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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.

The purpose of the paper is to propose an efficient, simple and concise unified robot calibration method that simultaneously considers errors in hand-eye parameters, kinematic parameters and tool center point (TCP) position.

This paper proposes a unified robot calibration method. First, the initial hand-eye matrix and TCP position are computed without considering kinematic parameter errors. Second, the nominal TCP positions in the laser tracker coordinate system {S} are computed. The actual TCP positions in {S} are directly measured. Third, a unified parameter error calibration model is established, and the sequential quadratic programming algorithm is used for error identification. Finally, the identified errors are used for direct error compensation.

Simulation results prove that the proposed scheme can accurately calibrate the hand-eye parameters, kinematic parameters and TCP position simultaneously. Experimental results reveal that the maximum value of the absolute positioning errors is reduced from 5.4725 mm to 0.4095 mm (reduced by 92.52%). Thus, the proposed approach meets the accuracy requirements of most robotic applications.

The main contributions of this paper are: (1) this scheme is efficient. The method can achieve fully automatic calibration by incorporating Kronecker products for the initial hand-eye matrix and TCP position computation. Thereby significantly improving the calibration efficiency and liberating the labor force. (2) This scheme is simple and concise. The hand-eye parameters, kinematic parameters and TCP position errors are modeled in a unified framework. Furthermore, the related redundant parameters are deleted.

The shape of a product plays a crucial role in shaping consumer behavior. Despite the voluminous research on factors influencing consumers’ shape preferences, there remains a limited understanding of how the busy mindset, a mentality increasingly emphasized by marketing campaigns, works. This study aims to fill this gap by exploring the relationship between a busy mindset and the preference for angular-shaped versus circular-shaped products and brand logos.

This research consists of seven experimental studies using various shape stimuli, distinct manipulations of busy mindset, different assessments of shape preference and samples drawn from multiple countries.

The findings reveal that a busy mindset leads to a preference for angular shapes over circular ones by amplifying the need for uniqueness. In addition, these effects are attenuated when products are scarce.

This research represents one of the pioneering efforts to study the role of a busy mindset on consumers’ aesthetic preferences. Beyond yielding insights for practitioners into visual marketing, this research contributes to the theories on the busy mindset and shape preference.

This study aims to investigate the effect of the virtual spokesperson type on the consumers' preference for new products. To meet the consumer needs of Generation Z, virtual spokespeople have become new assistants in brand marketing. However, how virtual spokespersons drive consumer preference for new products is minimally understood.

This research conducts three experiments to investigate the influence of virtual spokesperson type on consumers' preference for new products.

The research shows that, for radically new products, competent virtual spokespersons improve consumers' perception of self-efficacy and thus consumers' preference; for incrementally new products, warm virtual spokespersons improve consumers' perception of social connection and thus consumers' willingness to buy.

This study broadens research on brand spokespersons and virtual spokespersons. This research also enriches and expands research on the consideration of new product types in brand spokespersons.

The digitalization of archival management has rapidly developed with the maturation of digital technology. With data's exponential growth, archival resources have transitioned from single modalities, such as text, images, audio and video, to integrated multimodal forms. This paper identifies key trends, gaps and areas of focus in the field. Furthermore, it proposes a theoretical organizational framework based on deep learning to address the challenges of managing archives in the era of big data.

Via a comprehensive systematic literature review, the authors investigate the field of multimodal archive resource organization and the application of deep learning techniques in archive organization. A systematic search and filtering process is conducted to identify relevant articles, which are then summarized, discussed and analyzed to provide a comprehensive understanding of existing literature.

The authors' findings reveal that most research on multimodal archive resources predominantly focuses on aspects related to storage, management and retrieval. Furthermore, the utilization of deep learning techniques in image archive retrieval is increasing, highlighting their potential for enhancing image archive organization practices; however, practical research and implementation remain scarce. The review also underscores gaps in the literature, emphasizing the need for more practical case studies and the application of theoretical concepts in real-world scenarios. In response to these insights, the authors' study proposes an innovative deep learning-based organizational framework. This proposed framework is designed to navigate the complexities inherent in managing multimodal archive resources, representing a significant stride toward more efficient and effective archival practices.

This study comprehensively reviews the existing literature on multimodal archive resources organization. Additionally, a theoretical organizational framework based on deep learning is proposed, offering a novel perspective and solution for further advancements in the field. These insights contribute theoretically and practically, providing valuable knowledge for researchers, practitioners and archivists involved in organizing multimodal archive resources.

The purpose of this study is to develop a transient wheel–rail rolling contact model to primarily investigate the rail damage under wet condition when the train passes through the welded joints.

The impact force induced by welded joints is obtained through vehicle–track coupling dynamics. The normal and tangential wheel–rail contact pressures were solved by elastohydrodynamic lubrication (EHL) theory and simplified third-body layer theory, respectively. Then, the obtained tangential pressure and normal pressure were applied to the finite element model as moving loads, simulating cyclic loading. Finally, the shakedown map and critical plane method were used to predict rolling contact fatigue (RCF) and the initiation of fatigue cracks.

The results indicate that RCF will occur and fatigue cracks are more prone to appear on the subsurface of the rail, specifically around 2.7 mm below the rail surface in the vicinity of the welded joint and its heat-affected zone.

The cosimulation of numerical model and finite element model was implemented. The influence of surface roughness and fluids was considered. In this model, the normal and tangential wheel–rail contact pressure, the stress and strain and the rail fatigue cracks were obtained under a rail-welded joint excitation.

Employee green behavior (EGB) is a type of pro-environment behavior at the workplace strategized by organizations to attain sustainable development goals. While organizations have prioritized eco-friendly practices to attain sustainability objectives, EGB has emerged as an essential area of research. Considering the need for sustained employee green behavior, it is important to understand what stimulates such behaviors in an organization. Therefore, we propose a theoretical model grounded in social exchange theory to assess the effect of organizational commitment on employee green behavior, work-related use of social media, social well-being and psychological well-being.

A questionnaire-based survey approach was used to collect data from 203 employees of Indian manufacturing and service industries. Partial least square structural equation modeling (PLS-SEM) analysis was applied to examine the proposed hypothesis.

The results revealed positive and significant effects of organizational commitment on psychological well-being, social well-being, work-related social media use and employee green behavior. Further, psychological well-being mediates the association between work-related social media use and employee green behavior.

This is one of the first studies to examine the effect of organizational commitment on employee green behavior to the best of our knowledge. Additionally, the findings empirically establish organizational commitment, work-related social media use and psychological well-being as antecedents to employee green behavior, thus offering novel insights and theoretically contributing to the employee green behavior, well-being and organizational literature.

This paper aims to prepare third-order nonlinear optical (NLO) organic materials with large nonlinear optimization value, high damage threshold and ultrafast response time.

A series of novel symmetric and asymmetric compounds possessing third-order NLO properties were synthesized using 1,3,5-tribromobenzene as the basis. The photophysical and electrochemical properties, as well as the click reactions, were characterized by means of UV–VIS–NIR absorption spectroscopy and cyclic voltammetry.

The donor–acceptor chromophores were inserted into compound, making the molecule to have a broader absorption in the near-infrared regions and a narrower optical and electrochemical band gap. It also formed an electron-delocalized organic system, which has larger effects on achieving a third-order NLO response. The third-order NLO phenomenon of benzene ring complexes was experimentally studied at 532 nm using Z-scan technology, and some compounds showed the expected NLO properties.

The click products exhibit more NLO phenomena by performing different click combinations to the side groups, opening new perspectives on using the system in a variety of photoelectric applications.