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
Dialogue and fusion of horizons are two important concepts of Gadamer’s philosophical hermeneutics,which falls into the pedagogical category of teaching English News Listening Classes.The course of English News Liste...Dialogue and fusion of horizons are two important concepts of Gadamer’s philosophical hermeneutics,which falls into the pedagogical category of teaching English News Listening Classes.The course of English News Listening is one of the most fundamental and difficult courses in the curriculum for college students who are English majors.The simultaneous interpreting training method of shadowing is used in English News Listening Classes in helping students improve their language skill of listening and speaking.In fulfilling a teacher’s pedagogical performance of dialoguing and fusion of horizons,still one thing is important,i.e.,solidarity triggered between students and teacher,which is the good or the ethical choice between students and teacher.In English News Listening Classes,“道”or“the way(Dao)”is shadowing.In teaching English News Listening,a“dialogue”of shadowing could be achieved between students and teacher is even more significant than that of other courses.This paper intends to present the dialogic ethical triggering of fusion of horizons in class.In another word,students’knowing could be guided by teacher’s dialogic ethical triggering in English News classes.In voicing out the language,knowing in listening and speaking could help students have confidence in not only their language skills but in conquering their difficulties in their life.Teaching English News Listening at Northeastern University(NEU)in this way since 2013 has turned out to be good for students’growth and maturation in life.展开更多
Letϕbe a smooth radial weight that decays faster than the class Gaussian ones.We obtain certain estimates for the reproducing kernels and the Lp-estimates for solutions of theδ-equation on the weighted Fock spaces F_...Letϕbe a smooth radial weight that decays faster than the class Gaussian ones.We obtain certain estimates for the reproducing kernels and the Lp-estimates for solutions of theδ-equation on the weighted Fock spaces F_(ϕ)^(p)(1≤p≤∞),which extends the classical Hörmander Theorem.Furthermore,for a suitable f,we completely characterize the boundedness and compactness of the Hankel operator H_(f):F_(ϕ)^(p)→L^(q)(C,e^(qϕ(·))dm)for all possible 1≤p,q<∞and also characterize the Schatten-p class Hankel operator Hf from F_(ϕ)^(2)to L^(2)(C,E^(-2ϕ)dm) for all 0<p<∞. As an application, we give a complete characterization of the simultaneously bounded, compact and Schatten-p classes Hankel operators H_(f) and h_(f)^(-) on F_(ϕ)^(2).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
Abs As a crucial vehicle for young children’s artistic enlightenment,music appreciation holds an irreplaceable value in cognitive development,emotional edification,and the cultivation of aesthetic abilities.Currently...Abs As a crucial vehicle for young children’s artistic enlightenment,music appreciation holds an irreplaceable value in cognitive development,emotional edification,and the cultivation of aesthetic abilities.Currently,in music appreciation activities for senior kindergarten classes,there is a widespread phenomenon of homogenized teaching content and mechanized teaching methods,which results in insufficient enthusiasm for participation among young children and a superficial understanding of music.The situational teaching method,by constructing concrete and immersive learning scenarios,can effectively activate young children’s multi-dimensional sensory experiences.Its characteristics of intuitiveness and interactivity are highly consistent with the traits of young children’s concrete thinking,thus providing a new approach to resolving the current predicament.The research focuses on the practical pain points in music appreciation activities for senior kindergarten classes and proposes targeted solutions from four dimensions:content design,method innovation,resource integration,and teacher training,aiming to reconstruct a child-centered,in-depth music learning model.Practice has shown that the situational teaching method can not only enhance young children’s perceptual sensitivity to musical elements but also guide them to achieve emotional resonance through role-playing and life-related associations,laying a foundation for the sustainable development of young children’s musical literacy.展开更多
In this paper,the class of starlike functions of complex order γ(γ∈ℂ−{0})is extended from the case on unit disk U=(z∈C:|z|<1)to the case on the unit ball B in a complex Banach space or the unit polydisk U^(n) i...In this paper,the class of starlike functions of complex order γ(γ∈ℂ−{0})is extended from the case on unit disk U=(z∈C:|z|<1)to the case on the unit ball B in a complex Banach space or the unit polydisk U^(n) in C^(n).Let g be a convex function in U. We mainly establish the sharp bounds of all terms of homogeneous polynomial expansions for a subclass of g-parametric starlike mappings of complex order γ on B (resp.U^(n))when the mappings f are k-fold symmetric, k ∈ N. Our results partly solve the Bieberbach conjecture in several complex variables and generalize some prior works.展开更多
Let M(u) be an N function, A=D r+∑r-1k=0a k(x)D k a linear differential operator and W M(A) the Sobolev Orlicz class defined by M(u) and A. In this paper we give the asymptotic estimates...Let M(u) be an N function, A=D r+∑r-1k=0a k(x)D k a linear differential operator and W M(A) the Sobolev Orlicz class defined by M(u) and A. In this paper we give the asymptotic estimates of the n K width d n(W M(A),L 2[0,1]) .展开更多
