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The motivation of value co-creation among the multi-actor in sharing economy was an important topic in interactive marketing communication research. This study investigated how customer gratitude expression leads to value co-creation of PSPs in the sharing economy, and also investigates the moderating effect of platform benevolent climate.

A three-wave field survey (Study 1) and two experiments (Studies 2 and 3) were given to respondents with sharing economy practitioners.

First, customer gratitude expression positively influenced PSP's perceived meaningful work, which in turn enhanced their value co-creation intention. Second, PSP's perceived platform benevolent climate moderated the relationship between customer gratitude expression and PSP's perceived meaningful work.

Prior research discussed PSPs' value co-creation intention mainly from the perspective of platforms and PSPs, but few considered customer-PSP interaction perspective. This study revealed how customer gratitude expression influences PSP's value co-creation intention in highly interactive digital business context, examined the boundary condition of gratitude expression, and extended the application scenarios of social information processing theory.

The safety of high-speed rail operation environments is an important guarantee for the safe operation of high-speed rail. The operating environment of the high-speed rail is complex, and the main factors affecting the safety of high-speed rail operating environment include meteorological disasters, perimeter intrusion and external environmental hazards. The purpose of the paper is to elaborate on the current research status and team research progress on the perception of safety situation in high-speed rail operation environment and to propose directions for further research in the future.

In terms of the mechanism and spatio-temporal evolution law of the main influencing factors on the safety of high-speed rail operation environments, the research status is elaborated, and the latest research progress and achievements of the team are introduced. This paper elaborates on the research status and introduces the latest research progress and achievements of the team in terms of meteorological, perimeter and external environmental situation perception methods for high-speed rail operation.

Based on the technical route of “situational awareness evaluation warning active control,” a technical system for monitoring the safety of high-speed train operation environments has been formed. Relevant theoretical and technical research and application have been carried out around the impact of meteorological disasters, perimeter intrusion and the external environment on high-speed rail safety. These works strongly support the improvement of China’s railway environmental safety guarantee technology.

With the operation of CR450 high-speed trains with a speed of 400 km per hour and the application of high-speed train autonomous driving technology in the future, new and higher requirements have been put forward for the safety of high-speed rail operation environments. The following five aspects of work are urgently needed: (1) Research the single factor disaster mechanism of wind, rain, snow, lightning, etc. for high-speed railways with a speed of 400 kms per hour, and based on this, study the evolution characteristics of multiple safety factors and the correlation between the high-speed driving safety environment, revealing the coupling disaster mechanism of multiple influencing factors; (2) Research covers multi-source data fusion methods and associated features such as disaster monitoring data, meteorological information, route characteristics and terrain and landforms, studying the spatio-temporal evolution laws of meteorological disasters, perimeter intrusions and external environmental hazards; (3) In terms of meteorological disaster situation awareness, research high-precision prediction methods for meteorological information time series along high-speed rail lines and study the realization of small-scale real-time dynamic and accurate prediction of meteorological disasters along high-speed rail lines; (4) In terms of perimeter intrusion, research a multi-modal fusion perception method for typical scenarios of high-speed rail operation in all time, all weather and all coverage and combine artificial intelligence technology to achieve comprehensive and accurate perception of perimeter security risks along the high-speed rail line and (5) In terms of external environment, based on the existing general network framework for change detection, we will carry out research on change detection and algorithms in the surrounding environment of high-speed rail.

As the demand for human–robot collaboration in manufacturing applications grows, the necessity for collision detection functions in robots becomes increasingly paramount for safety. Hence, this paper aims to improve the existing method to achieve efficient, accurate and sensitive robot collision detection.

The external torque is estimated by momentum observers based on the robot dynamics model. Because the state of the joints is more accessible to distinguish under the action of the suppression operator proposed in this paper, the mutated external torque caused by joint reversal can be accurately attenuated. Finally, time series analysis (TSA) methods can continuously generate dynamic thresholds based on external torques.

Compared with the collision detection method based only on TSA, the invalid time of the proposed method is less during joint reversal. Although the soft-collision detection accuracy of this method is lower than that of the symmetric threshold method, it is superior in terms of detection delay and has a higher hard-collision detection accuracy.

