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E-hailing cars have become an important innovation in transportation operations. The E-hailing originated in the US and has developed rapidly around the world. As a laggard, China has experienced remarkable progress in exploring development period, rapid expansion period and adjustment period. The main reason is that China has a large user base, a sufficient labor force, a good network innovation environment, and a relatively relaxed regulatory environment. The new policy formulated by the Chinese government has caused a relatively large constraint on the Ehailing industry. Whether the E-hailing platform can maintain neutrality business and whether it can safeguarding consumer rights while developing the economy is an important challenge.

The support method in coal entry heading faces significantly impacts the stability of surrounding rock and heading efficiency. To enhance heading efficiency while ensuring the stability of surrounding rock, this study takes the 011813 headgate heading face in Jinfeng Mine. Addressing the technical conditions and the demand for rapid heading, an innovative partitioned support approach of “local anchoring + non-repeated temporary support” and “rapid reinforcement anchoring” is proposed. Field investigation, theoretical analysis, and numerical experiments are employed to systematically study the synergistic effect of anchoring and supporting in heading faces. The technical principles of synergistic anchoring and supporting are detailed, the mechanical model of surrounding rock under this system is established, and the strength of temporary support under this system is established. By using a systematic analysis method, parameters for local anchoring, temporary support, and reinforcement anchoring are proposed. Numerical experiments are conducted to comparatively analyze the stress evolution, damage and deformation characteristics of surrounding rock under conditions of no support, timely one-time anchoring, and synergistic anchoring and supporting. The influence of synergistic anchoring and supporting on the stability of surrounding rock is examined, the mechanism of synergistic anchoring and supporting is revealed, and the rapid heading process using this approach is optimized. Based on the findings, the feasibility of synergistic anchoring and supporting is evaluated from the perspectives of technical principles, surrounding rock stability characteristics, support mechanism, and rapid heading processes. The research indicates that the proposed approach holds great potential for field application in Jinfeng Mine. In subsequent heading practices, it is recommended to adjust the unsupported roof distance and unsupported roof distance based on actual conditions, fine-tune the partitioned anchoring parameters, focus on the effective control of surrounding rock through local anchoring, and enhance both the load-bearing capacity and cooperative deformation ability of temporary supports.

The Himalayan, Karakoram, and Hindu Kush (HKH-TMHA) are the three main mountain ranges in the high-mountain Asia region, covering the China–Pakistan Economic Corridor (CPEC). In this study, we identified glacial lakes in the HKH-TMHA region based on multitemporal Landsat images taken from 1990 to 2020. We analyzed the spatial distribution and evolution of glacial lakes in the HKH-TMHA region from the perspective of their elevation, size, and terrain aspect; then, we described their temporal changes. The results showed that approximately 84.56% of the glacial lakes (84.1% of the total lake area) were located at elevations between 4000 m and 5500 m, and glacial lakes with areas ranging from 0.01–0.5 km2 accounted for approximately 95.21% of the number and 63.01% of the total area of glacial lakes. The number (38.64%) and area (58.83%) of south-facing glacial lakes were largest in HKH-TMHA and expanded significantly over time. There were 5835 (664.84 ± 89.72 km2) glacial lakes in 1990; from 1990 to 2020, the number of glacial lakes in the HKH-TMHA region increased by 5974 (408.93 km2) in total; and the annual average increase in the area of glacial lakes reached 13.63 km2 (11.15%). In 2020, the total number of glacial lake reached to 9673 (899.66 ± 120.63 km2). In addition, most glacial lakes were located in the Eastern Himalayan, China, and the Indus Basin. Based on the precipitation and temperature analyses performed in our study area, we found inconsistent climate characteristics and changes in the three mountain ranges. In general, the daily precipitation (temperature) increased by 1.0766 mm (1.0311 °C), 0.8544 mm (0.8346 °C), and 0.8245 mm (−0.1042 °C) on the yearly, summer, and winter scales, respectively. Glacial melting and climate change are common contributors to glacial lake expansion. The investigation of glacial lakes in this region can provide basic supporting data for research on glacial lake-related disasters, land cover, and climate change in the high-mountain Asia region.

Chinese government has proposed ambitious targets to combat climate change. As carbon emissions of China’s transportation have been rapidly increasing in past decades, massive efforts for carbon reduction need to be taken by transportation sector. Research on practical action paths for transportation sector’s low-carbon development are critical to achieving the Paris Agreement goals and China’s “Dual-Carbon” Target. Based on the transportation’s historical carbon emissions and the new possible trends in the future, this paper uses a forecast model to predict transportation’s carbon emission. Then we adopt a scenario analysis to analyze the total transportation demand in the transportation sector from 2021 to 2060. We quantitatively simulated the emission reduction effects of different policy measures under different scenarios, such as optimization of transportation structure, application of energy-saving and emission-reduction technologies, and new energy vehicles. The results provide paths and measures for the low-carbon development of transportation, and provides policy suggestion for the scientific formulation of the low-carbon development.

