Search results

The purpose of this research is to improve the antibacterial and anti-pilling properties of polyester-cotton fabrics by applying a chitosan-silica coating through sol-gel technology. By optimizing the process parameters, the study aims to enhance the fabrics’ resistance to germs, prevent pilling and maintain their mechanical and functional properties.

A transparent sol-gel was obtained by hydrolysis and condensation reactions using silane coupling agent (KH-560), ethyl orthosilicate (TEOS), chitosan and ethanol as precursors and co-solvents, respectively. This sol-gel was employed for the purpose of applying antimicrobial and anti-pilling multifunctional finishes to PC fabrics. An orthogonal experimental design method was employed to optimize the process parameters. The surface morphology and chemical structure of the fabrics were studied using scanning electron microscopy and infrared spectroscopy, both before and after finishing. The fabrics were subjected to testing and analysis to evaluate their antimicrobial and anti-pilling properties, as well as the wearing performance.

The best antibacterial and anti-pilling properties are achieved when the volume ratio of TEOS to KH-560 is 1:3, the concentration of chitosan is 10 g/L, the dipping time is 60 min and the water content is 1:2. The fabric exhibits an anti-pilling grade of 4–5 and an antimicrobial rate of 99.99%. The silica/chitosan gel generated a thin and elastic coating on the fiber surface, acting as a protective barrier against external abrasion and enhancing the anti-pilling property by 2–3 grades. The fabric strength increased significantly, while the air permeability remained practically unaltered compared to untreated fabric.

The development of advanced materials such as chitosan-modified silica sols holds significant social implications. These materials, with their enhanced properties, can lead to innovations in healthcare, environmental remediation and energy storage, improving living standards and fostering sustainable development. Their widespread adoption could also stimulate economic growth and job creation, fostering a more resilient society.

This research introduces an innovative approach using sol-gel technology to enhance the antibacterial and anti-pilling properties of polyester-cotton fabrics. By optimizing the ratio of TEOS to KH-560, chitosan concentration, dipping time and water content, it achieves remarkable results in both performance metrics, offering significant practical value for the textile industry, especially in healthcare and fashion sectors.

The paper aims to build a finite element simulation model for pilling of polyester hairiness on the fabric to study the effects of hairiness performance on pilling and reveal pilling mechanisms.

The finite element simulation model of pilling of polyester hairiness was established by ABAQUS. Polyester hairiness was treated as elastic thin rod, which was divided by two-node linear three-dimensional truss element. The effects of hairiness elastic modulus, hairiness friction coefficient and hairiness diameter on frictional dissipation energy, strain energy and kinetic energy produced by pilling have been studied. The analysis solution values were compared with the finite element simulation results, which was used to verify finite element simulation.

The paper provides new insights about how to reveal pilling mechanisms of polyester hairiness with different performance. Comparing finite element simulation results with analysis solutions shows that the fitness is greater than 0.96, which verifies finite element simulation. Larger hairiness elastic modulus gives rise to higher friction dissipation energy and strain energy of hairiness but lower kinetic energy. Increasing friction coefficient enhances friction dissipation and strain energy of hairiness. However, kinetic energy decreases with the increase of friction coefficient. Hairiness diameter also has an important effect on hairiness entanglement and pilling. Increasing hairiness diameter can decrease friction dissipation energy but enhance strain energy and kinetic energy.

Finite element simulation was verified by analysis solutions. The solutions include friction dissipation energy, strain energy and kinetic energy, which cannot measured b experiment. Therefore, researchers are encouraged to simulate pilling to obtain pilling grades, which be compared with experiment results.

Pilling of polyester hairiness was simulated by ABAQUS. This method makes pilling process visualization, and pilling mechanisms was revealed from non-linear dynamics.

This study aims to introduce the achievements and benefits of applying wheel/rail-force–based maintenance interval extension of the C80 series wagon in China.

Chinese wagons' existing maintenance strategy had left a certain safety margin for the characteristics of widely running range, unstable service environment and submission to transportation organization requirements. To reduce maintenance costs, China railway (CR) has attempted to extend the maintenance interval since 2020. The maintenance cycle of C80 series heavy haul wagons is extended by three months (no stable routing) or 50,000 km (regular routing). However, in the meantime, the alarming rate of the running state, a key index to reflect the severe degree of hunting stability, by the train performance detection system (TPDS) for the C80 series heavy haul wagons has increased significantly.

