Transportation Network Density,Domestic Market Integration and Excess Sensitivity of Household Consumption

From the perspective of domestic market integration,this paper systematically examines the impact of transportation infrastructure conditions on excess sensitivity of household consumption based on the China Family Panel Survey(CFPS)and multi-level matching panel data of transportation network density.The results show that the fast-growing development of the transportation infrastructure network has a significant alleviating effect on excess sensitivity of household consumption along the route,and the conclusion is still robust after the use of the multi-dimensional instrumental variable method and a series of robustness tests.According to the heterogeneity tests,in terms of the alleviating effect of transportation infrastructure,railroads rank the first,highways the second,substandard roads the third,waterways the fourth,and roads of other grades at the bottom.The mechanism test reveals that the improvement of domestic market integration is an important channel for transportation infrastructure to alleviate excess sensitivity of household consumption.This paper confirms that improving the transportation infrustructure system is conducive to the construction of a unified national market,alleviating excess sensitivity of consumption and stimulating consumption.This paper provides suggestions for implementing the strategy of boosting domestic demand,and helps the government understand households'consumption decision-making from a broader perspective.This study also provides a theoretical basis for the economic spillover effect of transportation infrastructure.

Probabilistic seismic inversion based on physics-guided deep mixture density network

Deterministic inversion based on deep learning has been widely utilized in model parameters estimation.Constrained by logging data,seismic data,wavelet and modeling operator,deterministic inversion based on deep learning can establish nonlinear relationships between seismic data and model parameters.However,seismic data lacks low-frequency and contains noise,which increases the non-uniqueness of the solutions.The conventional inversion method based on deep learning can only establish the deterministic relationship between seismic data and parameters,and cannot quantify the uncertainty of inversion.In order to quickly quantify the uncertainty,a physics-guided deep mixture density network(PG-DMDN)is established by combining the mixture density network(MDN)with the deep neural network(DNN).Compared with Bayesian neural network(BNN)and network dropout,PG-DMDN has lower computing cost and shorter training time.A low-frequency model is introduced in the training process of the network to help the network learn the nonlinear relationship between narrowband seismic data and low-frequency impedance.In addition,the block constraints are added to the PG-DMDN framework to improve the horizontal continuity of the inversion results.To illustrate the benefits of proposed method,the PG-DMDN is compared with existing semi-supervised inversion method.Four synthetic data examples of Marmousi II model are utilized to quantify the influence of forward modeling part,low-frequency model,noise and the pseudo-wells number on inversion results,and prove the feasibility and stability of the proposed method.In addition,the robustness and generality of the proposed method are verified by the field seismic data.

An Aircraft Trajectory Anomaly Detection Method Based on Deep Mixture Density Network

The timely and accurately detection of abnormal aircraft trajectory is critical to improving flight safety.However,the existing anomaly detection methods based on machine learning cannot well characterize the features of aircraft trajectories.Low anomaly detection accuracy still exists due to the high-dimensionality,heterogeneity and temporality of flight trajectory data.To this end,this paper proposes an abnormal trajectory detection method based on the deep mixture density network(DMDN)to detect flights with unusual data patterns and evaluate flight trajectory safety.The technique consists of two components:Utilization of the deep long short-term memory(LSTM)network to encode features of flight trajectories effectively,and parameterization of the statistical properties of flight trajectory using the Gaussian mixture model(GMM).Experiment results on Guangzhou Baiyun International Airport terminal airspace show that the proposed method can effectively capture the statistical patterns of aircraft trajectories.The model can detect abnormal flights with elevated risks and its performance is superior to two mainstream methods.The proposed model can be used as an assistant decision-making tool for air traffic controllers.

Evaluation and optimization analysis of high-speed rail network structure in Northeast China under the background of northeast revitalization

