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This paper aims to address the gaps in current research by exploring how blockchain technology influences corporate green innovation.

This study investigates the potential of blockchain technology to stimulate the green innovation of companies using the difference-in-difference model with a panel data set of 1,803 Chinese listed companies from 2012 to 2019.

The application of blockchain significantly increases the number of green invention patents obtained by companies but has no significant impact on green utility model patents, that is, blockchain applications improve the quality rather than the quantity of green innovation. The role of blockchain in promoting green innovation is particularly pronounced in state-owned enterprises, non-heavily polluting industries and older companies. The use of blockchain technology helps reduce sales costs and boosts research and development investments, thereby encouraging green innovation. Additionally, a company’s internal control quality plays a moderating effect.

Firstly, previous research on blockchain has primarily centered on its relationship with supply chain management. This article empirically tests the impact of blockchain applications on the green innovation of companies using the DID method. Secondly, current studies mainly explore the influencing factors on green invention patents. This article examines the impact of blockchain applications on both green invention patents and green utility model patents and identifies distinct influencing effects. Finally, this article introduces the internal control mechanism of enterprises into the DID model and explores the potential impact of the quality of internal control on the relationship between blockchain and green innovation.

The application of galvanized steel is widespread across industries due to its protective zinc coating that protects against atmospheric corrosion. However, previous studies have primarily focused on long-term corrosion rates rather than the full-scale corrosion behavior of the zinc. This paper aims to study the full-scale corrosion evolution of galvanic steel under simulated marine atmospheric environment using real-time EIS measurement.

Electrochemical impedance spectroscopy (EIS) provides an advanced method in monitoring such behavior. Therefore, the EIS method has been used to conduct a comprehensive investigation on the corrosion behavior of galvanic steel in a full-time manner.

The results indicate that the corrosion process of galvanic steel can be divided into three stages: an initial stage with an increased corrosion rate, a subsequent stage with a reduced corrosion rate, and finally a third stage with the lowest and constant corrosion rate. The evolution of corrosion resistance is closely related to changes in composition and structure of the patina layer. In the initial stage, galvanized steel undergoes the formation of soluble ZnCl₂ and needle-like Zn₅(OH)₈Cl₂·H₂O, which promotes the generation and maintenance of an electrolyte layer, consequently leading to an increase in corrosion rate. With prolonged corrosion time, there is a continuous accumulation of Zn₅(OH)₈Cl₂·H₂O within the patina layer, which reduces the content of soluble components and promotes the development of a denser inner layer, thus enhancing corrosion resistance.

This work holds significance in the monitoring of corrosion, understanding the evolution of corrosion and predicting the lifespan of galvanized steel.

This paper considers an e-tailer planning to distribute a product under one direct sales channel and multiple asymmetric agency platforms. Based on the multinomial logit (MNL) choice model, this study optimizes the pricing strategy and channel selection strategy to maximize the e-tailer’s profit.

A two-stage channel selection and pricing problem is formulated, where the profit-maximizing e-tailer first optimally selects a specified number of agency platforms from a set of alternatives to distribute the product and then determines the optimal prices in those channels.

An optimal pricing strategy is proposed to maximize the e-tailer’s total profit on multiple asymmetric channels. The results show that the e-tailer can obtain a higher profit by selling products on more asymmetric agency platforms. Moreover, an effective channel selection algorithm is provided to help the e-tailer optimally select the M agency platforms from N alternatives.

This study enriches the relevant research on multichannel selection and pricing by proposing an optimal pricing strategy and an effective channel selection algorithm. Evaluation results based on real-world industrial data show that the proposed optimal multichannel pricing strategy in this paper can significantly improve the profit of a real-world e-tailer compared to the e-tailer’s actual profit.

This study aims to explore the role of ChatGPT literacy within the technology acceptance model (TAM) framework and its potential for learners of English as a foreign language (EFL), whereby ChatGPT is integrated into their informal digital learning of English activities.

Data from 543 Chinese EFL learners were collected using a cross-sectional quantitative method. The relationships between six factors, namely, ChatGPT literacy, perceived ease of use, perceived usefulness, attitude, behavioral intention and actual use, were conceptualized and tested based on the TAM framework. The conceptualized model was verified using partial least squares structural equation modeling.

