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Available information for evaluating the possibility of hospitality firm failure in emerging countries is often deficient. Oversampling can compensate for this but can also yield mixed samples, which limit prediction models’ effectiveness. This research aims to provide a feasible approach to handle possible mixed information caused by oversampling.

This paper uses mixed sample modelling (MSM) when evaluating the possibility of firm failure on enlarged hospitality firms. The mixed sample is filtered out with a mixed sample index through control of the noisy parameter and outliner parameter and meta-models are used to build MSM models for hospitality firm failure prediction, with performances compared to traditional models.

The proposed models are helpful in predicting hospitality firm failure in the mixed information situation caused by oversampling, whereas MSM significantly improves the performance of traditional models. Meanwhile, only partial mixed hospitality samples matter in predicting firm failure in both rich- and poor-information situations.

This research is helpful for managers, investors, employees and customers to reduce their hospitality-related risk in the emerging Chinese market. The two-dimensional sample collection strategies, three-step prediction process and five MSM modelling principles are helpful for practice of hospitality firm failure prediction.

This research provides a means of processing mixed hospitality firm samples through the early definition and proposal of MSM, which addresses the ranking information within samples in deficient information environments and improves forecasting accuracy of traditional models. Moreover, it provides empirical evidence for the validation of sample selection and sample pairing strategy in evaluating the possibility of hospitality firm failure.

Ammonia injection grid (AIG) is used as an input device for ammonia which reacts with NO_x in the selective catalytic reduction (SCR) reactor. However, non-uniform concentration distribution of ammonia could produce partially poisoning or deposits of the catalyst. In this work, for making ammonia widely distributed throughout the flue gas and fully mixed, an optimization method of AIG is proposed.

Depending on the complexity of fluid flow, the relation between the concentration distributions of ammonia and the geometric parameters of AIG is nonlinear. Based on a certain amount of AIG samples, the computational fluid dynamics (CFD) simulations are applied to propose the agent model which describes the functional relation of the deviation of ammonia concentration and the geometric parameters of AIG. The optimization model of AIG based on the agent model is established. The optimized AIG based on the agent model can be used to produce uniform concentration distributions of ammonia, especially in the case that velocity distribution of flue gas is non-uniform.

For qualitatively confirming this optimization method, the three-dimensional CFD simulation of the optimized AIG is carried out. The results reveal that the diffusion process of ammonia gas is consistent with the development of the local vortices, which have a certain relation with the velocity distribution of the flue gas. The unequal ammonia injection designed by the optimization based on the agent model promotes a better mixing of ammonia and flue gas.

In this work, first, the method for optimizing AIG based on the agent model is proposed. Second, the three-dimensional CFD modeling and simulation of the optimized AIG is carried out, and the mixing effects of ammonia and flue gas are presented.

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.

High-ductility cementitious composites (HDCC) have an excellent crack controlled capacity and corrosion resistance capacity, which has a promising application in structure engineering under harsh environment. The purpose of this study is to explore the corrosion mechanism of steel bar in HDCC.

Intact and the pre-cracked HDCC specimens under the coupled action of different dry–wet cycles and chloride attack were designed, and intact normal concrete (NC) was also considered for comparison. Corrosion behavior of a steel bar embedded in HDCC was analyzed by an electrochemical method, a chloride permeability test and X-ray computed tomography.

Steel corrosion probability is related to the chloride permeability of the HDCC cover, and the chloride permeability resistance of HDCC is better than that of NC. Besides, crack is the key factor affecting the corrosion of steel bars, and the HDCC with narrower cracks have a lower corrosion rate. Slight pitting occurs at the crack tips. In addition, the self-healing products and corrosion products fill up the cracks in HDCC, preventing the external aggressive ions from entering and thereby decreasing the steel corrosion rate.

HDCC has a superior corrosion resistance than that of NC, effects of variable crack width on corrosion behavior of steel bar in HDCC under the coupled actions of different dry–wet cycles and chloride attack are investigated, which can provide the guide for the design application of HDCC material in structure engineering exposed to marine environment.

