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Reliable Rock Mass Classification for Tunneling:Hole-Level MWD Data Modeling with Cost-Sensitive Bagging 认领 引用 被引量：1
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作者 Yue-ming Yuan Jin-rui Duan +4 位作者 Zhao Han Yi-guo Xue Zhi-ping Sun Fan-meng Kong Chuan-gui Li 《Applied Geophysics》 SCIE CSCD 2026年第1期86-98,428,共13页
Accurate identification of surrounding rock quality is critical for safe and efficient tunneling.A cost-sensitive bagging framework is developed to map engineering risk preference into the learning objective through a... Accurate identification of surrounding rock quality is critical for safe and efficient tunneling.A cost-sensitive bagging framework is developed to map engineering risk preference into the learning objective through an asymmetric cost matrix,with a confidence-gating rule to defer low-confidence predictions.Measurement-while-drilling(MWD)records from 1,115 boreholes are aggregated at the hole level into a 64-dimensional representation derived from six drilling channels and two indicators,each summarized by eight robust statistics.Stratified K-fold evaluation under class imbalance is conducted against RUSBoost,logistic regression,and weighted SVM;feature interpretation is performed via importance ranking and partial dependence.Results show ROC-AUC 0.958 and PR-AP 0.588,with reduced under-support at practitionerfavored operating points;the expected misclassication cost is minimized near t≈0.50.Penetration rate is negatively associated with poor rock,whereas pressure-related variables and derived indicators are positively associated.In summary,the framework provides accurate,interpretable,and risk-aware predictions that support real-time tunnel support planning under variable geology. 展开更多
关键词 Measurement-while-drilling(MWD) Rock mass classification Cost-sensitive learning Feature engineering Interpretability class imbalance risk-aware decision-making
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Effective Token Masking Augmentation Using Term-Document Frequency for Language Model-Based Legal Case Classification 认领 引用
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作者 Ye-Chan Park Mohd Asyraf Zulkifley +1 位作者 Bong-Soo Sohn Jaesung Lee 《Computers, Materials & Continua》 SCIE EI 2026年第4期928-945,共18页
Legal case classification involves the categorization of legal documents into predefined categories,which facilitates legal information retrieval and case management.However,real-world legal datasets often suffer from... Legal case classification involves the categorization of legal documents into predefined categories,which facilitates legal information retrieval and case management.However,real-world legal datasets often suffer from class imbalances due to the uneven distribution of case types across legal domains.This leads to biased model performance,in the form of high accuracy for overrepresented categories and underperformance for minority classes.To address this issue,in this study,we propose a data augmentation method that masks unimportant terms within a document selectively while preserving key terms fromthe perspective of the legal domain.This approach enhances data diversity and improves the generalization capability of conventional models.Our experiments demonstrate consistent improvements achieved by the proposed augmentation strategy in terms of accuracy and F1 score across all models,validating the effectiveness of the proposed method in legal case classification. 展开更多
关键词 Legal case classification class imbalance data augmentation token masking legal NLP
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Flood predictions from metrics to classes by multiple machine learning algorithms coupling with clustering-deduced membership degree 认领 引用
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作者 ZHAI Xiaoyan ZHANG Yongyong +5 位作者 XIA Jun ZHANG Yongqiang TANG Qiuhong SHAO Quanxi CHEN Junxu ZHANG Fan 《Journal of Geographical Sciences》 SCIE CSCD 2026年第1期149-176,共28页
Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting... Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting flood resource variables using single or hybrid machine learning techniques.However,class-based flood predictions have rarely been investigated,which can aid in quickly diagnosing comprehensive flood characteristics and proposing targeted management strategies.This study proposed a prediction approach of flood regime metrics and event classes coupling machine learning algorithms with clustering-deduced membership degrees.Five algorithms were adopted for this exploration.Results showed that the class membership degrees accurately determined event classes with class hit rates up to 100%,compared with the four classes clustered from nine regime metrics.The nonlinear algorithms(Multiple Linear Regression,Random Forest,and least squares-Support Vector Machine)outperformed the linear techniques(Multiple Linear Regression and Stepwise Regression)in predicting flood regime metrics.The proposed approach well predicted flood event classes with average class hit rates of 66.0%-85.4%and 47.2%-76.0%in calibration and validation periods,respectively,particularly for the slow and late flood events.The predictive capability of the proposed prediction approach for flood regime metrics and classes was considerably stronger than that of hydrological modeling approach. 展开更多
关键词 flood regime metrics class prediction machine learning algorithms hydrological model
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Distinct gas production characteristics from laboratory-synthesized ClassⅠ,Ⅱ,and Ⅲ hydrate reservoirs:A novel thermally-segmented rotatable approach 认领 引用
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作者 Hongyu Ye Jie Li +5 位作者 Yuanxin Yao Daoyi Chen Jun Duan Xuezhen Wu Dayong Li Mucong Zi 《International Journal of Mining Science and Technology》 SCIE EI CAS CSCD 2026年第3期651-665,共15页
Natural gas hydrate in Class Ⅰ reservoirs holds significant commercial potential,as demonstrated by production trials in the South China Sea.However,experimental studies have focused largely on Class Ⅲ systems,with ... Natural gas hydrate in Class Ⅰ reservoirs holds significant commercial potential,as demonstrated by production trials in the South China Sea.However,experimental studies have focused largely on Class Ⅲ systems,with Class Ⅰ/Ⅱ reservoirs remaining underrepresented due to the difficulties in simulating the geothermal gradient and interlayer interactions.This study investigates depressurization performance across all three classes using a novel 360°rotatable reactor with segmented temperature control,enabling precise simulation of reservoir conditions.Results reveal:(i)Class Ⅰ shows two-stage gas production,with 50%from early free gas enabling rapid depressurization,followed by dissociated gas dominance.They achieve 38.4%-78.3%higher cumulative production and superior gas-to-water ratios due to efficient energy use.(ii)The free gas layer in Class Ⅰ accelerates pressure and heat transfer.Class Ⅱ’s water layer provides sensible heat but causes water blocking,impairing heat flow.Class Ⅲ exhibits rapid initial dissociation but a quick decline without fluid support.(iii)Low temperature,low hydrate saturation,and high production pressure collectively reduce efficiency by increasing flow resistance,limiting gas supply,and reducing dissociation drive.Over-depressurization risks hydrate reformation and ice blockage.This work bridges experimental gaps for Class Ⅰ/Ⅱ reservoirs,offering key insights for optimizing recovery. 展开更多
关键词 Natural gas hydrate ClassⅠ,Ⅱ,andⅢreservoirs Rotatable reactor Depressurization Gas production characteristics Sensitivity analysis
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The Relationships among Exercise Participation,Self-Compassion and Academic Stress in Classroom Contexts:Based on Latent Profiles and Mediation Analyses 认领 引用
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作者 Guofeng Qu Fengwei Jia +5 位作者 Jing Liu XishuaiWang Guoyue Tang Zhonghu Gu Yuyang Nie Cong Liu 《International Journal of Mental Health Promotion》 2026年第5期110-122,共13页
