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Using Hourly Measurements to Explore the Role of Secondary Inorganic Aerosol in PM_(2.5)during Haze and Fog in Hangzhou, China

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Advances in Atmospheric Sciences 2014年第6期31卷 1427-1434页

作者： Roeland Cornelis JANSEN SHI Yang CHEN Jianmin HU YunJie XU Chang HONG Shengmao LI Jiao ZHANG Min Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention Department of Environmental Science & Engineering Fudan University Hangzhou Municipal Environmental Monitoring Center Key Laboratory of Data Analysis and Application the First Institute of OceanographyState Oceanic Administration

This paper explores the role of the secondary inorganic aerosol （SIA） species ammonium,NH4＋,nitrate,NO3-,and sulfate,SO24-,during haze and fog events using hourly mass concentrations of PM2.5 measured at a suburban site in Hangzhou,China.A total of 546 samples were collected between 1 April and 8 May *** samples were analyzed and classified as clear,haze or fog depending on visibility and relative humidity （RH）.The contribution of SIA species to PM2.5 mass increased to ～50％ during haze and *** mass contribution of nitrate to PM2.5 increased from 11％ during clear to 20％ during haze *** mass exceeded sulfate mass during haze,while near equal concentrations were observed during fog *** role of RH on the correlation between concentrations of SIA and visibility was examined,with optimal correlation at 60％-70％ *** total acidity during clear,haze and fog periods was 42.38,48.38 and 45.51 nmol m-3,respectively,indicating that sulfate,nitrate and chloride were not neutralized by ammonium during any *** nitrate to sulfate molar ratio,as a function of the ammonium to sulfate molar ratio,indicated that nitrate formation during fog started at a higher ammonium to sulfate molar ratio compared to clear and haze *** haze and fog,the nitrate oxidation ratio increased by a factor of 1.6-1.7,while the sulfur oxidation ratio increased by a factor of 1.2-1.5,indicating that both gaseous NO2 and SO2 were involved in the reduced visibility.

关键词： haze secondary inorganic aerosol PM2.5 Yangtze River Delta

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Heterogeneous-attributes enhancement deep framework for network embedding

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Frontiers of Computer Science 2021年第6期15卷 121-131页

作者： Lisheng QIAO Fan ZHANG Xiaohui HUANG Kai LI Enhong CHEN Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of ChinaHefei 230022China National Key Laboratory of Blind Signal Processing Chengdu 610041China School of Computer Science and Technology University of Science and Technology of ChinaHefei 230022China

Network embedding,which targets at learning the vector representation of vertices,has become a crucial issue in network ***,considering the complex structures and heterogeneous attributes in real-world networks,existing methods may fail to handle the inconsistencies between the structure topology and attribute ***,more comprehensive techniques are urgently required to capture the highly non-linear network structure and solve the existing inconsistencies with retaining more *** that end,in this paper,we propose a heterogeneous-attributes enhancement deep framework(HEDF),which could better capture the non-linear structure and associated information in a deep learningway,and effectively combine the structure information of multi-views by the combining *** this line,the inconsistencies will be handled to some extent and more structure information will be preserved through a semi-supervised *** extensive validations on several real-world datasets show that our model could outperform the baselines,especially for the sparse and inconsistent situation with less training data.

关键词： network embedding heterogeneous-attributes deep framework inconsistent

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Adaptive offloading scheme of fine-grained tasks based on reinforcement learning in multi-access edge computing

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Simulation Modelling Practice and Theory 2025年 143卷

作者： Jie Li Ge Chen Kansong Chen School of Computer Science Hubei University Wuhan 430062 China Hubei Key Laboratory of Big Data Intelligent Analysis and Application (Hubei University) Wuhan 430062 China Engineering Research Center of Hubei Province in Intelligent Government Affairs and Application of Artificial Intelligence Wuhan 430062 China Key Laboratory of Intelligent Sensing System and Security (Hubei University) Ministry of Education Wuhan 430062 China

In the context of multi-access edge computing, the constraints on device resources and energy pose a challenge in meeting the requirements of delay-sensitive and high-resource demand tasks. In order to address this issue, this paper puts forth an adaptive offloading decision based on fine-grained task sequencing (AOD-FTS). To enhance task execution efficiency and resource utilization, we design scheduling optimization for subtasks algorithm within the fine-grained task model to optimize the execution order of subtasks and improve subtask parallelism. Additionally, to reduce task delay and energy consumption, we propose an adaptive multi-objective reinforcement learning offloading algorithm based on ordered subtasks, formulated through multi-objective Markov decision processes. The scheme dynamically adjusts optimization preferences according to environmental changes and task completion states to make offloading decisions. Simulation results demonstrate that compared with two benchmark algorithms, the proposed AOD-FTS scheme achieves: (1) higher task completion rates, (2) improved resource utilization efficiency, (3) 81.66 % and 51.53 % reductions in delay respectively, and (4) 39.72 % and 71.36 % reductions in energy consumption respectively.

