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Characteristics of carbon emission point sources and industry analysis in the Guangdong-Hong Kong-Macao Greater Bay Area 认领 引用 被引量：2
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作者 Ying Teng Jiajie Li +10 位作者 Yiqi Chen Meiyu Guo Tian Gao Ji Kong Yuze Wang Pengfei Wang Jinlong Zhu Songbai Han Senyou An Jianbo Zhu Heping Xie 《Energy Geoscience》 EI CAS CSCD 2026年第1期179-194,共16页
Carbon Capture,Utilization,and Storage(CCUS)technology has gained widespread attention in recent years as a critical strategy to combat global climate change,particularly in achieving carbon neutrality goals.The Guang... Carbon Capture,Utilization,and Storage(CCUS)technology has gained widespread attention in recent years as a critical strategy to combat global climate change,particularly in achieving carbon neutrality goals.The Guangdong-Hong Kong-Macao Greater Bay Area(GBA),as one of China's most economically active regions,serves as a key engine for economic growth while also facing considerable carbon emission challenges.This study analyzes the industrial emission volume and geographical distribution of key emitting enterprises in the GBA,summarizes their technological processes and main carbonemitting equipment,and provides scientific support for precise mitigation policies and low-carbon development.Based on data from 176 key emitting enterprises,the study reveals that Guangzhou and Dongguan host the largest number of such enterprises.Carbon emissions are primarily concentrated in the power sector,dominated by coal-and gas-fired power units,characterized by significant spatial dispersion and uneven distribution.Beyond the power sector,the paper industry has a high number of enterprises but lower emissions.Key facilities such as boilers,cogeneration systems,and production lines are predominantly located near tributaries rivers in Dongguan and Jiangmen.The building materials sector,primarily cement production,ranks as the second-largest emitter,with hightemperature kilns and grinding equipment,particularly rotary kilns and glass furnaces,as the main sources.The petrochemical and chemical sectors have fewer enterprises and lower emissions in the GBA,mainly located in suburban industrial clusters.Carbon emissions in the GBA exhibit distinct industry concentration and geographical distribution disparities.This study provides crucial data and theoretical insights for the development of targeted emission reduction strategies,optimization of source-sink matching,and the advancement of CCUS technologies in the region,particularly from the GBA to the northern South China Sea. 展开更多
关键词 Greater Bay Area Key emitting enterprise Carbon emission point source Source-sink matching CCUS
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Development of nontarget method based on GC-QTOF-HRMS for analyzing organic pollutants in human serum 认领 引用 被引量：1
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作者 Congcong Yue Chang He +5 位作者 Hailing Li Zhiquan Yuan Guiying Li Shengtao Ma Xin Zhang Taicheng An 《Journal of Environmental Sciences》 SCIE EI CAS CSCD 2026年第7期379-386,共8页
Traditional targeted analyses often overlook unknown or emerging contaminants,highlighting the significance of nontarget and suspect screening approaches.A novel and high-sensitivity methodology for nontarget analysis... Traditional targeted analyses often overlook unknown or emerging contaminants,highlighting the significance of nontarget and suspect screening approaches.A novel and high-sensitivity methodology for nontarget analysis of organic pollutants in human serum was newly-developed based on gas chromatography coupled with quadrupole time-of-flight high-resolution mass spectrometry.The extraction protocol employing an acetonitrile-ethyl acetate(9:1,V:V)mixture significantly improved the extraction efficiency while minimizing matrix effect.A hybridized analytical strategy integrating nontarget and suspect screening was developed to achieve comprehensive identification and classification of pollutants,employing the National Institute of Standards and Technology(NIST)20 library and Agilent Technologies Personal Compound Database and Library(PCDL).This approach successfully characterized 273 organic contaminants spanning 12 categories,including polycyclic aromatic hydrocarbons(PAHs)and their derivatives,esters,and phenolic compounds in human serum,with a significant increase in detection specificity compared to conventional workflows.The methodology used serum samples of the workers from coking industry,revealing widespread contamination dominated by PAHs and PAH derivatives.Among the target analytes,three were identified solely by NIST and six solely by PCDL,indicating the complementary benefits of combining these different databases.Notably,this work reported the first confirmed detection of 2-naphthalenamine in human serum.This optimized approach demonstrates enhanced sensitivity and reliability in serum analysis,advancing biomonitoring capabilities and providing a deep understanding of human exposure to environmental pollutants. 展开更多
