Experimental Study of Breakage of Particles under Compression

Granular materials are used widely and can be seen in natural and industrial applications such as sand bags or pharmaceutical pills. During their manufacturing, processing, transport and use, granular materials are subjected to various kinds of loadings. If the amplitude of the loading is above the strength threshold, particles constituting granular materials may fracture. It is very important to understand the failure of particles under these loading conditions to prevent or control their failure during all stages of their manufacturing and use. Better characterization of the fracture behavior of particles composed of different materials and sizes will allow more precise application and better maintenance of granular materials in commercial usage. The effects of size and material properties on the deformation and fracture behavior of granular particles are studied by investigating particles from three different size ranges for three different materials. The mechanical behavior is characterized by force-displacement and stress-strain plots under quasi-static compression (strain rate = 10-2s-1). Along with the deformation behavior, the strengths of particles are also recorded and Weibull distribution is fitted to the fracture stresses. It was observed that the smaller particles break at lower forces but actually withstand higher stress at fracture. The calculated Weibull moduli for different size range and materials show that the flaw population from the manufacturing process is different for different sizes and materials. This study shows that size and material properties alter the fracture stresses. Future experiment can be performed for the same particles under dynamic compression to better understand effects of strain rate on the fracture of particles

Baichuan 2: Open Large-scale Language Models

Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing the need for extensive feature engineering. However, most powerful LLMs are closed-source or limited in their capability for languages other than English. In this technical report, we present Baichuan 2, a series of large-scale multilingual language models containing 7 billion and 13 billion parameters, trained from scratch, on 2.6 trillion tokens. Baichuan 2 matches or outperforms other open-source models of similar size on public benchmarks like MMLU, CMMLU, GSM8K, and HumanEval. Furthermore, Baichuan 2 excels in vertical domains such as medicine and law. We will release all pre-training model checkpoints to benefit the research community in better understanding the training dynamics of Baichuan 2.Comment: Baichuan 2 technical report. Github: https://github.com/baichuan-inc/Baichuan

The 1782 Taiwan Zhangzhou-Quanzhou Feud: A Case Study on Qing Dynasty Communal Violence

With the aid of Chinese primary sources and supplementary secondary sources, this essay seeks to analyze the 1782 Taiwan “subethnic feud” between the Quanzhou and Zhangzhou communities of Zhanghua County, which began with a personal dispute but soon escalated into a lethal rural conflict. The term “subethnic feud (分类械斗),” widely referring to early modern conflicts between different Chinese communities, emphasizes the dominant role of local identity conflicts. However, I argue that such outbursts of violence were complicated phenomena. Instead of the maturation of supposed “ethnic rivalries,” the escalation of the conflict from a personal dispute to a full-scale “rural war” is more likely the joint consequence of three contributing factors: the strong patterns of Taiwanese social organization along subethnic lines, mercenary and thug activities, and the inactivity of the local government

Integrated numerical simulation and quality attributes of soybean protein isolate extrusion under different screw speeds and combinations

A numerical simulation of the fluid-dynamic parameters (shear rate distribution, shear viscosity distribution and residence time) inside the barrel combined with extrudate properties, is a potential novel approach for investigating molten soybean protein isolate (SPI) under different screw speeds and combinations. Through finite element simulation, computer fluid dynamics and particle tracking simulation analysis, it was found that increasing the screw speed can increase the shear rate, decreased the shear viscosity of the SPI fluid, and reduced the RDT, thereby promoting the dispersion degree. The maximum shear rate and minimum shear viscosity were generated at the screw flight flanks, and the fluid underwent an alternate shearing force in the barrel. A small axial channel width can significantly promote the fluidity of molten proteins. In conclusion, SPI extrudates with a homogenous structure, smooth surface, and favourable colour and textual profile were produced at a relatively high screw speed (140 rpm)

Conductivity Classification of Multi-Shape Nonmagnetic Metal Considering Spatial Position Drift Effect with a Triple-Coil Electromagnetic Sensor

The primary step in metal recovery is metal classification. During eddy current testing (ECT), the shape of the sample can have an impact on the measurement results. To classify nonmagnetic metals in three shapes—planar, cylindrical, and spherical—a triple-coil electromagnetic sensor that operates as two coil pairs is used, and the difference in the phase tangent of the impedance change of the two coil pairs is used as a feature for the classification. The effect of spatial position drift between the sensor and the sample divided into lift-off vertically and horizontal drift horizontally on this feature is considered. Experimental results prove that there is a linear relationship between the feature and lift-off regardless of the metal shape, whereas horizontal drift has no effect on this feature. In addition, the slope of the curve between the feature and the lift-off is different for different shapes. Finally, a classification method eliminating the effect of lift-off variation has been constructed, and the classification accuracy of Cu-Al-Zn-Ti metals reached 96.3%, 96.3%, 92.6%, and 100%, respectively, with an overall correct classification rate of 96.3%