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This paper aims to propose a real-time evolutionary multi-objective semi-analytical inverse kinematics (IK) algorithm (EMSA-IK) for solving the multi-objective IK of redundant manipulators.

Within EMSA-IK, the parameterization method is applied to reduce the number of optimization variables of the evolutionary algorithm and calculate semi-analytical solutions that meet high target pose accuracy. The original evolutionary algorithm is improved with the proposed adaptive search sub-space strategy so that the improved evolutionary algorithm can be used to efficiently perform global search within the parametric joint space to obtain the global optimal parametric joint angles that satisfy multi-objective constraints.

Ablation experiments show the effectiveness of the improved strategy used for evolutionary algorithms. Comparative experiments on different manipulators demonstrate the advantages of EMSA-IK in terms of generalizability and balancing multiple objectives, for example, motion continuity, joint limits and obstacle avoidance. Real-world experiments further validate the effectiveness of the proposed algorithm for real-time application.

The semi-analytical IK solution that simultaneously satisfies high target pose accuracy and multi-objective constraints can be obtained in real time. Compared to existing semi-analytical IK algorithms, the proposed algorithm achieves obstacle avoidance for the first time. The proposed algorithm demonstrates superior generalizability, applicable to not only redundant manipulators with revolute joints but also those with prismatic joints.

This study aims to investigate the factors influencing service employee work and personal well-being affecting their intention to leave the organization. This research explored the effects of service climate, resilience and workplace well-being (WWB) on service employee perceptions of subjective well-being and turnover intention. PERMA framework of individual flourishing and well-being (Seligman, 2011) was used to measure employee WWB and reflected their positive emotions, engagement, relationships, meaning and accomplishment.

Service employees (n = 250) completed an online self-administered survey. partial least squares structural equation (PLS-SEM) modeling and multi-group analysis (PLS-MGA) were utilized to test how gender differences influenced personal and organizational factors, and their impacts on PERMA dimensions and outcomes.

Results revealed a significant effect of service climate and resilience on PERMA. Only service employee work-meaning positively influenced SWB and negatively turnover intention. Examining each dimension of employee engagement showed similar impacts of service climate and resilience for both men and women, while absorption increased turnover intention for men.

To the best of the authors’ knowledge, this study is one of the first to test the PERMA framework as service employee WWB. The study advances the employee well-being line of research by exploring the impacts of service climate and resilience on PERMA dimensions. The PERMA framework was extended to examine three sub-dimensions of employee engagement as unique PERMA dimensions. This study advances the limited knowledge of how work and personal factors affect service employees’ work and subjective well-being from a gender perspective.

This paper aims to analyze the direct and indirect effects of investor protection on forced CEO turnover.

The authors investigate 5,175 firm-year observations from 16 European countries over 2012–2018, collect data on four national investor protection indicators, identify 196 forced CEO turnovers and use multiple logistic regression models.

The results show that a reduction in the degree of investor protection significantly increases the probability of a forced change of the company’s CEO. Furthermore, when the degree of investor protection increases, directors are attributed a lower degree of responsibility in the event of a decline in earnings performance. Therefore, the relation between a decrease in profitability and a forced change of CEO is reduced.

The research is focused on countries belonging to the European Economic Area and most of the investor protection indicators are derived from surveys. Concerning policy implications, the findings suggest that regulators should focus on the effective enforcement of investor protection mechanisms.

The results confirm that characteristics at the country level have an impact on corporate decisions, highlighting the importance of increasing the degree of investor protection as a means of mitigating agency conflicts and improving stewardship.

To the best of the authors’ knowledge, this study explores a relatively underinvestigated topic as it uses investor protection indicators to jointly evaluate both direct and indirect effects on forced changes of CEO through cross-national research.

This study aims to examine why executives increase investments in corporate social responsibility (CSR) as a strategic action to protect their firms’ reputation from the possibility of a contagion effect following CSR-related corporate misconduct in the industry by drawing on an impression management perspective. This study also examines internal and external governance mechanisms as boundary conditions.

The sample includes panel data of firms listed in the S&P 500 index from 2009 to 2013. The authors used firm fixed-effects models to test the hypotheses.

The results show that recent CSR-related corporate misconduct occurred in other firms, inducing executives to increase investments in CSR. Moreover, internal and external governance mechanisms, which are CEO incentives and institutional ownership, moderated the relationship.

This study contributes to prior literature on the factors influencing CSR at the multilevel of analysis by examining how recent CSR-related corporate misconduct in the industry interacts with corporate governance mechanisms as boundary conditions to influence firm commitment to CSR.