The present paper addresses a big data statistical way to evaluate the risk of allowing the C80 series heavy haul wagons to remain in operation longer than stipulated by the maintenance interval initial set. Through the maintenance and wayside-detector data, which is divided into three stages, the extension period (three months), the current maintenance period and the previous maintenance period, this method reveals the alarming rate of hunting was correlated with maintenance interval. The maintainability of wagons will be achieved by utilizing wagon performance degradation modeling with the state of the wheelset and the often-contact side bearing. This paper also proposes a statistical model to return to the average safety level of the previous maintenance period's baseline through correct alarming thresholds for unplanned corrective maintenance.

The paper proposes an approach to reduce safety risk due to maintenance interval extension by effective maintenance program. The results are expected to help the railway company make the optimal solution to balance safety and the economy.

The paper aims to build a new objective evaluation method of fabric pilling by combining an integrated image analysis technology with a deep learning algorithm.

Series of image analysis techniques were adopted. First, a Fourier transform transformed images into the frequency domain. The optimal resolution matrix of an exponential high-pass filter was determined by combining the energy algorithm. Second, the multidimensional discrete wavelet transform determined the optimal division level. Third, the iterative threshold method was used to enhance images to obtain a complete and clear pilling ball images. Finally, the deep learning algorithm was adopted to train data from pilling ball images, and the pilling levels were classified according to the learning features.

The paper provides a new insight about how to objectively evaluate fabric pilling grades. Results of the experiment indicate that the proposed objective evaluation method can obtain clear and complete pilling information and the classification accuracy rate of the deep learning algorithm is 94.2%, whose structures are rectified linear unit (ReLU) activation function, four hidden layers, cross-entropy learning rules and the regularization method.

Because the methodology of the paper is based on woven fabric, the research study’s results may lack generalizability. Therefore, researchers are encouraged to test other kinds of fabric further, such as knitted and unwoven fabrics.

Combined with a series of image analysis technology, the integrated method can effectively extract clear and complete pilling information from pilled fabrics. Pilling grades can be classified by the deep learning algorithm with learning pilling information.

This paper aims to explore the impact of domestic market fragmentation on the innovation performance of enterprises and its mechanism from the perspective of market segmentation, a government behavior with Chinese characteristics.

In order to verify the theoretical hypothesis proposed in the previous article, that is, whether domestic market fragmentation can effectively improve the innovation performance of enterprises, this paper bases on the data of listed companies from 2010 to 2016, empirically testing the theoretical hypothesis by constructing a measurement model.

Domestic market fragmentation has a significant inhibitory effect on enterprise innovation performance. Domestic market fragmentation has heterogeneous effects on innovation performance of enterprises and regions. It is undeniable that domestic market fragmentation does have a certain support effect on state-owned enterprises but the support effect is achieved by distorting regional resource allocation and creating an unfair market environment.

Firstly, this paper explores the impact mechanism of domestic market fragmentation on corporate innovation performance from the perspective of market segmentation, a government behavior with Chinese characteristics, so as to expand and enrich the relevant research on enterprise innovation. Secondly, from the perspective of corporate innovation performance, this paper provides new evidence for the “curse effect” of domestic market fragmentation. Thirdly, this paper tries to shake the domestic market fragmentation support theory from the perspective of distortion effect brought by the “hand of support” of domestic market fragmentation.

Drawing upon the social identity approach, this research examines whether and how leader–subordinate congruence at high levels of proactive personality facilitates subordinate creativity.

Two different data sets (Study 1: N = 205; Study 2: N = 222) were collected from leader–subordinate dyads in China to provide stronger empirical evidence regarding our hypotheses. Polynomial regression and response surface analyses were used to test our predictions.

Subordinate creativity in the scenario in which the leader and subordinate shared a highly proactive personality (i.e. high–high congruence) was higher than that in the incongruence or low–low congruence scenario. The subordinate's identification with the leader mediated the above relationships such that the indirect relationship between leader–subordinate proactive personality and subordinate creativity via identification with the leader was maximized in the high–high congruence scenario.

The findings suggest that organizations should consider selecting both highly proactive leaders and highly proactive subordinates to facilitate the subordinates' identification and subsequent creativity.

This research highlights the crucial role of leader–subordinate congruence in strong proactive personality for the promotion of creativity and reveals that identification with the leader accounts for the above relationship.

This paper examines the impact of corporate digital transformation on employee satisfaction. Therefore, this study extends our understanding of the economic consequences of corporate digital transformation from employees’ perspectives.