The construction of high-speed rail(HSR)network has promoted the social-economic ties of cities,accelerated the compression of time and space,and changed the pattern of regional development.In this paper,with the adoption of the operation frequency data of HSR from 12306 website,and based on the HSR connection strength model and social network analysis model,as well as according to the HSR connection strength,HSR network density,centrality,agglomeration subgroup,and other indicators,we analyzed the characteristics of HSR network structure in Northeast China.Results show that the number of HSR cities in Northeast China is small,cities in HSR network generally exhibit weak connectivity,and the existence of HSR network marginalizes cities such as Ulanhot,Baicheng,and Songyuan,which significantly reduce the overall network connectivity of Northeast China.The overall centrality of HSR network in Northeast China is characterized by“one axis,four edges”;specifically,the one axis is located in Harbin-Dalian transportation line and the four edges are located on both sides of the main axis of Harbin-Dalian transportation line.Eight agglomeration subgroups(four double city subgroups and four multi city subgroups)have formed in Northeast China.The core status of Shenyang in HSR network is improved significantly,and“one axis and two wings”HSR network in Liaoning Province is improved significantly.With the gradual expansion of Chaoyang-Fuxin,Dandong-Benxi,and Jilin-Yanji branch networks,the“point axis”HSR network mode in Northeast China has gradually developed and matured.In the future,it is recommended to rely on eight agglomerating subgroups to encrypt HSR network structure,create secondary node central cities,and gradually build a new pattern of opening up in Northeast China.

A Harmonic Approach to Handwriting Style Synthesis Using Deep Learning

The challenging task of handwriting style synthesis requires capturing the individuality and diversity of human handwriting.The majority of currently available methods use either a generative adversarial network(GAN)or a recurrent neural network(RNN)to generate new handwriting styles.This is why these techniques frequently fall short of producing diverse and realistic text pictures,particularly for terms that are not commonly used.To resolve that,this research proposes a novel deep learning model that consists of a style encoder and a text generator to synthesize different handwriting styles.This network excels in generating conditional text by extracting style vectors from a series of style images.The model performs admirably on a range of handwriting synthesis tasks,including the production of text that is out-of-vocabulary.It works more effectively than previous approaches by displaying lower values on key Generative Adversarial Network evaluation metrics,such Geometric Score(GS)(3.21×10^(-5))and Fréchet Inception Distance(FID)(8.75),as well as text recognition metrics,like Character Error Rate(CER)and Word Error Rate(WER).A thorough component analysis revealed the steady improvement in image production quality,highlighting the importance of specific handwriting styles.Applicable fields include digital forensics,creative writing,and document security.

Characterization of Complex Fracture System in Volume Fracturing of Shale Gas Reservoir

After volume fracturing of horizontal wells in shale gas reservoir, an extremely complex fracture system is formed. The space area of the fracture system is the reservoir reconstruction volume of shale gas reservoir. The geometric parameters such as crack length, crack width, crack height, and characteristic parameters such as crack permeability and fracture conductivity proposed for a single crack in conventional fracturing are insufficient to describe and characterize the complex network fracture system after volume fracturing. In this paper, the discrete fracture modeling method is used to establish the volume fracturing network fracture model of horizontal wells in shale gas reservoir by using the random modeling method within the determined reservoir space. The model is random and selective, and can fully provide different forms of volume fracturing fracture expansion, such as conventional fracture morphology, line network model and arbitrarily distributed network fractures. The research results provide a theoretical basis for the development plan and stimulation plan of shale gas reservoir, and have important reference value and significance for other unconventional gas reservoir fracturing.

Structure and Connectivity Analysis of Financial Complex System Based on G-Causality Network

The recent financial crisis highlights the inherent weaknesses of the financial market. To explore the mechanism that maintains the financial market as a system, we study the interactions of U.S. financial market from the network perspective. Applied with conditional Granger causality network analysis, network density, in-degree and out-degree rankings are important indicators to analyze the conditional causal relationships among financial agents, and further to assess the stability of U.S. financial systems. It is found that the topological structure of G-causality network in U.S. financial market changed in different stages over the last decade, especially during the recent global financial crisis. Network density of the G-causality model is much higher during the period of 2007-2009 crisis stage, and it reaches the peak value in 2008, the most turbulent time in the crisis. Ranked by in-degrees and out-degrees, insurance companies are listed in the top of 68 financial institutions during the crisis. They act as the hubs which are more easily influenced by other financial institutions and simultaneously influence others during the global financial disturbance.

