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Chemical evolution characteristics and influencing factors of groundwater in the saline and fresh water funnel area in Hengshui City, North China.
Sci Rep
January 2025
School of Urban Geology and Engineering, Hebei GEO University, 050031, Shijiazhuang, China.
Both over-exploitation and exploitation reduction of groundwater can alter the conditions of groundwater recharge and discharge, thereby impacting the overall quality of groundwater. This study utilizes hydrogeochemical methods and statistical analysis to explore the spatial and temporal evolution characteristics and influencing factors of groundwater chemistry in the saline-freshwater funnel area of Hengshui City under exploitation reduction. The results showed that: With the exception of the deep freshwater funnel area in the western region, which exhibits a trend of water quality deterioration (Cl accounted for more than 25%), groundwater quality in the other funnel areas demonstrates an improving trend (HCO[Formula: see text] accounted for more than 25%).
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College of Forestry, Northeast Forestry University, Harbin 150040, China.
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National Engineering Laboratory for Efficient Utilization of Soil and Fertilizer Resources, College of Resources and Environment, Shandong Agricultural University, Tai'an 271018, China.
Agricultural land classification plays a pivotal role in food security and ecological sustainability, yet achieving accurate large-scale mapping remains challenging. This study presents methodological innovations through a multi-level feature enhancement framework that transcends traditional time series analysis. Using Shandong Province, northern China's agricultural heartland, as a case study, we first established a foundation with time series red-edge vegetation indices (REVI) from Sentinel-2 imagery, uniquely combining the normalized difference red edge index (NDRE) and plant senescence reflectance index (PSRI).
View Article and Find Full Text PDFA Deep-Learning Method for Remaining Useful Life Prediction of Power Machinery via Dual-Attention Mechanism.
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School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430070, China.
Remaining useful life (RUL) prediction is a cornerstone of Prognostic and Health Management (PHM) for power machinery, playing a crucial role in ensuring the reliability and safety of these critical systems. In recent years, deep learning techniques have shown great promise in RUL prediction, providing more reliable and accurate outcomes. However, existing models often struggle with comprehensive feature extraction, especially in capturing the complex behavior of power machinery, where non-linear degradation patterns arise under varying operational conditions.
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The Higher Educational Key Laboratory for Measuring & Control Technology and Instrumentation of Heilongjiang Province, Harbin University of Science and Technology, Harbin 150080, China.
Video instance segmentation, a key technology for intelligent sensing in visual perception, plays a key role in automated surveillance, robotics, and smart cities. These scenarios rely on real-time and efficient target-tracking capabilities for accurate perception and intelligent analysis of dynamic environments. However, traditional video instance segmentation methods face complex models, high computational overheads, and slow segmentation speeds in time-series feature extraction, especially in resource-constrained environments.
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