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
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
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
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
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
The high polymorphism of histocompatibility complex class Ⅱ(MHC-Ⅱ)alleles and limited immunopeptidomic data hinder pan-species epitope prediction.In this study,leveraging the predictive power of AlphaFold(AF)and the...The high polymorphism of histocompatibility complex class Ⅱ(MHC-Ⅱ)alleles and limited immunopeptidomic data hinder pan-species epitope prediction.In this study,leveraging the predictive power of AlphaFold(AF)and the conserved structural features of the core region of MHC-Ⅱ-binding peptides,derived from a comprehensive analysis of MHC-Ⅱ structure data in the PDB database,we developed a new tool,AF-prediction(AF-pred),with explicit quantitative criteria for MHC-Ⅱ-restricted epitope prediction.We validated AF-pred across human,porcine,bovine,and bat MHC-Ⅱ molecules through large-scale in silico analyses using known immunopeptidome datasets(1000 positive and 1000 negative antigenic peptides),together with in vitro binding assays and crystallographic characterization of newly predicted epitopes.Using uncharacterized bat MHC-Ⅱ structures,we demonstrated that AF-pred’s amino-acid interaction prediction underpins its pan-prediction capability and the underlying rationale of the method.Conversely,this characteristic limits the prediction of atypical MHC-Ⅱ peptide-binding modes.Compared with sequence-based tools,AF-pred demonstrates enhanced cross-species MHC-Ⅱ binding prediction,with higher accuracy and interpretability,and further reveals that iterative AF updates improve AF-pred performance.AF-pred has the potential to facilitate the development of novel T-cell epitope vaccines and advance the“One Health”initiative.展开更多
师幼互动质量不仅是幼儿园教育质量的关键要素,还是我国学前教育内涵式发展的重要方面。运用文献资料、逻辑分析等方法,从CLASS理论视域下探析师幼互动的关联逻辑、困境与创新路径。研究发现:CLASS理论与师幼互动的关联逻辑在于理论同...师幼互动质量不仅是幼儿园教育质量的关键要素,还是我国学前教育内涵式发展的重要方面。运用文献资料、逻辑分析等方法,从CLASS理论视域下探析师幼互动的关联逻辑、困境与创新路径。研究发现:CLASS理论与师幼互动的关联逻辑在于理论同源性与实践契合性。CLASS视域下师幼互动面临情感支持的缺乏、课堂组织的失衡、教学支持的脱节的现实困境。基于此,提出情感交融,以情育情;协调秩序,灵活组织;回应需求,弥合引导的创新路径。The quality of teacher-child interaction is not only a key element of the quality of kindergarten education, but also an important aspect of the connotative development of pre-school education in China. Using literature, logical analysis and other methods, we analyze the associated logic, dilemma and innovative path of teacher-child interaction from the perspective of CLASS theory. The study found that the logic of CLASS theory and teacher-child interaction lies in the homology of the theory and the fit of practice, and that teacher-child interaction in the CLASS perspective faces the dilemmas of lack of emotional support, imbalance of classroom organization, and disconnection of pedagogical support. Based on this, we propose innovative paths of emotional integration, nurturing emotions with emotions, coordinating order, flexible organization, responding to needs, and bridging guidance.展开更多
The prevalence of Class Ⅲ malocclusion varies among different countries and regions. The populations from Southeast Asian countries (Chinese and Malaysian) showed the highest prevalence rate of 15.8%, which can serio...The prevalence of Class Ⅲ malocclusion varies among different countries and regions. The populations from Southeast Asian countries (Chinese and Malaysian) showed the highest prevalence rate of 15.8%, which can seriously affect oral function, facial appearance, and mental health. As anterior crossbite tends to worsen with growth, early orthodontic treatment can harness growth potential to normalize maxillofacial development or reduce skeletal malformation severity, thereby reducing the difficulty and shortening the treatment cycle of later-stage treatment. This is beneficial for the physical and mental growth of children. Therefore,early orthodontic treatment for Class Ⅲ malocclusion is particularly important. Determining the optimal timing for early orthodontic treatment requires a comprehensive assessment of clinical manifestations, dental age, and skeletal age, and can lead to better results with less effort. Currently, standardized treatment guidelines for early orthodontic treatment of Class Ⅲ malocclusion are lacking. This review provides a comprehensive summary of the etiology, clinical manifestations, classification, and early orthodontic techniques for Class Ⅲ malocclusion, along with systematic discussions on selecting early treatment plans. The purpose of this expert consensus is to standardize clinical practices and improve the treatment outcomes of Class Ⅲ malocclusion through early orthodontic treatment.展开更多