Background Brominated Flame Retardants (BFRs) have attracted widespread concern due to their environmental persistence and potential toxicity. This study aims to examine the association between BFRs exposure and hypertension. Methods We used data from the National Health and Nutrition Examination Survey (NHANES) spanning 2005 to 2016 for the cross-sectional analysis. To evaluate the individual and combined impacts of BFRs exposure on hypertension, we utilized multivariate models, including generalized additive models, weighted quantile sum (WQS) regression, and Bayesian kernel machine regression (BKMR) models. Results 9882 individuals (48% male) aged ≥ 20 were included in the final analysis, of whom 4114 had hypertension. After controlling for potential covariates, higher serum concentrations of PBDE100 (OR: 1.26; 95% CI: 1.01, 1.57) and PBDE153 (OR: 1.50; 95% CI: 1.18, 1.88) were significantly associated with hypertension. A nonlinear relationship between PBDE28 and hypertension was observed ( P  = 0.03). Moreover, BFRs mixture were positively associated with the prevalence of hypertension in both the WQS (β:1.09; 95% CI: 1.02, 1.17; P  = 0.02) and BKMR models. Conclusion Our study suggested that BFRs exposure is positively associated with hypertension in the general population. To confirm this association and elucidate the mechanisms, further research is required.

Ice–snow freezing may disrupt the growth condition and structure of forest vegetation, increasing combustible loads and thus triggering forest fires. China’s subtropical regions are rich in forest resources, but are often disturbed by ice–snow freezing, especially due to climate change. Clarifying the responsive areas and times of forest fires to ice-snow freezing in this region is of vital importance for local forest fire management. In this study, meteorological data from 2001 to 2019 were used to extract the precipitation and its duration during the freezing period in order to analyze the freezing condition of forest vegetation in subtropical China. To improve the accuracy of identifying forest fires, we extracted forest fire information year-by-year and month-by-month based on the moderate resolution imaging spectroradiometer (MODIS) active fire data (MOD14A2) using the enhanced vegetation index (EVI), and analyzed the forest fire clustering characteristics in the region using the Moran’s Index. Then, correlation analysis between forest fires and freezing precipitation was utilized to explore the responsive areas and periods of forest fires caused by ice–snow freezing. Our analysis shows the following: (1) during the period of 2001–2019, the ice–snow freezing of forest vegetation was more serious in Hunan, Jiangxi, Hubei, and Anhui provinces; (2) forest fires in subtropical China have shown a significant downward trend since 2008 and their degree of clustering has been reduced from 0.44 to 0.29; (3) forest fires in Hunan, Jiangxi, and Fujian provinces are greatly affected by ice–snow freezing, and their correlation coefficients are as high as 0.25, 0.25, and 0.32, respectively; and (4) heavy ice–snow freezing can increase forest combustibles and affect forest fire behavior in February and March. This research is valuable for forest fire management in subtropical China and could also provide a reference for other regions.

Surface ice/snow is a vital resource and is sensitive to climate change in many parts of the world. The accurate and timely measurement of the spatial distribution of ice/snow is critical for managing water resources. Object-oriented and pixel-oriented methods often have some limitations due to the image segmentation scale, the determination of the optimal threshold and background heterogeneity. Therefore, this study proposes a method for automatically extracting large-scale surface ice/snow from Landsat series images, which takes advantage of the combination of image segmentation, the watershed algorithm and a series of ice/snow indices. We tested our novel method in three different regions in the Karakoram Mountains, and the experimental results show that the produced ice/snow map obtained a user’s accuracy greater than 90%, a producer’s accuracy greater than 97%, an overall accuracy greater than 98% and a kappa coefficient greater than 0.93. Comparing the extraction results under segmentation scales of 10, 15, 20 and 25, the user’s accuracy and producer’s accuracy from the proposed method are very similar, which indicates that the proposed method is more reliable and stable for extracting ice/snow objects than the object-oriented method. Due to the different reflectivity values in the near-infrared band in the snow and water categories, the normalized difference forest snow index (NDFSI) is suitable for Landsat TM and ETM+ images. This study can serve as a reliable, scientific reference for rapidly and accurately extracting ice/snow objects.

