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This paper studies the relationships among consumers’ perceptions of brand personality, consumers’ brand attitudes and brand-owned social media content marketing (SMCM). The moderating effect of the brand content relevancy was also assessed.

A conceptual model was established and examined using two experiments with a total of 363 participants. Hierarchical regression analysis and an analysis of variance were performed to test seven research hypotheses.

Results show that the three forms of brand-owned SMCM, namely: conversation, storytelling and customer interaction and participation, are positively correlated with consumers’ brand personality perceptions and brand attitudes. Also, consumers’ perceptions of brand personality can partially mediate the relationship between brand-owned content marketing and consumers’ brand attitudes. Furthermore, the brand content relevancy does not show a moderating effect on the relationship between content marketing and consumers’ brand personality perceptions or brand attitudes.

First, a framework was established to delineate those paths by which owned social media content marketing (OSMCM) influences consumers’ attitudes towards a brand. Second, the study demonstrates the importance of conversation as a powerful method of OSMCM. Third, with respect to content in marketing strategies, firms do not need to confine themselves to a narrow scope of content or information that is closely related to the brands alone.

The purpose of this paper is to examine and compare the entire impact of various execution skills of oppositional biogeography-based optimization using the current optimum (COOBBO) algorithm.

The improvement measures tested in this paper include different initialization approaches, crossover approaches, local optimization approaches, and greedy approaches. Eight well-known traveling salesman problems (TSP) are employed for performance verification. Four comparison criteria are recoded and compared to analyze the contribution of each modified method.

Experiment results illustrate that the combination model of “25 nearest-neighbor algorithm initialization+inver-over crossover+2-opt+all greedy” may be the best choice of all when considering both the overall algorithm performance and computation overhead.

When solving TSP with varying scales, these modified methods can enhance the performance and efficiency of COOBBO algorithm in different degrees. And an appropriate combination model may make the fullest possible contribution.

Metaheuristic algorithms based on biology, evolutionary theory and physical principles, have been widely developed for complex global optimization. This paper aims to present a new hybrid optimization algorithm that combines the characteristics of biogeography-based optimization (BBO), invasive weed optimization (IWO) and genetic algorithms (GAs).

The significant difference between the new algorithm and original optimizers is a periodic selection scheme for offspring. The selection criterion is a function of cyclic discharge and the fitness of populations. It differs from traditional optimization methods where the elite always gains advantages. With this method, fitter populations may still be rejected, while poorer ones might be likely retained. The selection scheme is applied to help escape from local optima and maintain solution diversity.

The efficiency of the proposed method is tested on 13 high-dimensional, nonlinear benchmark functions and a homogenous slope stability problem. The results of the benchmark function show that the new method performs well in terms of accuracy and solution diversity. The algorithm converges with a magnitude of 10-4, compared to 102 in BBO and 10-2 in IWO. In the slope stability problem, the safety factor acquired by the analogy of slope erosion (ASE) is closer to the recommended value.

This paper introduces a periodic selection strategy and constructs a hybrid optimizer, which enhances the global exploration capacity of metaheuristic algorithms.

The purpose of this paper is to propose an algorithm that combines the particle swarm optimization (PSO) with the biogeography-based optimization (BBO) algorithm.

The BBO and the PSO algorithms are jointly used in to order to combine the advantages of both algorithms. The efficiency of the proposed algorithm is tested using some selected standard benchmark functions. The performance of the proposed algorithm is compared with that of the differential evolutionary (DE), genetic algorithm (GA), PSO, BBO, blended BBO and hybrid BBO-DE algorithms.

Experimental results indicate that the proposed algorithm outperforms the BBO, PSO, DE, GA, and the blended BBO algorithms and has comparable performance to that of the hybrid BBO-DE algorithm. However, the proposed algorithm is simpler than the BBO-DE algorithm since the PSO does not have complex operations such as mutation and crossover used in the DE algorithm.

The proposed algorithm is a generic algorithm that can be used to efficiently solve optimization problems similar to that solved using other popular evolutionary algorithms but with better performance.

Hardgrove grindability index (HGI) is an important physical parameter used to demonstrate the relative hardness of coal particles. Modeling of HGI based on coal conventional properties is a quite complicated procedure. The paper aims to develop a new accurate model for prediction of HGI that is called optimized evolutionary neural network (OPENN).

The procedure for generation of the proposed OPENN predictive model was performed in two stages. In the first stage, as the high dimensionality involved in the input space, a correlation-based feature selection (CFS) algorithm was used to select the most important influencing variables for HGI prediction. In the second stage, a combination of differential evolution (DE) and biography-based optimization (BBO) algorithms as a global search method were applied to evolve weights of a multi-layer perception neural network.

The proposed OPENN was examined and compared with other typical models using a wide range of Kentucky coal samples. The testing results showed that the accuracy of the proposed OPENN model is significantly better than the other typical models and can be considered as a promising alternative for HGI prediction.

As HGI test is relatively expensive procedure, there is an economical interest on HGI modeling based on coal conventional properties (proximate, ultimate and petrography); the proposed OPENN model to estimate HGI would be a valuable and practical tool for coal industry.

The survival and sustainable growth of Cai Luong (Renovated Theatre) theatre companies as well as Cai Luong theatrical art in Vietnam necessitate the sharing of acting and singing skills between generations of actors. The purpose of this paper is to investigate the role of theatre members’ perception of psychological contract in predicting their sharing of knowledge. Another research purpose is to assess if corporate social responsibility (CSR) of theatre companies can activate the effect chain through psychological contract to knowledge sharing. The last research purpose sheds light on the moderating role of entrepreneurial orientation (EO) for the relationship between psychological contract and knowledge sharing among members of Cai Luong theatre companies in Vietnam setting.

