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This paper aims to propose a dynamic parameter identification method based on sensitivity analysis for the 5-degree of freedom (DOF) hybrid robots, to solve the problems of too many identification parameters, complex model, difficult convergence of optimization algorithms and easy-to-fall into a locally optimal solution, and improve the efficiency and accuracy of dynamic parameter identification.

First, the dynamic parameter identification model of the 5-DOF hybrid robot was established based on the principle of virtual work. Then, the sensitivity of the parameters to be identified is analyzed by Sobol’s sensitivity method and verified by simulation. Finally, an identification strategy based on sensitivity analysis was designed, experiments were carried out on the real robot and the results were verified.

Compared with the traditional full-parameter identification method, the dynamic parameter identification method based on sensitivity analysis proposed in this paper converges faster when optimized using the genetic algorithm, and the identified dynamic model has higher prediction accuracy for joint drive forces and torques than the full-parameter identification models.

This work analyzes the sensitivity of the parameters to be identified in the dynamic parameter identification model for the first time. Then a parameter identification method is proposed based on the results of the sensitivity analysis, which can effectively reduce the parameters to be identified, simplify the identification model, accelerate the convergence of the optimization algorithm and improve the prediction accuracy of the identified model for the joint driving forces and torques.

Black boys report experiencing more school-based racial discrimination than any other group (Butler-Barnes et al., 2019). Additionally, Black boys are viewed as older and less innocent than their peers beginning as early as 10 years old (Goff et al., 2014). Black boys are also suspended and expelled at much higher rates than other students (Graves & Wang, 2022). As such, there needs to be an investment in asset-based research designed to understand the factors that can help Black boys cope with these perceptions. Consequently, this chapter will discuss strengths based protective factors that will aid in the promotion of positive outcomes in Black boys.

This study evaluates the integrative role of justice theory, religiosity, satisfaction and trust in influencing customer loyalty to Islamic mobile banking.

This study surveyed 370 customers who used Islamic mobile banking. The authors employed SEM-PLS to estimate the proposed model and answer the hypotheses.

Empirical facts show that distributive justice, procedural justice and interactional justice can increase loyalty through the role of satisfaction. On the other hand, distributive justice, procedural justice and religiosity can predict loyalty through the role of trust.

This study encourages Islamic mobile banking providers to improve the quality of benefit distribution, the application of procedures and interaction among all levels of users. In addition, religious education innovation is also important to increase customer activity in using Islamic mobile banking.

Until now, none of the studies have estimated the loyalty of Islamic mobile banking users based on the integrative roles of justice theory, religiosity, satisfaction and trust. It, therefore, highlights the originality of this study.

This study aims to explore the role of international ambidexterity (IA) in improving the innovation capability of emerging market multinationals. In particular, the main purpose of this research is to study the relationship amongst digitalisation, IA and innovation performance (IP) amongst multinational enterprises in China’s healthcare industry.

The data for this investigation were collected from 134 listed companies in China’s healthcare industry during the study period. This study tested the hypotheses by constructing a two-way fixed-effects model.

The results show that both the balance dimension and the combined dimension of IA have significant positive effects on IP. Digitalisation not only has a direct positive effect on IP but also positively moderates the positive correlation between IA and IP.

Previous studies have not captured the relationship between ambidexterity, digitalisation and IP, and this study helps to fill in the gap and examine these associations in China’s healthcare industry. The results of this study provide valuable insights for healthcare industry managers to understand the role of ambidexterity and digitalisation in innovation in the context of internationalisation.

The present study aimed to examining the association between work–family conflict and turnover intention by exploring the mediating effect of job satisfaction and the moderating effect of perceived organizational support on preschool teachers in China.

A survey of 827 preschool teachers was conducted, and the data were analyzed using correlation analysis, hierarchical linear regression and path analysis with a structural equation model.

The results revealed that work–family conflict was significantly and positively associated with preschool teachers' turnover intention. Job satisfaction partially mediated the relationship between work–family conflict and turnover intention, while perceived organizational support moderated the association between work–family conflict and job satisfaction, thus mitigating the negative impact of work–family conflict on job satisfaction.

These findings contribute to the understanding of turnover among preschool teachers and suggest the need to enhance perceived organizational support to promote job satisfaction and reduce turnover in this profession.

Software defect prediction (SDP) is a critical aspect of software quality assurance, aiming to identify and manage potential defects in software systems. In this paper, we have proposed a novel hybrid approach that combines Gray Wolf Optimization with Feature Selection (GWOFS) and multilayer perceptron (MLP) for SDP. The GWOFS-MLP hybrid model is designed to optimize feature selection, ultimately enhancing the accuracy and efficiency of SDP. Gray Wolf Optimization, inspired by the social hierarchy and hunting behavior of gray wolves, is employed to select a subset of relevant features from an extensive pool of potential predictors. This study investigates the key challenges that traditional SDP approaches encounter and proposes promising solutions to overcome time complexity and the curse of the dimensionality reduction problem.

The integration of GWOFS and MLP results in a robust hybrid model that can adapt to diverse software datasets. This feature selection process harnesses the cooperative hunting behavior of wolves, allowing for the exploration of critical feature combinations. The selected features are then fed into an MLP, a powerful artificial neural network (ANN) known for its capability to learn intricate patterns within software metrics. MLP serves as the predictive engine, utilizing the curated feature set to model and classify software defects accurately.

The performance evaluation of the GWOFS-MLP hybrid model on a real-world software defect dataset demonstrates its effectiveness. The model achieves a remarkable training accuracy of 97.69% and a testing accuracy of 97.99%. Additionally, the receiver operating characteristic area under the curve (ROC-AUC) score of 0.89 highlights the model’s ability to discriminate between defective and defect-free software components.

