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With the improvement of modern aircraft requirements for safety, long life and economy, higher quality aircraft assembly is needed. However, due to the manufacturing and assembly errors of the posture adjustment mechanism (PAM) used in the digital assembly of aircraft large component (ALC), the posture alignment accuracy of ALC is difficult to be guaranteed, and the posture adjustment stress is easy to be generated. Aiming at these problems, this paper aims to propose a calibration method of redundant actuated parallel mechanism (RAPM) for posture adjustment.

First, the kinematics model of the PAM is established, and the influence of the coupling relationship between the axes of the numerical control locators (NCL) is analyzed. Second, the calibration method based on force closed-loop feedback is used to calibrate each branch chain (BC) of the PAM, and the solution of kinematic parameters is optimized by Random Sample Consensus (RANSAC). Third, the uncertainty of kinematic calibration is analyzed by Monte Carlo method. Finally, a simulated posture adjustment system was built to calibrate the kinematics parameters of PAM, and the posture adjustment experiment was carried out according to the calibration results.

The experiment results show that the proposed calibration method can significantly improve the posture adjustment accuracy and greatly reduce the posture adjustment stress.

In this paper, a calibration method based on force feedback is proposed to avoid the deformation of NCL and bracket caused by redundant driving during the calibration process, and RANSAC method is used to reduce the influence of large random error on the calibration accuracy.

With the continuous increase of space activities and insight into space exploration, the velocity of near space hypersonic vehicles becomes higher and higher, which leads to the aerothermodynamic phenomenon getting worse around a vehicle; therefore, the exploration of numerical scheme applicability is essential for hypersonic flow simulations.

The implicit finite volume schemes are derived from axisymmetric Navier–Stokes equations for chemical equilibrium flow and programmed in Fortran. Taking the atmosphere at 30 km as an example, the performance of spatial discretization schemes such as AUSMPW and AUSMPW+ are analyzed in a range of Mach numbers from 17 to 32.

The AUSMPW scheme appears pressure jump near the stagnation if the Mach number is over 18, but AUSMPW+ scheme shows better performance in comparison.

This study will help the aerothermodynamic design in near space hypersonic vehicles.

This chapter examines China’s corporate governance and accounting environment that shapes the adoption of internationally acceptable principles and standards. Specifically, it examines international influences, including supranational organizations; foreign investors and international accounting firms; domestic institutional influences, including the political system, economic system, legal system, and cultural system; and accounting infrastructure. China’s convergence is driven by desired efficiency of the corporate sector and legitimacy of participating in the global market. Influenced heavily by international forces in the context of globalization, corporate governance and accounting practices are increasingly becoming in line with internationally acceptable standards and codes. While convergence assists China in obtaining legitimacy, improving efficiency is likely to be adversely affected given that corporate governance and accounting in China operate in an environment that differs considerably from those of Anglo-American countries. An examination of the corporate governance and accounting environment in China suggests heavy government involvement within underdeveloped institutions. While the Chinese government has made impressive progress in developing the corporate governance and accounting environment for the market economy, China’s unique institutional setting is likely to affect how the imported concepts are interpreted and implemented.

Grey modeling technique is an important element of grey system theory, and academic articles applied to agricultural science research have been published since 1985, proving the broad applicability and effectiveness of the technique from different aspects and providing a new means to solve agricultural science problems. The analysis of the connotation and trend of the application of grey modeling technique in agricultural science research contributes to the enrichment of grey technique and the development of agricultural science in multiple dimensions.

Based on the relevant literature selected from China National Knowledge Infrastructure, the Web of Science, SpiScholar and other databases in the past 37 years (1985–2021), this paper firstly applied the bibliometric method to quantitatively visualize and systematically analyze the trend of publication, productive author, productive institution, and highly cited literature. Then, the literature is combed by the application of different grey modeling techniques in agricultural science research, and the literature research progress is systematically analyzed.

The results show that grey model technology has broad prospects in the field of agricultural science research. Agricultural universities and research institutes are the main research forces in the application of grey model technology in agricultural science research, and have certain inheritance. The application of grey model technology in agricultural science research has wide applicability and precise practicability.

By analyzing and summarizing the application trend of grey model technology in agricultural science research, the research hotspot, research frontier and valuable research directions of grey model technology in agricultural science research can be more clearly grasped.

This research aims to explore generation (Gen) Z's personal branding on social media when job seeking.

Gen Z students, in their final year of university, were interviewed about personal branding, as well as recruiters and career advisors to gain insights into the recruitment process and expectations of online personal brands. Before interviewing, Gen Z students' LinkedIn profiles were examined, and then fed into the interview process.