The findings indicated that ChatGPT literacy is a significant predictor of perceived ease of use and perceived usefulness, which were the two core variables with a significant impact on attitude. Perceived ease of use positively influenced perceived usefulness, indicating the mediating role of perceived usefulness in this path. Attitude significantly and positively influenced behavioral intention, which positively predicted actual use. Moreover, ChatGPT literacy moderated the relationship between behavioral intention and actual use.

This study extends the TAM framework by incorporating ChatGPT literacy as a predictor and moderator between behavioral intention and actual use. Empirical evidence is further offered for including ChatGPT as a language-learning instrument with great potential for EFL learners in extramural EFL learner settings.

Drawing on the tenets of institutional theory, the purpose of this study is to examine the impact of Confucianism on technology for social good, while also considering the moderating influence of extrinsic informal institutions (foreign culture) and intrinsic formal institutions (property rights).

This study constructs a comprehensive database comprising 9,759 firm-year observations in China by using a sample of Chinese A-share listed firms from 2016 to 2020. Subsequently, the hypotheses are examined and confirmed, with the validity of the results being upheld even after conducting endogenous and robustness tests.

The findings of this study offer robust and consistent evidence supporting the notion that Confucianism positively affects technology for social good through both incentive effect and normative effect. Moreover, this positive influence is particularly prominent in organizations with limited exposure to foreign culture and in nonstate-owned enterprises.

The findings contribute to the literature by fostering a deep understanding of technology for social good and Confucianism research, and further provide a nuanced picture of the role of foreign culture and property rights in the process of technology for social good in China.

With respect to severe working conditions such as heavy load and impact, this paper aims to investigate the friction reduction and anti-wear performance of kaolin and molybdenum dialkyl dithiophosphate (MoDDP) composite lubricant additives to improve the lubrication effect of a single additive.

A four-ball friction test was carried out to determine the optimal concentration of kaolin and organic molybdenum additives and the tribological properties of the kaolin/MoDDP composite lubricant additives. A ring block test of composite lubricant additives was designed to investigate its lubrication performance under the severe working conditions of low speed, heavy load and impact.

The results showed that the optimal addition mass fractions of kaolin and MoDDP were 4.0 and 1.5 Wt.%, respectively, when kaolin and MoDDP were used as single lubricant additives. Compared with the single additive, the 4.0 Wt.% kaolin/1.5 Wt.% MoDDP composite lubricant additive showed excellent friction reduction and anti-wear effects under heavy load and impact conditions. Physicochemical analysis of the wear surface revealed that the lamellar kaolin additive and MoDDP had excellent synergistic effects, and the friction process promoted the generation of lubricant films containing a chemically reactive layer of MoS₂, MoO₂, FeS₂ and Fe₂O₃ and a physically adsorbent layer containing SiO₂ and Al₂O₃, which play important roles in anti-wear and friction reduction.

The excellent friction reduction and anti-wear effects of lamellar silicate minerals and the excellent antioxidant properties and good synergistic effects of molybdenum were comprehensively used to develop the composite additives with great lubricating properties.

Damage of engineering structures is a nonlinear evolutionary process that spans across both material and structural levels, from mesoscale to macroscale. This paper aims to provide a comprehensive review of damage analysis methods at both the material and structural levels.

This study provides an overview of multiscale damage analysis of engineering structures, including its definition and significance. Current status of damage analysis at both material and structural levels is investigated, by reviewing damage models and prediction methods from single-scale to multiscale perspectives. The discussion of prediction methods includes both model-based simulation approaches and data-driven techniques, emphasizing their roles and applications. Finally, summarize the main findings and discuss potential future research directions in this field.

In the material level, damage research primarily focuses on the degradation of material properties at the macroscale using continuum damage mechanics (CDM). In contrast, at the mesoscale, damage research involves analyzing material behavior in the meso-structural domain, focusing on defects like microcracks and void growth. In structural-level damage analysis, the macroscale is typically divided into component and structural scales. The component scale examines damage progression in individual structural elements, such as beams and columns, often using detailed finite element or mesoscale models. The structural scale evaluates the global behavior of the entire structure, typically using simplified models like beam or shell elements.