Cross-sectional data for SEM-based analysis was collated from 226 respondents of Cai Luong theatre companies in Vietnam.

Research results unveil the predicting role that CSR played on the relationship between psychological contract and knowledge sharing among members in Cai Luong theatre companies. This relationship was also found to be moderated by EO.

Research results extend knowledge management literature through the inclusion of CSR and psychological contract as antecedents of knowledge sharing.

Attention is one of the most important factors to affect the academic performance of students. Effectively analyzing students' attention in class can promote teachers' precise teaching and students' personalized learning. To intelligently analyze the students' attention in classroom from the first-person perspective, this paper proposes a fusion model based on gaze tracking and object detection. In particular, the proposed attention analysis model does not depend on any smart equipment.

Given a first-person view video of students' learning, the authors first estimate the gazing point by using the deep space–time neural network. Second, single shot multi-box detector and fast segmentation convolutional neural network are comparatively adopted to accurately detect the objects in the video. Third, they predict the gazing objects by combining the results of gazing point estimation and object detection. Finally, the personalized attention of students is analyzed based on the predicted gazing objects and the measurable eye movement criteria.

A large number of experiments are carried out on a public database and a new dataset that is built in a real classroom. The experimental results show that the proposed model not only can accurately track the students' gazing trajectory and effectively analyze the fluctuation of attention of the individual student and all students but also provide a valuable reference to evaluate the process of learning of students.

The contributions of this paper can be summarized as follows. The analysis of students' attention plays an important role in improving teaching quality and student achievement. However, there is little research on how to automatically and intelligently analyze students' attention. To alleviate this problem, this paper focuses on analyzing students' attention by gaze tracking and object detection in classroom teaching, which is significant for practical application in the field of education. The authors proposed an effectively intelligent fusion model based on the deep neural network, which mainly includes the gazing point module and the object detection module, to analyze students' attention in classroom teaching instead of relying on any smart wearable device. They introduce the attention mechanism into the gazing point module to improve the performance of gazing point detection and perform some comparison experiments on the public dataset to prove that the gazing point module can achieve better performance. They associate the eye movement criteria with visual gaze to get quantifiable objective data for students' attention analysis, which can provide a valuable basis to evaluate the learning process of students, provide useful learning information of students for both parents and teachers and support the development of individualized teaching. They built a new database that contains the first-person view videos of 11 subjects in a real classroom and employ it to evaluate the effectiveness and feasibility of the proposed model.

This paper investigates the role of organization engagement and extra-role behavior in nonprofit sport clubs and examines how team dynamics and eudaimonic well-being in sport club members are influential for organization engagement. We further investigate the moderating influence of organizational tenure between team reflexivity and team identification.

We collected data from 545 sport club members in the UK and applied structural equation modeling (SPSS AMOS 29) to test the research model.

The results show that organization engagement is positively associated with extra-role behavior, except the social dimension of engagement, which is negatively associated. We established that team identification and eudaimonic well-being fully mediate the association between team reflexivity and organization engagement. The moderating effect of organizational tenure supports that team reflexivity is more effective for members with short-term tenure to strengthen team identification.

Managers in sport clubs are advised to consider the type of information exchanged, the way it is discussed, the intensity or regularity of team reflexivity and the degree of interactivity between members as critical factors that influence team dynamics and organization engagement.

We contribute to research in two major ways. First, we extend previous research on organization engagement by offering a multidimensional investigation of organization engagement in a nonprofit sport club context. Second, we extend previous research on organization engagement by introducing new antecedents and consequences in this context.

Using the Social Network Analysis(SNA) method, this paper examines inter-country relationships between countries that may be part of the New Silk Road. Based on bilateral-trade data from more than 70 countries, the paper provides a more vivid understanding of overall features and effects of the New Silk Road policy. According to the results, India, Turkey, and Russia have the highest degree centrality, indicating that the success of the New Silk Road policy depends mainly on the ability of the Chinese government to incorporate these countries. Among European countries, only Germany can be successfully incorporated into the New Silk Road network. In addition, Central Asian countries such as Kazakhstan and Uzbekistan show no potential as hubs in the network. Most importantly, China has a dominant position in the New Silk Road network. China's focal and dominating status is also supported by the fact that there is no change in the clustering coefficient in the network, which implies that the Chinese government has to absorb into the system those countries that are less likely to benefit from the policy.

This review aims to give a critical view of the wheel/rail high frequency vibration-induced vibration fatigue in railway bogie.

Vibration fatigue of railway bogie arising from the wheel/rail high frequency vibration has become the main concern of railway operators. Previous reviews usually focused on the formation mechanism of wheel/rail high frequency vibration. This paper thus gives a critical review of the vibration fatigue of railway bogie owing to the short-pitch irregularities-induced high frequency vibration, including a brief introduction of short-pitch irregularities, associated high frequency vibration in railway bogie, typical vibration fatigue failure cases of railway bogie and methodologies used for the assessment of vibration fatigue and research gaps.

The results showed that the resulting excitation frequencies of short-pitch irregularity vary substantially due to different track types and formation mechanisms. The axle box-mounted components are much more vulnerable to vibration fatigue compared with other components. The wheel polygonal wear and rail corrugation-induced high frequency vibration is the main driving force of fatigue failure, and the fatigue crack usually initiates from the defect of the weld seam. Vibration spectrum for attachments of railway bogie defined in the standard underestimates the vibration level arising from the short-pitch irregularities. The current investigations on vibration fatigue mainly focus on the methods to improve the accuracy of fatigue damage assessment, and a systematical design method for vibration fatigue remains a huge gap to improve the survival probability when the rail vehicle is subjected to vibration fatigue.

The research can facilitate the development of a new methodology to improve the fatigue life of railway vehicles when subjected to wheel/rail high frequency vibration.