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The purpose of our research is to explore the role of employee AI identity in influencing employee proactive behavior and its boundary conditions in AI workplace.

Based on the IT identity theory and motivation theory, our research discusses the effects of employee AI identity on employee proactive behavior and regarded the proactive work intention as a mediating variable. Meanwhile, we considered organization inducement as a boundary condition and discussed the moderating effects of it and its two sub-dimensions (development rewards and material rewards). Data were collected from 326 employees and partial least squares structural equation modeling was used to analyzed and draw the conclusions.

Findings showed that employee AI identity significantly affects employee proactive behavior, in which the proactive work intention play a mediating role. Moreover, three subdimensions (relatedness, emotional energy, dependence) of employee AI identity have different effects on formation of employee AI identity. And organization inducement acts as a positive moderating role, development rewards and material rewards play different roles in the formation of organization inducements.

Our research explores the different paths that influence employee proactive behavior and their boundary moderation, while analyzing the results of these influences in different subdimensions, deepening the research on employee AI identity and organization inducement. Our research is conducive to the development of the identity theory and organizational behavior research and provide suggestions for managers to improve their organizational management level.

This paper aims to facilitate reliable online diagnosis of early faults in the stator winding inter-turn short circuits of induction motors (IMs) under various operating conditions.

A novel fault characteristic component, the characteristic current amplitude, is proposed for the fault. Defined as the product of short-circuit coefficient and short-circuit current, the characteristic current is derived from the positive and negative-sequence components of the stator-side current and voltage.

Simulation models of the IMs pre- and postfault, along with an experimental platform for the motor’s inter-turn short circuit, were established. The characteristic current amplitude proves more robust against voltage unbalance and load variations, which offers enhanced reliability and sensitivity for early fault diagnosis of inter-turn short circuit in IMs stator windings.

A novel feature is proposed. Compared with negative-sequence current, which is considered as a traditional fault feature, the characteristic current amplitude exhibits a greater robustness against the imbalanced conditions, which simultaneously possesses the attributes of both reliability and expeditiousness in fault detection.

The concept of cost stickiness has emerged as a crucial factor affecting organizational resilience. While prior literature has mainly focused on the short-term performance implications of cost stickiness, this study aims to investigate its influence on organizational resilience over the long term.

Using data from Chinese A-share listed companies spanning from 2010 to 2019, we examine how cost stickiness impacts organizational resilience and explore the underlying mechanisms. This study contributes to the theoretical understanding of cost stickiness and organizational resilience and offers practical insights for businesses aiming to strengthen their resilience amid economic uncertainties.

Our findings suggest that cost stickiness not only promotes sustained performance growth and stabilizes financial outcomes but also enhances organizational resilience. Moreover, we find that macroeconomic policy uncertainty and micro-level corporate governance positively moderate the relationship between cost stickiness and organizational resilience. Additionally, our analysis reveals that organizational slack plays a partial mediating role in this relationship.

It is essential to clarify the concepts of cost and cost stickiness. Enterprises should scientifically implement the policy concept of “cutting overcapacity and reducing costs” while maintaining a reasonable cost stickiness.

Shareholders and institutional investors of enterprises should pay close attention to the behavior of managers to reduce the possibility of “inferior” stickiness due to agency problems. Both external institutional investors and inner shareholders play essential roles in supervising managers.

Enterprises should focus on cultivating organizational resilience. Resilience is vital for an enterprise to adapt quickly to external changes, overcome adversity, and make progress. Enterprises should reduce the excessive emphasis on short-term performance, focus on long-term development, cultivate strategic thinking, and establish a corporate culture.

The results show that cost stickiness can enhance organizational resilience. Economic policy uncertainty, the shareholding ratio of institutional investors, and the shareholding ratio of the largest shareholder can inhibit the opportunistic behavior of management, thereby positively regulating the relationship between cost stickiness and organizational resilience.

This study aims to evaluate the thermal performance of sodium alginate (SA) aerogel attached to nano SiO₂ and its radiative cooling effect on firefighting clothing without environmental pollution.

SA/SiO₂ aerogel with refractory heat insulation and enhanced radiative cooling performance was fabricated by freeze-drying method, which can be used in firefighting clothing. The microstructure, chemical composition, thermal stability, and thermal emissivity were analyzed using Fourier transform infrared spectroscopy, scanning electron microscopy, thermogravimetric analyzer and infrared emissivity measurement instrument. The radiative cooling effect of aerogel was studied using thermal infrared imager and thermocouple.