In this study we review the occurrence of different types (A, B, C, M, and X classes) of solar flares during different solar cycle phases from 1996 to 2019 covering the solar cycles 23 and 24. During this period, a to...In this study we review the occurrence of different types (A, B, C, M, and X classes) of solar flares during different solar cycle phases from 1996 to 2019 covering the solar cycles 23 and 24. During this period, a total of 19,126 solar flares were observed regardless the class: 3548 flares in solar cycle 23 (SC23) and 15,668 flares in solar cycle 24 (SC24). Our findings show that the cycle 23 has observed the highest occurrences of M-class and X-class flares, whereas cycle 24 has pointed out a predominance of B-class and C-class flares throughout its different phases. The results indicate that the cycle 23 was magnetically more intense than cycle 24, leading to more powerful solar flares and more frequent geomagnetic storms, capable of generating significant electromagnetic emissions that can affect satellites and GPS signals. The decrease in intense solar flares during cycle 24 compared to cycle 23 reflects an evolution in solar activity patterns over time.展开更多
Credit risk assessment is a crucial task in bank risk management.By making lending decisions based on credit risk assessment results,banks can reduce the probability of non-performing loans.However,class imbalance in ...Credit risk assessment is a crucial task in bank risk management.By making lending decisions based on credit risk assessment results,banks can reduce the probability of non-performing loans.However,class imbalance in bank credit default datasets limits the predictive performance of traditional machine learning and deep learning models.To address this issue,this study employs the conditional variational autoencoder-Wasserstein generative adversarial network with gradient penalty(CVAE-WGAN-gp)model for oversampling,generating samples similar to the original default customer data to enhance model prediction performance.To evaluate the quality of the data generated by the CVAE-WGAN-gp model,we selected several bank loan datasets for experimentation.The experimental results demonstrate that using the CVAE-WGAN-gp model for oversampling can significantly improve the predictive performance in credit risk assessment problems.展开更多
Graphene,owing to its exceptional electronic,optical,thermal,and mechanical properties,has emerged as a highly promising material.Currently,the synthesis of large-area graphene films on metal substrates via chemical v...Graphene,owing to its exceptional electronic,optical,thermal,and mechanical properties,has emerged as a highly promising material.Currently,the synthesis of large-area graphene films on metal substrates via chemical vapor deposition remains the predominant approach for producing high-quality graphene.To realize the potential applications of graphene,it is essential to transfer graphene films to target substrates in a manner that is non-destructive,clean,and efficient,as this significantly affects the performance of graphene devices.This review examines the current methods for graphene transfer from three perspectives:non-destructive transfer,clean transfer,and high-efficiency transfer.It analyzes and compares the advancements and limitations of various transfer techniques.Finally,the review identifies the key challenges faced by current graphene transfer methods and anticipates future developmental prospects.展开更多
Through literature analysis and case study, the introduction history, variety selection (high bush, half high bush, low bush) and regional cultivation techniques of blueberry in China were summarized, and the practica...Through literature analysis and case study, the introduction history, variety selection (high bush, half high bush, low bush) and regional cultivation techniques of blueberry in China were summarized, and the practical effects of precision cultivation (water and fertilizer integration, wild planting) and under-forest economic model (forest-blueberry-fungus system, ecological tourism) were evaluated. It provided a technical reference for expanding the planting scale of blueberry and improving the fruit quality.展开更多