关键词： Reinforcement learning

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Multi-dimensional graph linear canonical transform

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Digital Signal Processing: A Review Journal 2025年 165卷

作者： Li, Na Zhang, Zhichao School of Mathematics and Statistics Center for Applied Mathematics of Jiangsu Province Jiangsu International Joint Laboratory on System Modeling and Data Analysis Nanjing University of Information Science and Technology Nanjing 210044 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai 200240 China Key Laboratory of Computational Science and Application of Hainan Province Haikou 571158 China

Multi-dimensional (M-D) graph signals represent fundamental data structures in various practical applications, including digital imaging systems, sensor network telemetry, and meteorological observation records. The development of efficient transformation methodologies for processing such M-D graph signals within the linear canonical transform domain continues to present significant challenges. This paper introduces two novel transforms: the two-dimensional graph linear canonical transform based on central discrete dilated Hermite functions (2-D CDDHFs-GLCT) and the two-dimensional graph linear canonical transform based on chirp multiplication-chirp convolution-chirp multiplication decomposition (2-D CM-CC-CM-GLCT). These foundational constructs are extended to the multi-dimensional domain, resulting in M-D CDDHFs-GLCT and M-D CM-CC-CM-GLCT. In terms of computational complexity, additivity and reversibility, M-D CDDHFs-GLCT and M-D CM-CC-CM-GLCT are compared. Theoretical analyses reveal that the M-D CM-CC-CM-GLCT algorithm achieves substantial computational complexity reduction compared to its CDDHFs-based counterpart. Numerical simulations further establish that while maintaining comparable additivity performance, the M-D CM-CC-CM-GLCT exhibits enhanced reversibility characteristics. To validate practical applicability, the proposed M-D GLCT framework is implemented in data compression scenarios. Experimental results demonstrating superior algorithmic efficiency and implementation effectiveness compared to multi-dimensional graph fractional Fourier transform. © 2025 Elsevier Inc.

关键词： data compression Graph signal processing Linear canonical transform Multi-dimensional signal processing

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Noise-enhanced graph contrastive learning for multimodal recommendation systems

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Knowledge-Based Systems 2025年 324卷

作者： Ke Shi Yan Zhang Miao Zhang Kui Xiao Xiaoju Hou Zhifei Li School of Computer Science Hubei University Wuhan 430062 China Key Laboratory of Intelligent Sensing System and Security (Hubei University) Ministry of Education Wuhan 430062 China Hubei Key Laboratory of Big Data Intelligent Analysis and Application (Hubei University) Wuhan 430062 China Institute of Vocational Education Guangdong Industry Polytechnic University Guangzhou 510300 China

Multimodal recommendation systems enhance recommendation accuracy through the fusion of different types of information. Graph Contrastive Learning (GCL) has recently been widely applied in recommendation systems. However, the application of GCL mainly focuses on general user–item interactions, and few models apply it to multimodal information learning. This limitation may hinder the full potential of GCL in more complex multimodal scenarios. To address this issue, we propose a N oise- E nhanced G raph C ontrastive L earning for Multimodal Recommendation Systems (NEGCL). As different data augmentation strategies yield distinct outcomes in GCL, we have examined their relative strengths and weaknesses. Introducing innovative contrastive learning into the multimodal information extraction process helps address the limitations of this method in multimodal recommendation, supplemented by an improved transformer for learning in general interactions. A longitudinal comparison of contrastive learning across different methods reveals that noise-based contrastive learning substantially reduces the additional overhead potentially introduced by data augmentation. Furthermore, the introduction of appropriate noise allows contrastive learning to develop more robust nodes. Extensive experiments on three public datasets reveal that NEGCL surpasses previous benchmark models, achieving performance improvements ranging from 8.46% to 11.62% on the Clothing dataset. The source code is publicly available at https://***/HubuKG/NEGCL .