关键词 Nontarget analysis Biomonitoring method Human serum sample Organic pollutants GC-QTOF-HRMS
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Non-landslide sample for landslide susceptibility prediction modeling:A review of selection strategies and their influence rules 认领 引用 被引量：1
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作者 Zhuo Jia Zhijin Cheng +3 位作者 Zhilu Chang Qin Li Faming Huang Yuhao Peng 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第4期2859-2880,共22页
A proper non-landslide sample selection strategy can improve landslide susceptibility prediction(LSP)accuracy.However,there may be uncertainties regarding the compatibility between different selection strategies and m... A proper non-landslide sample selection strategy can improve landslide susceptibility prediction(LSP)accuracy.However,there may be uncertainties regarding the compatibility between different selection strategies and machine learning models,as well as in the extent of LSP performance enhancement after their coupling.To overcome these uncertainties,this study takes Wuning county of China as a case area,collecting 24 conditioning factors and 379 landslides data.Four non-landslide sample selection strategies,namely random selection,low-slope,buffer zone,and semi-supervised strategies,are then combined with landslide samples in a 1:1 ratio to serve as input variables for constructing LSP models using support vector machine(SVM),logistic regression(LR),random forest(RF)and extreme gradient boosting(XGBoost).Finally,the uncertainty of semi-supervised machine learning coupled models with a 1:2 ratio of landslide to non-landslide samples is analyzed and compared.The results show that:(1)The semi-supervised and low-slope strategies demonstrate higher prediction accuracy compared to the buffer zone and random selection strategies.Moreover,the RF coupled models are the most reliable,followed by the XGBoost,SVM,and LR coupled models;(2)Compared to a 1:1 ratio,a 1:2 ratio of landslide to non-landslide samples significantly improves prediction accuracy,suggesting that appropriately increasing the proportion of non-landslide samples helps to mitigate overfitting and enhance the identification of landslide samples;and(3)LSP is more sensitive to non-landslide sample selection strategies than to the choice of machine learning models.In conclusion,prioritizing reliable non-landslide samples is crucial for improving accuracy of LSP. 展开更多
关键词 Landslide susceptibility prediction Non-landslide sample selection Machine learning models Uncertainty analysis
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A red-emissive carbon dots-based fluorescence and colorimetric dual-mode probe for rapid and sensitive detection of perfluorooctanoic acid in environmental samples 认领 引用 被引量：1
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作者 Yongli Liu Guobei Ge +6 位作者 Huanjia Liu Yuxin Wang Penghui Zhou Xiaoyan Su Tianmiao Jin Guifen Zhu Qingxiang Zhou 《Journal of Environmental Sciences》 SCIE EI CAS CSCD 2026年第6期464-472,共9页
Perfluorooctanoic acid(PFOA)is a ubiquitous persistent organic pollutant.Hence,developing effective strategies for its fast and efficient detection in environmental samples is imperative for ecological and health safe... Perfluorooctanoic acid(PFOA)is a ubiquitous persistent organic pollutant.Hence,developing effective strategies for its fast and efficient detection in environmental samples is imperative for ecological and health safety.Herein,red-emissive carbon dots(R-CDs)were innovatively fabricated and employed to construct a novel eco-friendly fluorescence and colorimetric dual-mode probe for the effective determination of PFOA.The R-CDs were sourced from meso‑tetra(4-carboxyphenyl)porphine,ethanolamine,and oxalic acid via a facile hydrothermal method.The fluorescent intensity of the R-CDs at 652 nm and the absorbance at 415 nm decreased gradually with increasing concentration of PFOA.The experimental results demonstrated the probe exhibited good linearity across PFOA concentration ranges of 1–100 ng/mL,100–900 ng/mL,and 1000–4000 ng/mL based on fluorescence quenching attributable to the static quenching.Additionally,the probe achieved high sensitivity with a detection limit as low as 0.0012 ng/mL.On the other hand,the probe could detect PFOA in the range of 1000–7000 ng/mL with a detection limit of 54.9 ng/mL according to colorimetric principle.This probe exhibited excellent reliability and satisfactory recoveries when analyzing the trace PFOA in actual water and soil samples.The proposed probe provided a simple,rapid,economical,and environmentally friendly platform for effectively,selectively,and sensitively detecting PFOA in environmental samples. 展开更多
