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The purpose of this paper is to investigate the oscillating trajectory of the paddle of a fin-wheel underwater robot to enhance its propulsion efficiency in water. This robot can be used for underwater detection and military operations.

By studying the propulsion mode of underwater fin-based robots, it is found that such robots periodically generate a large reverse thrust during the swing process, resulting in low propulsion efficiency. Therefore, according to the propulsion characteristics of the oscillating paddle in the underwater environment, the hydrodynamic model and physical constraints of the oscillating paddle are established. Then, the oscillating gait trajectory of the paddle is optimized by the trajectory optimization method. The performance of the optimized trajectory was tested in the simulation environment and the actual underwater environment.

The prototype of the robot was built and tested in a small swimming pool. The research results confirm that the propulsion efficiency of the optimized trajectory is higher than that of the traditional trajectory under the condition of the same amplitude and period. Specifically, the maximum speed of the robot can reach 0.24 m/s when using the optimized trajectory, which is about 50% higher than that before optimization.

The optimized trajectory with the generated impulse as the optimization target is applied to the paddle oscillation, which can improve the thrust impulse generated by the fin-wheel underwater robot during underwater motion, thereby greatly improving the underwater propulsion efficiency and moving speed.

This study aims to get a better understanding of the impact of streamlined high-speed trains (HSTs) with geometric uncertainty on aerodynamic performance, as well as the identification of the key parameters responsible for this impact. To reveal the critical parameters, this study creates a methodology for evaluating the uncertainty and sensitivity of drag coefficient induced by design parameters of HST streamlined shapes.

Bézier curves are used to parameterize the streamlined shape of HSTs, and there are eight design parameters required to fit the streamlined shape, followed by a series of steady Reynolds-averaged Navier–Stokes simulations. Combining the preparation work with the nonintrusive polynomial chaos method results in a workflow for uncertainty quantification and global sensitivity analysis. Based on this framework, this study quantifies the uncertainty of drag, pressure, surface friction coefficient and wake flow characteristics within the defined ranges of streamline shape parameters, as well as the contribution of each design parameter.

The results show that the change in drag reaches a maximum deviation of 15.37% from the baseline, and the impact on the tail car is more significant, with a deviation of up to 23.98%. The streamlined shape of the upper surface and the length of the pilot (The device is mounted on the front of a train’s locomotive and primarily serves to remove obstacles from the tracks, thereby preventing potential derailment.) are responsible for the dominant factors of the uncertainty in the drag for HSTs. Linear regression results show a significant quadratic polynomial relationship between the length of the pilot and the drag coefficient. The drag declines as the length of the pilot enlarges. By analyzing the case with the lowest drag, the positive pressure area in the front of pilot is greatly reduced, while the nose tip pressure of the tail is enhanced by altering the vortices in the wake. The counter-rotating vortex pair is significantly attenuated. Accordingly, exerts the impacts caused by geometric uncertainty can be found on the wake flow region, with pressure differences of up to 900 Pa. The parameters associated with the shape of the upper surface contribute significantly to the uncertainty in the core of the wake separation region.

The findings contribute to a better understanding of the impact of streamlined HSTs with geometric uncertainty on aerodynamic performance, as well as the identification of the key parameters responsible for this impact. Based on this study, future research could delve into the detailed design of critical areas in the streamlined shape of HSTs, as well as the direction of shape optimization to more precisely and efficiently reduce train aerodynamic drag under typical conditions.

The purpose of this study is to examine the impact of customers' environmental concern on corporate green innovation and its underlying mechanisms.

This study empirically examines the impact of customer environmental concern on corporate green innovation using 967 company-customer-year observations of Chinese A-share listed companies over the period 2012–2022.

The empirical results show that customer environmental concern significantly enhances corporate green innovation. Furthermore, executive environmental awareness and research and development (R&D) investment play a partial mediating role in this relationship. The heterogeneity analysis reveals that state-owned customers, customers located in the same province with the corporate and the intellectual property model cities contribute to strengthening this relationship. Moreover, corporate performance analysis shows that customer environmental concern can significantly increase corporate financial performance and sustainable performance.

This study innovatively proposes a measure of customer environmental concern and examines its impact on corporate green innovation and its underlying mechanisms. In addition, this study also proposes some insights for policymakers.