Background:Physical exercise is recognized as an effective means of alleviating academic stress,and physical education(PE)classes constitute a primary source of such activity for middle school students.This study aime... Background:Physical exercise is recognized as an effective means of alleviating academic stress,and physical education(PE)classes constitute a primary source of such activity for middle school students.This study aimed to delve into the diversity of PE class participation patterns among these students,examine their relationship with academic stress,and specifically investigate the mediating role of self-compassion in this process.Methods:A cross-sectional survey was conducted among 849 Chinese middle school students.Data were collected via online questionnaires using validated measurement instruments,which included the degree of participation in PE classes,academic stress,and the self-compassion scale.SPSS 27.0 was used to perform correlation and mediation analyses,and Mplus 8.3 was utilized for latent profile analysis(LPA).Results:The study identified four distinct patterns of participation:the avoidant group(13.31%),the moderate participation group(10.75%),the interest-driven group(29.71%),and the active participation group(46.23%).Additionally,compared to the“Avoidant group”,students in the other three groups showed significantly higher levels of self-compassion and reported significantly lower levels of academic stress.Specifically,the relative indirect effects for the Moderately Engaged group,Interest-Driven Engagers group,and Actively Engaged group were-0.060(95%CI:[-0.180,0.055]),-0.123(95%CI:[-0.220,-0.021]),and-0.234(95%CI:[-0.333,-0.137]),respectively.Conclusion:These results underscore the importance of PE participation patterns and highlight that optimizing PE class design to stimulate students’intrinsic interest,thereby enhancing their engagement,represents an effective strategy for promoting the overall psychosocial well-being of middle school students. 展开更多
关键词 Physical education class sports participation self-compassion academic stress adolescents
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Major histocompatibility complex class I chain-related A and B molecules and their potential role in virus-associated cancers 认领 引用
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作者 Abdellatif Bouayad 《World Journal of Virology》 2026年第1期139-146,共8页
Infections with certain viruses are strong risk factors for specific cancers.The human major histocompatibility complex class I chain-related genes A(MICA)and B(MICB)are polymorphic,non-classical major histocompatibil... Infections with certain viruses are strong risk factors for specific cancers.The human major histocompatibility complex class I chain-related genes A(MICA)and B(MICB)are polymorphic,non-classical major histocompatibility complex class I genes located within the human leukocyte antigen region.Polymorphisms in these genes have been associated with susceptibility and outcomes of several virus-associated cancers.The underlying mechanisms involve modulation of natural killer cell-and CD8+T cell-mediated cytotoxicity by disrupting the natural killer group 2-member D-MICA/B axis.The resulting soluble forms of both MICA and MICB have recently gained attention as potential predictive biomarkers for virus-induced malignancies and disease severity.Therapeutic strategies targeting this axis show considerable promise.This minireview summarizes the genetics and biology of MICA and MICB,highlighting their emerging importance in the pathogenesis of virus-associated cancers. 展开更多
关键词 Major histocompatibility complex class I chain-related genes A Major histocompatibility complex class I chain-related genes B Virus-associated cancers Polymorphism Natural killer group 2-member D Natural killer-cell cytotoxicity
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Resilient Class-Incremental Learning:On the Interplay of Drifting,Unlabeled and Imbalanced Data Streams 认领 引用
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作者 Jin Li Kleanthis Malialis Marios M.Polycarpou 《Artificial Intelligence Science and Engineering》 2026年第1期49-65,共17页
In today's connected world,the generation of massive streaming data across diverse domains has become commonplace.In the presence of concept drift,class imbalance,label scarcity,and new class emergence,these chall... In today's connected world,the generation of massive streaming data across diverse domains has become commonplace.In the presence of concept drift,class imbalance,label scarcity,and new class emergence,these challenges jointly degrade representation stability,bias learning toward outdated distributions,and reduce the resilience and reliability of detection in dynamic environments.This paper proposes a streaming classincremental learning(SCIL)framework to address these issues.The SCIL framework integrates an autoencoder(AE)with a multi-layer perceptron for multi-class prediction,employs a dual-loss strategy(classification and reconstruction)for prediction and new class detection,uses corrected pseudo-labels for online training,manages classes with queues,and applies oversampling to handle imbalance.The rationale behind the method's structure is elucidated through ablation studies,and a comprehensive experimental evaluation is performed using both real-world and synthetic datasets that feature class imbalance,incremental classes,and concept drifts.Our results demonstrate that SCIL outperforms strong baselines and state-of-the-art methods.In line with our commitment to Open Science,we make our code and datasets available to the community. 展开更多
