Comprehensive analysis of risk factors in Internet agricultural finance based on neural network model

Agricultural industrialization is a major reform and practice in the process of agricultural development and requires theoretical guidance. However, the current theoretical research on financial support for the development of agricultural industrialization is insufficient, which to a certain extent seriously affects the development speed of agricultural industrialization. This paper studies the nature of the part and tail probability of dependent random variable sequences with different distributions, and focuses on the random variable sequences with wide dependent structures, and obtains the relevant probability estimation formulas. At the same time, this paper also considers the application of the main results in complete convergence. Moreover, based on the research on the nature of dependent random variable sequences, the dependent risk model is discussed, which combines Internet finance with the development of agricultural industrialization. In addition, this article uses agricultural industrialization theory and Internet finance theory to study the support of Internet finance for the development of agricultural industrialization in my country. The research results show that the model constructed in this paper has a certain effect.

Survival Analysis of Fatigue and Rutting Failures in Asphalt Pavements

Fatigue and rutting are two primary failure mechanisms in asphalt pavements. The evaluations of fatigue and rutting performances are significantly uncertain due to large uncertainties involved with the traffic and pavement life parameters. Therefore, deterministically it is inadequate to predict when an in-service pavement would fail. Thus, the deterministic failure time which is known as design life (yr) of pavement becomes random in nature. Reliability analysis of such time (t) dependent random variable is the survival analysis of the structure. This paper presents the survival analysis of fatigue and rutting failures in asphalt pavement structures. It is observed that the survival of pavements with time can be obtained using the bathtub concept that contains a constant failure rate period and an increasing failure rate period. The survival function (S(t)), probability density function (pdf), and probability distribution function (PDF) of failure time parameter are derived using bathtub analysis. It is seen that the distribution of failure time follows three parametric Weibull distributions. This paper also works out to find the most reliable life (YrR) of pavement sections corresponding to any reliability level of survivability.

Characteristics of basin inflows a statistical analysis for long-term/mid-term hydrothermal scheduling

The presented paper focuses on the characteristics of reservoir inflows and the appropriate inflow model for long-term/mid-term hydrothermal scheduling. The goal was to find the type of distribution that best fits the observed series of monthly and weekly average inflows in most cases for a model which considers the inflows as independent random variables without time correlation. Also, the objective was to explore the correlation between the inflows during time periods (for weekly and monthly intervals, respectively), and to investigate whether the more complex model of reservoir inflow as a dependent random variable is advisable for optimal long-term/mid-term hydrothermal scheduling. Differences in the characteristics of monthly and weekly inflows, which have been noticed during the analysis, are discussed. Numerical results are presented.

GENERALIZED COVARIATION AND EXTENDED FUKUSHIMA DECOMPOSITION FOR BANACH SPACE-VALUED PROCESSES: APPLICATIONS TO WINDOWS OF DIRICHLET PROCESSES

This paper is concerned with the notion of covariation for Banach space-valued processes. In particular, we introduce a notion of quadratic variation, which is a generalization of the classical restrictive formulation of Métivier and Pellaumail. Our approach is based on the notion of χ-covariation for processes with values in two Banach spaces B1 and B2, where χ is a suitable subspace of the dual of the projective tensor product of B1 and B2. We investigate some C1 type transformations for various classes of stochastic processes admitting a χ-quadratic variation and related properties. If 𝕏1 and 𝕏2 admit a χ-covariation, Fi : Bi → ℝ, i = 1, 2 are of class C1 with some supplementary assumptions, then the covariation of the real processes F1(𝕏1) and F2(𝕏2) exist. A detailed analysis is provided on the so-called window processes. Let X be a real continuous process; the C([-τ, 0])-valued process X(⋅) defined by Xt(y) = Xt+y, where y ∈ [-τ, 0], is called window process. Special attention is given to transformations of window processes associated with Dirichlet and weak Dirichlet processes. Those will constitute a significant Fukushima decomposition for functionals of windows of (weak) Dirichlet processes. As application, we provide a new technique for representing a path-dependent random variable as its expectation plus a stochastic integral with respect to the underlying process.

A Zero-One Result for the Least Squares Estimator

The least squares estimator for the linear regression model is shown to converge to the true parameter vector either with probability one or with probability zero. In the latter case, it either converges to a point not equal to the true parameter with probability one, or it diverges with probability one. These results are shown to hold under weak conditions on the dependent random variable and regressor variables. No additional conditions are placed on the errors. The dependent and regressor variables are assumed to be weakly dependent—in particular, to be strong mixing. The regressors may be fixed or random and must exhibit a certain degree of independent variability. No further assumptions are needed. The model considered allows the number of regressors to increase without bound as the sample size increases. The proof proceeds by extending Kolmogorov's 0-1 law for independent randomvariables to strong mixing random variables.