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Applications of advanced Optimized Neuro Fuzzy Models for Enhancing Daily Suspended Sediment Load Prediction

Computer Modeling in engineering & Sciences 2025年第4期143卷 1249-1272页

作者： Rana Muhammad Adnan Mo Wang Adil Masood Ozgur Kisi Shamsuddin Shahid Mohammad Zounemat-Kermani College of Architecture and Urban Planning Guangzhou UniversityGuangzhou510006China Center for Global Health Research Saveetha Institute of Medical and Technical SciencesChennai600001India Department of Natural and Applied Sciences TERI School of Advanced StudiesNew Delhi110070India Department of Civil Engineering Lübeck University of Applied ScienceLübeck23562Germany Department of Civil Engineering School of TechnologyIlia State UniversityTbilisi0162Georgia School of Civil Environmental and Architectural EngineeringKorea UniversitySeoul02841Republic of Korea School of Civil Engineering Faculty of EngineeringUniversiti Teknologi Malaysia(UTM)Johor Bahru81310Malaysia Department of Civil Engineering Shahid Bahonar University of KermanKerman***Iran

Accurate daily suspended sediment load(SSL)prediction is essential for sustainable water resource management,sediment control,and environmental ***,SSL prediction is highly complex due to its nonlinear and dynamic nature,making traditional empirical models *** study proposes a novel hybrid approach,integrating the Adaptive Neuro-Fuzzy Inference System(ANFIS)with the Gradient-Based Optimizer(GBO),to enhance SSL forecasting *** research compares the performance of ANFIS-GBO with three alternative models:standard ANFIS,ANFIS with Particle Swarm Optimization(ANFIS-PSO),and ANFIS with Grey Wolf Optimization(ANFIS-GWO).Historical SSL and streamflow data from the Bailong River Basin,China,are used to train and validate the *** input selection process is optimized using the Multivariate Adaptive Regression Splines(MARS)*** performance is evaluated using statistical metrics such as Root Mean Square Error(RMSE),Mean Absolute Error(MAE),Mean Absolute Percentage Error(MAPE),Nash Sutcliffe Efficiency(NSE),and Determination Coefficient(R^(2)).Additionally,visual assessments,including scatter plots,Taylor diagrams,and violin plots,provide further insights into model *** results indicate that including historical SSL data improves predictive accuracy,with ANFIS-GBO outperforming the other ***-GBO achieves the lowest RMSE and MAE and the highest NSE and R^(2),demonstrating its superior learning ability and *** findings highlight the effectiveness of nature-inspired optimization algorithms in enhancing sediment load forecasting and contribute to the advancement of AI-based hydrological *** research should explore the integration of additional environmental and climatic variables to enhance predictive capabilities further.

关键词： Suspended sediment load prediction neuro-fuzzy gradient-based optimizer ANFIS

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Effect of Cu content on microstructural evolution and mechanical behavior of Al–Cu–Mg–Ag alloys

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Journal of Materials Research and technology 2025年 36卷 5222-5236页

作者： Lianzhou Li Wentao Liu Namin Xiao Xingwu Li Haoyan Meng Jingjing Ruan Liang Jiang Lilong Zhu Institute for Advanced Studies in Precision Materials Yantai University Yantai Shandong 264005 China School of Materials Science and Engineering Shandong University of Science and Technology Qingdao Shandong 266590 China AECC Beijing Institute of Aeronautical Materials Beijing 100095 China

