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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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

Logistical risks are now significantly more acute due to epidemics, economic recession and local political turmoil. Cross-border logistics risks are particularly prevalent given the large number of participants and the long transport distances involved. This paper develops a system dynamics model to analyse the key factors of cross-border logistics risk. In previous studies, people usually consider the risk factors as many independent individuals. But actually, there is a causal relationship as well as conduction effects between risk factors. Therefore, this paper analyses the relationship between risk factors through qualitative analysis and establishes a system dynamics model to indicate the direction of risk transmission. The rate of risk transmission was analysed using quantitative methods. Finally, this paper conducts simulations using survey data of cross-border e-commerce enterprises in Chengdu, indicating that the industry currently has more prominent customs risks, process risks, and cooperation risks. For these more serious risks, this paper proposes corresponding prevention and control measures. [ABSTRACT FROM AUTHOR]

The estimation of present passenger flow state plays a vital role in the urban rail transit (URT) operation process and it is the basis of passenger flow control and train dispatching. Most of the existing researches used the Automatic Fare Collection (AFC) System data as the single data source for estimating present passenger flow state. In consideration of the delay in the data uploading process, the obtained estimation results based on the single AFC data source are not accurate enough, which could lead to the improper handling and decision failure and cause high risk and low efficiency for the operation and management of URT. To address problems mentioned above, this paper adopted the automatic differentiation method as the framework for the fusion of multi‐source heterogeneous data (including uploaded AFC data, the mobile phone signaling data provided by mobile phones, and the historical passenger flow data, etc.). Then, this paper proposed the online estimation model and the error layered optimization algorithm to implement the estimation of present passenger flow state. The calculation results indicate that the proposed model and algorithm can obtain reasonable and reliable URT passenger flow state. Finally, the study developed a present passenger flow state estimation system using the proposed model and algorithm. The system has been deployed in Chengdu Metro that serves about 3 million passengers per day from October 2018 to April 2020, with the overall error within 3% as of now. [ABSTRACT FROM AUTHOR]

Predicting and understanding travellers' mode choices is crucial to developing urban transportation systems and formulating traffic demand management strategies. Machine learning (ML) methods have been widely used as promising alternatives to traditional discrete choice models owing to their high prediction accuracy. However, a significant body of ML methods, especially the branch of neural networks, is constrained by overfitting and a lack of model interpretability. This study employs a neural network with feature selection for predicting travel mode choices and Shapley additive explanations (SHAP) analysis for model interpretation. A dataset collected in Chengdu, China was used for experimentation. The results reveal that the neural network achieves commendable prediction performance, with a 12% improvement over the traditional multinomial logit model. Also, feature selection using a combined result from two embedded methods can alleviate the overfitting tendency of the neural network, while establishing a more robust model against redundant or unnecessary variables. Additionally, the SHAP analysis identifies factors such as travel expenditure, age, driving experience, number of cars owned, individual monthly income, and trip purpose as significant features in our dataset. The heterogeneity of mode choice behaviour is significant among demographic groups, including different age, car ownership, and income levels. [ABSTRACT FROM AUTHOR]

The analysis of the spatial connection and structural characteristics evolution of the railway passenger transport network in Chengdu-Chongqing Economic Circle is of great significance to the future network construction and layout optimization. This paper constructs the railway passenger transport network of Chengdu-Chongqing Economic Circle based on the 46 863 617 railway passenger transport data, and uses GIS spatial analysis and social network analysis methods to analyze the space-time evolution of railway passenger transport links, the evolution of structure characteristics and the impact of network structure on economy and population in continuous time series. The results show that the polarization of railway passenger transport links in Chengdu Chongqing Economic Circle is obvious, and the passenger transport flow is mainly concentrated in Chengdu, followed by Mianyang, the main districts of Chongqing and Deyang, and other cities account for less than 20% of the passenger transport flow. The density of railway passenger transport network in Chengdu-Chongqing Economic Circle is increasing year by year, but it is still at a low level. The railway passenger transport network has a low average path length and a high average clustering coefficient, and it is characterized by a small world. The degree centrality and betweenness centrality of Chengdu and the main districts of Chongqing are far more than those of other cities. The accessibility of railway passenger transport in Chengdu-Chongqing Economic Circle is largely due to the intermediary role of Chengdu and the main districts of Chongqing. There is no railway passenger transport connection between most cities. The degree centrality, proximity centrality and intermediate centrality have positive effects on GDP, and pass the 1% significance level test. The degree centrality and intermediate centrality have negative impacts on the population, and pass the significance level tests of 5% and 10% respectively. Cities in the central position are not conducive to the permanent residence of the population. [ABSTRACT FROM AUTHOR]

