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The purpose of this study is to investigate the pricing strategies for knowledge payment products, taking into account the quality level of pirated knowledge products, in the context of platform copyright supervision.

This study abstracts the knowledge payment transaction process and aims to maximize producer's revenue by constructing a pricing model for knowledge payment products. It discusses pricing strategies for knowledge payment products under two scenarios: traditional supervision and blockchain supervision. The analysis explores the impact of pirated knowledge products quality level and blockchain technology on pricing strategies and consumer surplus, while providing threshold conditions for effective strategies.

Deploying blockchain technology in platform operations can significantly reduce costs and increase efficiency. In both scenarios, knowledge producer needs to balance factors such as the quality of pirated knowledge products, the supervision level of platform, and consumer surplus to dynamically adjust pricing strategies in order to maximize his own revenue.

This study enriches the literature on the pricing models of knowledge payment products and has practical significance in guiding knowledge producer to develop effective pricing strategies under copyright supervision.

This paper aims to investigate the lubrication characteristics of siliconized graphite with a wavy-tilt-dam (WTD) pattern applied to the hydrodynamic face seals.

It focuses on two friction pairs, carbon graphite versus tungsten carbide (CG-TC) and siliconized graphite versus siliconized graphite (SG-SG), through a three-dimensional elastic hydrodynamic lubrication numerical model that integrates finite difference method and finite element method. The consequence of axial elastic deformation of sealing pair materials on film thickness, film pressure, cavitation and sealing performance for a WTD mechanical face seal under full working conditions of ΔP = 0.8, 5.3 and 15.8 MPa are analyzed theoretically.

The nuclear hydrodynamic WTD face seal generates a convergent gap and exhibits a dual-characteristic behavior of hydrodynamic and hydrostatic effects under various ΔP. Compared to the CG-TC, the SG-SG shows a lower minimum film thickness, decreasing by 3.9%, 17.3% and 35.1%. The flow leakage rate decreases by 47.8%, 52.1% and 75.4%. In addition, the film stiffness increases by 46.8%, 49.8% and 97.8%. Thus, the SG-SG better deals with the dynamic tracking problem, and the sealing performance is stable. The strength and hardness of siliconized graphite enhance WTD sealing performance and improve cavitation control in high-pressure applications.

The lubrication characteristics of the siliconized graphite with a WTD pattern could inform the future design of hydrodynamic shallow groove wavy seals in boiler feedwater engineering implements under high-pressure conditions for the nuclear power industry.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-10-2024-0382/

The purpose of this study is to reveal the significant contribution of MXene on enhancing tribological properties and to obtain the influence mechanism of various factors on friction characteristics of rolling bearing under extreme conditions.

Under extreme working conditions, the friction characteristics of rolling bearings directly determine the safety and reliability of the transmission system. In this study, MXene is added to the origin lubricating grease (OLG) of rolling bearing to enhance their friction characteristics. Then, the effects of inner ring speed, radial load, grease filling volume and other factors on the friction coefficient of rolling bearing are analyzed using the Taguchi method.

The results indicate that the ranking of various factors affecting the friction coefficient is: radial load, inner ring speed, MXene additive content in grease and grease filling volume. Especially, the radial load and inner ring speed exhibit extremely significant effects, while the MXene additive content in grease (P < 0.05) has a significant influence on the friction coefficient of rolling bearing. The optimal condition for rolling bearing lubricated with MXene additives lubricating grease (MALG) achieves the lowest friction coefficient of 0.0049 under 1,000 rpm, 9 kN and 35% grease filling volume.

This study could offer reference solution for utilizing MXene nano-lubrication to fufill the demands of precision, heavy-load, or long-lifespan bearings. Furthermore, the lubrication approach has the potential to be expanded into aerospace, defense, and various industrial fields, thereby significantly promoting its practial engineering applications.

The objective of this study is to investigate the impact of the COVID-19 pandemic on the consumer payment behavior in Italy by correlating financial literacy with digital payment awareness, examining media anxiety and financial security, and including a gender analysis.

Consumers’ attitudes toward cashless payments were investigated using an online survey conducted from November 2021 to February 2022 on a sample of 836 Italian citizens by considering the behavioral characteristics and aspects of financial literacy. Structural equation modeling (SEM) was used to test the hypotheses and to determine whether the model was invariant by gender.

The analysis showed that the fear of contracting COVID-19 and the level of financial literacy had a direct influence on the payment behavior of Italians, which was completely different in its weighting. Fear due to the spread of news regarding the pandemic in the media indirectly influenced consumers’ noncash attitude. The preliminary results of the gender multigroup analysis showed that cashless payment was the same in the male and female subpopulations.

This research is noteworthy because of its interconnected examination. It examined the effects of the COVID-19 pandemic on people’s payment choices, assessed their knowledge, and considered the influence of media-induced anxiety. By combining these factors, the study offered an analysis from a gender perspective, providing understanding of how financial behaviors were shaped during the pandemic.

(1) The shear lag effect and its additional deflection contribution to composite beams based on spatial grid elements were presented. (2) A refined spatial grid element analysis method that can simultaneously obtain the internal forces, displacements and stresses of various parts of a composite beam.

