Life prediction of ZPW-2000A track circuit equipment based on SVDD and gray prediction

Evaluation of the health state and prediction of the remaining life of the track circuit are important for the safe operation of the equipment of railway signal system.Based on support vector data description(SVDD)and gray prediction,this paper illustrates a method of life prediction for ZPW-2000A track circuit,which combines entropy weight method,SVDD,Mahalanobis distance and negative conversion function to set up a health state assessment model.The model transforms multiple factors affecting the health state into a health index named H to reflect the health state of the equipment.According to H,the life prediction model of ZPW-2000A track circuit equipment is established by means of gray prediction so as to predict the trend of health state of the equipment.The certification of the example shows that the method can visually reflect the health state and effectively predict the remaining life of the equipment.It also provides a theoretical basis to further improve the maintenance and management for ZPW-2000A track circuit.

Application of Grey Model and Neural Network in Financial Revenue Forecast

There are many influencing factors of fiscal revenue,and traditional forecasting methods cannot handle the feature dimensions well,which leads to serious over-fitting of the forecast results and unable to make a good estimate of the true future trend.The grey neural network model fused with Lasso regression is a comprehensive prediction model that combines the grey prediction model and the BP neural network model after dimensionality reduction using Lasso.It can reduce the dimensionality of the original data,make separate predictions for each explanatory variable,and then use neural networks to make multivariate predictions,thereby making up for the shortcomings of traditional methods of insufficient prediction accuracy.In this paper,we took the financial revenue data of China’s Hunan Province from 2005 to 2019 as the object of analysis.Firstly,we used Lasso regression to reduce the dimensionality of the data.Because the grey prediction model has the excellent predictive performance for small data volumes,then we chose the grey prediction model to obtain the predicted values of all explanatory variables in 2020,2021 by using the data of 2005–2019.Finally,considering that fiscal revenue is affected by many factors,we applied the BP neural network,which has a good effect on multiple inputs,to make the final forecast of fiscal revenue.The experimental results show that the combined model has a good effect in financial revenue forecasting.

A Criterion for Crack Initiation and Propagation of Intact Rocks under Creep Condition

The mechanism and criterion of crack initiation and propagation of rocks were investigated by many researchers. And the creep behaviour of rocks was also theoretically and experimentally studied by some scientists and engineers. The characteristics of crack initiation and propagation of rocks under creep condition, however, are very important for rock engineering and still not paid enough attention by researchers. In this paper, the criterion and mechanism of crack initiation and propagation under creep condition were investigated using specimens collected from sandstone rock formations outcropping in the Emei Mountain, the Sichuan Province of China. Cuboid specimens under three point bending were used in this investigation. All specimens were classified into four sorts and used for Mode I fracture or creep fracture tests. The experimental result shows that due to creep deformation, rock crack will inevitably initiate and propagate under a load of K I , which is less than fracture toughness K IC but not less than a constant (marked as K IC2 ). K IC2 indicates the ability of rock to resist crack initiation and propagation under creep conditions and is less than fracture toughness K IC , defined as creep fracture toughness in this paper. K IC2 should be considered as an important parameter on design and computation of rock engineering. The microstructural mechanism for crack initiation and propagation of rock materials under creep condition was introduced based on competitive model between softening effect and hardening effect, and the validity of test result was explained. The test result was also verified in rheological theory. When K I is more than K IC2 but less than K IC , rock crack will initiate and propagate after a time interval of sustained loading under creep condition. In order to find the relation between duration of sustained loading, which can lead to crack initiation and propagation, and the initial stress intensity factor K I , an unequal interval time sequence forecasting and predicting model was introduced, and the relation was obtained for homogeneous and isotropic fine grained red sandstone. Finally a modified fracture toughness formula was given, in which the influence of fracture process zone(FPZ) was fully considered.

Big Data Interprets US Opioid Crisis

Since 2010,there has been a new round of drug crises in the United States.The abuse of opioids has led to a sharp increase in the number of people involved in drug crimes in the United States.There is an urgent need to explore solutions to the drug crisis in the United States.In this paper,the model of in-depth analysis is established under the condition of obtaining the opioid data and the influence factor data of the large sample of five state[1].In the first part,we use the Highway Safety Research Institute model based on the differential equation model to predict the initial value,find the initial position of the drug transfer,and obtain the curve of the number of different groups over time by fitting the data,so that the curves can be predicted the changing trends of the groups in the future.It was found that in Kentucky State,the county's most likely to start using opioids were Pike and Bale.In Ohio,the county's most likely to start using opioids are Jackson and Scioto.In Pennsylvania State,Mercer and Lackawanna are the counties most likely to start using opioids.Martinsville and Galax are the counties where Virginia State is most likely to start using opioids.Logan and Mingo are the counties where West Virginia State is most likely to start using opioids.In the second part,the gray prediction model is used to further analyze the time series of each factor,the maximum likelihood estimation method is used to obtain the weight of each factor,and the weight coefficient matrix is used to simulate the multivariate regression equation,and the factors that have the greatest influence on opioid abuse are educational background and family composition.In the third part,the hypothesis test model of two groups(the data type is proportional)is used to verify the difference between the influence factors(including the predicted values)in the first two parts of the states,thus verifying the feasibility between them.At the same time,we put forward a few suggestions to combine the current situation in the United States with the CDC data.We believe that in order to address the opium crisis,the U.S.government needs to strengthen not only oversight of doctors'prescriptions,but also make joint efforts of all sectors of society to fundamentally reduce the barriers to the use of opioids.

Gray Matter-Based Age Prediction Characterizes Different Regional Patterns

Dear Editor,The brain experiences ongoing changes across different ages to support brain development and functional reorganization.During the span of adulthood,although the brain has matured from a neurobiological perspective,it is still continuously shaped by external factors such as habits,the family setting,socioeconomic status,and the work environment [1].In contrast to chronological age (CA),brain(or biological) age (BA) is conceptualized as an important index for characterizing the aging process and neuropsychological state,as well as individual cognitiveperformance.Growing evidence indicates that BA can be assessed by neuroimaging techniques,including MRI [2].

Spatio-temporal evolution and dynamic simulation of the urban resilience of Beijing-Tianjin-Hebei urban agglomeration

The continuous growth of urban agglomerations in China has increased their complexity as well as vulnerability. In this context, urban resilience is critical for the healthy and sustainable development of urban agglomerations. Focusing on the Beijing-Tianjin-Hebei(BTH) urban agglomeration, this study constructs an urban resilience evaluation system based on four subsystems: economy, society, infrastructure, and ecology. It uses the entropy method to measure the urban resilience of the BTH urban agglomeration from 2000 to 2018.Theil index, standard deviation ellipse, and gray prediction model GM(1,1) methods are used to examine the spatio-temporal evolution and dynamic simulation of urban resilience in this urban agglomeration. Our results show that the comprehensive evaluation index for urban resilience in the BTH urban agglomeration followed a steady upward trend from 2000 to 2018,with an average annual growth rate of 6.72%. There are significant differences in each subsystem’s contribution to urban resilience;overall, economic resilience is the main factor affecting urban resilience, with an average annual growth rate of 8.06%. Spatial differences in urban resilience in the BTH urban agglomeration have decreased from 2000 to 2018, showing the typical characteristic of being greater in the central core area and lower in the surrounding non-core areas. The level of urban resilience in the BTH urban agglomeration is forecast to continue increasing over the next ten years. However, there are still considerable differences between the cities. Policy factors will play a positive role in promoting the resilience level. Based on the evaluation results, corresponding policy recommendations are put forwar to provide scientific data support and a theoretical basis for the resilience construction of the BTH urban agglomeration.