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• Previous studies have not explored the causal relationship between sadism and Internet Trolling from an environmental perspective. • Furthermore, from an individual perspective, the source of the pleasure of high sadistic Internet Trolling was determined. • The results suggest that high sadism takes Internet Trolling in anonymous scenarios, in order to feel more pleasure from the negative emotional reactions of other network users. Internet Trolling refers to a behavior pattern in which individuals deliberately disrupt normal network communication, provoking other network users' negative emotional reactions. Most previous researches focused on the correlation between sadism and Internet Trolling, but did not explore the impact of environmental factors on the two. Thus, Study 1 explored the causal relationships among sadism, Internet anonymity and Internet Trolling. And based on this, the intrinsic motivation of sadistic Internet Trolling was further identified. Study 2 explored the role of hurting others' pleasure in the relationship between sadism and Internet Trolling through three sub-studies (2a, 2b, and 2c). The results suggest that high sadism takes Internet Trolling in anonymous scenarios, in order to feel more pleasure from the negative emotional reactions of other network users. [ABSTRACT FROM AUTHOR]

This paper addresses the fault-tolerant attitude tracking control problem for spacecraft with limited communication capability and input saturation. The design of a hysteresis quantizer aims to alleviate communication burden between the controller module and the actuator module. Then, the attitude tracking problem for spacecraft with input quantization, input saturation, actuator faults and external disturbances is transformed into an attitude control problem with uncertain input coefficients and bounded disturbances. Thereafter a dynamic loop gain function-based approach and a hyperbolic tangent function term with a time-varying boundary layer are introduced to address the uncertainties of input coefficients and the bounded disturbances, respectively. To facilitate the boundness analysis of the signals in the closed-loop system, a pertinent lemma about the dynamic loop gain function is proved. Finally, numerical simulations are employed to validate the effectiveness of the proposed scheme. • The spacecraft attitude tracking problem with disturbances and uncertainties. • The quantized control law alleviates communication burdens of spacecraft. • A dynamic loop gain function method is used to address the impact of uncertain. • A hyperbolic tangent term is devised to counteract bounded disturbances. [ABSTRACT FROM AUTHOR]

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]