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ALSTNet:Autoencoder fused long-and short-term time-series network for the prediction of tunnel structure

Deep Underground Science and Engineering 2025年第1期4卷 72-82页

作者： Bowen Du Haohan Liang Yuhang Wang Junchen Ye Xuyan Tan Weizhong Chen State Key Laboratory of Software Development Environment Beihang UniversityBeijingChina State Key Laboratory of Geomechanics and Geotechnical Engineering Institute of Rock and Soil MechanicsChinese Academy of SciencesWuhanChina

It is crucial to predict future mechanical behaviors for the prevention of structural *** for underground construction,the structural mechanical behaviors are affected by multiple internal and external factors due to the complex *** that the existing models fail to take into account all the factors and accurate prediction of the multiple time series simultaneously is difficult using these models,this study proposed an improved prediction model through the autoencoder fused long-and short-term time-series network driven by the mass number of monitoring ***,the proposed model was formalized on multiple time series of strain monitoring ***,the discussion analysis with a classical baseline and an ablation experiment was conducted to verify the effectiveness of the prediction *** the results indicate,the proposed model shows obvious superiority in predicting the future mechanical behaviors of *** a case study,the presented model was applied to the Nanjing Dinghuaimen tunnel to predict the stain variation on a different time scale in the future.

关键词： autoencoder deep learning structural health monitoring time-series prediction

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Rumor Detection on Social Media with Temporal Propagation Structure Optimization 31

Rumor Detection on Social Media with Temporal Propagation St...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Peng, Xingyu Wu, Junran Liu, Ruomei Xu, Ke State Key Laboratory of Complex & Critical Software Environment Beihang University Beijing China

ISBN: (纸本)9798891761964

Traditional methods for detecting rumors on social media primarily focus on analyzing textual content, often struggling to capture the complexity of online interactions. Recent research has shifted towards leveraging graph neural networks to model the hierarchical conversation structure that emerges during rumor propagation. However, these methods tend to overlook the temporal aspect of rumor propagation and may disregard potential noise within the propagation structure. In this paper, we propose a novel approach that incorporates temporal information by constructing a weighted propagation tree, where the weight of each edge represents the time interval between connected posts. Drawing upon the theory of structural entropy, we transform this tree into a coding tree. This transformation aims to preserve the essential structure of rumor propagation while reducing noise. Finally, we introduce a recursive neural network to learn from the coding tree for rumor veracity prediction. Experimental results on two common datasets demonstrate the superiority of our approach. © 2025 Association for Computational Linguistics.

关键词： Graph neural networks

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Multi-orbital electronic coupling mediated by integrating multiple-metal hybridizations at ultrafast heating accumulation for efficient electrochemical urea synthesis

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Journal of Energy Chemistry 2025年第4期103卷 657-664页

作者： Rong Huang Fang Xue Peifang Wang Chongchong Liu Peilin Huang Lizhe Liu Hao Li Yitingyu Zhou Gang Zhou Key Laboratory of Integrated Regulation and Resource Development on Shallow Lake of Ministry of Education College of Environment Hohai University College of Mechanical and Electrical Engineering Handan University Jiangsu Key Laboratory for Nanotechnology and Collaborative Innovation Center of Advanced Microstructures National Laboratory of Solid State Microstructures Nanjing University

Electrocatalytic C—N coupling is an environmentally friendly pathway for reducing CO2emissions,nitrate wastewater treatment,and urea ***2is a commonly used electrocatalyst for urea synthesis,but its yield was restricted by the deficiency of active sites and the high barrier for C—N ***,we employed transient heating to introduce oxygen vacancies as sites for the deposition of single metal atoms,thereby maximizing the atomic utilization as active sites for urea *** as-prepared CuFe-V-CeO2electrocatalyst exhibits the outstanding urea yield rate(3553 mg h-1gcat-1.) at-1.5 V versus reversible hydrogen electrode（RHE）,surpassing the performance of previously reported electrochemical urea *** calculation further revealed the roles of Ce,Cu,and Fe sites in active hydrogen（*H） generation,nitrate treatment,and CO2stabilization,*** work offers a novel and effective pathway for the design of electrocatalysts and developing an efficient C—N coupling system for urea production.