When the addition of SiO₂ is 25% of SA, the prepared aerogel has excellent heat insulation and a high radiative cooling effect. Under a clear sky, the temperature of SA/SiO₂ aerogel is 9.4°C lower than that of pure SA aerogel and 22.1°C lower than that of the simulated environment. In addition, aerogel has more exceptional heat insulation effect than other common fabrics in the heat insulation performance test.

SA/SiO₂ aerogel has passive radiative cooling function, which can efficaciously economize global energy, and it is paramount to environment-friendly cooling.

This method could pave the way for high-performance cooling materials designed for firefighting clothing to keep maintain the wearing comfort of firefighters.

SA/SiO₂ aerogel used in firefighting clothing can release heat to the low-temperature outer space in the form of thermal radiation to achieve its own cooling purpose, without additional energy supply.

Small-to-medium-sized enterprises (SMEs) in e-commerce often invest in information technology (IT) to stay competitive. However, whether and how IT capability (ITC) translates into financial performance requires further research. This paper examines the role of ITC in enabling value proposition innovation (VPI) as an important mechanism that improves financial performance for Chinese e-commerce SMEs during the COVID-19 pandemic. We argue that ITC is critical for enabling innovation because it elevates SMEs’ understanding of changing customer needs, especially when SMEs operate on multiple e-commerce platforms (multihome).

We used partial least squares structural equation modeling (PLS-SEM) and tested the hypotheses that ITC mediated by VPI and moderated by multihoming increases the financial performance of e-commerce SMEs through a survey among 206 Chinese SMEs operating on Taobao.

We find that not only higher levels of ITC lead to better financial performance, but also that the effect is fully mediated by VPI. Moreover, the effect of ITC on innovation is enhanced when vendors operate on multiple platforms.

The study identifies VPI as an important mechanism through which SMEs can leverage their ITC to adapt, innovate and thrive in competition. Our work suggests that using technology to develop innovative ideas and identify opportunities (which are reflected in VPI) is key to success and that doing so is more likely when vendors multihome. Thus, this study contributes to the innovation literature by explicating a concrete link between ITC, multihoming, VPI and increased financial performance. Different e-commerce stakeholders, including SME owners, IT and service providers and e-commerce platforms, can benefit from the findings of this work.

This study aims to explore the traditional plant dyeing of Xinjiang Atlas silk fabrics, providing references for the comprehensive utilization of plant dyes in intangible cultural heritage.

The focus of this study is on dyeing experiments of Atlas silk fabrics using safflower extracts, constrained by regional resources. Safflower dry flowers grown in Xinjiang were selected, rinsed with pure water and rubbed. Yellow pigments were removed by adding edible white vinegar. Red pigments from safflower were extracted using an alkaline solution prepared with Populus euphratica ash, a special product of Xinjiang. The extraction rate was analyzed under varying material-to-liquor ratios, pH values, times and temperatures. Direct dyeing process experiments were conducted to obtain different colorimetric L, a, b and K/S values for comparison. Samples with good color development were selected to test the impact of dyeing immersions on color development, and their color fastness, UV protection and antibacterial effects were verified.

The dyeing experiments on silk fabrics confirmed their UV protection capabilities and antibacterial properties, demonstrating effectiveness against E. coli and Staphylococcus aureus. As a major producer of safflower, Xinjiang underscores the significance of safflower as an essential plant dyes on the Silk Road. This study reveals its market potential and suitability for use in the plant dyeing process of Atlas silk, producing vibrant red and pink colors.

The experiments indicated that after removing yellow pigments, the highest extraction rate of red pigment from safflower was achieved at a pH value of 10–11, a temperature of 30°C and an extraction time of 40 min. The best bright red color effect with strong color fastness was obtained with a material-to-liquor ratio of 1:20, a temperature of 40°C and three immersions. The best light pink color effect with strong color fastness was a material-to-liquor ratio of 1:80, a temperature of 30°C and two immersions.

The primary goal of domestic washing and drying processes is to deliver clean and dry clothes, but they often result in excessive wrinkling of fabrics. This study aims to evaluate the wrinkling of linen fabric using image processing techniques after these processes and to quantify various drying performance indicators.

An image processing software was developed to quantify the smoothness level of linen fabrics subjected to domestic washing and drying processes. Additionally, different washing parameters in a front-loading washer that affect fabric wrinkling were evaluated. Three drying cycles (easy-iron, normal and extra-dry) in a condensation dryer were also analyzed in terms of moisture and fabric wrinkling parameters.

The results demonstrated a close relationship between water extraction from the fabrics and wrinkle formation. The shortest drying cycle with milder temperatures produced wetter and less wrinkled fabrics at the end of the drying cycle. Conversely, the longer, higher-temperature programs completely removed the moisture from the linen fabrics but resulted in greater wrinkling.

