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This study investigated the effects of additives on fermentation quality, in vitro digestibility and microbial communities of Korshinsk peashrub (Caragana korshinskii Kom.) silages at different maturity stages. Korshinsk peashrub was harvested at budding (S1) and blooming (S2) stages, and ensiled without additives (CON), or with 5% molasses (M0), or a combination of 5% molasses and either L. plantarum CLP (M1) or L. plantarum L694 (M2). Each treatment was triplicated and ensiled for 60 days. Delayed harvest increased water soluble carbohydrates (WSC), neutral detergent fibre (aNDF) and acid detergent fibre (ADF) in silages at S2 stage, whereas crude protein (CP) was lower in silages at S1 stage. Compared with the CON group, the additives treated silages had lower pH, greater lactic acid (LA), acetic acid (AA), and WSC concentrations (p <.05). Application of molasses alone or in combination with CLP had a noticeable effect on bacterial communities, with an increase in Lactobacillus in silages at both stages, whereas M2 silages presented more unfavourable microorganisms. Spearman's rank correlation analysis showed that LA concentration in silage was positively correlated with Lactobacillus relative abundance at both stages, while ammonia nitrogen (NH3‐N) concentration was positively correlated with the relative abundances of unclassified‐f‐Enterobacteriaceae and Enterobacter. In conclusion, delayed harvest could result in greater WSC and aNDF but lower CP in Korshinsk peashrub silage. Low WSC is the primary limiting factor affecting silage quality, and ensiling with molasses accelerated lactic acid fermentation in Korshinsk peashrub silage, providing a high‐quality forage resource for livestock production. [ABSTRACT FROM AUTHOR]

The rapid expansion of social networks has generated a growing need for scalable algorithms capable of effectively predicting links. Link prediction is a crucial area of study within complex networks research. Link prediction aims to predict future connections between nodes from the current snapshot of the network and plays a vital role in estimating the growth of social networks. This article introduces an improved approach to link prediction in social networks by exploiting an extended version of local random walk as semi-local random walk (SLRW) for multilayer social networks. Here, taking into account the connectivity and structural similarity of the involved nodes, we propose the SLRW method to acquire nodes sequence with the highest similarity. Also, SLRW metric includes a distributed technique to identify the nearest neighbours by considering the extended neighbourhood concept. To ensure optimal performance, we conduct extensive studies on various hyperparameters of the proposed metric. The experimental results conducted on different datasets demonstrate that the proposed metric achieves improvements in the field of link prediction compared to the state-of-the-art baselines. [ABSTRACT FROM AUTHOR]