Teaching large,heterogeneous English class is now a teaching fact in China.It brings a lot of practical problems to English teachers who teach that kind of class because of the existence of differences among learners....Teaching large,heterogeneous English class is now a teaching fact in China.It brings a lot of practical problems to English teachers who teach that kind of class because of the existence of differences among learners.This paper aims to provide practical principles illustrated by some scholars to address problems appeared in those classes and achieve the aim of better learning for all members of the class.展开更多
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
As blockchain technology rapidly evolves,smart contracts have seen widespread adoption in financial transactions and beyond.However,the growing prevalence of malicious Ponzi scheme contracts presents serious security ...As blockchain technology rapidly evolves,smart contracts have seen widespread adoption in financial transactions and beyond.However,the growing prevalence of malicious Ponzi scheme contracts presents serious security threats to blockchain ecosystems.Although numerous detection techniques have been proposed,existing methods suffer from significant limitations,such as class imbalance and insufficient modeling of transaction-related semantic features.To address these challenges,this paper proposes an oversampling-based detection framework for Ponzi smart contracts.We enhance the Adaptive Synthetic Sampling(ADASYN)algorithm by incorporating sample proximity to decision boundaries and ensuring realistic sample distributions.This enhancement facilitates the generation of high-quality minority class samples and effectively mitigates class imbalance.In addition,we design a Contract Transaction Graph(CTG)construction algorithm to preserve key transactional semantics through feature extraction from contract code.A graph neural network(GNN)is then applied for classification.This study employs a publicly available dataset from the XBlock platform,consisting of 318 verified Ponzi contracts and 6498 benign contracts.Sourced from real Ethereum deployments,the dataset reflects diverse application scenarios and captures the varied characteristics of Ponzi schemes.Experimental results demonstrate that our approach achieves an accuracy of 96%,a recall of 92%,and an F1-score of 94%in detecting Ponzi contracts,outperforming state-of-the-art methods.展开更多
Objective:Sepsis exhibits remarkable heterogeneity in disease progression trajectories,and accurate identificationof distinct trajectory-based phenotypes is critical for implementing personalized therapeutic strategie...Objective:Sepsis exhibits remarkable heterogeneity in disease progression trajectories,and accurate identificationof distinct trajectory-based phenotypes is critical for implementing personalized therapeutic strategies and prognostic assessment.However,trajectory clustering analysis of time-series clinical data poses substantial methodological challenges for researchers.This study provides a comprehensive tutorial framework demonstrating six trajectory modeling approaches integrated with proteomic analysis to guide researchers in identifying sepsis subtypes after laparoscopic surgery.Methods:This study employs simulated longitudinal data from 300 septic patients after laparoscopic surgery to demonstrate six trajectory modeling methods(group-based trajectory modeling,latent growth mixture modeling,latent transition analysis,time-varying effect modeling,K-means for longitudinal data,agglomerative hierarchical clustering)for identifying associations between predefinedsequential organ failure assessment trajectories and 25 proteomic biomarkers.Clustering performance was evaluated via multiple metrics,and a biomarker discovery pipeline integrating principal component analysis,random forests,feature selection,and receiver operating characteristic analysis was developed.Results:The six methods demonstrated varying performance in identifying trajectory structures,with each approach exhibiting distinct analytical characteristics.The performance metrics revealed differences across methods,which may inform context-specificmethod selection and interpretation strategies.Conclusion:This study illustrates practical implementations of trajectory modeling approaches under controlled conditions,facilitating informed method selection for clinical researchers.The inclusion of complete R code and integrated proteomics workflows offers a reproducible analytical framework connecting temporal pattern recognition to biomarker discovery.Beyond sepsis,this pipeline-oriented approach may be adapted to diverse clinical scenarios requiring longitudinal disease characterization and precision medicine applications.The comparative analysis reveals that each method has distinct strengths,providing a practical guide for clinical researchers in selecting appropriate methods based on their specificstudy goals and data characteristics.展开更多
