Dioscorea polystachya ‘Huilou’ is a famous Chinese yam cultivar and cultivated in Shangqiu city of China. In this study, we sequenced, assembled, and analyzed the complete chloroplast (cp) genome of ‘Huilou’ yam. Our results showed that the complete cp genome of ‘Huilou’ yam is a typical tetrad structure with a total length of 153,234 bp and encoded 132 genes. Phylogenetic analysis strongly supported that ‘Huilou’ yam is closely related to D. polystachya ‘Anping’. This study is highly significant for enriching the yam genetic resources and promoting yam breeding research.

The role of AI-mediated metacognitive interventions in EFL writing development: A cross-sectional study of textual accuracy, lexical sophistication, and cohesion.

Inebilizumab and rituximab (RTX) are anti-CD19 and anti-CD20 B cell-depleting antibodies, respectively. They are both used in the treatment of neuromyelitis optica spectrum disorders (NMOSD). However, limited data on both drugs exist regarding real-world clinical applications. This study aimed to assess the efficacy and safety of inebilizumab vs low-dose RTX in NMOSD patients through a retrospective-prospective multicenter analysis. This study collected data on aquaporin-4 immunoglobulin G seropositive NMOSD patients from 6 cities in China among those receiving either inebilizumab or low-dose RTX (500 mg), with a 1-year follow-up. Analyses assessed outcomes using inverse probability of treatment weighting and doubly robust models. We included a total of 229 patients, comprising 119 treated with inebilizumab and 110 with low-dose RTX. The cohort was predominantly female (217/229, 94.76%). The median follow-up duration was 12.0 months (range: 4.0-12.0) in the inebilizumab group and 12.0 months (range: 7.0-12.0) in the low-dose RTX group. Relapses occurred in 8/119 (6.72%) inebilizumab recipients vs 24/110 (21.82%) low-dose RTX recipients (hazard ratio [HR] 3.77, 95% confidence interval [CI] 1.56-9.14; p = 0.003). Adjusted annualized relapse rates were significantly lower in the inebilizumab group (0.06) than in the low-dose RTX group (0.24), corresponding to an incidence rate ratio (IRR) of 3.65 (95% CI 1.59-8.39; p = 0.003). Adverse events (AEs) occurred in 35 patients (29.42%) with inebilizumab, including 2 serious adverse events (SAEs; 1.68%), vs 44 patients (40.00%) with low-dose RTX (4 SAEs [3.64%]). Both AE and SAE rates showed no statistical difference between groups. The rate of patients with at least 1 infection in inebilizumab group was lower than that in the low-dose RTX group (p = 0.003). In this short-term study, inebilizumab demonstrated greater efficacy in reducing relapse risk, along with significantly lower rates of key adverse events, compared with low-dose RTX in patients with NMOSD. These findings support the use of inebilizumab as an effective and well-tolerated therapeutic option in a broader NMOSD population. This short-term study with a 12-month observation period provides Class III evidence that in patients with NMOSD, inebilizumab is more effective than low-dose RTX in reducing relapses.

How the integration of technology and finance empowers the collaborative development of digitalization and greenization of Chinese cities

PM2.5 pollution remains a critical environmental and public health challenge in China despite post-2015 improvements. However, our understanding of the spatially heterogeneous and nonlinear associations of its driving factors remains limited. To fill this gap, we employ an explainable geospatial artificial intelligence (GeoAI) framework that integrates the geographical random forest (GRF) model and the Shapley additive explanations (SHAP) approach to examine the associations between 16 determinants and PM2.5 concentrations. Based on a nationwide and multi-year analysis across 288 cities selected from all 336 Chinese cities between 2015 and 2022, the results show that GRF achieves at least a 0.04 higher R² than baseline models. Our analysis reveals three findings. First, population density is the most influential factor in 52.39% of cities; combined with temperature, road density, and gas supply, these four dominate over 95% of cities. Second, drivers exhibit significant spatially varying and nonlinear associations. For instance, population density correlates positively with PM2.5 in the North China Plain but negatively in sparsely populated areas; and the association of temperature follows an inverted U-shaped pattern. Third, these spatial and nonlinear associations undergo temporal changes. These findings offer insights for future environmental management strategies to mitigate the negative impacts of various drivers.

Growth mindset and academic resilience: The mediating role of grit among Chinese university students.

Innovative city program as a game changer: Rethinking local land supply in Chinese cities

From ambiguity to precision: Digitized traffic rules for autonomous driving.

How teachers' real-time monitoring behaviors shape students' on-task engagement in technology-enhanced physical education.

Creative leadership and kindergarten teachers' creative teaching performance: A moderated mediation model.