Using impression management theory, the findings show that Gen Z perceive online personal brands as a crucial tool to gain more advantage in job markets. A gap was found between desired and perceived selves in Gen Z's online personal brands. Strategies such as effective self-reflection, authentic communication, self-promotion processes, awareness of risks and constantly controlling digital footprints were suggested to build stronger and more coherent personal brands. Gen Z are in favour of a more dynamic, interactive, work-in-process of authentic personal brands.

This research demonstrates the importance of authentically building online personal branding strategies and tactics to bridge the divide between Gen Z's desired and perceived images in personal branding on social media when job seeking.

This review aims to give a critical view of the wheel/rail high frequency vibration-induced vibration fatigue in railway bogie.

Vibration fatigue of railway bogie arising from the wheel/rail high frequency vibration has become the main concern of railway operators. Previous reviews usually focused on the formation mechanism of wheel/rail high frequency vibration. This paper thus gives a critical review of the vibration fatigue of railway bogie owing to the short-pitch irregularities-induced high frequency vibration, including a brief introduction of short-pitch irregularities, associated high frequency vibration in railway bogie, typical vibration fatigue failure cases of railway bogie and methodologies used for the assessment of vibration fatigue and research gaps.

The results showed that the resulting excitation frequencies of short-pitch irregularity vary substantially due to different track types and formation mechanisms. The axle box-mounted components are much more vulnerable to vibration fatigue compared with other components. The wheel polygonal wear and rail corrugation-induced high frequency vibration is the main driving force of fatigue failure, and the fatigue crack usually initiates from the defect of the weld seam. Vibration spectrum for attachments of railway bogie defined in the standard underestimates the vibration level arising from the short-pitch irregularities. The current investigations on vibration fatigue mainly focus on the methods to improve the accuracy of fatigue damage assessment, and a systematical design method for vibration fatigue remains a huge gap to improve the survival probability when the rail vehicle is subjected to vibration fatigue.

The research can facilitate the development of a new methodology to improve the fatigue life of railway vehicles when subjected to wheel/rail high frequency vibration.

The purpose of this paper is to summarize the existing point cloud target detection algorithms based on deep learning, and provide reference for researchers in related fields. In recent years, with its outstanding performance in target detection of 2D images, deep learning technology has been applied in light detection and ranging (LiDAR) point cloud data to improve the automation and intelligence level of target detection. However, there are still some difficulties and room for improvement in target detection from the 3D point cloud. In this paper, the vehicle LiDAR target detection method is chosen as the research subject.

Firstly, the challenges of applying deep learning to point cloud target detection are described; secondly, solutions in relevant research are combed in response to the above challenges. The currently popular target detection methods are classified, among which some are compared with illustrate advantages and disadvantages. Moreover, approaches to improve the accuracy of network target detection are introduced.

Finally, this paper also summarizes the shortcomings of existing methods and signals the prospective development trend.

This paper introduces some existing point cloud target detection methods based on deep learning, which can be applied to a driverless, digital map, traffic monitoring and other fields, and provides a reference for researchers in related fields.

The deposition of particles onto a substrate during the cold spraying (CS) process relies on severe plastic deformation, so there are various micro-defects induced by insufficient deformation and severe crushing. To solve the problems, many post-treat techniques have been used to improving the quality by eliminating the micro-defects. This paper aims to help scholars and engineers in this field a better and systematic understand of CS technology by summarizing the post-treatment technologies that have been investigated recently years.

This review summarizes the types of micro-defects and introduces the effect of micro-defects on the properties of CS coating/additive manufactured, illustrates the post-treatment technologies and its effect on the microstructure and performances, and finally outlooks the future development trends of post-treatments for CS.

There are significant discoveries in post-treatment technology to change the performance of cold spray deposits. There are also many limitations for post-treatment methods, including improved performance and limitations of use. Thus, there is still a strong requirement for further improvement. Hybrid post-treatment may be a more ideal method, as it can eliminate more defects than a single method. The proposed ultrasonic impact treatment could be an alternative method, as it can densify and flatten the CS deposits.

It is the first time to reveal the influence factors on the performances of CS deposits from the perspective of microdefects, and proposed corresponding well targeted post-treatment methods, which is more instructive for improving the performances of CS deposits.

The purpose of this paper is to put forward a nvew reconfigurable multi-mode walking-rolling robot based on the single-loop closed-chain four-bar mechanism, and the robot can be changed to different modes according to the terrain.

Based on the topological analysis, singularity analysis, feasibility analysis, gait analysis and the motion strategy based on motor time-sharing control, the paper theoretically verified that the robot can switch between the four motion modes.

The robot integrates four-bar walking, self-deforming and four-bar and six-bar rolling modes. A series of simulation and prototype experiment results are presented to verify the feasibility of multiple motion modes of the robot.

The work presented in this paper provides a good theoretical basis for further exploration of multiple mode mobile robots. It is an attempt to design the multi-mode mobile robot based on single loop kinematotropic mechanisms. It is also a kind of exploration of the new unknown movement law.