关键词 concept drift data stream mining class-incremental learning class imbalance
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Research on the Approaches to Optimize Classroom Interaction 认领 引用
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作者 CHE Ningwei 《US-China Education Review(A)》 2026年第3期129-132,共4页
This paper,exploring pre-class tasks in college English class,aims to optimize class interaction.The findings of research indicate that scientifically designed pre-class tasks can activate students'prior knowledge... This paper,exploring pre-class tasks in college English class,aims to optimize class interaction.The findings of research indicate that scientifically designed pre-class tasks can activate students'prior knowledge and enhance their classroom participation.In addition,pre-class task design with the principles of interest,relevance and task continuity,together with classroom evaluation incentives and the dynamic adjustments of teaching strategies,can significantly alleviate class silence and improve the quality of teacher-student and student-student interaction. 展开更多
关键词 class interaction pre-class tasks teaching optimization
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CAWASeg:Class Activation Graph Driven Adaptive Weight Adjustment for Semantic Segmentation 认领 引用
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作者 Hailong Wang Minglei Duan +1 位作者 Lu Yao Hao Li 《Computers, Materials & Continua》 SCIE EI 2026年第3期1071-1091,共21页
In image analysis,high-precision semantic segmentation predominantly relies on supervised learning.Despite significant advancements driven by deep learning techniques,challenges such as class imbalance and dynamic per... In image analysis,high-precision semantic segmentation predominantly relies on supervised learning.Despite significant advancements driven by deep learning techniques,challenges such as class imbalance and dynamic performance evaluation persist.Traditional weighting methods,often based on pre-statistical class counting,tend to overemphasize certain classes while neglecting others,particularly rare sample categories.Approaches like focal loss and other rare-sample segmentation techniques introduce multiple hyperparameters that require manual tuning,leading to increased experimental costs due to their instability.This paper proposes a novel CAWASeg framework to address these limitations.Our approach leverages Grad-CAM technology to generate class activation maps,identifying key feature regions that the model focuses on during decision-making.We introduce a Comprehensive Segmentation Performance Score(CSPS)to dynamically evaluate model performance by converting these activation maps into pseudo mask and comparing them with Ground Truth.Additionally,we design two adaptive weights for each class:a Basic Weight(BW)and a Ratio Weight(RW),which the model adjusts during training based on real-time feedback.Extensive experiments on the COCO-Stuff,CityScapes,and ADE20k datasets demonstrate that our CAWASeg framework significantly improves segmentation performance for rare sample categories while enhancing overall segmentation accuracy.The proposed method offers a robust and efficient solution for addressing class imbalance in semantic segmentation tasks. 展开更多
关键词 Semantic segmentation class activation graph adaptive weight adjustment pseudo mask
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Context Patch Fusion with Class Token Enhancement for Weakly Supervised Semantic Segmentation 认领 引用
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作者 Yiyang Fu Hui Li Wangyu Wu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第1期1130-1150,共21页
