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The exploration of ecological security patterns (ESPs) can help people find those areas that are in urgent need of restoration, which is an effective way to realize ecological protection. It is of utmost significance for promoting regional sustainable development to construct ESP and put forward sub-regional optimization suggestions based on the supply and demand ratio of ecosystem services (ESs). In this paper, we assessed the level of supply and demand for five ESs based on multi-source data in 2020 with the help of InVEST, ArcGIS, and IUEMS. Based on the results of supply and demand, we calculated the supply and demand ratio of ESs and extracted the ecological source areas (ESAs) on this basis. Then, we used the Linkage Mapper tool to construct the ESP based on the principle of the minimum cumulative resistance (MCR) model and circuit theory in the Chengdu–Chongqing economic circle (CCEC). Our results indicated that there were apparent spatial differences in the supply and demand of five ESs. There were 35 ESAs in the ESP network, covering an area of about 7914 km2, and most of their land use types were woodland. The CCEC was interconnected by a network of 91 ecological corridors (ECs), spanning a total length of approximately 10,701 km. From the ECs, we extracted 29 ecological pinch points (EPPs) and 16 ecological barrier points (EBPs), which each accounted for about 0.3% of the planned area of the CCEC. Finally, we divided the ecological spaces into four types and put forward the corresponding optimization suggestions. Among them, the proportion of ecological restoration area was 7.7%, which was located in Chengdu City, northwest of the study area. The findings of this paper can give some theoretical guidance and serve as a reference for making decisions in the pursuit of ecological civilization in this region. [ABSTRACT FROM AUTHOR]

This paper utilizes an improved undesirable output DEA model to measure the eco-efficiency of cities in five major urban agglomerations in China during 2006–2020. It employs the Theil Index and Geodetector to investigate the spatial-temporal distribution differentiation characteristics and driving factors of urban eco-efficiency. The main findings are as follows. Firstly, the eco-efficiency of all urban agglomerations showed a fluctuating upward trend, but the eco-efficiency performance of different urban agglomerations in China shows a stratification characteristic. Specifically, the Pearl River Delta urban agglomeration consistently ranks first in China, while the mean values of the Yangtze River Delta urban agglomeration, Beijing-Tianjin-Hebei urban agglomeration, and Chengdu-Chongqing urban agglomeration are lower than the national average. Secondly, the overall differences in the urban eco-efficiency of all sample cities show a consistently fluctuating downward trend. The factor that affects the level differences of eco-efficiency in different cities is the intra-regional differences. Last but not least, the top three factors affecting the spatial distribution difference of urban eco-efficiency in the whole sample are environmental pollution control investments, innovation level, and environmental infrastructure investments. In the end, this paper proposes that reducing the intra-regional differences is the primary task to achieve the coordinated improvement of urban eco-efficiency in urban agglomerations, and then puts forward some policy suggestions to improve eco-efficiency for the five major urban agglomerations. [ABSTRACT FROM AUTHOR]

Industrial land is essential for supply-side structural reforms, particularly in the Chengdu–Chongqing area, Western China's most densely populated and industrially robust region. This area, a pivotal hub linking Southwest China with South Asia and Southeast Asia, is critical for the national strategic layout and regional economic restructuring. Despite its substantial industrial foundation as an old industrial base, internal developmental stagnation has led to an irrational industrial land use structure. This paper analyzed land transaction data from the China Land Market Network (2010–2021) using methods such as kernel density estimation, the standard deviation ellipse method, and Global Moran's I index. The analysis focuses on the spatiotemporal evolution of industrial land marketization and its driving factors in 44 cities within the Chengdu–Chongqing economic circle. The findings aim to enhance the strategic implementation of national policies and regional economic optimization, suggesting intensified development efforts in key cities and promoting integrated growth in potential areas like Suining and Ziyang to foster a conducive environment for high-quality regional development. [ABSTRACT FROM AUTHOR]

This paper aims to examine the impact of digital transformations on the business performance of the meetings, expositions, events, and conventions (MEEC) industry in Chengdu by conducting an empirical analysis of 68 MEEC enterprises in the city. The study employed the fuzzy-set qualitative comparative analysis (fsQCA) method to explore the pathways and factors involved. Drawing on the theory of synergetics, nine condition variables were carefully selected from four dimensions: digital technologies, digital strategy, digital infrastructure, and digital innovation. The findings revealed that digital transformations were an intricate systematic process, whereby a single factor alone did not serve as a necessary condition for MEEC enterprises to achieve high business performance. The analysis identified five distinct pathways that would lead to high performance, which can be categorized into three distinct models: the "organization-human resources" dominated model, the "human resources-infrastructure" driven model, and the multi-factor coordination model. The analysis also identified two pathways that would result in low performance, categorized as the "basic condition absent" model. Notably, there was substantial heterogeneity observed in the configurational pathways in terms of the magnitude of the impact of digital transformation on business performance across MEEC enterprises of different sizes. This paper suggests that the government should assume the role of a platform, providing policy and financial support to MEEC enterprises to help guide them through the process of digital transformation and that MEEC enterprises themselves should seize the opportunities arising from digital transformation to strengthen their digital infrastructure construction, human resources development, and innovation in brand building. This study presents empirical evidence that contributes to the understanding of the transformation process of MEEC enterprises within the digital economy era, as well as valuable insights into understanding the channels and internal conditions involved in digital transformations. [ABSTRACT FROM AUTHOR]

Conducting an assessment of ecosystem service values and examining the influence of changes in land utilization on these values in Chengdu City help to deepen our comprehension of the ecological and environmental impacts of such changes. This, in turn, provides valuable insights into the construction of an ecological civilization and the sustainable utilization of land resources in Chengdu City. Based on land-use data from 2005 to 2020, this paper employs a dynamic model of land-use change, the land-use structure change index method, and an ecosystem service value measurement model to analyze the impact of land-use change on ecosystem service value in Chengdu City. The results showed that (1) the area of construction land in Chengdu City increased and those of cultivated land and unused land decreased; (2) from 2005 to 2014, the ecosystem service value in Chengdu City witnessed a rise of 1.559 billion yuan, while from 2014 to 2020, it experienced a decline of 947.77 million yuan; and (3) the sensitivity index of the ecosystem value shows that forest land contributes the most to the total value of ecosystem services. This paper comprehensively analyzes the impact of land-use change in Chengdu City from 2005 to 2020 on the ecosystem service value. It offers a reference foundation for advancing the efficient allocation of land resources in Chengdu City and nurturing the balanced and sustainable development of the economy and ecology. [ABSTRACT FROM AUTHOR]

The inconsistency between the whitening differential equation and the grey basic form of the non-homogeneous continuous grey model CFNGM(1,1,k,c) will result in internal errors. Thus this paper proposes a CFNGMA(1,1,k,c) model with adjustable parameters, which improves the accuracy of the CFNGM(1,1,k,c). This paper first elucidates reasons for the internal errors generated by the continuous grey model CFNGM(1,1,k,c), and explains the classic method, the discrete grey forecasting model, of eliminating internal errors. On the basis of an in-depth analysis of the modeling mechanism of CFNGM(1,1,k,c) model, a new parameter adjustable grey forecasting model is proposed by introducing parameter adjustment factors to modify model's parameters. Finally, the new model is applied to explore the gross regional product of Chengdu and Deyang in the Chengdu metropolitan area. The calculation results indicate that the newly proposed model can obtain more accurate results. [ABSTRACT FROM AUTHOR]

