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The screw extruder is applied in cement-three-dimensional (3D) printing. The cement paste flow in 3D printing is the typical Herschel–Bulkley fluid. To understand the flow in the channel, the improved lattice Boltzmann method (LBM) is proposed.

For Herschel–Bulkley flow, an improved LBM is presented to avoid the poor stability and accuracy. The non-Newtonian effect is regard as a special forcing term. The Poiseuille flow is taken to discuss the detailed process of the method. With the method, the analytical solution and numerical solution are obtained and compared. Then, the effect of the initial yield stress on the numerical solution is both explored by the shear-thickening fluid and the shear-thinning fluid. Moreover, the variations of the relative errors under different lattice nodes and different power-law indexes are analyzed. Finally, the method is applied into the simulation of the flow in the extruder of cement-3D printing.

The results show that the improved method is effective for Herschel–Bulkley fluids, which can simulate the flow in the extruder stably and accurately.

The simulation can contribute to understand the cement paste flow in the screw extruder, which helps to optimize the structure of the extruder in the following periods.

The improve method provide a new way to analyze the flow in the extruder of cement-3D printing. Also, in the past research, LBM for Herschel–Bulkley fluid is ignored, whereas the study can provide the reference for the numerical simulation.

Platform certification constitutes an effective mechanism for managing the lemon problem concerning food e-commerce. This work aims to evaluate the market effect of platform certification and analyzes its correction mechanism for lemon problem combined with reputation mechanism.

Utilizing the Gold Seller certification of Taobao.com to serve as an illustration, the authors conducted an empirical study based on the sales data of hairy crabs among 2,239 sample sites over six points in time from October to December 2019, systematically examining the market effect of food e-commerce platform certification along with the interaction between food e-commerce platform certification and reputation mechanisms, followed by a heterogeneity test by product price.

This study finds that sellers with platform certification can significantly increase their sales. The market effect of platform certification is more easily observed in the low-price product market. In addition, platform certification and reputation mechanisms have complementary effects. In a low-price product market, the complementary effect of platform certification and product reputation diminishes, while the complementary effect of platform certification and seller reputation disappears.

This study explores the market effect of food e-commerce platform certification, reveals the market effect of certification mechanism when multiple signaling mechanisms exist simultaneously and conducts an empirical test based on real market data. It provides a better comprehension of how platform certifications work in food e-commerce.

With the development and application of mobile internet access, social media represented by Weibo, WeChat, etc. has become the main channel for information release and sharing. High-impact users in social networks are key factors stimulating the large-scale propagation of information within social networks. User influence is usually related to the user’s attention rate, activity level, and message content. The paper aims to discuss these issues.

In this paper, the authors focused on Sina Weibo users, centered on users’ behavior and interactive information, and formulated a weighted interactive information network model, then present a novel computational model for Weibo user influence, which combined multiple indexes such as the user’s attention rate, activity level, and message content influence, etc., the model incorporated the time dimension, through the calculation of users’ attribute influence and interactive influence, to comprehensively measure the user influence of Sina Weibo users.

Compared with other models, the model reflected the dynamics and timeliness of the user influence in a more accurate way. Extensive experiments are conducted on the real-world data set, and the results validate the performance of the approach, and demonstrate the effectiveness of the dynamics and timeliness. Due to the similarity in platform architecture and user behavior between Sina Weibo and Twitter, the calculation model is also applicable to Twitter.

This paper presents a novel computational model for Weibo user influence, which combined multiple indexes such as the user’s attention rate, activity level, and message content influence, etc.