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With the development of the automobile industry, artificial intelligence, big data, 5G, and other technologies, the Internet of Vehicles (IoV) industry has entered a stage of rapid development. In this paper, a pollutant diffusion model based on an artificial neural network is designed in the context of a vehicle network. The application of artificial neural networks in haze prediction is studied. This paper first analyzes the causes and influencing factors of haze and selects the most representative and relatively large meteorological factors from temperature, wind, relative humidity, and several pollutant factors. Through training and simulation, a haze prediction model in the Beijing, Tianjin, and Hebei regions of China is established. Finally, according to the collected meteorological data, the pollutant diffusion model is established. The model is deduced by a standard mathematical formula, which makes the prediction results more accurate and rigorous, and the main conclusions and feasible scientific suggestions are obtained. The simulation results show that the method is effective. By strengthening the service system of the IoV, meteorological services can be more intelligent, and the information acquisition and service ability of the vehicle network can be effectively improved. [ABSTRACT FROM AUTHOR]

Recycling of construction waste has been a hot issue in road engineering research, and microscopic characteristics and composition of construction waste-recycled aggregate (RA) significantly influence their macroscopic service characteristics. In this paper, the composition, microstructure, and strength formation mechanism of natural aggregate (NA), recycled concrete aggregate (RCA), and recycled brick (RCB) were systematically studied by X-ray diffraction analysis (XRD), scanning electron microscopy (SEM), fractal dimension and porosity determination, unconfined compressive strength test, and indirect tensile test. The results showed that RA's microscopic morphological characteristics and chemical components were analyzed by combining XRD and SEM electron microscopy techniques. RA has prominent fractal features, and the smaller bulk density and more extensive crushing value index of RA correspond to its larger porosity as well as fractal dimension. The attributes of RA lead to the peak of 7 d unconfined compressive strength and 7 d splitting strength of the mix. The reasonable control of RA admixture benefits the improvement of mix strength. This study relies on engineering to carry out theoretical and experimental research, which provides essential support and guarantee for the application of construction waste in the field of highway transportation in Hebei Province. [ABSTRACT FROM AUTHOR]

The volatility of solar energy, geographic location, and weather factors continues to affect the stability of photovoltaic power generation, reliable and accurate photovoltaic power prediction methods not only effectively reduce the operating cost of the photovoltaic system but also provide reliable data support for the energy scheduling of the light storage microgrid, improve the stability of the photovoltaic system, and provide important help for the optimization operation of the photovoltaic system. Therefore, it is an important study to find reliable photovoltaic power prediction methods. In recent years, researchers have improved the accuracy of photovoltaic power generation forecasting by using deep learning models. Compared with the traditional neural network, the Transformer model can better learn the relationship between weather features and has good stability and applicability. Therefore, in this paper, the transformer model is used for predicting ultra-short-term photovoltaic power generation, and the photovoltaic power generation data and weather data in Hebei are selected. In the experiment, the prediction result of the transformer model was compared to the GRU and DNN models to show that the transformer model has better predictive ability and stability. Experimental results demonstrated that the proposed Transformer model outperforms the GRU model and DNN model by a difference of about 0.04 kW and 0.047 kW in the MSE value, and 22.0% and 29.1% of the MAPE error. In addition, the public DC competition dataset is selected for control experiments to demonstrate the general applicability of the transformer model for PV power prediction in different regions. [ABSTRACT FROM AUTHOR]

The paper aims to propose a new method to state the credit risk characteristics of the regional listed companies in China and makes the listed companies avoid involving in credit crisis. The paper selects fifty-four listed companies of Hebei Province as the research sample and establishes the index system of listed company's credit risk evaluation from four financial index categories which included profitability, operating capacity, solvency, and growth capability. The paper first filtrates fifteen indexes by using the gray clustering method from the four financial categories and finds out the effective variables of the prediction model. Then the paper predicates the credit risk probability of the listed companies by using the logistic regression model. Finally, by analyzing the financial data of annual reports of fifty-four listed companies in Hebei Province from 2012 to 2017 as sample data, the simulation experiment empirical test is carried out by using SPSS software. The results show that the logistic regression model with gray clustering analysis has high predictive accuracy and has a strong predictive ability to evaluate the credit risk of listed companies. The gray logistics evaluation plays a very good role in financial early warning for regional listed companies in China. [ABSTRACT FROM AUTHOR]

