Background Reed has high lignin content, wide distribution and low cost. It is an ideal raw material for replacing wood in the paper industry. Reeds are rich in resources, but the density of reeds is low, leading to high transportation and storage costs. This paper aims to study the compression process of reeds and the creep behaviour of compressed reeds, and provide theoretical guidance for the reed compressor management, bundling equipment and the stability of compressed reed bales. Results We have established a multi-layer perceptron network prediction model for the creep characteristics of reeds, and the prediction rate R2 of this model is greater than 0.997. The constitutive equation, constitutive coefficient and creep quaternary model of the reed creep process were established by using the prediction model. The creep behaviour of the reed bale is positively correlated with the initial maximum compressive stress (σ0). During the creep of the reed, the elastic power and the viscous resistance restrict each other. The results show that the proportion of elastic strain in the initial stage is the largest, and gradually decreases to 99.19% over time. The viscoelastic strain increases rapidly with time, then slowly increases, and finally stabilizes to 0.69%, while the plastic strain accounts for the proportion of the total strain. The specific gravity of the reed increases linearly with the increase of creep time, and finally accounts for 0.39%, indicating that as time increases, the damage of the reed's own structure gradually increases. Conclusions We studied the relationship between the strain and time of the reed and the strain and creep behaviour of the reed bag under different holding forces under constant force. It is proved that the multi-layer perceptron network is better than the support vector machine regression in predicting the characteristics of reed materials. The three stages of elasticity, viscoelasticity and plasticity in the process of reed creep are analysed in detail. This article opens up a new way for using machine learning methods to predict the mechanical properties of materials. The proposed prediction model provides new ideas for the characterization of material characteristics.

In this paper, we propose a new a new risk model with two-type by-claims and delay period based on compound binomial distribution, in which every main claim induces two kinds of by-claims but either of the by-claims maybe delayed to the next period. The nature of the risk model with time-correlated claims is studied based on recursion of joint distribution functions. Through introducing three submodels, we obtain the expression of the joint distribution ( , , ) f u x y of the surplus just before ruin and deficit at ruin when initial surplus is 0 and the recursive formula of the joint distribution when initial surplus is u . In addition, when initial surplus is 0 the ruin probability of the risk model is given. At last, we provide the method to compute the ruin probability when initial surplus is u .

Background: Reed has high lignin content, wide distribution and low cost. It is an ideal raw material for replacing wood in the paper industry. Reeds are rich in resources, but the density of reeds is low, leading to high transportation and storage costs. This paper aims to study the compression process of reeds and the creep behaviour of compressed reeds, and provide theoretical guidance for the reed compressor management, bundling equipment and the stability of compressed reed bales.Results: We studied the strain-time relationship and strain of the reed bales compressed under constant force and the creep behaviour of the reed bales under different holding forces. Additionally, the creep behaviour of reed bales under various retention forces was investigated. The test curves are fitted by Machine Learning Prediction Algorithms and Support Vector Machine Regression. And use machine learning prediction algorithm model to establish a quaternary model of reed creep. The results show that the creep behaviour of a reed bale was positively correlated with the initial maximum compressive stress. The established Burgers four-element model was capable of simulating the creep process of reed bales. The test curves coincided well with the model-simulated curves. Reed bales were found to exhibit viscoelasticity. During the creep process, the elastic dynamic force and the viscous resistance were mutually constrained. The strain of reeds was composed of elastic, viscoelastic and plastic εs. And elaborated the three stages of the creep process in detail.Conclusions: We studied the relationship between the strain and time of the reed and the strain and creep behaviour of the reed bag under different holding forces under constant force. It is proved that the multi-layer perceptron network is better than the support vector machine regression in predicting the characteristics of reed materials. The three stages of elasticity, viscoelasticity and plasticity in the process of reed creep are analysed in detail. This article opens up a new way for using machine learning methods to predict the mechanical properties of materials. The proposed prediction model provides new ideas for the characterization of material characteristics.