关键词 Red-emissive CDs Perfluorooctanoic acid(PFOA) Dual-mode probe Static quenching effect Environmental samples
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Spatially biased collections and the failure to cover all wild genetic clusters in plant populations under ex situ conservation 认领 引用 被引量：1
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作者 Zhiqiang Xiao Hui Liu +5 位作者 Guiyun Huang Di Wu Liwen Qiu Jinhua Wu Xinzeng Wei Mingxi Jiang 《Plant Diversity》 SCIE CAS CSCD 2026年第1期75-83,共9页
Successful ex situ conservation of plant populations requires a high degree of genetic representativeness.However,spatially biased sampling in ex situ conservation efforts may fail to capture all wild genetic clusters... Successful ex situ conservation of plant populations requires a high degree of genetic representativeness.However,spatially biased sampling in ex situ conservation efforts may fail to capture all wild genetic clusters for species with range-wide genetic structure.To investigate the extent of spatially biased sampling in living collections and the coverage of wild genetic clusters in plant populations under ex situ conservation worldwide,we combined a global synthesis of ex situ conservation efforts with a case study of an endangered riparian plant species,Myricaria laxiflora.Our analysis of ex situ conservation worldwide revealed that the majority(82.6%)of ex situ populations fail to cover all wild genetic clusters,largely due to spatially biased sampling with low geographic coverage.Our case study of M.laxiflora showed that genetic diversity differed between the ex situ and upstream populations,while it was comparable between ex situ populations and other wild populations.However,current ex situ populations did not cover all wild genetic clusters,as the upstream genetic cluster was previously uncollected.Our study suggests that the failure to cover all wild genetic clusters in ex situ populations is a widespread issue,and ex situ populations with high genetic diversity can also fail to cover all wild genetic clusters.In future ex situ conservation programs,both the importance of high genetic diversity and the high coverage of wild genetic clusters should be prioritized. 展开更多
关键词 Conservation genomics Genetic representativeness Ex situ conservation Genetic composition Geographic coverage Spatially biased sampling
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In-situ quality monitoring in LPBF via melt-pool radiation:Compressive sampling and deep feature extraction 认领 引用 被引量：1
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作者 Hanxiang Zhou Yongqiang Yang +5 位作者 Vyacheslav Trofimov Hui Li Zibin Liu Yunmian Xiao Tuixin Chen Changhui Song 《Additive Manufacturing Frontiers》 CAS CSCD 2026年第2期26-38,共13页
In-situ monitoring methods and deep learning models are increasingly being used for the quality assessment of parts fabricated using laser powder bed fusion to overcome the limitations of poor process repeatability.Ho... In-situ monitoring methods and deep learning models are increasingly being used for the quality assessment of parts fabricated using laser powder bed fusion to overcome the limitations of poor process repeatability.However,the massive data collection required for part-quality monitoring results in high transmission loads and storage costs.To address this problem,this study utilized the compressed sensing theory to acquire compressed photodiode signals.These signals were then used to train and test convolutional neural networks(CNN)to identify the lack-of-fusion,normal,and keyhole modes.At a compressive-sampling rate of 25%,the classification accuracy decreased from 93.1%(raw signals)to 79.3%.However,increasing the compression rate from 25%to 90%did not significantly decrease the classification accuracy.The linear mapping of the raw signal via a Gaussian measurement matrix causes coordinate information folding,thereby impairing the representation of latent features.Therefore,Gaussian process modeling was adopted for the features extracted using a pretrained CNN to mitigate the temporal information collapse and allow the compressed signals to achieve an accuracy comparable to that of the raw data.Furthermore,the sparsity and rank complexity of the melt-pool radiation signals were evaluated using sparse representation and principal component analysis. 展开更多
关键词 Laser powder bed fusion Compressive sampling In-situ quality monitoring Convolutional neural network Gaussian process
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面向数字装备性能鉴定的试验样本量确定方法 认领 引用 被引量：1
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作者 贾祥 潘正强 程志君 《系统工程与电子技术》 EI CSCD 北大核心 2026年第6期1965-1971,共7页
装备试验鉴定发挥着严把定型关口的作用,性能鉴定是其中的一个环节。数字化技术的发展带来了数字化试验的新形式,即研发数字装备随实装交付。针对数字装备的性能鉴定,确定试验样本量是试验方案设计的基础,由于数字装备的多输出参数和多... 装备试验鉴定发挥着严把定型关口的作用,性能鉴定是其中的一个环节。数字化技术的发展带来了数字化试验的新形式,即研发数字装备随实装交付。针对数字装备的性能鉴定,确定试验样本量是试验方案设计的基础,由于数字装备的多输出参数和多鉴定方式,现有方法不能直接确定样本量,为此首先提出基于数字装备输出参数和实装等试验数据一致性检验的数字装备鉴定框架,然后给出只考虑实装试验方式下的最小试验样本量,再根据各种试验方式的关系进行样本等效和合并,最终得到鉴定所需的试验样本量。通过算例分析,说明了所提方法的有效性。 展开更多
