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Heavy metals prediction in coastal marine sediments using hybridized machine learning models with metaheuristic optimization algorithm

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Chemosphere 2024年 352卷 141329页

作者： Yaseen, Zaher Mundher Melini Wan Mohtar, Wan Hanna Homod, Raad Z. Alawi, Omer A. Abba, Sani I. Oudah, Atheer Y. Togun, Hussein Goliatt, Leonardo Ul Hassan Kazmi, Syed Shabi Tao, Hai Civil and Environmental Engineering Department King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia Interdisciplinary Research Center for Membranes and Water Security King Fahd University of Petroleum & Minerals (KFUPM) Dhahran Saudi Arabia Department of Civil Engineering Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia UKM Selangor Bangi 43600 Malaysia Environmental Management Centre Institute of Climate Change Universiti Kebangsaan Malaysia Selangor UKM Bangi 43600 Malaysia Department of Oil and Gas Engineering Basrah University for Oil and Gas Basra Iraq Department of Thermofluids School of Mechanical Engineering Universiti Teknologi Malaysia UTM Skudai Johor Bahru 81310 Malaysia Department of Computer Sciences College of Education for Pure Science University of Thi-Qar Nasiriyah 64001 Iraq Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Nasiriyah 64001 Iraq Department of Mechanical Engineering College of Engineering University of Baghdad Baghdad Iraq Computational and Applied Mechanics Department Federal University of Juiz de Fora 36036-900 Brazil Guangdong Provincial Key Laboratory of Marine Disaster Prediction and Prevention and Guangdong Provincial Key Laboratory of Marine Biotechnology Shantou University Shantou 515063 China School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun 558000 China Institute of Big Data Application and Artificial Intelligence Qiannan Normal University for Nationalities Guizhou Duyun 558000 China Faculty of Data Science and Information Technology INTI International University 71800 Malaysia

This study proposes different standalone models viz: Elman neural network (ENN), Boosted Tree algorithm (BTA), and f relevance vector machine (RVM) for modeling arsenic (As (mg/kg)) and zinc (Zn (mg/kg)) in marine sediments owing to anthropogenic activities. A heuristic algorithm based on the potential of RVM and a flower pollination algorithm (RVM-FPA) was developed to improve the prediction performance. Several evaluation indicators and graphical methods coupled with visualized cumulative probability function (CDF) were used to evaluate the accuracy of the models. Akaike (AIC) and Schwarz (SCI) information criteria based on Dickey-Fuller (ADF) and Philip Perron (PP) tests were introduced to check the reliability and stationarity of the data. The prediction performance in the verification phase indicated that RVM-M2 (PBAIS = -o.0465, MAE = 0.0335) and ENN-M2 (PBAIS = 0.0043, MAE = 0.0322) emerged as the best model for As (mg/kg) and Zn (mg/kg), respectively. In contrast with the standalone approaches, the simulated hybrid RVM-FPA proved merit and the most reliable, with a 5 % and 18 % predictive increase for As (mg/kg) and Zn (mg/kg), respectively. The study's findings validated the potential for estimating complex HMs through intelligent data-driven models and heuristic optimization. The study also generated valuable insights that can inform the decision-makers and stockholders for environmental management strategies. © 2024 Elsevier Ltd

关键词： Artificial intelligence Heavy metals Sensitivity analysis Soil contamination

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FedLGMatch: Federated semi-supervised learning via joint local and global pseudo labeling

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Knowledge-Based Systems 2025年 320卷

作者： Zhao, Qing Chu, Jielei Li, Zhaoyu Huang, Wei Luo, Zhipeng Li, Tianrui School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu611756 China China Railway Engineering Group Limited Beijing100039 China China Railway Eryuan Engineering Group Co. Ltd. Sichuan Chengdu610031 China College of Computer and Data Science Fuzhou University China

The bulk of existing Federated Learning (FL) algorithms pay attention to supervised setting and assume that clients have fully labeled data. However, it may be impractical for all clients to obtain plenty of labels due to high annotation costs. Hence, the Federated Semi-Supervised Learning (FSSL) as a promising paradigm has better prospect in many realistic applications (e.g. medical scenario). Under Labels-at-Server scenario, most pseudo labeling based FSSL approaches use only the global model to generate pseudo-labels for unlabeled data, while the local models are ignored. When the local data distribution is much more different from the central server (e.g., Non-IID setting), the generated pseudo-labels may contain much noise, thus, resulting in more serious confirmation bias. To tackle the crucial issue, a novel Federated Semi-Supervised Learning via Joint Local and Global Pseudo Labeling (FedLGMatch) framework is proposed in this paper. The prominent advantage of the proposed FedLGMatch is that it allows local models trained in the last communication round to assist global model in generating pseudo-labels, which successfully emphasizes more clean pseudo-label learning at each client. Experimental results also show that FedLGMatch achieves significant performance improvements than other state-of-the-art models on the standard benchmark datasets. © 2025 Elsevier B.V.

