ABSTRACT
BACKGROUND: Cold chain distribution with multiple links maintains low temperatures to ensure the quality of meat products, whereas temperature fluctuations during this are often disregarded by the industry. The present study simulated two distinct temperatures cold chain distribution processes. Quality indicators and high-throughput sequencing were employed to investigate the effects of temperature fluctuations on the quality and microbial diversity of beef meatballs during cold chain distribution. RESULTS: Quality indicators revealed that temperature fluctuations during simulated cold chain distribution significantly (P < 0.05) exacerbated the quality deterioration of beef meatballs. High-throughput sequencing demonstrated that temperature fluctuations affected the diversity and structure of microbial community. Lower microbial species abundance and higher microbial species diversity were observed in the temperature fluctuations group. Proteobacteria and Pseudomonas were identified as the dominant phylum and genus in beef meatballs, respectively, exhibiting faster growth rates and greater relative abundance under temperature fluctuations. CONCLUSION: The present study demonstrates that temperature fluctuations during simulated cold chain distribution can worsen spoilage and shorten the shelf life of beef meatballs. It also offers certain insights into the spoilage mechanism and preservation of meat products during cold chain distribution. © 2024 Society of Chemical Industry.
ABSTRACT
In this paper, the effects of chitosan film containing star anise essential oil nanofiltration (CFSAO) and superchilled (SC) temperature on the changes of physicochemical and microbiological indexes of rabbit meat patties within 15 days of storage were studied. The total aerobic bacteria counts, malondialdehyde content, protein carbonyl content, total sulfhydryl content, and metmyoglobin content continued to grow throughout the entire experimental period, and the maximum absorption peak at the soret region of myoglobin gradually decreased. Along with the storage time extended, the brightness and redness of rabbit meat significantly decreased (P < 0.05), while the yellowness significantly increased (P < 0.05). The results of storage experiments showed that chitosan composite films and SC temperature had good inhibition on lipid oxidation, myoglobin oxidation and degradation, sulfhydryl content reduction, and microbial growth of rabbit meat after 15 days of storage, and could slow down the change of rabbit meat color.
ABSTRACT
Counterfactual regret minimization (CFR) is a popular method for finding approximate Nash equilibrium in two-player zero-sum games with imperfect information. Solving large-scale games with CFR needs a combination of abstraction techniques and certain expert knowledge, which constrains its scalability. Recent neural-based CFR methods mitigate the need for abstraction and expert knowledge by training an efficient network to directly obtain counterfactual regret without abstraction. However, these methods only consider estimating regret values for individual actions, neglecting the evaluation of state values, which are significant for decision-making. In this article, we introduce deep dueling CFR (D2CFR), which emphasizes the state value estimation by employing a novel value network with a dueling structure. Moreover, a rectification module based on a time-shifted Monte Carlo simulation is designed to rectify the inaccurate state value estimation. Extensive experimental results are conducted to show that D2CFR converges faster and outperforms comparison methods on test games.
ABSTRACT
The mechanism design theory can be applied not only in the economy but also in many fields, such as politics and military affairs, which has important practical and strategic significance for countries in the period of system innovation and transformation. As Nobel Laureate Paul said, the complexity of the real economy makes it difficult for "Unorganized Markets" to ensure supply-demand balance and the efficient allocation of resources. When traditional economic theory cannot explain and calculate the complex scenes of reality, we require a high-performance computing solution based on traditional theory to evaluate the mechanisms, meanwhile, get better social welfare. The mechanism design theory is undoubtedly the best option. Different from other existing works, which are based on the theoretical exploration of optimal solutions or single perspective analysis of scenarios, this paper focuses on the more real and complex markets. It explores to discover the common difficulties and feasible solutions for the applications. Firstly, we review the history of traditional mechanism design and algorithm mechanism design. Subsequently, we present the main challenges in designing the actual data-driven market mechanisms, including the inherent challenges in the mechanism design theory, the challenges brought by new markets and the common challenges faced by both. In addition, we also comb and discuss theoretical support and computer-aided methods in detail. This paper guides cross-disciplinary researchers who wish to explore the resource allocation problem in real markets for the first time and offers a different perspective for researchers struggling to solve complex social problems. Finally, we discuss and propose new ideas and look to the future.
