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The purpose of this paper is to evaluate the forecasting performance of grey prediction models on educational attainment vis-à-vis that of exponential smoothing combined with multiple linear regression employed by the National Center for Education Statistics (NCES).

An out-of-sample forecasting experiment was carried out to compare the forecasting performances on educational attainments among GM(1,1), GM(1,1) rolling, FGM(1,1) derived from the grey system theory and exponential smoothing prediction combined with multivariate regression. The predictive power of each model was measured based on MAD, MAPE, RMSE and simple F-test of equal variance.

The forecasting efficiency evaluated by MAD, MAPE, RMSE and simple F-test of equal variance revealed that the GM(1,1) rolling model displays promise for use in forecasting educational attainment.

Since the possible inadequacy of MAD, MAPE, RMSE and F-type test of equal variance was documented in the literature, further large-scale forecasting comparison studies may be done to test the prediction powers of grey prediction and its competing out-of-sample forecasts by other alternative measures of accuracy.

The findings of this study would be useful for NCES and professional forecasters who are expected to provide government authorities and education policy makers with accurate information for planning future policy directions and optimizing decision-making.

As a continuing effort to evaluate the forecasting efficiency of grey prediction models, the present study provided accumulated evidence for the predictive power of grey prediction on short-term forecasts of educational statistics.

Based on generic reviews of research on school leadership, this study aims to conceptualize an emerging leadership competence model for further prioritizing school leadership training needs by decision-making trial and evaluation laboratory (DEMATEL)-based analytic network process (ANP). Targeting at a specific group of junior high school principals in Taiwan, the synthesized prioritization of core competences and competence indicators computed by DEMATEL-based ANP can be used as a blueprint for systematically developing school principal preparation programs elsewhere based on contextually specific needs and concerns.

For the purpose of detecting the priority of competences extracted from a plethora of literature in relation to school leaders’ skills, abilities and knowledge, a hybrid MCDM model, known as DEMATEL-based ANP, is used to snapshot critical leadership competences for tailoring the intended junior high school principal preparation program.

Results of global weight computations by DEMATEL-based ANP show that the overall prioritization of the five dimensions in ranking order are “Redesigning the organization,” “Related leadership practices,” “Setting Directions,” “Personal Characteristics” and “Developing People.” Factor-level analysis indicated that “Legality,” “Consensus Building,” “Shared Vision,” “Strategic Management,” “Authoritarianism,” “Modeling Behavior” and “High Performance Emphasis” were prioritized factors to be included in developing the intended school principal preparation program.

Contextually based findings yielded from DEMATEL-based ANP would only be applicable to the target context under investigation. Generalizing the findings to junior high school principals elsewhere might not be appropriate. Methods introduced in this study are extensible to further studies aiming at shaping leadership trainings in other school settings to achieve maximum impact based on contextually specific needs. Limitations associated with sophisticated mathematical computations involved in performing high-leverage MCDM models would shed light on the importance of interdisciplinary collaborations between scholars, practitioners and methodologists.

To efficiently deliver training for junior high school principals, the prioritization of core competences and competence indicators solicited by DEMATEL-based ANP can be used as a scenario-based reference for planning the intended school principal preparation program planning, as well as a mechanism for selecting and evaluating potential and incumbent school principals with desired leadership competences.

This study has used DELATEL-based ANP as a decision-making tool to disclose a plethora of competences extracted from different threads in the literature into a prioritized competence framework that differs from past one-size-fit-all approach to design and plan school principal preparation programs.

This study aims at investigating the heat transfer characteristics of a nonsquare enclosure when hydrodynamic resistance is altered discontinuously along its inner surface. Particularly, it focuses on investigating how several essential factors collaboratively influence the natural convection, including the Rayleigh number (Ra), the aspect ratio (AR), the nanoparticle volume fraction (ϕ) and the locations of changing hydrodynamic resistance.

To achieve these objectives, an L-shaped enclosure of various AR is adopted, while zero local shear resistance is applied and modeled by stress-free (SF) patches of four distinct arrangements (corresponding to Cases 1–4). The nanofluid is modeled by Buongiorno’s two-phase model. The effects are explored using an in-house numerical framework based on a hybrid lattice Boltzmann-finite difference method with the total variation minimization scheme.