The accurate evaluation and prediction of highway network traffic state can provide effective information for travelers and traffic managers. Based on the deep learning theory, this paper proposes an evaluation and prediction model of highway network traffic state, which consists of a Fuzzy C-means (FCM) algorithm-based traffic state partition model, a Long Short-Term Memory (LSTM) algorithm-based traffic state prediction model, and a K-Means algorithm-based traffic state discriminant model. The highway network in Hebei Province is employed as a case study to validate the model, where the traffic state of highway network is analyzed using both predicted data and real data. The dataset contains 536,823 pieces of data collected by 233 continuous observation stations in Hebei Province from September 5, 2016, to September 12, 2016. The analysis results show that the model proposed in this paper has a good performance on the evaluation and prediction of the traffic state of the highway network, which is consistent with the discriminant result using the real data. [ABSTRACT FROM AUTHOR]

Based on the PSR conceptual evaluation model of urban green and low-carbon development competitiveness, this paper evaluates the green and low-carbon development competitiveness of 13 cities in Beijing-Tianjin-Hebei from 2010 to 2019 and reveals their spatial effects and obstacle factors combined with the spatial autocorrelation and obstacle model. The results show the following: (1) In terms of the overall situation, the evaluation index of urban green and low-carbon development competitiveness in the Beijing-Tianjin-Hebei region shows a fluctuating upward trend as a whole from 2010 to 2019. Among them, the comprehensive evaluation indexes of Beijing, Zhangjiakou, and Tianjin are very competitive, while Hengshui, Handan, and Xingtai are relatively weak. (2) In terms of spatial data analysis, the global and local Moran's I of Beijing-Tianjin-Hebei cities from 2010 to 2019 were positive and passed the 1% significant level test. This shows that the comprehensive evaluation index of urban green low-carbon development competitiveness of Beijing-Tianjin-Hebei cities has a positive spatial correlation, and the spatial agglomeration effect is significant. On the whole, it presents a spatial agglomeration pattern of "high in the north, partial jump, and low in the south". (3) In the aspect of obstacle factor analysis, the proportion of the tertiary industry output value in GDP, energy consumption of ten thousand yuan of GDP, the urban green technology innovation level, and industrial soot emission of ten thousand yuan of GDP are the main obstacle factors restricting the improvement of urban green low-carbon development competitiveness in Beijing-Tianjin-Hebei cities. [ABSTRACT FROM AUTHOR]

There are still some problems in China's ice and snow environmental sports industry, such as an unbalanced industrial structure and insufficient utilization of ecological resources. In order to achieve leap-forward development, the ice and snow sports industry needs to further optimize the allocation of input-output resources, enhance the overall technical capability, and improve the efficiency level of the industry. This study takes five cities of Heilongjiang, Jilin, Liaoning, Hebei, and Beijing as the research objects, constructs the input-output evaluation system of the ice-snow sports industry, measures the input-output-efficiency level of ice-snow environmental sports industry in China, and studies and analyzes the relationship between efficiency level and industrial input-output. Furthermore, this paper compares and analyzes the traditional total factor productivity and green total factor productivity of the ice and snow environmental sports industry and discusses the influencing mechanism of ecosystem efficiency of the ice-snow sports industry. The results show that the input/output ratio of different regions in 2018 was Heilongjiang (1.12) > Liaoning (1.09) > Beijing (1.08) > Hebei (1.06) > Jilin (0.81). The change in input and output level is the direct influencing factor leading to the change of efficiency, while efficiency is the core to promote the development of the ice and snow environmental sports industry. Comparing traditional and green total factor productivity, it can be found that the GameMaker Language (GML) index of green total factor productivity in Heilongjiang, Jilin, Liaoning, Hebei, and Beijing has increased by 10.50%, 9.59%, 12.22%, 11.34%, and 20.29%, respectively, in 2013. In most cases, the green total factor productivity is higher than the traditional total factor productivity, showing an environment-friendly development. The development of the ice and snow environmental sports industry, talents in the ice and snow tourism industry, urbanization level and promotion of sports cultural products in the ice and snow environment are the main external influencing factors, while the internal influencing factors are mainly reflected in the industrial structure, ownership structure, industrial relevance, industrial agglomeration degree, industrial layout, industrial scale, and differentiation of industrial product. [ABSTRACT FROM AUTHOR]