The addition of cold flow improvers(CFIs)is considered as the optimum strategy to improve the cold flow properties(CFPs)of diesel fuels,but this strategy is always limited by the required large dosage.To obtain low-do...The addition of cold flow improvers(CFIs)is considered as the optimum strategy to improve the cold flow properties(CFPs)of diesel fuels,but this strategy is always limited by the required large dosage.To obtain low-dosage and high-efficiency CFIs for diesel,1,2,3,6-tetrahydrophthalic anhydride(THPA)was introduced as a third and polar monomer to enhance the depressive effects of alkyl methacrylatetrans anethole copolymers(C_(14)MC-TA).The terpolymers of alkyl methacrylate-trans anethole-1,2,3,6-tetrahydrophthalic anhydride(C_(14)MC-TA-THPA)were synthesized and compared with the binary copolymers of C_(14)MC-TA and alkyl methacrylate-1,2,3,6-tetrahydrophthalic anhydride(C_(14)MC-THPA).Results showed that C_(14)MC-THPA achieved the best depressive effects on the cold filter plugging point(CFPP)and solid point(SP)by 11℃and 16℃at a dosage of 1250 mg/L and monomer ratio of 6:1,while 1500mg/L C_(14)MC-TA(1:1)reached the optimal depressive effects on the CFPP and SP by 12℃and 18℃.THPA introduction significantly improved the depressive effects of C_(14)MC-TA.Lower dosages of C_(14)MCTA-THPA in diesel exerted better improvement effects on the CFPP and SP than that of C_(14)MC-TA and C_(14)MC-THPA.When the monomer ratio and dosage were 6:0.6:0.4 and 1000 mg/L,the improvement effect of C_(14)MC-TA-THPA on diesel reached the optimum level,and the CFPP and SP were reduced by 13℃and 19℃,respectively.A 3D nonlinear surface diagram fitted by a mathematical model was also used for the first time to better understand the relationships of monomer ratios,dosages,and depressive effects of CFIs in diesel.Surface analysis results showed that C_(14)MC-TA-THPA achieved the optimum depressive effects at a monomer ratio of 6:0.66:0.34 and dosage of 1000 mg/L,and the CFPP and SP decreased by 14℃ and 19℃,respectively.The predicted results were consistent with the actual ones.Additionally,the improvement mechanism of these copolymers in diesel was also explored.展开更多
This paper addresses a fundamental question in rock mechanics:Are there Class Ⅱ rocks?The historical development of servo-controlled rock testing machines is reviewed,followed by a brief review of some stiff testing ...This paper addresses a fundamental question in rock mechanics:Are there Class Ⅱ rocks?The historical development of servo-controlled rock testing machines is reviewed,followed by a brief review of some stiff testing machines.The pioneering work of some researchers is reviewed,and the misconception of classifying rocks into Class Ⅰ and Class Ⅱ is discussed.The mechanism of post-peak Class Ⅱ behavior is discussed based on some recent test results.When a brittle hard rock is tested using a soft testing machine under axial-strain-controlled loading,violent failure can occur when the peak strength is reached,and the post-peak stress-strain curve cannot be obtained.However,a Class Ⅱ post-peak stress-strain curve can be obtained when the rock is tested under lateral-strain-controlled loading.If a stiff testing machine is used,Class Ⅰ and Class Ⅱ post-peak stress-strain curves will be obtained under axial-and lateral-strain-controlled loadings,respectively.It is therefore not appropriate to classify rocks into Class Ⅰ or Class Ⅱ rocks.The influences of other conditions,such as rock type,confinement,and specimen height-to-diameter ratio,on the type(Class Ⅰ or Class Ⅱ)of post-peak stress-strain curves are also discussed.Finally,some misconceptions in the rock mechanics community,stemming from the concept of“Class Ⅱ rock”,are discussed.By clarifying these concepts related to Class Ⅰ and Class Ⅱ behaviors,this paper seeks to clarify misunderstandings and misapplications related to post-peak strength and deformation properties in the field.展开更多
Exploring new innovative approaches and models for medical school class advisors to participate in student management is essential under the comprehensive promotion of moral education and talent cultivation.Taking the...Exploring new innovative approaches and models for medical school class advisors to participate in student management is essential under the comprehensive promotion of moral education and talent cultivation.Taking the“Five-Dimensional Education”model as an example,the School of Anesthesiology of Wannan Medical College redefines the roles of class advisors as builders of class ecology,leaders of value creation,companions on the growth journey,practitioners of lifelong learning,and connectors of human efforts,forming a comprehensive and multi-dimensional framework for student education management.This model effectively enhances the quality of talent cultivation in anesthesiology and optimizes the efficiency of educational management.By implementing effective assessment mechanisms,it ensures that class advisors can perform ideological and political education and academic guidance in an efficient,high-quality,and orderly manner.This study not only helps to cultivate medical talents with both moral integrity and professional competence,but also provides valuable theoretical and practical references for reforming student management in medical institutions,thereby promoting the sustainable development of medical education.展开更多
