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With the rapid development of O2O, offline experience and online purchase have become a method of purchase for more and more customers. Through offline experience, consumers can feel the quality of products directly. Such channel switching behavior of consumers will produce a "showroom" effect and affect the return rate of online channels. This study adopts the multinomial logit model to maximize profits by considering the difference in quality between online and offline products, quality defects, and offline service. Then, a pricing decision model is developed to analyze the influence of returning goods due to quality problems on the retailers' optimal pricing and profit. The result shows that retailers can obtain the optimal profit when the offline service is maintained at a certain level. As the return rate increases, the optimal pricing increases, but the maximum profit decreases. The optimal pricing decreases with the increase in online product quality, but the maximum profit increases accordingly. In the omni-channel environment, customers can freely switch between channels according to utility and preference when purchasing products. Based on customer returns, retailers can dynamically adjust their service, control product quality, and set optimal product pricing, thus achieving maximum profits. This study can provide a theoretical basis and decision support for omni-channel retailers in platform operation and revenue management. [ABSTRACT FROM AUTHOR]

With recent developments in information technology and the extensive promotion of Internet Plus, the use of online centralized procurement by governments and enterprise groups has become progressively more common, and the winning bid evaluation decision making method is particularly important in this context. However, experts might not be completely rational during the process of bid evaluation, which may induce the enhancement or repression of bid scores. To address such behaviors during the process of bid evaluation, an automatic mechanism to identify and correct such tendencies is proposed in this study. Because experts have different preferences for different alternatives, which are directly reflected in the evaluation of attribute values. Based on selection preference, this paper proposes a selection preference method for solving the subjective weight of attributes. Firstly, the weights of relevant attributes are first determined via the entropy weight method and the selection preference method, and the weights corresponding to groups are determined according to the differences between the scores assigned by experts. Then, a grouped multi-attribute bid evaluation decision making method is proposed based on the selection preference. Finally, an example is used to verify the effectiveness of the method and its superiority over existing methods. Thus, a theoretical basis and a decision support mechanism are provided in this study for centralized procurement departments of governments and enterprises. Further, it also provides guidance for multi-attribute decision making problems with identical grouped features. [ABSTRACT FROM AUTHOR]

Isothermal thermal decomposition method was used to study the thermal behavior of the HMX-based PBX explosive JOL-1. The gas pressure versus time curves of the isothermal decomposition of JOL-1 were obtained within the temperature range of 90°C to 130°C. Kinetic parameters such as activation energy and frequency factor for the thermal decomposition of JOL-1 were obtained using the Arrhenius equation and model-fitting method. The model-fitting method further proved that the isothermal decomposition of JOL-1 at 90°C, 100°C, 110°C, 120°C, and 130°C is consistent with the No. 8 mechanism function, namely, the anti-Jander equation, controlled by 3D diffusion. The storage life of JOL-1 was estimated by the Semenov equation and the extent of reaction reached 0.1–0.4%. Results showed that the effective storage life of JOL-1 at 25°C, 40°C, and 60°C are 68 years, 10 years, 1 year, respectively, when the extent of reaction reached 0.1%. [ABSTRACT FROM AUTHOR]

Efficient and accurate measurement of material mechanical properties is important for material development. The mechanical properties of materials are comprehensively affected by factors such as material composition and microstructure. The existing single-mode prediction methods have the problems of poor mapping integrity and low prediction accuracy. In this paper, a multimodal fusion prediction model integrating material microstructure information and material composition information is proposed. Firstly, Convolutional neural network(CNN) is used to extract the material microstructure features, and Multilayer perceptron(MLP) is used to extract the component features. Then, the adaptive feature vector fusion module designed to adjust the influence of different modal information on the mechanical properties is used to achieve high-level feature fusion, and neural network is used to complete the mechanical properties prediction. The method is verified on the data of an alternative composite material. The method proposed provides a promising solution for the measurement and prediction of material properties. [Display omitted] • The proposed model takes into account both the microstructure and composition of the material. • The addition of CBAM makes the model pay more attention to the features of the particle region in the SEM image. • Using branch network makes it easier to learn the mass proportion feature and crystal type feature interactively. • An adaptive feature vector fusion module is proposed which can control the influence weight of different modal information. [ABSTRACT FROM AUTHOR]