At present, many colleges attach more and more importance to the cultivation of scientific research ability of college students. However, there is no unified standard to effectively measure the cultivation effect. The current performance inference method cannot comprehensively evaluate the scientific research ability of college students. Few scholars have directly analyzed the college students' scientific research ability from the internal and external influencing factors. Therefore, this paper tries to design and implement a neural network analysis system for scientific research ability evaluation of college students. After surveying the status quo of scientific research ability evaluation of college students in northern China's Hebei Province, a hierarchical evaluation index system was constructed for college students' scientific research ability, referring to the existing evaluation index systems, and the weights were designed for the evaluation indices. Next, the backpropagation (BP) neural network was optimized by chaotic sine-cosine grasshopper optimization algorithm (CSCGOA) and used to establish a neural network analysis system for scientific research ability evaluation of college students. The proposed system was proved effective through experiments. The relevant results effectively enhance the scientific level and accuracy of the evaluation of college students' scientific research ability, improve the cultivation of college students, and provide a scientific basis for colleges to understand the scientific research ability of their students. [ABSTRACT FROM AUTHOR]

This article aims to improve the accuracy and predictability of rebound method in compressive strength evaluation of concrete members of the Yanchong Expressway, which is an important traffic guarantee project for the 2022 Winter Olympics Games in Zhangjiakou city, Hebei Province, China. For this purpose, concrete specimens were prepared by the mixing ratios of C35, C40, and C50. First of all, the paper investigated the effects of curing age, types, and dosage of mineral admixtures (fly ash and slag) on concrete rebound number and compressive strength. Then, the special testing strength curves of 7 d and 90 d curing age were proposed. The results showed that the average relative errors of the special testing strength curves were reduced by 35.99% and 20.26% respectively, compared with the national testing strength curve. Fly ash and slag were beneficial to the growth of concrete rebound numbers but cannot contribute to the growth of concrete compressive strength (except for fly ash single-mixed with 15% dosage). The present results can provide a significant reference for the compressive strength evaluation and later maintenance of concrete members of the Yanchong Expressway or other projects with tight construction period in this area. [ABSTRACT FROM AUTHOR]

Multiscale brings great benefits for people to observe objects or problems from different perspectives. Multiscale clustering has been widely studied in various disciplines. However, most of the research studies are only for the numerical dataset, which is a lack of research on the clustering of nominal dataset, especially the data are nonindependent and identically distributed (Non-IID). Aiming at the current research situation, this paper proposes a multiscale clustering framework based on Non-IID nominal data. Firstly, the benchmark-scale dataset is clustered based on coupled metric similarity measure. Secondly, it is proposed to transform the clustering results from benchmark scale to target scale that the two algorithms are named upscaling based on single chain and downscaling based on Lanczos kernel, respectively. Finally, experiments are performed using five public datasets and one real dataset of the Hebei province of China. The results showed that the method can provide us not only competitive performance but also reduce computational cost. [ABSTRACT FROM AUTHOR]