The results show that when Ra is sufficiently large, i.e. Ra = 10⁵, SF patches can generally enhance the heat transfer performance regardless of other factors. However, the ways of achieving those enhancements are different, which mainly depend on the arrangement of the SF patches and AR but are nearly independent of ϕ. The maximum improvement of heat transfer can be achieved in Case 3 with AR = 0.6, Ra = 10⁵ and ϕ = 0.04, where the averaged Nusselt number is enhanced by 8.89%.

This study presents a new scenario where the SF patches of various arrangements are applied to enhance the nanofluid natural convection of a nonsquared enclosure, and it reveals how the improvement is achieved and cooperatively affected by several important factors.

In response to the complex external structure of high-precision aviation plugs, which makes it difficult to search outside the hole and adjust inside the hole during automated assembly. This paper aims to propose an assembly framework that combines multi-agent search and variable parameter compliant control to solve this problem.

First, a multi-agent search strategy (MAS) based on Gaussian Mixture Model and Deep Q-Network was proposed to optimize displacement direction and actions, thereby improving search speed and success rate. Then, a variable parameter admittance control method (RL-VPA) based on dual delay depth deterministic policy gradient (TD3) was proposed, which dynamically optimized the internal parameters of the admittance controller and adopted state space discretization to improve convergence speed and assembly efficiency.

Compared to spiral search and single-agent search, the average search success rate has improved by approximately 10% and 6.6%. Compared to fixed admittance control and other RL-based methods, the average assembly success rate has increased by approximately 38.6%, 22% and 8.6%. Compared with the training results of the model without state discretization, it was found that state discretization helps the model converge quickly. To verify the generalization ability of the assembly framework, experiments were conducted on three different pin counts of aviation plugs, the assembly success rate reached 86.7%, all of which showed good assembly results. Finally, combining state space discretization to reduce the impact of environmental noise, improve training effectiveness and convergence speed.

MAS has been proposed to optimize displacement direction and action, improving search speed and success rate. RL-VPA is designed to dynamically optimize the internal parameters of the admittance controller, enhancing the robustness and generalization ability of the model. Additionally, state space discretization is combined to improve training effectiveness and convergence speed.

Construction workers’ safety behavior has been proven to be crucial in preventing occupational injuries and improving workplace safety, and organizational safety support provides essential resources to promote such behavior. However, the specific mechanisms of how organizational safety support affects safety behavior have not been thoroughly explored. Therefore, this study explored the relationship between workers’ perceived organizational safety support (perceived supervisor/coworker safety support) and safety behavior (safety task/contextual behavior), while considering the mediating effects of safety motivation, emotional exhaustion, and the moderating effect of psychosocial safety climate.

Based on the quantitative research method, the hypothesis was tested. The data were collected from 500 construction workers using a structured questionnaire. Observed variables were tested using confirmatory factor analysis, and the path coefficient of fitted model was then analyzed including the associated mediating and moderating effects.

The study found that (1) safety support from both supervisors and coworkers directly forecasted both types of safety behavior, (2) safety motivation was primarily predicted by perceived supervisor safety support, and perceived coworker safety support better predicted emotional exhaustion. Safety motivation mediated the relationship between perceived supervisor safety support and safety contextual behavior, and emotional exhaustion mediated the relationship between both types of safety support and both types of safety behavior, (3) psychosocial safety climate moderated the pathway relationships mediated by safety motivation and emotional exhaustion, respectively.

The samples of this study were mostly immersed in eastern culture and the construction industry, and the cultural and industry diversity of the samples deserves further consideration to enhance the universality of the results. The cross-sectional approach may have some impact on the accuracy of the results. In addition, other potential mediating variables deserve to be explored in future studies.

This study provides a new basis for extending current theoretical frameworks of organizational safety support and safety behavior by using a moderated mediation model. Some practical insights on construction safety management have also been proposed based on the research findings. It is recommended that practitioners should further raise awareness of the critical role of supervisor-worker and worker-coworker relationships, as high levels of safety support from the supervisor/worker respectively effectively encourage safety motivation, alleviate emotional exhaustion, and thus improve workers’ safety performance. Meanwhile, the psychosocial health conditions of workers should also receive further attention.

The aim of this paper is to demonstrate the application of optimal globalization grey relational analysis (GGRA) as a workable decision-making tool to prioritize emotional intelligence (EI) training needs of specific groups of academic leaders.

The study involved administrating the emotional skills assessment process to 50 academic leaders in the USA and 50 in Taiwan. Optimal GGRA was utilized to prioritize EI training needs of the two distinct groups of academic leaders.