Weakly Supervised Semantic Segmentation(WSSS),which relies only on image-level labels,has attracted significant attention for its cost-effectiveness and scalability.Existing methods mainly enhance inter-class distinct... Weakly Supervised Semantic Segmentation(WSSS),which relies only on image-level labels,has attracted significant attention for its cost-effectiveness and scalability.Existing methods mainly enhance inter-class distinctions and employ data augmentation to mitigate semantic ambiguity and reduce spurious activations.However,they often neglect the complex contextual dependencies among image patches,resulting in incomplete local representations and limited segmentation accuracy.To address these issues,we propose the Context Patch Fusion with Class Token Enhancement(CPF-CTE)framework,which exploits contextual relations among patches to enrich feature repre-sentations and improve segmentation.At its core,the Contextual-Fusion Bidirectional Long Short-Term Memory(CF-BiLSTM)module captures spatial dependencies between patches and enables bidirectional information flow,yield-ing a more comprehensive understanding of spatial correlations.This strengthens feature learning and segmentation robustness.Moreover,we introduce learnable class tokens that dynamically encode and refine class-specific semantics,enhancing discriminative capability.By effectively integrating spatial and semantic cues,CPF-CTE produces richer and more accurate representations of image content.Extensive experiments on PASCAL VOC 2012 and MS COCO 2014 validate that CPF-CTE consistently surpasses prior WSSS methods. 展开更多
关键词 Weakly supervised semantic segmentation context-fusion class enhancement
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Cascading Class Activation Mapping:A Counterfactual Reasoning-Based Explainable Method for Comprehensive Feature Discovery 认领 引用
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作者 Seoyeon Choi Hayoung Kim Guebin Choi 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第2期1043-1069,共27页
Most Convolutional Neural Network(CNN)interpretation techniques visualize only the dominant cues that the model relies on,but there is no guarantee that these represent all the evidence the model uses for classificati... Most Convolutional Neural Network(CNN)interpretation techniques visualize only the dominant cues that the model relies on,but there is no guarantee that these represent all the evidence the model uses for classification.This limitation becomes critical when hidden secondary cues—potentially more meaningful than the visualized ones—remain undiscovered.This study introduces CasCAM(Cascaded Class Activation Mapping)to address this fundamental limitation through counterfactual reasoning.By asking“if this dominant cue were absent,what other evidence would the model use?”,CasCAM progressively masks the most salient features and systematically uncovers the hierarchy of classification evidence hidden beneath them.Experimental results demonstrate that CasCAM effectively discovers the full spectrum of reasoning evidence and can be universally applied with nine existing interpretation methods. 展开更多
关键词 Explainable AI class activation mapping counterfactual reasoning shortcut learning feature discovery
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LOWER CLASSES AND CHUNG'S LILS OF THE FRACTIONAL INTEGRATED GENERALIZED FRACTIONAL BROWNIAN MOTION 认领 引用
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作者 Mengjie LYU Min WANG Ran WANG 《Acta Mathematica Scientia》 SCIE CSCD 2026年第3期1518-1535,共18页
Let{X(t)}t≥0 be the generalized fractional Brownian motion introduced by Pang and Taqqu(2019):{XT}t≥0d={∫R((t-u)α+-(-u)α+|u|-γ/2B(du)}whereγ∈[0,1),α∈(-1/2+γ/2,1/2+γ/2)are constants.For anyθ... Let{X(t)}t≥0 be the generalized fractional Brownian motion introduced by Pang and Taqqu(2019):{XT}t≥0d={∫R((t-u)α+-(-u)α+|u|-γ/2B(du)}whereγ∈[0,1),α∈(-1/2+γ/2,1/2+γ/2)are constants.For anyθ>0,let Yt=1/Г(θ)∫0t(t-u)θ-1X(u)du,t≥0,Building upon the argument of Talagrand(1996),we develop integral criteria characterizing the lower classes of the process Y at t=0 and at infinity.As a consequence,we derive its Chung-type laws of the iterated logarithm.In the proofs,the small ball probability estimates play important roles. 展开更多
关键词 fractional Brownian motion small ball probability lower classes Chung’s LIL
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Optimizing CNN Class Granularity for Power-Efficient Edge AI in Sudden Unintended Acceleration Verification 认领 引用
