Impact of Corn Starch Molecular Structures on Texture, Water Dynamics, Microstructure, and Protein Structure in Silver Carp (Hypophthalmichthys molitrix) Surimi Gel.

This study systematically investigates the impact of corn starch molecular structures on the quality attributes of surimi gel products. Employing molecular analyses to characterize corn starch, three amylopectin fractions (A, B1, and B2), categorized by the degree of polymerization ranges (6 < X ≤ 12, 12 < X ≤ 24, and 24 < X ≤ 36, respectively) were specifically focused on. The surimi gel quality was comprehensively assessed through texture profile analysis, nuclear magnetic resonance, scanning electron microscopy, stained section analysis, and Fourier transform infrared spectroscopy. Results indicated the substantial volume expansion of corn amylopectin upon water absorption, effectively occupying the surimi gel matrix and fostering the development of a more densely packed protein network. Starch gels with higher proportions of A, B1, and B2 exhibited improved hardness, chewiness, and bound water content in the resultant surimi gels. The weight-average molecular weight and peak molecular weight of corn starch showed a strong positive correlation with surimi gel hardness and chewiness. Notably, the secondary structure of proteins within the surimi gel was found to be independent of corn starch's molecular structure. This study provides valuable insights for optimizing formulations in surimi gel products, emphasizing the significance of elevated A, B1, and B2 content in corn starch as an optimal choice for crafting dense, chewy, water-retaining surimi gels.

HRL4EC: Hierarchical reinforcement learning for multi-mode epidemic control.

Infectious diseases, such as Black Death, Spanish Flu, and COVID-19, have accompanied human history and threatened public health, resulting in enormous infections and even deaths among citizens. Because of their rapid development and huge impact, laying out interventions becomes one of the most critical paths for policymakers to respond to the epidemic. However, the existing studies mainly focus on epidemic control with a single intervention, which makes the epidemic control effectiveness severely compromised. In view of this, we propose a Hierarchical Reinforcement Learning decision framework for multi-mode Epidemic Control with multiple interventions called HRL4EC. We devise an epidemiological model, referred to as MID-SEIR, to describe multiple interventions' impact on transmission explicitly, and use it as the environment for HRL4EC. Besides, to address the complexity introduced by multiple interventions, this work transforms the multi-mode intervention decision problem into a multi-level control problem, and employs hierarchical reinforcement learning to find the optimal strategies. Finally, extensive experiments are conducted with real and simulated epidemic data to validate the effectiveness of our proposed method. We further analyze the experiment data in-depth, conclude a series of findings on epidemic intervention strategies, and make a visualization accordingly, which can provide heuristic support for policymakers' pandemic response.