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The purpose of this study is to improve the position and posture accuracy of posture alignment mechanism. The automatic drilling and riveting machine is an important equipment for aircraft assembly. The alignment accuracy of position and posture of the bracket type posture alignment mechanism has a great influence on the operation effect of the machine. Therefore, it is necessary to carry out the kinematic calibration.

Based on analysis of elastic deformation of the bracket and geometric errors of the posture alignment mechanism, an improved method of kinematic calibration was proposed. The position and posture errors of bracket caused by geometric errors were separated from those caused by gravity. The method of reduction of dimensions was applied to deal with the error coefficient matrix in error identification, and it did not change the coefficient of the error terms. The target position and its posture were corrected to improve the error compensation accuracy. Furthermore, numerical simulation and experimental verification were carried out.

The simulation and experimental results show that considering the influence of the elastic deformation of the bracket on the calibration effect, the error identification accuracy and compensation accuracy can be improved. The maximum value of position error is reduced from 5.33 mm to 1.60 × 10⁻¹ mm and the maximum value of posture error is reduced from 1.07 × 10⁻³ rad to 6.02 × 10⁻⁴ rad, which is superior to the accuracy without considering the gravity factor.

This paper presents a calibration method considering the effects of geometric errors and gravity. By separating position and posture errors caused by different factors and correcting the target position and its posture, the results of the calibration method are greatly improved. The proposed method might be applied to any parallel mechanism based on the positioner.

The purpose of this paper is to enhance the method developed by previous researchers. In addition to using the combined interference matrix, the combined connection matrix and the combined contact matrix of product components, the disassembly sequence matrix and the combined instability matrix with platform to evaluate instability of sub‐assemblies are built, and effects of changes of sub‐assembly disassembly directions or tools and the effect of gravity are considered to obtain the best disassembly sequence for a product with many components. A computer program is generated and results of two cases are compared with those of the available studies.

The methodology includes the combined interference matrix, the combined contact matrix and the combined connection matrix of components for a product. The combined instability matrix of sub‐assemblies, changes of sub‐assembly disassembly direction or tools, and the effect of gravity during operation are considered. The binary number system is used to simplify relations among components of a product.

This methodology enhances the existing method and software is generated. Results of two cases are compared and show the same optimum disassembly processes as those obtained from other researchers.

All matrices are defined by the directions of x, y and z with three axes perpendicular to each other. The computer program generated cannot be used for a product with components that must be disassembled in the directions different from the axes.

Two cases are used to investigate feasibility of the proposed methodology with the computer program generated. The first one is an electric drill, and the second one is a flash lighter.

The methodology described in this paper is feasible for study of disassembly processes of products. The software generated can be used to obtain the optimum disassembly process of products.

Firms aiming to enhance firm performance require specific investment and qualification of capability. However, the relationship between these factors and firm performance is influenced by boundary conditions. This study focuses on the role of shared values as a governance mechanism in moderating the relationship between specific investment, qualification of capability, and firm performance.

Drawing on transaction cost analysis, the authors develop a theoretical model to explore how shared values moderate the relationship between specific investment, qualification of capability, and firm performance. The authors collected data from 156 firms in Cote d’Ivoire, resulting in a sample of 216 observations. The authors employed hierarchical regression analysis and the “pick-a-point approach” to examine how specific investment and qualification of capability impact firm performance at different levels of shared values.

The results indicate that specific investment and qualification of capability have a partially positive impact on firm performance. Interestingly, shared values are an important moderating variable, acting as a boundary condition that affects the relationship between specific investment, qualification of capability, and firm performance. Specifically, specific investment leads to excellent firm performance only when shared values are not sufficiently high, whereas qualification of capability leads to superior firm performance only when shared values are sufficiently high.

This study has three research implications. First, this study enriches TCA literature by identifying shared values as a boundary condition and examining the moderating role of shared values. Second, the study findings discover new insights into how specific investment and qualification of capability enhance or inhabit organizational performance at different levels of shared values. Third, this study extends the existing research and reveals the specific conditions for positive or negative relationships between specific investment and organizational performance and qualification of capability and organizational performance.

