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Simple Summary: Our objective is to establish an in vitro rumen fermentation model that can dynamically simulate the fermentation process of various total mixed ration (TMR) diets in the rumen of dairy cows, enabling a quantitative investigation of rumen methane and rumen acetic acid concentrations. The models were assessed for their prediction accuracy and precision, while the independent verification experiments confirmed the models' generalization ability across different total mixed ration (TMR) ratios (C:F). These results show that in vitro rumen models constructed by machine learning methods can be used as a tool to quantify rumen fermentation parameters (methane and acetic acid) and guide the dietary structure optimization of dairy cows. The aim of this study is to identify an alternative approach for simulating the in vitro fermentation and quantifying the production of rumen methane and rumen acetic acid during the rumen fermentation process with different total mixed rations. In this experiment, dietary nutrient compositions (neutral detergent fiber (NDF), acid detergent fiber (ADF), crude protein (CP), and dry matter (DM)) were selected as input parameters to establish three prediction models for rumen fermentation parameters (methane and acetic acid): an artificial neural network model, a genetic algorithm-bp model, and a support vector machine model. The research findings show that the three models had similar simulation results that aligned with the measured data trends (R2 ≥ 0.83). Additionally, the root mean square errors (RMSEs) were ≤1.85 mL/g in the rumen methane model and ≤2.248 mmol/L in the rumen acetic acid model. Finally, this study also demonstrates the models' capacity for generalization through an independent verification experiment, as they effectively predicted outcomes even when significant trial factors were manipulated. These results suggest that machine learning-based in vitro rumen models can serve as a valuable tool for quantifying rumen fermentation parameters, guiding the optimization of dietary structures for dairy cows, rapidly screening methane-reducing feed options, and enhancing feeding efficiency. [ABSTRACT FROM AUTHOR]

In the context of the digital economy era, it is urgent for Chinese state-owned enterprises (SOEs) to engage in social value cocreation activities. The government and consumers' roles in SOEs' social value cocreation system cannot be ignored. Therefore, it is necessary to explore the tripartite social value cocreation model involving the government, SOEs, and consumers. In this respect, this study constructs a tripartite evolutionary game model of the government, SOEs, and consumers, and explores the influencing factors and evolutionary mechanism of the system overall. Matlab software is used to analyze the simulation data. The results reveal that the prerequisite for SOEs' successful social value cocreation is that consumers receive additional social value benefits greater than the level of improvement in social welfare. The allocation coefficient of consumers' additional social value benefits, the degree of the government's digital empowerment subsidy, and the level of the punishment for SOEs that violate the government's cocreation requirements will accelerate the achievement of equilibrium in the social value cocreation system, without affecting the final equilibrium result. By analyzing the strategic choices and interactive relationships among the government, SOEs, and consumers in social value cocreation in-depth, this study offers suggestions to promote the government, SOEs, and consumers' participation in social value cocreation. This research contributes to clarifying SOEs' social value cocreation model and has significant implications for promoting enterprises' high-quality development., Competing Interests: Declaration of conflicting interestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.