Major Cities In China Research Articles

Boba Shop, Coffee Shop, and Urban Vitality and Development—A Spatial Association and Temporal Analysis of Major Cities in China from the Standpoint of Nighttime Light

Nighttime light (NTL) is a critical indicator of urban vitality and development. Using NTL as a representation of urban vitality and development, the study explores how different fresh-made beverage shops, namely boba and coffee shops, proxy various facets of urban vitality and development in four megacities in China. Existing studies mostly discuss urban vitality as a broad concept and seldom investigate the diverse urban vitality and development represented by different indicators. This study selects Beijing, Shanghai, Guangzhou, and Shenzhen as case study regions and explores (1) their urban vitality pattern represented by NTL. (2) the heterogeneous spatial distribution of boba and coffee shops; (3) how boba and coffee shops represent urban vitality differently; and (4) how boba and coffee shops portray the economy and population growth aspect of urban development differently. We acquired NTL data from remote sensing images to measure urban vitality and development. Cross-sectionally, the majority of urban vitality and development represented by NTL concentrates in urban centers. The distribution of coffee shops assimilates the spatial pattern of urban vitality represented by NTL while boba shops have a greater spatial extent in metropolitan fringes. Longitudinally, from 2012 to 2020, the global and local bivariate Moran’s I analysis between NTL and beverage shops shows that the coffee shops capture urban vitality and development better than boba shops in Beijing, while the pattern is reversed in Guangzhou and Shenzhen. Examining the evolving spatial dynamics between beverage shops’ growth and urban development using bivariate Moran’s I and Getis–Ord Gi/Mann–Kendall emerging hot spot analysis, we found that the locations with the most intense economic growth have seen the most spatial expansion of coffee shops. In contrast, those with the fastest population growth have seen the greatest spatial development of boba businesses. These results indicate that coffee shops represent the economic aspect of urban vitality while boba shops emphasize the population growth aspects. By examining the dynamic spatio–temporal relationship between small beverage shops and urban vitality and development represented by NTL data, this study broadens the usage of remote sensing data in urban studies and expands on previous research and offers insights for urban planners and geographers to reference when choosing indicators of urban vitality and growth.

UGS-1m: fine-grained urban green space mapping of 31 major cities in China based on the deep learning framework

Abstract. Urban green space (UGS) is an important component in the urban ecosystem and has great significance to the urban ecological environment. Although the development of remote sensing platforms and deep learning technologies have provided opportunities for UGS mapping from high-resolution images (HRIs), challenges still exist in its large-scale and fine-grained application due to insufficient annotated datasets and specially designed methods for UGS. Moreover, the domain shift between images from different regions is also a problem that must be solved. To address these issues, a general deep learning (DL) framework is proposed for UGS mapping in the large scale, and fine-grained UGS maps of 31 major cities in mainland China are generated (UGS-1m). The DL framework consists of a generator and a discriminator. The generator is a fully convolutional network designed for UGS extraction (UGSNet), which integrates attention mechanisms to improve the discrimination to UGS, and employs a point-rending strategy for edge recovery. The discriminator is a fully connected network aiming to deal with the domain shift between images. To support the model training, an urban green space dataset (UGSet) with a total number of 4544 samples of 512×512 in size is provided. The main steps to obtain UGS-1m can be summarized as follows: (a) first, the UGSNet will be pre-trained on the UGSet in order to obtain a good starting training point for the generator. (b) After pre-training on the UGSet, the discriminator is responsible for adapting the pre-trained UGSNet to different cities through adversarial training. (c) Finally, the UGS results of 31 major cities in China (UGS-1m) are obtained using 2179 Google Earth images with a data frame of 7′30′′ in longitude and 5′00′′ in latitude and a spatial resolution of nearly 1.1 m. An evaluation of the performance of the proposed framework by samples from five different cities shows the validity of the UGS-1m products, with an average overall accuracy (OA) of 87.56 % and an F1 score of 74.86 %. Comparative experiments on UGSet with the existing state-of-the-art (SOTA) DL models proves the effectiveness of UGSNet as the generator, with the highest F1 score of 77.30 %. Furthermore, an ablation study on the discriminator fully reveals the necessity and effectiveness of introducing the discriminator into adversarial learning for domain adaptation. Finally, a comparison with existing products further shows the feasibility of the UGS-1m and the great potential of the proposed DL framework. The UGS-1m can be downloaded from https://doi.org/10.57760/sciencedb.07049 (Shi et al., 2023).