Network analysis of eight industrial symbiosis systems

Industrial symbiosis is the quintessential characteristic of an eco-industrial park. To divide parks into different types, previous studies mostly focused on qualitative judgments, and failed to use metrics to conduct quantitative research on the intemal structural or functional characteristics of a park. To analyze a park＇s structural attributes, a range of metrics from network analysis have been applied, but few researchers have compared two or more symbioses using multiple metrics. In this study, we used two metrics （density and network degree centraliza- tion） to compare the degrees of completeness and dependence of eight diverse but representative industrial symbiosis networks. Through the combination of the two metrics, we divided the networks into three types： weak completeness, and two forms of strong completeness, namely ＂anchor tenant＂ mutualism and ＂equality-oriented＂ mutualism. The results showed that the networks with a weak degree of completeness were sparse and had few connections among nodes; for ＂anchor tenant＂ mutualism, the degree of completeness was relatively high, but the affiliated members were too dependent on core members; and the members in ＂equality-oriented＂ mutualism had equal roles, with diverse and flexible symbiotic paths. These results revealed some of the systems＇ internal structure and how different structures influenced the exchanges of materials, energy, and knowledge among members of a system, thereby providing insights into threats that may destabilize the network. Based on this analysis, we provide examples of the advantages and effectiveness of recent improvement projects in a typical Chinese eco-industrial park （Shandong Lubei）.

A review of functional slit lamp biomicroscopy

Functional slit lamp biomicroscopy(FSLB)is a novel device which consists of a traditional slit-lamp and a digital camera.It can quantitatively assess vessel diameter,blood flow velocity,and blood flow rate and can create noninvasive microvascular perfusion maps(nMPMs).At present,FSLB is mainly used in contact lens(CL)and dry eye disease(DED)studies to advance our understanding of ocular surface microcirculation.FSLB-derived blood flow and vessel density measures are significantly altered in CL wearers and DED patients compared to normal people.These subtle changes in the ocular surface microcirculation may contribute to the monitoring of potential diseases of the body and provide a new way to diagnose dry eye disease.Therefore,this may also indicate that FSLB can be more widely applied in the study of other diseases to reveal the relationship between changes in ocular surface microcirculation and systemic diseases.The purpose of this paper is to summarize the functions of FSLB and the related studies especially in CL and DED.

Representative volume element of asphalt pavement for electromagnetic measurements

The motivation for this study was to investigate the representative volume element （RVE） needed to correlate the nondestructive electromagnetic （EM） measurements with the con- ventional destructive asphalt pavement quality control measurements. A large pavement rehabilitation contract was used as the test site for the experiment. Pavement cores were drilled from the same locations where the stationary and continuous Ground Penetrating Radar （GPR） measurements were obtained. Laboratory measurements included testing the bulk density of cores using two methods, the surface-saturated dry method and determining bulk density by dimensions. Also, Vector Network Analyzer （VNA） and the through specimen transmission configuration were employed at microwave frequencies to measure the reference dielectric constant of cores using two different footprint areas and therefore vol- ume elements. The RVE for EM measurements turns out to be frequency dependent; therefore in addition to being dependent on asphalt mixture type and method of obtaining bulk density, it is dependent on the resolution of the EM method used. Then, although the average bulk property results agreed with theoretical formulations of higher core air void content giving a lower dielectric constant, for the individual cores there was no correlation for the VNA measurements because the volume element seizes deviated. Similarly, GPR technique was unable to capture the spatial variation of pavement air voids measured from the 150-mm drill cores. More research is needed to determine the usable RVE for asphalt.

Synthesis of electric vehicle charging data: A real-world data-driven approach

Nowadays,electric vehicles(EVs)are increasingly equipped with advanced onboard devices capable of collecting and recording real-time charging data.The analysis of such data from a large-scale EV fleet plays a crucial role in supporting decision-making processes,particularly in the deployment of charging infrastructure and the formulation of EV-focused policies.Nevertheless,the challenges of collecting these data are significant,primarily due to privacy concerns and the high costs associated with data access.In response,this study introduces an innovative methodology for generating large-scale and diverse EV charging data,mirroring real-world patterns for cost-efficient and privacy-compliant use.Specifically,this approach combines Gibbs sampling and conditional density networks and was trained and validated using a real-world dataset consisting of approximately 1.65 million charging events from 3,777 battery EVs(BEVs)in Shanghai over a year.Results illustrate that the proposed model can effectively capture the underlying distribution of the original charging data,enabling the generation of synthetic samples that closely resemble real-world charging events.The approach is readily employed for data imputation and augmentation,and it can also help simulate future charging distributions by conditional generation based on anticipated development premises.