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A large exchange of material elements occurs during glass weathering, which will affect the identification and judgment of ancient glass. To this end, the data is first used to give the basis for dividing lead barium and high potassium glass, and the difference between the chemical components of each type of glass can be compared to obtain a conclusion. Since the final number of subclasses is unknown, this paper uses systematic cluster analysis to obtain the number of subclasses and classification results at the same time. In order to identify the glass types of unknown cultural relics, this paper uses the existing data to establish a random forest model and construct a method and standard for dividing glass types.

This research combines “big data” and “thick data” approaches to examine the correlation and causation between residential neighborhood features and people’s daily commuting and traveling patterns by integrating two datasets: household survey data and mobile phone data. We focus on “lilong” neighborhoods—a primary form of traditional residential neighborhood in central Shanghai. The characteristics of lilong neighborhoods are assessed using “thick data” from surveys in 105 lilongs, while residents’ daily activities are mapped out using “big data” from two weeks of mobile phone usage. We match these two datasets at neighborhood level based on their geospatial references. Four multinomial logistic regression models are developed to examine neighborhood effects on lilong residents’ daily activities. Our research confirms the major mechanisms of neighborhood effects and unravels their relative importance in shaping the patterns of residents’ daily activities. Conceptually, this study sheds new light on the understanding of how people’s life quality and wellbeing are affected by neighborhood characteristics through highlighting the importance of social interactions and the access to/quality of public facilities. Methodologically, incorporating household survey data (thick data) and mobile phone data (big data) is proven to be a novel and effective approach for examining neighborhood effects at a relatively large scale.

Background Aiming at the problem of pilots with poor acceleration endurance, the effects of hyperbaric oxygen pretreatment (HBOP) on improving the lower body negative pressure endurance and centrifuge acceleration endurance of pilots with poor acceleration endurance were investigated. Methods Thirty pilots with poor acceleration endurance were subjected to multiple hyperbaric oxygen chambers and 3 oxygen exposures. The lower body negative pressure endurance, centrifuge acceleration endurance, blood pressure (BP), heart rate (HR),blood lactic acid (BLA), hydrogen ion concentration, partial pressure of carbon dioxide (PCO2), partial pressure of oxygen (PO2) and adrenal corticotropic hormone (ACTH) were measured before and after HBOP. Results The cumulative stress index increased after HBOP compared with before HBOP (P

Pulmonary thromboembolism (PTE) is a fatal clinical syndrome that usually occurs in elderly individuals. The present study aimed to identify functional and key genes involved in the early diagnosis of PTE using bioinformatics analysis. The GSE84738 dataset was retrieved from the Gene Expression Omnibus database. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were subsequently performed. In addition, Cytoscape software v.3.7.2 was used to construct a protein-protein interaction (PPI) network. Serum samples from patients with PTE and healthy individuals were collected and the expression levels of Toll-like receptor (TLR)4, TLR2, IL-1β, JUN, prostaglandin-endoperoxide synthase 2 (PTGS2), osteopontin (SPP1) and endothelin-1 (ET-1) were analyzed by reverse transcription-quantitative PCR. A total of 160 upregulated and 159 downregulated differentially expressed genes were identified between patients with PTE and healthy individuals. TNF, IL-1β, JUN, TLR4, PTGS2, vascular cell adhesion molecule 1, SPP1, ryanodine receptor 2, TLR2 and ET-1 were considered as hub genes, which are defined as the genes with the highest degree of interaction in the enrichment and PPI network analyses. The top 10 common genes with the highest degree in the PPI network and the top 10 genes in modules 1 and 2 were TLR4, TLR2, IL-1β, JUN, PTGS2, SPP1 and ET-1. Taken together, the present study suggested that TLR4, TLR2, IL-1β and SPP1 were enriched in patients with PTE, thus providing novel potential biomarkers for the diagnosis of PTE.