The impact of the implementation of ‘double reduction’ policy on tutors in shadow education: legislation goals and early experiences

ABSTRACT Private supplementary tutoring, also known as shadow education, has had a profound influence on Chinese education during the past twenty years. The rapid expansion of shadow education has attracted the attention and increased regulation of the Chinese government. In July 2021, the Chinese government adopted one of the most restrictive legislative constraints on shadow education: the so-called ‘double reduction’ policy. This policy aims to reduce the dual burden for students of on-campus classes and off-campus tutoring. Through analysis of China’s central and provincial double reduction policies, this study examines the policy goals, accompanying instruments, and delineated implementation strategies. Interviews with tutors from shadow education institutions in five major cities in China uncover the strategies shadow education tutors have applied to mitigate the policy constraints and offer criticism of the Chinese political system and deficiencies in the neoliberal education system.

Psychological Impact of COVID-19 Lockdown and Its Evolution: A Case Study Based on Internet Searching Data during the Lockdown of Wuhan 2020 and Shanghai 2022.

It has been three years since the initial outbreak of COVID-19 in Wuhan, China, which incurred huge damage both physically and psychologically on human's normal life. As a prevention measure, the lockdown was first adopted by Wuhan, then by a long list of Chinese cities and many other major cities around the world. Lockdown is the most restrictive social distancing strategy, turning out effective in mitigating the spreading of COVID-19 on the community level, which, however, cuts off all social interactions and isolates healthy people from each other. The isolated nature of the lockdown could induce severe mental health issues, forming one major source of depression and domestic violence. Given the potential side effect, a comprehensive investigation based on reliable data sources is needed to evaluate the real psychological impact of COVID-19 lockdown and its evolution over time, particularly in the time when the Omicron variant, known for its low death risk, dominates the pandemic. Based on the Baidu Searching Index data collected for Wuhan and Shanghai, two major cities in China that suffered from long-lasting (over two months) lockdowns in 2020 and 2022, respectively, it is found that the major psychological issue during the lockdown period is not induced by the spreading of COVID-19, but by the execution of lockdown. With the deepening of knowledge about COVID-19 and the decrease in the death risk, the psychological impact of lockdown keeps increasing, while the impact of virus spreading becomes less important and even irrelevant to depression and domestic violence issues. The findings reveal that from the psychological perspective, the negative effect of lockdown already overweighs the positive one, which is especially true for the Omicron variant provided its almost ignorable death risk. Therefore, it is necessary to re-evaluate the yield and cost of lockdown for those countries where the COVID-19 pandemic has not yet come to an end.

ResInformer: Residual Transformer-Based Artificial Time-Series Forecasting Model for PM2.5 Concentration in Three Major Chinese Cities

Many Chinese cities have severe air pollution due to the rapid development of the Chinese economy, urbanization, and industrialization. Particulate matter (PM2.5) is a significant component of air pollutants. It is related to cardiopulmonary and other systemic diseases because of its ability to penetrate the human respiratory system. Forecasting air PM2.5 is a critical task that helps governments and local authorities to make necessary plans and actions. Thus, in the current study, we develop a new deep learning approach to forecast the concentration of PM2.5 in three major cities in China, Beijing, Shijiazhuang, and Wuhan. The developed model is based on the Informer architecture, where the attention distillation block is improved with a residual block-inspired structure from efficient networks, and we named the model ResInformer. We use air quality index datasets that cover 98 months collected from 1 January 2014 to 17 February 2022 to train and test the model. We also test the proposed model for 20 months. The evaluation outcomes show that the ResInformer and ResInformerStack perform better than the original model and yield better forecasting results. This study’s methodology is easily adapted for similar efforts of fast computational modeling.

