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Debris avalanches and dry granular flows exhibit similar characteristics. In order to comprehend the fundamental mechanisms and improve the accuracy in predicting disasters such as landslides, debris flows, and rock avalanches, the collapse characteristics of a binary granular column are investigated through a three‐dimensional discrete element model. A novel approach is proposed by incorporating the concept of local granular velocity fluctuation and applying a cluster analysis method. Then, the flow mechanism of the binary granular column is analyzed, by considering the inhomogeneous particle size distribution. The research results show that: (1) The normalized final packing height of the granular column gradually increases when the content of coarse particles exceeds 20% and when the coarse‐fine particle size ratio increases. Conversely, the normalized run‐out distance of the granular column decreases gradually with the increase in coarse particle content and the coarse‐fine particle size ratio. (2) The particles with higher granular velocity fluctuations tend to move together and form clusters, demonstrating dynamical heterogeneity. As the coarse particle content and coarse‐fine particle size ratio increase, there is a greater tendency for particles to assemble into larger‐scale active clusters. This means that a larger number of particles exhibit collective behavior during the collapse process, resulting in increased resistance to shear deformation. Ultimately, this leads to a greater packing height and a reduced run‐out distance when observed from a macroscopic perspective. [ABSTRACT FROM AUTHOR]

The cause of action represents the nature of the legal relationships involved in the case.A scientific and rational choice of the cause of action will facilitate the correct application of laws and enable the courts to perform classification management of cases.Cause of action prediction aims to endow computers with the ability to automatically predict the cause category based on the textual case description.Due to the small number of the samples of low-frequency categories and the difficulty of learning effective features,previous methods usually filters out the samples of low-frequency category in data preprocessing.However,in the problem of predicting the cause of action,the key challenge is how to make an accurate prediction for the cases of low-frequency cause categories.To solve this problem,in this paper,we propose a novel hierarchical learning method based on unbalanced samples for predicting cause of action.Firstly,all causes are divided into the first-level and second-level causes according to their inherent hierarchical structure.Then,the tailed ones in second-level causes can be merged into a new first-level category with sufficient samples,and the hierarchical learning is applied to realize the prediction of cause of action.Finally,we refine the loss function to alleviate the problem of data imbalance.Experimental results show that the proposed method is significantly superior over the baseline methods,leading to an improvement of 4.81% in terms of accuracy.Also,we verify the benefits of introducing the hierarchical learning as well as refining the loss function for unbalanced data.

Objective To assess the feasibility of the rbcL sequence of chloroplast DNA as a genetic marker to identify Cannabis sativa L. Methods The rbcL sequences in 62 Cannabis sativa L. samples, 10 Humulus lupulus samples and 10 Humulus scandens DNA samples were detected, and 96 rbcL sequences of the Cannabaceae family were downloaded from Genbank. Sequence alignment was performed by MEGA X software, the intraspecific and interspecific Kimura-2-Parameter （K2P） genetic distances were calculated, and the system clustering tree was constructed. Results The rbcL sequence length acquired by sequencing of Cannabis sativa L. and Humulus scandens were 617 bp and 649 bp, respectively, and two haplotypes of Cannabis sativa L. were observed in the samples. The BLAST similarity search results showed that the highest similarity between the sequences acquired by sequencing and Cannabis sativa L. rbcL sequences available from Genbank was 100%. The genetic distance analysis showed that the maximum intraspecific genetic distance （0.004 9） of Cannabis sativa L. was less than the minimum interspecific genetic distance （0.012 9）. The results of median-joining network and system clustering tree analysis showed that Cannabis sativa L. and other members of the Cannabaceae family were located in different branches. Conclusion The rbcL sequence could be used as a DNA barcode for identifying Cannabis sativa L., and combined with comparative analysis of the rbcL sequence and system cluster analysis could be a reliable and effective detection method for Cannabis sativa L. identification in forensic investigation.

The quantity of intestinal flora is large and diverse, its interaction with the host. The intestinal flora is closely related to the development and the recovery of many human diseases. Intestinal microorganisms affect the occurrence and development of metabolic diseases by regulating the metabolism of substances such as sugar, lipids and amino acids; intestinal flora disorders can also promote inflammatory bowel disease and certain intestinal tumors, and supplementing probiotics can contribute to treat those diseases; In addition, the intestinal flora is closely related to other system such as the neuropsychiatric system, the cardiovascular system, and the urinary system. Therefore, studying the correlation between intestinal flora and disease is of great significance for the prevention and treatment of those diseases and the maintenance of human health.