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Aircraft ducts play an indispensable role in various systems of an aircraft. The regular inspection and maintenance of aircraft ducts are of great significance for preventing potential failures and ensuring the normal operation of the aircraft. Traditional manual inspection methods are costly and inefficient, especially under low-light conditions. To address these issues, we propose a new defect detection model called LESM-YOLO. In this study, we integrate a lighting enhancement module to improve the accuracy and recognition of the model under low-light conditions. Additionally, to reduce the model's parameter count, we employ space-to-depth convolution, making the model more lightweight and suitable for deployment on edge detection devices. Furthermore, we introduce Mixed Local Channel Attention (MLCA), which balances complexity and accuracy by combining local channel and spatial attention mechanisms, enhancing the overall performance of the model and improving the accuracy and robustness of defect detection. Finally, we compare the proposed model with other existing models to validate the effectiveness of LESM-YOLO. The test results show that our proposed model achieves an mAP of 96.3%, a 5.4% improvement over the original model, while maintaining a detection speed of 138.7, meeting real-time monitoring requirements. The model proposed in this paper provides valuable technical support for the detection of dark defects in aircraft ducts.

As it is central to sustainable urban development, urban mobility has primarily been scrutinised for its scaling and hierarchical properties. However, traditional analyses frequently overlook spatial directionality, a critical factor in city centre congestion and suburban development. Here, we apply vector computation to unravel the spatial directionality of urban mobility, introducing a two-dimensional anisotropy-centripetality metric. Utilising travel data from 90 million mobile users across 60 Chinese cities, we effectively quantify mobility patterns through this metric, distinguishing between strong monocentric, weak monocentric, and polycentric patterns. Our findings highlight a notable difference: residents in monocentric cities face increasing commuting distances as cities expand, in contrast to the consistent commuting patterns observed in polycentric cities. Notably, mobility anisotropy intensifies in the outskirts of monocentric cities, whereas it remains uniform in polycentric settings. Additionally, centripetality wanes as one moves from the urban core, with a steeper decline observed in polycentric cities. Finally, we reveal that employment attraction strength and commuting distance scaling are key to explaining these divergent urban mobility patterns. These insights are important for shaping effective policies aimed at alleviating congestion and guiding suburban housing development., (© 2024. The Author(s).)

Governments worldwide have announced stimulus packages to remobilize the labor force after COVID-19 and therefore to cope with the COVID-19-related recession. However, it is still unclear how to facilitate large-scale work resumption. This paper aims to clarify the issue by analyzing the large-scale prefecture-level dataset of human mobility trajectory information for 320 million workers and about 500,000 policy documents in China. We model work resumption as a collective behavioral change due to configurations of capacity, motivation, and policy instruments by using qualitative comparative analysis. We find that the effectiveness of post-COVID-19 recovery stimulus varied across China depending on the fiscal and administrative capacity and the policy motivation of the prefecture. Subnational fiscal and procurement policies were more effective for the wholesale and retail sector and the hotel and catering sector, whereas the manufacturing and business services sectors required more effort regarding employment policies. Due to limited prefectural capacity and wavering policy motivation, the simultaneous adoption of fiscal, employment, and procurement policy interventions endangered post-COVID-19 work resumption. We highlight the necessity of tailored postcrisis recovery strategies based on local fiscal and administrative capacity and the sectoral structure.

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., Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2022.)

Rural mobility inequality is an important aspect of inequality-focused Sustainable Development Goals. To reduce inequality and promote global sustainable development, more insight is needed into human mobility patterns in rural areas. However, studies on rural human mobility are scarce, limiting our understanding of the spatial and social gaps in rural human mobility and our ability to design policies for social equality and global sustainable development. This study, therefore, explores human mobility patterns in rural China using mobile phone data. Mapping the relative frequency of short-distance trips across rural towns, we observed that geographically peripheral populations tend to have a low percentage of short-distance flows. We further revealed social gaps in mobility by fitting statistical models: as travel distances increased, human movements declined more rapidly among vulnerable groups, including children, older people, women, and low-income people. In addition, we found that people living with low street density, or in rural towns in peripheral cities with long distances to city borders, are more likely to have low intercity movement. Our results show that children, older adults, women, low-income individuals, and geographically peripheral populations in rural areas are mobility-disadvantaged, providing insights for policymakers and rural planners for achieving social equality by targeting the right groups., (© 2023. The Author(s).)

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: χ 2 /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., (© 2022 Elsevier Ltd. All rights reserved.)

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., Competing Interests: The authors declare that they have no competing interests., (Copyright: © Lu et al.)

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., (© 2021 Elsevier Ltd. All rights reserved.)

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., (© 2021 Elsevier Ltd. All rights reserved.)