Efficiency Evaluation of New Energy Light-duty Truck Policy in Major Cities of China

The electrification level of commercial vehicles in China has achieved remarkable development with the support of fiscal and tax policies. In this paper, 15 major cities are selected as samples, and the combined effect of national and local fiscal and tax policies is carefully assessed using the DEA model. The results show that: (1) the comprehensive efficiency of the sample cities is high, indicating that the current policy portfolio has achieved great efficiency in general. (2) Shenzhen, Chengdu and Shanghai are taking the lead in the promotion and penetration of new energy trucks, but the 3 cities fail to achieve the best overall efficiency, with lower scale efficiency and diminished returns to scale. (3) Most of the cities suffer input redundancy, and should further optimize the policy structure and synergy to ensure the efficient implementation of various policies.

Modeling multi-regional temporal correlation with gated recurrent unit and multiple linear regression for urban traffic flow prediction

Urban traffic flow prediction has received much attention in the past few years, especially after the availability of huge traffic data. In addition, the efficacy of some existing traffic flow techniques heavily depends on some influential external factors (like weather, geographical information, point-of-interests (POIs), or road information). However, exploitable data containing such auxiliary information are extremely limited. One primary reason is that some rising cities lack the resources necessary for collecting such data. Therefore, it is difficult to pick an effective method that can accurately predict the traffic state when the data is limited. Due to that, this paper proposes a framework for achieving accurate predictions based on four data characteristics and the temporal correlation of neighbors without using additional influential factors. Specifically, we first propose a novel Multi-Region Correlation (MRC) method to make regions cooperate and share their traffic flow and patterns. Then, a deep recurrent network is used to capture the neighbors’ temporal correlation by integrating their traffic history that flow among regions and their neighbors. When analyzing the traffic data history, we model four data characteristics (seasonality, trend, residual, and cyclic). And lastly, we employ a Multiple Linear Regression Unit (MLRU) to predict the future traffic directly from the neighbors’ traffic. We conducted extensive experiments on two real-world datasets collected from two major cities in China, Chengdu and Xi’an. The results demonstrate that the proposed model can achieve superior performance compared to many existing models in terms of accuracy and efficiency.

Comparison of PM2.5 prediction performance of the three deep learning models: A case study of Seoul, Daejeon, and Busan

In this study, the PM2.5, prediction performances of long short-term memory (LSTM), gated recurrent unit (GRU), and bidirectional LSTM (Bi-LSTM) were compared using data from Seoul, Daejeon, and Busan, which are representative cities in Korea. The data analysis period was from 9:00 on May 16, 2014, to 23:00 on December 31, 2021, based on data at 1 h intervals. The causal factors affecting the change in PM2.5 of three cities in Korea, and five major cities in China were determined. The analysis revealed that the three models showed similarly high performances in short-term prediction within 24 h (R2 ≥ 0.9). The Bi-LSTM model using both past and future time information showed high prediction accuracy for long-term prediction (R2 ≥ 0.6). Using the PM2.5 data of the five major Chinese cities, it was confirmed that the accuracy of the PM2.5 prediction model for Seoul, Daejeon, and Busan improved. The deep learning model showed a high accuracy even when the Fine Dust Act measures were implemented. This study can facilitate governments to prepare measures against air pollution with a high regional prediction performance by identifying the causal factors affecting PM2.5, specific to the city, and designing different models for each forecasting period.

The Impacts of Vegetation on Summer Extreme Temperature of 4 Major Cities in China Based on Scatter Analysis and Linear Trend Analysis

In the summer of 2022, the extreme high temperature phenomenon is particularly obvious in China, especially in Chongqing, Zhengzhou, Guangzhou, and Shanghai. Some studies have pointed out that there is a strong correlation between vegetation activity and regional climate change, and some vegetation types can mitigate the rise of extreme temperature. However, few papers can put forward inductive suggestions on what types of vegetation can effectively alleviate the extreme temperature in China. Therefore, in this paper the extreme temperature data of CMIP6 (tasmax) is used to explore the correlation between the vegetation cover rate and the extreme temperature of the four major cities in China through the scatter analysis and linear trend analysis. It is found that the vegetation cover rate has a significant correlation (p<0.05) with the extreme temperature: The high vegetation cover rate has a negative correlation with the extreme temperature (p=0.01), and the low vegetation cover rate has a positive correlation with the extreme temperature (p=5.86 x 10-9). According to the geographical location, 4 cities are divided into two types: inland cities and coastal cities. The results show that: (1) planting high vegetation may be more conducive to the mitigation of extreme temperature; (2) the increase of low vegetation coverage may be more conducive to the reduction of temperature rise rate; (3) inland cities may need to plant more high vegetation than coastal cities to mitigate extreme temperature.

