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The purpose of this study is to identify the dynamic parameters of journal bearings in asymmetric rotor systems without additional test runs or excitations.

An asymmetric rotor-bearing test rig was set up for the identification experiment. Comparations were made between the measured response of the asymmetric rotor and the symmetric rotor. The mathematical model of the asymmetric rotor is established by the finite element method. The identification algorithm is based on the model of the rotor and the measured vibration response to identify bearing parameters. The influence of modeling error and measurement noise on the identification results are numerically analyzed. The dynamic parameters of the journal bearings under different rotational speeds are identified and compared with the theoretical values calculated by the perturbation method.

The experiment results show that the vibration characteristics of the asymmetric rotor and the symmetric rotor are different. The numerical evaluation of the identification algorithm shows that the algorithm is accurate and has good robustness to modeling error and measurement noise. The identified dynamic parameters agree reasonably well with the parameters derived from the theoretical bearing model.

The proposed identification method uses the unique vibration characteristics of asymmetric rotors to identify the bearing dynamic parameters. As the method does not require excitations or additional test runs, it is suitable for the field test.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2024-0096/

This study aims to investigate whether and how public air environmental concern (PAEC) affects corporate environmental, social and governance (ESG) performance in emerging markets.

This study measured PAEC using the Baidu index search keyword “雾霾 (PM2.5)” and assessed its impact on corporate ESG among Chinese A-share listed companies from 2011 to 2020 through regression analysis.

The empirical results indicate a positive relationship between PAEC and corporate ESG. Moreover, PAEC facilitates enhanced corporate ESG performance by mediating through corporate reputation and government environmental regulations. Heterogeneity analysis shows that the promotion effect of PAEC on ESG is more pronounced in the subgroups of companies with an excellent green image, low perceived uncertainty, strong management political connections, low short-termism, high industry technological levels and low pollution levels.

The practical implications of this study underscore the importance for policymakers, investors and companies to prioritize PAEC and its influence on corporate ESG performance.

This study contributes to ESG literature by highlighting the positive impact of external oversight, such as PAEC.

In the context of fixed-wing aircraft wing assembly, there is a need for a rapid and precise measurement technique to determine the center distance between two double-hole components. This paper aims to propose an optical-based spatial point distance measurement technique using the spatial triangulation method. The purpose of this paper is to design a specialized measurement system, specifically a spherically mounted retroreflector nest (SMR nest), equipped with two laser displacement sensors and a rotary encoder as the core to achieve accurate distance measurements between the double holes.

To develop an efficient and accurate measurement system, the paper uses a combination of laser displacement sensors and a rotary encoder within the SMR nest. The system is designed, implemented and tested to meet the requirements of precise distance measurement. Software and hardware components have been developed and integrated for validation.

The optical-based distance measurement system achieves high precision at 0.04 mm and repeatability at 0.02 mm within a range of 412.084 mm to 1,590.591 mm. These results validate its suitability for efficient assembly processes, eliminating repetitive errors in aircraft wing assembly.

This paper proposes an optical-based spatial point distance measurement technique, as well as a unique design of a SMR nest and the introduction of two novel calibration techniques, all of which are validated by the developed software and hardware platform.

The purpose of this paper is to explore the effect of ZnO on the structure and properties of micro-arc oxidation (MAO) coating on rare earth magnesium alloy under large concentration gradient.

The macroscopic and microscopic morphology, thickness, surface roughness, chemical composition and structure of the coating were characterized by different characterization methods. The corrosion resistance of the film was studied by electrochemical and scanning Kelvin probe force microscopy. The results show that the addition of ZnO can significantly improve the compactness and corrosion resistance of the MAO coating, but the high concentration of ZnO will cause microcracks, which will reduce the corrosion resistance to a certain extent.

When the concentration of zinc oxide is 8 g/L, the compactness and corrosion resistance of the coating are the best, and the thickness of the coating is positively correlated with the concentration of ZnO.

Too high concentration of ZnO reduces the performance of MAO coating.

The MAO coating prepared by adding ZnO has good corrosion resistance. Combined with organic coatings, it can be applied in corrosive marine environments, such as ship parts and hulls. To a certain extent, it can reduce the economic loss caused by corrosion.

The effect of ZnO on the corrosion resistance of MAO coating in electrolyte solution was studied systematically, and the conclusion was new to the common knowledge.

The construction of smart cities holds the potential to drive digital innovation in the construction industry through various means, such as enhancing supply and demand. This study echoes the urgent need for the construction industry to overcome development challenges. Hence, it is necessary to study the extent and ways in which smart city policies promote digital innovation in the construction industry.

This study treats China’s smart city policies as quasi-natural experiments. Using a dataset of Chinese prefecture-level cities from 2007 to 2021 and a difference-in-differences model, the study scrutinizes the impact of smart city policies on digital innovation within the construction industry.

The study reveals a substantial positive influence of smart city policies on digital innovation in the construction industry. In addition, the study explains these results by analysing supply-side and demand-side mechanisms. Moreover, the effect of smart city pilot policies on promoting digital innovation within the construction industry displays noteworthy heterogeneity across cities at different regional and political levels.

By exploring the impact and mechanisms of smart city policies on digital innovation in the construction industry, this research contributes to a more comprehensive and profound comprehension of the role of policies in facilitating the digital transformation of the construction sector. It is a valuable reference for policymakers and industry practitioners aiming to advance digital development.