关键词： Semi-supervised learning

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A New Free-Standing Aqueous Zinc-Ion Capacitor Based on MnO2–CNTs Cathode and MXene Anode

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Nano-Micro Letters 2019年第4期11卷 227-238页

作者： Siliang Wang Qiang Wang Wei Zeng Min Wang Limin Ruan Yanan Ma Key Laboratory of Intelligent Computing and Signal Processing Ministry of EducationAnhui UniversityNo.3 Feixi RoadHefei 230039Anhui ProvincePeople’s Republic of China National Engineering Research Center for Agro-Ecological Big Data Analysis and Application School of Electronics and Information EngineeringAnhui UniversityNo.111 Jiulong RoadHefei 230601Anhui ProvincePeople’s Republic of China School of Sciences Hubei University of Automotive TechnologyNo.167 Checheng West RoadShiyan 442002Hubei ProvincePeople’s Republic of China

Restricted by their energy storage mechanism,current energy storage devices have certain drawbacks,such as low power density for batteries and low energy density for ***,the nearest ion capacitors,such as lithium-ion and sodium-ion capacitors containing battery-type and capacitor-type electrodes,may allow achieving both high energy and power *** the inspiration,a new zinc-ion capacitor(ZIC)has been designed and realized by assembling the free-standing manganese dioxide-carbon nanotubes(MnO2-CNTs)battery-type cathode and MXene(Ti3C2Tx)capacitortype anode in an aqueous *** ZIC can avoid the insecurity issues that frequently occurred in lithium-ion and sodium-ion capacitors in organic *** expected,the ZIC in an aqueous liquid electrolyte exhibits excellent electrochemical performance(based on the total weight of cathode and anode),such as a high specific capacitance of 115.1 F g?1(1 mV s?1),high energy density of 98.6 Wh kg?1(77.5 W kg?1),high power density of 2480.6 W kg?1(29.7 Wh kg?1),and high capacitance retention of^83.6%of its initial capacitance(15,000 cycles).Even in an aqueous gel electrolyte,the ZIC also exhibits excellent *** work provides an essential strategy for designing next-generation high-performance energy storage devices.

关键词： Energy storage Zinc-ion capacitor Battery-type and capacitor-type electrodes MXene Electrochemical performance

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An automatic detection scheme of acute Stanford type A aortic dissection based on DCNNs in CTA images 19

An automatic detection scheme of acute Stanford type A aorti...

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4th International Conference on Multimedia Systems and Signal Processing, ICMSSP 2019

作者： Xu, Xin He, Zhiqiang Niu, Kai Zhang, Yijie Tang, Hao Tan, Ling Key Laboratory of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing China Beijing University of Posts and Telecommunications Beijing China Guizhou Provincial Engineering Research Center for Livelihood Big Data Comprehensive Application China Department of Cardiovascular Surgery Second Xiangya Hospital Central South University Changsha Hunan China

ISBN: (纸本)9781450371711

As a severely life-threatening disease, acute Stanford type A aortic dissection brings patients with high potential of death. Computed Tomography Angiography (CTA) images become the effective diagnostic basis of aortic dissection. In this paper, we propose a scheme for automatic patient-level and site-level detection of aortic dissection based on Deep Convolutional Neural Networks (DCNNs) in order to relieve the burden on doctors and diagnose accurately and timely. Firstly, Mask R-CNN is adopted to precisely detect and segment the aorta from the original CTA images. Taking advantage of spatial continuity among CTA images, we put forward an area-wise screening method to remove a handful of unsatisfactory segmentation results. Then we process the segmented aorta images to extract the edges by Canny edge detector. Finally, two ResNets are employed in processed aorta images for aortic dissection detection. Specially, we can detect not only the ascending and descending aorta, but also the aortic arch, which may be the site of onset. We tested on over 3500 images, achieving an accuracy of 98.66% in slice-level detection, 96.51% in patient-level detection and 91.47% in site-level detection. © 2019 Association for Computing Machinery.