The development of an image processing technique to quantify the wrinkling of linen fabric allowed for more objective evaluation of this parameter. Additionally, this study allowed for the correlation of various drying indicators with the wrinkling generated in the linen fabric.

Previous studies have rarely integrated the financing modes of a capital-constrained manufacturer with the choices of online sales strategies. To address this gap, the authors study how a manufacturer selects optimal financing modes under different sales strategies in three dual-channel supply chains.

This paper considers three sales strategies, namely, combining a traditional retailer channel with one of the direct selling, reselling and agency selling channels, and two common financing modes, namely, bank financing and retailer financing. The authors obtain equilibrium outcomes of the manufacturer and traditional retailer and then provide the conditions for them to select optimal financing modes under three sales strategies.

The results indicate that the manufacturer’s financing decisions rely on the initial capital and interest rates, and the manufacturer selects retailer financing only if the initial capital is relatively larger. In terms of financing mode options, the retailer financing mode is more beneficial for the manufacturer under the three sales strategies. From the perspective of sales strategies, the direct selling model is more beneficial. In addition, the higher the consumer acceptance of the online channel, the more profits the manufacturer obtains.

This paper provides suggestions on how the capital-constrained manufacturer chooses financing modes and sales strategies.

This paper integrates the financing mode and different sales strategies to investigate the manufacturer’s optimal operational decisions. These sales strategies allow us to investigate the manufacturer’s optimal financing modes in the presence of both different financing modes and sales strategies.

Linear addition is commonly used to generate ensemble forecasts for decomposition ensemble models but traditionally treats individual modes with equal weights for simplicity. Using Taiwan air passenger flow as an empirical case, this study examines whether incorporating weighting for individual single-mode forecasts assessed by grey relational analysis into linear addition can improve the accuracy of the decomposition ensemble models used to forecast air passenger demand.

Data series are decomposed into several single modes by empirical mode decomposition, and then different artificial intelligence methods are applied to individually forecast these decomposed modes. By incorporating the correlation between each forecasted mode series and the original time series into linear addition for ensemble learning, a genetic algorithm is applied to optimally synthesize individual single-mode forecasts to obtain the ensemble forecasts.

The empirical results in terms of level and directional forecasting accuracy showed that the proposed decomposition ensemble models with linear addition using grey relational analysis improved the forecasting accuracy of air passenger demand for different forecasting horizons.

Accurately forecasting air passenger demand is beneficial for both policymakers and practitioners in the aviation industry when making operational plans.

In light of the significance of improving the accuracy of decomposition ensemble models for forecasting air passenger demand, this research contributes to the development of a weighting scheme using grey relational analysis to generate ensemble forecasts.

The rapid growth of 3D printing has transformed the cost-effective production of prototypes and functional items, primarily using extrusion technology with thermoplastics. This study aims to focus on optimizing mechanical properties, precisely highlighting the crucial role of mechanical compressive strength in ensuring the functionality and durability of 3D-printed components, especially in industrial and engineering applications.

Using the Box−Behnken experimental design, the research investigated the influence of layer thickness, wall perimeter and infill level on mechanical resistance through compression. Parameters such as maximum force, printing time and mass utilization are considered for assessing and enhancing mechanical properties.

The layer thickness was identified as the most influential parameter over the compression time, followed by the degree of infill. The number of surface layers significantly influences both maximum strength and total mass. Optimization strategies suggest reducing infill percentage while maintaining moderate to high values for surface layers and layer thickness, enabling the production of lightweight components with adequate mechanical strength and reduced printing time. Experimental validation confirms the effectiveness of these strategies, with generated regression equations serving as a valuable predictive tool for similar parameters.

This research offers valuable insights for industries using 3D printing in creating prototypes and functional parts. By identifying optimal parameters such as layer thickness, surface layers and infill levels, the study helps manufacturers achieve stronger, lighter and more cost-efficient components. For industrial and engineering applications, adopting the outlined optimization strategies can result in components with enhanced mechanical strength and durability, while also reducing material costs and printing times. Practitioners can use the developed regression equations as predictive tools to fine-tune their production processes and achieve desired mechanical properties more effectively.

This research contributes to the ongoing evolution of additive manufacturing, providing insights into optimizing structural rigidity through polylactic acid (PLA) selection, Box−Behnken design and overall process optimization. These findings advance the understanding of fused deposition modeling (FDM) technology and offer practical implications for more efficient and economical 3D printing processes in industrial and engineering applications.