Who Gains, Who Suffers? The impact of industrial transfer on carbon emission intensity in Chinese cities

Driving digital innovation through quality incentives: Evidence from Chinese cities in the digital transformation era

Subtropical high-driven ozone pollution episodes in coastal cities in southeastern China

Geospatial analysis of urban village redevelopment in major Chinese cities using a deep learning approach

Can new energy vehicles cut carbon emissions? Evidence from 201 Chinese cities

Does urban agglomeration policy improve urban land use efficiency? A quasi-natural experiment based on 281 Chinese cities

• Employ Bayes’ rules to infer carsharing travel purposes of 4.8 million trips across 58 cities. • Investigates distinctive characteristics of carsharing usage patterns in different cities. • Examine how urban and system characteristics influence usage under various trip purposes. • Support policy formulation for local governments and operational strategies for carsharing companies. Carsharing provides a convenient travel option and has attracted increasing attention in Chinese market in recent years. This study conducted an analysis of carsharing travel patterns and purposes across multiple Chinese cities. We collected over 4.8 million trips from 58 cities, employed Bayes’ rules to infer users’ trip purposes and analyzed their travel patterns within a comparative analytic framework. The analysis explores variations in carsharing usage and trip purposes across cities and examines how urban characteristics influence various trip purposes. The results reveal a strong preference for leisure-oriented travels using carsharing services. There are more work-related trips during daytime and leisure activities in the evenings on weekdays and predominantly leisure activities on weekends. Additionally, there is considerable variability in the distribution of trip purposes across cities. The megacity demonstrates a more balanced usage pattern, whereas the Super-Large City shows a higher demand for commuting trips. In Large City I and Large City II, carsharing is primarily used for leisure and recreational trips. Some carsharing system characteristics, including the number of carsharing parking spaces and shared vehicles, are associated positively with carsharing utilization for different trip purposes, while other factors, like population and road densities, show varying impacts. This study offers a systematic exploration of carsharing services in China and contributes to improving the efficiency of carsharing services in diverse urban contexts.

This study aims to propose integrated process mining (IPM) approach, a novel process mining method that integrates knowledge graph, and tests its performance of model generation and utility of the generated model in identifying urban cascading disaster risks. To achieve the goal, we evaluated the effectiveness of the IPM through three sets of experiments. The first experiment tests the effectiveness of the proposed method in generating reliable disaster process models using real data of eleven Chinese cities with largely varying city features. Second, we examine whether the generated model works effectively in identifying cascading disaster risks. The third experiment analyzes performance of the IPM approach under temporal and spatial constraints to understand its applicability scope using data collected from Wuhan, Yantai, and Xiamen city. The results show that (1) the IPM method can generate process models that effectively represent past disaster patterns with even incomplete process trace samples; (2) the generated process models outperform traditional methods in identifying diverse cascading disaster risks and uncovering their complex associations; and (3) performance of the proposed method is strengthened in cities with similar city features and high temporal proximity. Finally, the article provides strategic policy recommendations: (i) Leveraging multi-source media data to decode historical process patterns; (ii) enhancing process mining through domain knowledge transfer to address data constraints; and (iii) implementing spatially and temporally precise risk identification frameworks.

A digital path to carbon neutrality: Heterogeneous green innovations and their spatial impact for Chinese cities

How heritage cities can sustain everyday living support while preserving historical value is a central question in sustainable urban governance. Existing studies have focused mainly on heritage identification, conservation performance, or tourism development but have paid less attention to how relatively fixed heritage endowments are converted into contemporary living support. This study examines 143 national historical and cultural cities in China. It develops a fixed heritage endowment index (fixed HEI) and comparable living support indices (comparable LSI) for 2012 and 2022. Within a dual-scale framework of 5km grids and city/county units, Markov transition matrices, transition type profiling, and XGBoost-SHAP are combined to identify living support conversion in high-heritage resource units and its associated conditions. The share of nonzero LSI grids increased from 45.58% in 2012 to 61.27% in 2022, indicating an expansion of observable living support intensity under a harmonized POI framework rather than a precise increase in the absolute stock of facilities. In the high-resource sample, 70.22% of the HL grids remained HL, 29.78% shifted to HH, and 98.97% of the HH grids remained stable. The main model-based association structure focused on road network density, the impervious surface area ratio, and population density, and spatial lag robustness checks further revealed that neighborhood road network density provided additional explanatory information. These results show that high heritage endowments do not automatically translate into high levels of living support. This study moves research on historic cities from static resource evaluation to resource conversion diagnosis and suggests that heritage city governance should shift from resource identification to the governance of resource conversion capacity.