Green human resource management (GHRM) is critical to enhancing the ability of the companies' green innovation, but this link is rarely explored or empirically tested in the literature. Drawing upon human capital theory, the study examines a conceptual model that incorporates the effects of green human capital and management environment concern.

Data were collected from 143 firms in China, and the regression analysis and bootstrapping test were used to assess the hypothesis.

Our findings indicate that GHRM can positively influence green innovation, and green human capital mediated the link between GHRM and green innovation. In addition, management environment concern moderates the effect of GHRM on green human capital. The results further explore that the indirect effect of GHRM on green innovation through green human capital is significant for the firms with a high management environment concern, but not for this relationship with a low management environment concern.

The findings further extend the scope of GHRM research, and theoretical and practical implications of GHRM are presented to enhance environment sustainability.

The purpose of this paper is to examine how advertising is used by real estate companies as an instrument for managing the adverse effects of a catastrophe.

Through a theoretical analysis, types of post-disaster advertising messages were identified. On the basis of the likely variations in post-disaster advertising, a content analysis was conducted of a sample of 4,150 property print advertisements to identify advertising messages related to the earthquake. Finally, the message changes in these earthquake-related advertisements were evaluated and compared with the dimension of time to explore the development of advertising strategies.

The authors found that 12 types of advertising messages were used by developers in response to the Wenchuan earthquake. The initial advertising strategy was mainly to manage public relations, then the strategy was to reduce or compensate for the increased earthquake risk perceptions of buyers.

The findings provide valuable references for helping enterprises adopt effective advertising messages and strategies to reduce the negative effects of disasters.

There are only a few studies on advertising campaigns, especially in the real estate industry, that have been conducted in the wake of catastrophes. This study sought to expand upon the scarce findings in this particular field.

This paper aims to present a simplified solution method for the elasto-plastic consolidation problem under different stress paths.

First, a double-yield-surface model is introduced as the constitutive model framework, and a partial derivative coefficient sequence is obtained by using numerical approximation using Gauss nuclear function to construct a discretization constitutive model which can reflect the influence of different stress paths. Then, the model is introduced to Biot’s consolidation theory. Volumetric strain of each step as the right-hand term, the continuity equation is simplified as a Poisson equation and the fundamental solution is derived by the variable separation method. Based on it, a semi-analytical and semi-numerical method is presented and implemented in a finite element program.

The method is a simplified solution that is more convenient than traditional coupling stiffness matrix method. Moreover, the consolidation of the semi-infinite foundation model is analyzed. It is shown that the numerical method is sufficiently stable and can reflect the influence of stress path, loading distribution width and some other factors on the deformation of soil skeleton and pore water pressure.

Original features of this research include semi-numerical semi-analytical consolidation method; pore water pressure and settlements of different stress paths are different; maximum surface uplift at 3.5a; and stress path is the main influence factor for settlement when loading width a > 10 m.

Examining the influence of ethnic food tourists' perception of hygiene on their attitudinal loyalty formation is the purpose of this study. Specifically, How to demonstrate touristsʼ perception of ethnic food hygiene is the key question, and moreover, the study also investigates whether and how the stages of attitudinal loyalty in this study, which are perceived authenticity, positive emotion, and perceived value, are illustrated in this mechanism and are affected by tourist perceptions of hygiene?

By engaging in the critical debate around the topic of hygiene perception, this study explores the influence of this factor on tourist's attitudinal loyalty, including the cognitive, affective and conative aspects, to ethnic food through the adoption of perceived authenticity, positive emotion and perceived value. A survey was conducted at the Xijiang Miao Village, a very popular ethnic tourism destination in China.

This study reveals that ethnic food tourists' perceptions of hygiene have five dimensions. One of these plays a direct predictor role in developing effective conative loyalty (perceived value). Tourists' perceptions of authenticity and positive emotion representing cognitive and affective loyalty are confirmed in their direct effect on conative loyalty as well. The five dimensions of perceptions of hygiene identified have varying degrees of influence on the three stages of attitudinal loyalty.

The unique contribution of this study lies in two points: (1) it has discovered the way that tourists' perceptions of the hygienic preparation of ethnic food in the ethnic destination is constructed, and (2) it investigated the relationship between tourists' perceptions of hygiene and the three stages of attitudinal loyalty.