The object of this study is a clustering algorithm using various technologies. This paper compares clustering algorithms that are more commonly used to analyze urban road network trajectories, the growth curve model, with the elbow method and the x-means algorithm. Experiments were conducted with various volumes of big data to determine calculation time, accuracy, and ways to increase calculation time. These results can be used to manage traffic jams in congested areas and city streets. Considering the widespread use of clustering algorithms for solving various problems, this study proposes to introduce GCM, SPGK methods for monitoring and analyzing the state of congestion on city roads. The work was carried out in the following steps: research and selection of methods based on efficiency and time, implementation of parallel computing technologies to improve computation speed, demonstration of the selected method based on collected data from a real city with visualization of the results. The growth curve model algorithm has been proven to be almost 5 times more effective than the elbow method and the x-means algorithm. The time allocated for data processing has been calculated. An increase in the volume of processed data showed an almost stable execution time t=3 s for the GCM algorithm for data with a volume of up to almost 2,000 units. The effectiveness of SPGK-means was shown for different values of the number of points. Models of the Chengdu transport network obtained using a clustering algorithm with maximum grid density of neighborhoods are presented. There are some deviations between the grid and the road network due to the large grid size. This error is explained by an error of up to one between the points and the real grid. The results obtained clearly show how optimization of congested roads can be influenced. They provide information to obtain data on available routes, which allows you to analyze the road network individually and as a whole. [ABSTRACT FROM AUTHOR]

The high-quality development of service industry has become an important engine for promoting sustainable economic development. This paper first constructed the evaluation index system of high-quality development of service industry, based on panel data from 2005 to 2020. Second, Kernel density, Markov chain and Dagum Gini coefficient were used to represent the regional differences and dynamic evolution of service industry, and the Koo method was used to explore the characteristics of spatial agglomeration. Finally, social network analysis was used to identify core indicators. The study found that: (1) From 2005 to 2020, the overall level of service industry first decreases and then increases, with Chengdu and Chongqing leading other cities. (2) The development of service industry in the CCEC has large spatial differences, mainly due to inter-regional differences. (3) The level of spatial agglomeration is less variable, with high agglomeration mainly in Chengdu. (4) Indicators such as the level of human capital are the core factors of its high-quality development. This study is of great theoretical and practical significance for the optimization and upgrading of service industry in the CCEC and the synergetic development of the region. [ABSTRACT FROM AUTHOR]

With the advent of the era of Industry 4.0, big data and the Internet of Things came into being. The rise of big data and the Internet of Things has had an impact on all walks of life. The development of big data and the Internet of Things provides an opportunity for the evolution of street form. With the rapid development of smart cities, the street shape is also changing more and more towards the direction of intelligence. Taking Chunxi Road in Chengdu as an example, this paper studies the generation mechanism of street morphology. The factors influencing the generation of street form are divided into two categories: (1) legal factors. (2) Architectural design factors. By building a model, the article quantitatively analyzes the impact of these two types of factors on street form. At the same time, the article conducts an in-depth study on the evolution of ancient Chinese street morphology. The ancient Chinese street form is divided into four stages, namely the Tang and Song Dynasties, the Yuan, Ming and Qing Dynasties, the Late Qing Dynasty, and the founding of New China. The influencing factors of street shape are divided into four categories, namely, the influence of commodity economy, the influence of foreign culture, the influence of building materials, and the influence of traffic. This paper elaborates the influence mechanism of these four factors on the street form in China. The article finally puts forward the development trend of my country's future street form, and points out that the evolution of my country's street form will develop in the direction of intelligence, and intelligent transportation, intelligent garbage classification system, etc. will be applied. The number of brick-and-mortar stores in my country's street layout may decrease. [ABSTRACT FROM AUTHOR]

Introduction: The stress-strain relationship of clays characterized by strain hardening exhibits varying curves under different confining pressures and dry densities. Methods: Considering the viscoelastic properties of clays, a normalized viscoelastic model of strain-hardening clay was established based on fractional derivatives, and normalization factors were proposed. Results: The experimental results showed that the stress-strain relationship of the clay was strain hardening. It shows that Chengdu clay has better normalization conditions. Furthermore, the normalized analysis of this clay through the viscoelastic normalization model revealed that the straight line of normalized data displayed a goodness-of-fit of over 0.98. The obtained values were consistent with experimental results, suggesting the reasonability of the normalized strain-hardening parameters and elastic moduli. Discussion: In addition, the superiority of the developed model was verified by testing the strain-hardening clays in Wuhan, China and Bangkok, Thailand. After analyzing the strain-hardening parameters and normalization factors of our model, it was found that the slope of the normalized line can accurately reflect the strain-hardening ability of the clay. These findings demonstrated that the proposed normalization factor is preferred for a normalized viscoelastic model. It shows that the model proposed in this paper has clearer physical meaning and advancement. [ABSTRACT FROM AUTHOR]

With the popularity of shared bicycles in urban areas, more and more residents choose this fast and convenient mode of transportation for short-distance travel. By optimizing the layout of shared bicycle parking areas and delivery quantity, the investment cost of shared bicycle enterprises can be effectively reduced, and the convenience of residents' travel can be improved at the same time. In this paper, we develop a collaborative optimization model for the layout of the shared bicycle parking area and delivery quantity, aiming at minimizing the walking distance of residents and the investment cost of enterprises, while considering the constraints of the parking area's attractive range and the number of bicycles placed. Aiming at the characteristics of this mixed integer nonlinear problem, an improved genetic algorithm incorporating symmetric individual precision control mechanism is designed. Finally, taking the planned area between the Second Ring Road and the Third Ring Road in the northern part of Jin-niu District, Chengdu as the background, the proposed collaborative optimization model for the layout of shared bicycle parking areas and delivery quantity is applied to a real scene. The results show that after optimization, the number of parking areas is reduced by 2, and the total investment cost is reduced by about 12.2%. [ABSTRACT FROM AUTHOR]

With the deepening of urbanization in China, the coordinated development of cities in different regions is an important part of the sustainable development of the country, and the reasonable quantification of the unbalanced development of cities in different regions is an important issue facing the society nowadays. Previous studies usually use population data to analyze the power-law distribution law to quantify the imbalance of urban development in different regions, but China's population data span a large number of years and numerous division criteria, and the results obtained from different population data are widely disparate and have obvious limitations. The paper starts from a fractal perspective and utilizes OpenStreetMap (OSM) data to extract national road intersections from 2015 to 2022, calculates critical distance thresholds for eight years using urban expansion curves, generates urban agglomerations in China, and quantifies the imbalance of urban development in different regions by calculating the urban agglomeration power-law index. The results indicate that (1) the critical distance threshold of urban expansion curves exhibits a slight overall increase and stabilizes within the range of 120–130 m, (2) the number of urban agglomerations in China has been increasing significantly year by year, but the power-law index has been decreasing from 1.49 in 2015 to 1.36 in 2022, and (3) the number of urban agglomerations and the power–law index of the Beijing–Tianjin–Hebei, Yangtze River Delta, Pearl River Delta, and Chengdu–Chongqing regions, which is consistent with the national scale trend, indicates that the scale distribution of urban agglomerations in China at this stage does not conform to Zipf's law, and there is a certain Matthew effect among cities in different geographic areas with a large unevenness. The results of the study can provide new ideas for assessing the coordinated development of cities in different regions. It compensates for the instability of population and economic data in traditional studies. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of digital reading during educational disruption on science and engineering students' learning experience. Before the pandemic, some studies explored whether university students preferred using printed or digital resources for their academic readings. Amidst the pandemic, online learning became essential. Several studies showed students' preference for printed text. This paper extends a pilot study that was conducted during the first COVID-19 wave in China. A survey consisting of Likert questions and open questions was designed using MS-Forms. The survey was shared with the science and engineering students in Years 2–4 (Levels 1–3) of their study at SWJTU-Leeds Joint School, Southwest Jiaotong University in Chengdu, China. This covered students from four undergraduate programs: Civil Engineering with Transport, Electronic and Electrical Engineering, Mechanical Engineering, and Computer Science. In total, 223 students participated in this study. The survey was anonymous and was made available to students for a month. The participation rate is nearly 27%. Findings indicate that the behavior of science and engineering students toward digital reading was different than other majors, and it is generally favorable. The necessity for online learning during educational disruption has encouraged some students to develop their digital reading skills. [ABSTRACT FROM AUTHOR]