The synergy between CO2 emission reduction and air pollution control in urban China under scale-oriented strategy: A perspective of efficiency

At present, energy shortages and air pollution are of increasing concern worldwide. During the "14th Five-Year Plan" period, China will more firmly implement the green development strategy. In order to explore the synergistic emission reduction effects of urban greenhouse gases and air pollutants under different strategies, this paper takes 30 major cities in China from 2011 to 2019 as the research object, and analyses the input-output performance of 30 cities under the scale-oriented strategy. The empirical results show that most large cities in China are still in a state of low efficiency and insufficient growth momentum.

Influence of Varied Ambient Population Distribution on Spatial Pattern of Theft from the Person: The Perspective from Activity Space

The ambient population has been regarded as an important indicator for analyzing or predicting thefts. However, the literature has taken it as a homogenous group and seldom explored the varied impacts of different kinds of ambient populations on thefts. To fill this gap, supported by mobile phone trajectory data, this research investigated the relationship between ambient populations of different social groups and theft in a major city in China. With the control variables of motivated offenders and guardianship, spatial-lag negative binominal models were built to explore the effects of the ambient populations of different social groups on the distribution of theft. The results found that the influences of ambient populations of different social groups on the spatial distribution of theft are different. Accounting for the difference in the “risk–benefit” characteristics among different activity groups to the offenders, individuals from the migrant population are the most likely to be potential victims, followed by suburban and middle-income groups, while college, affluent, and affordable housing populations are the least likely. The local elderly population had no significant impact. This research has further enriched the studies of time geography and deepened routine activity theory. It suggests that the focus of crime prevention and control strategies developed by police departments should shift from the residential space to the activity space.

Local Housing Market Sentiments and Returns: Evidence from China.

This paper examines the impacts of local housing sentiments on the housing price dynamics of China. With a massive second-hand transaction dataset, we construct monthly local housing sentiment indices for 18 major cities in China from January 2016 to October 2020. We create three sentiment proxies representing the local housing market liquidity and speculative behaviors from the transaction dataset and then use partial least squares (PLS) to extract a recursive look-ahead-bias-free local housing sentiment index for each city considered. The local housing sentiments are shown to have robust predictive powers for future housing returns with a salient short-run underreaction and long-run overreaction pattern. Further analysis shows that local housing sentiment impacts are asymmetric, and housing returns in cities with relatively inelastic housing supply are more sensitive to local housing sentiments. We also document a significant feedback effect between housing returns and market sentiments, indicating the existence of a pricing-sentiment spiral which could potentially enhance the ongoing market fever of Chinese housing markets. The main estimation results are robust to alternative sentiment extraction methods and alternative sentiment proxies, and consistent for the sample period before COVID-19.

Urban Land-Cover Changes in Major Cities in China from 1990 to 2015.

The accelerated urbanization process in China has led to land-cover changes, triggering a series of environmental issues as one of the major drivers of global change. We studied the land-cover changes in the built-up areas of 50 major cities in China from 1990 to 2015 with Landsat data combined with spectral unmixing methods and decision tree classification. The overall accuracy of urban land-cover type products with 30 m resolution was obtained as 84%, which includes impervious surfaces, bare soil, vegetation, and water bodies. Based on these land-cover type products, the results show that the urbanization of major cities in China manifests itself as a steep expansion of impervious surfaces (+32.91%) and vegetation (+36.93%), while the proportion of bare soil (-68.64%) and water bodies (-1.20%) decreases. The increase in vegetation indicates an increasing emphasis on greening during urbanization, which is especially vital for the sustainability of urban ecosystems. Increasing economic standards and population sizes are significantly correlated with impervious surface expansion and may be the main drivers of urbanization. Nationwide, there is a decreasing trend of shape complexity among different large cities, which indicates that landscape shapes will gradually become regular when cities grow to a certain level. Greenspace areas in the cities increased significantly during 1990-2015 and became more fragmented and tended to disperse across cities. These changes reflect the government's efforts to enhance urban ecosystem functions to serve the rapidly increasing urban population in China over the past three decades.