PM2.5 concentration is an important indicator to measure air quality. Its value is affected by meteorological factors and air pollutants, so it has the characteristics of nonlinearity, irregularity, and uncertainty. To accurately predict PM2.5 concentration, this paper proposes a hybrid prediction system based on the Synchrosqueezing Wavelet Transform (SWT) method, Quantum Particle Swarm Optimization (QPSO) algorithm, and Long Short-Term Memory (LSTM) model. First, the original data are denoised by the SWT method and taken as the input of the prediction model. Then, the main parameters of the LSTM model are optimized by global search based on the QPSO algorithm, which solves the problems of slow convergence and local extremum of traditional parameter training algorithms. Finally, the PM2.5 daily concentration data of Chengdu, Shijiazhuang, Shenyang, and Wuhan are predicted by the proposed SWT-QPSO-LSTM model, and the prediction results are compared with those of single prediction models and hybrid prediction models. The experimental results show that the proposed model achieves higher prediction precision and lower prediction error than other models. [ABSTRACT FROM AUTHOR]

Simple Summary: Accurate and efficient automatic individual recognition of livestock such as goats is one of the key tasks to achieve smart farming. However, this task is challenging due to factors such as the dense distribution of livestock in a barn. To accurately recognize Chengdu ma goats, a high-quality sheep breed with local characteristics, we construct a Chengdu ma goat dataset containing nearly one thousand images with manual annotation information and propose a method for the automatic individual recognition of Chengdu ma goats using a computer. Experiments demonstrate the usability of our method to accurately recognize Chengdu ma goats under extreme conditions such as dense target distribution and drastic differences in scale. It is worth noting that the method is scalable, which enhances the network's representation ability for data by learning the structural information of goats' head area. The location and category information obtained by our automatic individual recognition method is the basis for realizing a series of subsequent intelligent breeding tasks, such as precision feeding based on age and sex. Our method provides a new idea for automatic livestock recognition and can be extended to be applied to additional types of livestock. The Chengdu ma goat is an excellent local breed in China. As one of the breeds listed in the National List of Livestock and Poultry Genetic Resources Protection, the protection of its germplasm resources is particularly important. However, the existing breeding and protection methods for them are relatively simple, due to the weak technical force and lack of intelligent means to assist. Most livestock farmers still conduct small-scale breeding in primitive ways, which is not conducive to the breeding and protection of Chengdu ma goats. In this paper, an automatic individual recognition method for Chengdu ma goats is proposed, which saves labor costs and does not depend on large-scale mechanized facilities. The main contributions of our work are as follows: (1) a new Chengdu ma goat dataset is built, which forms the basis for object detection and classification tasks; (2) an improved detection algorithm for Chengdu ma goats based on TPH-YOLOv5 is proposed, which is able to accurately localize goats in high-density scenes with severe scale variance of targets; (3) a classifier incorporating a self-supervised learning module is implemented to improve the classification performance without increasing the labeled data and inference computation overhead. Experiments show that our method is able to accurately recognize Chengdu ma goats in the actual indoor barn breeding environment, which lays the foundation for precision feeding based on sex and age. [ABSTRACT FROM AUTHOR]

The increasing need for capacity has led the railway industry to explore new train control systems based on a concept called virtual coupling. Inspired by the platooning of autonomous vehicles, the safe operation of virtual coupling is guaranteed by a relative brake distance-based train separation method. This paper proposes a novel long short-term memory (LSTM)-based model predictive control (MPC) method for train operations. An MPC-based control design for virtual coupled train operations is presented. The LSTM is introduced to model the dynamics of the preceding train to approximate the actual train operations. With the train dynamics models, the operation trajectories of the preceding train are predicted based on planned control inputs. A study of a metro line in Chengdu was chosen to analyze the proposed control approach. The simulation results of different scenarios show that compared with the conventional MPC methods, the proposed LSTM-based MPC can reduce the speed differences and position differences of tracking trains by up to 35 % and 25 % , respectively. [ABSTRACT FROM AUTHOR]