In daily operation, urban rail transit (URT) systems often experience disturbances that result in a partial reduction in transport capacity [partial disturbances (PDs)] rather than disturbances leading to a complete reduction in transport capacity. However, research that assesses the resilience of URT networks under PDs remains limited. This paper addresses this gap by proposing a passenger-oriented resilience assessment model for URT networks under PDs, considering the travel behaviors of passengers and different relations (i.e., linear, concave, and convex) between the velocity coefficient and failure severity. A simulation-based assessment approach was developed to solve the resilience assessment model. A numerical experiment was conducted on the Chengdu subway network in China. The results demonstrate that the performance indicator employed herein reflects the impact of passenger travel time distribution on network performance. Deliberate PDs cause more significant performance losses than random PDs. Moreover, the network is the least resilient under PDs considering the convex relation between the velocity coefficient and failure severity. The resilience-based critical link of each line is not fixed and varies with the failure severity and disturbance occurrence time. Increasing the failure severity of PDs results in more severe performance losses than increasing the number of PDs. Additionally, the passenger-oriented resilience of a URT network can be enhanced by improving the passengers’ tolerable delay time and disturbance duration. [ABSTRACT FROM AUTHOR]

Smart cities can effectively improve urban life quality. But, with the rise in population size, there is an increase in the use of vehicles for transportation. In smart city development, traffic control and route planning are the primary tasks to address the problem of travel time estimation. It has become complex due to concerns of wrapped spatio-temporal data on dynamic real-time traffic conditions. The existing methods failed to estimate travel time efficiently due to not considering the spatio-temporal features and lack of computing resources. There is a need for a system like intelligent transportation system to monitor and accurately predict traffic flow to avoid traffic congestion and reduce the impact on the ecological system. This paper developed a novel hybrid deep learning model to estimate optimized travel time and possible trajectories. In this work, U-Net used to reduce the number of feature points for each temporal data and GNN is used to build the graph with connectivity between vehicular nodes. This hybrid model helps us predict traffic flow and aims to estimate accurate travel time from one location (node) to another. The model's performance is evaluated on the standard benchmark datasets Q-Traffic, TaxiBJ, and Chengdu. The experimental results show that the proposed framework significantly improves performance in extracting travel patterns and provides an optimal route with an estimated travel time than existing methods with RMSE 4%, MAE 20.49%, and MAPE 18%. The proposed model has accurately predicted the estimation of travel time of the vehicle for the given urban traffic data. [ABSTRACT FROM AUTHOR]

The recommended bearing capacity of medium weathering mudstone foundation is less than the capacity of the rock structure to withstand loads in Southwest China. A comprehensive failure characterization of medium weathering mudstone in Chengdu has been performed including bearing plate test (BPT), binocular vision measurement (BVM) test, uniaxial compressive strength test, trial trench test of shallow rock surface and 3D imaging in this paper. Failure behavior of rock has been modeled with 3D imaging algorithm that utilizes Zhang's calibration method in BVM system combination with trial trench test of shallow rock surface. The bearing capacity of medium weathering mudstone foundation were extracted from uniaxial experiments and BPT-BVM test by fitting relevant material properties to the data. The results revealed that: Bearing capacity of medium weathering mudstone of layered isotropic in Chengdu is undervalued. Specifically, the characteristic load carrying value is in the range 1500–2500 kP, that is 50% higher than in the local standard system. Failure process is different from Hoek–Brown Failure Criterion, presenting a wave peak transfer phenomenon of the increment displacement into the distance. Thus, it can be reduced to that of punching failures for thin bedded structures of Moudstone foundations. Compressive strength of soft rock proves to be main factor limiting the bearing capacity, a clear correlation between the uniaxial compressive strength reduction coefficient and the bearing capacity has been used to establish, leading to the proposal of a load bearing capacity prediction model. [ABSTRACT FROM AUTHOR]

This paper aimed to explore the potential factors of the coronavirus disease 2019 (COVID-19) pandemic on Chinese undergraduates' psychological distress (PD), as well as the moderating impact of coping styles and social support in this relationship. 5638 Chinese college students in Chengdu were randomly selected for an online survey. 10-item Kessler Psychological Distress Scale (K10), the Simplified Coping Style Questionnaire (SCSQ) and the Social Support Rating Scale (SSRS) were used in this paper. In SPSS 17, one-way ANOVA, multiple linear regression and 95% confidence were used to observe the relationship between sociodemographic characteristics, COVID-19 exposure, coping styles, social support and PD of college students. The PD prevalence rate of our sample during the pandemic was 45.5%. Males, students with a romantic relationship, those spending more time on COVID-19, worrying about getting infected, those whose cohabitants were nervous about COVID-19, higher scores of negative coping style, lower positive coping style and social support scores had a higher risk of experiencing PD. The relationship between the nervousness of cohabitants about COVID-19 and the occurrence of PD was moderated by positive coping. Based on the serious psychological condition of Chinese college students during COVID-19. There is an urgent need to enhance the positive coping styles of college students, and systematic support for their mental health is crucial. This article provides theoretical research guidance for students and schools to further prevent and intervene in PD among college students. [ABSTRACT FROM AUTHOR]

Based on the analysis of the spatial distribution characteristics and influencing factors of 2879 attractions POI in Chengdu, this paper studies the spatial distribution types, spatial concentration, and equilibrium of attractions by using standard deviation ellipse, geographic concentration index, and kernel density estimation. Buffer analysis, near analysis, correlation analysis, and grey relation analysis are used to study the influencing factors of tourism in Chengdu. The results show that: (1) The overall attractions POI in Chengdu is in an east–west direction, and except for other types of attractions POI, the remaining types are also in an east–west direction. (2) The spatial agglomeration areas of different types of attractions POI in Chengdu were quite different. Among them, parks and city squares were mainly concentrated in the central urban areas, namely Jinjiang, Qingyang, Jinniu, Wuhou, and Chenghua, forming a prominent gathering area. (3) There were differences in the influence degree of each factor on the spatial distribution of attractions POI. Topography and drainage, transportation, tourist source market, and other factors significantly correlated with the distribution of attractions, which were the main factors affecting the spatial distribution of attractions. Industrial development and economic population were also important factors affecting the distribution of attractions. Tourism is an important thrust for urban development. The study of the spatial distribution of tourism and its influencing factors has clear guiding significance for later tourism development, which can help optimize the integration of tourism resources, promote the cooperation of regional tourism development and provide a basis for decision-making for tourism development. [ABSTRACT FROM AUTHOR]

