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The purpose of this paper is to develop a new time-dependent KdV6 equation. The authors derive multiple soliton solutions and multiple complex soliton solutions for a time-dependent equation.

The newly developed time-dependent model has been handled by using the Hirota’s direct method. The authors also use the complex Hirota’s criteria for deriving multiple complex soliton solutions.

The examined extension of the KdV6 model exhibits complete integrability for any analytic time-dependent coefficient.

The paper presents a new efficient algorithm for constructing extended models which give a variety of multiple real and complex soliton solutions.

The paper introduced a new time-dependent KdV6 equation, where integrability is emphasized for any analytic time-dependent function.

The findings are new and promising. Multiple real and multiple complex soliton solutions were formally derived.

This is an entirely new work where a new time-dependent KdV6 equation is established. This is the first time that the KdV6 equation is examined as a time-dependent equation. Moreover, the complete integrability of this newly developed equation is emphasized via using Painlevé test.

The increasingly active data practice in academic environments makes investigating college faculty users’ potential needs for library data services (LDS) essential. Guided by a conceptual framework rooted in the data lifecycle and the extended technology acceptance model, this study aims to investigate the relationship between faculty’s data engagement (DE) and their attitudes toward multiaspect LDS.

An online survey at a master’s college was conducted to collect data regarding faculty data practice, potential needs for data services (DS) and attitudes toward multiaspect LDS. Based on 139 complete and valid responses, the study built three conceptual models to demonstrate faculty users’ potential acceptance of LDS for research and teaching.

Participants’ research and teaching-related DE and background factors directly or indirectly affect their attitudes toward general DS, an institutional data repository if available and repository-based data curation.

The study contributes to DS and librarianship research by offering three conceptual models to explore LDS’ holistic support for faculty research and teaching. Moreover, the study provides insights into faculty’s job-related DE factors and calls for future research on effective DS in more college communities.

Fashion subscription service is a newly emerged retailing model that provides an innovative way of shopping to meet consumers’ fashion needs. From the perspective of innovation adoption, the purpose of this paper is to provide an insight of consumers’ perceptions as well as adoption intention of this innovative retailing format.

This research is qualitative in nature, utilizing focus group study approach. In this paper, content analysis was applied to analyze the data.

While possessing varying degrees of knowledge about fashion subscription retailing, the participants shared the following perceptions of relative advantages, including convenience, personalization, consumer excitement, opportunities to try new styles, and opportunity to better manage their apparel budget. Concerns mainly focused on missing social shopping experiences and the hassle in the cancellation process. The overall adoption intention was high.

Due to the nature of this research, the sample size was limited and results may not be generalized. This research paid less attention to individual differences, in terms of demographic and psychographic characteristics.

Future marketing could focus more on educating consumers about the attributes of the services they provide. Retailers can strategically leverage the positively perceived advantages in their marketing communications to enhance consumers’ adoption intention of their services.

The paper fills a gap in the literature on consumer behavior toward fashion subscription retailing and sheds light for companies in their endeavors to excel in this new retailing venue.

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.

Abundant studies of outpatient visits apply traditional recurrent neural network (RNN) approaches; more recent methods, such as the deep long short-term memory (DLSTM) model, have yet to be implemented in efforts to forecast key hospital data. Therefore, the current study aims to reports on an application of the DLSTM model to forecast multiple streams of healthcare data.

As the most advanced machine learning (ML) method, static and dynamic DLSTM models aim to forecast time-series data, such as daily patient visits. With a comparative analysis conducted in a high-level, urban Chinese hospital, this study tests the proposed DLSTM model against several widely used time-series analyses as reference models.

The empirical results show that the static DLSTM approach outperforms seasonal autoregressive integrated moving averages (SARIMA), single and multiple RNN, deep gated recurrent units (DGRU), traditional long short-term memory (LSTM) and dynamic DLSTM, with smaller mean absolute, root mean square, mean absolute percentage and root mean square percentage errors (RMSPE). In particular, static DLSTM outperforms all other models for predicting daily patient visits, the number of daily medical examinations and prescriptions.

With these results, hospitals can achieve more precise predictions of outpatient visits, medical examinations and prescriptions, which can inform hospitals' construction plans and increase the efficiency with which the hospitals manage relevant information.

To address a persistent gap in smart hospital and ML literature, this study offers evidence of the best forecasting models with a comparative analysis. The study extends predictive methods for forecasting patient visits, medical examinations and prescriptions and advances insights into smart hospitals by testing a state-of-the-art, deep learning neural network method.

Consistency and consensus are two important research issues in group decision-making (GDM). Considering some drawbacks associated with these two issues in existing GDM methods with intuitionistic multiplicative preference relations (IMPRs), a new GDM method with complete IMPRs (CIMPRs) and incomplete IMPRs (ICIMPRs) is proposed in this paper.

A mathematically programming model is constructed to judge the consistency of CIMPRs. For the unacceptably consistent CIMPRs, a consistency-driven optimization model is constructed to improve the consistency level. Meanwhile, a consistency-driven optimization model is constructed to supplement the missing values and improve the consistency level of the ICIMPRs. As to GDM with CIMPRs, first, a mathematically programming model is built to obtain the experts' weights, after that a consensus-driven optimization model is constructed to improve the consensus level of CIMPRs, and finally, the group priority weights of alternatives are obtained by an intuitionistic fuzzy programming model.

The case analysis of the international exchange doctoral student selection problem shows the effectiveness and applicability of this GDM method with CIMPRs and ICIMPRs.