A refined spatial grid element analysis method.

The proposed method can directly obtain the internal forces and displacements of the joints of the composite beam roof, floor and web.

To comprehensively comprehend the mechanical behavior of double-girder steel plate composite girder bridge structures and facilitate refined analysis, this paper introduces a refined spatial grid element analysis model applicable to both the global and local domains.

This contribution investigates the numerical solution of Volterra integral equations with auto-convolution of the third kind (AVIE). The numerical method applied in this investigation employs a collocation method based on the moving least squares (MLS) approximation. The MLS approximation is an effective way of approximating an unknown function by taking a disordered dataset. This method is a mesh-free approach since it does not require background interpolation or approximation cells, and is independent of domain geometry. The proposed method reduces the solution of third-kind AVIEs to the solution of systems of algebraic equations. By employing the Gauss–Legendre integration formula, we can estimate all the integrals of these equations. The applicability and validity of this method are demonstrated by numerical experiments, and its efficiency and robustness are proven by comparison with existing methods.

The numerical method applied in this study uses a collocation method based on moving least squares (MLS) approximation. This method is a mesh-free approach since it requires no background interpolation or approximation cells and is independent of domain geometry. Using the Gauss–Legendre integration formula, we can estimate all the integrals of these equations.

The authors declare that they have no known competing financial interests.

The manuscript has not been copyrighted or published previously and is not under consideration for publication elsewhere.

The purpose of this paper is to analyze whether and how intergovernmental fiscal transfers (IFTs) affect technological innovation.

China’s provincial panel data from 2007 to 2019 are used in an empirical study to examine the effect of IFTs on technological innovation and the role of fiscal spending policy in the relationship between the two by using the spatial Durbin model.

Results show an evident spatial correlation for the effect of IFTs on technological innovation, indicating that IFTs have a significant negative influence on technological innovation in local and surrounding regions. IFTs also inhibit technological innovation by negatively affecting science and technology spending and education spending.

These findings can aid policymakers in advancing technological innovation by improving the system of fiscal transfers and optimizing the structure of fiscal spending.

Although the determinants of technological innovation have been analyzed, no studies have investigated the effect of IFTs on technological innovation. Thus, this paper aims to address this gap.

This study aims to examine the relationship between managerial ability and innovation efficiency, the mediating effect of digital transformation and the moderating effect of internal control.

This study collected A-share manufacturing listed companies in China from 2008 to 2019 and analyzed the data by means of multiple regression analysis, mediating effect test, moderating effect test and heterogeneity test. Finally, the authors conducted robustness test by remeasuring key variables and adding control variables.

The empirical results show that the higher managerial ability can improve innovation efficiency, internal control has a positive moderating effect and digital transformation plays a partial mediating effect on the relationship between managerial ability and innovation efficiency. Specially, it is found that the mediating effect of digital transformation is not significant in non-state-owned firms.

This study suggests that it is necessary to focus on the managerial ability in terms of both cultivation and supervision, to further deepen the digital transformation from the aspects of firms, government and society, especially to support the digital transformation of non-state-owned firms, and to make efforts to improve the corporate governance mechanism and internal control system, so as to better comprehensively realize the improvement of enterprise innovation efficiency.

Based on the mediating effect analysis of digital transformation and the moderating effect analysis of internal control, this study explores the role of managerial ability on innovation efficiency from a new perspective, expanding the related theoretical framework and research boundaries.

This paper aims to introduce a novel structure for the physical internet (PI)–enabled sustainable supplier selection and inventory management problem under uncertain environments.

To address hybrid uncertainty both in the objective function and constraints, a novel interactive hybrid multi-objective optimization solution approach combining Me-based fuzzy possibilistic programming and interval programming approaches is tailored.

Various numerical experiments are introduced to validate the feasibility of the established model and the proposed solution method.

Due to its interconnectedness, the PI has the opportunity to support firms in addressing sustainability challenges and reducing initial impact. The sustainable supplier selection and inventory management have become critical operational challenges in PI-enabled supply chain problems. This is the first attempt on this issue, which uses the presented novel interactive possibilistic programming method.

This paper aims to examine the impact of bank managerial ability on the cost of loans using an extensive data set of US banks from 2001:Q1 to 2021:Q2.

First, the authors use multiple fixed-effects estimation to estimate the impact of managerial ability on bank loan pricing. In addition, the authors also perform different econometrics regression methods to test the robustness of the model including: Prais–Winsten regression, Newey–West regression and the Fama McBeth method, instrumental variable regression, propensity score matching method and quantile regression.

The results show that banks with higher managerial ability tend to provide loans with lower costs, and this effect is stronger at larger banks. In addition, research results from the quantile regression model show that the negative impact of managerial ability on the cost of loans will be stronger at banks with higher lending costs.

To the best of the authors’ knowledge, this study is one of the first to investigate this relationship between managerial ability and bank loan pricing from the supply side. In addition, this paper examines the impact of bank managerial ability on the loan costs of banks of different sizes, as well as differs across the distribution of the dependent variable. Therefore, this paper is of particular interest to regulators and policymakers.