This study systematically investigates the effect of Cu content (2.77–6.80 wt%) on the microstructural evolution and mechanical behavior of Al–Cu–Mg–Ag alloys through comprehensive characterization using scanning electron microscope (SEM), electron backscatter diffraction (EBSD) and transmission electron microscopy (TEM). The relationship between microstructure and yield strength (YS) was quantitatively analyzed using multiple strengthening models. Experimental results demonstrate that increasing Cu content promotes eutectic phase formation (θ-Al 2 Cu, T-Al 20 Cu 2 Mn 3 and Ag-containing S-Al 2 CuMg) in as-cast microstructures, accompanied by a transition from dendritic to cellular dendritic grain morphology. Coarse surplus phases (CSPPs) facilitate particle-stimulated nucleation (PSN), enhancing recrystallization to a maximum of 88.91 % at 6.80 wt% Cu. The number density of the Ω phase exhibits a non-monotonic dependence on Cu content, reaching a maximum of 6.72E+22 m −2 at 4.68 wt% Cu, while θ′ phase precipitation is significantly suppressed. Mechanical properties and grain boundary characteristics show strong Cu content dependence: increasing Cu content reduces precipitate-free zone (PFZ) width and transforms grain boundary precipitates (GBPs) from discontinuous to continuous distributions. Yield strength demonstrates a positive correlation with Cu content, while ductility follows an inverse trend. Strengthening mechanism analysis reveals that Ω phase precipitation dominates the yield strength enhancement.

关键词： Al–Cu–Mg–Ag alloys Microstructural evolution Precipitation Mechanical properties Strengthening mechanism

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MPEC: Manifold-Preserved EEG Classification via an Ensemble of Clustering-Based Classifiers

MPEC: Manifold-Preserved EEG Classification via an Ensemble ...

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International CSI Computer Conference, CSICC

作者： Shermin Shahbazi Mohammad–Reza Nasiri Majid Ramezani Department of Electrical and Computer Engineering University of Zanjan Zanjan Iran Department of Computer Science and Information Technology Institute for Advanced Studies in Basic Sciences (IASBS) Zanjan Iran

ISBN: (数字)9798331523114

ISBN: (纸本)9798331523121

Accurate classification of EEG signals is crucial for brain-computer interfaces (BCls) and neuroprosthetic applications, yet many existing methods fail to account for the non-Euclidean, manifold structure of EEG data, resulting in suboptimal performance. Preserving this manifold information is essential to capture the true geometry of EEG signals, but traditional classification techniques largely overlook this need. To this end, we propose MPEC (Manifold-Preserved EEG Classification via an Ensemble of Clustering-Based Classifiers), that introduces two key innovations: (1) a feature engineering phase that combines covariance matrices and Radial Basis Function (RBF) kernels to capture both linear and non-linear relationships among EEG channels, and (2) a clustering phase that employs a modified K-means algorithm tailored for the Riemannian manifold space, ensuring local geometric sensitivity. Ensembling multiple clustering-based classifiers, MPEC achieves superior results, validated by significant improvements on the BCI Competition IV dataset 2a.

关键词： Manifolds Geometry Support vector machines Technological innovation Accuracy Sensitivity Brain modeling Electroencephalography Brain-computer interfaces Reliability

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Principal component regression approach for measuring the impact of built environment variables on multiple air pollutants in Delhi

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Discover Atmosphere 2025年第1期3卷 1-18页

作者： Jain, Deepty Bhatnagar, Smriti Rathi, Vanshika Sachdeva, Kamna Tewani, Ankush Sharma, Gautam Transportation Research and Injury Prevention Centre Indian Institute of Technology Delhi New Delhi India Department of Sustainable Engineering TERI School of Advanced Studies Delhi India Department of Natural and Applied Sciences TERI School of Advanced Studies Delhi India Delhi Skill and Entrepreneurship University Delhi India Central Pollution Control Board Delhi India