This chapter focuses on the impact of measures to contain COVID-19. The outbreak of COVID-19 in China resulted in a nationwide mobility reduction, which has important implications for access to food, job and health facilities. The evolution of the disease and its impacts on mobility are highly uncertain which makes the decision making of mobility policies an arduous quest. In order to understand possible wider social impacts of immobility due to the disease, this chapter identifies two types of immobility—self-motivated and imposed immobility—by comparing the mobility behaviour in February and March 2020. Further, it explores how these two immobility groups have been influenced by the disease differently. Interviews with some fifty individual and twenty households are being conducted with residents in Kunming where citizens had overlooked the severity of COVID-19 and put up relatively strong resistance against mobility policies at the beginning of the outbreak, probably because the whole province had been a non-infected area during SARS.

Background Individuals have experienced various degrees of accessibility loss during the COVID-19 pandemic, which may consequently have influenced their mental health. Although efforts have been made to understand the mental health consequences of the pandemic and corresponding containment measures, the impacts of accessibility loss remain underexplored. Methods Based on 186 family interviews, a 569-respondent panel survey was designed and distributed monthly from February to October 2020 in Kunming, China. A 3-wave cross-lagged panel model was developed to understand the causal relationship between mental health and perceived accessibility of daily necessities, key services, and social activities. Results Goodness-of-fit indicators imply that the hypothesised model fits the observed data well: χ2/df = 2.221, AGFI = 0.910, NFI = 0.907, CFI = 0.933, RMSEA = 0.052. The results indicate that perceived accessibility of daily necessities and social activities had lagged effects on mental health status. The within-wave effects show that perceived accessibility of daily necessities (0.619, p < 0.01) and social activities (0.545, p < 0.01) significantly influenced respondents' mental health during the peak of the pandemic whilst perceived accessibility of social activities dominantly influenced their mental health after restrictions were lifted (0.779, p < 0.01). Perceived accessibility of public services such as healthcare did not significantly influence respondents’ mental health in any wave. COVID-19 containment policies had different mental outcomes across population groups. Disadvantaged people experienced mental health issues due to accessibility loss for daily necessities and social activities until the lifting of compulsory QR-code-for-buses, whilst better-off populations had better mental health during the early phase of the outbreak and rapidly recovered their mental health after mobility restrictions eased. Conclusion Reduced perceived accessibility of daily necessities and social activities may be an underlying cause of mental health problems. Relative accessibility deprivation exacerbated mental health inequities during the COVID-19 pandemic.

Since many parents travel separately for escorting and commuting, certain hidden daily car trips may have been ignored in previous research regarding parental escort behaviors. By defining an escort-space model using the spatial relationships between home, the workplace, and school, this study focuses on the daily modal split among parental chauffeurs using data from Qujing, China, while focusing on the effects of different escort-space models: spatial aggregation, job-housing separation and school-housing separation. The descriptive statistics of parental chauffeurs’ travel mode choices under the influences of these three escort-space models are presented. The statistical results demonstrate that the modal splits of parental chauffeurs perform significantly differently under these three escort-space models. Furthermore, the determinants of the daily travel mode of parental chauffeurs, including escort-spaces and other selected variables, are investigated using a multinomial logit model. A model without the escort-space model is also presented for comparison. The results show that the model with the escort-space model has a more significant goodness-of-fit than the model without the escort-space model. Both the job-housing separation and school-housing separation of parental chauffeurs result in the increase of car trips, while the usage amount of car in daily journeys is higher than that in escort trips. Moreover, car ownership, bike ownership, household income, residential location, age, gender, income, and education level all significantly impact the daily travel mode choices of parental chauffeurs. These findings can help policymakers create suitable policies to reduce excessive car trips by parental chauffeurs.