关键词 数字装备 性能鉴定 试验样本量 样本等效 样本合并
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Schemes of IPsec integrated with quantum key distribution 认领 引用
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作者 Chun-Hui Zhang Wen-Xuan Zhang +4 位作者 Xing-Yu Zhou Yuan Cao Jun Wang Jian Li Qin Wang 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第4期273-285,共13页
With the rapid advancement of quantum computing,traditional security protocols based on classical encryption algorithms are increasingly vulnerable to potential quantum attacks.The current IPsec protocol,which relies ... With the rapid advancement of quantum computing,traditional security protocols based on classical encryption algorithms are increasingly vulnerable to potential quantum attacks.The current IPsec protocol,which relies on classical cryptographic methods,is insufficient to withstand such threats,thereby compromising the security of long-term data transmission.To address this issue,we propose integrating quantum key distribution(QKD)into the internet protocol security(IPsec)protocol,thereby enhancing its resilience against quantum computing attacks.Here,two schemes that merge QKDgenerated keys with classical cryptographic keys are designed to enhance both security and stability.Furthermore,we conduct a comprehensive evaluation of the performance of various QKD protocols implemented with the scheme,along with an assessment of its overall efficacy across a topological network configuration.This approach not only ensures secure data transmission in the era of quantum computing but also highlights the potential application value of integrating QKD with IPsec,providing valuable insights for the design and implementation of future quantum-secure communication systems. 展开更多
关键词 quantum key distribution internet protocol security internet key exchange
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GlycoPro:A High-Throughput Sample-Processing Platform for Multi-Glycosylation-Omics Analysis 认领 引用
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作者 Xuejiao Liu Yue Meng +4 位作者 Bin Fu Haoru Song Bing Gu Ying Zhang Haojie Lu 《Engineering》 SCIE EI CSCD 2026年第2期43-57,共15页
Glycosylation-omics has emerged as a prominent field for early detection and diagnosis by identifying alterations in glycosylation patterns linked to cancer.In the realm of clinical multi-glycosylation-omics applicati... Glycosylation-omics has emerged as a prominent field for early detection and diagnosis by identifying alterations in glycosylation patterns linked to cancer.In the realm of clinical multi-glycosylation-omics applications,there is a critical need for robust,efficient,and cost-effective preprocessing methodologies capable of handling large sample cohorts.To bridge this gap,we introduce the GlycoPro platform,an innovative solution designed to overcome the limitations of existing analysis methods.Tailored for multi-glycosylation-omics sample preprocessing,GlycoPro refines existing workflows by seamlessly integrating steps including protein extraction,desalting,digestion,derivatization,and enrichment.The GlycoPro platform employs a 96-well plate format,enabling the efficient enrichment or desalting of up to 384 samples in a single day.This capability represents a significant increase in throughput,meeting the demands of large-scale clinical sample preprocessing for mass spectrometry analysis.The GlycoPro platform was used to successfully enrich serum N-glycans from breast cancer patients,revealing unique glycomic signatures that distinguish malignant from benign conditions.We have developed a robust Nglycan biomarker panel,demonstrating a sensitivity of 88.24%and a specificity of 78.95%in diagnostics. 展开更多
关键词 Multi-glycosylation-omics High-throughput Sample preparation Biomarkers Breast cancer
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Albumin-based fluorescent sensor array for differentiating of tetracyclines through host-guest recognitions 认领 引用
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作者 Zhongyong Xu Jun Peng +3 位作者 Wenxing Zhang Lei Wang Xiongzhi Xiang Bin Liu 《Chinese Chemical Letters》 SCIE CAS CSCD 2026年第6期448-451,共4页
