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Enhanced carbonate weathering and CO2 release in a typical karst watershed (Southwest China): Evidence from hydrochemical and multi-isotopic data

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Science of the Total Environment 2024年 957卷 177372页

作者： Liu, Lei Lan, Jiacheng Ren, Kun Huang, Mingzhi Mao, Ruitao Zhang, Xianwen Yue, Kunqian School of Karst Science / State Engineering Technology Institute for Karst Desertiffcation control Guizhou Normal University Guiyang550001 China Institute of Karst Geology Chinese Academy of Geological Sciences Key Laboratory of Karst Dynamics Ministry of Natural Resources & Guangxi Guilin541004 China International Research Centre on Karst under the Auspices of UNESCO National Center for International Research on Karst Dynamic System and Global Change Guilin541004 China Pingguo Guangxi Karst Ecosystem National Observation and Research Station Pingguo531406 China

Carbon sinks formed by carbonate weathering are a major component of the carbon sink deficit and play a significant role in the carbon cycle. However, exogenous acids (HNO3 and H2SO4) are also involved in carbonate weathering, complicating the overestimation of the formation of carbon sinks and fluxes. In this study, we analyzed the Hongjiadu karst underground river basin in Guizhou using isotopes (δ13C-DIC, δ34S-SO4, δ18O-SO4, δ18O-H2O, and δ2H-H2O) and hydrochemical methods to investigate carbon cycling characteristics and its influencing factors, and quantitatively evaluate the effects of HNO3 + H2SO4 on CO2 sink fluxes in the basin. The following results were obtained: (1) The nitrate in the watershed mainly comes from fertilizers, and the sulfate mainly comes from local coal seams. (2) The relationship between isotopes (δ13C-DIC, δ34S-SO4, δ18O-SO4, δ18O-H2O, and δ2H-H2O) and hydrochemicals (Ca2+, Mg2+, NO3–, HCO3–, and SO42–) indicates that H2CO3, HNO3, and H2SO4 are involved in carbonate rock dissolution. Most of the samples in the watershed, particularly the acid mine drainage water, were affected by acid rain and sulfide oxidation, resulting in increased δ13C-DIC values. Another portion of the springs was affected by soil CO2, resulting in decreased δ13C-DIC values. (3) Quantitative calculations showed that exogenous acids enhanced the weathering rate of carbonate rocks in groundwater by 67.24 % (dry season) and 76.31 % (wet season) while decreasing the carbon sink flux in groundwater by 55.31 % (dry season) and 66.64 % (wet season). Rainfall enhances the weathering of carbonate rocks. In the process, CO2 is released, increasing the carbon source. Our study highlights the influence of anthropogenic, natural, and hydrological changes on karst carbon sinks in mixed-pollution karst basins. This information is important for future studies on the carbonate weathering processes in karst regions. © 2024 Elsevier B.V.

关键词： Groundwater pollution

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Switch Machine Fault Diagnosis Method Based on Kalman Filter and Support Vector Machines 4th

Switch Machine Fault Diagnosis Method Based on Kalman Filter...

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4th International Conference on Electrical and Information Technologies for Rail Transportation, EITRT 2019

作者： Li, Xiang Qin, Yong Wang, Zhipeng Kan, Jiayu Zhang, Xiaofeng State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China National Research Center of System Engineering of Railway Intelligent Transport Beijing100081 China National Research Center of System Engineering of Railway Intelligent Transport Signal & Communication Research Institute Beijing100044 China

ISBN: (纸本)9789811528651

Switch machines are used for operating railway turnout;its error can cause delays, increase operating costs and may even lead to train accidents. Therefore, the fault diagnosis technology for the switch machine has received more and more attention. This paper proposes a fault diagnosis method based on the action current of switch machine. Firstly, the Kalman filter is used to preprocess the collected action current to reduce the influence of the unavoidable error of the measurement. In addition, we can further improve the accuracy of fault diagnosis by extracting the characteristics of the action current curve, like the maximum, minimum and average value, etc. Finally, we use DAG-SVMs to intelligently diagnose switch failures. Experiments show that the accuracy of classification after Kalman filter preprocessing is better than that of direct classification. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Kalman filters

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A Priori Based Deep Unfolding Method for mmWave Channel Estimation in MIMO Radar Aided V2X Communications

A Priori Based Deep Unfolding Method for mmWave Channel Esti...