First, compared to specific investment, qualification of capability has greater effect on organizational performance. Second, when considering whether to increase specific investment or/and improve qualification of capability, executives are encouraged to first evaluate their firm's level of shared values and then make appropriate strategic decision accordingly. Third, six tactics are recommended for enhancing shared values.

This study enriches the literature on transaction cost analysis and contributes to understanding the moderating role of shared values. The findings shed light on the specific investment, qualification of capability, and firm performance relationships. Additionally, this research highlights the importance of considering shared values as a boundary condition in examining these relationships.

This paper aims to present a review on utilizing shape memory technology (SMT) for active assembly/disassembly, i.e. assembly/disassembly without physically touching.

The fundamentals behind the shape memory effect (SME) in materials, in particular shape memory alloys (SMAs) and polymers, which are the cornerstones of SMT, are introduced, together with the possible approaches to implement this effect in active assembly/disassembly. Example applications for not only active assembly/ disassembly, but also programmed active disassembly are presented.

The advantages of utilizing SMT over conventional assembly/disassembly techniques are identified.

The paper introduces the fundamentals behind the SME and the basic approaches to implement the SMT in not only active assembly/disassembly, but also programmed active assembly.

This paper examines how mathematics teacher educators (MTEs) learn to enact equitable mathematics instruction using technology through lesson study (LS).

A LS team with three MTEs conducted three iterations of LS on teaching the Pythagorean Theorem in an in-person, technology-mediated environment. Many forms of data were collected: Desmos activities, videos of research lessons (RLs), videos of MTE RL debriefings, artifacts of student learning in the Desmos Dashboard, and MTEs' written self-reflection. The authors investigate the teacher educators' learning through LS by analyzing the MTE debriefings of the RLs using Bannister’s (2015) framework for teacher learning in communities of practice.

The MTEs learned to enact equitable mathematics instruction using technology through addressing emerging issues related to intellectual authority and use of student thinking. Throughout the LS, the MTEs sought ways of promoting students' mathematical authority and using student thinking through features of the Desmos platform.

This study focuses on MTEs' learning without examining participating preservice teachers' learning. It demonstrates the benefits of LS for MTEs' professional learning.

This study showcases how a research-based Desmos activity is used and refined to promote MTE learning how to implement equitable mathematics instruction.

The study contributes to better understanding of how LS could be used to develop MTEs' professional learning. Moreover, the dual process of participation and reification was concretized through diagnostic and prognostic frames in the LS context, which enriches the concept of community of practice.

This study aims to identify how perceived destination social responsibility (DSR) drives destination brand loyalty through a jointly and independently mediated mechanism of cognitive and affective components (e.g. tourist-destination identification, cognitive image, affective image and tourist satisfaction) and to examine the moderating role of individual-level collectivist values in linking perceived DSR and tourist behaviors.

An online survey is conducted to collect the data of 351 domestic tourists visiting an urban tourism destination (e.g. Danang City) in Vietnam. A serial multiple mediation model and moderation model were examined by applying covariance-based structural equation modeling.

This research’s results highlight the leading factors of perceived DSR in the process of forming destination brand loyalty and confirm the vital role of the intermediary mechanism of tourists' cognition and affect during this process. The chain of causal relationships DSR → TDI → CI → AI → TS → DBL confirms the role of perceived DSR as an essential prerequisite factor of DBL, creating a close connection to tourists' cognition and affect and contributing to improving destination brand loyalty. Individual-level collectivist values were found to positively moderate the links between perceived DSR and tourist-destination identification, affective image and destination brand loyalty.

Future research would provide insights into the links between perceived DSR and tourist behaviors by considering moderating variables (e.g. cultural distance and tourist types) and uncovering specific insights into each destination stakeholder's DSR activities.

A new integrated model of destination brand loyalty development is proposed to explore a new path for destination brand loyalty formation through cognitive, affective and cognitive-affective pathways. This moderating stream of examining individual-level collectivist values can make a significant contribution to the extant tourism literature by promoting a more positive tourist perception of DSR, thereby increasing tourists' knowledge, beliefs and emotions and enhancing destination brand loyalty.