Du-Bus: A Realtime Bus Waiting Time Estimation System Based On Multi-Source Data

Realtime bus waiting time information is of great importance to the intelligent public transportation system and is beneficial for improving user satisfaction by online map services. While there are limited realtime bus waiting time services in a city, because of the expensive cost of sensor deployment and sophisticated traffic conditions. To address the above problem, we propose Du-Bus, a multi-source data fusion based system, which estimates the realtime bus waiting time based on approximating the realtime locations of buses without GPS sensors, by a variety of urban datasets, including historical bus trip data reported by a limited number of GPS equipped buses, transportation network data, traffic condition data, user mobility data, and temporal data. Du-Bus approximates the realtime locations of buses without GPS sensors by jointly modeling the bus timetable and the bus realtime travel time, which can be estimated by a variety of data sources. Specifically, we first propose a BiLSTM based end-to-end model for each bus route to estimate the bus departure interval and generate the corresponding departure timetable. Then, we estimate the travel time for each individual bus via a deep neural network component by incorporating the traffic conditions, geolocation, and map query information. Finally, we estimate the bus waiting time for arbitrary stations in the city by jointly modeling the estimated bus departure timetable and travel time. We evaluate our system on two real-world datasets, and the results verify the effectiveness of Du-Bus compared with historical average based and headway based methods. Since early 2019, Du-Bus has been deployed on Baidu Maps, one of the world’s largest map services, servicing over 20 major cities in China.

Spatial Coupling Coordination Evaluation of Mixed Land Use and Urban Vitality in Major Cities in China

Based on the data from 35 major cities in China in 2020, this paper applies the Simpson's diversity index, the entropy value method, and the coupling coordination degree model to comprehensively measure the coupling coordination level of mixed land use and urban vitality in major cities in China and further analyze their spatial distribution characteristics. In addition, this paper analyzes the factors affecting the spatial variation of the coupling coordination level with the help of the geographic probe model. The study finds that: (1) The overall level of coupling coordination between mixed land use and urban vitality is high in 35 major cities in China. There is no disorder between mixed land use and urban vitality. (2) In terms of the spatial distribution of the coupling coordination between mixed land use and urban vitality in 35 cities in China, five cities, namely Beijing, Shenzhen, Shanghai, Guangzhou, and Chengdu, have the highest level of coupling coordination between mixed land use and urban vitality, reaching "good coordination" with a discrete spatial distribution. Central cities such as Hangzhou and Nanjing have the second highest level of coupling coordination and are at the "intermediate coordinate" with a "strip-like distribution" in space. Twenty cities in the north and south have the lowest coupling coordination levels and are in the "primary coordination." Among these twenty cities, seven cities in the south have a higher level of coupling coordination than thirteen cities in the north, with a spatial distribution of a "C" shape. The northern cities have the lowest level of coupling coordination, with a "W"-shaped distribution in space. (3) Population size plays an essential role in guiding the level of coupling coordination between mixed land use and urban vitality in major cities in China, followed by government regulation and economic level. At the same time, transportation conditions and industrial structure have the weakest influence on the level of coupling coordination between mixed land use and urban vitality in major cities in China.

