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The purpose of this paper is to study the correlation between the dynamic features of the running-in attractor and the wear particle group, so as to characterize the running-in attractor by means of the wear particle group.

Wear particles are collected in phased wear experiments, and their dynamic features are investigated by the equivalent mean chord length L. Then, the correlation between the equivalent mean chord length L and the correlation dimension D of the running-in attractor is studied.

In the wear process, the equivalent means chord length L first decreases, then remains steady, and finally increases, this process agrees with the increase, stabilization and decrease of the correlation dimension D. Therefore, the wear particle group has a dynamic nature, which characterizes the formation, stabilization, and disappearance of a running-in attractor. Consequently, the dynamic characteristics and evolution of a running-in attractor can be revealed by the wear particle group.

The intrinsic relationship between the wear particle group and the running-in attractor is proved, and this is advantageous for further revealing the dynamic features of the running-in attractor and identifying the wear states.

This study aims to investigate the determinants of herd behavior in online hotel service evaluations, focusing on the cultural and geographic distance characteristics of customers.

On the basis of 381,462 TripAdvisor reviews of hotels in the USA written by more than 100,000 customers from 92 countries, this study uses the empirical analysis to explore the collective roles of cultural distance, geographic distance and hospitality experience on herd behavior in online hotel ratings.

Cultural and geographic distances between customers and product and service locations positively affect herding and these two effects are substitutable. The hospitality experience of customers attenuates the impacts of distances on herding. These results are robust for multiple hotel service ratings.

Findings help hotels understand perceptual biases of customers on hotel services under the social influence and consequently develop effective marketing strategies to boost hotel revenues and increase profitability.

The research contributes to hospitality and online review literature by understanding how cultural and geographic distances shape online hotel service evaluations under the root of the uncertainty of decision-making and the observation of others’ behavior. The research also contributes to the distances in international business literature by deepening the understanding of the substitution and heterogeneity of distance effects. Methodologically, a time-varying and monotonously increasing variable is constructed to depict customers’ hospitality experience. The extensive data volume ensures the generalizability of our results.

Most epidemic transmission forecasting methods can only provide deterministic outputs. This study aims to show that probabilistic forecasting, in contrast, is suitable for stochastic demand modeling and emergency medical resource planning under uncertainty.

Two probabilistic forecasting methods, i.e. quantile regression convolutional neural network and kernel density estimation, are combined to provide the conditional quantiles and conditional densities of infected populations. The value of probabilistic forecasting in improving decision performances and controlling decision risks is investigated by an empirical study on the emergency medical resource planning for the COVID-19 pandemic.

The managerial implications obtained from the empirical results include (1) the optimization models using the conditional quantile or the point forecasting result obtain better results than those using the conditional density; (2) for sufficient resources, decision-makers' risk preferences can be incorporated to make tradeoffs between the possible surpluses and shortages of resources in the emergency medical resource planning at different quantile levels; and (3) for scarce resources, the differences in emergency medical resource planning at different quantile levels greatly decrease or disappear because of the existing of forecasting errors and supply quantity constraints.

Very few studies concern probabilistic epidemic transmission forecasting methods, and this is the first attempt to incorporate deep learning methods into a two-phase framework for data-driven emergency medical resource planning under uncertainty. Moreover, the findings from the empirical results are valuable to select a suitable forecasting method and design an efficient emergency medical resource plan.

The stress–strain behaviors of rockfill materials in dams are significantly affected by the anisotropy and grain crushing. However, these factors are rarely considered in numerical simulations of high rockfill dams. This study intends to develop a reasonable and practical constitutive model for rockfill materials to overcome the above problems.

The effects of anisotropy and grain crushing are comprehensively considered by the spatial position of the reference state line. After the improved generalized plasticity model for rockfill materials (referred to as the PZR model) is developed and verified by laboratory tests, it is used with the finite element method to simulate the stress–strain behaviors of the Nuozhadu high core rockfill dam.

The simulated results agree well with the laboratory tests data and the situ monitoring data, verifying the reliability and practicability of the developed PZR model.

A new anisotropic state parameter is proposed to reflect the nonmonotonic variation in the strength as the major principal stress direction angle varies. This advantage is verified by the simulation of a set of conventional triaxial tests with different inclination angles of the compaction plane. 2) This is the first time that the elastoplastic model is verified by the situ monitoring data of high core rockfill dams. The numerical simulation results show that the PZR model can well reflect the stress–strain characteristics of rockfill materials in high core rockfill dams and is better than the traditional EB model.

This chapter seeks to investigate the role of student emotional intelligence (EI) in countering in-class cyberloafing behaviour by students, by exploring the mediating role of boredom proneness through a quantitative approach. A sample of 163 postgraduate university students in India was selected. The authors assessed the mediation model using PROCESS macro. The authors found that students who are more aware of their emotions are better equipped to handle internal and external distractions and work towards a desired goal or outcome and therefore are less likely to experience boredom. Results from this study revealed a significant direct and indirect negative relationship between students’ EI and cyberloafing behaviour. This chapter contributes to the body of literature by highlighting the positive effects of EI as an important antidote to student cyberloafing behaviour. On the practical front, the findings of this study can be used by academicians who are charged with the responsibility of understanding and enhancing student learning by diminishing cyberloafing behaviour among them. The proposed framework could provide a foundation for countering cyberloafing behaviour in educational settings.