protein hydrolysis on quality of fermented milk: The changes in structure and processing characteristics

The functions and sensory properties of fermented dairy products are influenced by numerous factors, including the type of milk, the strain's degree of protein hydrolysis, and the additives of fermented milk. Currently, a large number of studies have demonstrated that the proteolytic ability of probiotics significantly affects the gel properties of fermented milk. However, few studies have examined the effects of probiotics with varying proteolytic abilities on the functions and sensory properties of fermented dairy products from the protein conformation perspective. Therefore, this study screened Lactobacillus helveticus KLDS1.8701 with a high protein hydrolysis rate and Lactobacillus helveticus KLDS1.0633 with a low hydrolysis rate to investigate the effects of varying degrees of protein hydrolysis on the gel properties, sensory properties and protein conformation of fermented milk. The results showed that the fermented milk containing Lactobacillus helveticus KLDS1.0633 with a low protein hydrolysis degree had high WHC, hardness and viscosity, a dense and uniform structure, abundant pores, and good texture and sensory properties. In addition, compared with fermented milk with high protein hydrolysis, low protein hydrolysis also increased the content of disulfide bonds, low surface hydrophobicity, and more hydrophilic structures such as a-helix. This study explains the impact of differences in protein hydrolysis degree among strains on fermented milk from a new perspective of protein secondary and tertiary conformations, which provides new insights for selecting suitable strains to produce fermented milk with better taste and nutrition.

Energy harvesting properties for potassium-sodium niobate piezoceramics through synergistic effect of phase structure and texturing engineering

In order to address the energy dilemma and meet environmental protection requirements, it is an urgent need to develop lead-free piezoelectric energy harvesters (PEHs). However, the low output power density has seriously hindered the application of lead-free piezoceramics as high efficiency energy harvesters. To solve above question, the combination of phase structure regulation and texturing technique in the K0.5Na0.5NbO3-xBi0.5Na0.5ZrO3-0.5%wtBiFeO3 (KNN-xBNZ) ceramics was adopted in this work. Because of both the Rhombohedral-Orthorhombic-Tetragonal (R-O-T) multiphases coexistence and texturing engineering, an ultrahigh piezoelectric activity quality factor d33×g33 ∼ 18324 ×10-15 m2·N-1 was achieved in the textured KNN-0.05BNZ (T-KNN-0.05BNZ) ceramics, which is about third times higher than that of random KNN-0.05BNZ (R-KNN-0.05BNZ) (d33×g33 ∼ 6672 ×10-15 m2·N-1) ones. A high output power of ∼ 0.32 mW was also obtained in the T-KNN-0.05BNZ ceramics by cantilever beam-based energy harvester devices under the resonance frequency of 75 Hz and matched RL of 1.2 MΩ, suggesting that the T-KNN-0.05BNZ ceramics have a great potential for application in the PEHs devices. This result also reveals that the combination of phase structure regulation and texturing technique is an effective way to achieved a high energy harvesting performances.

Innovative rotary swaging method drives high performance of n-Type Bi2(Te, Se)3 thermoelectrics

The thermoelectric transport of n-type Bi2Te3 heavily depends on grain alignment, causing performance limitations that severely restrict the demand for low-grade waste heat recovery. Here, the n-type Bi2Te2.7Se0.3 material with a certain textured structure is prepared by an innovative rotary swaging method. It is found that various defects including Te vacancies, dislocations, and grain boundaries significantly strengthen the phonon scattering. With an obviously suppressed thermal conductivity and well-maintained carrier mobility, the obtained rods extending up to several tens of centimeters achieve a peak ZT of 1.2 at 450 K and an average ZT of 1.0 (300–550 K), with Vickers hardness and compressive strength increased to 0.42 GPa and 52.6 MPa, respectively. Moreover, the assembled 17-pair thermoelectric module achieves a competitive conversion efficiency of up to 6.3% and a high output power of 0.93 W at a temperature difference of 250 K. This study develops an effective strategy for synergistically enhancing the thermoelectric and mechanical properties of n-type Bi2(Te,Se)3.

Adaptive Detection of Radar Range-Doppler Dual-Spread Targets in Lognormal-Texture Clutter

This paper investigates the problem of adaptive detection of radar targets in non-Gaussian clutter, where the target to be detected is considered to behave the dual-spread in the Doppler frequency dimension and the range dimension. The clutter is assumed to follow the compound Gaussian model with lognormal texture and unknown covariance matrix structure. The multi-rank linear subspace model and the range-spread model are employed to depict the Doppler and range spread characteristics of target echoes. Then, the range-Doppler dual-spread adaptive radar target detector with lognormal-texture is proposed using the two-step generalized likelihood ratio criteria, which replaces the true values of the unknown parameters with their maximum likelihood and maximum a posteriori estimates. Experimental results on simulated and measured data demonstrate that the proposed detector shows superior performance in different clutter and target parameters compared to the competitors.

Effect of modified trehalose on regulation of gluten protein structure and steamed bread quality

The trehalose (TL) was modified with sodium alginate, starch, and the effects of modified trehalose particle on the gluten protein and steamed bread properties were investigated. The results showed that for dough frozen after 30 d, the addition of 0.8 % modified TL significantly increased water-holding capacity by 12.12 %, improved the freezing rate, and the change of binding water could be better controlled. Besides, the α-helix content and random coil content of gluten protein was increased and decreased after adding modified TL, respectively, improving the ordered structure of wheat gluten protein and maintaining its aggregation characteristics and structural stability to some extent. Meanwhile, the brightness, water-holding capacity and specific volume of steamed bread was increased after adding 0.8 % modified TL, which improved and maintained the normal texture and internal structure of steamed bread. The results can provide a basis for the application of trehalose in flour products.