A full-level fused cross-task transfer learning method for building change detection using noise-robust pretrained networks on crowdsourced labels

Accurate building change detection is crucial for understanding urban development. Although fully supervised deep learning-based methods for building change detection have made progress, they tend to fuse temporal information only at a single level (e.g., input, feature, or decision levels) to mitigate the data distribution differences between time-series images, which is highly prone to introduce a large number of pseudo changes. Moreover, these methods rely on a large number of high-quality pixel-level change labels with high acquisition costs. In contrast, available crowdsourced building data are abundant but are less considered for change detection. For example, OpenStreetMap (OSM), Google Map, and Gaode Map provide lots of available building labels, yet they usually contain noise such as false alarms, omissions, and mismatches, limiting their wide application. In addition, when the building extraction task is transferred to the building change detection task, the temporal and regional differences between different images may lead to undesired pseudo changes. Given these issues, we propose a full-level fused cross-task transfer learning method for building change detection using only crowdsourced building labels and high-resolution satellite imagery. The method consists of three steps: 1) noise-robust building extraction network pretraining; 2) uncertainty-aware pseudo label generation; and 3) full-level fused building change detection. We created building extraction and building change detection datasets. The former (building extraction dataset) contains 30 scenes of ZY-3 images covering 27 major cities in China and crowdsourced building labels from Gaode Map for training, while the latter (building change dataset) contains bi-temporal ZY-3 images in Shanghai and Beijing for testing. The results show that the proposed method can identify changed buildings more effectively and better balance false alarms and omissions, compared to the existing state-of-the-art methods. Further analysis indicates that the inclusion of samples from multiple cities with various spatial heterogeneities is helpful to improve the performance. The experiments show that it is promising to apply the proposed method to situations where true labels are completely lacking or limited, thus alleviating the issue of high label acquisition cost. The source code will be available at https://github.com/lauraset/FFCTL.

Real-world prescription patterns for reproductive assistance patients in China: A trend analysis from 2016 to 2020

Background: Pharmacotherapy is one of the primary treatments for patients with Assisted reproductive technology (ART). Despite the publication of various research on ART treatment, there is no clear conclusion regarding the choice of drug treatment in China. Our research intends to examine the trend of widely prescribed medications for ART patients in China. For instance, the study examines the logic of drug indications, usage, and dose in patient prescriptions. Methods: We did a cross-sectional study of the data from the hospital prescription analysis cooperation project supervised by the China Medical Association. The information is extracted from the prescriptions of reproductive assistance outpatients from January 2016 to December 2020. We used the U.S. Food and Drug Administration (FDA) classification to quantify the frequency of drug use and the categories of drugs. We manually extract the information of patients who require ART treatment, divide the patients into various age groups and geographies, followed by study the indications, utilization, and rationale of the most important therapeutic medications. Results: Among the 225225 patients included in this study, Guangzhou (47.83%), Shanghai (19.84%), and Zhengzhou (9.36%) were the top three cities. In the past 5years, the average age was 32.99, and 60.38% of women were between the age of 25 and 34. The main therapeutic medicines taken by each patient, primarily hormone therapies, were tallied. Eleven types of primary therapeutic medicines were employed. Different progesterone preparations (47472, 21.08%), chorionic gonadotrophin gondotrophin for injection (38932, 17.29%), dydrogesterone tables (33591, 14.91%), and triptorelin for injection (26959, 11.97%) rounded out the top five. According to the data on outpatient medications in major cities in China, the variety and proportion of injections are the highest, including the most frequent types of ovulation induction and urotropia, as well as triptorelin and progesterone. Even though the total dosage of urotropin was the highest in 5years, it showed a declining trend. The dosages of progesterone and didroxyprogesterone increased, with progesterone showing the most rapid increase. The top five most expensive prescription medications are triptorelin, urotropin, progesterone, didroxyprogesterone, and leuprorelin, in that order. Goserelin, leuprorelin, triptorelin, growth hormone, and didroxyprogesterone are among the top five most expensive medications per capita. Conclusion: The average age of patients has not increased considerably over the past 5years. However, the opportunity cost of childbirth for women has increased, which has significantly enhanced their willingness for childbearing intentions. The medication selection is reasonable overall. In this study, the recommended dosages of first-line medicines (urotropin and chorionic gonadotropin) are likewise high. In contrast, the dosage of oral first-line treatment for ovarian stimulation in unexplained infertility is modest, and the dosage of progesterone is steadily increasing. In addition, the price of certain medicines is high, which will increase the patients' financial burden. Future research will focus on enhancing the degree of rational drug use among outpatients and realizing the economical, safe, and effective use of pharmaceuticals to lessen the economic burden of patients.