Owing to the mutated external torque caused by joint reversal, which seriously affects the stability of time series models, the collision detection method based only on TSA cannot detect continuously. The consequences are disastrous if the robot collides with people or the environment during joint reversal. After multiple experimental verifications, the proposed method still exhibits detection capabilities during joint reversal and can implement real-time collision detection. Therefore, it is suitable for various engineering applications.

The diagnosis and prediction methods used for estimating the health conditions of the bearing are of great significance in modern petrochemical industries. This paper aims to discuss the accuracy and stability of improved empirical mode decomposition (EMD) algorithm in bearing fault diagnosis.

This paper adopts the improved adaptive complementary ensemble empirical mode decomposition (ICEEMD) to process the nonlinear and nonstationary signals. Two data sets including a multistage centrifugal fan data set from the laboratory and a motor bearing data set from the Case Western Reserve University are used to perform experiments. Furthermore, the proposed fault diagnosis method, combined with intelligent methods, is evaluated by using two data sets. The proposed method achieved accuracies of 99.62% and 99.17%. Through the experiment of two data, it can be seen that the proposed algorithm has excellent performance in the accuracy and stability of diagnosis.

According to the review papers, as one of the effective decomposition methods to deal with nonlinear nonstationary signals, the method based on EMD has been widely used in bearing fault diagnosis. However, EMD is often used to figure out the nonlinear nonstationarity of fault data, but the traditional EMD is prone to modal confusion, and the white noise in signal reconstruction is difficult to eliminate.

In this paper only the top three optimal intrinsic mode functions (IMFs) are selected, but IMFs with less correlation cannot completely deny their value. Considering the actual working conditions of petrochemical units, the feasibility of this method in compound fault diagnosis needs to be studied.

Different from traditional methods, ICEEMD not only does not need human intervention and setting but also improves the extraction efficiency of feature information. Then, it is combined with a data-driven approach to complete the data preprocessing, and further carries out the fault identification and classification with the optimized convolutional neural network.

This study investigates how role overload in the sharing economy leads to emotional exhaustion, which restricts value co-creation activity, and also investigates the moderating effect of perceived platform support.

Two experimental investigations and field research questionnaires were given to respondents with shared mobility industry expertise.

First, role overload detrimentally affects service providers' value co-creation behavior; second, emotional exhaustion acts as a mediator between role overload and value co-creation behavior; and finally, perceived platform support moderates the adverse effect of role overload on emotional exhaustion.

To the best of the authors' knowledge, this study is the first to explore the antecedents of value co-creation behavior from the service provider's perspective, extending the application of COR theory in a sharing economy context.

First, alternative mediators between role overload and emotional exhaustion were not identified. Second, other dimensions of role overload and their impacts were not examined. Lastly, this study did not explore broader perspectives beyond algorithms.

This study recommends that managers reduce role overload ex ante in terms of clarifying responsibilities and obligations, providing substantive resource support and rationalizing order allocation, respectively.

This study aims to extract knowledge of ancient Chinese scientific and technological documents bibliographic summaries (STDBS) and provide the knowledge graph (KG) comprehensively and systematically. By presenting the relationship among content, discipline, and author, this study focuses on providing services for knowledge discovery of ancient Chinese scientific and technological documents.

This study compiles ancient Chinese STDBS and designs a knowledge mining and graph visualization framework. The authors define the summaries' entities, attributes, and relationships for knowledge representation, use deep learning techniques such as BERT-BiLSTM-CRF models and rules for knowledge extraction, unify the representation of entities for knowledge fusion, and use Neo4j and other visualization techniques for KG construction and application. This study presents the generation, distribution, and evolution of ancient Chinese agricultural scientific and technological knowledge in visualization graphs.

The knowledge mining and graph visualization framework is feasible and effective. The BERT-BiLSTM-CRF model has domain adaptability and accuracy. The knowledge generation of ancient Chinese agricultural scientific and technological documents has distinctive time features. The knowledge distribution is uneven and concentrated, mainly concentrated on C1-Planting and cultivation, C2-Silkworm, and C3-Mulberry and water conservancy. The knowledge evolution is apparent, and differentiation and integration coexist.