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IEEE International Conference on Communications (ICC)

作者： Jiapan Yang Xiao Gong Bo Ai Wei Chen State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University China Frontiers Science Center for Smart High-speed Railway System Beijing China Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications Beijing China School of Information Engineering Zhengzhou University Zhengzhou China Key Laboratory of Railway Industry of Broadband Mobile Information Communications Beijing China

Due to the inherent high-mobility features in the Vehicles-to-Everything (V2X) scenarios, accurate channel estimation is essential to ensure the quality of communication services. Recently, multiple-input multiple-output (MIMO) radar has shown the potential to aid channel estimation. In this paper, we consider the MIMO radar aided V2X communication systems and propose a prior information aided deep learning method for channel estimation. Specifically, we use the MIMO radar to measure the angle information of moving vehicles. Based on the estimated angles, we obtain the non-zero position information of sparse angle-frequency channel. Then, by formulating the channel estimation as solving a group row sparse recovery problem, we propose a new shrinkage function and derive a priori assisted deep unfolding method. Experimental results show that the proposed method achieves the highest channel estimation accuracy compared with existing compressive sensing algorithms and deep-learning-based baseline methods.

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Device-Edge Digital Semantic Communication with Trained Non-Linear Quantization

Device-Edge Digital Semantic Communication with Trained Non-...

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IEEE Conference on Vehicular Technology (VTC)

作者： Lei Guo Wei Chen Yuxuan Sun Bo Ai State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China Frontiers Science Center for Smart High-Speed Railway System School of Electronic and Information Engineering Beijing Jiaotong University Beijing China Key Laboratory of Railway Industry of Broadband Mobile Information Communications Beijing Engineering Research Center of High-Speed Railway Broadband Mobile Communications School of Information Engineering Zhengzhou University Zhengzhou China

Powered by deep learning, semantic communication is an intelligent communication paradigm, aiming to transmit useful information in the semantic domain. In most existing work, robust semantic features can be learned against wireless channel degradation, and directly transmitted in an analog fashion. However, analog semantic communication raises various challenges to the existing system from hardware/protocol to encryption issues. In this paper, we propose a novel non-linear quantization module to efficiently quantify semantic features. A sparse scaling vector is further incorporated to reduce the dimension of transmitted semantic features. Experimental results demonstrate that the proposed nonlinear quantization achieves better performance than the linear quantization method, and the performance of the digital system achieves better performance.

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Bandgap engineering of Resorcinol-Formaldehyde Resin for Efficient H2o2 Photosynthesis from Air and Water

SSRN

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SSRN 2024年

作者： Wang, Wuming Tang, Yuyang Ran, Jiaqi Teng, Wei Peng, Cheng Xu, Zuxin Chu, Wenhai State Key Laboratory of Pollution Control and Resources Reuse College of Environmental Science and Engineering Tongji University Shanghai200092 China Shanghai Institute of Pollution Control and Ecological Security Shanghai200092 China International Joint Research Center for Sustainable Urban Water System Tongji University Shanghai200092 China Key Laboratory of Interfacial Physics and Technology Shanghai Institute of Applied Physics Chinese Academy of Sciences Shanghai201800 China Tongji University China

On-site photosynthesis of hydrogen peroxide (PHP) from water and oxygen presents a promising vision for decentralized production technology of H2O2 at low concentration. Bandgap structure is crucial to photocatalyst for efficient H2O2 photosynthesis. Here we presented a sulfur-doping strategy to modulate the bandgap structure of resorcinol-formaldehyde (RF) resin to enhance the photocatalytic activity and stability. Results revealed the optimized bandgap structure of sulfur-doped RF enables an outstanding performance, with the solar-to-chemical conversion efficiency exceeding 0.6% after 24 h. Over sulfur-doped RF, oxygen can not only undergo a one-step two-electron reduction to produce H2O2, but an additional pathway, a two-step single-electron indirect reduction pathway, also occurs. Besides, PHP solution exhibited adequate disinfection efficiency in E. coli suspend and natural water, while its disinfection by-product (DBP) formation was negligible. This study provides valuable insights into designing efficient photocatalyst for practical applications, offering a sustainable and low-energy consumption technology for decentralized water disinfection. © 2024, The Authors. All rights reserved.

关键词： Photosynthesis

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Deep Learning Method for Path Loss Prediction in Mobile Communication systems 13

Deep Learning Method for Path Loss Prediction in Mobile Comm...

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13th International Symposium on Antennas, Propagation and EM Theory, ISAPE 2021

作者： Xu, Zhuocheng Cao, Huiyun Yin, Yue Zhang, Xiaohan Wu, Lina He, Danping Wang, Yuanbo Beijing Jiaotong University School of Electronic and Information Engineering Beijing China State Key Laboratory of Rail Traffic Control and Safety Beijing China Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications Beijing China Frontiers Science Center for Smart High-speed Railway System Beijing China Chn Energy Shuohuang Railway Development Company Ltd Hebei China

ISBN: (纸本)9781665418126

The study of wireless channel propagation characteristics is important to channel availability analysis and wireless network optimization. The existing deterministic channel models are greatly restricted by the high complexity. This paper proposes a deep learning (DL) method for path loss (PL) prediction in mobile communication systems, which uses satellite images and neural network (NN) to achieve efficient and reliable prediction. A Deep Neural Network (DNN) framework is established based on a Convolutional Neural Network (CNN) and a Back Propagation Neural Network (BPNN), where CNN extracts environmental features of propagation paths from satellite images and BPNN predicts PL according to the distance features and environmental features. The results show that DNN proposed in this paper can reduce the time cost with satisfying accuracy compared to the traditional Ray-tracing method with the lack of a detailed model of the scene and material parameters. The DL method for making a quick prediction of the PL in this paper can be used to optimize the mobile communication systems. © 2021 IEEE.

