Study on energy distribution of discharge plasma and its effect on crater formation in EDM

A comprehensive understanding of the relationship between public transportation supply and demand is crucial for the construction and sustainable development of urban transportation. Due to the spatial and networked nature of public transportation, revealing the spatial configuration and structural disparities between public transportation supply and demand networks (TSN and TDN) can provide significant insights into complex urban systems. In this study, we explored the spatial configuration and structural disparities between TSN and TDN in the complex urban environment of Beijing. By constructing subdistrict-scale TSN and TDN using urban public transportation operation data and mobile phone data, we analyzed the spatial characteristics and structural disparities of these networks from various dimensions, including global indicators, three centralities, and community structure, and measured the current public transportation supply and demand matching pattern in Beijing. Our findings revealed strong structural and geographic heterogeneities of TSN and TDN, with significant traffic supply–demand mismatch being observed in urban areas within the Sixth Ring Road. Moreover, based on the percentage results of supply–demand matching patterns, we identified that the current public transportation supply–demand balance in Beijing is approximately 64%, with around 18% of both excess and shortage of traffic supply. These results provide valuable insights into the structure and functioning of public transportation supply–demand networks for policymakers and urban planners; these can be used to facilitate the development of a sustainable urban transportation system.

/pdf/exploring-public-transportation-supply-demand-structure-of-24wbgpa9.pdf

Exploring Public Transportation Supply–Demand Structure of Beijing from the Perspective of Spatial Interaction Network

Characterization of plasma discharge channel in EDM using photography and spectroscopy

Shared bicycle systems play a crucial role in promoting sustainable urban transportation, addressing challenges such as traffic congestion and air pollution. Understanding the spatiotemporal patterns of shared bike usage is essential for optimizing bike-sharing infrastructure and improving transportation planning. In this study, we analyzed 2.4 million records of shared bicycle data to explore the spatial distribution, interaction patterns, and flow dynamics within Beijing’s urban central area. We found that bike distribution peaks during commuting hours, particularly in central regions with employment centers. Complex networks are an important method for studying travel flows. Through a spatial interaction network, we identified key streets with high node strength and popularity, often concentrated in central areas. They experience heavy shared bicycle use during peak hours due to their employment-centric location. Conversely, peripheral areas see increased usage in the evenings, reflecting distinct commuting patterns. The morning exhibits higher positive central values compared to the evening, while negative values show the opposite trend. Based on these findings, we recommend enhancing bike infrastructure in high-density areas with bike lanes and ample shared bikes during peak hours. Implementing mixed-use zoning policies in the central region can reduce traffic congestion. Expanding shared bike services to peripheral regions can promote equitable access. This research underscores the importance of considering spatial and temporal factors in urban transportation planning. Future work should incorporate additional data sources, explore environmental impacts, and analyze usage in different seasons and special events, further contributing to sustainable urban mobility development.

Analyzing the Spatial Interaction Characteristics of Urban Area Shared Bicycle Systems: A Case Study of Beijing’s Central Area

Examining the clustering characteristics and fluctuations within urban areas during peak hours through the lens of bike-sharing is of utmost importance in the optimization of bike-sharing systems and urban transportation planning. This investigation adopts the principles of urban spatial interaction network construction and employs streets as the fundamental units of analysis to model bike-sharing activities during morning and evening peak hours within Beijing’s six central districts. Subsequent to this, a comprehensive analysis of the network’s structural attributes was carried out. A Walktrap method, rooted in modularity analysis, was introduced to discern and scrutinize the clustering patterns and characteristics of communities within the network across different temporal intervals. Empirical findings reveal a predominant usage pattern of shared bicycles for short-distance travel during both morning and evening peak hours. Notably, distinctive community structures manifest during these periods, characterized by two large communities and multiple smaller ones during the morning peak, while the evening peak showcases a single large community alongside several medium-sized and smaller ones. Moreover, the extended interaction radius points to an expanded geographic range of interactions among streets. These findings bear significant implications for the management of urban transportation, bike-sharing enterprises, and urban residents, proffering valuable insights for the optimization of bike-sharing schemes and transportation strategies. These research findings not only contribute to enhancing urban transportation planning and bike-sharing systems but also provide robust guidance for advancing more efficient and sustainable urban transportation solutions, thereby fostering the sustainable development of cities. 

