Exploration of the prediction and generation patterns of heterocyclic aromatic amines in roast beef based on Genetic Algorithm combined with Support Vector Regression.

Heterocyclic aromatic amines (HAAs) are harmful byproducts in food heating. Therefore, exploring the prediction and generation patterns of HAAs is of great significance. In this study, genetic algorithm (GA) and support vector regression (SVR) are used to establish a prediction model of HAAs based on heating conditions, reveal the influence of heating temperature and time on the precursor and formation of HAAs in roast beef, and study the formation rules of HAAs under different processing conditions.

Study on failure mechanism of tight sandstone based on moment tensor inversion.

Understanding deep rocks' mechanical properties and failure evolution is crucial for efficient resource development. This study investigates the mechanical properties of tight sandstone and analyzes its acoustic emission (AE) characteristics using a combined discrete element model and moment tensor inversion. The AE activity during loading is categorized into three stages: crack initiation, stable crack propagation, and unstable crack propagation.