To contribute to achieving the Sustainable Development Goals, this study aims to explore how to encourage innovative green behaviors among college students and the mechanisms behind the formation of green innovation behavior. Specifically, this study examines the influences of schools, mentors and college students themselves.

A multilevel, multisource study involving 261 students from 51 groups generally supported this study’s predictions.

Proenvironmental and responsible mentors significantly predicted innovative green behavior among college students. In addition, creative motivation mediated the logical chain among green intellectual capital, emotional intelligence and green innovation behavior.

The study findings offer new insights into the conditions required for college students to engage in green innovation. In addition, they provide practical implications for cultivating green innovation among college students.

The authors proposed and tested a multilevel theory based on the ability–motivation–opportunity framework. In this model, proenvironmental and responsible mentors, green intellectual capital and emotional intelligence triggered innovative green behavior among college students through creative motivation.

This paper aims to present the fabrication of 6061 aluminum alloy (AA6061) using a promising laser additive manufacturing process, called the laser-foil-printing (LFP) process. The process window of AA6061 in LFP was established to optimize process parameters for the fabrication of high strength, dense and crack-free parts even though AA6061 is challenging for laser additive manufacturing processes due to hot-cracking issues.

The multilayers AA6061 parts were fabricated by LFP to characterize for cracks and porosity. Mechanical properties of the LFP-fabricated AA6061 parts were tested using Vicker’s microhardness and tensile testes. The electron backscattered diffraction (EBSD) technique was used to reveal the grain structure and preferred orientation of AA6061 parts.

The crack-free AA6061 parts with a high relative density of 99.8% were successfully fabricated using the optimal process parameters in LFP. The LFP-fabricated parts exhibited exceptional tensile strength and comparable ductility compared to AA6061 samples fabricated by conventional laser powder bed fusion (LPBF) processes. The EBSD result shows the formation of cracks was correlated with the cooling rate of the melt pool as cracks tended to develop within finer grain structures, which were formed in a shorter solidification time and higher cooling rate.

This study presents the pioneering achievement of fabricating crack-free AA6061 parts using LFP without the necessity of preheating the substrate or mixing nanoparticles into the melt pool during the laser melting. The study includes a comprehensive examination of both the mechanical properties and grain structures, with comparisons made to parts produced through the traditional LPBF method.

Robotic arms play a crucial role in various industrial operations, such as sorting, assembly, handling and spraying. However, traditional robotic arm control algorithms often struggle to adapt when faced with the challenge of dynamic obstacles. This paper aims to propose a dynamic obstacle avoidance method based on reinforcement learning to address real-time processing of dynamic obstacles.

This paper introduces an innovative method that introduces a feature extraction network that integrates gating mechanisms on the basis of traditional reinforcement learning algorithms. Additionally, an adaptive dynamic reward mechanism is designed to optimize the obstacle avoidance strategy.

Validation through the CoppeliaSim simulation environment and on-site testing has demonstrated the method's capability to effectively evade randomly moving obstacles, with a significant improvement in the convergence speed compared to traditional algorithms.

The proposed dynamic obstacle avoidance method based on Reinforcement Learning not only accomplishes the task of dynamic obstacle avoidance efficiently but also offers a distinct advantage in terms of convergence speed. This approach provides a novel solution to the obstacle avoidance methods for robotic arms.

This paper aims to present a direct analysis to demonstrate why markedly different tensile and compressive behaviors of concretes could not be simulated with the Drucker–Prager yield criterion.

This study proposed an extended form of the latter for establishing a new elastoplasticity model with evolving yield strengths.

Explicit closed-form solutions to non-symmetric tensile and compressive responses of uniaxial specimens at finite strain are for the first time obtained from hardening to softening.

With such exact solutions, the yield strengths in tension and compression can be explicitly prescribed by uniaxial tensile and compressive stress-strain functions. Then, the latter two are further provided in explicit forms toward accurately simulating tensile and compressive behaviors. Numerical examples are supplied for meso-scale heterogeneous concrete (MSHC) and high-performance concrete (HPC), etc. Model predictions are in good agreement with test data.

The composition of author teams is a significant factor affecting the novelty of academic papers. Existing research lacks studies focusing on institutional types and measures of novelty remained at a general level, making it difficult to analyse the types of novelty in papers and to provide a detailed explanation of novelty. This study aims to take the field of natural language processing (NLP) as an example to analyse the relationship between team institutional composition and the fine-grained novelty of academic papers.

Firstly, author teams are categorized into three types: academic institutions, industrial institutions and mixed academic and industrial institutions. Next, the authors extract four types of entities from the full paper: methods, data sets, tools and metric. The novelty of papers is evaluated using entity combination measurement methods. Additionally, pairwise combinations of different types of fine-grained entities are analysed to assess their contributions to novel papers.

The results of the study found that in the field of NLP, for industrial institutions, collaboration with academic institutions has a higher probability of producing novel papers. From the contribution rate of different types of fine-grained knowledge entities, the mixed academic and industrial institutions pay more attention to the novelty of the combination of method indicators, and the industrial institutions pay more attention to the novelty of the combination of method tools.

This paper explores the relationship between the team institutional composition and the novelty of academic papers and reveals the importance of cooperation between industry and academia through fine-grained novelty measurement, which provides key guidance for improving the quality of papers and promoting industry–university–research cooperation.