关键词： C[sbnd]N coupling Electrocatalytic Nitrate treatment Urea synthesis

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ICCG:low-cost and efficient consistency with adaptive synchronization for metadata replication

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Frontiers of Computer Science 2025年第1期19卷 53-70页

作者： Chenhao ZHANG Liang WANG Jing SHANG Zhiwen XIAO Limin XIAO Meng HAN Bing WEI Runnan SHEN Jinquan WANG State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China School of Cyberspace Security Hainan UniversityHaikou 570228China China Mobile Information Technology Center Beijing 100033China

The rapid growth in the storage scale of wide-area distributed file systems (DFS) calls for fast and scalable metadata management. Metadata replication is the widely used technique for improving the performance and scalability of metadata management. Because of the POSIX requirement of file systems, many existing metadata management techniques utilize a costly design for the sake of metadata consistency, leading to unacceptable performance overhead. We propose a new metadata consistency maintenance method (ICCG), which includes an incremental consistency guaranteed directory tree synchronization (ICGDT) and a causal consistency guaranteed replica index synchronization (CCGRI), to ensure system performance without sacrificing metadata consistency. ICGDT uses a flexible consistency scheme based on the state of files and directories maintained through the conflict state tree to provide an incremental consistency for metadata, which satisfies both metadata consistency and performance requirements. CCGRI ensures low latency and consistent access to data by establishing a causal consistency for replica indexes through multi-version extent trees and logical time. Experimental results demonstrate the effectiveness of our methods. Compared with the strong consistency policies widely used in modern DFSes, our methods significantly improve the system performance. For example, in file creation, ICCG can improve the performance of directory tree operations by at least 36.4 times.

关键词： metadata management metadata replication consistency directory tree replica index

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Photo-transformation of nitrate and fulvic acid driven by guest iron minerals

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Frontiers of environmental Science & Engineering 2025年第1期19卷 115-128页

作者： Na Huang Yuanyuan Chen Xuyin Yuan Yingying Li Yin Lu Yilan Jiang Huacheng Xu Lingxiao Ren Dawei Wang Key Laboratory of Integrated Regulation and Resource Development of Shallow Lakes(Ministry of Education) College of EnvironmentHohai UniversityNanjing 210098China State Key Laboratory of Lake Science and Environment Nanjing Institute of Geography and LimnologyChinese Academy of SciencesNanjing 210008China School of Environmental Engineering Nanjing Institute of TechnologyNanjing 210008China

The photochemical interactions between nitrate(NO_(3)^(-))and natural organic matter(NOM)are vital for aquatic ***,the effects of guest iron minerals,which may enter the aquatic environments due to both human and natural activities,on those interactions are widely *** work evaluated the effects of hematite(α-Fe_(2)O_(3))on the photochemical conversion products and pathways of NO_(3)^(-),fulvic acid(FA)under 12 h of ultraviolet *** addition of 0.4 g/L of guestα-Fe_(2)O_(3)accelerated the reduction of NO_(3)^(-)by 24.3%,with NH4+as the primary reduction product,and hampered the mineralization of *** effects were dependent on the dosage amount ofα-Fe2O3 and FA *** studies on the molecule-level changes of FA revealed that the complete oxidation to CO_(2)and the partial oxidation pathways that alter the molecular composition of FA were suppressed,and the mineralization rate decreased by 27.8%.Particularly,the conversion rates of CHON and CHONS were reduced by 21.0%and 20.3%,respectively,increasing the unsaturated *** scavenging experiments and quantitative measurements of hydroxyl radicals(•OH)proposed that the photogenerated electrons and holes fromα-Fe_(2)O_(3)were the key for the altered transformation of NO3-and *** work revealed the guest effects of iron mineral particles on the photochemical interactions between NO_(3)^(-)and NOM in the natural surface waters.