With the rapid development of the economy, the impact of light pollution on human life has become increasingly serious. For a long time, people have only paid attention to the positive effects of light, while ignoring the harmful consequences of light pollution. In order to evaluate the degree of light pollution in the region, this research paper has presented the multilevel evaluation model of light pollution (MELP) model. First, the MELP model selected 14 generally applicable indicators, and these indicators are easy to obtain. Second, the MELP model considered the correlation between indicators and utilizes analytic hierarchy process-entropy weight method (AHP-EWM) to determine the weight of indicators. Third, the normal cumulative distribution-Monte Carlo method was used to simulate regional light pollution data. Finally, based on the TOPSIS scoring results, the severity of light pollution was categorized into three levels: slight pollution (I–III), moderate pollution (IV–VI), and severe pollution (VII–IX). A study case is Chengdu City, Southwest China. According to the results of the MELP model, the light pollution level of Chengdu is VIII, which belongs to severe pollution. This study aimed to investigate and reduce the impact of light pollution on humans and the environment, and it can also provide a reference for similar studies around the world. [ABSTRACT FROM AUTHOR]

In the Chengdu-Chongqing economic circle in China, the recommended bearing capacity of a medium-weathering mudstone foundation is less than the capacity of the rock structure to withstand loads. The aim of this paper was to perform plate loading tests (PLTs) with binocular vision measurement (BVM) to obtain the accurate deformation of the foundation under vertical loading. The results revealed that vertical load–bearing foundations have a failure mode that can be reduced to that of thrust failures for thin bedded structures of mudstone foundations. By analyzing the differences in results between the PLTs and the uniaxial compressive strength tests under the same conditions, a new formalism for calculating foundation-bearing capacity was proposed; there is only a 5% difference compared to in situ tests. The results of this research provide theoretical support and a practical benchmark for the full development of the load-bearing capability of soft red rocks. As the Chengdu-Chongqing economic circle progresses toward the international urban cluster, inevitably, soft rock is used as a foundation retaining layer in the construction of superhigh and super-rise buildings in the region. However, owing to antiquated testing methods and poor intelligibility of the testing equipment, the bearing capacity of such a mass of rock has yet to be fully developed and cannot meet the dual-carbon economic goals and sustainable development of architectural design. Based on this, the study introduces a noncontact method to measure the bearing capacity of soft rock within the traditional method of in situ testing and combines the concept of big data analysis to advance an empirical method for calculating the bearing capacity accurately. Based on the results of this study, the concept of natural foundations can be adopted and the engineering dilemma that the treatment of building foundations cannot be economical and practical can be solved. [ABSTRACT FROM AUTHOR]

Due to the flammable, explosive, corrosive, radioactive and other characteristics of dangerous goods, if a traffic accident or leakage accident occurs during transportation, it will seriously endanger public safety and the safety of people's lives and property. It is of great significance for urban safety and stability to establish a risk early warning mechanism, ensure the safe transportation of dangerous goods, and formulate emergency rescue measures. In view of the problems that may occur during the transportation of dangerous goods, such as weak supervision, low efficiency in obtaining accident information, and difficulty in emergency treatment, this paper constructed a comprehensive perception and collaborative integration of urban emergency response system by integrating vehicle target intelligent monitoring platform and decision support platform. Under the support of target detection and tracking technology, PyQt5 is used to build an intelligent vehicle target monitoring platform based on satellite video data, which realizes the real-time tracking, monitoring and early warning functions for dangerous goods transport vehicles, and can quickly complete the location of emergencies and the statistics of environmental road information after an accident. With the support of database technology and ArcGIS Engine embedded component library, a decision support platform integrating statistics and spatial analysis is constructed. GIS technology is used to realize the spatial analysis of emergency points and basic information management functions. At the same time, the platform is based on the two basic starting points of fast response to emergency events and high scheduling efficiency, which realizes the rapid determination of the accident impact area and the planning of emergency rescue routes. Taking Chengdu as an example, the emergency response system constructed in this paper can monitor dangerous goods transport vehicles in the urban area in real time, and formulate emergency response measures in seconds. The system is conducive to reducing the damage and loss caused by emergencies and improving the informatization level of emergency related departments. [ABSTRACT FROM AUTHOR]

Ensuring the basic stability of supply and prices of agricultural products bears on people's wellbeing and contributes to social development and stability. However, the outbreak of COVID-19 and a series of rules and regulations confining socialization adopted to deal with the epidemic have led to the prominent contradiction between supply and demand in the agricultural market, and the sharp fluctuations in the prices of agricultural products. In this paper, the price data of agricultural products in the main urban area of Chengdu in the 10 weeks before and 20 weeks after the Spring Festival from 2018 to 2020 were used to empirically study the impact of COVID-19 on local agricultural product prices by generalized multiple difference method (DID). The empirical results show that, first, compared with the Spring Festival of 2018 and 2019, the COVID-19 epidemic in the Spring Festival of 2020 led to an average increase of 105.02% in the retail prices of agricultural products in Chengdu, among which the increase of livestock and poultry, fruits and vegetables, and aquatic products was the most obvious, while the change of grain and oil prices was not significant. Second, compared with the demand side, the COVID-19 pandemic has a major impact on agricultural prices from the supply side. Third, the impact of COVID-19 on the retail prices of agricultural products is more obvious in areas where the primary industry is relatively small and industrialization is fast. Fourth, in the short term, the government's policy of suppressing prices does not restrain all agricultural prices. The above research findings provide a reference for understanding the adjustment mechanism of agricultural prices under the impact of the epidemic, and for effectively formulating relevant policies to stabilize the price of agricultural products, ensure supply, and alleviate the pressure on people's livelihood. [ABSTRACT FROM AUTHOR]

This paper presents an in-depth analysis and study of traditional corridor construction and tourism system planning using intelligent sensing networks. This paper proposes a node deployment and coverage optimization method for HWSNs based on particle swarm optimization emperor butterfly algorithm and extreme learning machine, with the objectives of reducing node deployment cost, reducing coverage redundancy and voids, improving monitoring area coverage, and increasing the agility of data processing by 15.03%, efficiency by 12.15%, and stability by 11.31%. In the paper, the static parameter table of the sensor is stored inside the smart sensor or stored on the IoT cloud platform to achieve automatic registration of the static parameters of the smart sensor. According to the characteristics of dynamic parameters of smart sensors, an autonegotiation algorithm is designed to realize the dynamic parameters of smart sensors to be determined by autonegotiation and complete registration without human intervention, which includes four parts: utility function, concussion protocol, proposal generation strategy, and response mechanism. The intelligent sensing system is composed of three parts: smart sensor, IoT gateway, and IoT cloud platform; the functions of each part in the system are analyzed, and MAR and STM32F4 are selected as the core board of smart sensor and IoT gateway. And for the problems of time-consuming and tedious access to smart sensors in the system, we propose to study the adaptive sensor UART serial port parameters and automatic registration of sensor parameters. Based on the analysis of the current environment and resource characteristics of the traditional corridor, the current situation of ecotourism resources of the traditional corridor is sorted out, an evaluation index system is established to evaluate ecotourism resources, and the current development basis is analyzed from four aspects: natural environment, tourism resources, product development, and infrastructure. Finally, to promote the sustainable development of the ecotourism corridor in the Chengdu section of the southern foot of the Qinling Mountains, the planning and development strategies to match the ecotourism corridor in the Chengdu section of the southern foot of the Qinling Mountains are proposed in four aspects: transportation organization optimization, ecological landscape enhancement, support system construction, and regional tourism development. [ABSTRACT FROM AUTHOR]

