CRRC (China)

Understanding the spatial dynamics and inequality of human water use is essential for achieving sustainable water management. Although previous studies have examined the spatial evolution of regional water use, comprehensive assessments that couple sectoral decomposition with long-term (> 50 years) national-scale dynamics remain limited. To address this gap, we compiled multi-sectoral water use data from 1970 to 2020 and analyzed spatial trends and inequalities in China's water use using the center-of-gravity approach and Gini coefficients. Our results reveal distinct directional shifts among sectors: irrigation and industrial water use moved northeastward and southwestward, respectively, while total water use exhibited minimal spatial displacement. Water-use inequality declined from high to moderate levels before 2000 and has since stabilized. Irrigation water use dominated both the spatial redistribution and inequality of total water use, driven primarily by changes in irrigated area and industrial scale. Inter-provincial disparities accounted for the largest share of overall water-use inequality. These findings offer new insights into the spatial evolution of China's water use and provide an empirical basis for promoting more equitable and sustainable water allocation policies.

Abstract High-temperature superconducting (HTS) maglev offers non-contact and passively stable levitation, yet its large-scale deployment is hindered by the high cost and material utilization efficiency of permanent magnet guideways (PMGs). This paper proposes a discretization-based inverse design framework where the PMG cross-section is meshed into elemental units, treating the magnetization direction of each unit as a design variable to maximize levitation force. Case studies on two engineering PMGs demonstrate that the algorithm robustly converges to consistent optimal physical configurations regardless of initialization, with the primary variation observed only in computational convergence rates. To ensure engineering manufacturability, the framework incorporates fabrication constraints, including discrete angles, block-merging algorithms, and symmetry rules. Furthermore, the strategy is extended to a V-shaped PMG to verify its universality for complex geometries. Finally, the optimization is validated via levitation force experiments on 3D-printed scaled-down prototypes. The results demonstrate an 10% improvement in maximum levitation force over a initial scaled-down V-shaped Halbach baseline with an identical cross-sectional area. These findings confirm the feasibility and effectiveness of the proposed strategy, providing both theoretical insights and practical guidelines for next-generation HTS maglev systems.

Volatile fatty acids (VFAs) derived from organic waste offer promising and cost-effective carbon sources for the production of microbial lipids. This study demonstrates the significant influence of nitrogen nutrition on cell proliferation and microbial lipid synthesis in Yarrowia lipolytica during high-concentration acid cultivation. Further investigations into nitrogen sources revealed that NH4Cl and urea are suitable options for cultivating Y. lipolytica to produce microbial lipids, resulting in lipid yields ranging from 2.00 to 2.50g/L. Moreover, pH fluctuations were found to be influenced by both the nitrogen source and acid utilisation, with pH adaptation helping alleviate acid inhibition caused by high-concentration VFAs. Under optimised cultivation conditions, the highest yield of microbial lipids reached 4.00g/L, accompanied by a dry cell weight of 9.91g/L and a microbial lipid content of 40.37%, consisting predominantly of C16 ~ 18 fatty acids. These findings highlight the central role of nitrogen metabolism and pH adaptation in enhancing VFA assimilation, offering guidance for cost-effective microbial lipid production from organic waste streams.

Metro trains often operate in highly complex environments, characterized by illumination variations, high-speed motion, and adverse weather conditions. These factors pose significant challenges for visual perception systems, especially those relying solely on conventional RGB cameras. To tackle these difficulties, we explore the integration of event cameras into the perception system, leveraging their advantages in low-light conditions, high-speed scenarios, and low power consumption. Specifically, we focus on Kilometer Marker Recognition (KMR), a critical task for autonomous metro localization under GNSS-denied conditions. In this context, we propose a robust baseline method based on a pre-trained RGB OCR foundation model, enhanced through multi-modal adaptation. Furthermore, we construct the first large-scale RGB-Event dataset, EvMetro5K, containing 5,599 pairs of synchronized RGB-Event samples, split into 4,479 training and 1,120 testing samples. Extensive experiments on EvMetro5K and other widely used benchmarks demonstrate the effectiveness of our approach for KMR. Both the dataset and source code will be released on <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Event-AHU/EvMetro5K_benchmark</uri>

The fast expansion of deep learning models has led to an increasing need for well-annotated datasets, while traditional manual annotation cannot meet this requirement. Current research on annotation mainly focuses on automating the annotation process. These studies typically rely on a set of predefined functionalities. However, in complex scenarios, for example, autonomous driving, annotation workflow, and postprocessing functions must be tailored to specific tasks. The challenge here lies in ensuring that newly generated functions integrate with the existing function set of the system, which requires the pipeline to understand the user requirement and real-time system context to generate appropriate input-output data structures. Previous annotation methods relied on human programming to meet this requirement. This dependency on professional assistance restricts the generalizability of annotation methods. Drawing on modern software engineering principles, we introduce an interactive code generation pipeline based on natural language input to address this challenge. Our approach supports real-time code generation by natural language input. To the best of our knowledge, this is one of the latest applications that apply customizable functional extensions in the annotation pipeline. Our evaluations on public datasets and a self-built real-world dataset demonstrate that our method significantly enhances the range of application scenarios for annotation tools while reducing manual intervention. Upon acceptance, the code will be open source.