Results of the optimal GGRA suggest that context-specific EI interventions focusing on enhancing weak areas of emotional competency are made possible through optimal globalization grey analysis.

Optimal GGRA is introduced as a valid decision-making technique for planning effective EI interventions. The results have implications for designing training courses targeting on enhancing weak areas of emotional competency.

The utility of optimal GGRA as a decision-making tool to prioritizing training needs for the two cultural groups of academic leaders in the study aims at going beyond the narrow psychometric perspectives of measurements on leadership potentials and reaching out to practical approaches to leadership development and training.

In spite of unprecedented levels of social, economic and political development, Chinese politics is characterized by increasingly salient social contradictions, conflicts and even protests. As for the various measures taken by all levels of governments to maintain social stability in changing times, the effect is not obvious, and all kinds of social conflicts are still on the rise. The purpose of this paper is to find out the relationship between political elites and social conflicts and give advice on the adjustment of the governance mode of social conflicts in current China.

This paper attempts to conduct a detailed analysis of the case of Tang Hui re-education through labour to explore how social conflicts arise, upgrade and dissolve within the intention of political elites to maintain social stability in current China.

Political elites who are subject to the pressure of maintaining social stability cannot rely on public interests and civil rights as the full justification for their actions. Above all, they must place social stability first. In this sense, political elites are constructing the network of maintaining social stability and shaping social protests. Social elites use the media or the internet platform to exert public opinion influence and try to push social protests to final solutions. In this way, social elites deconstruct political elites’ network of maintaining social stability. However, the forces that enable social conflicts and protests to be resolved remain to be the network of maintaining social stability itself.

The case of Tang Hui re-education through labour is believed to have hastened the abolition of the system of re-education through labour. As it completely interprets the whole process of the generation, evolution and resolution of social conflicts in current China, this typical case can be considered as an ideal lens through which we can see the unique relationship between political elites and social conflicts, as well as the governance mode of social conflicts in current China.

This study assessed the relevance of Vroom’s expectancy motivational framework in a cross-cultural context. Differences in attitudes for task investment, preferences for work related achievements, and the reward potential of outcomes was assessedwith Australians and Malaysians who were employed in similar work contexts of the beauty care industry. Reasons why the Australian employees reported significantly higher job motivation than the Malaysian respondents were identified by examining the three main components of expectancy, instrumentality and valence, of Vroom’s framework. The study findings are discussed in terms of the implications they have for the necessary organizational development with Australians and Malaysians who were employed in similar work contexts of the beauty care industry. Reasons why the Australian employees reported significantly higher job motivation than the Malaysian respondents were identified by examining the three main components of expectancy, instrumentality and valence, of Vroom's framework. The study findings are discussed in terms of the implications they have for the necessary organizational development of businesses in the competitive Asia-Pacific region.

Machine learning is an algorithmic-based auto-learning mechanism that improves from its experiences. It makes use of a statistical learning method that trains and develops on its own without the assistance of a person. Data, characteristics deduced from the data, and the model make up the three primary parts of a machine learning solution. Machine learning generates an algorithm from subsets of data that can utilise combinations of features and weights different from those obtained from basic principles. In this paper, an analysis of customer behaviour is predicted using different machine learning algorithms. The results of the algorithms are validated using python programming.

This paper, which originates in an English-as-a-foreign-language (EFL) classroom activity in Hong Kong, aims to explore English learners’ expressive and creative potential in writing by studying their work in the literary narrative genre.

A group of upper secondary students (15-16 years of age) with limited English resources and competence was enlisted to remake a folktale with visual and written prompts.

The writing samples demonstrate that these low-level EFL writers are able to refashion the narrative elements, and to communicate meanings for their own purposes. They exhibit logicality and problem-solving skills in their attempts to challenge and transform idea and to include themes of interest to them. There is also evidence of creative play with language in their use of dialogues and figures of speech.

These writing outcomes suggest the need to re-vision English language arts practices in increasingly diverse education systems. Genre-based instruction, with its emphasis on “writing to mean” as a social activity supported by learning to use language, could lead to widening EFL learners’ access to genre knowledge and to greater life chances.

A linguistics-based pedagogy scaffolding less able EFL writers while they learn to build effective narratives is identified as a way forward.

Although the idea of using narratives to engage EFL learners in writing is not entirely new, this paper contributes to the field by responding to low-level learners’ writing that goes beyond linguistic “correctness”, and developing strategies for supporting creativity and language play.