First, a novel consistency definition of CIMPRs is presented. Then, a consistency-driven optimization model is constructed, which supplements the missing values and improves the consistency level of ICIMPRs simultaneously. Therefore, this model greatly improves the efficiency of consistency improving. Experts' weights determination method considering the subjective and objective information is proposed. The priority weights of alternatives are determined by an intuitionistic fuzzy (IF) programming model considering the risk preference of experts, so the method determining priority weights is more flexible and agile. Based on the above theoretical basis, a new GDM method with CIMPRs and ICIMPRs is proposed in this paper.

This study reveals the trigger of innovative behavior from the perspective of intrinsic and extrinsic spiritual inspiration and provides a new research idea for the formation mechanism of innovative behavior. The purpose of this study is to provide certain guidance and implications for enterprises to cultivate and enhance employees’ innovative behavior.

We conducted three studies, collected multi-source data (N = 1,175) from different countries longitudinally, as well as used hierarchical regression analysis and fuzzy-set quantitative comparative analysis to verify the theoretical model.

According to the findings, both spiritual leadership and career calling have a positive impact on employees’ innovative behavior through the mediating effect of autonomous motivation and the moderating effect of person-vocation fit.

Innovative behavior is the positive professional pursuit of employees, which is difficult to form without the motivation of spiritual factors. Spirituality is a complex concept that contains intrinsic and extrinsic spiritual factors, both of which could stimulate employees’ innovative behavior. Although many discussions have been held on this topic in recent years, little attention has been paid simultaneously to the motivating effects of the two perspectives. Drawn from self-determination theory, this study explores the mechanisms of two spiritual motivation paths (i.e. the intrinsic and extrinsic spiritual motivation paths) in the improvement of employees’ innovative behavior.

This paper aims to investigate effect of porous polystyrene microspheres encapsulated inhibitor on the protection performance of epoxy resin coating.

Porous polystyrene (PS) microspheres were synthesized by soap-free emulsion polymerization. The morphology of microspheres was observed by scanning electron microscopy and transmission electron microscopy. Corrosion inhibitor benzotriazole was encapsulated into porous PS microspheres. The protection performance of epoxy resin coating with different contents of PS microspheres was tested by polarization curve.

The findings of electrochemical impedance spectroscopy and scanning vibrating electrode technique showed that addition of corrosion inhibitor to porous PS microspheres further improved the protection performance of the coatings.

Porous PS microspheres could be used as nanocontainer to encapsulate corrosion inhibitor.

Addition of porous PS microspheres with corrosion inhibitor improved the protection performance of the coatings.

This paper aims to investigate whether a complementary effect exists between government R&D subsidies (GRSs) and intellectual property rights (IPR) protection on innovation performance in the pharmaceutical manufacturing industry in China. This paper also attempts to understand whether this complementary effect is significantly different across China’s eastern, central and western regions.

The study sample comprised 28 provinces involved in the pharmaceutical manufacturing industry in China from 2003 to 2014. Ordinary least squares was used to test the complementary effects of GRSs and IPR protection on innovation performance. The seemingly unrelated estimation test was also applied to ascertain whether differences existed between the eastern and western regions.

A complementary effect between GRSs and IPR protection in terms of improving the innovation of pharmaceutical manufacturers was found to exist only in eastern and western regions. There was no significant difference in the complementary effect between GRSs and IPR protection in terms of improving innovation among the three regions. Without considering the interaction effect of GRSs and IPR protection, the GRSs were negatively associated with innovation in the eastern region, but positively related to the innovation in central region.

This study contributes to the literature on government policies and innovation by incorporating the interaction effect of GRSs and IPR protection on innovation in the context of a high-tech industry (pharmaceutical manufacturing) in an emerging economy (China). It also explores the internal differences in the influence of this complementary effect on innovation in an emerging economy, enriching the institution-based view.

Intelligent new energy vehicles (INEVs) are becoming the competitive hotspot for the automobile industry. The major purpose of this study is to determine how to increase innovation efficiency through knowledge sharing and technology spill between new energy vehicle (NEV) enterprises and technology enterprises. This will help to improve the core competence of the automobile industry in China. Also, it serves as a guide for the growth of other strategic.

The authors construct a tripartite evolutionary game model to study the cross-border cooperative innovation problem. Firstly, the payment matrix of NEV enterprise, technology enterprise and government is established, and the expected revenue of each participant is determined. Then, the replication dynamic equations and evolutionary stability strategies are analyzed. Finally, the theoretical research is validated through numerical simulation.

Results showed that: (1) An optimal range of revenue distribution coefficient exists in the cross-border cooperation. (2) Factors like research and development (R&D) success rate, subsidies, resource and technology complementarity, and vehicles intelligence positively influence the evolution towards cooperative strategies. (3) Factors like technology spillover risk cost inhibit the evolution towards cooperative strategies. To be specific, when the technology spillover risk cost is greater than 2.5, two enterprises are inclined to choose independent R&D, and the government chooses to provide subsidy.

The research perspective and theoretical analysis are helpful to further explore the cross-border cooperation of the intelligent automobile industry. The findings suggest that the government can optimize the subsidy policy according to the R&D capability and resource allocation of automobile industry. Moreover, measures are needed to reduce the risk of technology spillovers to encourage enterprise to collaborate and innovate. The results can provide reference for enterprises’ strategic choice and government’s policy making.

The INEV industry has become an important development direction of the global automobile industry. However, there is limited research on cross-border cooperation of INEV industry. Hence, authors construct a tripartite evolutionary game model involving NEV enterprise, technology enterprise and the government, and explore the relationship of cooperation and competition among players in the INEV industry, which provides a new perspective for the development of the INEV industry.