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The globalisation of higher education has created new challenges for educators to remain dynamic in teaching science undergraduates with diverse cultural backgrounds. The current study explores this challenge by evaluating the different personal factors among biosciences and pharmacy undergraduates that affect their learning approaches as well as to identify course-specific teaching approaches in order to determine their impacts on students' learning.

A cross-sectional survey design based on study process questionnaire (SPQ), and approaches and study skills inventory for students (ASSIST) was utilised to collect quantitative data from 129 biosciences and pharmacy undergraduates.

Biosciences undergraduates adopted deep and achieving learning approaches as they progressed through the curriculum, whilst surface learners dominated the pharmacy course. Ethnicity was shown to affect the learning approaches among the undergraduates. Although male undergraduates from both courses were more competitive than the female, elder female undergraduates tend to adopt deep learning approach. Pre-university qualifications did not significantly influence the undergraduate's learning approach. The contrasting finding on the preferred teaching approaches between biosciences and pharmacy undergraduates has highlighted the importance of providing tailored teaching and learning strategies to cater different students' learning needs.

This study has demonstrated the combinational use of SPQ and ASSIST instruments to compare undergraduate's learning approaches with preferred teaching approaches. The current finding could support the development of teaching and learning practice among the biosciences and pharmacy undergraduates with multicultural background.

The purpose of this paper is to investigate the perceived impact of just‐in‐time (JIT) implementation on operations performance, identify the relationship between elements of JIT (integrated and individual) and performance metrics, and based on the results, present some helpful suggests for improving JIT implementation in manufacturing industry.

Framework and hypotheses based on literature review were first constructed, then a questionnaire was developed to collect data from mainland China; in total, 224 information responses were obtained, then statistical analysis was carried out to test the hypotheses. Based on the data analysis results, conclusion and managerial insight were summarized.

Results show that, no matter what kind of industry or scale of sale of the firm, implementation of an aggregate bundle of JIT elements can improve production operations performance. However, for individual elements of JIT, their impacts are different; some basic elements of JIT like 5S, multiple‐skill employee and JIT purchasing can play a more remarkable role than other elements.

The conclusions of this paper can benefit firms in improving implementation of JIT in practice, especially for those firms in developing countries.

Although a number of studies on JIT implementation have been reported in developed countries, little attention has been paid to the status of Chinese firms; this paper has value for examining the practice of JIT in China and reference value for other developing countries’ firms.

Using the theoretical frameworks of public diplomacy and public relations, we mapped how the Chinese government has used panda imagery to build its national brand on Twitter and how this ‘panda diplomacy’ has facilitated its para-diplomatic actions. Our findings uncover new attempts by the Chinese government to engage in digital diplomacy. Mobilizing panda imagery on Twitter enhanced friendly relations with foreign political leaders and people and established a friendly and peaceful image of China on Twitter.

The purpose of this paper is to address the problem of profiling human behaviour patterns captured in surveillance videos for the application of online normal behaviour recognition and anomaly detection.

A novel framework is developed for automatic behaviour profiling and online anomaly detection without any manual labeling of the training dataset.

Experimental results demonstrate the effectiveness and robustness of the authors' approach using noisy and sparse datasets collected from one real surveillance scenario.

To discover the topics, co‐clustering topic model not only captures the correlation between words, but also models the correlations between topics. The major difference between the conventional co‐clustering algorithms and the proposed CCMT is that CCMT shows a major improvement in terms of recall, i.e. interpretability.

Although the concept of branding has been considered extensively in products and services, branding in Chinese is a relatively emerging phenomenon. This paper aims to present the enlivenment of branding in Chinese within the cross‐strait markets of Taiwan and Mainland China, which underlies various ideologies.

This study primarily reviews literatures of brand and brand name translation, defines the essentiality of brand naming, and outlines the branding strategies for entering cross‐strait markets. Furthermore, this study validates the using of substantially interpreted brands that support the authors' four developed propositions.

This study compares substantially interpreted brands in cross‐strait markets with a reference to commonly used translation methods. The results illustrate interesting ideologies among cross‐strait markets and can help managers achieve global brand recognition.

Since China and Taiwan share the same Chinese culture, the qualitative method proposed by the present authors is more applicable to practitioners who are eager to pursue branding in cross‐strait markets. Thus, the relevant techniques may not be applicable to people less familiar with Chinese culture.

The qualitative case study provides an advisable method for branding in Chinese. The results of this study can provide greater understanding of the various ideologies in cross‐strait markets, as well as help managers achieve global brand recognition.

The various ideologies from branding is complex, especially for those involved with linguistic essentials. Previous research has mainly focused on managerial‐based branding and customer‐based branding. This paper extends the interest into enlivening inspirations.

In recent years, electronic word-of-mouth (e-WOM) concerning travel products reflected in online review information has become an important reference for tourists to make their product purchase decisions, while for travel service providers (TSPs), monitoring and improving the e-WOM of their travel products is always an important task. Therefore, based on the online review information, how to capture e-WOM of travel products and find out specific ways to improve the e-WOM is a noteworthy research problem. The purpose of this paper is to develop a method for capturing and analyzing e-WOM toward travel products based on sentiment analysis and stochastic dominance.