The overuse and improper disposal of tetracyclines raise significant environmental and public health concerns due to their persistent ecotoxicological effects.However,there is still a lack of simple,readily available,... The overuse and improper disposal of tetracyclines raise significant environmental and public health concerns due to their persistent ecotoxicological effects.However,there is still a lack of simple,readily available,and effective method for simultaneously detecting multiple tetracyclines.Herein,we present a simple fluorescent sensor array for the detection and identification of multiple tetracyclines(including tetracycline,oxytetracycline,chlortetracycline,and doxycycline)based on host-guest recognitions between albumin(host)and tetracycline(guest).Upon entering the hydrophobic cavity of albumin,tetracycline exhibits a significant enhancement in its intrinsic fluorescence.The differential binding affinity of two albumins to four tetracyclines resulted in different fluorescent responses,creating distinct fluorescence patterns for each tetracycline.With the assistance of machine learning technique,including linear discriminant analysis(LDA)and hierarchical cluster analysis(HCA),this sensor array demonstrated the significant discrimination and classification capabilities for four common tetracyclines and their mixtures with 100%accuracy.Additionally,the array has been successfully applied to differentiate tetracyclines in real food samples.The spiked antibiotics in water sample were determined with a satisfactory recovery of 96.33%-106.5%.This work offers a simple but promising method for differentiating tetracycline antibiotics and presents a versatile strategy for sensor array design. 展开更多
关键词 Tetracyclines Fluorescent sensor array Albumin Host-guest Food samples
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Decade-long fertilization and Bradyrhizobium inoculation reconfigure soybean rhizosphere microecology through fungal community assembly and metabolic niche partitioning 认领 引用
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作者 Wanling Wei Mingchao Ma +3 位作者 Xin Jiang Fangang Meng Ping He Jun Li 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2026年第5期2093-2108,共16页
Soil microbial-metabolite interactions influence crop productivity,yet their responses to long-term nutrient management in legume systems warrant further investigation.This study examined how fertilization and Bradyrh... Soil microbial-metabolite interactions influence crop productivity,yet their responses to long-term nutrient management in legume systems warrant further investigation.This study examined how fertilization and Bradyrhizobium inoculation reshape soybean rhizosphere fungal-metabolite networks to improve soil health.Through a decade-long field trial utilizing internal transcribed spacer(ITS) sequencing and liquid chromatography-mass spectrometry(LC-MS) metabolomics,four treatments were evaluated:no fertilizer application(CK);phosphorus and potassium fertilization(PK);PK chemical fertilizers combined with urea(PK+N);PK fertilization with Bradyrhizobium japonicum 5821 inoculation(PK+R).Results indicated that nitrogen fertilization increased fungal diversity at maturity and enhanced co-occurrence network complexity(displaying the highest node and edge counts),while Bradyrhizobium inoculation promoted stochastic assembly.Soil fungi exhibited notable correlations with 3-hydroxymethylantipyrine,chrysophanol,3,7-dihydroxyflavone and triethylamine.Metabolite profiling revealed nitrogen suppression of stress-resistant flavonoids(3-hydroxymethylantipyrine,chrysophanol,3,7-dihydroxyflavone),whereas Bradyrhizobium enhanced these key metabolites.KEGG enrichment identified tryptophan and caffeine metabolism as central during flowering-podding stage,coordinating nitrogen assimilation and defense responses.Additionally,the key metabolites correlated significantly with soil total nitrogen,organic matter,and available nitrogen.These findings reveal that Bradyrhizobium acts synergistically with fertilization to activate fungal-driven metabolic pathways,offering a microbiome-based approach to enhance nitrogen efficiency and reduce agrochemical dependency in soybean systems. 展开更多
关键词 fungal community key differential metabolites Bradyrhizobium soil microecology
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The overlooked role of individual variability in autumn xylem phenology and carbon sequestration 认领 引用
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作者 Chunsong Wang Jean-Daniel Sylvain +3 位作者 Roberto Silvestro Guillaume Drolet Keyan Fang Sergio Rossi 《Forest Ecosystems》 SCIE CAS CSCD 2026年第2期267-275,共9页
Accurate modeling of carbon sequestration by forests requires scaling wood formation processes from trees to the landscape.The quantification of growth and carbon dynamics requires deep knowledge of the variability in... Accurate modeling of carbon sequestration by forests requires scaling wood formation processes from trees to the landscape.The quantification of growth and carbon dynamics requires deep knowledge of the variability in xylem phenology among individuals.This study presents a comprehensive assessment of seasonal and individual variability in xylem phenology based on more than 800 balsam firs(Abies balsamea(L.)Mill.)monitored weekly across 33 plots from 2018 to 2022 in Montmorency Forest,Quebec,Canada.Wood microcores were collected from April to October to quantify the timings of cambial activity and xylem development on anatomical sections observed at high magnification under the