Farmers may add chemical additives to crops to enhance their appearances/tastes 5 or decrease their costs, which may also increase the food demand and sales profits. Man-6 ufacturers buy products from farmers and sell them to consumers, where the government 7 benefits from tax income based on sales revenues. However, once the contaminated food 8 is consumed, customers could get sick. The government would, thus, be partially respon-9 sible for society's health risks from the chemical additives. The punishment policies are 10 set up by the government to regulate and deter farmers' and manufacturers' risky behav-11 ior, balancing tax income, punishment income, and society's health risks. Based on the 12 observation of government regulations, the farmers strategically choose the optimal level of 13 * , and manufacturers pay the appropriate price to farmers. To our knowl-14 edge, little work has studied the strategic interactions among the regulating government, 15 manufacturers, and farmers with endogenous customer demand. This paper fills this gap by 16 building a Government-Manufacturer-Farmer model with three decentralized and centralized 17 sub-models. The models are validated and illustrated through applying the 2008 Sanlu food 18 contamination data. Our results show that (a) the higher the food price is, the higher the 19 punishment is needed to deter the use of chemicals; (b) the optimal chemical level increases 20 in the payment to the farmer when it is low and decreases in the government punishment; 21 (c) the manufacturer's payment to the farmer decreases in the government punishment; (d) 22 the chemical level is significantly higher in the centralized model than in the decentralized 23 model especially when the food price and slope for sales amount are high, or the base sales 24 demand, tax rate, and chemical cost are low; and (e) the decentralized model leads to the 25 lowest chemical level at equilibrium. This paper provides some novel policy insights for food 26 supply chain risk management.27

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Take-away food (also referred to as “take-out” food in different regions of the world) is a very convenient and popular dining choice for millions of people. In this article, we collect online textual data regarding “take-away food safety” from Sina Weibo between 2015 and 2018 using the Octopus Collector. After the posts from Sina Weibo were preprocessed, users’ emotions and opinions were analyzed using natural language processing. To our knowledge, little work has studied public opinions regarding take-away food safety. This paper fills this gap by using latent Dirichlet allocation (LDA) and k-means to extract and cluster topics from the posts, allowing for the users’ emotions and related opinions to be mined and analyzed. The results of this research are as follows: (1) data analysis showed that the degree of topics have increased over the years, and there are a variety of topics about take-away food safety; (2) emotional analysis showed that 93.8% of the posts were positive; and (3) topic analysis showed that the topic of public discussion is diverse and rich. Our analysis of public opinion on take-away food safety generates insights for government and industry stakeholders to promote the healthy and vigorous development of the food industry.

In the food industry, manufacturers may add some chemical additives to augment the appearance or taste of food. This may increase the food demand and sales profits, but may also cause health problems to consumers. The government could use a punishment policy to regulate and deter such risky behavior but could also benefit from economic prosperity and tax income based on their revenues. This generates a tradeoff for the government to balance tax income, punishment income, and health risks. Adapting to government regulations, the manufacturers choose the level of chemical additives, which impacts the consumer demand. To our knowledge, no prior work has studied the strategic interactions of regulating the government and the manufacturers, faced with strategic customers. This paper fills this gap by (a) building a governmentmanufacturer model and comparing the corresponding decentralized and centralized models; and (b) applying the 2008 Sanlu food contamination data to validate and illustrate the models.

In a security screening system, a tighter screening policy not only increases the security level, but also causes congestion for normal people, which may deter their use and decrease the approver's payoff. Adapting to the screening policies, adversary and normal applicants choose whether to enter the screening system. Security managers could use screening policies to deter adversary applicants, but could also lose the benefits of admitting normal applicants when they are deterred, which generates a tradeoff. This paper analyzes the optimal screening policies in an imperfect two-stage screening system with potential screening errors at each stage, balancing security and congestion in the face of strategic normal and adversary applicants. We provide the optimal levels of screening strategies for the approver and the best-response application strategies for each type of applicant. This paper integrates game theory and queueing theory to study the optimal two-stage policies under discriminatory and non-discriminatory screening policies. We extend the basic model to the optimal allocation of total service rate to the assumed two types of applicants at the second stage and find that most of the total service rate are assigned to the service rate for the assumed "Bad" applicants. This paper provides some novel policy insights which may be useful for security screening practices.