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A novel non-pneumatic safety tire, namely mechanical elastic wheel (ME-Wheel), has been developed recently to solve problems, including tire bursting and flat tire, of typical pneumatic tires. This paper aims to predict the life of the ME-Wheel accurately and settle the problem of lower lifespan.

This study proposes a new method to establish a novel model of virtual proving ground based on finite element analysis, and by combining with the random vibration damage analysis method, the life of an ME-Wheel is predicted precisely. Next, the weak parts and infinite life parts of the ME-Wheel are investigated from the perspective of constant life design, and an approximate response surface model is developed, which is suitable for the ME-Wheel. Then, the optimal results of the ME-Wheel are obtained, including tire modal, mass and its lifetime.

It is found that the proposed methods can provide a reliable theoretical basis for further improving the structural design and material selection of ME-Wheel parts. The results show that the improved ME-Wheel can reduce the weight and greatly improve the ability of anti-resonance, and the lifetime of ME-Wheel has significantly improved.

A new type of non-pneumatic tire (ME-Wheel) has been developed to avoid problems with traditional tires, such as tire leaking or puncture. A new method to establish a novel model of virtual proving ground based on finite element analysis has been proposed. The weakest key component of the ME-Wheel is determined. The life, mass and modal of the ME-Wheel are optimized.

Simulation is a well-known technique for using computers to imitate or simulate the operations of various kinds of real-world facilities or processes. The facility or process of interest is usually called a system, and to study it scientifically, we often have to make a set of assumptions about how it works. These assumptions, which usually take the form of mathematical or logical relationships, constitute a model that is used to gain some understanding of how the corresponding system behaves, and the quality of these understandings essentially depends on the credibility of given assumptions or models, known as VV&A (verification, validation and accreditation). The main purpose of this paper is to present an in-depth theoretical review and analysis for the application of VV&A in large-scale simulations.

After summarizing the VV&A of related research studies, the standards, frameworks, techniques, methods and tools have been discussed according to the characteristics of large-scale simulations (such as crowd network simulations).

The contributions of this paper will be useful for both academics and practitioners for formulating VV&A in large-scale simulations (such as crowd network simulations).

This paper will help researchers to provide support of a recommendation for formulating VV&A in large-scale simulations (such as crowd network simulations).

The emission of greenhouse gases has become an increasingly relevant topic in supply chain management. The steel industry is a highly intensive manufacturing industry with significant emission levels, particularly Scope 3 emissions, which are the indirect emissions from suppliers. Since a supply chain is seen as a non-mandatory measurement item within GHG measurement protocols, this article contributes to the literature on assessing the suppliers of a focal company relative to their emissions for complying with Scope 3 (indirect emissions). It adds to the evolving literature on low-carbon supply chains.

This study first conducted a survey with 110 suppliers from a focal transnational buyer company. A cluster analysis was performed, and ANOVA compared constructs relating to public or private ownership and country of origin. Finally, regression tested the relationship between the motivators and governance in the mitigation strategies.

Using cluster analysis, two groups of companies were found that have statistically significant differences. The influence of the country of origin was also found in relation to governance and mitigation strategies, as was the influence of the type of company on governance. Furthermore, the more motivated the suppliers and the more governance measures they adopt, the more companies adopt their own GHG mitigation strategies. These findings are summarized by way of an analytical framework that integrates the constructs with empirical evidence.

The steel industry is a sector that is particularly energy-intensive and produces millions of tons of CO₂ per year. Emissions from its SC (Scope 3) are relevant but still seen as a non-mandatory item for measurement purposes within the GHG measurement protocols, which leads to less attention being paid to the subject. This study contributes by way of its analytical framework that is validated by empirical data that can be tested in further studies.

This study aims to analyze those factors affecting the rural resident’s willingness to adopt solar photovoltaic (PV) which is important for accelerating the popularization of clean energy in China.

This study contained a sample of 653 households in 8 provinces/regions by stratified, and random sampling in rural China. Descriptive analysis, exploratory factor analysis and confirmatory factor analysis techniques have been used for analytical purposes.