Funding: National Natural Science Foundation of China (41925003), Beijing Social Science Foundation (18JZD029), UKRI’s Global Challenge Research Fund (No. ES/P011055/1) and Ministry of Education Key Projects of Philosophy and Social Sciences Research (No. 18JZD029). The outbreak of COVID-19 in China started at the end of December 2019. This led to a series of containment measurements to control the spread of COVID-19. Despite of the widely reported effects of these measures, inadequate attention has gone to their social impacts. The elderly, as one of the most susceptible populations, has experienced a considerable reduction in mobility. This paper explores the role mobility played and how the social environment influenced elderly mobility in the first 2 months of the COVID-19 outbreak. We surveyed 186 families with a total of 248 elderly people in Kunming. The results show that mobility improves the quality of daily living, such as access to grocery shopping, maintenance of outdoor activities for health cultivation and preserving social networks even during the pandemic. Four themes relating to social environment emerged from the data as elements influencing elderly mobility during the pandemic: social pressure, practice of the virtue of Xiao, the social norm of respecting the aged and the impacts of technological advances. Among them, the virtue of Xiao enabled the elderly to stay in place in the early phase of COVID-19 by fulfilling their needs for daily necessities and social interactions, whilst being less technology-savvy further excluded them socially by restraining them from restoring mobility after the lifting of travel restrictions. Postprint

Knowing how workers return to work is a key policymaking issue for economic recovery in the post-COVID-19 era. This paper uses country-wide time-series mobile phone big data (comparing monthly and annual figures), obtained between February 2019 and October 2019 and between February 2020 and October 2020, to discover the spatial patterns of rural migrant workers’ (RMWs’) return to work in China’s three urban agglomerations (UAs): the Beijing–Tianjin–Hebei Region, the Yangtze River Delta and the Pearl River Delta. Spatial patterns of RMWs’ return to work and how these patterns vary with location, city level and human attribute were investigated using the fine-scale social sensing related to post-pandemic human mobility. The results confirmed the multidimensional spatiotemporal differentiations, interaction effects between variable pairs and effects of the actual situation on the changing patterns of RMWs’ return to work. The spatial patterns of RMWs’ return to work in China’s major three UAs can be regarded as a comprehensive and complex interaction result accompanying the nationwide population redistribution, which was affected by various hidden factors. Our findings provide crucial implications and suggestions for data-informed policy decisions for a harmonious society in the post-COVID-19 era.

The weak and sustained tropical cyclone (TC) Rumbia (2018) and its associated heavy precipitation after landfall are simulated by the Weather Research and Forecasting model using three microphysics parameterizations (MP) and two planetary boundary layer (PBL) schemes. Key factors regulating the heavy rainfall distribution of landed TC at three stages (landfall, inland slow-moving and recurving stage) and their sensitivities to multi-physics parameterizations are examined. Results show that heavy rainfall distribution is largely regulated by the intensity of TC itself at landfall, but more affected by environmental factors at the two inland stages, including the environmental vertical wind shear, the associated upper-level wind divergence and low-level moisture convergence. Different MP and PBL schemes mainly affect the simulation of TC thermodynamic structure and key environmental factors, leading to their different forecast skills at three stages. Specifically, the Yonsei University (YSU) PBL scheme systematically outperforms the Mellor-Yamada-Nakanishi-Niino (MYNN) scheme in simulating stronger TC and heavy rainfall intensity. With the advantageous YSU PBL scheme, the Ferrier MP scheme only shows a slight advantage in simulating the intensity of TC and heavy rainfall at landfall. However, the WRF single-moment 6-class (WSM6) and Thompson scheme produce more graupel or snow, and better simulate the key environmental factors, showing their respective advantages in simulating the heavy rainfall structure and location at the two inland stages. This implies that the use of nonlocal YSU PBL scheme and the MP schemes with sophisticated ice processes shows superiority in simulating the postlandfall heavy rainfall induced by weak TCs.