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Adam revisited:a weighted past gradients perspective

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Frontiers of Computer Science 2020年第5期14卷 61-76页

作者： Hui Zhong Zaiyi Chen Chuan Qin Zai Huang Vincent W.Zheng Tong Xu Enhong Chen Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of ChinaHefei230027China Zhejiang Cainiao Supply Chain Management Co.Ltd Hangzhou311122China Advanced Digital Sciences Center Singapore138602Singapore

Adaptive learning rate methods have been successfully applied in many fields,especially in training deep neural *** results have shown that adaptive methods with exponential increasing weights on squared past gradients(i.e.,ADAM,RMSPROP)may fail to converge to the optimal *** many algorithms,such as AMSGRAD and ADAMNC,have been proposed to fix the non-convergence issues,achieving a data-dependent regret bound similar to or better than ADAGRAD is still a challenge to these *** this paper,we propose a novel adaptive method weighted adaptive algorithm(WADA)to tackle the non-convergence *** AMSGRAD and ADAMNC,we consider using a milder growing weighting strategy on squared past gradient,in which weights grow *** on this idea,we propose weighted adaptive gradient method framework(WAGMF)and implement WADA algorithm on this ***,we prove that WADA can achieve a weighted data-dependent regret bound,which could be better than the original regret bound of ADAGRAD when the gradients decrease *** bound may partially explain the good performance of ADAM in ***,extensive experiments demonstrate the effectiveness of WADA and its variants in comparison with several variants of ADAM on training convex problems and deep neural networks.

关键词： adaptive learning rate methods stochastic gradient descent online learning

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Lymph node metastasis mapping and prognostic analysis of early gastric cancer

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Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract 2025年 102082页

作者： Wenqian Ma Honghai Guo Peigang Yang Jinchen He Tongkun Li Haotian Wu Jiaxiang Wu Jiaxuan Yang Yuan Tian Shuo Guo Gongning Wang Hongcai Wang Qi Wang Limian Er Ping'an Ding Qun Zhao The Third Department of Surgery the Fourth Hospital of Hebei Medical University Shijiazhuang 050011 China Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer Shijiazhuang 050011 China Big data analysis and mining application for precise diagnosis and treatment of gastric cancer Hebei Provincial Engineering Research Center Shijiazhuang 050011 China Department of Endoscopy The Fourth Hospital of Hebei Medical University Shijiazhuang 050011 China. Big data analysis and mining application for precise diagnosis and treatment of gastric cancer Hebei Provincial Engineering Research Center Shijiazhuang 050011 China. Big data analysis and mining application for precise diagnosis and treatment of gastric cancer Hebei Provincial Engineering Research Center Shijiazhuang 050011 China. Electronic address: ding_ping_an@***. Big data analysis and mining application for precise diagnosis and treatment of gastric cancer Hebei Provincial Engineering Research Center Shijiazhuang 050011 China. Electronic address: zhaoqun@***.

BACKGROUND:Many studies have focused on the analysis of risk factors for lymph node metastasis in early gastric cancer, but few studies analyze the drainage patterns of metastatic lymph nodes. METHODS:data was retrospectively analyzed from a database of gastric cancer resections from 2014-2018. The cohort included 786 pT1 patients with complete data. Outcomes evaluated were lymph node metastasis frequencies, survival analyses, and risk factors impacting prognosis. Lymph node mapping was conducted to explore the distribution pattern of metastatic nodes based on tumor location. RESULTS:The overall lymph node metastasis rate was 23.7%. Lymph node mapping showed that No. 7 (67.7%) and No. 3 (45.7%) group had the highest metastasis rates and regardless of tumor location, metastases were concentrated in perigastric lymph node groups (No. 3, 4, 5, 6, and 7), while involvement of second-tier station nodes was rare. The 5-year overall survival rate (54.8% vs 95.7%; P < 0.001) and disease-free survival rate (48.4% vs 95.7%; P < 0.001) of patients with node-positive disease were significantly worse than those of patients with node-negative disease. Multivariable Cox regression identified tumor size > 2cm (P = 0.007, < 0.001), poor differentiation (P = 0.007, < 0.001), T1b stage (all P < 0.001), lymph node metastasis (all P < 0.001), and vascular invasion (all P = 0.002, 0.016) as independent negative prognostic factors affecting 5-year OS and DFS in patients with early gastric cancer. Postoperative chemotherapy (P < 0.001, 0.019）was an independent positive prognostic factor. CONCLUSION:This retrospective study demonstrates that lymph node metastasis in early gastric cancer is widely and disorderly not depending on the location. Lymph node metastasis mainly occurs in the perigastric station, supporting a specific lymph node dissection strategy may be sufficient for some early gastric cancer. Meanwhile its prognosis is closely related to lymph node metastasis. AVAILABILITY OF DA