Both acceleration and pseudo-acceleration response spectra play important roles in structural seismic design.However,only one of them is generally provided in most seismic codes.Therefore,many studies have attempted t...Both acceleration and pseudo-acceleration response spectra play important roles in structural seismic design.However,only one of them is generally provided in most seismic codes.Therefore,many studies have attempted to develop conversion models between the acceleration response spectrum(SA)and the pseudo-acceleration response spectrum(PSA).Our previous studies found that the relationship between SA and PSA is affected by magnitude,distance,and site class.Subsequently,we developed an SA/PSA model incorporating these effects.However,this model is suitable for cases with small and moderate magnitudes and its accuracy is not good enough for cases with large magnitudes.This paper aims to develop an efficient SA/PSA model by considering influences of magnitude,distance,and site class,which can be applied to cases not only with small or moderate magnitudes but also with large ones.For this purpose,regression analyses were conducted using 16,660 horizontal seismic records with a wider range of magnitude.The magnitude of these seismic records varies from 4 to 9 and the distances vary from 10 to 200 km.These ground motions were recorded at 338 stations covering four site classes.By comparing them with existing models,it was found that the proposed model shows better accuracy for cases with any magnitudes,distances,and site classes considered in this study.展开更多
文摘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.
基金supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF),funded by the Ministry of Education(RS-2023-00249743).
文摘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.
基金National Key Research and Development Program of China,No.2023YFC3006704National Natural Science Foundation of China,No.42171047CAS-CSIRO Partnership Joint Project of 2024,No.177GJHZ2023097MI。
文摘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.
基金supported by the Funds for Central-Guided Local Science and Technology Development(Grant No.202407AC110005)Key Technologies for the Construction of a Whole-Process Intelligent Service System for Neuroendocrine Neoplasm.Supported by 2023 Opening Research Fund of Yunnan Key Laboratory of Digital Communications(YNJTKFB-20230686,YNKLDC-KFKT-202304).
文摘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.
基金supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)[RS-2021-II211341,Artificial Intelligence Graduate School Program(Chung-Ang University)],and by the Chung-Ang University Graduate Research Scholarship in 2024.
文摘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.
基金supported by the National Key Research and Development Program of China(grant number 2021YFD1800100 to N.Z.)the National Natural Science Foundation of China(grant number 32172871 to N.Z.)the 2115 Talent Development Program of China Agricultural University to N.Z.This study was supported by High-performance Computing Platform of China Agricultural University.
文摘The high polymorphism of histocompatibility complex class Ⅱ(MHC-Ⅱ)alleles and limited immunopeptidomic data hinder pan-species epitope prediction.In this study,leveraging the predictive power of AlphaFold(AF)and the conserved structural features of the core region of MHC-Ⅱ-binding peptides,derived from a comprehensive analysis of MHC-Ⅱ structure data in the PDB database,we developed a new tool,AF-prediction(AF-pred),with explicit quantitative criteria for MHC-Ⅱ-restricted epitope prediction.We validated AF-pred across human,porcine,bovine,and bat MHC-Ⅱ molecules through large-scale in silico analyses using known immunopeptidome datasets(1000 positive and 1000 negative antigenic peptides),together with in vitro binding assays and crystallographic characterization of newly predicted epitopes.Using uncharacterized bat MHC-Ⅱ structures,we demonstrated that AF-pred’s amino-acid interaction prediction underpins its pan-prediction capability and the underlying rationale of the method.Conversely,this characteristic limits the prediction of atypical MHC-Ⅱ peptide-binding modes.Compared with sequence-based tools,AF-pred demonstrates enhanced cross-species MHC-Ⅱ binding prediction,with higher accuracy and interpretability,and further reveals that iterative AF updates improve AF-pred performance.AF-pred has the potential to facilitate the development of novel T-cell epitope vaccines and advance the“One Health”initiative.