关键词： Deep neural networks

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S$^{\text{4}}$TP: Social-Suitable and Safety-Sensitive Trajectory Planning for Autonomous Vehicles

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2023年第2期9卷 3220-3231页

作者： Xiao Wang Ke Tang Xingyuan Dai Jintao Xu Quancheng Du Rui Ai Yuxiao Wang Weihao Gu Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education Anhui University Hefei China Haomo Technology Company Ltd. Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

In public roads, autonomous vehicles (AVs) face the challenge of frequent interactions with human-driven vehicles (HDVs), which render uncertain driving behavior due to varying social characteristics among humans. To effectively assess the risks prevailing in the vicinity of AVs in social interactive traffic scenarios and achieve safe autonomous driving, this article proposes a social-suitable and safety-sensitive trajectory planning (S $^{\text{4}}$ TP) framework. Specifically, S $^{\text{4}}$ TP integrates the Social-Aware Trajectory Prediction (SATP) and Social-Aware Driving Risk Field (SADRF) modules. SATP utilizes Transformers to effectively encode the driving scene and incorporates an AV's planned trajectory during the prediction decoding process. SADRF assesses the expected surrounding risk degrees during AVs-HDVs interactions, each with different social characteristics, visualized as two-dimensional heat maps centered on the AV. SADRF models the driving intentions of the surrounding HDVs and predicts trajectories based on the representation of vehicular interactions. S $^{\text{4}}$ TP employs an optimization-based approach for motion planning, utilizing the predicted HDVs' trajectories as input. With the integration of SADRF, S $^{\text{4}}$ TP executes real-time online optimization of the planned trajectory of AV within low-risk regions, thus improving the safety and the interpretability of the planned trajectory. We have conducted comprehensive tests of the proposed method using the SMARTS simulator. Experimental results in complex social scenarios, such as unprotected left-turn intersections, merging, cruising, and overtaking, validate the superiority of our proposed S $^{\text{4}}$ TP in terms of safety and rationality. S $^{\text{4}}$ TP achieves a pass rate of 100% across all scenarios, surpassing the current state-of-the-art methods Fanta of 98.25% and Predictive-Decision of 94.75%.

关键词： Trajectory Safety Vehicles Trajectory planning Autonomous vehicles Risk management Behavioral sciences

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Deep neural network based interpolation of sparse samples for time-dense power load forecasting 4

Deep neural network based interpolation of sparse samples fo...

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4th International Conference on Computer engineering and Application, ICCEA 2023

作者： Li, Bo Zhao, Ruifeng Lu, Jiangang Xin, Kuo Huang, Jinhua Lin, Guanqiang Chen, Jinrong Pang, Xueyue Wu, Shunxin Electric Power Dispatching and Control Center of Guangdong Power Grid Co. Ltd. Guangdong Guangzhou China Electric Power Dispatching and Control Center of China Southern Power Grid Co. Ltd. Guangdong Guangzhou China Electric Power Research Institute of Guangdong Power Grid Co. Ltd. Guangdong Guangzhou China Huizhou Power Supply Bureau of Guangdong Power Grid Co. Ltd. Guangdong Huizhou China Foshan Power Supply Bureau of Guangdong Power Grid Co. Ltd. Guangdong Foshan China China Energy Engineering Group Guangdong Electric Power Design Institute Co. Ltd. Guangdong Guangzhou China School of Electrical Engineering Chongqing University State Key Laboratory of Power Equipment & System Security and New Technology Chongqing China

ISBN: (纸本)9798350347548

Large amount of power load data with high-sampling rate plays a crucial role in the training of time-dense power load forecasting network. However, in many cases, there is a large amount of data with low-sampling rate, which can't directly train a time-dense forecasting network. Therefore, this paper proposes a deep learning based dataset interpolation method, which uses the available high-sampling rate power load datasets to train a dataset interpolation network, then interpolates the low - sampling rate datasets with trained network to obtain high-sampling rate datasets, and finally uses the interpolated datasets for time-dense forecasting network training. The results show that the data obtained based on the method proposed in this paper fits the ground-truth closer than the traditional interpolation, and the results of the network trained based on this dataset are more accurate in the subsequent forecasting tasks. © 2023 IEEE.