microscope.The first enlarging cells appeared between late May and early June(day of the year(DOY)153-167),and cell-wall thickening ended in late August(DOY 223-238),resulting in a growing season of 63-79 days.Xylem production ranged from 27.4 to 47.9 radial cells.While the onset of xylogenesis was well synchronized among individuals,within 2 weeks,the cessation of growth showed a greater variability,reaching up to 3 weeks.This autumnal variability was positively correlated with wood production,as higher cambial activity increases the accumulation of xylem cells to be differentiated.Our findings provide empirical evidence that individual variability in growth cessation reflects the underlying heterogeneity in cambial activity among trees of the same stand.Our results demonstrate the role of xylem phenology,especially during the autumn,in shaping forest growth.The assessment of both seasonal and individual variability in phenology is an essential step to improve the representation of autumn processes in forest carbon models,which can help to reduce the uncertainty in predictions of boreal forest growth under current or future climate scenarios. 展开更多
关键词 Xylogenesis Cell production Cell differentiation Microcore Sample size Abies balsamea
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融合Transformer与CNN的正样本缓冲滑坡易发性评价模型:以宝鸡市为例 认领 引用 被引量：2
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作者 瞿伟 边子策 +2 位作者 李久元 唐兴友 陈沛男 《武汉大学学报（信息科学版）》 EI CAS CSCD 北大核心 2026年第4期726-740,共15页
宝鸡市滑坡灾害频发,但当前该区域滑坡编录数据样本较少,同时若仅采用滑坡点样本训练模型会导致模型的空间代表性不足,从而制约对该区域滑坡易发性评价的精度。针对此,结合卷积神经网络模型(convolutional neural net‐work,CNN)的局部... 宝鸡市滑坡灾害频发,但当前该区域滑坡编录数据样本较少,同时若仅采用滑坡点样本训练模型会导致模型的空间代表性不足,从而制约对该区域滑坡易发性评价的精度。针对此,结合卷积神经网络模型(convolutional neural net‐work,CNN)的局部特征提取优势和注意力机制Transformer的全局建模能力,提出一种融合Transformer与CNN的正样本缓冲滑坡易发性评价模型。依据滑坡规模设置90~130 m动态缓冲区扩展正样本,并综合考虑地形地貌、地质条件、水文气象和人类工程等选取13类滑坡影响因子,通过多重共线性分析后构建了滑坡评价体系。研究结果显示,采用缓冲区将随机森林、CNN、Transformer、Transformer-CNN模型四者的ROC曲线下的面积(area under the curve,AUC)从0.834、0.852、0.847、0.875分别提升至0.883、0.913、0.926、0.959。此外,Transformer-CNN相较CNN、Transformer,未缓冲时AUC分别从0.852、0.847提升至0.875,进行缓冲时分别从0.913、0.926提升至0.959;基于夏普利加性解释算法可解释性分析进一步揭示出岩性、年降雨、坡向三类因子对滑坡易发性预测贡献度最大,贡献度分别达0.55、0.47、0.43,且三者交互效应显著,为锁定区域滑坡高易发区提供了可量化的依据。 展开更多
关键词 滑坡 易发性评价 正样本缓冲 CNN Transformer SHAP
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Improved probabilistic seismic AVO inversion constrained by instantaneous phase using quadratic PP-reflectivity approximation and IA2RMS-Gibbs algorithm 认领 引用
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作者 Shuang-Shuang Zhou Xing-Yao Yin +1 位作者 Kun Li Ya-Ming Yang 《Petroleum Science》 SCIE EI CAS CSCD 2026年第1期127-142,共16页
Seismic amplitude variation with offset(AVO)inversion is a cornerstone of oil and gas reservoir prediction,enabling the estimation of subsurface elastic parameters and characterization of stratigraphic interfaces.Howe... Seismic amplitude variation with offset(AVO)inversion is a cornerstone of oil and gas reservoir prediction,enabling the estimation of subsurface elastic parameters and characterization of stratigraphic interfaces.However,balancing inversion accuracy and computational efficiency remains a critical challenge.To address this,we propose a novel probabilistic AVO inversion framework integrating three key innovations.First,we derive a high-precision quadratic approximation for compressional(P-wave)reflectivity by retaining first-and second-order terms from the exact Zoeppritz equations through a perturbation strategy.This approach significantly enhances accuracy compared to conventional linear approximations,particularly in reflecting the true amplitude variation at large angles.Subsequently,to improve lateral continuity and stratigraphic resolution,we introduce an instantaneous phase constraint derived via the Hilbert transform.This constraint leverages phase sensitivity to seismic waveform coherence,ensuring geologically consistent interface characterization during stochastic inversion.Furthermore,we develop a hybrid Markov Chain Monte Carlo(MCMC)algorithm combining adaptive Gibbs sampling with the independent doubly adaptive rejection Metropolis sampling(IA2RMS)method.This framework efficiently samples high-dimensional posterior probability density functions(PDFs)of elastic pa rameters:Gibbs sampling gene rates adaptive proposal distributions,while IA2RMS accele rates Markov chain convergence through location-and scale-adjustable proposals.Numerical experiments and field seismic data demonstrate the robustness and feasibility of the proposed probabilistic AVO inversion method. 展开更多
关键词 Quadratic approximation Instantaneous phase constraint Gibbs sampling Independent doubly adaptive rejection metropolis sampling(IA2RMS) Probability density functions(PDFs)
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DeepClassifier:A Data Sampling-Based Hybrid BiLSTM-BiGRU Neural Network for Enhanced Type 2 Diabetes Prediction 认领 引用