The purpose of this paper is to expand the investor sentiment’s effect on investment efficiency to the layer of “credit financing,” studying whether investor sentiment can affect credit financing level and the inner mechanism of the effect.

The authors obtain firm-level data from the Shanghai and Shenzhen stock markets and using panel estimation techniques examine whether investor sentiment can affect credit financing level and the inner mechanism of the effect.

This paper finds that credit financing plays the role of partial media in the process of investor sentiment affecting investment efficiency. Based on the funds increasing effect, with the high-investor sentiment and increasing credit financing, corporations alleviate the financing constraints, but also provide a convenient for the abuse of corporate funds. So, investor sentiment positively associates with enterprises’ overinvestment, while investor sentiment negatively associates with enterprises’ underinvestment. Relying on the particular system background and property right environment in China, this paper finds that investor sentiment has an effect on the overinvestment of state-owned enterprises and the underinvestment of private enterprises through credit financing channel, while it does not function in the overinvestment of private enterprises. The reason of the difference is that under the soft budget constraint in the country, the credit preference of state-owned enterprises and the creditor’s rights management of banks are partially absent.

By fusing the special financial environment and institutional background, this thesis further includes in the analysis frame the difference in governance effect by credit financing between state-owned and privately owned listed companies, and further analyzes the difference in impact on investment efficiency in enterprises of different natures after investor sentiment has affected enterprise credit financing.

This paper has verified the constraint assumption and deepened the research work on bank credit supply and answered practical questions such as whether the banks in the country exercise supervision function over the listed companies and on which kind of listed companies the supervision function plays a more effective role.

As an unofficial substitution mechanism, bank-enterprise relationship can elevate the investment efficiency by private owned enterprises. Based on the timely research results on credit financing, reference is provided for private listed companies to utilize investor sentiment to improve its investment efficiency.

This paper has proved the specific path which creates the dual effects on resources allocation by investor sentiment, that is, the intermediary transmission in credit financing, clarifying the mechanism of action by which investor sentiment affects the efficiency of enterprise investment and making incremental contribution to the research of how investor sentiment affects the efficiency of enterprise investment.

Stock selection has long been identified as a challenging task. This line of research is highly contingent upon reliable stock ranking for successful portfolio construction. The purpose of this paper is to employ the methods from computational intelligence (CI) to solve this problem more effectively.

The authors develop a risk-adjusted strategy to improve upon the previous stock selection models by two main risk measures – downside risk and variation in returns. Moreover, the authors employ the genetic algorithm for optimization of model parameters and selection for input variables simultaneously.

It is found that the proposed risk-adjusted methodology via maximum drawdown significantly outperforms the benchmark and improves the previous model in the performance of stock selection.

Future work considers an extensive study for the risk-adjusted model using other risk measures such as Value at Risk, Block Maxima, etc. The authors also intend to use financial data from other countries, if available, in order to assess if the method is generally applicable and robust across different environments.

The authors expect this risk-adjusted model to advance the CI research for financial engineering and provide an promising solutions to stock selection in practice.

The originality of this work is that maximum drawdown is being successfully incorporated into the CI-based stock selection model in which the model's effectiveness is validated with strong statistical evidence.

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 understand genomics scientists’ perceptions in data quality assurances based on their domain knowledge.

The study used a survey method to collect responses from 149 genomics scientists grouped by domain knowledge. They ranked the top-five quality criteria based on hypothetical curation scenarios. The results were compared using χ2 test.

Scientists with domain knowledge of biology, bioinformatics, and computational science did not reach a consensus in ranking data quality criteria. Findings showed that biologists cared more about curated data that can be concise and traceable. They were also concerned about skills dealing with information overloading. Computational scientists on the other hand value making curation understandable. They paid more attention to the specific skills for data wrangling.

This study takes a new approach in comparing the data quality perceptions for scientists across different domains of knowledge. Few studies have been able to synthesize models to interpret data quality perception across domains. The findings may help develop data quality assurance policies, training seminars, and maximize the efficiency of genome data management.