文摘师幼互动质量不仅是幼儿园教育质量的关键要素,还是我国学前教育内涵式发展的重要方面。运用文献资料、逻辑分析等方法,从CLASS理论视域下探析师幼互动的关联逻辑、困境与创新路径。研究发现:CLASS理论与师幼互动的关联逻辑在于理论同源性与实践契合性。CLASS视域下师幼互动面临情感支持的缺乏、课堂组织的失衡、教学支持的脱节的现实困境。基于此,提出情感交融,以情育情;协调秩序,灵活组织;回应需求,弥合引导的创新路径。The quality of teacher-child interaction is not only a key element of the quality of kindergarten education, but also an important aspect of the connotative development of pre-school education in China. Using literature, logical analysis and other methods, we analyze the associated logic, dilemma and innovative path of teacher-child interaction from the perspective of CLASS theory. The study found that the logic of CLASS theory and teacher-child interaction lies in the homology of the theory and the fit of practice, and that teacher-child interaction in the CLASS perspective faces the dilemmas of lack of emotional support, imbalance of classroom organization, and disconnection of pedagogical support. Based on this, we propose innovative paths of emotional integration, nurturing emotions with emotions, coordinating order, flexible organization, responding to needs, and bridging guidance.
文摘The prevalence of Class Ⅲ malocclusion varies among different countries and regions. The populations from Southeast Asian countries (Chinese and Malaysian) showed the highest prevalence rate of 15.8%, which can seriously affect oral function, facial appearance, and mental health. As anterior crossbite tends to worsen with growth, early orthodontic treatment can harness growth potential to normalize maxillofacial development or reduce skeletal malformation severity, thereby reducing the difficulty and shortening the treatment cycle of later-stage treatment. This is beneficial for the physical and mental growth of children. Therefore,early orthodontic treatment for Class Ⅲ malocclusion is particularly important. Determining the optimal timing for early orthodontic treatment requires a comprehensive assessment of clinical manifestations, dental age, and skeletal age, and can lead to better results with less effort. Currently, standardized treatment guidelines for early orthodontic treatment of Class Ⅲ malocclusion are lacking. This review provides a comprehensive summary of the etiology, clinical manifestations, classification, and early orthodontic techniques for Class Ⅲ malocclusion, along with systematic discussions on selecting early treatment plans. The purpose of this expert consensus is to standardize clinical practices and improve the treatment outcomes of Class Ⅲ malocclusion through early orthodontic treatment.
文摘In this study we review the occurrence of different types (A, B, C, M, and X classes) of solar flares during different solar cycle phases from 1996 to 2019 covering the solar cycles 23 and 24. During this period, a total of 19,126 solar flares were observed regardless the class: 3548 flares in solar cycle 23 (SC23) and 15,668 flares in solar cycle 24 (SC24). Our findings show that the cycle 23 has observed the highest occurrences of M-class and X-class flares, whereas cycle 24 has pointed out a predominance of B-class and C-class flares throughout its different phases. The results indicate that the cycle 23 was magnetically more intense than cycle 24, leading to more powerful solar flares and more frequent geomagnetic storms, capable of generating significant electromagnetic emissions that can affect satellites and GPS signals. The decrease in intense solar flares during cycle 24 compared to cycle 23 reflects an evolution in solar activity patterns over time.
基金supported by National Key R&D Program of China(2022YFA1008000)the National Natural Science Foundation of China(12571297,12101585)+1 种基金the CAS Talent Introduction Program(Category B)the Young Elite Scientist Sponsorship Program by CAST(YESS20220125).
