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The current study was conducted to investigate the feasibility of high concentration diet (HCD) supplementation with Dimethyl Silicone Oil (DSO) to prevent frothy rumen bloat in goats. The treatments were control group (group C, feeding HCD) and test group (group T, feeding HCD supplemented with 0.1%DSO). The results showed that compared with the group C, the ruminal pH value, Microbial Crude Protein content of group T was extremely significantly higher (p < 0.01), the levels of acetic acid and propionic acid were significantly (p < 0.05) and extremely significantly (p < 0.01) lower in group T, respectively. The foam production and foam strength of the rumen fluid in the group T was extremely significantly lower (p < 0.01), the viscosity was extremely significantly (p < 0.01) higher than those of group C. The total gastrointestinal apparent digestibility of various nutrients, the rumen microbial relative abundance at the phylum level and genus level were not significantly different (p > 0.05). The results indicated that the supplementation of 0.1% DSO in HCD can significantly eliminate foam of the rumen fluid, and didn't disturb the ruminal microorganisms, no negatively affect on digestibility of nutrients in goats, thereby has the application prospect of preventing frothy rumen bloat. The gas produced by rumen fermentation is wrapped in foam and cannot be discharged is the root cause of frothy bloat induced by a high concentration diet. In the present study, the feasibility of dietary supplementation with Dimethyl Silicone Oil (DSO) to prevent frothy bloat was preliminarily evaluated. The results indicated that DSO can significantly eliminate foam of the rumen fluid, and has not negatively effect on the ruminal microorganisms and the digestibility of nutrients in goats, thereby has the application prospect of preventing frothy bloat. [ABSTRACT FROM AUTHOR]

This article proposes a variable-sample method for the computation of stochastic stable Nash equilibrium, in which the objective functions are approximated, in each iteration, by the sample average approximation with different sample sizes. We start by investigating the contraction mapping properties under the variable-sample framework. Under some moderate conditions, it is shown that the accumulation points attained from the algorithm satisfy the first-order equilibrium conditions with probability one. Moreover, we use the asymptotic unbiasedness condition to prove the convergence of the accumulation points of the algorithm into the set of fixed points and prove the finite termination property of the algorithm. We also verify that the algorithm converges to the equilibrium even if the optimization problems in each iteration are solved inexactly. In the numerical tests, we comparatively analyze the accuracy error and the precision error of the estimators with different sample size schedules with respect to the sampling loads and the computational times. The results validate the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

The accurate prediction of Li-ion battery capacity is important because it ensures mission and personnel safety during operations. However, the phenomenon of capacity recovery (CR) may impede the progress of improving battery capacity prediction performance. Therefore, in this study, we focus on the phenomenon of capacity recovery during battery degradation and propose a hybrid lithium-ion battery capacity prediction framework based on two states. First, to improve the density of capacity-related information, the simultaneous Markov blanket discovery algorithm (STMB) is used to screen the causal features of capacity from the initial feature set. Then, the life-long cycle sequence of batteries is partitioned into global degradation regions and recovery regions, as part of the proposed prediction framework. The prediction branch for the global degradation region is implemented through a long short-term memory network (LSTM) and the other prediction branch for the recovery region is implemented through Gaussian process regression (GPR). A support vector machine (SVM) model is applied to identify recovery points to switch the branch of the prediction framework. The prediction results are integrated to obtain the final prediction results. Experimental studies based on NASA's lithium battery aging data highlight the trustworthy capacity prediction ability of the proposed method considering the capacity recovery phenomenon. In contrast to the comparative methods, the mean absolute error and the root mean square error are reduced by up to 0.0013 Ah and 0.0043 Ah, which confirms the validity of the proposed method. [ABSTRACT FROM AUTHOR]

Investigating the availability of water in very deep soil layers for vegetation is important for assessing the eco‐hydrological effects of the thick loess soil and optimizing soil water management and vegetation restoration initiatives. However, the proportion of soil water sourced from the soil layer deeper than 300 cm by typical afforestation species on the Loess Plateau, along with its seasonal variations, remains unclear. In this study, we examined the composition of the water sources used by two typical afforestation plant species (Caragana korshinskii and Medicago sativa) from soil depths of up to 10 m during the growing season, using isotopic analysis. The results showed that soil desiccation occurred in the soil layers of 260–1000 cm and 260–700 cm at the upper and lower slope positions, respectively, for both species plots. Relative stability of the isotopic values in >260 cm soil layers explained a piston flow occurred in these layers. The contribution proportion of soil water in 260–1000 cm was at 41.7% ± 9.0% and 51.3% ± 8.8% for C. korshinskii on upper and lower slope, respectively and was at 67.6% ± 6.7% and 73.7% ± 7.0% for M. sativa on upper and lower slope, respectively. The serious soil desiccation was induced by the dependence on very deep soil water and the slow recharge with the piston flow. Meanwhile, C. korshinskii converted the main water source between 0–260 cm and 260–1000 cm in different seasons. In contrast, M. sativa used water mainly from soil layers deeper than 260 cm in June and increased the utilization proportion of deeper water sources in July and September. The persistent dependence on very deep soil water for M. sativa, induced a deeper desiccation depth as compared to C. korshinskii. Thus, thinning to the optimal leaf area index and converting to natural grassland were recommended for C. korshinskii and M. sativa plots, respectively to restore the depleted soil water and to promote water cycle. [ABSTRACT FROM AUTHOR]