关键词： Deep neural networks

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Climate Change and Ecological Engineering Jointly Induced Vegetation Greening in Global Karst Regions From 2001 to 2020

Research Square

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research Square 2021年

作者： Huang, Jing Ge, Zhongxi Huang, Yuqing Tang, Xuguang Shi, Zhan Lai, Peiyu Song, Zengjing Hao, Binfei Yang, Hong Ma, Mingguo Southwest University School of Geographical Sciences Chongqing Jinfo Mountain Field Scientific Observation and Research Station for Karst Ecosystem Ministry of Education Chongqing400715 China Chongqing Engineering Research Center for Remote Sensing Big Data Application School of Geographical Sciences Southwest University Tiansheng Road No.2 Beibei District Chongqing400715 China Chongqing Key Laboratory of Karst Environment School of Geographical Sciences Southwest University Chongqing400715 China Department of Geography and Environmental Science University of Reading Whiteknights Reading BerkshireRG6 6AB United Kingdom Key Laboratory of Beibu Gulf Environment Change and Resource Use Ministry of Education Nanning Normal University Nanning530000 China College of Electronics and Information Engineering Guangdong Ocean University Zhanjiang524088 China

Backgrounds Vegetation dynamic plays a dominant role in the global carbon cycle and climate, especially in vulnerable karst ecosystem. Many studies have examined past several decades changes in vegetation greenness and the associated with climate drivers. Yet, few studies have analyzed the vegetation change in global karst regions particularly in the last decades when climate change and anthropogenic disturbance widely occurred. Methods In this study, we investigated the spatio-temporal variations of vegetation dynamic using the Seasonally Integrated Normalized Difference Vegetation Index (SINDVI) and examined their relationship to climate changes by correlation analysis, the ordinary least squares method investigate the variation trends and the Mann-Kendal test to detect the turning point from 2001 to 2020. Results As expected, there have greening trend in global karst SINDVI from 2001 to 2020, with significant increasing trend in China (range = 0.836, P © 2021, CC BY.

关键词： Temperature

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Time-aware bike flow prediction framework with dynamic edge fusion and memory integration

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Knowledge-Based Systems 2025年 322卷

作者： M.Rajeh, Taha Li, Tianrui Luo, Zhpeng Javed, Muhammad Hafeez Alhaek, Fares School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu611756 China Stirling College Chengdu University Chengdu610106 China Computing Science Faculty of Natural Sciences University of Stirling StirlingFK9 4LA United Kingdom

Bike-Sharing Systems (BSSs) have become a popular solution to urban sustainability concerns, providing affordable and environmentally friendly transportation. However, modeling and predicting shared-bike flow patterns are essential for developing intelligent transportation systems and facilitating city management. Despite the existence of several inflow and outflow prediction models, certain unresolved issues remain, including the inability to differentiate and capture heterogeneous dynamic relationships within daily flows, and the suboptimal utilization of historical flow data for forecasting. To address these limitations, we propose MVRC (Multi-View Relational Framework based on Clustering), a novel deep-learning framework for precise inflow and outflow prediction. The MVRC framework models three types of heterogeneous dynamic flow relationships during different periods of the day. Initially, we introduce a novel clustering method to group nearby stations exhibiting similar patterns, followed by creating a dynamic memory module to store and update edge attributes based on the time of day. Furthermore, the dynamic edge fusion mechanism effectively integrates heterogeneous, time-evolving flow features, capturing their varying impacts. To further enhance the model's capabilities, an offline component that leverages historical data is incorporated, improving performance during testing. Extensive experiments conducted on real datasets from New York City and Washington, D.C., demonstrate the superior prediction efficiency and framework effectiveness of the MVRC framework compared to existing state-of-the-art techniques. These results highlight the practicality and efficacy of MVRC in terms of prediction accuracy and stability. © 2025

关键词： Urban transportation

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Clinical characteristics and plasma antibody titer of patients with COVID-19 in Zhejiang,China

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Journal of Zhejiang University-Science B(Biomedicine & Biotechnology) 2020年第12期21卷 955-960页

作者： Wei-ling XIANG Jing-jing CHENG Lian-peng WU Bing-yu CHEN Wen-xin LI Dan-ying QIU Wei ZHANG Fei-hang GE Dong CHEN Zhen WANG Research Center of Blood Transfusion Medicine Ministry of Education Key Laboratory of Laboratory MedicineDepartment of Blood TransfusionZhejiang Provincial People's HospitalPeople's Hospital of Hangzhou Medical CollegeHangzhou 310014China Department of Laboratory Medicine Wenzhou Central Hospital and Sixth People's Hospital of WenzhouWenzhou 325000China Rehabilitation&Sports Medicine Research Institute of Zhejiang Province Zhejiang Provincial People's HospitalPeople's Hospital of Hangzhou Medical CollegeHangzhou 310014China Research on the Application of Big Data-driven Integrated Blood Collection and Supply Auxiliary Diagnosis and Treatment System Dongyang People's HospitalDongyang 322100China