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作者 Abdullahi Abubakar Imam Sahalu Balarabe Junaidu +9 位作者 Hussaini Mamman Ganesh Kumar Abdullateef Oluwagbemiga Balogun Sunder Ali Khowaja Shuib Basri Luiz Fernando Capretz Asmah Husaini Hanif Abdul Rahman Usman Ali Fatoumatta Conteh 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第3期1017-1049,共33页
Artificial Intelligence(AI)in healthcare enables predicting diabetes using data-driven methods instead of the traditional ways of screening the disease,which include hemoglobin A1c(HbA1c),oral glucose tolerance test(O... Artificial Intelligence(AI)in healthcare enables predicting diabetes using data-driven methods instead of the traditional ways of screening the disease,which include hemoglobin A1c(HbA1c),oral glucose tolerance test(OGTT),and fasting plasma glucose(FPG)screening techniques,which are invasive and limited in scale.Machine learning(ML)and deep neural network(DNN)models that use large datasets to learn the complex,nonlinear feature interactions,but the conventional ML algorithms are data sensitive and often show unstable predictive accuracy.Conversely,DNN models are more robust,though the ability to reach a high accuracy rate consistently on heterogeneous datasets is still an open challenge.For predicting diabetes,this work proposed a hybrid DNN approach by integrating a bidirectional long short-term memory(BiLSTM)network with a bidirectional gated recurrent unit(BiGRU).A robust DL model,developed by combining various datasets with weighted coefficients,dense operations in the connection of deep layers,and the output aggregation using batch normalization and dropout functions to avoid overfitting.The goal of this hybrid model is better generalization and consistency among various datasets,which facilitates the effective management and early intervention.The proposed DNN model exhibits an excellent predictive performance as compared to the state-of-the-art and baseline ML and DNN models for diabetes prediction tasks.The robust performance indicates the possible usefulness of DL-based models in the development of disease prediction in healthcare and other areas that demand high-quality analytics. 展开更多
关键词 Diabetes deep learning prediction BiLSTM BiGRU classification data sampling
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Ratiometric fluorescent probes based on nitrogen-doped carbon dots for the fluorescence detection of sulfide ions 认领 引用
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作者 CAI Zhifeng ZHANG Yiran +3 位作者 CAI Qun JIA Miao FENG Yaxuan ZHANG Yuqi 《无机化学学报》 SCIE CAS CSCD 北大核心 2026年第5期1015-1025,共11页
Herein,ratiometric fluorescence-based carbon dots(N-CDs)with blue emission were prepared by using simple one-step hydrothermal methods from benzimidazole and L-tryptophan as precursors.Dual emission peaks were observe... Herein,ratiometric fluorescence-based carbon dots(N-CDs)with blue emission were prepared by using simple one-step hydrothermal methods from benzimidazole and L-tryptophan as precursors.Dual emission peaks were observed at 356 and 442 nm under the excitation wavelength of 303 nm.Upon addition of sulfide ions(S2-),the fluorescence intensity at 442 nm decreased significantly,while that at 356 nm increased.The F442/F356 intensity ratio(where F356 and F442 refer to the fluorescence intensity at 356 and 442 nm,respectively)exhibited a linear relationship with the concentration of S2-(0-60.0μmol·L-1),and the detection limit was determined to be 0.076μmol·L-1.The fluorescence detection mechanism was ascribed to the static quenching effect.Furthermore,this fluorescence probe was successfully used for the determination of S2-in real samples with satisfactory recoveries.Finally,the analytical greenness metric for sample preparation(AGREEprep)and blue applicability grade index(BAGI)tools indicated the high sustainability of this platform. 展开更多
关键词 amino acid-based carbon dots ratiometric determination sulfide ions real samples
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基于VMD重构数据增强的不平衡少样本轴承故障识别方法 认领 引用 被引量：5
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作者 张锐 赵锦钰 +5 位作者 郭洪飞 王燕 杨思妍 刘婷婷 周卫斌 游国栋 《计算机集成制造系统》 EI CSCD 北大核心 2026年第1期339-354,共16页
滚动轴承在机械设备中至关重要,其健康状态直接关系到机械设备安全运行和整体性能,然而,实际运行中获取足够的故障样本进行研究是一项挑战。因此,针对实际工况下故障样本数量缺少、与正常样本数量相比形成类不平衡的情形,提出一种基于... 滚动轴承在机械设备中至关重要,其健康状态直接关系到机械设备安全运行和整体性能,然而,实际运行中获取足够的故障样本进行研究是一项挑战。因此,针对实际工况下故障样本数量缺少、与正常样本数量相比形成类不平衡的情形,提出一种基于变分模态分解(VMD)重构数据增强的故障识别模型。首先,通过VMD分解和滤波调整将轴承故障信号重构为平衡数据集。其次,建立各故障类型样本特征参数与不同故障尺寸间关联性,实现生成样本特征评估。最后,通过深度学习YOLOv8算法对各不平衡比例数据集进行深入分析。分析实验结果表明,所提方法能有效扩充少样本场景下的轴承故障数据,提高故障识别精度,从数据层面解决类不平衡问题,对于轴承不平衡样本故障识别具有可行性和有效性。 展开更多
关键词 故障识别 不平衡样本 变分模态分解 数据增强 滚动轴承
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Analysis of urban atmospheric influence on free-space quantum key distribution 认领 引用
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作者 Hai-Long Zhang Xing-Ran Chen Tan Li 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第4期286-294,共9页