Blockchains used by e-commerce consortia are a novel form of governance that facilitates coordination and collaboration among the numerous organisations that comprise e-commerce supply chains. Despite the increasing prevalence of consortium blockchain networks for e-commerce, there is a limited understanding of the economic and social dynamics that influence the behaviour of blockchain consortium members. By utilising transaction cost theory and social exchange theory, this research investigates the interplay between blockchain transaction-specific investment (BTSI), trust, adaptive collaboration (ADC) and the overall performance of supply chains in consortium blockchains

A quantitative research approach was employed to collect data from a representative sample of blockchain organisations affiliated with e-commerce consortium blockchains worldwide. Following this, the data obtained from 361 participants were analysed using descriptive and inferential statistics.

The results of our study indicate that BTSI has a substantial impact on trust. Furthermore, trust plays a pivotal role in shaping ADC, and ADC, in turn, acts as a mediator in the relationship between trust and performance outcomes.

This study underlines these economic and social dynamics in the evolving context of consortium blockchain networks, offering insights into their significance within a technology-driven environment.

This paper aims to improve the corrosion resistance of AH36 carbon steel, an epoxy resin (EP)-based superhydrophobic coating was prepared on the surface of AH36 carbon steel.

The hydroxylated multi-walled carbon nanotubes were used as nanocontainers, and the corrosion inhibitor L-proline was loaded by negative pressure method and then modified it with 3-aminopropyltriethoxysilane and 3-mercaptopropyltrimethoxysilane, got functionalized hydroxy carbon nanotubes (KH-CNTs@LP). The KH-CNTs@LP was mixed with the EP, and the KH-CNTs@LP/EP superhydrophobic coating was successfully prepared on the surface of the AH36 carbon steel matrix by spraying.

The results showed that the water contact angle of the KH-CNTs@LP/EP superhydrophobic coating is 155.2° and the rolling angle is 5°. The KH-CNTs@LP/EP superhydrophobic coating had a good corrosion resistance in the pH = 4 corrosion environment, |Z|0.01 Hz was 7.21 × 10⁷ Ω·cm².

The KH-CNTs@LP/EP superhydrophobic coating is pH-responsive and releases L-proline, which increased the impedance of the coating and can effectively improve the protection efficiency of the coating on the metal. The active protection is provided by loaded L-proline inhibitor from KH-CNTs@LP, whereas the passive protection is achieved through the water rejection of superhydrophobic surfaces.

Rapid industrialization and construction generate substantial concrete waste, leading to significant environmental issues. Nearly 10 billion metric tonnes of concrete waste are produced globally per year. In addition, concrete also accelerates the consumption of natural resources, leading to the depletion of these natural resources. Therefore, this study uses artificial intelligence (AI) to examine the utilization of recycled concrete aggregate (RCA) in concrete.

An extensive database of 583 data points are collected from the literature for predictive modeling. Four machine learning algorithms, namely artificial neural network (ANN), random forest (RF), ridge regression (RR) and least adjacent shrinkage and selection operator (LASSO) regression (LR), in predicting simultaneously concrete compressive and tensile strength were evaluated. The dataset contains 10 independent variables and two dependent variables. Statistical parameters, including coefficient of determination (R²), mean square error (MSE), mean absolute error (MAE) and root mean square error (RMSE), were employed to assess the accuracy of the algorithms. In addition, K-fold cross-validation was employed to validate the obtained results, and SHapley Additive exPlanations (SHAP) analysis was applied to identify the most sensitive parameters out of the 10 input parameters.

The results indicate that the RF prediction model performance is better and more satisfactory than other algorithms. Furthermore, the ANN algorithm ranks as the second most accurate algorithm. However, RR and LR exhibit poor findings with low accuracy. K-fold cross-validation was successfully applied to validate the obtained results and SHAP analysis indicates that cement content and recycled aggregate percentages are the effective input parameter. Therefore, special attention should be given to sensitive parameters to enhance the concrete performance.

This study uniquely applies AI to optimize the use of RCA in concrete production. By evaluating four machine learning algorithms, ANN, RF, RR and LR on a comprehensive dataset, this study identities the most effective predictive models for concrete compressive and tensile strength. The use of SHAP analysis to determine key input parameters and K-fold cross-validation for result validation adds to the study robustness. The findings highlight the superior performance of the RF model and provide actionable insights into enhancing concrete performance with RCA, contributing to sustainable construction practice.

This paper explores the application of supervised machine learning (ML) classification models to address supplier performance analysis and risk profiling as a multi-class classification problem. The research highlights that current applications of machine learning in supplier selection primarily focus on binary classification problems, underscoring a significant gap in the literature.