Chengdu-Chongqing economic circle (hereinafter referred to as "Chengyu Region") is a key construction region of China's major development strategy. The development of regional tourism plays an important role in the optimization of regional economy and industrial structure. In this paper, ArcGIS 10.5 was used as the main analysis tool to analyze the temporal and spatial distribution of A-level tourist attractions in Chengyu Region, and makes factor analysis and interactive analysis on the factors affecting the distribution of tourist attractions by geographic detector model. The results show that: 1. The distribution of tourism attractions in Chengyu Region is mainly concentrated type, and the distribution of the natural landscape, cultural landscape, rural pastoral and modern entertainment in Chengyu Region are all concentrated type. 2. In terms of the distribution characteristics of kernel density, there is a significant deviation between the spatial distribution of A-level tourist attractions and tourism income in Chengyu Region. Through the analysis of the dynamic development of A-level tourist attractions in 2010, 2015 and 2021, tourism in Chengyu Region show a good situation of "driven by two cities and blooming in many places." Among them, Chengdu and Chongqing have obvious advantages, and Yibin has become an important city second only to Chengdu and Chongqing. 3. In terms of spatial correlation, the spatial distribution of A-level tourist attractions in Chengyu Region has a significant spatial autocorrelation. The local spatial autocorrelation of A-level tourist attractions includes H–H (high-high), H–L (high-low) and L–H (low–high) clustering types. 4. In terms of influencing factors, traffic location, water system, topography and social and economic development level are the important factors affecting the spatial distribution of regional A-level tourist attractions. Among them, the level of social and economic development has the greatest impact on regional tourism. Finally, based on relevant theories, this paper puts forward countermeasures and suggestions for regional coordinated development, so as to guide the sustainable development and management innovation of regional tourism industry. [ABSTRACT FROM AUTHOR]

The study takes 10 urban agglomerations in China as the research object, focusing on the Chengdu-Chongqing urban agglomeration, and applies Data Envelopment Analysis (DEA) to measure and compare their scientific input and output efficiency of universities. First, this paper analyzes the input and output of scientific research in universities in major provinces in China in detail. Second, according to the construction principles of the indicator system, using qualitative interview to construct evaluation indicators of university research efficiency. Third, using DEA method, first analyze the input and output profile of some urban agglomeration universities such as Chengdu-Chongqing economic circle, measure and compare their research input and output efficiency, then compare and analyze the research efficiency of research-type sample universities within Chengdu-Chongqing economic circle, and conduct a projection study of non-DEA effective sample universities. The main conclusions are as follows: first, the average efficiency of scientific research in universities in Chengdu-Chongqing and other urban agglomerations in 2020 has slightly increased compared with that in 2016, but the gap between urban agglomerations is prominent, and the innovation level of scientific research in higher education institutions in urban agglomerations needs to be improved. Second, there is a mismatch between the themes of research, funding and human resources in research-oriented universities in the Chengdu-Chongqing economic circle. Third, there is considerable room for improvement in research efficiency, and the influence of scale on overall efficiency is weak. We found that excessive investment in scientific research in universities is the main reason for the non-effect. [ABSTRACT FROM AUTHOR]

Due to the limited space in urban area, subway tunnels have to be designed and constructed with marginal clearance between each other. In the construction of subway tunnels, the installation and dismantling of temporary lining prone to affect the existing subway and new subway. In this paper, based on the Nijiaqiao Station Project in Chengdu Metro, stress state and deformation of the existing subway and the new subway, induced by dismantling of temporary lining, are compared and analyzed. It is found that different installation sequence of lining and dismantling temporary lining will affect the deformation of the subway, especially in the floor. The deformation and stress conditions of the structure are the least affected when all secondary linings are constructed in the first place and then all temporary linings are dismantled; and condition II is the worst and leads to excessive deformation and stress. Based on the analysis of the construction efficiency and influence of dismantling temporary lining, it is suggested that the combination form of divisional lining and dismantling temporary lining (i.e., condition III) can be adopted for the construction of closely-undercrossing subway. Finally, it is found that the field monitoring results are basically consistent with the simulation results, which verifies the validity of the numerical simulation. The results obtained in this paper can provide some vital references for the design and construction of similar projects (i.e., closely-undercrossing cases) in the future. [ABSTRACT FROM AUTHOR]

In the era of the urban economy and service economy, the decoupling of service industry land expansion from economic growth has always been a key measure to evaluate sustainable and healthy development. Based on the decoupling model and GIS spatial analysis method, this paper conducted an empirical study of Chinese cities from 2012 to 2019. Results: (1) Increasing spatial heterogeneity, correlation and agglomeration of land expansion were found in China's urban service industry and its economic growth; (2) Most cities were in weak decoupling, with evolved, degraded and unchanged cities accounting for one-third each, and the number of cities in negative decoupling was increasing, leading to increasingly diverse and complex decoupling relationships; (3) From the perspective of changes in the urban service industry land and its decoupling from economic growth, HH cities were clustered in the Yangtze River Delta, Pearl River Delta, Beijing–Tianjin–Hebei and Chengdu–Chongqing urban agglomerations in a continuous belt pattern, while LL and HL cities were mostly in the north, especially in the northeast, creating many cluster-like agglomerations that have become problematic spaces; (4) A significant synergistic effect was identified between the factors of urban permanent population, value added of the secondary industry, per capita GDP, government financial expenditure, international trade, foreign direct investment, total retail of commodities, and authorized patents, with factor pairs formed showing nonlinear enhancement. The factor value added of the secondary industry had the largest direct impact, while urban permanent population and foreign direct investment led in terms of net synergies; (5) It is recommended to introduce classified and differentiated urban service industry land use policies, plan and build a number of national, provincial and municipal modern service industry clusters, demonstrate changes in land supply and use, build a scientific and efficient land resource allocation and management system, guard against and prevent recoupling and effectively improve the ability of cities to achieve high-quality economic development. [ABSTRACT FROM AUTHOR]

Resilient cities provide a new operating mechanism for sustainable urban development and can effectively reduce urban disaster losses. Urban resilience has become an important research topic, but few scholars focus on the urban resilience of urban agglomerations in western China. Therefore, this paper takes the Chengdu–Chongqing urban agglomeration of China as the study area and aims to evaluate the resilience level of cities in typical regions of western China. This study uses multiple interdisciplinary methods, such as the entropy weight method, Theil index, and geographically and temporally weighted regression, to evaluate the resilience levels of 16 cities in the region and discuss the influencing factors of regional urban resilience. The results show that the urban resilience of cities in the Chengdu–Chongqing urban agglomeration has evolved from a low to high level. Additionally, there are significant spatial differences in urban resilience in the Chengdu–Chongqing urban agglomeration, and the resilience levels of cities in the east and west of the region are relatively high, while the resilience levels of cities in the south and north are relatively low. Further research found that factors such as administrative level, marketization level, industrial structure, population density, urbanization level, and emergency facility level all have a significant positive impact on the improvement of urban resilience, but this impact has spatial and temporal heterogeneity. Based on the above research results, the strategies have been proposed from the perspective of sustainable urban development to provide a new theoretical support and decision-making reference for improving the resilience level of urban agglomerations in western China. [ABSTRACT FROM AUTHOR]

Traffic congestion and frequent traffic accidents have become the main problems affecting urban traffic. The effective location prediction of vehicle trajectory can help alleviate traffic congestion, reduce the occurrence of traffic accidents, and optimize the urban traffic system. Vehicle trajectory is closely related to the surrounding Point of Interest (POI). POI can be considered as the spatial feature and can be fused with trajectory points to improve prediction accuracy. A Local Dynamic Graph Spatiotemporal–Long Short-Term Memory (LDGST-LSTM) was proposed in this paper to extract and fuse the POI knowledge and realize next location prediction. POI semantic information was learned by constructing the traffic knowledge graph, and spatial and temporal features were extracted by combining the Graph Attention Network (GAT) and temporal attention mechanism. The effectiveness of LDGST-LSTM was verified on two datasets, including Chengdu taxi trajectory data in August 2014 and October 2018. The accuracy and robustness of the proposed model were significantly improved compared with the benchmark models. The effects of major components in the proposed model were also evaluated through an ablation experiment. Moreover, the weights of POI that influence location prediction were visualized to improve the interpretability of the proposed model. [ABSTRACT FROM AUTHOR]