目的:利用胶体金快速检测试剂盒测定恢复期患者血浆中IgG/IgM的抗体滴度,回顾性分析新型冠状病毒肺炎(COVID-19)患者临床特征、抗体效价变化特点,以及两者间的相关性.创新点:利用胶体金检测试剂盒检测恢复期血浆抗体滴度,结合重症及危... 详细信息

目的:利用胶体金快速检测试剂盒测定恢复期患者血浆中IgG/IgM的抗体滴度,回顾性分析新型冠状病毒肺炎(COVID-19)患者临床特征、抗体效价变化特点,以及两者间的相关性.创新点:利用胶体金检测试剂盒检测恢复期血浆抗体滴度,结合重症及危重症患者病例特点及实验室检测指标分析血浆抗体效价变化规律,及其临床特征的关系,为临床重症及危重症患者诊治以及筛选恢复期血浆供者提供参考依据.方法:收集293份来自COVID-19康复者随访过程中的血液标本,取血浆原液,用生理盐水按不同比例(1:80、1:160、1:320和1:640)稀释后进行胶体金试纸法IgG/IgM抗体检测,并记录相应患者检验指标水平.通过电子病历系统检索回顾性分析温州市第六人民医院截至2020年2月24日确诊的COVID-19患者的流行病学特征和临床信息.通过线性回归分析两变量之间的相关性,P<0.05时具有统计学意义.结论:症状出现4~6周时,COVID-19患者恢复期血浆IgG滴度水平可达到1:320~1:640或者更高水平.此时部分患者的血浆IgM仍为阳性.COVID-19患者恢复期血浆IgM抗体滴度检测结果显示男性较女性高.本研究中危重症患者均需要无创通气来维持生命,且重症和极危重症患者均易发生水、电解质紊乱.COVID-19发病早期发生凝血功能障碍及炎症,与恢复期血浆中IgG和IgM的抗体滴度水平存在一定相关性.

关键词：新型冠状病毒肺炎(COVID-19) 恢复期血浆临床特征抗体滴度

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Multiview feature selection combining latent space and similarity structure learning

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Pattern Recognition 2026年 169卷

作者： Zhuowen Li Hongmei Chen Tengyu Yin Zhong Yuan Chuan Luo Shi-Jinn Horng Tianrui Li School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu 611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu 611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu 611756 PR China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu 611756 PR China College of Computer Science Sichuan University Chengdu 610065 China Department of Computer Science and Information Engineering Asia University Taichung 41354 Taiwan Department of Medical Research China Medical University Hospital China Medical University Taichung 404327 Taiwan

Unsupervised multiview feature selection dependent on similar or clustering structures has dramatically progressed, but both ignore the mutually reinforcing relationship between structure learning. Firstly, the two methods of appeal ignore the possibility of shared joint learning, although they can learn coherent information. Secondly, they are inadequate for data structure learning and lack awareness of co-learning global and local structures. This paper proposes an unsupervised feature selection method (SCSF_FS) that integrates similarity and clustering structures learning to capture intrinsic information in data. Specifically, we use latent space learning to partition multiple data matrices into view-specific base matrices and clustering metrics. Structural learning is used to transform specific clustering metrics into coherent clustering structures. In addition, adaptive weights are used on each view’s similarity matrix to learn a consistent similarity structure, while laplacian graph learning is introduced to unify the similarity and clustering structures. Consequently, a unified framework is designed to unify multiview consistency learning, global structure, and local structure preservation. Moreover, an optimization iteration algorithm is designed to solve it. Comparison with eight algorithms shows the effectiveness of the proposed method.

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Associations of Serum 25-Hydroxyvitamin D Concentrations with Risks of Mortality and Cardiovascular Disease among Individuals with Psoriasis

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Journal of the American Academy of Dermatology 2025年

作者： Kaiqing Lin Ya Miao Lu Gan Bin Zhao Fang Fang Ru Wang Xiang Chen Jiaqi Huang National Clinical Research Center for Metabolic Diseases Metabolic Syndrome Research Center Key Laboratory of Diabetes Immunology Ministry of Education and Department of Metabolism and Endocrinology The Second Xiangya Hospital of Central South University Changsha Hunan 410011 China Xiangya School of Public Health Central South University Changsha China CSU-Sinocare Research Center for Nutrition and Metabolic Health Changsha China Furong Laboratory Changsha China. Electronic address: JIAQI.HUANG@***. Furong Laboratory Changsha China. Institute of Environmental Medicine Karolinska Institutet Stockholm Sweden. School of Exercise and Health Shanghai University of Sport Shanghai China. Electronic address: WANGRU@***. Department of Dermatology Hunan Engineering Research Center of Skin Health and Disease Hunan Key Laboratory of Skin Cancer and Psoriasis Xiangya Clinical Research Center for Cancer Immunotherapy Xiangya Hospital Central South University Changsha Hunan China National Clinical Research Center for Geriatric Disorders Xiangya Hospital Central South University Changsha Hunan China National Engineering Laboratory of Medical Big Data Application Technology (Central South University) Changsha China Furong Laboratory Changsha China. Electronic address: CHENXIANGCK@***.