Specifically, online review information of travel products is first crawled and preprocessed. Second, sentiment strengths of online review information toward travel products concerning each feature are judged. Then, the matrix of structured online review information toward travel products is formed. Further, the matrix of e-WOM comparisons between any two travel products is constructed, and e-WOM ranking concerning each travel product is determined. Finally, trade-off chart models are constructed to conduct the e-WOM improvement analyses concerning the travel products.

An empirical study based on the online review information toward six travel products crawled from the Tuniu.com website is given to illustrate the use of the proposed method.

The proposed method can not only realize the real-time e-WOM monitoring to travel products but also be useful for TSPs to improve the e-WOM of their travel products.

The purpose of this paper is to provide an understanding of principal preparation and training in China by providing a background discussion of principal preparation in a number of countries. As an illustration, it provides an overview of the curriculum used in the initial preparation of school principals at Beijing Normal University.

The paper draws mainly on writing and research from China, Australia and the USA to explore principal preparation and training in China.

In addition to providing a rich description of principal preparation in China, the paper's main findings comprise seven key challenges that confront China as it endeavours to provide quality principal preparation. These challenges include China's diversity and uneven social, cultural and educational development; limited resources in some regions throughout China; the place and importance of study tours for principal preparation; the teaching approach used to train principals; the process used for assessing principal learning during their training programs; the limited transfer of learning from the classroom to the school environment; and the timing of training for principals.

Each of the challenges arising here raises important practical implications for developers of principal training programs.

The paper paints a picture of principal preparation in China and raises a number of issues and challenges with which it continues to grapple. Of note is that China is not alone in facing some of these ongoing concerns.

The overall purpose of this research paper largely depends on developing an easy method to synthesis a material suitable for supercapacitor application. This paper includes the synthesis of, α-Co(OH)₂, its structural, elemental and morphological properties and its supercapacitor properties.

Firstly, the electrolyte is prepared using binder free method, then electrodeposition is used to synthesize α-Co(OH)₂ at 2 V. X-ray diffraction (XRD), energy dispersive spectroscopy (EDS) and scanning electron microscope (SEM) are used to study the structural, elemental and morphological characteristics. The supercapacitor properties are investigated by using cyclic voltammetry, charging-discharging graph, stability test and electrochemical impedance spectroscopy (EIS).

Synthesis of α-Co(OH)₂ is a tedious job as the temperature and use of weak base plays an important role. However, throughout electrodeposition, temperature is maintained using a water bath and weak base as the precursor. The presence of nitrate anions shows more interlayer space than that of ß-Co(OH)₂ because of which free diffusion of the electrolyte is possible. Sheets structures are more visible in SEM images. Nanosheet like structure is observed in the film and such kind of structure provide higher surface area and higher specific capacitance. Usually, the surface morphology of cobalt hydroxide shows flower-like, spherical and nanocubes particles. The cross-section of the deposited film and it is found to be approximately 100 µm. In the forward and backward scan, oxidation and reduction peaks are clearly visible. However, such a behavior is reported as stable because of no further peaks of oxidation.

XRD and EDS confirms the growth of α-Co(OH)₂. SEM images shows the porous nature of the film. Specific capacitance and energy density has been estimated at 5 mV s⁻¹ is 780 F g⁻¹ and 82 W h kg⁻¹, respectively. The film was stable for 600 cycles showing 75 per cent capacitance retention. The voltage drop is 0.02 V for 0.5 A cm⁻², indicating low resistance and good conductivity of the film. The specific power is estimated to be 15 W kg⁻¹ for 1 A cm⁻². The value of R_ESR, R_CT, C_DL and W is 4.83 Ohm, 1.273 Ohm, 0.00233 C and 0.717, respectively. Thus indicating α-Co(OH)₂ to be better candidate for supercapacitor applications.

With the development of deep learning-based analytical techniques, increased research has focused on fatigue data analysis methods based on deep learning, which are gaining in popularity. However, the application of deep neural networks in the material science domain is mainly inhibited by data availability. In this paper, to overcome the difficulty of multifactor fatigue life prediction with small data sets,

A multiple neural network ensemble (MNNE) is used, and an MNNE with a general and flexible explicit function is developed to accurately quantify the complicated relationships hidden in multivariable data sets. Moreover, a variational autoencoder-based data generator is trained with small sample sets to expand the size of the training data set. A comparative study involving the proposed method and traditional models is performed. In addition, a filtering rule based on the R2 score is proposed and applied in the training process of the MNNE, and this approach has a beneficial effect on the prediction accuracy and generalization ability.

A comparative study involving the proposed method and traditional models is performed. The comparative experiment confirms that the use of hybrid data can improve the accuracy and generalization ability of the deep neural network and that the MNNE outperforms support vector machines, multilayer perceptron and deep neural network models based on the goodness of fit and robustness in the small sample case.

The experimental results imply that the proposed algorithm is a sophisticated and promising multivariate method for predicting the contact fatigue life of a coating when data availability is limited.

A data generated model based on variational autoencoder was used to make up lack of data. An MNNE method was proposed to apply in the small data case of fatigue life prediction.