Estimated time of arrival (ETA) is an estimate of the vehicle travel time from the origin to destination in the roadworks. From the perspective of travel planning or resource allocation, accurate ETA is significantly important. In recent years, deep learning-based methods represented by recurrent neural networks has been widely used in travel time prediction tasks, but such methods cannot effectively learn data association at different moments. At the same time, the existing methods do not effectively leverage local traffic information. Targeting these challenges, this paper proposes a new model TransETA to predict vehicle travel time. The model includes three modules: the input feature transformation module uses graph convolutional network (GCN) to extract the local congestion feature, the deep forest module mainly deals with static trajectory data, and ETA-Transformer module processes the feature extraction of dynamic trajectory data. Finally, we conducted experiments on two large trajectory datasets. The experimental results show that the proposed hybrid deep learning method, TransETA, outperforms the state-of-the-art models. On the Chengdu and Porto datasets, our proposed method shows an improvement of 6s and 9s in mean absolute error compared to the current best performing method, respectively. Also the average absolute percentage error is reduced by 2.34% and 3.64% respectively. The effectiveness of each module was approved through ablation experiments. Specifically, local congestion information representation can effectively improve the accuracy of the prediction. ETA-Transformer module is more effective in extracting spatio-temporal feature correlation than the LSTM-based method. [ABSTRACT FROM AUTHOR]

Despite growing scholarly attention on the role of urban networks for understanding regional dynamics, there has been limited research examining the impact of cities' transportation network connections on regional market integration. This paper analyzes China's four major urban agglomerations: the Yangtze River Delta, the Pearl River Delta, Beijing-Tianjin-Hebei, and Chengdu-Chongqing. Applying a spatial Durbin model to cross-sectional datasets for 2019, we provide insight into the role of cities' transportation network connections in promoting regional market integration, considering both the potentially heterogeneous impact of network connections and the interplay between network and agglomeration externalities. Our results indicate that: (1) cities' transportation network connections have an inverted 'U'-shaped effect on regional market integration; (2) transportation network connections have spatial spillover effects; (3) the positive impact of transportation network connections on regional market integration becomes more pronounced as city size decreases; and (4) there are neither complementary nor substitution effects between network and agglomeration externalities. We reflect on the broader implications of our empirical findings for regional development strategies and discuss possible avenues for further research. [ABSTRACT FROM AUTHOR]

Design coordination and collaboration are crucial in the architecture, engineering, and construction (AEC) industries, necessitating the integration of diverse disciplines and expertise to achieve unified and functional designs. Traditionally, these disciplines operate in isolation, leading to a fragmented design process. Building Information Modeling (BIM), recognized for its collaborative capabilities, presents an opportunity to revolutionize traditional design practices. However, existing research on BIM primarily assumes an ideal environment where all major participants use BIM models, overlooking the dynamics of mixed 2D Computer-aided Design (CAD) and 3D BIM environments. Addressing this research gap, this study aims to establish a dual-level OpenBIM-enabled collaborative design framework, enhancing the design process across various disciplines. The study employs a case study approach, applying this framework to an airport project in Chengdu, China. The airport includes different corridors that are similar in scope and scale but distinct enough to allow for a comparative study. The results demonstrate a 27% faster completion rate, a 98% reduction in design errors, and improved user satisfaction with the proposed method. The paper concludes by discussing the limitations of the study and suggesting avenues for future research. [ABSTRACT FROM AUTHOR]

Educating preschool children on environmental sustainability is an excellent investment in shaping a sustainable society. In this study, the researchers conducted action research using Lilian Katz's Project Approach in cooperation with two teachers to explore the process of developing and implementing STEM modules in a preschool in Chengdu, China. Thirty children aged 5 to 6 years were involved in STEM modules in two cycles, each cycle was improved based on feedback from the previous cycle. Adopting the constructivist approach, these STEM modules are child-centered, inquiry-driven, and process-oriented. Through observation and interviews with teachers, children and principals, it was found that these STEM modules successfully engaged children to investigate, deepen their understanding of trees, motivate them to experiment and innovate, and enhance their self-learning ability. The children were happy and excited to learn, they expressed aspirations to create a green world, protect trees, and save and recycle paper. In short, they showed the beginning of understanding and awareness of environmental sustainability. [ABSTRACT FROM AUTHOR]

With the expansion of a city, the urban green space is occupied and the urban heat island effect is serious. Greening the roof surfaces of urban buildings is an effective way to increase the area of urban green space and improve the urban ecological environment. To provide effective data support for urban green space planning, this paper used high-resolution images to (1) obtain accurate building spots on the map of the study area through deep learning assisted manual correction; and (2) establish an evaluation index system of roof greening including the characteristics of the roof itself, the natural environment and the human society environment. The weight values of attributes not related to the roof itself were calculated by Analytic Hierarchy Process (AHP). The suitable green roof locations were evaluated by spatial join, weighted superposition and other spatial analysis methods. Taking the areas within the Chengdu city's third ring road as the study area, the results show that an accurate building pattern obtained by deep learning greatly improves the efficiency of the experiment. The roof surfaces unsuitable for greening can be effectively classified by the method of feature extraction, with an accuracy of 86.58%. The roofs suitable for greening account for 48.08%, among which, the high-suitability roofs, medium-suitability roofs and low-suitability roofs represent 45.32%, 38.95% and 15.73%. The high-suitability green buildings are mainly distributed in the first ring district and the western area outside the first ring district in Chengdu. This paper is useful for solving the current problem of the more saturated high-density urban area and allowing the expansion of the urban ecological environment. [ABSTRACT FROM AUTHOR]

Early work on China's Two-Child Policy identified cost as a major disincentive to having a second child, but financial constraints may work in tandem with cultural factors to shape childbearing desire. In addition, most research has excluded men from the sample. This paper develops Yan's (2003) concepts of 'new' and 'traditional' fertility cultures to childbearing desire, a key component of fertility decision-making that is poorly understood (Hayford & Agadjanian, 2017). In particular, we focus on how differing conceptions of children's needs relate to perceptions of cost. This paper reports on semi-structured interviews with 29 men and women in Chengdu, the capital of Sichuan province. We found that interviewees who espoused views consistent with a traditional fertility framework were more interested in having a second child, and were less concerned about cost. In addition, men were more likely to espouse views consistent with a traditional fertility cultural framework. [ABSTRACT FROM AUTHOR]