BACKGROUND:Evidence remains scarce regarding the relationship between serum 25-hydroxyvitamin D (25(OH)D) and the risk of mortality and cardiovascular disease (CVD) in psoriasis. OBJECTIVE:To explore the potential protective effect of serum 25(OH)D on the risk of mortality and CVD among patients with psoriasis. METHODS:This cohort study included 8,947 psoriasis patients and 178,937 controls identified at baseline in the UK Biobank cohort. Serum 25(OH)D levels were prospectively collected at the time of recruitment. Multivariable-adjusted Cox proportional hazards regression models were used to evaluate the associations between levels of serum 25(OH)D and risk of overall mortality and CVD incidence. RESULTS:Compared to the lowest level of serum 25(OH)D (<25 nmol/L), hazard ratios were 0.54 and 0.78 for overall mortality and incident CVD for the highest level of serum 25(OH)D (≥50 nmol/L) among psoriasis patients, respectively (all P<0.05). Psoriasis patients with serum 25(OH)D ≥50 nmol/L appeared to have no excess risk of mortality and CVD incidence, compared with matched controls. LIMITATIONS:Information on psoriasis subtypes was not available. CONCLUSION:A higher level of serum 25(OH)D was associated with a reduced risk of overall mortality and CVD in individuals with psoriasis.

关键词： cardiovascular disease cohort study epidemiology mortality psoriasis vitamin D

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A differentiability-based processes and parameters learning hydrologic model for advancing runoff prediction and process understanding

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Journal of Hydrology 2025年 661卷

作者： Chunxiao Zhang Heng Li Yuqian Hu Dingtao Shen Bingli Xu Min Chen Wenhao Chu Rongrong Li School of Information Engineering China University of Geosciences Beijing Beijing China Hebei Key Laboratory of Geospatial Digital Twin and Collaborative Optimization China University of Geosciences Beijing Beijing China Chinese Academy of Surveying and Mapping Beijing China Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province Central China Normal University Wuhan China College of Urban and Environmental Sciences Central China Normal University Wuhan China Department of Information and Communication Academy of Army Armored Forces Beijing China Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC) Nanjing Normal University Nanjing Jiangsu China International Research Center of Big Data for Sustainable Development Goals Beijing China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing Jiangsu China Institute of Space and Earth Information Science The Chinese University of Hong Kong Shatin New Territories Hong Kong China

Differentiable parameterized learning (dPL) represents a cutting-edge advancement in synergizing process-based models (PBMs) and machine learning to improve both the model prediction and interpretability. However, dPL mainly focuses on refining parameters within a predefined process-based structure, which limits its performance and process understanding to the constraints of existing PBM frameworks. To address this limitation, we propose an extended framework: Differentiable Process and Parameter Learnings (dP2Ls). dP2Ls incorporate regionalized neural networks (NNs) to simultaneously improve both the modeling of process variables and parameter learnings. Within dP2Ls, the EXP-HYDRO model serves as a differentiable physical backbone, where the potential evapotranspiration (PET) process is modeled using a regionalized long short-term memory (LSTM), while the dPL strategy is independently applied to learning hydrological parameters. Experiments with runoff prediction in ungauged catchments across the contiguous United States revealed that: (1) The median Nash-Sutcliffe efficiency (NSE) of regionalized dP2Ls in ungauged catchments surpassed that of all models using NNs for either process learning or parameter learning and approached the LSTM model (the median NSE differs by only 0.021); (2) Regionalized dP2Ls enhanced the interpretability of some intermediate variables by jointly learning the process variables and parameters, resulting in the correlation coefficients of snowpack and ET fitting the estimation products increased by 0.022 and 0.026, respectively; (3) The embedded regionalized LSTM outputs more interpretable PET than EXP-HYDRO, demonstrating the capability of NNs to improve traditional process understanding. In summary, this study demonstrates the advantages of dP2Ls in regionalized modeling, overcoming the limitations of traditional process-based structures through flexible NN configurations, and providing a reliable pathway for the future in-depth diagno

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