This study explores whether and how financial agglomeration affects enterprise specialization, and whether it weakens the division effects of logistics accessibility.

Using the panel data of A-share listed companies in the Chinese manufacturing industry from 2010 to 2018, the authors test the hypotheses of the research framework by constructing a double fixed effect model.

First, for every 1% increase in financial agglomeration and logistics accessibility, enterprise specialization will increase by 1.8% and 5.6%. Meanwhile, financial agglomeration weakens the division effects of logistics accessibility. After a multiple robustness test, the above conclusions still hold. Second, the mechanism test shows that financial agglomeration can promote enterprise specialization through cost saving effect, scale expansion effect and technological progress effect. Third, financial agglomeration has a stronger positive impact and negative moderating effect on enterprise specialization in periphery areas and small-scale enterprises, but logistics accessibility is more likely to increase enterprise specialization in core regions and small-scale firms.

In the context of the global value chain (GVC) reshaping caused by the transformation of the international situation, exploring the relationship among financial agglomeration, logistics accessibility and enterprise specialization based on China’s experience can be an important insight for other emerging economies to enhance their position in the GVC.

The above findings enrich the literature on the division of labor among firms and contribute to the further improvement of GVC theory and division theory.

Accurate prediction of the structural condition of urban critical infrastructure is crucial for predictive maintenance. However, the existing prediction methods lack precision due to limitations in utilizing heterogeneous sensing data and domain knowledge as well as insufficient generalizability resulting from limited data samples. This paper integrates implicit and qualitative expert knowledge into quantifiable values in tunnel condition assessment and proposes a tunnel structure prediction algorithm that augments a state-of-the-art attention-based long short-term memory (LSTM) model with expert rating knowledge to achieve robust prediction results to reasonably allocate maintenance resources.

Through formalizing domain experts' knowledge into quantitative tunnel condition index (TCI) with analytic hierarchy process (AHP), a fusion approach using sequence smoothing and sliding time window techniques is applied to the TCI and time-series sensing data. By incorporating both sensing data and expert ratings, an attention-based LSTM model is developed to improve prediction accuracy and reduce the uncertainty of structural influencing factors.

The empirical experiment in Dalian Road Tunnel in Shanghai, China showcases the effectiveness of the proposed method, which can comprehensively evaluate the tunnel structure condition and significantly improve prediction performance.

This study proposes a novel structure condition prediction algorithm that augments a state-of-the-art attention-based LSTM model with expert rating knowledge for robust prediction of structure condition of complex projects.

The evolution from emerging technologies to smart libraries is thoroughly analyzed thematically and bibliometrically in this research study, spanning 2013 through 2022. Finding and analyzing the significant changes, patterns and trends in the subject as they are represented in academic papers is the goal of this research.

Using bibliometric methodologies, this study gathered and examined a large corpus of research papers, conference papers and related material from several academic databases.

Starting with Artificial Intelligence (AI), the Internet of Things (IoT), Big Data (BD), Augmentation Reality/Virtual Reality and Blockchain Technology (BT), the study discusses the advent of new technologies and their effects on libraries. Using bibliometric analysis, this study looks at the evolution of publications over time, the geographic distribution of research and the most active institutions and writers in the area. A thematic analysis is also carried out to pinpoint the critical areas of study and trends in emerging technologies and smart libraries. Some emerging themes are information retrieval, personalized recommendations, intelligent data analytics, connected library spaces, real-time information access, augmented reality/virtual reality applications in libraries and strategies, digital literacy and inclusivity.

This study offers a thorough overview of the research environment by combining bibliometric and thematic analysis, illustrating the development of theories and concepts during the last ten years. The results of this study helps in understanding the trends and future research directions in emerging technologies and smart libraries. This study is an excellent source of information for academics, practitioners and policymakers involved in developing and applying cutting-edge technology in library environments.

This study aims at investigating how hybrid intelligence might enhance employee engagement in breakthrough innovation. Specifically, it empirically examines the mediating role of self-extinction and moderating role of social intelligence.

This study, using the lens of socio-technical system (STS) theory, collected data from 317 employees through cross-sectional survey. The hypotheses were tested using MPlus 8.3 by applying Structural Equation Modelling (SEM).