This study is the first to visually present the knowledge connotation and association of ancient Chinese STDBS. It solves the problems of the lack of in-depth knowledge mining and connotation visualization of ancient Chinese STDBS.

The purpose of this paper is to solve the abrupt deterioration of lubricant performance in high-temperature conditions.

Three silver pyrazolyl methyl pyridine complexes with different morphologies were synthesized. A four-ball tribometer was used to assess the tribological characteristics as an additive for pentaerythritol oleate both independently and compound with 1-hexyl-3-methylimidazolium bis(trifluoromethane sulfonyl)imide.

The results showed that when silver complexes and ionic liquids (IL) act independently, sheet silver complex 1 and rod silver complex 2 exhibit good lubricating performance; the optimal antifriction concentration of the ILs is 0.25 Wt.%. The tribological results of the compounds additive of ILs and silver complexes indicate that the wear scar diameter of compound 1 decreased by 16.914%, the wear volume reduced by 7.44% and the lubrication effect surpassed that of the two substances individually; rod compound 2 exhibited an antagonistic effect, intensifying wear; compound 3’s lubrication effect fell between that of the two individual components.

The compound of sheet silver complexes and ILs effectively solves the agglomeration problem of micro/nano lubricant additives. When the interface fails, self-repair is completed, improving the stability and antiwear performance of the lubricating oil.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2024-0128

To ensure the stable operation of ironmaking process and the quality and output of sinter, the multi-objective optimization of sintering machine batching process was carried out.

The purpose of this study is to establish a multi-objective optimization model with iron taste content and batch cost as targets, constrained by field process requirements and sinter quality standards, and to propose an improved balance optimizer algorithm (LILCEO) based on a lens imaging anti-learning mechanism and a population redundancy error correction mechanism. In this method, the lens imaging inverse learning strategy is introduced to initialize the population, improve the population diversity in the early iteration period, avoid falling into local optimal in the late iteration period and improve the population redundancy error correction mechanism to accelerate the convergence rate in the early iteration period.

By selecting nine standard test functions of BT series for simulation experiments, and comparing with NSGA-?, MOEAD, EO, LMOCSO, NMPSO and other mainstream optimization algorithms, the experimental results verify the superior performance of the improved algorithm. The results show that the algorithm can effectively reduce the cost of sintering ingredients while ensuring the iron taste of sinter, which is of great significance for the comprehensive utilization and quality assurance of sinter iron ore resources.

An optimization model with dual objectives of TFe content and raw material cost was developed taking into account the chemical composition and quality indicators required by the blast furnace as well as factors such as raw material inventory and cost constraints. This model was used to adjust and optimize the sintering raw material ratio. Addressing the limitations of existing optimization algorithms for sintering raw materials including low convergence accuracy slow speed limited initial solution production and difficulty in practical application we proposed the LILCEO algorithm. Comparative tests with NSGA-III MOEAD EO LMOCSO and NMPSO algorithms demonstrated the superiority of the proposed algorithm. Practical applications showed that the proposed method effectively overcomes many limitations of the current manual raw material ratio model providing scientific and stable decision-making guidance for sintering production operations.

The purpose of this paper is to determine the effect of clothing fabrics, sizes and air ventilation rate on the volume and thickness of the air gap under the air ventilation garments (AVGs).

The geometric models of the human body and clothing were obtained by using a 3D body scanner. Then the distribution of the volume and thickness of the air gap for four clothing fabrics and three air ventilation rates (0L/S, 12L/S and 20L/S) were calculated by Geomagic software. Finally, a more suitable fabric was selected from the analysis to compare the distribution of the air gap entrapped for four clothing sizes (S, M, L and XL) and the three air ventilation rates.

The results show that the influence of air ventilation rate on the air gap volume and thickness is more obvious than that of the clothing fabrics and sizes. The higher is the air ventilation rate, the thicker is the air gap entrapped, and more evenly distributed is the air gap. It can be seen that the thickness of the air gap in the chest does not change significantly with the changes of the air ventilation rates, clothing fabrics and sizes, while the air gap in the waist is affected significantly.

This research provides a better understanding of the distribution of the air gap entrapped in ventilated garments, which can help in designing the optimal air gap dimensions and thus provide a basis and a reference for the design of the AVGs.