The household is usually an essential element for activity-based travel decision-making of individuals. From the perspective of household context, activities are often allocated to individuals based on their household roles, thereby affecting individuals’ travel behavior. By defining the household role using spatial-temporal constraints which are associated with individual activities and household activities, this paper investigates the travel mode choice of individuals considering the effect of different household roles. The descriptive statistics of the household roles and their corresponding travel mode choice are presented using the data from Kunming, China. The statistical results show that the modal splits between females and males perform a significant difference in the same household roles. Furthermore, the travel mode choice of females and males are estimated separately using multinomial logistic regression model. The results show that those who face more space-time constraints associated with household tasks are less willing to travel by car. While with the increase of commuting constraints, household heads, especially female-heads, tend to use car to meet the travel demands of household activity. Besides, individuals’ age, education level, the number of cars and bikes in household also have a significant impact on travel mode choices of individuals.

Individuals have experienced various degrees of accessibility loss during the COVID-19 pandemic, which may consequently influence transport equity. However, conventional measurements of accessibility cannot capture individual experiences and perceptions of accessibility. Moreover, since many daily necessities and services can only be acquired online during the pandemic, the ease of using smartphone-based services play an essential role in people’s everyday lives. Therefore, this paper investigates the relationship between the ease of using smartphone-based services, perceived accessibility, and perceived transport equity during the pandemic. Based on 186 family interviews, a panel survey with 569 respondents was conducted monthly from February to October 2020 in Kunming, China, and a three-wave cross-lagged panel model was developed to understand the causal relationship between the three constructs. The results indicate that the ease of using smartphone-based services dominantly influence transport equity in the early phase of the pandemic, but its effect faded after the lifting of travel restrictions. Perceived accessibility to services appears a sound indicator for transport equity in the new normal, but perceived accessibility and transport equity are not strongly associated when staying at home is perceived as desirable. Moreover, we found that contemporary practices of smartphone-based new mobility services only favour those who already have convenient access to services and have further excluded and marginalised disadvantaged populations, which urgently require policy interventions.

China railway transportation is developing rapidly, the running speed of high-speed train is promoted. As the promoted speed, the running gear and gearbox of high-speed train produce more heat, causing hot axle easily. In view of the hot axle phenomenon of high-speed train during operating, in combination with the former failure condition of axle temperature monitoring system in terms of total composition, principle and failure condition during operating, analyzing the causes of system failure deeply, a new type of axle temperature monitoring system is designed. Through the test, it indicates that the system could monitor the axle temperature condition of high-speed operating train real-time and accurately, it plays an important role in ensuring the safe operation of high-speed train.

To encourage low-carbon travel, reduce car trips, and improve the urban traffic system, an understanding of travel mode choice for escorting children to and from school is important. Previous studies have focused mostly on walking and bicycling. However, the electric bicycle (e-bike—a mainstream escorting mode in China) rarely has been investigated. The factors that affect travel mode choice (walking, e-bike, or car) for escorting children to and from school are explored at the household level in a case study from Kunming, China. Intrahousehold interactions and parental space–time constraints are represented by the comparative working hours of the parents and space–time coordination in joint parent–child trips, respectively. A multinomial logit model was applied in mode analysis. The results reveal interesting phenomena in travel mode choices for escorting children to school. When the mother’s working hours are longer than the father’s, the travel mode choice for escorting a child to and from school is less likely to be walking or e-bike; in other words, a car is the preferred choice in mother-dominated households. The distance deviation between parents and children is a significant factor in choosing between a car or an e-bike. Children are more likely to be escorted by e-bikes as a result of distance deviations, and walking or e-bike is less likely to be chosen when the school is far from home. Results could be valuable for traffic demand management to reduce car use for trips to and from school. In addition, e-bike development should be considered seriously for the future development of China.