关键词： Early gastric cancer Lymph nodes Patterns Prognosis Risk factors

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Clause-level Relationship-aware Math Word Problems Solver

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Machine Intelligence Research 2022年第5期19卷 425-438页

作者： Chang-Yang Wu Xin Lin Zhen-Ya Huang Yu Yin Jia-Yu Liu Qi Liu Gang Zhou Anhui Province Key Laboratory of Big Data Analysis and Application School of Data ScienceUniversity of Science and Technology of ChinaHefei 230026China Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefei 230088China Laboratory of Mathematical Engineering and Advanced Computing Information Engineering UniversityZhengzhou 450001China

Automatically solving math word problems,which involves comprehension,cognition,and reasoning,is a crucial issue in artificial intelligence *** math word problem solvers mainly work on word-level relationship extraction and the generation of expression solutions while lacking consideration of the clause-level *** this end,inspired by the theory of two levels of process in comprehension,we propose a novel clause-level relationship-aware math solver(CLRSolver)to mimic the process of human comprehension from lower level to higher ***,in the lower-level processes,we split problems into clauses according to their natural division and learn their *** the higher-level processes,following human′s multi-view understanding of clause-level relationships,we first apply a CNN-based module to learn the dependency relationships between clauses from word relevance in a local ***,we propose two novel relationship-aware mechanisms to learn dependency relationships from the clause semantics in a global ***,we enhance the representation of clauses based on the learned clause-level dependency *** expression generation,we develop a tree-based decoder to generate the mathematical *** conduct extensive experiments on two datasets,where the results demonstrate the superiority of our framework.

关键词： Artificial intelligence(AI) artificial neural network(ANN) computational mathematics machine intelligence machine learning

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Legal Judgment Prediction with Multiple Perspectives on Civil Cases 1st

Legal Judgment Prediction with Multiple Perspectives on Civi...

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1st CAAI International Conference on Artificial Intelligence, CICAI 2021

作者： Zhao, Lili Yue, Linan An, Yanqing Liu, Ye Zhang, Kai He, Weidong Chen, Yanmin Yuan, Senchao Liu, Qi Anhui Province Key Laboratory of Big Data Analysis and Application School of Data Science and School of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (纸本)9783030930455

Legal Judgment Prediction, which aims at predicting the judgment result based on case materials, is an essential task in Legal Intelligence. Most existing studies have analyzed and modeled criminal cases as a whole, while a small part of the research focuses on civil cases which are also significant in the legal system. In these studies, most model on a certain type of civil causes and make the judgment prediction based on the ascertained fact from the perspective of the court. However, in real-world scenarios, there are different civil cases on various causes, and every case is judged from multiple perspectives (i.e., plaintiff, defendant, and the court). It is difficult to make judgment predictions on various civil causes. To address the above challenges, in this paper, we propose a novel Civil Case Judgment (CCJudge) prediction method by simulating the logic of judges. Following this logic, we construct an external knowledge base that contains the explanations and applications of every cause, it helps make the judgment on various causes. Furthermore, a special encoder layer and interaction layer are designed for learning linguistic semantics from multiple perspectives. We conduct extensive experiments on a real-world dataset. The experimental results demonstrate the effectiveness of our method. © 2021, Springer Nature Switzerland AG.

关键词： Forecasting

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Deep learning approaches in flow visualization

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Advances in Aerodynamics 2022年第1期4卷 327-340页

作者： Can Liu Ruike Jiang Datong Wei Changhe Yang Yanda Li Fang Wang Xiaoru Yuan Key Laboratory of Machine Perception(Ministry of Education) and School of AIPeking UniversityBeijingChina China Aerodynamics Research and Development Center MianyangChina National Engineering Laboratory for Big Data Analysis and Application Peking UniversityBeijingChina

With the development of deep learning(DL)techniques,many tasks in flow visualization that used to rely on complex analysis algorithms now can be replaced by DL *** reviewed the approaches to deep learning technology in flow visualization and discussed the technical benefits of these *** also analyzed the prospects of the development of flow visualization with the help of deep learning.

关键词： Deep learning Flow visualization data management Feature extraction Particle tracing

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