The empirical results indicate that financial incentive and social interaction have positive effects on rural residents’ adoption willingness, while village leaders’ engagement can indirectly influence their adoption willingness through social interaction and residents’ cognition.

This study mainly considers external and internal factors but ignores the effect of technical factors. In addition, the samples are just selected from the residents who have adopted solar PV.

This study is expected to be useful for the government, regulators, village leaders, village leaders and other institutions.

This study conducts a systematic analysis and clarifies the relationship between factors (external and internal) and rural residents’ adoption willingness. The village leaders’ engagement is first added to the conceptual model as an external factor, which is very essential in rural residents’ adoption in China.

This study aims to explore the impact of chief executive officers’ (CEOs) values on the socially responsible behaviors (SRBs) of top management teams. Drawing from the social learning framework, it examines the mechanisms through which CEOs’ values shape SRBs within organizational leadership.

Using the hierarchical regression model, this study assesses direct effects, while the Monte Carlo method is used to evaluate indirect effects. The analysis is based on time-lagged data collected from 122 CEOs and 287 corresponding top managers from small- and medium-sized enterprises in China.

The results indicate a positive correlation between CEOs’ self-transcendent values and their own SRBs (i.e. doing-good and avoiding harm behavior). This, in turn, promotes top managers’ SRBs.

By highlighting the micro-foundations of corporate social responsibility, this study enriches the understanding of SRBs enhancement in management teams. It reveals the significance of CEO self-transcendent values as a precursor to SRBs and elucidates the learning processes involved.

The aim of this study is to provide a holistic analysis of all possible maritime business logistics processes related to import and export shipments in a fuzzy environment through a case study of a maritime logistics company based on the as-is and to-be models within business process management (BPM).

The analyses considered the following perspectives: (i) in the stage of the process identification, the definition of the problem was carried out; (ii) in the stage of the process discovery, ocean department was divided into ocean export/import operation departments; ocean export/import operation were divided into freight collect/prepaid operation processes; ocean export/import logistics activity groups were broken down into sub-activities for freight collect/prepaid operation; the logistics activity groups and their sub-activities were defined; each sub-activity as either operation or documentation process group was classified; the durations of sub-activities were evaluated by decision-makers (DMs) as fuzzy sets (FSs); the monthly total jobs activities were estimated by DMs as FSs; the applied to monthly jobs activities of total shipments were estimated by DMs as FSs; the durations of each sub-activities were aggregated; the duration of the logistics activity groups and the sub-activities for per job were calculated; the cumulative workload of logistics activity groups and sub-activities were calculated; the duration of sub-activities for per job as operation or documentation departments were calculated, (iii) in the stage of the process analysis, cumulative ocean export/import workload as operation or documentation for freight collect/prepaid were calculated; duration of activity groups and sub-activities for per job as operation or documentation were calculated; cumulative workload activity groups and sub-activities as operation or documentation were calculated, (iv) in the stage of the process redesign, cumulative workload, process cycle time as operation and documentation group and required labor force were calculated; the process cycle time of the theoretical, the as-is model and the to-be model were calculated: (i) the theoretical minimum process cycle time without resource were calculated by the critical path method (CPM), (ii) the process cycle time of the as-is model perspective with the 1 person resource constraint and (iii) the process cycle time of the to-be model perspective with the 2-person resource constraint were calculated by the resource constrained project scheduling problem (RCPSP) method.

The methodology for analyzing the ocean department operation process was successfully implemented in a real-life case study. It is observed that the results of the to-be model can be applicable for the company. The BPM-proposed methodology is applicable for the maritime logistics industry in the present study; however, it can be applied to other companies in maritime logistics as well as other industries.

This study contributes to research using BPM methodology in maritime logistics. This is the first study the logistics process analyses were carried out in terms of including all operation processes for a company. All processes were analyzed by using BPM methodology in maritime logistics. This study demonstrated the application of the BPM as-is and to-be models to maritime logistics. The as-is and the to-be models of the BPM methodology were applied in maritime logistics.