A well-controlled electronic throttle not only improves the fuel economy, but also reduces the emission of harmful gases from incomplete combustion. With the development of the semiconductor industry, the computing performance of the electronic control unit has also increased, and it is also feasible to control the electronic throttle using model predictive control. In this work, a control-oriented model for the electronic throttle is proposed first. Then, based on the model identification, a nonlinear model predictive controller is developed. The optimal solution is obtained by using the sequential quadratic programming combing the multiple shooting tool. Simulation validation results show that the designed control system has acceptable performance.

Egalitarian thought has a long tradition in Chinese history. Synthesized with the socialist ideology, it was practiced nationwide in the first decades of Communist China. Alongside the well-reported quality of life improvements that the recent economic liberalisation reforms have brought about, Chinese people are experiencing an increasingly serious polarization between the rich and the poor. Consequently, an egalitarian tendency has represented itself within contemporary policy and popular discourse. This paper aims to explore how egalitarian thought has influenced public awareness of social inequities by using the case of public attitudes towards the Beijing congestion charge, which is currently still at its planning stage. Nine focus groups, with a total of 73 participants, were undertaken with residents living in different areas of the city. Results show that the perceptions of social inequities are significantly different between low-income and high-income people. Due to egalitarian thinking, low-income people expect the privileges of the rich to be abolished, however, they do not pay much attention to a wider redistribution of wealth and other social resources. By contrast, richer people tend to deny to a wider population the privileges they themselves received, and, to some extent, they are reluctant to accept policy outcomes that may favour those who are in lower social positions. The resentment against the rich, as another by-product of egalitarianism, considerably exaggerates actual social inequalities, thereby intensifying the feelings of being unequally treated.

Due to the coupling of different energy systems, optimization of different energy complementarities, and the realization of the highest overall energy utilization rate and environmental friendliness of the energy system, distributed energy system has become an important way to build a clean and low-carbon energy system. However, the complex topological structure of the system and too many coupling devices bring more uncertain factors to the system which the calculation of the interval power flow of distributed energy system becomes the key problem to be solved urgently. Affine power flow calculation is considered as an important solution to solve uncertain steady power flow problems. In this paper, the distributed energy system coupled with cold, heat, and electricity is taken as the research object, the influence of different uncertain factors such as photovoltaic and wind power output is comprehensively considered, and affine algorithm is adopted to calculate the system power flow of the distributed energy system under high and low load conditions. The results show that the system has larger operating space, more stable bus voltage and more flexible pipeline flow under low load condition than under high load condition. The calculation results of the interval power flow of distributed energy systems can provide theoretical basis and data support for the stability analysis and optimal operation of distributed energy systems.

Over the past decade, China has experienced a rapid increase in urbanization. The urban built-up areas (population) of Shanghai increased by 16.1% (22.9%) from 2006 to 2015. This study aims to analyze the variations of tropospheric NO2 over Yangtze River Delta region and the impacts of rapid urbanization during 2006-2015. The results indicate that tropospheric NO2 vertical column density (VCD) of all cities in the study area showed an increasing trend during 2006-2011 whereas a decreasing trend during 2011-2015. Most cities showed a lower tropospheric NO2 VCD value in 2015 compared to that in 2006, except for Changzhou and Nantong. Shanghai and Ningbo are two hotspots where the tropospheric NO2 VCD decreased most significantly, at a rate of 22% and 19%, respectively. This effect could be ascribed to the implementation of harsh emission control policies therein. Similar seasonal variability was observed over all cities, with larger values observed in the summer and smaller values shown in the winter. Further investigations show that the observed increasing trend of tropospheric NO2 during 2006-2011 could be largely explained by rapid urbanization linked to car ownership, GDP, power consumption, population and total industrial output. Such effect was not prominent after 2011, mainly due to the implementation of emission control strategies.

The purpose of this research is to explore the relationship between expatriate learning, expatriate adaptation, and international assignment experience, and to verify the relationships between the variables described. Thus, this chapter outlines the research methodology adopted in order to answer the research questions and contribute to the knowledge gap as discussed in the literature review.