文摘Credit risk assessment is a crucial task in bank risk management.By making lending decisions based on credit risk assessment results,banks can reduce the probability of non-performing loans.However,class imbalance in bank credit default datasets limits the predictive performance of traditional machine learning and deep learning models.To address this issue,this study employs the conditional variational autoencoder-Wasserstein generative adversarial network with gradient penalty(CVAE-WGAN-gp)model for oversampling,generating samples similar to the original default customer data to enhance model prediction performance.To evaluate the quality of the data generated by the CVAE-WGAN-gp model,we selected several bank loan datasets for experimentation.The experimental results demonstrate that using the CVAE-WGAN-gp model for oversampling can significantly improve the predictive performance in credit risk assessment problems.
基金supported by the National Key R&D Program of China(2020YFB2009002).
文摘Graphene,owing to its exceptional electronic,optical,thermal,and mechanical properties,has emerged as a highly promising material.Currently,the synthesis of large-area graphene films on metal substrates via chemical vapor deposition remains the predominant approach for producing high-quality graphene.To realize the potential applications of graphene,it is essential to transfer graphene films to target substrates in a manner that is non-destructive,clean,and efficient,as this significantly affects the performance of graphene devices.This review examines the current methods for graphene transfer from three perspectives:non-destructive transfer,clean transfer,and high-efficiency transfer.It analyzes and compares the advancements and limitations of various transfer techniques.Finally,the review identifies the key challenges faced by current graphene transfer methods and anticipates future developmental prospects.
文摘Through literature analysis and case study, the introduction history, variety selection (high bush, half high bush, low bush) and regional cultivation techniques of blueberry in China were summarized, and the practical effects of precision cultivation (water and fertilizer integration, wild planting) and under-forest economic model (forest-blueberry-fungus system, ecological tourism) were evaluated. It provided a technical reference for expanding the planting scale of blueberry and improving the fruit quality.
基金supported from the Natural Science Foundation Project of Shanghai(Nos.23ZR1425300 and 22ZR1426100)Experimental Technical Team Construction Project of Shanghai Education Commission(No.10110N230080)+1 种基金National Natural Science Foundation of China(No.22075183)Research and Innovation Project of Shanghai Municipal Education Commission(No.2023ZKZD54).
文摘The addition of cold flow improvers(CFIs)is considered as the optimum strategy to improve the cold flow properties(CFPs)of diesel fuels,but this strategy is always limited by the required large dosage.To obtain low-dosage and high-efficiency CFIs for diesel,1,2,3,6-tetrahydrophthalic anhydride(THPA)was introduced as a third and polar monomer to enhance the depressive effects of alkyl methacrylatetrans anethole copolymers(C_(14)MC-TA).The terpolymers of alkyl methacrylate-trans anethole-1,2,3,6-tetrahydrophthalic anhydride(C_(14)MC-TA-THPA)were synthesized and compared with the binary copolymers of C_(14)MC-TA and alkyl methacrylate-1,2,3,6-tetrahydrophthalic anhydride(C_(14)MC-THPA).Results showed that C_(14)MC-THPA achieved the best depressive effects on the cold filter plugging point(CFPP)and solid point(SP)by 11℃and 16℃at a dosage of 1250 mg/L and monomer ratio of 6:1,while 1500mg/L C_(14)MC-TA(1:1)reached the optimal depressive effects on the CFPP and SP by 12℃and 18℃.THPA introduction significantly improved the depressive effects of C_(14)MC-TA.Lower dosages of C_(14)MCTA-THPA in diesel exerted better improvement effects on the CFPP and SP than that of C_(14)MC-TA and C_(14)MC-THPA.When the monomer ratio and dosage were 6:0.6:0.4 and 1000 mg/L,the improvement effect of C_(14)MC-TA-THPA on diesel reached the optimum level,and the CFPP and SP were reduced by 13℃and 19℃,respectively.A 3D nonlinear surface diagram fitted by a mathematical model was also used for the first time to better understand the relationships of monomer ratios,dosages,and depressive effects of CFIs in diesel.Surface analysis results showed that C_(14)MC-TA-THPA achieved the optimum depressive effects at a monomer ratio of 6:0.66:0.34 and dosage of 1000 mg/L,and the CFPP and SP decreased by 14℃ and 19℃,respectively.The predicted results were consistent with the actual ones.Additionally,the improvement mechanism of these copolymers in diesel was also explored.