关键词： Interpolation

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Characteristics of Labile Organic Carbon Fractions Under Different Types of Subsidence Waterlogging Areas in the Coal Mining Area with High Ground Water Table: A Case Study in Xinglongzhuang Coal Mine, China

SSRN

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SSRN 2023年

作者： Fu, Yanhua Hu, Zhenqi Zhu, Qi Rong, Ying School of Economics and Management Tianjin Chengjian University Tianjin300384 China School of Environment Science and Spatial Informatics China University of Mining and Technology Xuzhou221116 China Chinese Research Academy of Environmental Sciences National Engineering Laboratory for Lake Pollution Control and Ecological Restoration State Environmental Protection Key Laboratory for Lake Pollution Control Beijing100012 China The Research Center of System and Industrial Technology Zhongyuan University of Technology Zhengzhou451191 China

The restoration of subsidence waterlogging areas has the potential to facilitate carbon accumulation in the coal mining areas with high ground water table. Nevertheless, the impact of restoration and management strategy on the soil organic carbon (SOC) and the labile organic carbon(LOC) have not been sufficiently demonstrated. In this study, SOC and LOC fractions, including light fraction organic carbon(LFOC), particulate organic carbon(POC), and dissolved organic carbon(DOC) within the 0-50cm profiles, were determined in the reference cropland(RC) and three types of subsidence waterlogging areas including aquaculture waterlogging area(AWA), deep waterlogging area(DWA) and shallow waterlogging area(SWA) in Xinglongzhuang Coal Mine, Shandong Province, China. Our results showed that compared with the reference cropland SOC and LOC decreased by 4.13%, 25.03%, and 77.91% in SWA, DWA, and AWA, respectively. The vital depletion was in the 0-10cm soil layer, which indicates that the recovery of total functional pools of SOC after subsidence and the succession of ecological systems takes a longer time. The liability of POC was highest among the three types of labile organic fractions which could indicate the mining subsidence triggered the soil redistribution and impact the carbon accumulation. With comparatively less interference on the soil profile, the liability of LFOC and POC decreased with soil depth, while DOC increased in DWA and SWA. And there was a large discrepancy in AWA, with the liability of LOC fractions fluctuating because of intensive engineering soil disturbance. Labile organic C fractions were closely associated and correlated with soil properties, especially TN and TP. Overall, the restoration and management have a vital impact on the soil organic carbon pool, it is suggested that shallow water, natural geomorphology-based topography, less soil disturbance, well development of vegetation communities, and continuous monitoring of soil and water properties

关键词： Subsidence

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Investigation on the reinforcement effect of a bedding slope affected by a landsliding block

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Arabian Journal of Geosciences 2022年第6期15卷 1-20页

作者： Zhang, Fei Yang, Tianhong Li, Lianchong Zhu, Xinping Key Laboratory of Roads and Railway Engineering Safety Control Shijiazhuang Tiedao University Ministry of Education Shijiazhuang China State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures Shijiazhuang Tiedao University Shijiazhuang China School of Civil Engineering Shijiazhuang Tiedao University Shijiazhuang China Center of Rock Instability and Seismicity Research School of Resources and Civil Engineering Northeastern University Shenyang Liaoning China China Coal Technology and Engineering Group Shenyang Research Institute Fushun China

The south slope of Fushun west open-pit mine located in Northeast China is a high-deep rock slope. In the last few years, a landsliding block has gradually formed in the south slope. Owing to the effect of sliding force caused by the sliding block, some damage has occurred on the foundation of Kengkou refinery which constructed in the west of the south slope. To improve the stability of the refinery foundation, three group micro-pile structures were employed to cut off the drag of the sliding block and reinforce the refinery on the southwest slope. In this study, a comprehensive method combining the microseismic monitoring technique and numerical simulation method was used, the stability of the southwest slope was evaluated, and reinforcement mechanism was deeply analyzed. A microseismic monitoring system is established on-site to obtain useful information about the damage energy, location, and magnitude of the rocks in the southwest slope. Thus, the west boundary of the landslide in the south slope of the open-pit is determined. By analyzing the numerical results, the stress concentration is discovered mainly around the micro-pile structures and near the west boundary. After reinforcement, the displacement decrease in the south-north direction is apparent. To investigate the interaction mechanism between the micro-pile and the surrounding rock mass, a local 3D numerical model is employed to help understand auxiliary information about the stress state and stress redistribution induced by failure in the surrounding rock mass. It is shown that the reinforcement effect becomes more and more evident with the increase in the number of employed micro-piles. The numerical results and in situ GPS displacement monitoring data illustrate that the micro-pile structures have an excellent strengthening result in controlling the deformation of the slope. The comprehensive methodology provides an effective way to estimate the stability and the reinforcement effect of the slope und

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