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This paper aims to study the establishment of cooperative supply game model considering transportation hub location, and design the profit allocation rule of the cooperative supply coalition.

Based on the economic lost-sizing (ELS) game model and considering the location of transportation hub and the topology design of basic traffic network, we build a supply game model to maximize the profit of cooperative supply coalition. Based on the principle of proportion and the method of process allocation, we suppose the procedural proportional solution of the supplier cooperative supply game.

Through numerical examples, the validity and applicability of the proposed model and the procedural proportional solution were verified by comparing the procedural proportional solution with the weighted Shapley value, the equal division solution and the proportional rule.

This paper constructs a feasible mixed integer programming model for cooperative supply game. We also provide the algorithm of the allocation rule of cooperative supply game and the property analysis of the allocation rule.

This paper aims to theoretically investigate a knowledge management model from the combined perspective of knowledge acquisition and knowledge application and its effect on organizational performance.

This study reviews prior research on knowledge acquisition and knowledge application, puts forward the concepts of “the extensiveness of knowledge acquisition” and “the concentration of knowledge application” and more importantly proposes an integrated model by combining these two dimensions. Four case examples of enterprises are subsequently described and analyzed to illustrate the sources of knowledge acquisition, the objects of knowledge application and their influences on organizational performance.

Four knowledge management modes and their impacts are confirmed in this study. Specifically, the organization of the turbojet engine mode (high extensiveness of knowledge acquisition and high concentration of knowledge application) can achieve good performance. The pipeline mode (high extensiveness of knowledge acquisition and low concentration of knowledge application) is the second, which has limited influence on good organizational performance. Organizations with the flashlight mode (low extensiveness of knowledge acquisition and high concentration of knowledge application) can achieve limited performance under the appropriate environment. The candle mode (low extensiveness of knowledge acquisition and low concentration of knowledge application) is the worst, performance of which is poor due to the break of the knowledge chain.

This paper holds that organizations should actively use the turbojet engine mode, adopt the pipeline mode and the flashlight mode cautiously, and avoid falling into the candle mode.

To the best of the authors’ knowledge, this study is among the first to propose the concepts of “the extensiveness of knowledge acquisition” and “the concentration of knowledge application,” and provides a combined model for analyzing differences in organizational performance from the perspective of knowledge.

This study aims to investigate the impact of environmental responsibility (ER) on consumers' green customization intention (GCI), as well as the separate and joint moderating effects of consumers' negative perceptions and environmental claim type (ECT) on the relationship between ER and GCI.

In this study, two online experimental questionnaires and one between-subject lab experiment were conducted to test all hypotheses.

This study found that high-ER consumers are more likely to choose environmental options in the product customization process (versus low ER). The effect of ER on consumers' GCI is moderated by consumers' negative perceptions of green consumption (e.g. green skepticism and perceived environmental premium (PEP)). Additionally, ECT's joint moderating effect and consumers' negative perception of green customization consumption can be decreased by a substantive (versus associative) claim type in message framing.

The findings provide new insights into the factors affecting consumers' GCIs and have significant practical implications. First, consumers' different ER levels should not be neglected when examining the value of green customization. Second, consumers' green skepticism and PEP could reduce consumers' GCI. Third, the use of substantive environmental claims may also be particularly helpful for companies' communication strategies regarding consumers' low-level negative perceptions of green options.

While previous studies state “environmental responsibility” as a key characteristic of general green consumption, the authors extend that to a green customization process and connect that with consumers' negative perceptions and ECT. In addition, the separate and joint moderating effects of consumers' negative perceptions and ECT were examined. Thus, these findings refine the existing understanding of the relationship between ER and green customization.

The purpose of this paper is to present a method that addresses the data sparsity problem in points of interest (POI) recommendation by introducing spatiotemporal context features based on location-based social network (LBSN) data. The objective is to improve the accuracy and effectiveness of POI recommendations by considering both spatial and temporal aspects.

To achieve this, the paper introduces a model that integrates the spatiotemporal context of POI records and spatiotemporal transition learning. The model uses graph convolutional embedding to embed spatiotemporal context information into feature vectors. Additionally, a recurrent neural network is used to represent the transitions of spatiotemporal context, effectively capturing the user’s spatiotemporal context and its changing trends. The proposed method combines long-term user preferences modeling with spatiotemporal context modeling to achieve POI recommendations based on a joint representation and transition of spatiotemporal context.

Experimental results demonstrate that the proposed method outperforms existing methods. By incorporating spatiotemporal context features, the approach addresses the issue of incomplete modeling of spatiotemporal context features in POI recommendations. This leads to improved recommendation accuracy and alleviation of the data sparsity problem.

The research has practical implications for enhancing the recommendation systems used in various location-based applications. By incorporating spatiotemporal context, the proposed method can provide more relevant and personalized recommendations, improving the user experience and satisfaction.

The paper’s contribution lies in the incorporation of spatiotemporal context features into POI records, considering the joint representation and transition of spatiotemporal context. This novel approach fills the gap left by existing methods that typically separate spatial and temporal modeling. The research provides valuable insights into improving the effectiveness of POI recommendation systems by leveraging spatiotemporal information.

A vital job for one sharing business is dynamically dispatching shared items to balance the demand-supply of different sharing points in one sharing network. In order to construct a highly efficient dispatch strategy, this paper proposes a new dispatching algorithm based on the findings of sharing network characteristics.