基金the Natural Science and Engineering Research Council of Canada(RGPIN/4052-16,ALLRP 560390-20).
文摘This paper addresses a fundamental question in rock mechanics:Are there Class Ⅱ rocks?The historical development of servo-controlled rock testing machines is reviewed,followed by a brief review of some stiff testing machines.The pioneering work of some researchers is reviewed,and the misconception of classifying rocks into Class Ⅰ and Class Ⅱ is discussed.The mechanism of post-peak Class Ⅱ behavior is discussed based on some recent test results.When a brittle hard rock is tested using a soft testing machine under axial-strain-controlled loading,violent failure can occur when the peak strength is reached,and the post-peak stress-strain curve cannot be obtained.However,a Class Ⅱ post-peak stress-strain curve can be obtained when the rock is tested under lateral-strain-controlled loading.If a stiff testing machine is used,Class Ⅰ and Class Ⅱ post-peak stress-strain curves will be obtained under axial-and lateral-strain-controlled loadings,respectively.It is therefore not appropriate to classify rocks into Class Ⅰ or Class Ⅱ rocks.The influences of other conditions,such as rock type,confinement,and specimen height-to-diameter ratio,on the type(Class Ⅰ or Class Ⅱ)of post-peak stress-strain curves are also discussed.Finally,some misconceptions in the rock mechanics community,stemming from the concept of“Class Ⅱ rock”,are discussed.By clarifying these concepts related to Class Ⅰ and Class Ⅱ behaviors,this paper seeks to clarify misunderstandings and misapplications related to post-peak strength and deformation properties in the field.
基金supported by Anhui Teaching Quality and Teaching Reform Project(2022jyxm1698)Special Fund Project for Party Construction of Wannan Medical College(WK2024DJ06).
文摘Exploring new innovative approaches and models for medical school class advisors to participate in student management is essential under the comprehensive promotion of moral education and talent cultivation.Taking the“Five-Dimensional Education”model as an example,the School of Anesthesiology of Wannan Medical College redefines the roles of class advisors as builders of class ecology,leaders of value creation,companions on the growth journey,practitioners of lifelong learning,and connectors of human efforts,forming a comprehensive and multi-dimensional framework for student education management.This model effectively enhances the quality of talent cultivation in anesthesiology and optimizes the efficiency of educational management.By implementing effective assessment mechanisms,it ensures that class advisors can perform ideological and political education and academic guidance in an efficient,high-quality,and orderly manner.This study not only helps to cultivate medical talents with both moral integrity and professional competence,but also provides valuable theoretical and practical references for reforming student management in medical institutions,thereby promoting the sustainable development of medical education.
基金National Natural Science Foundation of China under Grant No.52278135。
文摘Both acceleration and pseudo-acceleration response spectra play important roles in structural seismic design.However,only one of them is generally provided in most seismic codes.Therefore,many studies have attempted to develop conversion models between the acceleration response spectrum(SA)and the pseudo-acceleration response spectrum(PSA).Our previous studies found that the relationship between SA and PSA is affected by magnitude,distance,and site class.Subsequently,we developed an SA/PSA model incorporating these effects.However,this model is suitable for cases with small and moderate magnitudes and its accuracy is not good enough for cases with large magnitudes.This paper aims to develop an efficient SA/PSA model by considering influences of magnitude,distance,and site class,which can be applied to cases not only with small or moderate magnitudes but also with large ones.For this purpose,regression analyses were conducted using 16,660 horizontal seismic records with a wider range of magnitude.The magnitude of these seismic records varies from 4 to 9 and the distances vary from 10 to 200 km.These ground motions were recorded at 338 stations covering four site classes.By comparing them with existing models,it was found that the proposed model shows better accuracy for cases with any magnitudes,distances,and site classes considered in this study.
