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The purpose of this study is to provide an alternative approach to teacher learning on top of the usual practices of listening to experts' lectures and conducting school-based activities among peers in China. A boundary-crossing lesson study (BCLS) through school-university partnership served as an example to illustrate how a class teacher's mindset changed towards her students in equal interactions with university scholars.

With the lenses of action science theory and boundary-crossing learning theory, the study used qualitative research approach to collect and analyze data. One Chinese primary school class teacher from a workshop on narrative action research was selected as the case for this study. Interviewing, observation and document analysis were used to collect data. Data analysis methods included categorization and contextualization of the teacher's mindset change towards her student.

The case teacher, Mrs. Li, collaborated closely in paired teaching with her university partner AP Yu in all the four phases of their BCLS. Each phase was marked with an interactive event such as dialogic illumination, reflexive theorization, embodied conversation, and fusion of teacher and trainer roles. With inspirational trust as a major interactive mechanism, Mrs. Li jumped out of her single-loop learning (changing strategies according to results) to double-loop learning (changing both strategies and values/values). As a result, her mindset changed from attribution to appreciation towards her low-achieving student.

This study made contributions in two ways. First, it examined a class teacher's mindset change towards her student, rather than that of subject matter teachers towards their teaching materials and methods. Second, it revealed how reflective interactions in a special kind of BCLS by school teachers and university scholars may promote the teacher's mindset change. The findings further confirm that having differences as boundaries is not enough for teacher learning. For deep learning like Mrs. Li's mindset change, it requires a respectful and inspirational relationship between school teachers and university scholars in the BCLS.

Due to the increasing size and complexity of construction projects, construction engineering and management involves the coordination of many complex and dynamic processes and relies on the analysis of uncertain, imprecise and incomplete information, including subjective and linguistically expressed information. Various modelling and computing techniques have been used by construction researchers and applied to practical construction problems in order to overcome these challenges, including fuzzy hybrid techniques. Fuzzy hybrid techniques combine the human-like reasoning capabilities of fuzzy logic with the capabilities of other techniques, such as optimization, machine learning, multi-criteria decision-making (MCDM) and simulation, to capitalise on their strengths and overcome their limitations. Based on a review of construction literature, this chapter identifies the most common types of fuzzy hybrid techniques applied to construction problems and reviews selected papers in each category of fuzzy hybrid technique to illustrate their capabilities for addressing construction challenges. Finally, this chapter discusses areas for future development of fuzzy hybrid techniques that will increase their capabilities for solving construction-related problems. The contributions of this chapter are threefold: (1) the limitations of some standard techniques for solving construction problems are discussed, as are the ways that fuzzy methods have been hybridized with these techniques in order to address their limitations; (2) a review of existing applications of fuzzy hybrid techniques in construction is provided in order to illustrate the capabilities of these techniques for solving a variety of construction problems and (3) potential improvements in each category of fuzzy hybrid technique in construction are provided, as areas for future research.

Vector Autoregression (VAR) has been a standard empirical tool used in macroeconomics and finance. In this paper we discuss how to compare alternative VAR models after they are estimated by Bayesian MCMC methods. In particular we apply a robust version of deviance information criterion (RDIC) recently developed in Li, Zeng, and Yu (2014b) to determine the best candidate model. RDIC is a better information criterion than the widely used deviance information criterion (DIC) when latent variables are involved in candidate models. Empirical analysis using US data shows that the optimal model selected by RDIC can be different from that by DIC.

This paper presents an alternative approach to evaluating the overall efficiency and performance of Taiwanese container ports. Specifically, a parallel activity with series structure concept in the form of data envelopment analysis (MNDEA) is used to construct a model that applies to three different activities: harbor management, stevedoring and warehousing operations. We will further divide each activity into two process types, production processes and services processes. We will also adopt a Delphi survey approach and use the Analytic Network Process (ANP) to identify these processes’influence dependence and their degree of importance for the MNDEA model setting. An empirical application demonstrates the performance of Taiwanese container ports by using MNDEA with window analysis techniques via the directional distance functionThe results demonstrate that the application is effective in indicating and/or suggesting resource-adjustments, while considering which undesirable output levels and shared inputs were involved. The results also present directions for possible improvements in workplace efficiency.

