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This paper delves into cross-border E-business, unraveling its intricate dynamics and forecasting its future trajectory.

This paper projects the prospective market size of cross-border E-business in China for the year 2023 using the GM (1,1) gray forecasting model. Furthermore, to enhance the analysis, the paper attempts to simulate and forecast the size of China’s cross-border E-business sector using the GM (1,3) gray model. This extended model considers not only the historical trends of cross-border E-business but also the growth patterns of GDP and the digital economy.

The forecast indicates a market size of 18,760 to 18,934 billion RMB in 2023, aligning with the consistent growth observed in previous years. This suggests a sustained positive trajectory for cross-border E-business.

Cross-border e-commerce critically shapes China’s global integration and traditional industry development. The research in this paper provides insights beyond statistical trends, contributing to a nuanced understanding of the pivotal role played by cross-border e-commerce in shaping China’s economic future.

China has fully capitalized on the opportunities presented by the latest wave of technological revolution and industrial transformation, paving the way for a path with Chinese characteristics in the development of the digital economy. This paper analyzes the development of China’s digital economy, outlining its path, advantages and challenges. It aims to provide insights into how China capitalized on technological and industrial transformation to foster a digital economy with distinct Chinese characteristics.

This paper adopts a descriptive analytical approach to outline the evolution of China’s digital economy through various stages of development. It highlights the pivotal role of market demand, the intricate government-market relations and technological advancements in shaping this evolution. The approach also identifies key factors that have contributed significantly to China’s success in digital economy development.

The key findings reveal that China’s digital economy has grown rapidly, driven by market demand, technological innovation and government support. The “Chinese path” prioritizes consumer internet, leverages scale advantages and emphasizes data-driven development. However, challenges exist in balancing governance systems, endogenous growth and external environments.

This paper offers original insights into the unique development path of China’s digital economy, highlighting its advantages and challenges. It provides valuable insights for other countries seeking to foster their own digital economies, especially in managing government-market relations and leveraging domestic market demand.

The purpose of this paper is to propose a new indicator-gray porosity that can objectively evaluate real porosities of electromagnetic shielding (EMS) fabric based on computer image analysis, which aims to address current porosity evaluation by tightness.

A method for the fabric image acquisition is determined and a gray digital model is established. The porosity membership region of true porosity is judged according to the total gray wave. A bi-directional judgment method based on horizontal and vertical single gray waves is proposed to automatically identify the gray porosity in the porosity membership region. After experiments, the differences between the gray porosity indicator and the tightness indicator are analyzed, the influence of the gray porosity on the shielding effectiveness (SE) is discussed, and the advantages of the gray porosity indicator are detailed.

Results show that the proposed indicator can accurately represent the real porosity size of the EMS fabric without pre-acquiring the structure parameters of the fabric, which provides a reference for the study of the electromagnetic characteristic of the EMS fabric.

The gray porosity presented in this paper is a new method to objectively evaluate real porosities of the EMS fabric, and can be applied to the research and evaluation of the electromagnetic characteristic for the EMS fabric.

No adequate study on scientific analysis of surface metal fiber (SMF) arrangement of electromagnetic shielding fabric (EMSF) and influence on shielding effectiveness (SE) is available at present.

This paper recognizes the SMF region and constructs a binary feature matrix according to edge condition, width condition and gray condition using the computer image analysis technique based on the construction of the surface digitized image of the EMSF. Three parameters of coverage, dispersion and uniformity are proposed to describe the SMF arrangement. Then experiments and testing samples are designed to analyze the relationship between the three parameters and the SE.

Results show that the proposed method can accurately recognize the SMF of the EMSF, the coverage, dispersion and uniformity can describe three aspects of the SMF arrangement of percentage content, porosity and orientation, and the three parameters are positively, negatively and positively correlated to the SE, respectively.

The research in this paper provides the basis for further description of the SMF arrangement of the EMSF, possesses the significance for the study of the shielding mechanism, transmission model, electromagnetic performance and rapid non-destructive evaluation of the EMSF and provide a new idea for the study on the shielding theory and application of the EMSF.

The purpose of this paper is to investigate the oxidation behavior of Ti‐48Al‐8Cr‐2Ag (at.%) at 900°C and 1000°C for various different times.

Laboratory tests were performed to determine growth process of the oxide scale at 900°C and 1000°C for various different times with SEM/EDX, XRD and TEM.

Merely Al2O3 occurred on the Laves phase at the initial stage at 900°C, while a mixture of Al2O3+TiO2 formed at the initial stage at 1000°C. Oxidation rate of the alloy at 900°C after long‐term oxidation was higher than that at 1000°C because a dense Al2O3 scale formed on the surface at 1000°C.