Air pollution will likely increase as cities continue to intensify urban activities and expand their infrastructure. Delhi is one of the most polluted cities in the world. Yet, there is a lack of understanding of how the built environment (BE) strategies can address local air quality levels for the city. Additionally, the existing studies use the land use regression (LUR) technique, assuming independence between BE variables. We assessed the impact of BE variables measured at various spatial scales on Delhi's air pollutants (PM2.5, PM10, CO, NO2 and O3). This study used the Principal Component Regression (PCR) approach to account for the multicollinearity between BE variables. As per the analysis, PCR provided better estimates for PM10, PM2.5, and CO concentrations. LUR was found better for modelling NO2 and O3. The findings show that as built-up percentage and the metro station density increases the PM10 and CO levels are also likely to increase, while increasing green percentage is likely to result in decreasing pollutant concentrations. We also identify BE variables that affect a particular pollutant. Percentage institutional within 700 m buffer radii affects PM10, distance to CBD affects CO levels, and distance to the bus depot is affects both CO and NO2 levels. The PCR helped measure the joint effect of BE variables on pollutant concentrations in Delhi. Simultaneously modelling multiple air pollutants can help develop a better urban development strategy for addressing air pollution.

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Fast-Neutron Soft-Error Tolerance of a Radiation-Hardened Repairable Field Programmable Gate Array

Fast-Neutron Soft-Error Tolerance of a Radiation-Hardened Re...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Minoru Watanabe Makoto Kobayashi Mitsutaka Isobe Kunihiro Ogawa Shingo Tamaki Isao Murata Sachie Kusaka Faculty of Natural Science and Technology Okayama University National Institute for Fusion Science The Graduate University of Advanced Studies SOKENDAI Division of Sustainable Energy and Environmental Engineering Graduate School of Engineering Osaka University

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Radiation-hardened very large scale integrations (VLSIs) are required for nuclear power plants, particularly for decommissioning operations. Nevertheless, the totalionizing-dose tolerance of current radiation-hardened VLSIs remains insufficient. Even if a space-grade VLSI were used, the totalionizing-dose tolerance would be only 1–20 kGy, implying their life-time as about 1–20 hr under the intense radiation of 1000 Sv/h around the melted nuclear fuel of the Fukushima Daiichi Nuclear Power Plant. This paper presents a new radiation-hardened field programmable gate array (RHFPGA) incorpo-rating a triple-modular redundant configuration function. The RHFPGA sustained a 7.3 MGy total ionizing dose: 365-7,300 times higher than those of existing space-grade VLSIs. How-ever, during decommissioning operations, recriticality presents an important concern. This paper explains evaluation of the soft-error tolerance of RHFPGAs against a 15.0 MeV high-energy neutron beam. No soft error occurred on RHFPGAs.

关键词： Particle beams Nuclear energy Neutrons Logic gates Very large scale integration Total ionizing dose Power generation Field programmable gate arrays Consumer electronics

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Broadband Optical Nonlinearities and Ultrafast Carrier Dynamics of GO-CuS Hybrid Film

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Journal of Lightwave technology 2025年

作者： Zhang, Xuefeng Bai, Xuli Liu, Xiaofei Wang, Dan Han, Hong Xiao, Zhengguo Zhang, Jianguo Nie, Zhongquan Taiyuan University of Technology College of Materials Science and Engineering Institute of New Carbon Materials Taiyuan030024 China Taiyuan University of Technology Key Lab of Advanced Transducers and Intelligent Control System Ministry of Education and Shanxi Province College of Electronic Information and Optical Engineering Taiyuan030024 China Taiyuan Institute of Technology Department of Science Taiyuan030008 China Tongren University Department of Physics and Electronic Engineering Tongren China National University of Defense Technology College of Advanced Interdisciplinary Studies Changsha410073 China