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作者 HeeSeok Choi Joon-Min Gil 《Computers, Materials & Continua》 SCIE EI 2026年第5期1723-1742,共20页
Given the growing number of vehicle accidents caused by unintended acceleration and braking failure,verifying Sudden Unintended Acceleration(SUA)incidents has become a persistent challenge.A central issue of debate is... Given the growing number of vehicle accidents caused by unintended acceleration and braking failure,verifying Sudden Unintended Acceleration(SUA)incidents has become a persistent challenge.A central issue of debate is whether such events stem frommechanical malfunctions or driver pedalmisapplications.However,existing verification procedures implemented by vehiclemanufacturers often involve closed tests after vehicle recalls;thus raising ongoing concerns about reliability and transparency.Consequently,there is a growing need for a user-driven framework that enables independent data acquisition and verification.Although previous studies have addressed SUA detection using deep learning,few have explored howclass granularity optimization affects power efficiency and inference performance in real-time Edge AI systems.To address this problem,this work presents a cloud-assisted artificial intelligence(AI)solution for the reliable verification of SUA occurrences.The proposed system integrates multimodal sensor streams including camera-based foot images,On-Board Diagnostics II(OBD-II)signals,and six-axismeasurements to determine whether the brake pedal was actually engaged at themoment of a suspected SUA.Beyond image acquisition,convolutional neural network(CNN)models perform real-time inference to classify the driver’s pedal operation states with the resulting outputs transmitted and archived in the cloud.A dedicated dataset of brake and accelerator pedal images was collected from 15 vehicles produced by 6 domestic and international manufacturers.Using this dataset,transfer learning techniques were applied to compare and analyze model performance and generalization as the CNN class granularity varied from coarse to fine levels.Furthermore,classification performance was evaluated in terms of latency and power efficiency under different class configurations.The experimental results demonstrated that the proposed solution identified the driver’s pedal behavior accurately and promptly,with the two-class model achieving the highest F1-score and accuracy among all granularity settings. 展开更多
关键词 Edge artificial intelligence(Edge AI) real-time inference sudden unintended acceleration(SUA) convolutional neural networks(CNNs) class granularity optimization pedal placement analysis
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A data augmentation method for lacustrine shale lithofacies classification based on a conditional diffusion probabilistic model:A case study from the Dongying Depression,Bohai Bay Basin,China 认领 引用
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作者 Gui-Ang Li Cheng-Yan Lin +6 位作者 Chun-Mei Dong Li-Hua Ren Peng-Jie Ma Yu-Qi Wu Guo-Yin Zhang Xin-Yu Du Zi-Ru Zhao 《Petroleum Science》 SCIE EI CAS CSCD 2026年第6期3017-3036,共20页
Accurate identification of lithofacies is critical for shale hydrocarbon exploration and development.Although machine learning(ML)is one of the most effective approaches for predicting shale lithofacies,the inherent g... Accurate identification of lithofacies is critical for shale hydrocarbon exploration and development.Although machine learning(ML)is one of the most effective approaches for predicting shale lithofacies,the inherent geological heterogeneity results in scarce training samples and severely imbalanced class distributions,leading conventional ML methods to experience overfitting and reduced accuracy.To address this issue,we introduced a conditional diffusion probabilistic model(CDPM)to address these challenges and developed a comprehensive data augmentation framework for shale lithofacies prediction.Applying this framework to the Upper Fourth Member of the Shahejie Formation(Es4s)in the Dongying Depression,we successfully generated 3,600 class-balanced augmented samples from 926core-calibrated samples and eight conventional well log curves from well NY1,achieving an 878.3%increase in rare organic-rich fissile calcareous mudstone(L1)lithofacies.To