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The purpose of this paper is to formulate and simulate the interaction between particles and fields for gyroklystron amplifier rapidly and effectively.

From Maxwell's equations, transient electromagnetic field equations and particle motion equations, as the subject of a self‐consistent field theory, are obtained by semi‐analytical and semi‐numerical method. A numerical calculation model on the interaction between particles and fields is proposed and illustrated in detail. Based on above‐mentioned field theory, calculation model and the Runge‐Kutta method, a program to simulate the interaction between particles and fields is designed and its software implementation is achieved using Fortran language. To testify the correctness of the calculation model, a millimeter wave gyroklystron amplifier is simulated by the program, and some numerical results are presented and analyzed. Meanwhile, a contrast among the simulated frequency characteristic, the FDTD‐PIC results and the experiments is made. The computing resources needed by the program are compared with that needed by the FDTD‐PIC method.

The calculation results show that the model and the program take less CPU time and fewer computing resources than FDTD‐PIC simulation. Moreover, simulated results are in accord with the FDTD‐PIC results and the experiments.

A calculation model on the interaction between particles and fields is proposed and achieved in this paper. A program is designed and proved to be a fast and effective calculation tool for solving the simulation of the interaction. In addition, a detailed speed spread model of particles is studied. The calculation model considering speed spread, the program and the simulated results constitute the main contribution of this paper.

This study aimed to investigate positive associations between three main motivations (social interaction, information and entertainment) for the use of social network sites (SNSs) and users' well-being, as well as the multiple mediating effects of perceived social support, positive and honest self-presentation.

A sample of 759 active users of SNSs (WeChat Moments, Qzone and Weibo) aged 14–43 years was measured with online questionnaires. Correlation analysis and structural equation modeling were implemented to examine the corresponding hypotheses.

The results showed the overall intensity of motivations was positively associated with users' well-being; perceived social support and positive self-presentation played intermediary roles and honest self-presentation and perceived social support had a chain mediation effect. However, the motivations of social interaction, information and entertainment indirectly affected users' well-being through three different mediation paths.

Although some studies have investigated the effects of motivations (including social interaction, information and entertainment) for SNS use on users' well-being, there has not been a consistent conclusion. The findings may shed light on the motivations for SNS use and how they may affect people's well-being in the digital era, thereby promoting their healthy use of SNSs as well as improved interface design and user management of SNSs.

Traditional case-adaptation methods have poor accuracy, low efficiency and limited applicability, which cannot meet the needs of knowledge users. To address the shortcomings of the existing research in the industry, this paper proposes a case-adaptation optimization algorithm to support the effective application of tacit knowledge resources.

The attribute simplification algorithm based on the forward search strategy in the neighborhood decision information system is implemented to realize the vertical dimensionality reduction of the case base, and the fuzzy C-mean (FCM) clustering algorithm based on the simulated annealing genetic algorithm (SAGA) is implemented to compress the case base horizontally with multiple decision classes. Then, the subspace K-nearest neighbors (KNN) algorithm is used to induce the decision rules for the set of adapted cases to complete the optimization of the adaptation model.

The findings suggest the rapid enrichment of data, information and tacit knowledge in the field of practice has led to low efficiency and low utilization of knowledge dissemination, and this algorithm can effectively alleviate the problems of users falling into “knowledge disorientation” in the era of the knowledge economy.

This study provides a model with case knowledge that meets users’ needs, thereby effectively improving the application of the tacit knowledge in the explicit case base and the problem-solving efficiency of knowledge users.

The adaptation model can serve as a stable and efficient prediction model to make predictions for the effects of the many logistics and e-commerce enterprises' plans.

This study designs a multi-decision class case-adaptation optimization study based on forward attribute selection strategy-neighborhood rough sets (FASS-NRS) and simulated annealing genetic algorithm-fuzzy C-means (SAGA-FCM) for tacit knowledgeable exogenous cases. By effectively organizing and adjusting tacit knowledge resources, knowledge service organizations can maintain their competitive advantages. The algorithm models established in this study develop theoretical directions for a multi-decision class case-adaptation optimization study of tacit knowledge.