The paper shows that the oxidation behavior of TiAl alloy at initial stage is the basis of the revealing mechanism of oxidation. It is necessary to further investigate the oxidation of Ti‐Al‐Cr‐Ag alloy in more detail to clearly understand its oxidation process and growth process of the oxide scale.

The purpose of this paper is to employ a fractional approach to predict the permeability of nonwoven fabrics by simulating diffusion process.

The method described here follows a similar approach to anomalous diffusion process. The relationship between viscous hydraulic permeability and electrical conductivity of porous material is applied in the derivation of fractional power law of permeability.

The presented power law predicted by fractional method is validated by the results obtained from simulation of fluid flow around a 3D nonwoven porous material by using the lattice-Boltzmann approach. A relation between the fluid permeability and the fluid content (filling fraction), namely, following the power law of the form, was derived via a scaling argument. The exponent n is predominantly a function of pore-size distribution dimension and random walk dimension of the fluid.

The fractional scheme by simulating diffusion process presented in this paper is a new method to predict wicking fluid flow through nonwoven fabrics. The forecast approach can be applied to the prediction of the permeability of other porous materials.

The purpose of this paper is to focus on the design of automated fabric defect detection based on cascaded low-rank decomposition and to maintain high quality control in textile manufacturing.

This paper proposed a fabric defect detection algorithm based on cascaded low-rank decomposition. First, the constructed Gabor feature matrix is divided into a low-rank matrix and sparse matrix using low-rank decomposition technique, and the sparse matrix is used as priori matrix where higher values indicate a higher probability of abnormality. Second, we conducted the second low-rank decomposition for the constructed texton feature matrix under the guidance of the priori matrix. Finally, an improved adaptive threshold segmentation algorithm was adopted to segment the saliency map generated by the final sparse matrix to locate the defect regions.

The proposed method was evaluated on the public fabric image databases. By comparing with the ground-truth, the average detection rate of 98.26% was obtained and is superior to the state-of-the-art.

The cascaded low-rank decomposition was first proposed and applied into the fabric defect detection. The quantitative value shows the effectiveness of the detection method. Hence, the proposed method can be used for accurate defect detection and automated analysis system.

The purpose of this paper is to find an efficient fabric defect detection algorithm by means of exploring the sparsity characteristics of main local binary pattern (MLBP) extracted from the original fabric texture.

In the proposed algorithm, original LBP features are extracted from the fabric texture to be detected, and MLBP are selected by occurrence probability. Second, a dictionary is established with MLBP atoms which can sparsely represent all the LBP. Then, the value of the gray-scale difference between gray level of neighborhood pixels and the central pixel, and the mean of the difference which has the same MLBP feature are calculated. And then, the defect-contained image is reconstructed as normal texture image. Finally, the residual is calculated between reconstructed and original images, and a simple threshold segmentation method can divide the residual image, and the defective region is detected.

The experiment result shows that the fabric texture can be more efficiently reconstructed, and the proposed method achieves better defect detection performance. Moreover, it offers empirical insights about how to exploit the sparsity of one certain feature, e.g. LBP.

Because of the selected research approach, the results may lack generalizability in chambray. Therefore, researchers are encouraged to test the proposed propositions further.

In this paper, a novel fabric defect detection method which extracts the sparsity of MLBP features is proposed.

Fabric defect detection plays an important role in textile quality control. The purpose of this paper is to propose a fabric defect detection algorithm using learned dictionary-based visual saliency.

First, the test fabric image is splitted into image blocks, and the learned dictionary with normal samples and defective sample is constructed by selecting the image block local binary pattern features with highest or lowest similarity comparing with the average feature vector; second, the first L largest correlation coefficients between each test image block and the dictionary are calculated, and other correlation coefficients are set to zeros; third, the sum of the non-zeros coefficients corresponding to defective samples is used to generate saliency map; finally, an improve valley-emphasis method can efficiently segment the defect region.

Experimental results demonstrate that the generated saliency map by the proposed method can efficiently outstand defect region comparing with the state-of-the-art, and segment results can precisely localize defect region.

In this paper, a novel fabric defect detection scheme is proposed via learned dictionary-based visual saliency.

Fabric defect detection plays an important role in textile quality control. The purpose of this paper is to propose a fabric defect detection algorithm via context-based local texture saliency analysis.

In the proposed algorithm, a target image is first divided into blocks, then the Local Binary Pattern (LBP) technique is used to extract the texture features of blocks. Second, for a given image block, several other blocks are randomly chosen for calculating the LBP contrast between a given block and the randomly chosen blocks. Based on the obtained contrast information, a saliency map is produced. Finally, saliency map is segmented by using an optimal threshold, which is obtained by an iterative approach.

The experimental results show that the proposed algorithm, integrating local texture features and global image texture information, can detect texture defects effectively.

In this paper, a novel fabric defect detection algorithm via context-based local texture saliency analysis is proposed.