To achieve realistic simulations, it is essential to include as many mesoscale details as possible. However, this results in significant computational demands. To balance accuracy and efficiency, multiscale methods are employed. These methods are categorized into hierarchical approaches, where different scales are processed sequentially, and concurrent approaches, where multiple scales are solved simultaneously to capture complex interactions across scales.

This study aims to build on social comparison theory to develop a theoretical model of leader–member exchange (LMX) relationship to workplace ostracism through perceived organizational status by coworkers and envy. This study further proposes that warmth and competence may potentially moderate these two indirect effects.

This study tested the hypotheses in a battery manufacturing company located in South China by a survey of 216 employees organized in 55 work teams, using different sources. Additionally, the authors conduct two online vignette experiments to test this study’s mediation, proving the causality.

The authors found that high-level LMX leads to both envy and perceived organizational status by coworkers, which results in a mixed blessing on workplace ostracism toward the employee with high-level LMX. The focal employee’s warmth and competence moderate these indirect relationships.

The authors use LMX to explore antecedents of workplace ostracism and explain how and when these focal employees suffer workplace ostracism from their coworkers. The authors extend the research on LMX by examining the interpersonal risk of being a focal employee. The authors discover two critical boundary conditions – warmth and competence.

This study suggests that it is important to balance the level of the differential LMX; appropriately endorsing other members is a good way to avoid eliciting envy and opposition. Meanwhile, person-oriented citizenship behaviors such as demonstrations of concern or help may shortly build up an employee’s warm impression on their coworkers.

By discovering the bright and dark sides of LMX, this paper has the potential to advance theories on LMX and workplace ostracism. Therefore, the authors believe the current research will have an important impact on relevant research in the future.

The purpose of this study is to evaluate and address the energy efficiency prevalent within the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP) countries, a diverse group that constitutes a significant portion of the global gross domestic product. By assessing energy efficiency, the study aims to inform strategies that foster sustainable economic growth and environmental sustainability within these nations.

This research uses a three-stage slacks-based measure data envelopment analysis (SBM-DEA) model, enhanced by stochastic frontier analysis (SFA), to assess the energy efficiency of CPTPP countries over the period from 2000 to 2015. The three-stage SBM-DEA model allows for a comprehensive analysis by first measuring energy efficiency, then adjusting for external environmental factors and statistical noise through SFA, and finally, reevaluating efficiency with adjusted inputs.

The analysis reveals significant disparities in energy efficiency among CPTPP nations, identifying both high-performing countries and those needing considerable improvements. Developed countries within the CPTPP generally exhibit higher energy efficiency levels, which can be attributed to their adoption of advanced technologies and strong policy frameworks. In contrast, developing nations demonstrate greater vulnerability to external environmental factors affecting their energy efficiency.

The research fills a gap in the literature by providing a nuanced and comprehensive evaluation of energy efficiency across a significant and economically diverse group of nations, offering valuable insights for sustainable development within the CPTPP framework.

This research focuses on developing a dual-nozzle slurry-based extrusion 3D printer capable of fabricating intricate zirconia structures. The designed 3D printer combines material extrusion and photopolymerization technologies to improve material diversity, precision and cost-effectiveness.

The 3D printer design incorporates ultraviolet curing to instantly cure extruded zirconia slurry thereby, eliminating the need for a step-wise curing procedure. Printing parameters were optimized to achieve high-quality prints, and supports made of polyethylene terephthalate glycol were used for intricate geometries. The printability and mechanical properties were evaluated for two different zirconia slurry compositions: 70 / 30 and 80 / 20 powder-to-resin weight percentages. The printed green body was subjected to a two-phase sintering process.

The 3D printer fabricated structures with features subtending angles greater than 50 degrees and a filling density above 80% without any supports. Shrinkage analysis showed the 80 / 20 composition resulted in higher density parts, with shrinkage ratios of 25.23%, 26.23% and 27.26% along the X, Y and Z axes, respectively. The sintered objects displayed hardness (1525 HV) and flexural strength (117 MPa), with minimal porosity.

This study demonstrates the development of a cost-effective dual-nozzle 3D printer that can effectively fabricate functional parts with complex material compositions and geometries that can cater to the futuristic requirements of high-end industries.