Quantum key distribution(QKD)has been widely deployed in practical applications after decades of development.However,the QKD system is easily affected by the external environment,especially free-space QKD.In this text... Quantum key distribution(QKD)has been widely deployed in practical applications after decades of development.However,the QKD system is easily affected by the external environment,especially free-space QKD.In this text,we examine two scenarios of free-space QKD in urban environments:satellite to ground and intercity.For satellite to ground QKD,the effects of stray light are analyzed.For intercity link,we discuss the influence of sea salt particles in coastal cities,and insoluble and soot particles in inland cities.Our findings indicate that using a telescope with a smaller field of view(FOV)and larger aperture diameters in satellite-to-ground QKD can effectively reduce errors induced by stray light.However,the diameter cannot be increased infinitely,when exceeding 0.8 m,the number of stray photons entering receiver rises rapidly and the quantum bits error rate(QBER)shows no significant reduction.For intercity QKD,the strength of extinction varies with relative humidity and aerosol particle radius,consequently altering channel transmittance.We investigate the impact of sea salt,insoluble particles,and soot on the key rate,finding that under the number density N=106m-3,sea salt exhibits the strongest impacts on key rate,especially when radius exceed 2.5μm.The impacts of insoluble particles are weaker and soot is the weakest,which can be ignored until N reaches 109m-3.For larger particle density,we can get higher key rate and further transmission distances in a soot-dominated environment.Our work could provide a valuable reference for the practical implementation of QKD in urban atmospheres. 展开更多
关键词 free-space aerosol particles urban atmosphere key rate
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Intrinsic physical layer secure communication architecture aided by joint secure key distribution technique 认领 引用
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作者 Di Wu Yuan Yao +6 位作者 Taihang Qiu Hui Rong Hanwen Luo Lei Deng Qi Yang Deming Liu Mengfan Cheng 《Advanced Photonics Nexus》 CSCD 2026年第2期188-197,共10页
Physical layer security(PLS)has become a critical technique for the rapidly expanding communication network in the postquantum era,supporting superior secure communication rates with lower deployment cost.Typical PLS ... Physical layer security(PLS)has become a critical technique for the rapidly expanding communication network in the postquantum era,supporting superior secure communication rates with lower deployment cost.Typical PLS architecture follows the“key distribution before encrypted communication”paradigm,which causes redundant system structure and potential security issues.We propose an intrinsic physical layer secure communication architecture aided by a joint secure key distribution technique in this article.The architecture utilizes random time-varying polarization evolution induced by the fiber channel as the entropy source and estimated bit error rate information as the medium to implement an intrinsic key distribution scheme without requiring additional channels and complex devices.We realize simultaneous physical layer encrypted transmission and error-free joint secure key distribution(SKD)based on this architecture.The experimental demonstration verifies the feasibility of the scheme.We believe the highly integrated secure communication architecture with multiple guaranteed security measures may not only provide a feasible,low-cost solution for the deployment of the SKD system in a variety of communication scenarios,but also propose a“key distribution within encrypted communication”paradigm for future PLS solutions. 展开更多
关键词 physical layer security secure key distribution optical encrypted communication
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基于数字孪生的机械臂路径规划研究 认领 引用 被引量：2
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作者 吴海波 杨梦琦 +2 位作者 杨宇恒 郑成飘 杨磊 《仪器仪表学报》 EI CAS CSCD 北大核心 2026年第2期173-185,共13页
针对传统机械臂路径规划方法普遍存在仿真与现实差距大、搜索效率低以及路径可靠性和可执行性受限等问题,提出了一种基于数字孪生的机械臂路径规划方法。首先,基于工业机械臂的实际运行环境搭建了数字孪生平台,实现实体机械臂与机械臂... 针对传统机械臂路径规划方法普遍存在仿真与现实差距大、搜索效率低以及路径可靠性和可执行性受限等问题,提出了一种基于数字孪生的机械臂路径规划方法。首先,基于工业机械臂的实际运行环境搭建了数字孪生平台,实现实体机械臂与机械臂数字孪生模型之间的虚实双向映射与实时数据交互,为机械臂路径规划算法的仿真验证与实际执行提供实时、准确的数字孪生仿真平台;其次,在路径规划算法层面,提出一种基于自适应梯度采样的双向快速随机搜素树(AG-BI-RRT∗)算法,算法采用基于历史梯度反馈的自适应圆锥采样方法、3种扩展策略(目标偏置扩展、改进人工势场法扩展、随机方向扩展)以及多因素父节点重选策略,从搜索效率、避障能力和路径质量等方面对算法进行综合优化,有效提升了路径搜索效率与路径质量;最后,引入路径优化处理方法,通过贪婪剪枝和B样条平滑优化生成平滑无碰撞的路径。综合仿真实验与机械臂实物实验验证了该方法的可行性与优良性,AG-BI-RRT∗算法在路径长度、迭代时间、搜索节点数量、路径转向角度上均优于对比算法;机械臂数字孪生模型关节角度差异不超过±0.01°,机械臂实体与孪生模型之间平均响应时间为176.721 ms,符合数字孪生对实时性与一致性的要求,为机械臂在数字孪生环境下的路径规划提供了一种有效解决方案。 展开更多
关键词 机械臂 数字孪生 路径规划 自适应采样 双向映射
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