We report the broadband optical nonlinearities and ultrafast carrier dynamics of GO doped with CuS nanoparticles (GO-CuS) following the femtosecond pulse lasers. Both the pure GO and GO-CuS hybrid films are first synthesized by the vacuum filter deposition method and their optical nonlinearities are then examined in the wide wavelength range (400 nm-1064 nm) based on the femtosecond Z-scan technique. The results reveal that, compared to the pristine GO, the introduction of CuS not only makes the GO-CuS hybrid film switched from saturated absorption to reverse saturated absorption possible, but also achieves strong broadband optical nonlinear absorption responses and optical limiting effects. It is demonstrated that the strongest nonlinear absorption coefficient (∼3.6×10-7 m/W) appears at 400 nm excitation, which is dozens of times larger than that under the illumination of 1064 nm. By leveraging the femtosecond pump-probe technique, we further study the ultrafast carrier dynamics of the GO-CuS hybrid film carrying the fast and slow relaxation processes. Moreover, the physical mechanisms behind these excellent nonlinear optical responses are elucidated. The findings presented here indicate that the GO-CuS hybrid film can be regarded as a potential promising candidate in various ultrafast photonics devices. © 1983-2012 IEEE.

关键词： Femtosecond lasers

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Chemical characteristics and sources apportionment of volatile organic compounds in the primary urban area of Shijiazhuang,North China Plain

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Journal of Environmental Sciences 2025年第3期149卷 465-475页

作者： Xiao Zhang Junfeng Wang Jiangwei Zhao Junliang He Yali Lei Kai Meng RuiWei Xue Zhang Miaomiao Zhang Shuangying Ni Eleonora Aruffo College of geographical sciences and Environment Shijiazhuang UniversityShijiazhuang 050035China Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Collaborative Innovation Center of Atmospheric Environment and Equipment TechnologySchool of Environmental Science and EngineeringNanjing University of Information Science and TechnologyNanjing 210044China Hebei Province Ecology Environmental Monitoring Center Shijiazhuang 050037China Key Lab of Geographic Information Science of the Ministry of Education School of Geographic SciencesEast China Normal UniversityShanghai 200241China Hebei Provincial Institute of Meteorological Sciences Shijiazhuang 050021China Key Laboratory of Meteorology and Ecological Environment of Hebei Province Shijiazhuang 050021China Hebei Provincial Academy of Ecological Environmental Science Shijiazhuang 050037China Department of Advanced Technologies in Medicine&Dentistry University“G.d’Annunzio”of Chieti-PescaraChieti 66100Italy Center for Advanced Studies and Technology-CAST Chieti 66100Italy

VOCs(Volatile organic compounds)exert a vital role in ozone and secondary organic aerosol production,necessitating investigations into their concentration,chemical characteristics,and source apportionment for the effective implementation of measures aimed at preventing and controlling atmospheric *** to October 2020,onlinemonitoringwas conducted in the main urban area of Shijiazhuang to collect data on VOCs and analyze their concentrations and ***,the PMF(positive matrix factorization)method was utilized to identify the VOCs *** indicated that the TVOCs(total VOCs)concentration was(96.7±63.4μg/m^3),with alkanes exhibiting the highest concentration of(36.1±26.4μg/m^3),followed by OVOCs(16.4±14.4μg/m^3).The key active components were alkenes and aromatics,among which xylene,propylene,toluene,propionaldehyde,acetaldehyde,ethylene,and styrene played crucial roles as reactive *** sources derived from PMF analysis encompassed vehicle emissions,solvent and coating sources,combustion sources,industrial emissions sources,as well as plant sources,the contribution of which were 37.80%,27.93%,16.57%,15.24%,and 2.46%,***,reducing vehicular exhaust emissions and encouraging neighboring industries to adopt low-volatile organic solvents and coatings should be prioritized to mitigate VOCs levels.

关键词： Volatile organic compounds Pollution characteristics Ozone formation potential OH radical loss rate Source apportionment

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On the microstructural, mechanical, damping, wear properties of magnesium alloy AZ91-3 vol. % SiCP-3 vol. % fly ash hybrid composite and property correlation thereof

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Journal of Magnesium and Alloys 2025年

作者： Gollapalli, Prince Pant, Mridul Chandra, A.R. Anil Surappa, M.K. Department of Materials Engineering Indian Institute of Science Karnataka Bangalore560012 India Department of Polymer & Process Engineering Indian Institute of Technology Roorkee Saharanpur Campus Saharanpur247001 India Department of Mechanical Engineering B.M.S. College of Engineering Karnataka Bangalore560019 India INSA Senior Scientist and Adjunct Professor National Institute of Advanced Studies Karnataka Bangalore560012 India