Experimental implementations using machine learning-based techniques with feature reduction are conducted to validate the proposed solutions. The goal is to enhance SDP’s accuracy, relevance and efficiency, ultimately improving software quality assurance processes. The confusion matrix further illustrates the model’s performance, with only a small number of false positives and false negatives.

Existing studies suggest that negative impacts emanating from corporate fraud revelations may diffuse to other firms through lower trust and lower market participation. Extending this literature stream, the authors examine whether corporate fraud revelations are associated with higher costs of raising capital through initial public offerings (IPOs) for industry peers.

The authors employ several analysis techniques including univariate analysis, multivariate regressions, propensity score matching methodology, and probit estimation. The sample consists of 3,015 US IPO firms for the 1996–2021 period.

By adopting US private securities class action lawsuits as a proxy for the presence of corporate fraud, the authors find that fraud revelations are associated with higher IPO underpricing, higher post-IPO stock return volatility and increased likelihood of withdrawal from the offering for industry peers. The findings are robust to alternative industry definitions and litigation proxies and to the inclusion of a battery of controls, including industry, state and year fixed effects.

This study presents private firms with an additional industry litigation factor to consider when assessing the marginal costs of going public.

The need for precise synthesis of customized designs has resulted in the development of advanced coating processes for modern nanomaterials. Achieving accuracy in these processes requires a deep understanding of thermophysical behavior, rheology and complex chemical reactions. The manufacturing flow processes for these coatings are intricate and involve heat and mass transfer phenomena. Magnetic nanoparticles are being used to create intelligent coatings that can be externally manipulated, making them highly desirable. In this study, a Keller box calculation is used to investigate the flow of a coating nanofluid containing a viscoelastic polymer over a circular cylinder.

The rheology of the coating polymer nanofluid is described using the viscoelastic model, while the effects of nanoscale are accounted for by using Buongiorno’s two-component model. The nonlinear PDEs are transformed into dimensionless PDEs via a nonsimilar transformation. The dimensionless PDEs are then solved using the Keller box method.

The transport phenomena are analyzed through a comprehensive parametric study that investigates the effects of various emerging parameters, including thermal radiation, Biot number, Eckert number, Brownian motion, magnetic field and thermophoresis. The results of the numerical analysis, such as the physical variables and flow field, are presented graphically. The momentum boundary layer thickness of the viscoelastic polymer nanofluid decreases as fluid parameter increases. An increase in mixed convection parameter leads to a rise in the Nusselt number. The enhancement of the Brinkman number and Biot number results in an increase in the total entropy generation of the viscoelastic polymer nanofluid.

Intelligent materials rely heavily on the critical characteristic of viscoelasticity, which displays both viscous and elastic effects. Viscoelastic models provide a comprehensive framework for capturing a range of polymeric characteristics, such as stress relaxation, retardation, stretching and molecular reorientation. Consequently, they are a valuable tool in smart coating technologies, as well as in various applications like supercapacitor electrodes, solar collector receivers and power generation. This study has practical applications in the field of coating engineering components that use smart magnetic nanofluids. The results of this research can be used to analyze the dimensions of velocity profiles, heat and mass transfer, which are important factors in coating engineering. The study is a valuable contribution to the literature because it takes into account Joule heating, nonlinear convection and viscous dissipation effects, which have a significant impact on the thermofluid transport characteristics of the coating.

The momentum boundary layer thickness of the viscoelastic polymer nanofluid decreases as the fluid parameter increases. An increase in the mixed convection parameter leads to a rise in the Nusselt number. The enhancement of the Brinkman number and Biot number results in an increase in the total entropy generation of the viscoelastic polymer nanofluid. Increasing the strength of the magnetic field promotes an increase in the density of the streamlines. An increase in the mixed convection parameter results in a decrease in the isotherms and isoconcentration.

This study aims to elucidate the process and internal mechanism of place identity construction in traditional villages under the impact of tourism by taking Cuandixia village as a case. The research methods comprise participatory observation and in-depth interviews with the residents. The main results are as follows: the impact of tourism on traditional villages is mainly reflected in space reconstruction, livelihood change, social relations restructuring and culture change; under the impact of tourism, the representation of residents’ identity construction shows complexity, with positive and negative effects; and the place identity construction of residents affects their perception of and attitudes toward tourism. Moreover, self-esteem and self-efficacy principles play a key role in their perception of tourism. This study provides some reference for further investigation of the tourism development model and the mental mechanism of residents in traditional villages.

The purpose of this study is to model the theory of the low-velocity impact (LVI) process on sandwich beams consisting of flexible cores and face sheets reinforced with functionally graded carbon nanotubes (CNTs).

A series of parameters derived from molecular dynamics are used to consider the size scale in the mixture rule for the combination of CNTs and resin. A procedure involving the use of the first-order shear deformation theory of the beam is used to provide the displacement field of the sandwich beam. The energy method and subsequently the generalized Lagrange method are used to derive the motion equations. Due to the use of Hertz’s nonlinear theory to calculate the contact force, the equations of motion are nonlinear. Validation of the problem is carried out by comparing natural frequencies with other papers.

The influence of a series of parameters such as CNTs distributions pattern in the face sheets, the influence of the CNTs volume fraction and the influence of the core thickness to the face sheets thickness ratio in the issue of LVI on sandwich beams with clamped-clamped boundary conditions is investigated. The result shows that the type of CNTs pattern in the face sheet and the CNTs volume fraction have a very important effect on the answer to the problem, which is caused by the change in the value of the Young’s modulus of the beam at the contact surface. Changes in the core thickness to the face sheets thickness ratio has little effect on the impact response.

Considering the important application of sandwich structures in vehicles, aviation and ships, in this research, sandwich beams consisting of flexible core and CNTs-reinforced face sheets are investigated under LVI.