This methodology applied in this study can enable organizations operating in the time-urgent maritime logistics sector to manage their logistics processes more efficiently, increase customer satisfaction, reduce the risks of customer loss due to poor operational performance and increase profits in the long term. Through the use of these methodologies utilizing FSs, the CPM and the RCPSP methods, this study is expected to make contributions to the BPM literature and provide original insights into the field. Furthermore, this study will undertake a comprehensive analysis of maritime logistics with respect to BPM to deliver noteworthy contributions to the maritime logistics literature and provide original perspectives into the field.

This paper aims to propose the automatic carrier landing system with the fault-tolerant ability for carrier-based aircraft in the presence of deck motion, external airwake disturbance and actuator fault, which consists of the reference trajectory generation module and flight control module.

The longitudinal and lateral basic controllers are designed based on the optimal preview control (OPC), which can ensure favorable tracking performance and anti-disturbance ability of system. Furthermore, based on the OPC, the robust fault-tolerant preview control scheme is proposed to attenuate the impact of actuator fault on system, which ensures the safe landing of carrier-based aircraft in case of actuator failure.

Both the Lyapunov method and simulations prove that the tracking errors can converge to zero and system states can be asymptotically stable both in normal and fault operations.

The fault-tolerant control strategy is introduced into preview control to deal with actuator fault, which combines feedforward control based on future previewable information and feedback control based on current information to improve the system performance.

Drawing upon the social identity approach, this research examines whether and how leader–subordinate congruence at high levels of proactive personality facilitates subordinate creativity.

Two different data sets (Study 1: N = 205; Study 2: N = 222) were collected from leader–subordinate dyads in China to provide stronger empirical evidence regarding our hypotheses. Polynomial regression and response surface analyses were used to test our predictions.

Subordinate creativity in the scenario in which the leader and subordinate shared a highly proactive personality (i.e. high–high congruence) was higher than that in the incongruence or low–low congruence scenario. The subordinate's identification with the leader mediated the above relationships such that the indirect relationship between leader–subordinate proactive personality and subordinate creativity via identification with the leader was maximized in the high–high congruence scenario.

The findings suggest that organizations should consider selecting both highly proactive leaders and highly proactive subordinates to facilitate the subordinates' identification and subsequent creativity.

This research highlights the crucial role of leader–subordinate congruence in strong proactive personality for the promotion of creativity and reveals that identification with the leader accounts for the above relationship.

Nowadays, artificial intelligence (AI) technology has demonstrated extensive applications in the field of art design. Attribute editing is an important means to realize clothing style and color design via computer language, which aims to edit and control the garment image based on the specified target attributes while preserving other details from the original image. The current image attribute editing model often generates images containing missing or redundant attributes. To address the problem, this paper aims for a novel design method utilizing the Fashion-attribute generative adversarial network (AttGAN) model was proposed for image attribute editing specifically tailored to women’s blouses.

The proposed design method primarily focuses on optimizing the feature extraction network and loss function. To enhance the feature extraction capability of the model, an increase in the number of layers in the feature extraction network was implemented, and the structure similarity index measure (SSIM) loss function was employed to ensure the independent attributes of the original image were consistent. The characteristic-preserving virtual try-on network (CP_VTON) dataset was used for train-ing to enable the editing of sleeve length and color specifically for women’s blouse.

The experimental results demonstrate that the optimization model’s generated outputs have significantly reduced problems related to missing attributes or visual redundancy. Through a comparative analysis of the numerical changes in the SSIM and peak signal-to-noise ratio (PSNR) before and after the model refinement, it was observed that the improved SSIM increased substantially by 27.4%, and the PSNR increased by 2.8%, serving as empirical evidence of the effectiveness of incorporating the SSIM loss function.

The proposed algorithm provides a promising tool for precise image editing of women’s blouses based on the GAN. This introduces a new approach to eliminate semantic expression errors in image editing, thereby contributing to the development of AI in clothing design.