This study aims to obtain the more precise decision map to fuse the source images by Coefficient significance method. In the area of multifocus image fusion, the better decision map is very important the fusion results. In the processing of distinguishing the well-focus part with blur part in an image, the edge between the parts is more difficult to be processed. Coefficient significance is very effective in generating the better decision map to fuse the multifocus images.

The energy of Laplacian is used in the approximation coefficients of redundant discrete wavelet transform. On the other side, the coefficient significance based on statistic property of covariance is proposed to merge the detail coefficient.

Due to the shift-variance of the redundant discrete wavelet and the effectiveness of fusion rule, the presented fusion method is superior to the region energy in harmonic cosine wavelet domain, pixel significance with the cross bilateral filter and multiscale geometry analysis method of Ripplet transform.

In redundant discrete wavelet domain, the coefficient significance based on statistic property of covariance is proposed to merge the detail coefficient of source images.

“V-bending” is the most commonly used bending process in which the sheet metal is pressed into a “V-shaped” die using a “V-shaped” punch to form a required angular bend. When the punch is removed after the operation, because of elastic recovery, the bent angle varies. This shape discrepancy is known as spring back which causes problems in the assembly of the component in the modern aerospace industry. Regarding the optimization of spring-back accuracy, this research will illustrate the laws of the transition area (TA) of the nondeformation area (NDA) during the 90° “V-shape” bending process.

According to the traditional “V-bending” process to optimize the spring-back accuracy, the bent sheets are divided into deformation area (DA) and NDA. For this reason, the traditional “V-bending” process may prolong error to optimize the spring-back accuracy because NDA has a certain amount of deformation, which the researcher always avoids. Firstly, bent sheets are divided into three parts in this research: DA, TA and NDA to avoid the distortion error in TA that are not considered in the NDA in traditional theory. Then, the stress and strain in the DA and TA were discussed during theoretical derivation and some hypotheses were proposed. In this research, the interval, position and distortion degree of the TA of the bending sheet are used by finite element analysis. Finally, V-shape bending tests for aluminum alloy at room temperature are used and labeled all the work pieces' TAs to realize the deformation amount in the TA.

The bending radius does not affect the range of the TA, it only changes the position of TA in the bending sheet. It is evident that the laws of TA were explored in the width direction and gradually changed from the inner layer to the outer layer based on the ratio of width and thickness of the bending plate/sheet.

In the modern aerospace industry, aircraft manufacturing technology must maintain high accuracy. This research has practical value in the 90° “V-shape” bending of metal sheets and the development of its spring-back accuracy.

This paper aims to present an adaptive sliding mode control (ASMC) for tunnel boring machine cutterhead telescopic system with uncertainties to achieve a high-precision trajectory in complex strata. This method could be applied to solve the problems caused by linear and nonlinear model uncertainties.

First, an integral-type sliding surface is defined to reduce the static tracking error. Second, a projection type adaptation law is designed to approximate the linear and nonlinear redefined parameters of the electrohydraulic system. Third, a nonlinear robust term with a continuous approximation function is presented for handling load force uncertainty and reducing sliding mode chattering. Moreover, Lyapunov theory is applied to guarantee the stability of the closed-loop system. Finally, the effectiveness of the proposed controller is proved by comparative experiments on a scaled test rig.

The linear and nonlinear model uncertainties lead to large variations in the dynamics of the mechanism and the tracking error. To achieve precise position tracking, an adaptation law was integrated into the sliding mode control which compensated for model uncertainties. Besides, the inherent sliding mode chattering was reduced by a continuous approximation function, while load force uncertainty was solved by a nonlinear robust feedback. Therefore, a novel ASMC for tunnel boring machine cutterhead telescopic system with uncertainties can improve its tracking precision and reduce the sliding mode chattering.

To the best of the authors’ knowledge, the ASMC is proposed for the first time to control the tunnel boring machine cutterhead telescopic system with uncertainties. The presented control is effective not only in control accuracy but also in parameter uncertainty.