关键词： Guest iron minerals Photochemistry Nitrate Fulvic acid Conversion

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Propagation criterion of hydraulic fracture in rock based on the rock micro-cracking mechanism

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International Journal of Mining Science and Technology 2025年第3期35卷 433-449页

作者： Qingwang Cai Bingxiang Huang Xinglong Zhao Yuekun Xing School of Resources Environment and Safety Engineering University of South ChinaHengyang 421001 China State Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources China University of Mining and TechnologyXuzhou 221116 China

Hydraulic fracture(HF)formed in rock significantly helps with the development of geo-energy and *** HF formation condition was challenging to understand,with obscure rock micro-cracking mechanisms being a key *** rock micro-cracking mechanism under gradient pore water pressure was analyzed on the scale of mineral particles and it was combined with macroscopic boundary conditions of rock hydraulic fracturing,obtaining the propagation criterion of HF in rock based on the rock micro-cracking mechanism which was verified by *** results show that the disturbed skeleton stress induced by the disturbance of gradient pore water pressure in rock equals the pore water pressure *** overall range of the defined mechanical shape factor a/b is around 1,but greater *** the combined influence of pore water pressure differences and macroscopic boundary stresses on the rock micro-cracking,micro-cracks form among rock mineral particles,micro-cracks connect to form micro-hydraulic fracture surfaces,and micro-hydraulic fracture surfaces open to form macrohydraulic *** begins to form at the micro-cracking initiation pressure(MCIP),which was tested by keeping the HF tip near the initiation *** theoretical value of MCIP calculated by the proposed propagation criterion is close to MCIP tested.

关键词： Hydraulic fracture Propagation criterion Micro-cracking mechanism Pore pressure Stress singularity

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Deep learning-based software engineering: progress,challenges, and opportunities

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Science China(Information Sciences) 2025年第1期68卷 57-144页

作者： Xiangping CHEN Xing HU Yuan HUANG He JIANG Weixing JI Yanjie JIANG Yanyan JIANG Bo LIU Hui LIU Xiaochen LI Xiaoli LIAN Guozhu MENG Xin PENG Hailong SUN Lin SHI Bo WANG Chong WANG Jiayi WANG Tiantian WANG Jifeng XUAN Xin XIA Yibiao YANG Yixin YANG Li ZHANG Yuming ZHOU Lu ZHANG School of Journalism and Communication Sun Yat-sen University School of Software Technology Zhejiang University School of Software Engineering Sun Yat-sen University School of Software Dalian University of Technology School of Computer Science and Technology Beijing Institute of Technology Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of EducationSchool of Computer Science Peking University State Key Laboratory for Novel Software Technology Nanjing University School of Computer Science and Engineering Beihang University Institute of Information Engineering Chinese Academy of Sciences School of Computer Science Fudan University State Key Laboratory of Complex & Critical Software Environment (CCSE) School of Software Beihang University School of Computer and Information Technology Beijing Jiaotong University School of Computer Science and Technology Harbin Institute of Technology School of Computer Science Wuhan University Huawei Technologies

Researchers have recently achieved significant advances in deep learning techniques, which in turn has substantially advanced other research disciplines, such as natural language processing, image processing, speech recognition, and software engineering. Various deep learning techniques have been successfully employed to facilitate software engineering tasks, including code generation, software refactoring, and fault localization. Many studies have also been presented in top conferences and journals, demonstrating the applications of deep learning techniques in resolving various software engineering tasks. However,although several surveys have provided overall pictures of the application of deep learning techniques in software engineering,they focus more on learning techniques, that is, what kind of deep learning techniques are employed and how deep models are trained or fine-tuned for software engineering tasks. We still lack surveys explaining the advances of subareas in software engineering driven by deep learning techniques, as well as challenges and opportunities in each subarea. To this end, in this study, we present the first task-oriented survey on deep learning-based software engineering. It covers twelve major software engineering subareas significantly impacted by deep learning techniques. Such subareas spread out through the whole lifecycle of software development and maintenance, including requirements engineering, software development, testing, maintenance, and developer collaboration. As we believe that deep learning may provide an opportunity to revolutionize the whole discipline of software engineering, providing one survey covering as many subareas as possible in software engineering can help future research push forward the frontier of deep learning-based software engineering more systematically. For each of the selected subareas,we highlight the major advances achieved by applying deep learning techniques with pointers to the available datasets i