Study on Community Detection Method for Morning and Evening Peak Shared Bicycle Trips in Urban Areas: A Case Study of Six Districts in Beijing

Understanding the regularity and determinants of mobility is indispensable for the reasonable deployment of shared bicycles and urban planning. A spatial interaction network covering streets in Beijing’s six main districts, using bike sharing data, is constructed and analyzed. as Additionally, the exponential random graph model (ERGM) is used to interpret the influencing factors of the network structure and the mobility regularity. The characteristics of the spatial interaction network structure and temporal characteristics between weekdays and weekends show the following: the network structure on weekdays is obvious; the flow edge is always between adjacent blocks; the traffic flow frequently changes and clusters; the network structure on weekends is more complex, showing scattering and seldom changing; and there is a stronger interaction between blocks. Additionally, the predicted result of the ERGM shows that the influencing factors selected in this paper are positively correlated with the spatial interaction network. Among them, the three most important determinants are building density, housing prices and the number of residential areas. Additionally, the determinant of financial services shows greater effects on weekdays than weekends.

/pdf/spatial-interaction-analysis-of-shared-bicycles-mobility-1xtu7t7k.pdf

Spatial Interaction Analysis of Shared Bicycles Mobility Regularity and Determinants: A Case Study of Six Main Districts, Beijing

A parallel PIC-MCC simulation of microsecond discharge modeling in EDM

Aiming at the problem of the single data source in PM2.5 prediction, a PM2.5 DNN-LSTM hybrid neural network prediction model that takes into account climate factors is proposed. First, the DNNnetwork is used to abstract the characteristics of climate and seasonal factors and climate factors as an additional part of the prediction process. Input and analyze in collaboration with LSTM network. Experiments with pollution data and weather data (sampling interval of one hour) are collected from monitoring sites in Beijing from 2010 to 2014, and comparing the DNN-LSTM model with other prediction models, the results show that this model is compared to LSTM. The RMSE of the model is reduced by 10.71%, which is 5.52% lower than the RMSE of the multi-source data fusion LSTM model. Research shows that the multi-source data fusion DNN-LSTM model proposed in this paper has better predictive ability. Compared with the LSTM model, the RMSE of this model is reduced by 10.71%, compared with the multi-source data fusion LSTM model, the RMSE is reduced by 5.52%, compared with the LSTM model, the MAE is reduced by 21.55%, and compared with the multi-source data fusion LSTM model, the RMSE is reduced by 12.94%.

Study on PM2.5 DNN-LSTM hybrid neural network prediction model considering climate factors

The city center plays an important role in the functioning of the city and the multi-center spatial structure has become an important urban space model. In this paper, the urban POI (Points of Interest) data and road network data are used as the basic data sources, and the results of their kernel density analysis are used to construct an urban multi-center spatial structure identification method based on the localized contour tree. Taking the core area of Beijing as an example to achieve a more refined and efficient extraction of the multi-center spatial structure of the study area. Identifying the multi-center spatial structure of the core area of Beijing has reference significance for urban development planning.

Identification of multi-center spatial structure in Beijing core area based on multi-source data

Kojic acid is an antioxidant used by the cosmetics industry but the excessive usage of kojic acid may cause harm to human health. Therefore, it is important to detect the content of kojic acid in cosmetic products. In this work, the low-cost and simple paper-based analytical devices (μPADs) based on double-sided adhesive tape were developed for the colorimetric detection of kojic acid. The μPADs were fabricated by dissolving double-sided adhesive tape in toluene and then making the patterned adhesive tape penetrate into the filer paper. Ferric nitrate was used as the reagent to react with kojic acid, and the product of their chelating reaction caused the color change from colorless to orange-red. The ImageJ software was used to analyze the color intensity and the corresponding correction curve was determined. The μPADs for the detection of kojic acid exhibited a wide linear range (0.05 - 4 mM) and the detectable limit was 0.05 mM. The good selectivity and reproducibility were also demonstrated.

New Paper-based Microfluidic Analytical Devices for Kojic Acid Assay Based on Double-sided Adhesive Tape

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