To set up a logistic regression prediction model for hepatotoxicity of Chinese herbal medicines based on the efficacy of Chinese herbal medicines

In our previous research,a logistic regression prediction model for hepatotoxicity of Chinese herbal medicines based on the four properties,five flavors and channel tropism has been successfully established.However,could Chinese herbal medicines efficacy also be applied to predict the hepatotoxicity of Chinese herbal medicines?Therefore,a logistic regression prediction model for hepatotoxicity of Chinese herbal medicines based on Chinese herbal medicines efficacy has been tentatively set up to study the correlations of hepatotoxic and nonhepatotoxic Chinese herbal medicines with efficacy by using a chi-square test for two-way unordered categorical data.Logistic regression prediction model was established and the accuracy of the prediction by this model was evaluated.It has been found that the hepatotoxicity and nonhepatotoxicity of Chinese herbal medicines were weakly related to the efficacy,and the coefficient was 0.295.There were 20 variables from Chinese herbal medicines efficacy analyzed with unconditional logistic regression,and 6 variables,rectifying Qi and relieving pain,clearing heat and disinhibiting dampness,invigorating blood and stopping pain,invigorating blood and relieving swelling,killing worms and relieving fright were chosen to establish the logistic regression prediction model,with the optimal cutoff value being 0.250.Dissipating cold and relieving pain(DCRP),clearing heat and disinhibiting dampness,invigorating blood and relieving pain(IBRP),invigorating blood and relieving swelling,killing worms,and relieving fright were the variables to affect the hepatotoxicity and the established logistic regression prediction model had predictive power for hepatotoxicity of Chinese herbal medicines to a certain degree.

A New Hybrid Machine Learning Model for Short-Term Climate Prediction by Performing Classification Prediction and Regression Prediction Simultaneously

Machine learning methods are effective tools for improving short-term climate prediction.However,commonly used methods often carry out classification and regression prediction modeling separately and independently.Such a single modeling approach may obtain inconsistent prediction results in classification and regression and thus may not meet the needs of practical applications well.To address this issue,this study proposes a selective Naive Bayes ensemble model(SENB-EM)by introducing causal effect and voting strategy on Naive Bayes.The new model can not only screen effective predictors but also perform classification and regression prediction simultaneously.After being applied to the area prediction of summer western North Pacific subtropical high(WNPSH)from 2008 to 2021,it is found that the accuracy classification score(a metric to assess the overall classification prediction accuracy)and the time correlation coefficient(TCC)of SENB-EM can reach 1.0 and 0.81,respectively.After integrating the results of different models[including multiple linear regression ensemble model(MLR-EM),SENB-EM,and Chinese Multimodel Ensemble Prediction System(CMME)used by National Climate Center(NCC)]for 2017-2021,the TCC of the ensemble results of SENB-EM and CMME can reach 0.92(the highest result among them).This indicates that the prediction results of the summer WNPSH area provided by SENB-EM have a high reference value for the real-time prediction.It is worth noting that,except for the numerical prediction results,the SENB-EM model can also give the range of numerical prediction intervals and predictions for anomalous degrees of the WNPSH area,thus providing more reference information for meteorological forecasters.Overall,as a new hybrid machine learning model,the SENB-EM has a good prediction ability;the approach of performing classification prediction and regression prediction simultaneously through integration is informative to short-term climate prediction.

Median Unbiased Estimation of Bivariate Predictive Regression Models with Heavy-tailed or Heteroscedastic Errors

In this paper, we consider median unbiased estimation of bivariate predictive regression models with non-normal, heavy-tailed or heteroscedastic errors. We construct confidence intervals and median unbiased estimator for the parameter of interest. We show that the proposed estimator has better predictive potential than the usual least squares estimator via simulation. An empirical application to finance is given. And a possible extension of the estimation procedure to cointegration models is also described.

A dynamic logistic regression for network link prediction

In social network analysis, link prediction is a problem of fundamental importance. How to conduct a comprehensive and principled link prediction, by taking various network structure information into consideration,is of great interest. To this end, we propose here a dynamic logistic regression method. Specifically, we assume that one has observed a time series of network structure. Then the proposed model dynamically predicts future links by studying the network structure in the past. To estimate the model, we find that the standard maximum likelihood estimation(MLE) is computationally forbidden. To solve the problem, we introduce a novel conditional maximum likelihood estimation(CMLE) method, which is computationally feasible for large-scale networks. We demonstrate the performance of the proposed method by extensive numerical studies.

反渗透膜经济寿命分析

提出利用反渗透膜的经济寿命解决回用水水质控制不及时和反渗透膜更换浪费的问题。并提出通过应用经济寿命的分析方法来确定出反渗透膜(RO膜)的最佳更换时间。为了使所求得的经济寿命更为准确,RO膜的经济寿命模型中利用电导率和硬度对产水量进行回归预测。应用最佳更换时间解决回用水处理过程中可能出现的水质不稳定以及RO膜更换时间不确定而造成运行成本浪费的问题。

高速公路路堑风雪流灾害预测模型与应用

山区高速公路在风雪天气极易出现风雪流灾害,造成雪阻路断,影响正常通行。复杂的地形和路基断面往往是引导风雪流流向和雪粒沉积的关键因素,尤其路堑形式下,路面积雪更为严重。针对全路堑、迎风半路堑和背风半路堑三种路堑形式,对风雪流流场进行了数值模拟,分析流场的分布规律和雪害致灾机理。选定路面积雪范围为目标函数,以山体相对高度、路基与山体横向距离、路堑深度、路堑坡度及风速为主要影响因素,对大量案例进行了正交试验和回归分析,获得了路堑积雪的预测模型。该模型经过了显著性检验,可反映各因素对路面积雪的影响程度,并能体现各因素对路面积雪的耦合作用。最后,结合具体工程实例,对所建立的模型进行了应用与验证分析,发现预测模型与数值模拟结果误差不超过5%,验证了模型的有效性和实用性。

Sequential Big Data-Based Macroeconomic Forecast for Industrial Value Added

Macroeconomic situation is the overall performance of a country’s and regional economic situation.At present,the vast majority of macroeconomic indicators are obtained through sampling surveys,step-by-step reporting,statistical calculations,and other processes,which are publicly released by the Statistical Bureau.There are some shortcomings,such as lag and non-authenticity.Timely forecasting and early warning of macroeconomic trends are the important needs of government affairs.However,the timeliness of data has a direct impact on government decision-making.In this paper,the high frequency and relatively accurate big data sources are adopted to construct a multivariate regression prediction model for traditional national economic accounting indicators(such as industrial value added above the scale of Hefei),which is different from the traditional time series prediction model such as ARIMA model.Based on the macroeconomic prediction model of time series big data,multi-latitude data sources,sequential update,verification set screening model and other strategies are used to provide more reliable,timely,and easy-to-understand forecasting values of national economic accounting indicators.At the same time,the potential influencing factors of macroeconomic indicators are excavated to provide data and theoretical basis for macroeconomic analysis and decision-making.