The results support the proposed model, suggesting that hybrid intelligence fosters employees' breakthrough innovation engagement and such a relationship is fully mediated by self-extinction. Besides, the findings provide support for the positive moderating impact of social intelligence on such indirect relationships in a way that high social intelligence will further strengthen the relationship.

As a pioneering contribution, the study uncovers the social mechanism that underlies hybrid intelligence–breakthrough innovation engagement relationship via self-extinction. The research suggests managers leveraging employees' social intelligence for playing a critical role in countering the negative impact of self-extinction by enhancing the employees' engagement in the breakthrough innovation process.

The purpose of this paper is to investigate the effect of internal control (IC) on earnings quality from the perspective of the capital market. Specifically, it examines the effect of IC on earnings response coefficients.

This study uses the moderated regression analysis on a sample of 1,310 Chinese listed firms on the Shanghai Stock Exchange (SSE) from 2020 to 2022. It employed an earnings response coefficient model by Holthausen and Verrecchia (1988) and used the IC score produced by the index created by the Shenzhen Dibo Enterprise Risk Management Technology, i.e. DIB IC, and risk management database.

The study finds that the capital market placed lower earnings reliability on companies with high IC. This suggests that investors perceived negatively on the IC score of China listed companies, possibly due to their negative perception on the reason for implementation of high IC by those companies. A high IC score may raise suspicion amongst investors that the company has internal issues.

This study adds to the limited studies on less regulated internal governance mechanisms from the perspective of the capital market. The contradictory result suggests the need for more studies before deriving a solid conclusion.

This study focusses on the under research area of IC rather than the common board of directors and from the perspective of Chinese economies, limited studies of developed countries.

This study aims to enhance the bioavailability and antioxidant function of vitamin E (VE) by combining it with inositol and choline, exploring their synergistic effects and underlying mechanisms.

An oxidative stress model in mouse hepatocytes was established using hydrogen peroxide to assess the antioxidant effects of combined VE, inositol and choline treatments. Cell viability, the levels of reactive oxygen species (ROS) and malondialdehyde (MDA) and antioxidant enzymes, including superoxide dismutase (SOD) and catalase (CAT), were measured to evaluate protective effects. α-Tocopherol (α-T) and α-tocopherol transfer protein (α-TTP) levels were assessed to determine VE bioavailability. Transcriptome sequencing was conducted to identify gene expression changes and pathways impacted by the treatment.

An optimal oxidative stress model was established with 641 µM hydrogen peroxide for 6 h. The combination of 20 µM VE, 1.5 mM inositol and 0.3 mM choline significantly improved cell viability, reduced ROS and MDA levels and increased SOD and CAT activities, indicating enhanced antioxidant protection. In addition, α-T and α-TTP levels were higher with this combination, suggesting improved VE bioavailability. Transcriptome analysis revealed that the treatment modulated key pathways linked to inflammation, cancer and lipid metabolism, particularly impacting interleukin-17 and nuclear factor kappa-light-chain-enhancer of activated B cells signaling, cytokine interactions and the alcoholic liver disease pathway.

This study demonstrates a novel approach for improving VE’s bioavailability and effectiveness through its combination with inositol and choline, providing insights that could inform antioxidant therapies and liver health interventions.

This study aims to construct alternative models to establish the dynamic relationship between exchange rates and housing affordability by estimating both the short- and long-run relationship between exchange rates and housing affordability for 18 OECD countries from 1975Q1 to 2022Q4. After that, this study demonstrates how this nexus behaves during high and low inflation regimes and turbulent times.

This study uses the panel autoregressive distributed lag technique to examine the nexus between housing affordability to capture the distinct characteristics of the sample countries and estimate various short- and long-run dynamics in the relationship between housing affordability and exchange rate.

Exchange rate appreciation improves housing affordability in the short run, whereas this connection tends to dissipate in the long run. Moreover, inflation can worsen housing affordability during turbulent times, such as the global financial crisis, in both the short and long run. Ignoring these changes in the relationship between exchange rates and housing affordability during turbulent times can lead to incorrect conclusions.

To the best of the authors’ knowledge, this study is the first to examine the association between exchange rates and housing affordability by demonstrating how these variables behave in high and low inflation regimes and turbulent times.