The normal supply of energy is related to the stable development of the economy and society. Forecasting energy consumption helps prepare for the normal supply of energy. In the study of energy consumption forecasting, different scholars have used different forecasting models. This paper uses five-year energy consumption data in the Beijing-Tianjin-Hebei region and uses the grey fractional FGM(1, 1) model to analyze the next six years. Then, the energy consumption of three places is predicted. The advantage of the grey score FGM(1, 1) model is that it can get more accurate prediction results based on a small amount of information. In this study, relatively outdated information affects the accuracy of prediction results. However, other prediction models have great limitations on data. Choosing the grey number fractional model for prediction research can get a more reasonable prediction result. We use the FGM(1, 1) model to make predictions and get the prediction results. In Beijing, the growth rate of natural gas consumption has slowed down and will be basically stable by 2023. The average annual deceleration of coal consumption is 32%. The average annual deceleration of coke consumption is 10%. Crude oil consumption decreased by 6.3% annually. Gasoline consumption is slowly increasing. The consumption of kerosene increased about 8% annually. Diesel consumption is slowly decreasing. Fuel oil consumption is reduced by 17% annually. The average annual growth rate of power consumption exceeds 6%. In Tianjin, the annual growth rate of natural gas consumption is about 5%. Coal consumption is reduced by about 8% every year. The average annual deceleration of coke consumption is 7%. Crude oil consumption decreased by 2.4% annually. Gasoline consumption is slowly decreasing. The consumption of kerosene has increased by about 20% annually. Diesel consumption is slowly decreasing. Fuel oil consumption is reduced by 20% annually. Electricity consumption is slowly increasing. In Hebei Province, the annual growth rate of natural gas consumption is about 15%. Annual coal consumption is reduced by about 3%. Coke consumption remained stable. Crude oil consumption is reduced by 3% annually. Gasoline consumption is slowly increasing, and kerosene consumption has increased by about 31% annually. Diesel consumption is reduced by about 3% annually. Fuel oil consumption remained stable. Electricity consumption is slowly increasing. [ABSTRACT FROM AUTHOR]

Research on ecological total-factor energy efficiency (ETFEE) is conducive to energy conservation, emission reduction, and ecological protection. This paper focuses on the measurement and decomposition of the ETFEE in the Beijing-Tianjin-Hebei (BTH) region in China. In order to measure the ETFEE values, the window technology is combined with a nonradial and nonoriented SBM-undesirable model considering undesirable outputs to overcome the defect of insufficient data of research objects and ensure the calculation process to be implemented. The findings show that Beijing and Tianjin are DEA-efficient, while Hebei is not. The technological progress rates of Beijing and Tianjin reach up to 11.92% and 14.96%, while that of Hebei retrogresses by 4.47%. The scale efficiencies of Beijing, Tianjin, and Hebei are 97.75%, 86.60%, and 93.81%, respectively, which means that there are potentials for further optimization in the energy structures. The impulse response results between the energy structure and the ETFEE show that the proportions of coal and petroleum have negative effects on the ETFEE, while that of natural gas has a positive effect. The research results can provide reference for decision makers to formulate regional development plans. [ABSTRACT FROM AUTHOR]

In order to determine the key content of preventive maintenance of the cement concrete pavement in military airports in the seasonal frozen area, the relationship among pavement diseases is analyzed in this paper, and the degree of impact of each disease on pavement damage is quantified. Based on the survey data of 36 sample airports in 12 provinces, such as Hebei, Shandong, and Liaoning, a questionnaire covering many disease variables affecting pavement damage was designed. The exploratory factor analysis method and structural equation model (SEM) were used to analyze the interaction among joint disease, surface disease, vertical disease, repair disease, and fracture disease. And the influence degree of each disease on pavement damage was quantified as well. The research results show that the questionnaire has good reliability and validity and is suitable for confirmatory factor analysis. The SEM fits well with the observed data and meets the adaptation standard. Joint disease, surface disease, and vertical disease all have significant positive direct impact on pavement damage. Moreover, joint disease and surface disease can also indirectly affect pavement damage. The effects of surface disease (0.327), vertical disease (0.283), and joint disease (0.219) on pavement damage decrease in turn. Surface diseases have the greatest impact on pavement damage, which are the critical diseases to accelerate the pavement damage. By strengthening the prevention and control of surface diseases and delaying the conversion process of other diseases to surface diseases, the service life of the pavement can be greatly improved, and the maintenance cost can be reduced. [ABSTRACT FROM AUTHOR]