An improved model assessing variation characteristics of pore structure of sandy soil thawing from extremely low temperature using NMR technique

During the construction of subway connecting passages in major cities in China, in last decades the artificial freezing method has gradually been becoming one of the most safe and effective methods in excavating saturated sandy soils. This work aims at exploring the changes of pore size and relaxation time induced by temperature change using NMR technology. Sandy soil samples were retrieved from a subway construction site in Beijing. In the laboratory experiments, these samples were loaded by different extremely low temperatures and natural convection thawing. The T2 (i.e., relaxation time) distribution curves and real-time temperature throughout the thawing process were recorded. It was observed that the variation of T2 distribution curves and pore structure changes with temperature may be divided into four stages, i.e., Stage I: spherical and cylindrical pores with radius r = 0.3 nm are formed at temperature lower than −28.0 °C; Stage II: the water adsorbed in pores melts and pore radius increases to the range of 0.3 nm < r < 32.0 nm as temperature increases in the range of −28.0 °C ∼ -0.2 °C; Stage III: capillary water in the pores with radius >32.0 nm melts rapidly as temperature increases in the range of −0.2 °C ∼ 0.7 °C; Stage IV: small amount of pores transform from small-sized absorptive pores and large-sized capillary pores to medium-sized absorptive pores as temperature increases to above 0.7 °C°. The specific threshold temperature and the threshold relaxation time are found as −0.2 °C and 5.17 ms respectively. By quantitatively analyzing the incremental change of T2 distribution curves and the rising temperature, an improved Gibbs-Thomson equation and a Two-Staged model of T2LM-Tmel (the geometric mean of relaxation time - the absolute value of temperature) and T2-Tmel (relaxation time - the absolute value of temperature), which are applicable to NMR technology, were proposed. Based on these results, a new pore structure evolution model of frozen sandy soil thawing from extremely-low temperature was proposed.

Transmission Characteristics and Predictive Model for Recent Epidemic Waves of COVID-19 Associated With OMICRON Variant in Major Cities in China.

Objectives: Waves of epidemics associated with Omicron variant of Coronavirus Disease 2019 (COVID-19) in major cities in China this year have been controlled. It is of great importance to study the transmission characteristics of these cases to support further interventions. Methods: We simulate the transmission trajectory and analyze the intervention influences of waves associated with Omicron variant in major cities in China using the Suspected-Exposed-Infectious-Removed (SEIR) model. In addition, we propose a model using a function between the maximum daily infections and the duration of the epidemic, calibrated with data from Chinese cities. Results: An infection period of 5days and basic reproduction number R0 between 2 and 8.72 are most appropriate for most cases in China. Control measures show a significant impact on reducing R0, and the earlier control measures are implemented, the shorter the epidemic will last. Our proposed model performs well in predicting the duration of the epidemic with an average error of 2.49days. Conclusion: Our results show great potential in epidemic model simulation and predicting the end date of the Omicron epidemic effectively and efficiently.

The importance of the functional mixed entropy for the explanation of residential and transport CO2 emissions in the urban center of China

The influence of urban spatial form on the environment is complex and lengthy. The spatial analysis for the urban form and residential-related CO2 emissions at the city scale is challenging due to the lack of extensive urban form data and fine-grain CO2 emission maps. This research uses remote sensing data and downscaling interpolation to generate residential and transport (RTCE) maps in 130 m spatial resolution of urban center regions from 31 major cities in China, then investigates the relationship between 3 types of urban form indicators (Internal characteristics, external morphology, and development intensity) and RTCE through Geographical Weighted Regression method. The results reveal that urban form indicators could explain about 45.9% of RTCE. The 2D building shape indicator has the second greatest positive impact as the external morphology indicator, which complicates the influence. The internal characteristics indicators have relatively strong influences than the development intensity indicators. For instance, the influence of functional mixed entropy (FME) is the greatest positive influence and decreases exponentially with FME increases. Therefore, cities with the FME lower than the threshold (0.28) should increase it appropriately, while cities with the FME around and larger than 0.28 should maintain and probably reduce it.