ensure the reliability of the augmented data,a comprehensive quality assessment was conducted,demonstrating that augmented data effectively retained logging characteristics and petrophysical relationships,with a Fréchet Inception Distance(FID)of 22.9 and a maximum mean discrepancy(MMD)of 0.078.Building upon this highquality augmented dataset,we evaluated the impact of data augmentation on lithofacies classification performance across random forest(RF),support vector machine(SVM),and gradient boosted decision tree(GBDT)algorithms.The results showed substantial improvements,with average accuracy and F1 score increases of 13.6%and 16.5%,respectively,and a 33.1%improvement in L1 recall.To further validate the practical applicability of our approach,blind-well validation on independent wells from different structural positions demonstrated robust generalization capability,achieving significant improvement over traditional ML methods.This study pioneers a conditional diffusion model for predicting shale lithofacies,providing a novel framework for characterising lacustrine shale oil reservoirs and predicting sweet spots. 展开更多
关键词 Lacustrine shale Lithofacies prediction Conditional diffusion probabilistic model Data augmentation Class imbalance
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基于UPLC I-Class/Q Exactive-Orbitrap MS和生物信息学的凌术化浊方入血成分改善慢性萎缩性胃炎的作用机制研究 认领 引用
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作者 高凡 莫小萱 +4 位作者 许俸月 田佳业 赵鑫萌 刘兴超 郭秋红 《药物分析杂志》 CAS CSCD 北大核心 2026年第4期665-683,共19页
目的:基于UPLC I-Class/Q Exactive-Orbitrap MS、网络药理学方法预测凌术化浊方防治慢性萎缩性胃炎的核心靶点及通路,通过分子对接、分子动力学技术对网络药理学结果进行验证,探讨凌术化浊方入血成分抗炎作用的分子机制。方法:采用Wate... 目的:基于UPLC I-Class/Q Exactive-Orbitrap MS、网络药理学方法预测凌术化浊方防治慢性萎缩性胃炎的核心靶点及通路,通过分子对接、分子动力学技术对网络药理学结果进行验证,探讨凌术化浊方入血成分抗炎作用的分子机制。方法:采用Waters UPLC HSS T3色谱柱(100 mm×2.1 mm,1.8μm),柱温40℃,以0.1%甲酸水溶液(A)-甲醇(B)为流动相,梯度洗脱(0~1.0 min,2%B;1.0~41.0 min,100%B;41.0~50.0 min,100%B;50.0~50.1 min,2%B;50.1~52.0 min,2%B),流速0.3 mL·min-1,进样量10.0µL。采用配备热电喷雾离子源的四极杆轨道离子阱质谱仪(Q ExactiveTM),正、负离子的离子源电压分别为3.7 kV和3.5 kV,扫描范围m/z 100~1500。通过保留时间、相对分子质量、二级质谱碎片离子等信息,结合对照品及文献数据比对,鉴定凌术化浊方中化学成分及其血中移行成分;通过TCMSP等数据库筛选凌术化浊方入血成分对应靶点;从OMIM、TTD、DisGeNET等数据库获取CAG相关疾病靶点;构建“凌术化浊方-活性成分-靶点”网络;进行PPI分析和GO、KEGG富集分析;应用分子对接、分子动力学技术进行验证。结果:UPLC I-Class/Q Exactive-Orbitrap MS技术共鉴定出45个化学成分,21个入血成分,协同作用于33个靶点,参与胶原蛋白分解、细胞对活性氧的反应、炎症反应等231个生物学过程,涉及表皮生长因子受体、松弛素、肿瘤坏死因子、白细胞介素-17等68条信号通路。核心靶点为AKT丝氨酸和苏氨酸激酶1、前列腺素内过氧化物合酶2、基质金属蛋白酶2、类固醇受体辅激活因子、基质金属蛋白酶3、白细胞介素-1β等。分子对接显示6-表-狭叶香茶菜素和贵州冬凌草素与PTGS2的结合活性较高,分子动力学模拟进一步验证了6-表-狭叶香茶菜素和贵州冬凌草素分别与PTGS2的结合具有较好的结构稳定性及结合亲和力。结论:通过血清药物化学、网络药理学及分子动力学相互印证,揭示了凌术化浊方改善慢性萎缩性胃炎的分子机制,为其临床应用提供了科学依据。 展开更多
关键词 凌术化浊方 慢性萎缩性胃炎 网络药理学 分子动力学 超高效液相色谱-四极杆-静电场轨道阱高分辨质谱 入血成分
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A Survey on Moments of a Class of Error Terms 认领 引用
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作者 ZHAI Wenguang 《数学进展》 CSCD 北大核心 2026年第3期481-504,共24页
This is a survey article on the moments of a class of error terms in analytic number theory.We begin by reviewing the moments of the Dirichlet divisor problem,and then review the hybrid moments of error terms in the d... This is a survey article on the moments of a class of error terms in analytic number theory.We begin by reviewing the moments of the Dirichlet divisor problem,and then review the hybrid moments of error terms in the divisor problems.We give two new results on the joint distribution of sign changes ofΔ2(x)andΔ3(x).In the last section,we present some conjectures on the moment results in the well-known Selberg class. 展开更多
关键词 divisor problem error term moment sign change Selberg class
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Theoretical Construction and Practical Path of the Collaborative Education Mechanism between Graduate Counselors and Class Advisors under the Three-in-One Holistic Education System 认领 引用
17
作者 Shunjia Che Xiaohua Ning 《Journal of Contemporary Educational Research》 2026年第3期154-160,共7页