Several possible lithosphere–atmosphere–ionosphere coupling mechanisms before earthquake occurrence are presented in the literature. They are described by several models with different interaction channels (e.g., electromagnetic, mechanics, chemical, thermal), sometimes in conflict with each other. In this paper, we search for anomalies six months before the Lushan (China) 2013 earthquake in the three geo-layers looking for a possible view of the couplings and testing if one or another is more reliable to describe the observations. The Lushan earthquake occurred in China's Sichuan province on 20 April 2013, with a magnitude of Mw = 6.7. Despite the moderate magnitude of the event, it caused concern because its source was localized on the southwest side of the same fault that produced the catastrophic Wenchuan event in 2008. This paper applies a geophysical multi-layer approach to search for possible pre-earthquake anomalies in the lithosphere, atmosphere, and ionosphere. In detail, six main increases in the accumulated seismic stress were depicted. Anomalous geomagnetic pulsations were recorded in the Chengdu observatory, sometimes following the increased stress. Atmosphere status and composition were found to be anomalous in several periods before the earthquake, and, spatially, the anomalies seem to appear firstly far from the upcoming earthquakes and later approaching the Longmenshan fault where the Lushan earthquakes nucleated. The Formosat-3 data identified interesting anomalies in the altitude or electron content of the ionospheric F2 peak in correspondence with seismic and atmospheric anomalies 130 days before the earthquake. In addition, the total electron content showed high anomalous values from 12 to 6 days before the earthquake. We compared the anomalies and tried to explain their correspondences in different geo-layers by the lithosphere–atmosphere–ionosphere coupling models. In particular, we identified three possible couplings with different mechanisms: a first, about 130 days before the earthquake, with a fast (order of one day) propagation delay; a second, about 40 days before the earthquake occurrence, with a propagation delay of few days and a third from 2.5 weeks until one week before the event. Such evidence suggests that the geo-layers could interact with different channels (pure electromagnetic or a chain of physical-chemical processes) with specific propagation delays. Such results support the understanding of the preparation for medium and large earthquakes globally, which is necessary (although not sufficient) knowledge in order to mitigate their impact on human life. [ABSTRACT FROM AUTHOR]

China is attaching increasing importance to the creation of regional integration, high-quality economic development and ecological civilization. An accurate grasp of the traction effect of the increased level of integration of urban agglomerations on the eco-efficiency (EE) of cities in the region will help to promote the steady improvement of urban economic development and the ecological environment. This paper constructs an index system to measure the level of integration of the Chengdu–Chongqing urban agglomeration (CCUA) and the EE of each city within it from 2011 to 2020 and explores the impact of regional integration on urban EE and its mechanism of action. The study presents the follow findings: (1) The level of integration of the CCUA increased nearly 10 times from 2011 to 2020, with the government playing a significant leading role. (2) The positive and negative effects of the level of integration of the CCUA on urban EE depend on factors such as the level of economic development, the stage of development and the location. There are several relationships between the level of intra-regional integration and urban EE: first, a nearly linear increase, as in Chongqing and Chengdu; second, an increase in fluctuation, as in Dazhou, Guang'an and Leshan; and third, a fluctuation, decrease, flat or even no real increase, as in Luzhou, Ya'an and Zigong. (3) Based on this, this paper considers the mechanism of the level of integration within the region on urban EE in terms of both economic and eco-environmental effects, with a view to exploring the future green development path of the CCUA. [ABSTRACT FROM AUTHOR]

The Yangtze River Economic Belt (YREB), an important industrial belt for food security for China, is facing the challenge of decreasing cultivated land in the process of rapid urbanization. In this case, how to improve the cultivated land use efficiency (CLUE) has become the top priority. Based on data from 108 cities of YREB from 2001 to 2019, we measured CLUE using a slack-based measure with undesirable output (SBM-Undesirable). The high-value area of CLUE shows a trend from multi-core agglomeration to two-core agglomeration, mainly concentrated in Chengdu-Chongqing urban agglomeration and the northern part of the YREB. Then the paper examines the spatial effect of urbanization on CLUE using the Spatial Error Model (SEM). The result shows that population urbanization has significantly promoted the improvement of CLUE in YREB during the sample period. With each percentage point increase in population urbanization, CLUE will increase by 2.99%. Land urbanization has a negative impact on CLUE, for each percent increase in the expansion of urban spatial scope, CLUE will decrease by 0.06%. The spatial heterogeneity analysis shows that population urbanization in the lower reaches has significantly promoted CLUE, with a coefficient of 1.053. The population urbanization level in the middle and lower reaches of the region has no obvious effect on CLUE. The coefficient of land urbanization in the downstream region is 0.35, which significantly promotes CLUE. The coefficient in the middle is −0.26, which implies the CLUE decreases by 0.26% for every one percentage point increase in land urbanization. Land urbanization in the upper has no significant impact on the CLUE. Policy implications include improving the quality of the three major urban clusters along the YREB, building an ecologic protective screen in the upper, encouraging a new agricultural management system and detailed regulations related to the cultivated land protection in YREB. [ABSTRACT FROM AUTHOR]

China's foreign capital R&D network has become a core component of the high-quality development of the national innovation ecosystem. Narrowing the spatial difference in the foreign R&D network is considered an essential strategy to improve China's holistic innovation capability. Previous studies only examined the internal factors of the system while mostly ignoring the quantitative analysis of the spatial differences diffusion. Based on the dissipative structure theory, the current paper demonstrates the construction of positive and negative entropy indexes of foreign capital R&D networks in China. Rested on a survey dataset of 340 foreign R&D institutions in 21 Chinese provinces from 2016 to 2017, this paper analyzes the spatial differences and driving factors of the innovation development of foreign capital R&D networks across the country using the Brusselator model. The results show that China's entire deviation degree of foreign capital R&D networks is small. Still, there are significant spatial differences in the deviation between the entropy value and the ideal status of dissipative structure in foreign R&D networks, with the minor deviation-value areas located in the eastern regions. In addition, the central and western regions where the late-mover layout of the foreign R&D network deviates are far from the ideal status. Among them, the western (i.e., Xi'an and Chengdu) foreign R&D network is significantly better than the central innovation performance. It is found that policy traction of talent advantage and intellectual property rights protection positively impact the negative entropy flow of foreign R&D networks in China, which becomes an influential driving force for the future system equilibrium. The paper concludes with several policy implications to guide China's innovation and ecological optimization of foreign capital R&D networks. [ABSTRACT FROM AUTHOR]

The popularity of ride-hailing platforms has significantly improved travel efficiency by providing convenient and personalized transportation services. Designing an effective ride-hailing service generally needs to address two tasks: order matching that assigns orders to available vehicles and proactive vehicle repositioning that deploys idle vehicles to potentially high-demand regions. Recent studies have intensively utilized deep reinforcement learning to solve the two tasks by learning an optimal dispatching strategy. However, most of them generate actions for the two tasks independently, neglecting the interactions between the two tasks and the communications among multiple drivers. To this end, this paper provides an approach based on multi-agent deep reinforcement learning where the two tasks are modeled as a unified Markov decision process, and the colossal state space and competition among drivers are addressed. Additionally, a modifiable agent-specific state representation is proposed to facilitate knowledge transferring and improve computing efficiency. We evaluate our approach on a public taxi order dataset collected in Chengdu, China, where a variable number of simulated vehicles are tested. Experimental results show that our approach outperforms seven existing baselines, reducing passenger rejection rate, driver idle time and improving total driver income. [ABSTRACT FROM AUTHOR]

This paper investigates the risk quantification for Unmanned Aircraft (UA) in urban environments, focusing on the safety of ground people. An assessment methodology is proposed to quantify the flying risk, which indicates the ground fatalities resulted from different potential risk sources. With the knowledge of UA's specifications and ground environments, the flying risk of the target UA flying in the target area could be evaluated from the combination of results from independent assessment procedures focusing on multiple potential risk sources with specific safety metrics. A study case to assess the flying risk of the Talon and the DJI Inspire 2 flying in one piece of the region in Chengdu is presented. From the assessment result, the airspace management strategies for both Talon and DJI Inspires 2 could be easily developed to guarantee the safety of ground people, therefore, this risk quantification method could be a general tool to support decision-making in safety work. [ABSTRACT FROM AUTHOR]