This research paper opts for a structured approach to solve supplier selection and risk profiling using supervised machine learning multi-class classification models and prediction probabilities. The study involved a synthetic data set of 1,600 historical data points, creating a supplier selection framework that simulates current supply chain (SC) performance. The “Supplier Analysis and Selection ML Module” guided supplier selection recommendations based on ML analysis. Real-world variability is introduced through random seeds, impacting actual delivery dates, quantity delivered and quality performance. Supervised ML models, with hyperparameter tuning, enable multi-class classification of suppliers, considering past delivery performance and risk calculations.

The study demonstrates the effectiveness of the supervised ML-based approach in ensuring consistent supplier selection across multi-class classification problems. Beyond evaluating past delivery performance, it introduces a new dimension by predicting and assessing supplier risks through ML-generated prediction probabilities. This can enhance overall SC visibility and help organizations optimize strategies associated with risk mitigation, inventory management and customer service.

The findings highlight the adaptability of ML-based methodologies in dynamic SC environments, providing a proactive means to identify and manage risks. These insights are vital for organizations aiming to bolster SC resilience, particularly amid uncertainties.

The practical implications of this study are significant for both commercial and humanitarian supply chain management (SCM). For commercial applications, the ML-based methodology allows businesses to make more informed supplier selection decisions, reducing risks and improving operational efficiency. In disaster and humanitarian SC contexts, the use of ML can improve preparedness and resource allocation, ensuring that critical supplies reach affected areas promptly.

The study’s implications extend to disaster and humanitarian SCM, where timely and efficient delivery is critical for saving lives and alleviating suffering. ML tools can improve preparedness, resource allocation and coordination in these contexts, enhancing the resilience and responsiveness of humanitarian supply chains.

Unlike conventional methods focused on quality, cost and delivery performance aspects, the current study introduces supervised ML to identify and assess supplier risks through prediction probabilities for multi-class classification problems (delivery performance as late, on-time and ahead), offering a refined understanding of supplier selection in dynamic SC environments.

This study aims to explore the perceptions of young consumers, a rapidly growing market segment in Taiwan’s restaurant industry, and analyze the composition and structure of background music alongside the physical environment in luxury restaurants. This study identifies key factors that significantly influence customer emotions and examine the moderating effect of service encounter pace on customer emotions and satisfaction for offering practitioners practical insights to enhance young consumers’ dining experiences.

This study used partial least squares structural equation modeling (PLS-SEM) for hypothesis testing. The research model was evaluated within the context of luxury restaurants, and moderation analyses were conducted to assess the impact of service encounter pace on customer satisfaction.

The study reveals that luxury restaurants’ physical environment and background music significantly affect pleasure and arousal. Notably, arousal positively influences customer satisfaction, while pleasure does not. The findings also support that service encounter pace creates a fully moderated effect between customer emotion (pleasure and arousal) and customer satisfaction.

The findings assist luxury restaurant management in developing effective servicescapes that evoke positive customer emotions and establish an optimal service encounter pace, thereby enhancing overall customer satisfaction.

This study enriches our understanding of the dining experiences of Taiwanese Gen Y consumers in luxury restaurants. It also sheds light on emerging hospitality trends and explores the perceptions of younger generations, potentially including Generation Z.

Although a fashion subscription offers significant environmental benefits by transforming physical products into shared services, most customers are reluctant to adopt it. This hesitation, exacerbated by poor communication from brands that primarily emphasize its personal benefits, hinders its sustainable growth. This study aims to examine specifically which concerns increase hesitation, and the role of explicitly informing consumers about the service’s environmental benefits in mitigating the impact of consumer concerns on their hesitation.

Data were collected through an online experiment with more than a thousand U.S. adults nationwide and analyzed using a two-step analysis. First, theory-based causal modeling was conducted to examine the effects of consumer concerns on hesitation, accounting for ambivalence as a mediator and informed environmental benefits as a moderator. Second, machine learning was used to cross-validate the findings.

Results show that certain types of consumer concerns increase hesitation, significantly mediated by ambivalence, and confirm that informed environmental benefits mitigate the effects of some concerns on hesitation.

This study contributes to building on the hierarchy of effects theory by exploring negatively nuanced constructs – concerns, ambivalence and hesitation – beyond the traditional constructs representing the cognitive, affective and conative stages of consumer decision-making. Findings provide strategic guidance to brands on how to communicate the new service to consumers. Leveraging theory-based causal modeling with machine learning-based predictive modeling provides a novel methodological approach to explaining and predicting consumer hesitation toward new services.