Given the critical importance of green innovation (GI) for organizations in developing economies, this study aims to examine the interrelationship between knowledge management (KM) enablers, KM processes and GI. The research also indicates that certain combinations of KM enabler dimensions and KM processes can lead to better GI.

The study sample consists of 328 participants from Pakistan's medium- and large-sized manufacturing enterprises. Smart PLS 3.2.9 is used to verify the relationships. Moreover, the fuzzy set qualitative comparative analysis (fsQCA) investigates configurational paths for improving GI.

The results demonstrate that KM enablers significantly affect two aspects of GI – green product and process innovation – and KM processes. Moreover, KM processes significantly enhance two aspects of GI. The fsQCA findings indicate multiple combinations of KM enablers and KM processes dimensions that result in better GI.

To better understand the critical role of knowledge resources, future studies should explore the potential mediating mechanisms of KM processes or the moderating effects of strategic organizational factors in the relationship between KM enablers and GI.

The study offers valuable insight and a unique approach for policymakers and executives of corporations in developing countries to enhance their organizations' GI capacity through KM enablers and KM processes.

This research contributes to bridging research gaps in the literature and advances insights into the interrelationship among KM enablers, KM processes and GI. In addition, the study offers methodological significance by combining direct and configurational techniques to address two distinct facets of GI.

Continuous knowledge contribution behaviors (CKCB) are critical for the healthy development of online medical communities (OMCs). However, it is unclear that if and how contributors' prior actions and the responses they received from the community influence the nature of their future contributions. Drawing upon the Information Systems Continuance theory and Service Feedback theory, the purpose of the study is to examine the impact of knowledge contribution performance (KCP) on doctors' CKCB. Evaluation of social motivation, financial incentive and the moderating influence of expertise level (EL) provided further insight into the pathways that motivate various forms of CKCB.

In order to better understand the CKCB of physicians in OMCs, the authors divided it into two categories: A_CKCB (active CKCB) and P_CKCB (passive CKCB). Information Systems Continuance theory and Service Feedback theory are adapted and integrated with empirical findings from previous research on OMCs to develop a model of CKCB. This study used ordinary least squares (OLS) regression to test hypotheses in the preexisting research model based on data collected from a Chinese OMC platform.

The results show that KCP helps develop several facets of CKCB. According to the findings, doctors' CKCB improved dramatically after receiving feedback from A_CKCB and P_CKCB, but feedback from peers did not promote CKCB. This study found that financial rewards only have a significant positive effect on P_CKCB, and that the level of expertise has a negative effect on the effect. The findings also demonstrated that doctors' level of expertise moderates the relationship between fA_CKCB (a comprehensive evaluation of doctors' A_CKCB) and A_CKCB.

Future studies should look at the role of self-efficacy as a mediator and attitudes as a moderator in the link between KCP and various forms of CKCB. This will help authors figure out how important KCP is for physicians' CKCB. And future research should use more than one way to gather data to prove the above roles.

This study makes a significant contribution to understanding the association between CKCB and KCP by highlighting the significance of distinguishing between the various forms of CKCB and their underlying causes.

This research has advanced both the theory and practice of OMCs' user management by illuminating the central role of KCP in this context.

This study examines the relationship between higher education improvement and tacit knowledge importance. In this context, the scarcity of empirical and theoretical studies on acquiring tacit knowledge to enhance academic performance in higher education suggests that this research area holds significant importance for experts and policymakers. Consequently, this study aims to explore the factors that influence academic research performance at Chinese universities by acquiring tacit knowledge.

To achieve the study aims, the current approach utilizes the research technique based on the socialization, externalization, internalization and combination (SECI) model and knowledge management (KM) theory. To analyze the study objective, the authors collected data from post-graduate students at Chinese universities and analyzed it using structural equation modeling (SEM) to test the model and hypotheses.

The results indicated that social interaction, internalization and self-motivation have a positive impact on academic research performance through the acquisition of tacit knowledge. Furthermore, the findings suggest that academic researchers can acquire more knowledge through social interaction than self-motivation, thereby advancing research progress.

This study addresses the critical issues surrounding the acquisition of tacit knowledge and presents a comprehensive framework and achievements that can contribute to achieving exceptional academic performance.