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This chapter explores a descriptive theory of multidimensional travel behaviour, estimation of quantitative models and demonstration in an agent-based microsimulation.

A descriptive theory on multidimensional travel behaviour is conceptualised. It theorizes multidimensional knowledge updating, search start/stopping criteria and search/decision heuristics. These components are formulated or empirically modelled and integrated in a unified and coherent approach.

The theory is supported by empirical observations and the derived quantitative models are tested by an agent-based simulation on a demonstration network.

Based on artificially intelligent agents, learning and search theory and bounded rationality, this chapter makes an effort to embed a sound theoretical foundation for the computational process approach and agent-based micro-simulations. A pertinent new theory is proposed with experimental observations and estimations to demonstrate agents with systematic deviations from the rationality paradigm. Procedural and multidimensional decision-making are modelled. The numerical experiment highlights the capabilities of the proposed theory in estimating rich behavioural dynamics.

Discusses the 6th ITCRR, its breadth of textile and clothing research activity, plus the encouragement given to workers in this field and its related areas. States that, within the newer research areas under the microscope of the community involved, technical textiles focuses on new, ‘smart’ garments and the initiatives in this field in both the UK and the international community at large. Covers this subject at length.

Modelling and forecasting interval-valued time series (ITS) have received increasing attention in statistics and econometrics. An interval-valued observation contains more information than a point-valued observation in the same time period. The previous literature has mainly considered modelling and forecasting a univariate ITS. However, few works attempt to model a vector process of ITS. In this paper, we propose an interval-valued vector autoregressive moving average (IVARMA) model to capture the cross-dependence dynamics within an ITS vector system. A minimum-distance estimation method is developed to estimate the parameters of an IVARMA model, and consistency, asymptotic normality and asymptotic efficiency of the proposed estimator are established. A two-stage minimum-distance estimator is shown to be asymptotically most efficient among the class of minimum-distance estimators. Simulation studies show that the two-stage estimator indeed outperforms other minimum-distance estimators for various data-generating processes considered.

School leaders can significantly influence the conditions that affect teacher retention. Yet, leaders in rural and high-poverty schools often face limited opportunities to develop their abilities to enhance these conditions. In this case study, we examine how participating in a professional community supported school leaders' efforts to increase teacher retention and student learning outcomes.

We used case study methodology to study 14 leaders from rural, high-poverty or underperforming schools with greater-than-average levels of teacher turnover. The leaders were participating in a three-year research-practice partnership intended to assist them in using improvement science to address problems of practice related to teacher retention and student learning outcomes in their schools. We collected and analyzed data from interviews, exit surveys, artifacts and participant observations over a one-year period.

Participating in this professional community helped the leaders create the conditions for increased teacher retention and student learning outcomes by providing them with opportunities to collaborate with their peers, receive leadership coaching, exchange ideas and learn in a safe space.

These findings confirm and extend extant school leadership development research. A particularly interesting finding was the role of the professional community in reducing the leaders' feelings of isolation while providing them a safe space to learn. The findings also illustrate how universities and school districts can partner to provide professional learning opportunities that enhance school leaders' professional knowledge, leadership practices and well-being.

The purpose of this study is to examine how a school leadership team in a rural, high-poverty elementary school learned to lead continuous school improvement in a research–practice partnership.

This case study draws on qualitative research methods, improvement science and Deming's notion of a system of profound knowledge to identify how members of the school leadership team understood and approached their school improvement work differently as a result of engaging in continuous improvement processes in a research–practice partnership.

The findings illustrate how engaging in continuous improvement processes in the research–practice partnership enhanced the leadership team members' capacities to prioritize and solve problems, incorporate multiple and diverse perspectives in problem-solving efforts and establish a culture of increased risk-taking and ownership of teaching and learning outcomes. In sum, the members of the leadership team became the drivers of their own change processes.

The findings provide insight into how leaders in rural, high-poverty schools can build capacity within their schools to meet the demand for increased student achievement by leading collaborative, continuous improvement processes grounded in improvement science in research–practice partnerships.

The ever-increasing pressure for school improvement has led to a related increase in research-practice partnerships (RPPs) that address problems of practice. Yet, little research has centered on how the myriad challenges to such partnerships can be overcome, such as bridging the cultural divide between universities and their school-based partners. Thus, the purpose of this paper is to examine how social capital was developed among the members of a steering committee in a RPP between a university and a 12-district consortium of predominantly rural, high-poverty school districts to develop and implement a professional development initiative for rural school leaders.

Data for this phenomenological single case study were collected over a one-year period through participant observations, document analysis and semi-structured interviews with ten steering committee members. Data were inductively and deductively coded through multiple rounds of analysis, which drew on the structural and cognitive elements of social capital (Uphoff, 2000). Findings were triangulated and member checked for trustworthiness.

The analysis of the data revealed three key ways in which social capital was developed among members of the steering committee to overcome the cultural challenges of RPPs to develop and implement a professional development initiative for rural school leaders: providing an open but focused structure, ensuring inclusive and respectful discussion and negotiating roles and ideas.