关键词： deep learning software engineering software benchmark software artifact representation survey

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Machine learning for membrane bioreactor research:principles,methods,applications,and a tutorial

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Frontiers of environmental Science & Engineering 2025年第3期19卷 119-144页

作者： Yizhe Lai Kang Xiao Yifan He Xian Liu Jihua Tan Wenchao Xue Aiqian Zhang Xia Huang Beijing Yanshan Earth Critical Zone National Research Station College of Resources and EnvironmentUniversity of Chinese Academy of SciencesBeijing 101408China Key Laboratory of Earth System Numerical Modeling and Application University of Chinese Academy of SciencesBeijing 101408China Environmental Chemistry Faculty College of Resources and EnvironmentUniversity of Chinese Academy of SciencesBeijing 101408China State Key Laboratory of Environmental Chemistry and Ecotoxicology Research Center for Eco-Environmental SciencesChinese Academy of SciencesBeijing 100085China Department of Energy Environmentand Climate ChangeSchool of EnvironmentResourcesand DevelopmentAsian Institute of TechnologyP.O.Box 4Klong LuangPathumthani 12120Thailand State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijing 100084China

Membrane fouling poses a significant challenge to the sustainable development of membrane bioreactor(MBR)technologies for wastewater *** accurate prediction of the membrane filtration process is of great importance for identifying and controlling *** learning methods address the limitations of traditional statistical approaches,such as low accuracy,poor generalization ability,and slow convergence,particularly in predicting complex filtration and fouling processes within the realm of big *** article provides an in-depth exposition of machine learning *** study then reviews advances in MBRs that utilize machine learning methods,including artificial neural networks(ANN),support vector machines(SVM),decision trees,and ensemble *** on current literature,this study summarizes and compares the model input and output characteristics(including foulant characteristics,solution environments,filtration conditions,operating conditions,and time factors),as well as the selection of models and optimization *** modeling procedures of SVM,random forest(RF),back propagation neural network(BPNN),long short-term memory(LSTM),and genetic algorithm-back propagation(GA-BP)methods are elucidated through a tutorial *** simulation results demonstrated that all five methods yielded accurate predictions with R2>***,the existing challenges in the implementation of machine learning models in MBRs were *** is notable that integration of deep learning,automated machine learning(AutoML)and explainable artificial intelligence(XAI)may facilitate the deployment of models in practical engineering *** insights presented here are expected to facilitate the establishment of an intelligent control framework for MBR processes in future endeavors.

关键词： Membrane bioreactor Machine learning Pollutant removal Membrane fouling Model prediction

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Characterization and function of particulate organic matter:Evidence from lakes undergoing ecological restoration

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Journal of environmental Sciences 2025年第4期150卷 91-103页

作者： Letian Zhang Wenqiang Zhang Hao Guo Baoqing Shan Dongyang Wei State Key Laboratory of Environmental Aquatic Chemistry Research Center for Eco-Environment SciencesChinese Academy of SciencesBeijing 100085China School of Environmental and Municipal Engineering Lanzhou Jiaotong UniversityLanzhou 730070China Technical Centre for Soil Agriculture and Rural Ecology and EnvironmentMinistry of Ecology and EnvironmentNo.8DayangfangBeijing 100012China Environmental Development Center of Ministry of Ecology and Environment Beijing 100029China

