Structural Health Evaluation of Arch Bridge by Field Test and Optimized BPNN Algorithm

Arch bridges play an important role in rural roads in China. Due to insufficient funds and a lack of management techniques, many rural arch bridges are in a state of disrepair, unable to meet the increasing transportation needs. Thus, it is of great significance to develop a set of rapid and economic damage identification procedures for the management and maintenance of old arch bridges. Sanliushui Bridge, located in Chenggu County, Hanzhong, is selected as a model case. Field tests and numerical simulations were carried out to identify the damage states of Sanliushui Bridge. The sum square of wavelet packet energy change rate, a damage identification index based on wavelet packet analysis method was implemented to process the measured data of the load test and the simulated data of the numerical calculation model with assumed damage. BPNN, GA-BPNN, PSO-BPNN and test data analysis are adopted to compare the measured data with the simulated data to quantitatively identify the damage degree of the selected bridge. By comparing the results of the two methods mentioned above, it is found that the proposed damage identification approach realized a precise damage identification of the selected arch bridges

Structural Health Evaluation of Arch Bridge by Field Test and Optimized BPNN Algorithm

Arch bridges play an important role in rural roads in China. Due to insufficient funds and a lack of management techniques, many rural arch bridges are in a state of disrepair, unable to meet the increasing transportation needs. Thus, it is of great significance to develop a set of rapid and economic damage identification procedures for the management and maintenance of old arch bridges. Sanliushui Bridge, located in Chenggu County, Hanzhong, is selected as a model case. Field tests and numerical simulations were carried out to identify the damage states of Sanliushui Bridge. The sum square of wavelet packet energy change rate, a damage identification index based on wavelet packet analysis method was implemented to process the measured data of the load test and the simulated data of the numerical calculation model with assumed damage. BPNN, GA-BPNN, PSO-BPNN and test data analysis are adopted to compare the measured data with the simulated data to quantitatively identify the damage degree of the selected bridge. By comparing the results of the two methods mentioned above, it is found that the proposed damage identification approach realized a precise damage identification of the selected arch bridges

Has the Digital Economy Reduced Carbon Emissions?: Analysis Based on Panel Data of 278 Cities in China

China is undergoing an urbanization process at an unprecedented scale, and low-carbon urban development is of great significance to the completion of the “dual carbon goals”. At the same time, the digital economy has become an important engine for urban development, and its role in environmental improvement has become increasingly prominent. While the digital economy is booming, can it promote the low-carbon development of cities? Based on the panel data of 278 cities in China from 2011 to 2019, this paper discusses the impact of the digital economy on carbon emissions and the long-term development trend between the digital economy and carbon emissions, the impact of differences in the development level of the digital economy on carbon emissions reduction, and the impact of green energy efficiency in the relationship between the digital economy and carbon emissions. The results show that the digital economy has a significant inhibitory effect on carbon emissions, and with the development of the digital economy, more and more cities show an absolute decoupling of the digital economy and carbon emissions and are turning to low-carbon development. The development level of the digital economy has a heterogeneous impact on carbon emissions. With the improvement of the development level of the digital economy, the effect on emission reduction is more significant. As a threshold variable, green energy efficiency affects the relationship between digital economy and carbon emissions. When green energy efficiency is low, the digital economy promotes carbon emissions, and when green energy efficiency is high, the digital economy reduces carbon emissions

Empirical Likelihood for Partially Linear Single-Index Models under Negatively Associated Errors

In this paper, the authors consider the application of the blockwise empirical likelihood method to the partially linear single-index model when the errors are negatively associated, which often exist in sequentially collected economic data. Thereafter, the blockwise empirical likelihood ratio statistic for the parameters of interest is proved to be asymptotically chi-squared. Hence, it can be directly used to construct confidence regions for the parameters of interest. A few simulation experiments are used to illustrate our proposed method

Research on Quality Evaluation of Group Buying Websites Based on DEA and Fuzzy Comprehensive Evaluation

Group buying mode has been one of the hottest business models in the Chinese Internet area in recent years. Though the market competition is intense, the demand of group purchase is still on the rise. Nowadays, the research on comprehensive evaluation of group buying websites in our country is still tiny. The paper used DEA combined with fuzzy comprehensive evaluation to evaluate popular group buying websites. According to the characteristics of group buying websites, we evaluate the quality of group buying websites from three aspects. There are running efficiency, profit ability and promotion quality. Then we got the comprehensive evaluation results of each website. Some suggestions of website building for group purchasing enterprises are proposed

Multiplication Algorithms for Approximate Optimal Distributions with Cost Constraints