The findings provide a fine-grained illustration of how intentional efforts to develop social capital among members in a co-design team can assist in bridging the cultural boundaries often encountered in RPPs.

This study investigated four different economic level areas of China (Shanghai, Shandong, Shaanxi, and Guizhou) to analyze the eastern and western urban and rural media service status at different development stages. This set of data comes from the comparison of regional urban and rural areas and indicates the various aspects of differences in the survey area, including the media use habits, media resources, media consumer demand, evaluation of media services, the role of media in public life, public knowledge level, and so on. On analyzing data comprehensively, one thing can be found that there is a positive correlation between the public media contact degree and the public knowledge level. The media plays an extremely important role in public life and regional public knowledge gap between urban and rural areas exists. Furthermore, this gap is positively correlated to the media resources and media exposure. The trend of media using on mobile phone and computer in urban areas increases significantly greater than in rural areas. Then, how to narrow the urban–rural and regional public knowledge gap and reduce the negative impact of the digital divide will be an important urgent task.

The deposition of particles onto a substrate during the cold spraying (CS) process relies on severe plastic deformation, so there are various micro-defects induced by insufficient deformation and severe crushing. To solve the problems, many post-treat techniques have been used to improving the quality by eliminating the micro-defects. This paper aims to help scholars and engineers in this field a better and systematic understand of CS technology by summarizing the post-treatment technologies that have been investigated recently years.

This review summarizes the types of micro-defects and introduces the effect of micro-defects on the properties of CS coating/additive manufactured, illustrates the post-treatment technologies and its effect on the microstructure and performances, and finally outlooks the future development trends of post-treatments for CS.

There are significant discoveries in post-treatment technology to change the performance of cold spray deposits. There are also many limitations for post-treatment methods, including improved performance and limitations of use. Thus, there is still a strong requirement for further improvement. Hybrid post-treatment may be a more ideal method, as it can eliminate more defects than a single method. The proposed ultrasonic impact treatment could be an alternative method, as it can densify and flatten the CS deposits.

It is the first time to reveal the influence factors on the performances of CS deposits from the perspective of microdefects, and proposed corresponding well targeted post-treatment methods, which is more instructive for improving the performances of CS deposits.

This study proposes a predictive neural network model reference decoupling control method for the coupling problem between the leg joints of hydraulic quadruped robots, and verifies its decoupling effect..

The machine–hydraulic cross-linking coupling is studied as the coupling behavior of the hydraulically driven quadruped robot, and the mechanical dynamics coupling force of the robot system is controlled as the disturbance force of the hydraulic system through the Jacobian matrix transformation. According to the principle of multivariable decoupling, a prediction-based neural network model reference decoupling control method is proposed; each module of the control algorithm is designed one by one, and the stability of the system is analyzed by the Lyapunov stability theorem.

The simulation and experimental research on the robot joint decoupling control method is carried out, and the prediction-based neural network model reference decoupling control method is compared with the decoupling control method without any decoupling control method. The results show that taking the coupling effect experiment between the hip joint and knee joint as an example, after using the predictive neural network model reference decoupling control method, the phase lag of the hip joint response line was reduced from 20.3° to 14.8°, the amplitude attenuation was reduced from 1.82% to 0.21%, the maximum error of the knee joint coupling line was reduced from 0.67 mm to 0.16 mm and the coupling effect between the hip joint and knee joint was reduced from 1.9% to 0.48%, achieving good decoupling.

The prediction-based neural network model reference decoupling control method proposed in this paper can use the neural network model to predict the next output of the system according to the input and output. Finally, the weights of the neural network are corrected online according to the predicted output and the given reference output, so that the optimization index of the neural network decoupling controller is extremely small, and the purpose of decoupling control is achieved.

The purpose of this study is to examine the effect of customers’ socio-demographic characteristics on the formation of behavioral intention toward green hotels in Malaysia.

The current study used partial least square-structural equation modeling to analyze a sample of 400 green hotel customers in Malaysia. Further, multi-group analysis (MGA) is conducted to examine whether a significant difference exists across demographic groups.

Attitude and perceived behavioral control exert positive effects on desire, which in turn considerably influences behavioral intention. Results of MGA indicate that attributions have varying effects on the desire and behavioral intention with different socio-demographic characteristics. In particular, negative anticipated emotion negatively predicts desire among the customers of the older group, whereas positive anticipated emotion and subjective norm positively influence desire among the customers of the low educational level group.

The study examines green hotel customers’ behavior in Malaysia, and further research is needed to determine whether the impact of the proposed determinants across different industries. As the concept of corporate social responsibility (CSR) has started to influence customers’ behavior, the findings may be changing over time. Thus, a further longitudinal study would be beneficial to monitor the performance hotel CSR activities.

Although there is an increasing interest in CSR activities among Malaysian customers, there is a lack of information regarding customers’ motivation in the green hotel context. Furthermore, limited studies examined the role of customers’ demographic characteristics in the CSR literature. There is a need to understand green hotel industry and customers’ responses toward CSR activities.