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The purpose of this paper is to study and report the effects of silver (Ag) content, glass phase particle size and Ag/antimony-doped tin oxide (ATO) particle size on the properties of ATO thick film resistor pastes, especially on the microstructure, square resistance, temperature coefficient of resistance (TCR), resistance temperature curve and other properties of the pastes.

Thick film resistor pastes with different Ag content, glass phase particle size and ATO particle size were printed on stainless steel substrates by screen printing technology, and a series of Ag/ATO thick film resistors (TFRs) were obtained after high-temperature sintering. The electrical properties of TFRs were evaluated. The microstructure development, square resistance, TCR and other properties of the developed TFRs were evaluated with the change in Ag content and the particle size.

The results show that with the increase of Ag content, the square resistance of the pastes decreases and the TCR increases. The change rate of resistance after resintering is less than 4%, and the pastes show excellent antiaging properties. Meanwhile, with the increase of the particle size of the glass phase, the square resistance decreases first and then increases, and the TCR increases first and then decreases, which has little effect on the conductive behavior. The increase in ATO particle size leads to an increase in the square resistance of TFRs and a decrease in the TCR.

This paper provides a useful evaluation of the square resistance, TCR and other properties of Ag/ATO thick film resistor pastes, which are related to the Ag content, glass phase particle size and ATO particle size of the developed TFRs. The thick film resistor pastes with zero TCR can be obtained using Ag/ATO as the functional phase without Pd or Pt.

Given the widespread adoption of empowerment in the workplace, increasing research has investigated the influences of empowering leadership. However, previous research has found confounding effects of it. This study aims to examine how and when empowering leadership exhibits “double-edged sword” effects on followers’ work outcomes.

The authors used a three-wave survey with a final sample of 215 full-time employees to test the research model.

The results indicate that followers’ role-breadth self-efficacy (RBSE) interacted with empowering leadership to predict their hindrance-related stress, subsequently influencing their turnover intention. Specifically, empowering leadership is found to elicit hindrance-related stress among followers with low RBSE. Furthermore, empowering leadership indirectly affects turnover intention by eliciting hindrance-related stress only among followers with low RBSE.

This study broadens the exploration of the “dark side” of empowering leadership, offering a more nuanced explanation of how it can lead to both beneficial and detrimental outcomes. It refines the understanding of empowering leadership’s effectiveness by highlighting the role of followers’ RBSE rather than focusing solely on the degree of empowerment. In addition, by contributing to the stress theory, this research demonstrates how individual differences influence followers’ cognitive appraisal of stress, shaping distinct stress experiences and driving the adoption of varying work-related coping strategies.

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.

This study empirically evaluates the effect of China’s 2016 Green Financial System (GFS) framework on corporate green development, focusing on the role of green investment in achieving sustainability.

This study uses a quasinatural experiment design to combine difference-in-difference and propensity score matching methods for analysis. It examines 799 polluting and 1,130 nonpolluting firms from 2013 to 2020, enabling a comprehensive assessment of the GFS framework’s influence.

This study affirms a statistically significant positive influence of the GFS framework on escalating green investment levels in polluting firms. Robust sensitivity analyses, encompassing parallel trend assessment, entropy balancing test, and alternative proxies, corroborate these findings. A mediation analysis identifies the implementation of an environmental management system as the potential underlying mechanism. A cross-sectional analysis identifies high financial slack, high profitability, mandatory CSR regulations, and marketization level as the influencing factors.

The study’s findings have critical implications for policymakers, regulators, and companies. Demonstrating the effectiveness of the GFS framework in driving green investment underscores the importance of aligning financial systems with sustainability goals.

This study contributes novel empirical evidence on the positive effect of China’s GFS framework on corporate green development. The quasinatural experiment design, coupled with comprehensive sensitivity analyses, strengthens the robustness of the findings.

The paper aims to clarify the operation rationality of high speed trains (HSTs) under tunnel condition with the speed of 400 km/h through representative aerodynamic factors including running drag, eardrum comfort, carriages noise, aerodynamic loads on tunnel ancillary facilities and HST, micro-pressure waves, and then put forward engineering suggestions for higher speed tunnel operation based on the analysis.

Based on the field measurement data of CR400AF-C and CR400BF-J tunnel operation, correlations between each aerodynamic indicators with HST speed were established. By analyzing the safety reserve of aerodynamic indicators at 350 km/h and the sensitivity of each indicator to HST speed increasing and the indicators’ formation mechanism, the coupling relationship between various indicators was obtained.

The sensitivity of different aerodynamic indicators to speed variation differed. The aerodynamic indicators representing flow field around HST showed a linear relationship with HST speed including noise, eardrum comfort, aerodynamic load on HST body. The positive aerodynamic load on tunnel auxiliary facilities and the micro-pressure wave at the entrance of the tunnel have the same sensitivity to the 3th-power relation of HST speed. The over-limit proportion of micro-pressure wave was the highest among the indicators, and aerodynamic buffering measures were recommended for optimization. The open tunnel pressure relief structure is recommended, while allowing trains to pass through the tunnel at an unconditional speed of 380 km/h.