With the advantages of fast construction speed and small disturbance to stratum, shield tunneling is becoming the preferred construction scheme for metro construction. However, if the disturbance of stratum cannot be clearly understood, it will affect the safety of adjacent underground structures and aboveground buildings. Based on the construction of the interval tunnel between Shizishan station and Chuanshi station of Chengdu metro line 7 project, this paper proposed a simplified calculation method to analyze the impact of shield excavation on adjacent pile foundation. A two-stage method of modified Peck formula and Winkler elastic foundation model was used to investigate the additional displacement and internal force of pile. A three-dimensional numerical model validated with the field test data was performed to compare with the theoretical results. The impact factors and their impact degree were discussed. The results show that the additional displacement and internal force are at a reasonable and acceptable level. The permanent pile foundation should adopt the small diameter pile as far as possible. When the small diameter pile foundation cannot meet the stress requirements, the design scheme of the pile group should be adopted. The area around shield tunnel is divided into strong impact area of pile foundation (S ≤ 0.75 D), slight impact area (0.75 D ≤ S ≤ 1.5 D), and no impact area (S > 1.5 D). The pile researched in this paper (S = 0.83 D) is in the slight impact area. [ABSTRACT FROM AUTHOR]

Using the CRITIC method, comprehensive index evaluation method, VAR model, coupling coordination model and other methods, this paper evaluates the comprehensive development of the population economy and eco-geological environment composite system in the twin-city economic circle of Chengdu–Chongqing region from 2000 to 2020, verifies the dynamic coupling relationship between subsystems and measures the coupling coordination degree of complex system. Meanwhile, the differences in the development process of each subsystem are discussed and the spatial–temporal evolution characteristics and laws of coupling coordination of the composite system during the research period are analyzed. The results reveal the following: (1) There is a long-term coordinated relationship between population, economy and eco-geological environment in Chengdu–Chongqing region, which have the conditions for emergence and generation. (2) The subsystems of population, economy and eco-geological environment in Chengdu–Chongqing region show an overall upward trend, among which the Sichuan part obviously outperforms the Chongqing part. Besides, the growth rate of the economy subsystem is significantly higher than that of the population, eco-geological environment subsystem. (3) The coupling coordinated development of the composite system has shown a benign upward development trend, gradually changing from "composite spiral structure" to "two cores outstanding, peripheral collapse, the west superior to the east", while the main coordination state has developed from "Basic synergy—Economic lag" in 2000 to "Basic synergy—Population lag" in 2022. In addition, Cheng-De-Mian and Yuzhong districts with the better level of economic development have shown "Advanced synergy—Ecological lag". [ABSTRACT FROM AUTHOR]

Rail-transit hub classification in TOD refers to the categorization of transit stations based on their level of connectivity and ridership and the potential for development around them as part of a Transit-Oriented Development (TOD) strategy. TOD, as an essential concept in developing smart cities and public transportation accessibility, has attracted the focus of many policymakers. To this end, many research projects have been dedicated to classifying the rail-transit stations, although the necessity of integrated models for rail-transit hubs could have been mentioned in previous papers. Therefore, this parametric case study is directed to apply the Node–Place–Ridership–Time (NPRT) model to provide a logical classification model for Chengdu rail-transit hubs at the junctions of high-speed railway and subway stations. Multiple Linear Regression (MLR) provided a series of equations, including the effective parameters of the NPRT model. These equations were then verified by the Artificial Neural Network (ANN) to provide the effect of each node and place values on the integrated ridership of rail-transit hubs in different time periods. The results proved the consistent contribution of the integrated ANN-NPRT-HUB algorithm to the TOD concept for smart cities. [ABSTRACT FROM AUTHOR]

