Passengers’ boarding time interval is of great significance for analysis of collective mobility behaviors. In this paper, we empirically investigate the boarding time interval of mobility behaviors based on three large-scale reservation records of passengers traveling by three different types of transportation from a travel agency platform, namely airplane, intercity bus and car rental. The statistical results show that similar properties exist in the passengers’ mobility behaviors, for example, there are similar burstiness [Formula: see text] and memory [Formula: see text] for different time interval distribution, which indicates that the passengers’ mobility behaviors are periodical. Furthermore, we present a probability model to regenerate the empirical results by assuming that the passengers’ next boarding time interval will generate between a short time of 1–7 days with probability [Formula: see text] and a random long time with probability [Formula: see text]. The simulation results show that the presented model can reproduce the burstiness and memory effect of the boarding time interval when [Formula: see text] for three empirical datasets, which suggests the periodical behaviors with the probability [Formula: see text]. This work helps in deeply understanding the regularity of human mobility behaviors.

This paper examines a hotel reservation website's customer defection. Applying statistic and data mining technology including logistic regression and random forests, we examine customer database to identify the attributes that affect customer attrition and develop a model of customer defection in the hotel reservation website. The empirical evaluation results showed the model has 78.9% accuracy, which suggest that the proposed churn prediction technique exhibits satisfactory predictive effectiveness.

The aim of this paper is to study the performance of a composite floor system at different heat stages using artificial intelligence to derive a sustainable design and to select the most critical factors for a sustainable floor system at elevated temperatures. In a composite floor system, load bearing is due to composite action between steel and concrete materials which is achieved by using shear connectors. Although shear connectors play an important role in the performance of a composite floor system by transferring shear force from the concrete to the steel profile, if the composite floor system is exposed to high temperature conditions excessive deformations may reduce the shear-bearing capacity of the composite floor system. Therefore, in this paper, the slip response of angle shear connectors is evaluated by using artificial intelligence techniques to determine the performance of a composite floor system during high temperatures. Accordingly, authenticated experimental data on monotonic loading of a composite steel-concrete floor system in different heat stages were employed for analytical assessment. Moreover, an artificial neural network was developed with a fuzzy system (ANFIS) optimized by using a genetic algorithm (GA) and particle swarm optimization (PSO), namely the ANFIS-PSO-GA (ANPG) method. In addition, the results of the ANPG method were compared with those of an extreme learning machine (ELM) method and a radial basis function network (RBFN) method. The mechanical and geometrical properties of the shear connectors and the temperatures were included in the dataset. Based on the results, although the behavior of the composite floor system was accurately predicted by the three methods, the RBFN and ANPG methods represented the most accurate values for split-tensile load and slip prediction, respectively. Based on the numerical results, since the slip response had a rational relationship with the load and geometrical parameters, it was dramatically predictable. In addition, slip response and temperature were determined as the most critical factors affecting the shear-bearing capacity of the composite floor system at elevated temperatures.

It is of great economic significance to optimize the total cost and improve the performance of the supply chain. In this paper, we assume that the market demand is random, and the seller and the buyer share information and make decisions together. We analyze the optimal joint order quantity under probabilistic demand and design the quantity discount model and profit distribution mechanism. Under a certain quantity discount mechanism and profit distribution strategy, both the seller and the buyer can reduce costs. The quantity discount model and profit distribution mechanism designed require supply chain members to share information. In order to protect the privacy of members and improve the willingness of supply chain members to share information, we designed a privacy protection joint ordering policy protocol and privacy protection quantity discount policy based on Secure multiparty computation technology. Then, the joint ordering strategy, the privacy-preserving joint ordering strategy, and quantity discount protocol are numerically simulated. The numerical simulation results show that the privacy-preserving quantity discount coordination mechanism designed by us can reduce the cost of supply chain members to varying degrees and effectively protect the shared information of supply chain members. This work is helpful to the research of cost optimization of the system in complex supply chain systems.

Background It is of great economic significance to optimize the total cost of supply chain and improve the performance of supply chain. Information sharing can improve supply chain performance. A probabilistic demand system with one seller and one buyer studied by scholars can find that compared with the traditional decentralized system, the quantity coordination strategy can improve the performance of the supply chain. However, supply chain members are reluctant to provide private cost information because their customers may abuse this information, which will seriously affect their psychological security of privacy protection. The application of secure multiparty computing (SMC) is a possible way to solve these problems. SMC provides a framework for computing partners to make decisions to achieve global goals while protecting the privacy of any party's private information. Only a few literatures have discussed the application of SMC in supply chain collaboration. When applying SMC protocol to the joint ordering strategy with quantity discount under Probabilistic demand, the inventory problem has not been studied. Especially in terms of the actual indicators of emotional labor and work stress, no research has been carried out. Subjects and Methods Firstly, we analyze the optimal order quantity under Probabilistic demand, the optimal joint order quantity under Probabilistic demand, profit distribution and quantity discount design. Then, based on the SMC protocol, a joint ordering strategy under Probabilistic demand is proposed, and the corresponding privacy protection quantity discount of the joint ordering strategy without intermediary is given. In this regard, we give the numerical simulation of joint ordering and the numerical simulation of privacy protection joint ordering strategy and its quantity discount design. Last, Using the revised Lin Shangping's “organization's emotional labor burden scale” and “Minnesota Satisfaction Scale (MSQ), the study distributed 200 questionnaires to the subjects and recovered 196. The sample structure distribution is that men account for 51.7%, the lowest proportion under the age of 25 (2.5%), and the highest proportion over the age of 36 (62.6%) The proportion is the highest, accounting for 64.0%. In terms of service years, the proportion of 1-3 years is the lowest, accounting for 4.4%, and the proportion of more than 7 years is the highest, accounting for 84.2%. The total correlation coefficient and total reliability of the modified items were analyzed to delete the items without internal consistency in the questionnaire, so as to improve the reliability of the questionnaire and the quality of measurement tools. Results The privacy protection joint ordering strategy based on the probabilistic demand of SMC protocol can be implemented under the condition of any private (cost and capacity) information retention, which can be realized without mediation. The numerical simulation results of joint ordering show that joint ordering can reduce the total cost of supply chain. Through quantity discount design, the cost of each participant in the supply chain can be reduced to varying degrees. In terms of emotional labor, it has a positive and significant impact on job satisfaction, because appropriate emotional labor helps to reduce work pressure and naturally improve psychological safety. Conclusion SMC protocol gives the privacy protection joint ordering strategy under Probabilistic demand, and determines the privacy protection quantity discount of the joint ordering strategy under Probabilistic demand. The numerical simulation results of joint ordering show that joint ordering can reduce the total cost of supply chain. Through quantity discount design, the cost of each participant in the supply chain can be reduced to varying degrees. The above privacy protection agreement can be realized without the help of intermediaries. The buyer and the seller can obtain the optimal joint order quantity while protecting their own privacy information, which helps to improve the psychological security of supply chain members, and realize efficient operation and win-win of the supply chain. In the future, we will consider the existence of more buyers or multi-layer supply chain structure, and deduce the corresponding privacy protection algorithm. Acknowledgements Supported by a project grant from Zhejiang Philosophy and Social Sciences Foundation (Grant No.19NDJC145YB), and a project grant from National Natural Science Foundation of China (Grant No. 71203161), and a project grant from National Social Science Foundation of China (Grant No. 17BGL132).