Particulate organic matter(POM)plays a crucial role in the organic composition of lakes;however,its characteristics remain poorly *** study aimed to characterize the structure and composition of POM in Lake Baiyangdian usingmany kinds of techniques and investigate the effects of different extracted forms of POM on water *** suspended particulatematter in the lake had complex compositions,with its components primarily derived from aquatic plants and their *** organic matter content of the suspended particulatematterwas relatively high(organic carbon content 27.29–145.94 g/kg)for the sum of three extractable states(water-extracted organic matter[WEOM],humic acid,and fulvic acid)and one stable bound state(humin).Spatial distribution analysis revealed that the POM content in the water increased from west to east,which was consistent with the water flow pattern influenced by the Baiyangdian water diversion *** spectroscopy analysis of the WEOM showed three prominent peaks with excitation/emission wavelengths similar to those of dissolved organic matter *** peaks were potentially initial products of POM conversion into dissolved organic ***,the intensity of the WEOM fluorescence peak(total fluorescence peak intensity)was negatively correlated with the inorganic nitrogen concentration in water(p<0.01),while the intensity of the HA fluorescence peak showed a positive correlation with the inorganic nitrogen concentration(p<0.01).This suggested that exogenous organic matter inputs led to the diffusion of alkaline dissolved nitrogen from sediment intowater,while degradation processes of aquatic plant debris contributed to the decrease in inorganic nitrogen concentrations in the water *** findings enhance our understanding of POM characteristics in shallow lakes and the role of POM in shallow lake ecosystems.

关键词： Lake baiyangdian Particulate organic matter Physical structure Spatial distribution Three-dimensional fluorescence spectroscopy

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Effect of different climate zone’s humic and fulvic acid on aggregation of UV irradiated graphene oxide

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Frontiers of environmental Science & Engineering 2025年第3期19卷 27-41页

作者： Jawad Ali Xinfeng Wang Xinjie Wang Enxiang Shang Zahid Hussain Muhammad Mohiuddin Jian Zhao Xinghui Xia Yang Li Environment Research Institute Shandong UniversityQingdao 266237China State Key Laboratory of Water Environment Simulation School of EnvironmentBeijing Normal UniversityBeijing 100875China College of Science and Technology Hebei Agricultural UniversityHuanghua 061100China Department of Development Studies COMSATS University IslamabadAbbottabad CampusAbbottabad 22060Pakistan Environment Management Consultant(EMC)Pakistan Pvt Ltd. Karachi 75350Pakistan

UV light absorption by aquatic systems affect the physicochemical characteristics of graphene oxide(GO)nanoparticles which ultimately influence its aggregation behavior in *** this research,various humic and fulvic acids(HA/FA),extracted from China’s different climate zones,were treated with 2 h UV irradiated large(~500 nm)and(~200 nm)GO in 200 mmol/L *** irradiated GO particles displayed aggregation even at low humic acid/fulvic acid(HA/FA)concentrations ranging from 0.2 to 1.0 mgC/L,whereas pristine GO particles did not exhibit such *** of functional groups,containing Oxygen(C=O/C–O),via UV irradiation is responsible for this aggregation phenomenon and conversion of GO to reduced graphene oxide(rGO).Consequently,rGO exhibits lower dispersibility,facilitating its ***,both small and large-sized GO particles exhibited less aggregation in HAs compared to FAs due to large molecular weight and high polarity of *** of GO was more obvious with Makou FA and Maqin HA from Plateau and Mountain climate zone and Subtropical Monsoon climate zone,respectively,owing to DOM’s lower molecular weight and aromaticity that reduced their *** application of the Derjaguin-landau-verwey-overbeek(DLVO)theory did not reveal any significant interaction energy barrier between the 2 h UV irradiated GO particles even in the presence of DOM,indicating that aggregation prevailed despite the addition of *** findings highlight that UV irradiation poses a significant threat to the GO stability in aquatic environments,particularly in the presence of DOM.

关键词： Aggregation Fulvic acid Humic acid Molecular weight Reduced graphene oxide Ultraviolet irradiation

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