For a class of arbitrary large initial data with radial symmetry or cylindrical symmetry, we prove the existence of global solutions for the 3 D relativistic Vlasov–Poisson system for the plasma physics case. The compact support assumption is not imposed for both cases. The essential lower bound assumption of the angular momentum in the previous work of Glassey and Schaeffer (Math Methods Appl Sci 24:143–157, 2001) is not imposed on the initial data for the cylindrical symmetry case.

Amidst global economic fluctuations and escalating geopolitical risks, gold futures, as a pivotal safe-haven asset, demonstrate price dynamics that directly impact investor decision-making and risk mitigation effectiveness. Traditional forecasting models face significant limitations in capturing long-term trends, addressing abrupt volatility, and mitigating multi-source noise within complex market environments characterized by nonlinear interactions and extreme events. Current research predominantly focuses on single-model approaches (e.g., ARIMA or standalone neural networks), inadequately addressing the synergistic effects of multimodal market signals (e.g., cross-market index linkages, exchange rate fluctuations, and policy shifts) and lacking the systematic validation of model robustness under extreme events. Furthermore, feature selection often relies on empirical assumptions, failing to uncover non-explicit correlations between market factors and gold futures prices. A review of the global literature reveals three critical gaps: (1) the insufficient integration of temporal dependency and global attention mechanisms, leading to imbalanced predictions of long-term trends and short-term volatility; (2) the neglect of dynamic coupling effects among cross-market risk factors, such as energy ETF-metal market spillovers; and (3) the absence of hybrid architectures tailored for high-frequency noise environments, limiting predictive utility for decision support. This study proposes a three-stage LSTM–Transformer–XGBoost fusion framework. Firstly, XGBoost-based feature importance ranking identifies six key drivers from thirty-six candidate indicators: the NASDAQ Index, S&P 500 closing price, silver futures, USD/CNY exchange rate, China’s 1-year Treasury yield, and Guotai Zhongzheng Coal ETF. Second, a dual-channel deep learning architecture integrates LSTM for long-term temporal memory and Transformer with multi-head self-attention to decode implicit relationships in unstructured signals (e.g., market sentiment and climate policies). Third, rolling-window forecasting is conducted using daily gold futures prices from the Shanghai Futures Exchange (2015–2025). Key innovations include the following: (1) a bidirectional LSTM–Transformer interaction architecture employing cross-attention mechanisms to dynamically couple global market context with local temporal features, surpassing traditional linear combinations; (2) a Dynamic Hierarchical Partition Framework (DHPF) that stratifies data into four dimensions (price trends, volatility, external correlations, and event shocks) to address multi-driver complexity; (3) a dual-loop adaptive mechanism enabling endogenous parameter updates and exogenous environmental perception to minimize prediction error volatility. This research proposes innovative cross-modal fusion frameworks for gold futures forecasting, providing financial institutions with robust quantitative tools to enhance asset allocation optimization and strengthen risk hedging strategies. It also provides an interpretable hybrid framework for derivative pricing intelligence. Future applications could leverage high-frequency data sharing and cross-market risk contagion models to enhance China’s influence in global gold pricing governance.

PurposeThis paper aims to provide a comprehensive analysis of the strategic adjustments in China's transportation structure, with a particular focus on the pivotal role of railway freight and its integration into the modern logistics system. It seeks to address the need for a more nuanced understanding of the “road to rail” policy, emphasizing the importance of intermodal collaboration and service of fragmented market demands.Design/methodology/approachThe study employs a transport economics perspective to evaluate the achievements and shortcomings of China's transportation structure optimization. It bases its assessment of the current state of railway freight logistics, multi-modal transportation and the broader implications for the transportation service market on data analysis. The methodology includes a review of existing policies, an examination of industry practices and a comparative analysis with global trends in railway logistics.FindingsThe research underscores the importance of focusing on the development of non-bulk materials, noting the insufficiency in the development of China's rail multi-modal transportation and highlighting the instructive value of successful cases in open-top container road-rail intermodal transportation. The study posits that the railway sector must enhance cooperation with other market entities, aligning with the lead enterprises in the logistics chain that are characterized by speed, high value and strong coordination capabilities, in order to better serve the transportation market. This approach moves away from a reliance on the railway's own capabilities alone.Originality/valueThis paper offers original insights into the transformation of railway freight in China, contributing to the body of knowledge on transportation economics and logistics. It provides valuable recommendations for policymakers and industry practitioners, emphasizing the strategic importance of railway logistics in the context of China's economic development and intense competition in the supply chain. The value of the article lies in its comprehensive understanding of the complexities involved in the adjustment of transportation structures, providing direction for the market-oriented reform of China's railway freight sector.