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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.

The purpose of the article is to conduct a mathematical and theoretical analysis of soliton solutions for a specific nonlinear evolution equation known as the (2 + 1)-dimensional Zoomeron equation. Solitons are solitary wave solutions that maintain their shape and propagate without changing form in certain nonlinear wave equations. The Zoomeron equation appears to be a special model in this context and is associated with other types of solitons, such as Boomeron and Trappon solitons. In this work, the authors employ two mathematical methods, the modified F-expansion approach with the Riccati equation and the modified generalized Kudryashov’s methods, to derive various types of soliton solutions. These solutions include kink solitons, dark solitons, bright solitons, singular solitons, periodic singular solitons and rational solitons. The authors also present these solutions in different dimensions, including two-dimensional, three-dimensional and contour graphics, which can help visualize and understand the behavior of these solitons in the context of the Zoomeron equation. The primary goal of this article is to contribute to the understanding of soliton solutions in the context of the (2 + 1)-dimensional Zoomeron equation, and it serves as a mathematical and theoretical exploration of the properties and characteristics of these solitons in this specific nonlinear wave equation.

The article’s methodology involves applying specialized mathematical techniques to analyze and derive soliton solutions for the (2 + 1)-dimensional Zoomeron equation and then presenting these solutions graphically. The overall goal is to contribute to the understanding of soliton behavior in this specific nonlinear equation and potentially uncover new insights or applications of these soliton solutions.

As for the findings of the article, they can be summarized as follows: The article provides a systematic exploration of the (2 + 1)-dimensional Zoomeron equation and its soliton solutions, which include different types of solitons. The key findings of the article are likely to include the derivation of exact mathematical expressions that describe these solitons and the successful visualization of these solutions. These findings contribute to a better understanding of solitons in this specific nonlinear wave equation, potentially shedding light on their behavior and applications within the context of the Zoomeron equation.

The originality of this article is rooted in its exploration of soliton solutions within the (2 + 1)-dimensional Zoomeron equation, its application of specialized mathematical methods and its successful presentation of various soliton types through graphical representations. This research adds to the understanding of solitons in this specific nonlinear equation and potentially offers new insights and applications in this field.

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.

The precise and sensitive methods for authentication and differentiation of natural and synthetic indigo dyes are required for assurance of textile safety and public health. This study aims to develop a fast and simple method to distinguish natural indigo from synthetic one.

A static headspace gas chromatography-mass spectrometry (GC-MS) method was developed for identification of natural and synthetic indigo samples. Natural indigo samples prepared from three different plants and synthetic indigo samples from three famous manufacturers in China, were involved in this study, along with some nonindigo blue samples (such as direct blue, active blue and neutral blue). The yarns and fabrics dyed with natural and synthetic indigo were also analyzed by the GC-MS method.

High levels of aniline (21.87%–71.59%) or N-methylaniline (25.26%–38.73%) were detected only in synthetic indigo samples (1 g) using the static headspace GC-MS method. The yarns and fabrics dyed with the synthetic indigo were also detected with residual aniline (0.47%–14.86%) or N-methylaniline (6.59%–40.93%).

The results clearly demonstrated that aniline or N-methylaniline can be used a diagnostic marker for distinguishing natural indigo from synthetic indigo. The proposed static headspace GC-MS method is a rapid, simple and convenient approach for differentiation of natural and synthetic indigo, as well as for the yarns and fabrics dyed with synthetic indigo.

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.