Economic development and fast growing urbanization in China have caused severe air pollution, with frequent pollution episodes endangering the health of inhabitants and disturbing social activities, and as an expanding metropolis, Chengdu has suffered ever since. The concentration variations of main air pollutants, such as PM10, PM2.5 and NO2, often show periodicity because of meteorological impact and anthropic activities, and display orientation discrepancies due to influences of wind speed (WS), frequency and pollutant sources. These features have complicated the mechanisms of pollution episodes and deepened the difficulty in pollution control evaluation. The WS has significant influences on the periodicity and orientation variations in pollutant concentrations, and quantifying the influence of which is of high significance and provides sustainable foundations for pollution alleviation strategies. Different time-scale cycles (i.e., Diurnal, weekly, seasonal and annual), along with the WS, wind frequency, wind and spatial orientations in urban areas, were analyzed in this paper. Results show that the periodicity of diurnal, seasonal and annual cycles is remarkable, and weekly cycle is obvious by adding the influence of the WS in 16 orientations. The WS has direct impacts on pollutants varying in the range of 1.5–2.5 m/s, and has a remarkable diffusion effect on pollutants once above 2.5 m/s. Over heavy pollution hours in diurnal, weekly, annual cycles and transitional seasons, the WS had more significant influences on pollutants, and whereas the wind frequency is not the main impact factor for orientation variations. For Chengdu, the northeast orientation is suitable to construct a wind panel with a remarkable diffusion effect on pollutants, while air pollutions in the northwest and southwest orientations were severe with the WS below 1.5 m/s, and pollution diffusion in the north-northwest orientation was the worst. This work can provide guidance and reference for urban planning optimization and air environment protection in cities with air quality control considerations impacted by city wind. [ABSTRACT FROM AUTHOR]

Regarding the whole excavation process of the support system of the Southwest Jiaotong University Station of Chengdu Metro Line 6 (the deep foundation pit bored pile & steel support and support system) as the engineering background, this paper studies the deformation rule of the deep foundation pit bored pile & steel support of the sandy pebble foundation. The deformation rule of this support system, the settlement rule of the ground surface outside the pit, and the rule of the uplift of the loose at the bottom of the pit are studied. A key analysis of the positive corner of the foundation pit is conducted, and the rationality of the optimization of the support scheme is evaluated. This paper provides effective guidance for the subsequent deep foundation pit construction and provides a reference for deep foundation pit construction. [ABSTRACT FROM AUTHOR]

Attaining optimal eco-efficiency is of paramount importance in promoting the sustainable and harmonious development of the economy and environment within urban agglomerations. Firstly, this paper utilizes the Super-SBM model with undesirable output to measure the eco-efficiency (EE) of 64 cities in the Beijing–Tianjin–Hebei metropolitan region (BTHMR), the Yangtze River Delta (YRD), the Pearl River Delta (PRD), and the Chengdu–Chongqing Economic Zone (CCEZ) from 2006 to 2019. Secondly, this study puts forth a novel and comprehensive index system aimed at evaluating the urbanization efficiency and sheds light on the spatiotemporal changes in EE and urbanization efficiency. Finally, the STIRPAT model is used to examine the influencing factors of EE and to investigate the correlation between EE and urbanization efficiency. The study found that the overall EE of the four typical urban agglomerations is high, but the trend varies with a decrease of about 12.9% from 2006 to 2019. The mean EE is in the order of CCEZ > PRD > BTHMR > YRD, with mean values of 0.941, 0.909, 0.842, and 0.732, respectively. The level of science and technology and the urbanization efficiency have a significant positive impact on EE, while population, industrial structure, FDI, and greening level have an inhibitory effect on urban eco-efficiency. Based on the results, policy suggestions such as paying attention to regional heterogeneity and giving full play to the government's macro-regulatory role in shaping the economic and industrial structure are proposed to serve as a guide for the coordinated development of urban agglomerations under the Dual Carbon Target. [ABSTRACT FROM AUTHOR]