Preparation of cod protein composite gels for dysphagia by high-pressure homogenization: Egg white microgels-based high-phase emulsion as a texture modifier

The rising prevalence of dysphagia among the aging population presents significant challenges in developing specialized diets that combine soft textures with high nutritional value. This study investigates the formulation of cod protein composite gels (CPCGs) using high internal phase emulsions (HIPEs, 5–25 %) derived from cooked cod protein and egg white microgels. The CPCGs were prepared via high-pressure homogenization at 400 bar for 10 min. The texture, water retention, rheological properties, and network structure of the CPCGs were analyzed. Results demonstrated that incorporating HIPEs increased the hardness and whiteness of CPCGs. The water holding capacity peaked at 98.52 % with 15 % HIPEs. Additionally, as the concentration of HIPEs increased, the apparent viscosity, support properties, and thermal stability of CPCGs improved, although the critical strain point decreased from 475.33 % (control) to 199.33 % (25 % HIPEs). The network structure of CPCGs became denser and more uniform with higher HIPE concentrations, with noticeable changes in the gel skeleton when HIPEs exceeded 15 %. According to the International Dysphagia Diet Standardization Initiative (IDDSI) tests, all CPCG formulations met Level 6 - soft and bite-sized criteria. This study offers valuable insights into the development of fish protein-based dysphagia diets that are both nutritious and safe for swallowing.

A Strip Steel Surface Defect Salient Object Detection Based on Channel, Spatial and Self-Attention Mechanisms

Strip steel is extensively utilized in industries such as automotive manufacturing and aerospace due to its superior machinability, economic benefits, and adaptability. However, defects on the surface of steel strips, such as inclusions, patches, and scratches, significantly affect the performance and service life of the product. Therefore, the salient object detection of surface defects on strip steel is crucial to ensure the quality of the final product. Many factors, such as the low contrast of surface defects on strip steel, the diversity of defect types, complex texture structures, and irregular defect distribution, hinder existing detection technologies from accurately identifying and segmenting defect areas against complex backgrounds. To address the above problems, we propose a novel detector called S3D-SOD for the salient object detection of strip steel surface defects. For the encoding stage, a residual self-attention block is proposed to explore semantic information cues of high-level features to locate and guide low-level feature information. In addition, we apply a general residual channel and spatial attention to low-level features, enabling the model to adaptively focus on the key channels and spatial areas of feature maps with high resolutions, thereby enhancing the encoder features and accelerating the convergence of the model. For the decoding stage, a simple residual decoder block with an upsampling operation is proposed to realize the integration and interaction of feature information between different layers. Here, the simple residual decoder block is used for feature integration due to the following observation: backbone networks like ResNet and the Swin Transformer, after being pretrained on the large dataset ImageNet and then fine-tuned on a smaller dataset for strip steel surface defects, are capable of extracting feature maps that contain both general image features and the specific characteristics required for the salient object detection of strip steel surface defects. The experimental results on the SD-saliency-900 dataset show that S3D-SOD is better than advanced methods, and it has strong generalization ability and robustness.

Evaluation Indexes of Skid Resistance of Epoxy Chip Seal Based on Texture Features at Different Scales

Epoxy chip seals are widely used to improve the skid resistance of cement concrete pavements, which is significantly affected by surface texture. However, current methods for characterizing the surface texture structure are not precise, and the standard is not uniform. Therefore, a high-toughness modified epoxy resin chip seal structure was developed to conduct an indoor accelerated abrasion test. The elevation data of the epoxy chip seal under different abrasion times and different abrasion loads were obtained using laser scanning, and the density/sharpness combination parameters were obtained using the Hilbert–Huang transform and spectral analysis. The correlation between the texture parameters and the dynamic friction coefficient was analyzed using a stepwise multivariate quadratic polynomial method. The results showed that the texture structure of the epoxy chip seal surface is positively distributed, and the macroscopic texture occupies 49.45%, while the probability of the microscopic texture is only 4.55%. Meanwhile, the correlation coefficient between the texture parameters and the dynamic friction coefficient is 0.9307, which demonstrates that the selected texture parameters discard the texture components unrelated to skid resistance performance and reflect the distribution of the pavement texture and the skid resistance performance well.

Research on a novel non-uniform and asymmetric ultrasonic vibration system

Abstract A new type of non-uniform and asymmetric ultrasonic vibration system (UVS) is proposed and applied to the turning process. The research results show that the design theory of the non-uniform and non-symmetric UVS is accurate and reliable. The theoretical design frequency and modal simulation frequency error of ultrasonic vibration turning system is only 2.5%. The designed non-uniform and non-symmetric UVS has excellent vibration performance and stability, and the established simulation model can predict surface microtexture accurately. The vibration mode output by the non-uniform and non-symmetric UVS is longitudinal-bending vibration, and the actual vibration performance is excellent. Compared with conventional cutting, the addition of longitudinal-bending ultrasonic vibration under the same conditions can give the machined surface a microscopic textured structure, reduce surface roughness and water contact angle, and improve surface wettability. The minimum surface roughness obtained in the experiment is 1.005 μm. The orthogonal experiment results show that there is a correlation between the machining parameters, surface micro-structure parameters, surface wettability, and surface roughness.