Cost Forecasting Model of Transmission Project based on the PSO-BP Method

In order to solve being sensitive to the initial weights, slow convergence, being easy to fall into local minimum and other problems of the BP neural network, this paper introduces the Particle Swarm Optimization (PSO) algorithm into the Artificial Neural Network training, and construct a BP neural network model optimized by the particle swarm optimization. This method can speed up the convergence and improve the prediction accuracy. Through the analysis of the main factors on the cost of transmission line project, dig out the path and lead factors, topography and meteorological factors, the tower and the tower base materials and other factors. Use the PSO-BP model for the cost forecasting of transmission line project based on historical project data. The result shows that the method can predict the cost effectively. Compared with the traditional BP neural network, the method can predict with higher accuracy, and can be generalized and applied in cost forecasting of actual projects

3D printing high interfacial bonding polyether ether ketone components via pyrolysis reactions

Recently, 3D-printed polyether-ether-ketone (PEEK) components have been shown to offer many applications in state-of-the-art electronics, 5G wireless communications, medical implantations, and aerospace components. Nevertheless, a critical barrier that limits the application of 3D printed PEEK components is their weak interfacial bonding strength. Herein, we propose a novel method to improve this unsatisfied situation via the interface plasticizing effect of benzene derivatives obtained from the thermal pyrolysis of trisilanolphenyl polyhedral oligomeric silsequioxane (POSS). Based on this method, the bonding strength of the filaments and interlayers of 3D-printed POSS/PEEK components can reach 82.9 MPa and 59.8 MPa, respectively. Moreover, the enhancing mechanism of the pyrolysis products derived from the POSS is characterized using pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS), Fourier transform infrared spectroscopy (FTIR), and X-ray computed tomography (X-CT). Our proposed strategy broadens the novel design space for developing additional 3D-printed materials with satisfactory interfacial bonding strength

Assessing the Combined Effects of Transportation Infrastructure on Regional Tourism Development in China Using a Spatial Econometric Model (GWPR)

Transportation infrastructure plays an important role in tourism, and the spatial econometric model (GWPR) can offer quantitative support for regionalized development policies in transportation infrastructure. Panel data from 30 provinces were collected for a decade before the COVID-19 pandemic. We show that the GWPR model is a superior tool for assessing the combined impact of transportation infrastructure on tourism and its spatial heterogeneity. The effects of transportation infrastructure on tourism have historically been overwhelmingly positive, with the positive effect of high-speed rail expanding over the decade, while the positive effect of air travel contracted. The combined effects of transportation infrastructure vary across space and time. Additionally, the evolution of the effects exhibits spatial heterogeneity. The 30 provinces in this study are categorized into five types, and targeted implementation strategies for transportation infrastructure are formulated

Industrial Spatio-Temporal Distribution of High-Speed Rail Station Area from the Accommodation Facilities Perspective: A Multi-City Comparison

As a new engine of urban development, the high-speed rail (HSR) station area is an emerging location where the service industry is concentrated. This study aims to reflect the development of accommodation facilities in transport hub areas through the spatial distribution and agglomeration characteristics of the lodging industry in HSR station areas. HSR stations in Beijing, Tianjin, Nanjing, Jinan, Kunshan, and Xuzhou are selected. The Geodetector model is applied to analyze the pertinent driving factors. The findings indicate that: (1) The smaller the population size of the city, the closer the high agglomeration area of the accommodation industry in the HSR station area is to the HSR station. (2) The longer the HSR station is open, the stronger the agglomeration intensity of the accommodation industry is. (3) At HSR stations in various cities, the driving factors affecting the accommodation industry are heterogeneous. The interaction between the factors has a synergistic enhancement effect

Industrial Spatio-Temporal Distribution of High-Speed Rail Station Area from the Accommodation Facilities Perspective: A Multi-City Comparison

As a new engine of urban development, the high-speed rail (HSR) station area is an emerging location where the service industry is concentrated. This study aims to reflect the development of accommodation facilities in transport hub areas through the spatial distribution and agglomeration characteristics of the lodging industry in HSR station areas. HSR stations in Beijing, Tianjin, Nanjing, Jinan, Kunshan, and Xuzhou are selected. The Geodetector model is applied to analyze the pertinent driving factors. The findings indicate that: (1) The smaller the population size of the city, the closer the high agglomeration area of the accommodation industry in the HSR station area is to the HSR station. (2) The longer the HSR station is open, the stronger the agglomeration intensity of the accommodation industry is. (3) At HSR stations in various cities, the driving factors affecting the accommodation industry are heterogeneous. The interaction between the factors has a synergistic enhancement effect

Short-Term Power Load Point Prediction Based on the Sharp Degree and Chaotic RBF Neural Network

In order to realize the predicting and positioning of short-term load inflection point, this paper made reference to related research in the field of computer image recognition. It got a load sharp degree sequence by the transformation of the original load sequence based on the algorithm of sharp degree. Then this paper designed a forecasting model based on the chaos theory and RBF neural network. It predicted the load sharp degree sequence based on the forecasting model to realize the positioning of short-term load inflection point. Finally, in the empirical example analysis, this paper predicted the daily load point of a region using the actual load data of the certain region to verify the effectiveness and applicability of this method. Prediction results showed that most of the test sample load points could be accurately predicted