In this paper, a network coupling mechanism is studied to couple the air sector network, airport network, and air route network into a multilayer air network model. Then, the altitude layers are divided into three: high, medium, and low, and the arrival and departure flight procedures are also considered. By defining the association between the altitude layers and the waypoints, a multilayer air network model considering the altitude layer is constructed. Then, the line graph theory is used to redefine the nodes and edges, and the network is reconstructed to obtain a new single-layer one which is easy to calculate. Finally, a case study is carried out in Chengdu control area. The results show that the proposed model is closer to the reality, and the invulnerability performance is more referential. Besides, it also reflects the impact of altitude layers on the efficiency of airspace. The results are helpful for air traffic controllers to manage airspace better and have significance for promoting air traffic safety and stability. [ABSTRACT FROM AUTHOR]

The Local Climate Zone (LCZ) classification scheme is a vital method of building a category dataset for high-resolution urban land. For the development of urban meteorology, air pollution and related disciplines, the high-resolution classification data of urban buildings are very important. This study aims to create LCZ datasets with detailed architectural characteristics for major cities and urban agglomerations in China, and obtain more accurate results. We constructed 120 m resolution land use datasets for 63 cities (mainly provincial capitals, municipalities directly under the Central Government, important prefecture-level cities and special administrative regions) and 4 urban agglomerations in China based on the local climate zone (LCZ) classification scheme using the World Urban Database and Access Portal Tools method (WUDAPT). Nearly 100,000 samples were used, of which 76,000 training samples were used to provide spectral signatures and 23,000 validation samples were used to ensure accuracy assessments. Compared with similar studies, the LCZ datasets in this paper were generally of good quality, with an overall accuracy of 71–93% (mean 82%), an accuracy for built classifications of 57–83% (mean 72%), and an accuracy for natural classifications of 70–99% (mean 90%). In addition, 35% of 63 Chinese cities have construction areas of more than 5%, and the plateaus northwest of Chengdu and Chongqing are covered with snow all year round. Therefore, based on the original LCZ classification system, the construction area (LZC H) and the snow cover (LCZ I) were newly added as the basic classifications of urban LCZ classification in China. Detailed architectural features of cities and urban agglomerations in China are provided by the LCZ datasets in this study. It can be applied to fine numerical models of the meteorological and atmospheric environment and improve the prediction accuracy. [ABSTRACT FROM AUTHOR]

Taking the Chengdu Municipal Solid Waste Incineration (MSWI) Plant in China as object, totally 100 ambient soil samples from 20 sampling points were taken for experiment. This paper analysed the characteristics of Cd in soil. By analyses, the distribution and health risk assessment were taken into consideration. The results demonstrated that the Cd value in soil in the surroundings of the MSWI plant was beyond the acceptable level according to standards (GB15618-2018). The risk assessment in this experiment showed that the carcinogenic and noncarcinogenic risks via inhalation exposure were in the accessible levels. [ABSTRACT FROM AUTHOR]

The cross-impact of environmental pollution among cities has been reported in more research works recently. To implement the coordinated control of environmental pollution, it is necessary to explore the structural characteristics and influencing factors of the PM2.5 spatial correlation network from the perspective of the metropolitan area. This paper utilized the gravity model to construct the PM2.5 spatial correlation network of ten metropolitan areas in China from 2019 to 2020. After analyzing the overall characteristics and node characteristics of each spatial correlation network based on the social network analysis (SNA) method, the quadratic assignment procedure (QAP) regression analysis method was used to explore the influence mechanism of each driving factor. Patent granted differences, as a new indicator, were also considered during the above. The results showed that: (1) In the overall network characteristics, the network density of Chengdu and the other three metropolitan areas displayed a downward trend in two years, and the network density of Wuhan and Chengdu was the lowest. The network density and network grade of Hangzhou and the other four metropolitan areas were high and stable, and the network structure of each metropolitan area was unstable. (2) From the perspective of the node characteristics, the PM2.5 spatial correlation network all performed trends of centralization and marginalization. Beijing-Tianjin-Hebei and South Central Liaoning were "multi-core" metropolitan areas, and the other eight were "single-core" metropolitan areas. (3) The analysis results of QAP regression illustrated that the top three influencing factors of the six metropolitan areas were geographical locational relationship, the secondary industrial proportion differences, respectively, and patent granted differences, and the other metropolitan areas had no dominant influencing factors. [ABSTRACT FROM AUTHOR]