The data used to construct our main proxy of employee satisfaction are collected from Kanzhun.com, which provides reviews by rank-and-file employees on their employers. This study uses a large sample of Chinese firms and adopts various empirical methods to examine the impact of digital transformation on employee satisfaction.

We find a significant positive relationship between corporate digital transformation and employee satisfaction. Moreover, we document that the relationship between corporate digital transformation and employee satisfaction is more pronounced in firms with higher labor intensity and in state-owned enterprises (SOE).

One significant limitation is that corporate digital transformation is constructed based on word frequency analysis. This approach may be influenced by variations in corporate disclosure practices and might not accurately capture the true extent of corporate digital transformation. This limitation is not only present in our research but is also pervasive in many other studies that utilize similar methodologies. Therefore, our results should be interpreted with this caveat in mind.

Our study suggests that corporate digital transformation enhances employee satisfaction, providing direct evidence for managers and regulators to promote corporate digital transformation. Through digital transformation, companies can not only improve operational efficiency but also foster employee satisfaction. This dual benefit underscores the importance of investing in corporate digital transformation for long-term success.

Our study suggests that corporate digital transformation enhances employee satisfaction, providing direct evidence for managers and regulators to promote corporate digital transformation. Through digital transformation, companies can not only improve operational efficiency but also foster employee satisfaction. This dual benefit underscores the importance of investing in corporate digital transformation for long-term success.

Our study contributes to the literature on the economic consequences of corporate digital transformation and extends existing research on the determinants of employee satisfaction. Additionally, it provides a novel measurement of employee satisfaction for a large sample of Chinese firms.

This study aims to analyze the paths and mechanisms of firms’ sustainable high growth. Firms’ high growth is susceptible to interruption, stagnation or reversal. Thus, how firms can achieve sustainable high growth is an important topic that requires urgent discussion and has significant implications for sustainable economic development and employment.

This study applies a longitudinal case study approach to portray the process by which Jiashu orchestrated digital elements with traditional resources to continuously fulfill their user demands and ultimately achieve sustainable high growth.

This study reveals three resource orchestration strategies: trust-oriented, demand-oriented and efficiency-oriented. These strategies are adopted in an organization’s startup, expansion and maturity periods, respectively. By dynamically integrating and orchestrating digital elements with traditional resources, firms implement a growth strategy with expanding and stacking dimensions, leading to sustainable high growth. The replicability and connectivity resulting from orchestrating digital elements and traditional resources encourage firms to expand their dimensions of growth and achieve sustainable high growth in multiple dimensions.

This study conducts a preliminary exploration of the relationship between the integration of digital and traditional elements and the sustainable high growth of enterprises. A more stable theoretical relationship between the two requires further multi-case studies and empirical analysis for substantiation.

This study first clarifies the concept of sustainable high growth and reveals a unique nonlinear path characterized by growth with expanding and stacking dimensions. The findings contribute to deepening the theories of sustainable high growth and resource orchestration in the digital economy era and offer practical implications for the sustainable high-growth practices of firms.

Although manufacturer-initiated rewards are widely used to secure distributors’ compliance, the spillover effect on unrewarded distributors (i.e. observers) in the same distribution channel is under-researched. Using insights from social learning theory, this paper aims to investigate how manufacturer-initiated rewards affect observers’ expectation of reward and shape observers’ compliance toward the manufacturer. Furthermore, this paper explores how such effects are contingent upon distributor relationship features.

To test the hypotheses, hierarchical multiple regression and bootstrapping analyses were performed using survey data from 280 Chinese distributors.

The magnitude of a manufacturer-initiated reward to a distributor stimulates expectation of reward among observers, which enhances compliance; observers’ expectation of reward mediates the impact of reward magnitude on compliance. Moreover, network centrality (of the rewarded peer) negatively moderates the positive impact of reward magnitude on observers’ expectation of reward, whereas observers’ dependence (on the manufacturer) positively moderates this dynamic.

Manufacturers should pay attention to the spillover effects of rewards. Overall, they should use rewards of appropriate magnitude to show willingness to recognize outstanding distributors. This will inspire unrewarded distributors, which will then be more compliant. Furthermore, manufacturers should know that specific types of distributor relationship features may significantly vary the spillover effects.

This study illuminates the spillover effects of manufacturer-initiated reward by opening the “black box” of the link between reward magnitude and observers’ compliance and by specifying the effects’ boundary conditions.