Joint Institute of the Dalian University of Technology and Belarusian State University

Granular flow is widely found in nature or industrial production. Although the external driving force significantly affects the dynamic behavior of a granular system, a large number of numerical simulations have been conducted to study granular flows driven by gravity. In this study, a superquadric equation was used to construct spherical and cylindrical elements, and the flow processes of granular materials under external pressure were simulated by the discrete element method. To examine the validity of the DEM model, the Janssen effect of spherical particles, the static packing of cylindrical particles and the flow process of spherical particles under external pressure are simulated and compared with the previous experimental and theoretical results. Subsequently, the effects of blockiness, orifice diameter, and particle friction on the flow characteristics are investigated. Results show that the flow rate of spherical particles increases as the external pressure and opening diameter increase or the particle friction decreases. However, the flow rate of cylindrical particles decreases as the blockiness parameter increases, and the external pressure has little effect on the flow rate of the cylindrical particles when the blockiness parameter is greater than 4. Furthermore, the external pressure causes a change in the flow pattern of granular systems. In a gravity-driven granular flow, cylindrical particles appear in funnel flow, and spherical particles in both mass and funnel flows. In a pressure-driven granular flow, spherical particles appear in mass flow, and cylindrical particles in both mass and funnel flows. The critical height of the transition between mass and funnel flows decreases with increasing external pressure and eventually reaches a steady state. Meanwhile, the critical height increases with the blockiness parameter, which indicates that more cylindrical than spherical particles appear in funnel flow. Finally, the basic flow characteristics of granular materials under external pressure are further analyzed by the velocity uniformity index, the normal contact force between particles, and the bottom pressure. Overall, the numerical results are useful for understanding the changes in the flow characteristics of spherical and cylindrical granular materials under external pressure, and further provide guidance for the appropriate design and optimization of cylindrical hoppers.

Based on state-of-the-art ab initio potential energy functions and classical kinetic theory, some transport properties (diffusion, viscosity and thermal conductivity coefficients) of two-component dilute gas media of radium and halogen (F, Cl, Br, I) atoms were predicted as functions of the translation temperature up to 3000 K. Calculations were performed by sequential analytical and (or) numerical computations of deflection angle, cross-section and collision integrals. A detailed methodology for the calculation of the transport properties using the Morse potential was developed. Some numerical difficulties arising due to the singularity of the integrands and discontinuous character of the variable of integration are considered. The dependence of transport properties on isotope mass is also shown. Possible errors introduced by using the model Morse potential function instead of the real potential for the interaction between atoms are estimated. These data can be useful for the planning of the experiments on the direct laser cooling of the monohalides of alkaline earth metals.

In recent years, the omnichannel sales model has rapidly emerged and is widely used in a context in which consumer buying behavior is influenced by a variety of factors that produce complex changes. Among these factors, the impact of omnichannel promotions on consumer behavior is particularly critical, and thus this research area has received much attention. Predicting the shopping feedback of different types of consumers with different numbers of promotions can help companies develop targeted promotional strategies and improve the consumer shopping experience. In this paper, we classify consumers into different types by clustering them in three dimensions: basic personal information, shopping preference, and shopping channel preference. Based on this, a machine learning prediction model based on the random forest algorithm is built in each of the three dimensions, and the model's performance is evaluated by plotting ROC curves. In order to improve the model's performance, this paper uses the up-and-down sampling method to balance the data. The prediction model based on consumers' "basic personal information", "shopping preference," and "shopping channel preference" has been successfully developed, and the prediction results are excellent. This study provides guidance for companies to develop targeted promotion strategies and offers new ideas and methods for marketing and data analysis. In the future, we can continue to dig deeper into consumer behavior data, classify consumers in more dimensions, build more accurate prediction models, and continue to improve the scientific and accurate promotion decisions of enterprises.

Heterogeneous confinement systems attract increasing attention owing to their widespread applications in diverse areas. However, it is still lacking an in-depth understanding of the diffusion mechanism and physical properties of water in the heterogeneous nanochannel through molecular simulations. Here, high-precision TIP4P-BGWT water molecules confined in molybdenum disulfide (MoS2) and graphene walls are utilized to investigate the influences of variables, i.e., channel height, wettability of walls, charge of MoS2, and temperature, on the diffusion mechanism and physical properties. The simulation results indicate that the diffusion mechanism is significantly affected by the channel height and temperature but weakly influenced by the wettability of walls. Observable impacts on the physical properties can be observed with the channel height and temperature, but slight impacts are observed with the wettability of walls. Considered variables, excluding charge of MoS2, remarkably influence density distribution, while limiting mean square displacement at the channel height depends solely upon the effective diffusion distance. It is worth noting that, compared to the homostructure, significant discrepancy in the density distribution can be obtained from the heterogeneous nanochannel due to different solid–liquid interactions. The present study offers a solid foundation for the design of nanodevices, such as nanomembrane, nanosensor, microfluidic chip, etc.

Phase-change material (PCM) based optical switches enable low-power, large-scale integrated photonic networks through precise, nonvolatile splitting ratio control. The key technology lies in the material system and structural design of microsecond-scale, high-temperature-resistant microheaters. Indium tin oxide (ITO) microheaters offer advantages of low fabrication complexity and compatibility with passive photonic platforms. However, their thermal stability issues during switching operations lead to poor cycling reliability in PCM-based phase shifters. Here, we overcome this challenge through geometric optimization of ITO microheaters, suppressing current crowding to achieve transient thermal stability at 1400 K during 5-μs pulsed operation─significantly exceeding the temperature requirements for PCM quenching. We characterize the switching speed by constructing a Mach–Zehnder interferometer (MZI) optical switch, demonstrating a fast response time of 2 μs while maintaining a low insertion loss of only 0.5 dB. These results establish ITO as a transformative platform for energy-efficient, reconfigurable photonic routers in AI-driven computing architectures.