With the continuous expansion of China’s graduate education system and the diversification of training models,the new era has raised higher requirements for cultivating high-level talents.Under the“Three-in-One Holi... With the continuous expansion of China’s graduate education system and the diversification of training models,the new era has raised higher requirements for cultivating high-level talents.Under the“Three-in-One Holistic Education”framework,structural deficiencies in the ideological and political education management system for graduate students have emerged,where the lack of collaborative mechanisms among educational stakeholders hinders educational effectiveness.This study focuses on the coordination mechanism between graduate student counselors and class advisors,constructing a theoretically coherent and practically feasible collaborative education model.From a functionalist perspective,the research demonstrates the necessity and feasibility of introducing the“graduate class advisor”role,positioning it as a pivotal hub.Building upon this foundation,a five-dimensional collaborative education mechanism model is proposed,achieving optimized allocation of educational resources and enhanced effectiveness.This study provides a theoretical framework to address challenges in ideological and political education,offers practical pathways and policy insights for universities to establish educational management systems,and holds significant implications for deepening the“Three-in-One Holistic Education”reform and cultivating high-level talents. 展开更多
关键词 Holistic education Graduate class advisor Collaborative education Functionalism
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CLASS视角下区域活动中教师回应的小妙招 认领 引用
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作者 赵宇洁 《世界儿童》 2026年第1期0125-0127,共3页
学前教育在培养幼儿学习习惯、锻炼幼儿生活技能、发展幼儿交际能力等方面具有积极作用,幼儿园正是学前教育实践的“主战场”。为提升幼儿园教学质量,教师会按照幼儿需求精心打造不同教学区角,并在不同区角开展多元区域活动,以满足幼儿... 学前教育在培养幼儿学习习惯、锻炼幼儿生活技能、发展幼儿交际能力等方面具有积极作用,幼儿园正是学前教育实践的“主战场”。为提升幼儿园教学质量,教师会按照幼儿需求精心打造不同教学区角,并在不同区角开展多元区域活动,以满足幼儿在实践中积累直接经验的学习需求。由于区域活动多是两人及多人共同参与的集体性游戏,在游戏实践中幼儿间难免出现摩擦、分歧。因此,教师可以主动借鉴美国的CLASS评估系统,通过正向回应以身作则,带动幼儿快乐学习、快乐成长。 展开更多
关键词 幼儿园 CLASS师幼互动 区域活动 教师回应
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基于CLASS评估工具提升师幼互动质量——以大班沙水游戏“恰‘桥’遇见你”为例 认领 引用
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作者 龚丹 《东方娃娃（保育与教育）》 2026年第4期58-60,共3页
一、CLASS评估工具的内涵与价值。CLASS课堂互动评估系统(Classroom Assessment Scoring System)是聚焦师幼互动质量的专业化评估工具,以情感支持、班级组织、教育支持三大核心领域为框架,构建了多维度、可操作的观察指标体系,为学前教... 一、CLASS评估工具的内涵与价值。CLASS课堂互动评估系统(Classroom Assessment Scoring System)是聚焦师幼互动质量的专业化评估工具,以情感支持、班级组织、教育支持三大核心领域为框架,构建了多维度、可操作的观察指标体系,为学前教育工作者观察、诊断与优化师幼互动行为提供了科学、客观的实践依据。 展开更多
关键词 情感支持 CLASS评估工具 师幼互动质量
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Semi-Supervised Medical Image Classification Based on Sample Intrinsic Similarity Using Canonical Correlation Analysis 认领 引用
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作者 Kun Liu Chen Bao Sidong Liu 《Computers, Materials & Continua》 SCIE EI 2025年第3期4451-4468,共18页
Large amounts of labeled data are usually needed for training deep neural networks in medical image studies,particularly in medical image classification.However,in the field of semi-supervised medical image analysis,l... Large amounts of labeled data are usually needed for training deep neural networks in medical image studies,particularly in medical image classification.However,in the field of semi-supervised medical image analysis,labeled data is very scarce due to patient privacy concerns.For researchers,obtaining high-quality labeled images is exceedingly challenging because it involves manual annotation and clinical understanding.In addition,skin datasets are highly suitable for medical image classification studies due to the inter-class relationships and the inter-class similarities of skin lesions.In this paper,we propose a model called Coalition Sample Relation Consistency(CSRC),a consistency-based method that leverages Canonical Correlation Analysis(CCA)to capture the intrinsic relationships between samples.Considering that traditional consistency-based models only focus on the consistency of prediction,we additionally explore the similarity between features by using CCA.We enforce feature relation consistency based on traditional models,encouraging the model to learn more meaningful information from unlabeled data.Finally,considering that cross-entropy loss is not as suitable as the supervised loss when studying with imbalanced datasets(i.e.,ISIC 2017 and ISIC 2018),we improve the supervised loss to achieve better classification accuracy.Our study shows that this model performs better than many semi-supervised methods. 展开更多
关键词 Semi-supervised learning skin lesion classification sample relation consistency class imbalanced
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