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This study explores whether media coverage of Chinese food safety guide consumer behaviour and determines its impact.

Using data from the China Family Panel Studies, this study implements unsupervised machine learning methods to quantitatively identify themes in news media coverage of food safety across various provinces and regions. Based on these findings, this study examines the impact of coverage of food safety on consumer behaviour related to FAFH.

We find that media coverage of food safety in the restaurant sector significantly decreases household expenditure on FAFH relative to total expenditure. While negative coverage substantially decreases expenditure on FAFH, non-negative coverage significantly increases it. Reports of food safety incidents outside consumers’ province are negatively correlated with expenditure on FAFH, whereas reports within province significantly increases such spending. Further, the negative impact of media coverage on FAFH spending is less pronounced among higher-income families, households headed by individuals with high educational levels and those with low sensitivity to newspaper information. A robust government information infrastructure also mitigates this negative impact.

The findings have important policy reference value for promoting the healthy development of catering and other life services by improving news reporting and the regulatory system.

This article employs machine learning methods to identify news reports related to food safety in the catering industry quantitatively and incorporates them into the study of household consumption in China. Consequently, this not only fills a gap in the existing literature but also provides a new perspective for interdisciplinary research in economics, sociology and computer science.

The purpose of this paper is to present an oligopolistic version of the cobweb model that departs from the strict assumptions of perfect competition in the traditional cobweb model.

Introducing a model where n identical producers engage in Cournot competition, with output decisions influencing market prices. The paper retains the original assumptions of naive expectations and a linear model where price expectations of Cournot competitors are made simultaneously with production decisions. The investigation focuses on the model's behavior as the number of producers decreases or industry concentration increases. The authors also show empirical evidence when drawing the data from the pig sector in China and the USA.

The findings indicate that the cobweb model undergoes a transition from divergent to continuous and even convergent as the number of producers decreases or industry concentration increases. The incorporation of costs related to entry and exit from the market contributes to achieving a more stable equilibrium state.

The cobweb model has been primarily studied in an idealized market structure of perfect competition, and the assumptions that they share are not obviously appropriate to many agriculture markets. This study presents an alternative version of the cobweb model in an oligopolistic market that relaxes the strict assumptions of perfect competition. The authors show the dynamics of reduced competitor numbers or increased industry concentration on the convergence of the cobweb model based on subtle variations in parameters.

This study aims to address the challenge of the real-time state of charge (SOC) estimation for lithium-ion batteries in robotic systems, which is critical for monitoring remaining battery power, planning task execution, conserving energy and extending battery lifespan.

The authors introduced an optimal observer based on adaptive dynamic programming for online SOC estimation, leveraging a second-order resistor–capacitor model for the battery. The model parameters were determined by fitting an exponential function to the voltage response from pulse current discharges, and the observer's effectiveness was verified through extensive experimentation.

The proposed optimal observer demonstrated significant improvements in SOC estimation accuracy, robustness and real-time performance, outperforming traditional methods by minimizing estimation errors and eliminating the need for iterative steps in the adaptive critic and actor updates.

This study contributes a novel approach to SOC estimation using an optimal observer that optimizes the observer design by minimizing estimation errors. This method enhances the robustness of SOC estimation against observation errors and uncertainties in battery behavior, representing a significant advancement in battery management technology for robotic applications.