To modernize budgeting system is a challenge issue in many developing countries. To some scholars (Schick, 1998a, 1998b; Ma, 2009a), developing countries must first put into place basic budgetary controls before moving to more advanced models of budgeting. This approach of “basic first,” however, is questioned by others (e.g., Andrew, 2006). Drawing on China's recent budget reforms, this essay reconfirms the validity of the “basics first” approach. In China, budget reform since 1999 has begun to install budgetary controls for state finance, leading to an enhancement of budgeting capacity and financial accountability. However, governments at the same time have begun to be plagued by the unexpected problem of delays in spending and the accumulation of significant underexpenditures. Contrary to what many people may believe, we contend that this somewhat odd problem arises not because the new budgeting system has exercised too much control but rather because the new system is not yet effective in exerting budgetary controls.

Grey modeling technique is an important element of grey system theory, and academic articles applied to agricultural science research have been published since 1985, proving the broad applicability and effectiveness of the technique from different aspects and providing a new means to solve agricultural science problems. The analysis of the connotation and trend of the application of grey modeling technique in agricultural science research contributes to the enrichment of grey technique and the development of agricultural science in multiple dimensions.

Based on the relevant literature selected from China National Knowledge Infrastructure, the Web of Science, SpiScholar and other databases in the past 37 years (1985–2021), this paper firstly applied the bibliometric method to quantitatively visualize and systematically analyze the trend of publication, productive author, productive institution, and highly cited literature. Then, the literature is combed by the application of different grey modeling techniques in agricultural science research, and the literature research progress is systematically analyzed.

The results show that grey model technology has broad prospects in the field of agricultural science research. Agricultural universities and research institutes are the main research forces in the application of grey model technology in agricultural science research, and have certain inheritance. The application of grey model technology in agricultural science research has wide applicability and precise practicability.

By analyzing and summarizing the application trend of grey model technology in agricultural science research, the research hotspot, research frontier and valuable research directions of grey model technology in agricultural science research can be more clearly grasped.

This study aims to explore the factors influencing the evolution of emerging technology innovation network (ETIN) in combination with the key attributes and life cycle of emerging technologies, particularly the impact of multiple knowledge attributes and technology life cycle on the ETIN evolution.

This study collects 5G patent data and their citation information from the Derwent Innovations Index to construct a 5G technology innovation network (5GIN) as a sample network and conducts an empirical analysis of the 5GIN using the temporal exponential random graph model (TERGM).

The results indicate that during the 5GIN evolution, the network scale continues to expand and exhibits increasingly significant core-periphery structure, scale-free characteristic, small-world characteristic and community structure. Furthermore, the findings suggest that the multiple knowledge attributes based on the key attributes of emerging technologies, including knowledge novelty, coherence, growth and impact, have a significant positive influence on the ETIN evolution. Meanwhile, the temporal evolution of ETIN is also found to be correlated with the life cycle of emerging technologies.

This study extends the exploration of emerging technology research from a complex network perspective, providing a more realistic explanatory framework for the factors influencing ETIN evolution. It further highlights the important role that multiple knowledge attributes and the technology life cycle play within this framework.

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.

This paper aims to develop an efficient algorithm combining straightforward response surface functions with Monte Carlo simulation to conduct seismic reliability analysis in a systematical way.

The representative slip surfaces are identified and based on to calibrate multiple response surface functions with acceptable accuracy. The calibrated response surfaces are used to determine the yield acceleration in Newmark sliding displacement analysis. Then, the displacement-based limit state function is adopted to conduct seismic reliability analysis.

The calibrated response surface functions have fairly good accuracy in predicting the yield acceleration in Newmark sliding displacement analysis. The seismic reliability is influenced by such factors as PGA, spatial variability and threshold value. The proposed methodology serves as an effective tool for geotechnical practitioners.

The multiple sources of a seismic slope response can be effectively determined using the multiple response surface functions, which are easily implemented within geotechnical engineering.

This study aims to investigate the impact of physician efforts in online reviews on outpatient appointments, while also examining the moderating effect of physician title.

This study employs the heuristic-systematic model (HSM) to analyze the impact of physician efforts on outpatient appointments. Subsequently, a fixed effect model is employed to examine the research model using an 89-week panel dataset (from April 16, 2018 to December 29, 2019) comprising appointment and online review information pertaining to 8,157 physicians from a prominent online health community in China.

The findings suggest that physicians with lower professional titles exhibit a significantly higher inclination to enhance heuristic information (e.g. attracting helpful votes) compared to those with higher professional title. All physicians can enhance their outpatient appointments by dedicating efforts towards improving systematic review information, but physician title would weaken the relationship. Moreover, the effect of increasing review volume is considerably more substantial than that of increasing review length, which also surpasses the influence of providing managerial response.

Unlike previous studies that primarily focus on patients’ perspectives, this paper represents one of the pioneering effects to examine physicians’ engagement in online reviews.