Detection of subsurface hydrodynamic anomalies plays a significant role in groundwater resource management and environmental monitoring. In this paper, based on data from the groundwater level, atmospheric pressure, and precipitation in the Chengdu area of China, a method for detecting outliers considering the factors affecting groundwater levels is proposed. By analyzing the factors affecting groundwater levels in the monitoring site and eliminating them, simplified groundwater data is obtained. Applying sl-Pauta (self-learning-based Pauta), iForest (Isolated Forest), OCSVM (One-Class SVM), and KNN to synthetic data with known outliers, testing and evaluating the effectiveness of 4 technologies. Finally, the four methods are applied to the detection of outliers in simplified groundwater levels. The results show that in the detection of outliers in synthesized data, the OCSVM method has the best detection performance, with a precision rate of 88.89%, a recall rate of 91.43%, an F1 score of 90.14%, and an AUC value of 95.66%. In the detection of outliers in simplified groundwater levels, a qualitative analysis of the displacement data within the field of view indicates that the outlier detection performance of iForest and OCSVM is better than that of KNN. The proposed method for considering the factors affecting groundwater levels can improve the efficiency and accuracy of detecting outliers in groundwater level data. [ABSTRACT FROM AUTHOR]

The development of the automatic fare collection (AFC) systems provides significant support for predicting passenger flow on urban rail transit. This paper extracts passenger travel patterns using AFC data on urban rail transit in Chengdu, China, over a one-month period. Passengers are divided into two categories based on their travel habits and data mining models, and multinomial logit (MNL) models are separately used to predict their destinations. Furthermore, a two-way search algorithm is developed to search the optimal paths between origin-destination (OD) pairs by considering interchange constraints. Start a path search through the origin point and destination point, respectively, until the shortest path is found. The maximum effectiveness of a path is measured by travel time, interchange time, and the number of interchanges between the OD pairs. Finally, the validity of the proposed passenger flow path prediction method is verified by using the AFC data of Chengdu metropolitan rail transit from April 2018. [ABSTRACT FROM AUTHOR]

As high-speed rail (HSR) and air transportation developed rapidly in the last decade, their coopetition and interactional relationship has reshaped China's urban networks. Based on HSR and flight schedule data from 2009 to 2019, this paper constructs a weighted network to compare China's urban networks and their evolution, and employs machine learning to investigate the potential determinants. The results indicated that both networks tend toward polarization in the overall distribution and that the structure of urban networks under HSR networks is more hierarchical. That of HSR networks gradually forms a corridor structure along the trunk lines, while that of airline networks mainly shows a diamond spatial structure with Beijing, Shanghai, Guangzhou, and Chengdu as the cores. As for the evolution of urban networks, geographical factors, per capita GDP, and the tourism function of cities have more important impacts on that of HSR networks, while the network's topological structure and education resources have a greater impact on that of airline networks. Some socioeconomic attributes, such as urban administrative level, population, and the proportion of tertiary industry, have similar and limited influences on the two networks. • The paper constructed a comparative analysis on the evolution of China's urban networks during the last decade. • Employing machine learning to investigate the potential determinants. • The study examined the distinct attributes of the evolution and structure of high-speed rail and airline networks. • The study contrasts the determinants influencing the evolution of high-speed rail and airline networks at the city level. [ABSTRACT FROM AUTHOR]

Probabilistic seismic hazard analysis is an important scientific basis for building seismic fortification. Unlike most seismic hazard analyses starting from the perspective of magnitude, this paper presents a seismic hazard analysis method based on the monthly maximum peak ground acceleration (PGA) distribution. In this paper, cities of Chongqing, Chengdu, and Kunming in southwest China are taken as examples where historical earthquake events from 1920 to 2020 with surface wave magnitude (Ms) greater than or equal to 3 and epicentral distances within 400 km are selected for analysis. PGA data are back-calculated from Ms data recorded in the earthquake catalog using the attenuation relation of seismic parameters. By considering the monthly maximum PGA values of the three cities as statistical samples, regression analysis is performed to obtain the monthly maximum PGA distribution and the frequency-PGA relation respectively. Based on the Poisson model and the parameters obtained from the regression analysis, then seismic hazard analysis is conducted to derive year exceedance probability vs. PGA curves and recurrence interval vs. PGA curves. Finally, earthquake events with the epicentral distance within 200 km, 300 km, and 400 km are respectively analyzed to discuss the influence of the epicentral distance on results of seismic hazard analysis. [ABSTRACT FROM AUTHOR]

In recent years, the tourism industry has developed rapidly. However, traditional tourism information has the disadvantages of slow response speed and limited information content, which cannot reflect the evolution trend of spatial and temporal patterns of tourism information in time. Here, based on the Baidu Index, we construct an evaluation framework to analyse the spatial and temporal flow of tourism information in the Chengdu–Chongqing urban cluster from 2011 to 2021. Then, we analyse the urban links between different network levels from the evolution pattern. Finally, we use the geodetector model to analyse its driving mechanism. The results show that Chengdu and Chongqing are the most active cities in the study area in terms of tourism information. The unbalanced development of tourism information between Chengdu and Chongqing and other cities in the region gradually deepens during the period 2011–2019 (polarization effect), but the unbalanced development moderates after 2019. On the other hand, cities in the middle of the Chengdu–Chongqing cluster always have weak agglomeration effects of tourism information. Cities with high tourism information outflow rates in the Chengdu–Chongqing city cluster are mainly concentrated around Chengdu. The average outflow rate of Deyang is the highest, at 27.8%. Cities with low tourist information outflow rates are primarily located in the west, central and south. Ya'an is the city with the lowest outflow rate, with an average of −62.2%. Specifically, Chengdu is the dominant and most radiantly influential city. The tourism information of the Chengdu–Chongqing urban cluster shows a radial network with Chengdu and Chongqing as the core. The driving force analysis shows that the push factor of tourist source, such as the number of people buying pension insurance, is the core driving mechanism, while the pull factor of destination, such as the park green area, and resistance factors such as psychological distance, are in the secondary position. In general, this paper uses Internet tourism data to expand the traditional tourism information research of the Chengdu–Chongqing urban cluster, which can better respond to the changes and needs of the tourism market and provide reference for the spatial optimization of tourism destinations. [ABSTRACT FROM AUTHOR]

The development and functional perfection of urban areas have led to increasingly severe fire risks in recent decades. Previous urban fire risk assessment methods relied on subjective judgment, rough data collection, simple linear statistical methods, etc. These drawbacks can lead to low robustness of evaluation and inadequate generalization ability. To resolve these problems, this paper selects the indicator and regression models based on the high-resolution data of the spatial distribution characteristics of Longquanyi distinct in Chengdu, China. and proposes an integrated machine learning algorithm for fire risk assessment. Firstly, the kernel density analysis is used to map the fourteen urban characteristics related to fire risks. The contributions of these indicators (characteristics) to fire risk and its corresponding index are determined by Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and eXtreme Gradient Boosting (XGBoost). Then, the spatial correlation of fire risks is determined through Moran's I, and the spatial distribution pattern of indicator weights is clarified through the raster coefficient space analysis. Finally, with these selected indicators, we test the regression performance with a backpropagation neural network (BPNN) algorithm and a geographically weighted regression (GWR) model. The results indicate that numerical variables are more suitable than dummy variables for estimating micro-scale fire risks. The main factors with a high contribution are all numerical variables, including roads, gas pipelines, GDP, hazardous chemical enterprises, petrol and charging stations, cultural heritage protection units, assembly occupancies, and high-rise buildings. The machine learning algorithm integrating RF and BPNN shows the best performance (R2 = 0.97), followed by the RF-GWR integrated algorithm (R2 = 0.87). Compared with previous methods, this algorithm reduces the subjectivity of the traditional assessment models and shows the ability to automatically obtain the key indicators of urban fire risks. Hence, this new approach provides us with a more robust tool for assessing the future fire safety level in urban areas. [ABSTRACT FROM AUTHOR]