To that end, in this paper, the profit-changing process of single sharing points is modeled and analyzed first. And then, the characteristics of the whole sharing network are investigated. Subsequently, some interesting propositions are obtained, based on which an algorithm (named the Two-step random forest reinforcement learning algorithm) is proposed.

The authors discover that the sharing points of a common sharing network could be categorized into 6 types according to their profit dynamics; a sharing network that is made up of various combinations of sharing stations would exhibit distinct profit characteristics. Accounting for the characteristics, a specific method for guiding the dynamic dispatch of shared products is developed and validated.

Because the suggested method considers the interaction features between sharing points in a sharing network, its computation speeds and the convergence efficacy to the global optimum scheme are better than similar studies. It suits better to the sharing business requiring a higher time-efficiency.

The purpose of this paper is to bridge the understanding of apparent dichotomies such as East and West, philosophy and social sciences, and antiquity and modernity, and to continue the vibrant expansion of competitive dynamics study into the realm of East-West theoretical fusion.

The author looks to classical Chinese philosophy to discover the origins and nature of competitive dynamics. The paper develops the premise that the foundational thrusts of this contemporary Western management topic spring from ancient Eastern conceptions of duality, relativity, and time.

Research inroads are made along two paths. First, the paper traces the theoretical and philosophical underpinnings of competitive dynamics to Eastern thinking. Then by bridging what have customarily been perceived as fundamentally different paradigms, it reveals, in a new light, empirical findings in this strategy subfield.

Linking Western management science, and specifically the study of competitive dynamics, to classical Eastern philosophy raises new research questions in the areas of international management and management education as well as competitive dynamics. In the latter, the paper suggests opportunities for exploring connections between traditional Chinese concepts and contemporary organizational and competition research issues, including competitive and cooperative relationships at the industry level. Future research may also investigate the fundamental differences and similarities between Eastern and Western philosophies, and their implications for competitive strategies.

From a relatively obscure corner of business academia, competitive dynamics now occupies a distinct place in strategic management research and is a topic of intense interest to scholars in a variety of disciplines. The usual view is that competitive dynamics fits squarely in the spectrum of social sciences, an organically home-grown area of Western study. This paper examines the topic from a distinctly different angle – through the lens of ancient Eastern philosophy – to discern deeper a deeper meaning and wider application.

The electromechanical brake system is leading the latest development trend in railway braking technology. The tolerance stack-up generated during the assembly and production process catalyzes the slight geometric dimensioning and tolerancing between the motor stator and rotor inside the electromechanical cylinder. The tolerance leads to imprecise brake control, so it is necessary to diagnose the fault of the motor in the fully assembled electromechanical brake system. This paper aims to present improved variational mode decomposition (VMD) algorithm, which endeavors to elucidate and push the boundaries of mechanical synchronicity problems within the realm of the electromechanical brake system.

The VMD algorithm plays a pivotal role in the preliminary phase, employing mode decomposition techniques to decompose the motor speed signals. Afterward, the error energy algorithm precision is utilized to extract abnormal features, leveraging the practical intrinsic mode functions, eliminating extraneous noise and enhancing the signal’s fidelity. This refined signal then becomes the basis for fault analysis. In the analytical step, the cepstrum is employed to calculate the formant and envelope of the reconstructed signal. By scrutinizing the formant and envelope, the fault point within the electromechanical brake system is precisely identified, contributing to a sophisticated and accurate fault diagnosis.

This paper innovatively uses the VMD algorithm for the modal decomposition of electromechanical brake (EMB) motor speed signals and combines it with the error energy algorithm to achieve abnormal feature extraction. The signal is reconstructed according to the effective intrinsic mode functions (IMFS) component of removing noise, and the formant and envelope are calculated by cepstrum to locate the fault point. Experiments show that the empirical mode decomposition (EMD) algorithm can effectively decompose the original speed signal. After feature extraction, signal enhancement and fault identification, the motor mechanical fault point can be accurately located. This fault diagnosis method is an effective fault diagnosis algorithm suitable for EMB systems.

By using this improved VMD algorithm, the electromechanical brake system can precisely identify the rotational anomaly of the motor. This method can offer an online diagnosis analysis function during operation and contribute to an automated factory inspection strategy while parts are assembled. Compared with the conventional motor diagnosis method, this improved VMD algorithm can eliminate the need for additional acceleration sensors and save hardware costs. Moreover, the accumulation of online detection functions helps improve the reliability of train electromechanical braking systems.

This paper aims to prepare an oil-impregnated porous polytetrafluoroethylene (PTFE) composite with advanced tribological properties using citric acid as a novel pore-forming agent.

Citric acid (CA) was used to form pores in PTFE, and then oil-impregnated PTFE composites were prepared. The pore-forming efficiency of CA was evaluated. The possible mechanism of lubrication was proposed according to the tribological properties.

The results show CA is an efficient pore-forming agent and completely removed, and the porosity of the PTFE increases with the increase of the CA content. The oil-impregnated porous PTFE exhibits an excellent tribological performance, an increased wear resistance of 77.29% was realized in comparison with neat PTFE.

This study enhances understanding of the lubrication mechanism of oil-impregnated porous polymers and guides for their tribological applications.