In this paper, we study the D- and A-optimal assignment problems for regression models with experimental cost constraints. To solve these two problems, we propose two multiplicative algorithms for obtaining optimal designs and establishing extended D-optimal (ED-optimal) and A-optimal (EA-optimal) criteria. In addition, we give proof of the convergence of the ED-optimal algorithm and draw conjectures about some properties of the EA-optimal algorithm. Compared with the classical D- and A-optimal algorithms, the ED- and EA-optimal algorithms consider not only the accuracy of parameter estimation, but also the experimental cost constraint. The proposed methods work well in the digital example

Semiparametric estimation of regression functions in autoregressive models

This paper proposes a semiparametric method for an autoregressive model by combining a parametric regression estimator with a nonparametric adjustment. The regression has a parametric framework. After the parameter is estimated through a general parametric method, the obtained regression function is adjusted by a nonparametric factor, and the nonparametric factor is obtained through a natural consideration of the local L2-fitting criterion. Some asymptotic and simulation results for the semiparametric method are discussed.

Spatial Imbalance, Dynamic Evolution and Convergence of the Digital Economy: Analysis Based on Panel Data of 278 Cities in China

Accelerating the development of the digital economy is the way to build a modern industrial system and promote sustainable development. In order to accurately analyze the development status of China’s digital economy, this study introduced a text analysis method to construct an index of the digital economy and surveyed the digital economy based on the panel data of 278 Chinese cities from 2011 to 2019. Moran’s I index, the Dagum Gini coefficient, the kernel density and a Markov chain were used to reveal the space-time difference and dynamic change characteristics. Considering the impact of the spatial correlation and regional division on convergence, we compared the σ values and spatial σ values to study the convergence characteristics after grouping with the decision tree method. The research showed that the digital economy had greatly improved, but it showed a significant imbalance. The research on the regional division of cities according to their geographical distribution and grade showed that the development status of the digital economy was increasingly different, and there was no convergence feature. We chose continuous classification variables and used the decision tree method to divide cities into 10 groups to investigate the convergence. The results showed that the σ values and spatial σ values decreased significantly and showed convergence characteristics. The development of the digital economy showed convergence, indicating that the convergence was greatly affected by the geographical location and grouping basis. Overall, this study contributes to our understanding of the development status of the digital economy, and targeted policy recommendations were proposed to improve the level of digital economy development

Evaluating the Causal Effects of Emissions Trading Policy on Emission Reductions Based on Nonlinear Difference-In-Difference Model

Based on panel data from 30 provinces, cities, and autonomous regions from 2001 to 2019, this paper uses the nonlinear difference-in-difference (DID) method to estimate the distribution of causal effects of emissions trading policy on emission reduction in Chinese industrial enterprises, and examines the heterogeneity of the effects. The empirical results show that (1) the emissions trading policy has a significant effect on industrial SO2 emissions reduction in China, where the reduction effect is larger in non-pilot areas than in pilot areas; (2) the policy effects are not proportional to the regional SO2 emissions intensity, and the emissions trading policy is not more effective in regions with higher industrial SO2 emissions intensities. One advantage of this paper is the use of nonlinear DID to estimate the emissions reduction effect, which eliminates the bias problem caused by the strict linearity assumption of the classical DID method. Another advantage is that the combination of the random forest method avoids the subjectivity in the selection of control variables and uses distribution effects for multilevel comparisons. This method improves the validity of estimating the effect of emissions trading policy and provides targeted policy suggestions for the effective promotion of system implementation, all of which have academic and application value

Cichorium intybus L. Extract Suppresses Experimental Gout by Inhibiting the NF-κB and NLRP3 Signaling Pathways

Background: The production and maturation of interleukin (IL)-1β, regulated by the NF-κB and NLRP3 signaling pathways, lie at the core of gout. This study aimed to evaluate the antigout effect of Cichorium intybus L. (also known as chicory) in vivo and in vitro. Methods: A gout animal model was established with monosodium urate (MSU) crystal injections. Rats were orally administered with chicory extract or colchicine. Levels of ankle edema, inflammatory activity, and IL-1β release were observed. Several essential targets of the NF-κB and NLRP3 signaling pathways were detected. Primary macrophages were isolated to verify the antigout mechanism of chicory extract as well as chicoric acid in vitro. Results: Improvements of swelling degree, inflammatory activity, and histopathological lesion in MSU-injected ankles were observed in the treatment with chicory extract. Further, the chicory extract significantly decreased IL-1β release by suppressing the NF-κB and NLRP3 signaling pathways in gout rats. Similar to the in vivo results, IL-1β release was also inhibited by chicory extract and chicoric acid, a specific effective compound in chicory, through the NF-κB and NLRP3 signaling pathways. Conclusion: This study suggests that chicory extract and chicoric acid may be used as promising therapeutic agents against gout by inhibiting the NF-κB and NLRP3 signaling pathways