A combination of hard (SiCP) and soft (fly ash) particulate reinforcements could be a strategy to enhance combination of multiple properties of Magnesium and its alloys which otherwise suffer from low stiffness, low wear resistance, and many other critical properties. However, at present a comprehensive and robust map correlating different properties in particle-reinforced composites is much lacking. In this work, an industrial grade AZ91 magnesium alloy reinforced with hard SiC and soft fly ash particles (with 3 vol. % each), has been prepared using stir casting followed by hot extrusion at 325 ∘C with a ratio of 21.5. Microstructure of the hybrid composite was characterized using optical and scanning electron microscopes. The composite exhibited a reduction in average grain size from 13.6 to 7.1 µm, concomitantly an increase in Vickers hardness from 73 to 111 HV. The tension-compression yield asymmetry ratios of the unreinforced alloy and hybrid composite were 1.165 and 0.976, respectively indicating higher yield strength for the composite under compressive load. The composite exhibited 76% improvement in damping capacity under time sweep mode, and 28% improvement at 423 K under temperature sweep mode. The tribological characteristics of the composite under dry sliding conditions at sliding speeds and loads in the range of 0.5 to 1.5 m s-1 and 10 to 30 N, respectively showed higher wear resistance than the unreinforced alloy. The composite showed 23% improvement in sliding wear resistance at a load of 20 N and a speed of 1 m s-1. Finally, efforts have been made to understand the influence of one property on the other by developing statistical property correlation maps from the properties obtained in this study and from the literature. These maps are expected to help in the design of hybrid Metal Matrix Composites for a variety of targeted applications in different sectors. © 2025

关键词： Magnesium alloys

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Enhanced Lithium-Ion Battery Electrodes with Metal–Organic Framework Additives Featuring Undercoordinated Zr4+ Sites

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advanced Materials 2025年第0期 e2500909页

作者： Shen, Li Liu, Qian Huang, Xuecong Fortini, Alexis Li, Xianyang Tang, Mue Yin, Qingyang Yang, Jinhu Zhang, Chi Lu, Yunfeng School of Chemical Science and Engineering Institute for Advanced Studies Tongji University Shanghai200092 China Department of Chemical and Biomolecular Engineering University of California Los AngelesCA90095 United States School of Chemical Science and Engineering Tongji University Shanghai200092 China Beijing Advanced Innovation Center for Soft Matter Science and Engineering Beijing University of Chemical Technology Beijing100029 China

Performances of lithium-ion batteries (LIBs) are dictated by processes of electron-ion separation, transfers, and combination. While carbon additives are routinely used to ensure electronic conductivity, additives capable of simultaneously boosting ion conduction and delivering step-change performance remain elusive. Herein, metal–organic frameworks (MOFs) possessing coordinately unsaturated Zr4+ sites are exploited as a new material library of electrode additives. The MOFs imbue infused electrolytes with an expanded electrochemical stability window (0 to 5 V vs Li/Li⁺) and enhanced Li⁺ transport efficiency. Mechanistically, strong interactions between Zr4+ sites and Li+ solvation sheaths result in trimmed, anion-fixed, and solvent-separated ion pairs, mitigating electrostatic coupling and enabling efficient Li⁺ translocation in the porous nanospace. Concomitantly, these solvation structural modulations foster interfacial and electrochemical stabilities. When implemented at 1.7 wt.% in graphite and sub-Ah full cell, the MOF additives significantly improved Li+ diffusional kinetic, rate capability beyond 2C, and cycling longevity doubling lifespan. This work offers a straightforward yet effective route to remedy the bottlenecks of industrial LIBs. © 2025 Wiley-VCH GmbH.

关键词： Solvation

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Biomedical data and AI

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Science China Life Sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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