Comprehensive evaluation of multiple aerodynamic indicators for HST tunnel operation with higher speeds was realized. The main engineering requirements to release aerodynamic effect were identified and the optimization scheme is proposed.

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.

The aim of this study is to investigate the effects of the laser cladding process on the microstructure, hardness and corrosion resistance properties of high-entropy alloys (HEA).

Laser cladding technology was used, using AlCoCrFeNiCu HEA powder as the cladding material. HEA coatings were prepared on the surface of 45 steel using a coaxial powder feeding method. The microstructure, phase composition, hardness and corrosion resistance properties of the HEA cladding layer were analyzed using optical microscopy (OM), X-ray diffractometer, digital microhardness tester and electrochemical workstation.

Laser power affects the coating surface; lower power reveals more visible unmelted powder particles. Higher power results in increased melt width and height, a brighter, smoother surface. Phase structure remains consistent, but the coating hardness is significantly higher than the substrate. The hardness of the melted zone in the substrate peaks at approximately 890.5 HV. The cladding zone hardness is about 60 HV higher than the substrate zone. Electrochemical corrosion parameters of the cladding show that, compared to the substrate, Ecor shifts positively by 113 mV, Icor decreases by one order of magnitude and Rp increases by one order of magnitude. These results indicate that the cladding has superior corrosion resistance to the substrate. The bonding strength between the coating and the substrate is greater than 93.6 MPa.

First, based on preliminary pilot experiments, nine sets of single-factor experiments were designed. Through these experiments, a specimen with relatively favorable cross-sectional morphology was observed. This specimen was then subjected to coating research, revealing that its microstructure and properties had significantly improved compared to the substrate. This enhancement holds remarkable significance for prolonging the service life of components.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2024-0413/

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.

Despite the rapid growth of corporate social responsibility (CSR) in emerging markets, there remains a need in the current literature for deeper insights into the factors influencing CSR practices in these contexts. This study aims to address this research gap and enhance the discourse on CSR in emerging markets by exploring the reciprocal relationship between business and government and its potential role in driving firms’ CSR efforts in these burgeoning economies.

The study is grounded in the theory of reciprocity and integrates insights from existing literature to posit that, within a reciprocal relationship, firms respond positively to government regulatory support by actively participating in CSR initiatives. To test this hypothesis, data from prominent sources, including the CSRHub database, the World Bank’s Ease of Doing Business, and the International Country Risk Guide, are gathered, yielding a sample of nearly 1,500 firms operating in diverse emerging markets. A series of empirical tests are then conducted to validate the existence of the reciprocal relationship and its influence on firms’ CSR efforts.

The findings reveal strong evidence of a reciprocal relationship between business and government in emerging markets. When the government provides favorable regulatory support, firms tend to reciprocate by shouldering greater responsibility in promoting societal well-being, specifically through active participation in CSR initiatives directed toward the well-being of the community in which they operate. These findings are robust across various estimation methods.

The study advances the understanding of CSR in emerging markets and provides valuable insights into the role of reciprocity in promoting CSR in real-world settings. This offers promising avenues for future theoretical and empirical research in the field of CSR.

Policymakers are urged to recognize the significance of business-government relations in fostering CSR. Developing a supportive regulatory environment can motivate firms to invest in CSR, benefiting both businesses and the communities they serve. For businesses, aligning CSR initiatives with community needs can foster a mutually beneficial relationship with the government, leading to greater social benefits and competitive advantages.

To the best of the authors’ knowledge, this study pioneers the application of the reciprocity theory to explain the interplay between business and government in shaping firms’ CSR endeavors in emerging markets.

Current multi-source image fusion methods frequently overlook the issue of detailed features when employing deep learning technology, resulting in inadequate target feature information. In real-world mission scenarios, such as military information acquisition or medical image enhancement, the prominence of target feature information is of paramount importance. To address these challenges, this paper introduces a novel infrared-visible light fusion model.

Leveraging the foundational architecture of the traditional DenseFuse model, this paper optimizes the backbone network structure and incorporates a Unique Feature Encoder (UFE) to meticulously extract the distinctive features inherent in the two images. Furthermore, it integrates the Convolutional Block Attention Module (CBAM) and the Squeeze and Excitation Network (SE) to enhance and replace the original spatial and channel attention mechanisms.

Compared to other methods such as IFCNN, NestFuse, DenseFuse, etc., the values of entropy, standard deviation, and mutual information index of the method presented in this paper can reach 6.9985, 82.6652, and 13.6022, respectively, which are significantly improved compared with other methods.

This paper presents a UFEFusion framework that synergizes with the CBAM attention mechanism to markedly augment the extraction of detailed features relative to other methods. Moreover, the framework adeptly extracts and amplifies unique features from disparate images, thereby elevating the overall feature representation capability.