Most predictive maintenance studies have emphasized accuracy but provide very little focus on Interpretability or deployment readiness.This study improves on prior methods by developing a small yet robust system that ...Most predictive maintenance studies have emphasized accuracy but provide very little focus on Interpretability or deployment readiness.This study improves on prior methods by developing a small yet robust system that can predict when turbofan engines will fail.It uses the NASA CMAPSS dataset,which has over 200,000 engine cycles from260 engines.The process begins with systematic preprocessing,which includes imputation,outlier removal,scaling,and labelling of the remaining useful life.Dimensionality is reduced using a hybrid selection method that combines variance filtering,recursive elimination,and gradient-boosted importance scores,yielding a stable set of 10 informative sensors.To mitigate class imbalance,minority cases are oversampled,and class-weighted losses are applied during training.Benchmarking is carried out with logistic regression,gradient boosting,and a recurrent design that integrates gated recurrent units with long short-term memory networks.The Long Short-Term Memory–Gated Recurrent Unit(LSTM–GRU)hybrid achieved the strongest performance with an F1 score of 0.92,precision of 0.93,recall of 0.91,ReceiverOperating Characteristic–AreaUnder the Curve(ROC-AUC)of 0.97,andminority recall of 0.75.Interpretability testing using permutation importance and Shapley values indicates that sensors 13,15,and 11 are the most important indicators of engine wear.The proposed system combines imbalance handling,feature reduction,and Interpretability into a practical design suitable for real industrial settings.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
In recent years, the scale of enrollment of college students has been expanded. As a result, in colleges teachers are badly needed for the basic course of college English. To solve this problem, big classes are adopte...In recent years, the scale of enrollment of college students has been expanded. As a result, in colleges teachers are badly needed for the basic course of college English. To solve this problem, big classes are adopted for college English teaching in some universities. And scientific management can guarantee the quality of college English teaching of big classes. Finally, this article introduces four tactics to manage college English teaching of big classes.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
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
With the increasing severity of network security threats,Network Intrusion Detection(NID)has become a key technology to ensure network security.To address the problem of low detection rate of traditional intrusion det...With the increasing severity of network security threats,Network Intrusion Detection(NID)has become a key technology to ensure network security.To address the problem of low detection rate of traditional intrusion detection models,this paper proposes a Dual-Attention model for NID,which combines Convolutional Neural Network(CNN)and Bidirectional Long Short-Term Memory(BiLSTM)to design two modules:the FocusConV and the TempoNet module.The FocusConV module,which automatically adjusts and weights CNN extracted local features,focuses on local features that are more important for intrusion detection.The TempoNet module focuses on global information,identifies more important features in time steps or sequences,and filters and weights the information globally to further improve the accuracy and robustness of NID.Meanwhile,in order to solve the class imbalance problem in the dataset,the EQL v2 method is used to compute the class weights of each class and to use them in the loss computation,which optimizes the performance of the model on the class imbalance problem.Extensive experiments were conducted on the NSL-KDD,UNSW-NB15,and CIC-DDos2019 datasets,achieving average accuracy rates of 99.66%,87.47%,and 99.39%,respectively,demonstrating excellent detection accuracy and robustness.The model also improves the detection performance of minority classes in the datasets.On the UNSW-NB15 dataset,the detection rates for Analysis,Exploits,and Shellcode attacks increased by 7%,7%,and 10%,respectively,demonstrating the Dual-Attention CNN-BiLSTM model’s excellent performance in NID.展开更多
With more and more people who are jumping into the line of learning the second language (especially English),and also due to the limited resources in language teaching,many foreign language teachers are nowadays confr...With more and more people who are jumping into the line of learning the second language (especially English),and also due to the limited resources in language teaching,many foreign language teachers are nowadays confronting the large classes when teaching.So how to teach foreign language effectively in large classes has become an issue as well as a special field to make researches in.展开更多
Let M(u) be an N function, A=D r+∑r-1k=0a k(x)D k a linear differential operator and W M(A) the Sobolev Orlicz class defined by M(u) and A. In this paper we give the asymptotic estimates...Let M(u) be an N function, A=D r+∑r-1k=0a k(x)D k a linear differential operator and W M(A) the Sobolev Orlicz class defined by M(u) and A. In this paper we give the asymptotic estimates of the n K width d n(W M(A),L 2[0,1]) .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
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Liaoning Province Education and Scientific Research Young and Middle-Aged Teachers Special Project:Innovative Research and Practice of Chunk-Based Interpreting Teaching for IP’s Curriculum(Fund No.:JG24QGA06).
文摘Dialogue and fusion of horizons are two important concepts of Gadamer’s philosophical hermeneutics,which falls into the pedagogical category of teaching English News Listening Classes.The course of English News Listening is one of the most fundamental and difficult courses in the curriculum for college students who are English majors.The simultaneous interpreting training method of shadowing is used in English News Listening Classes in helping students improve their language skill of listening and speaking.In fulfilling a teacher’s pedagogical performance of dialoguing and fusion of horizons,still one thing is important,i.e.,solidarity triggered between students and teacher,which is the good or the ethical choice between students and teacher.In English News Listening Classes,“道”or“the way(Dao)”is shadowing.In teaching English News Listening,a“dialogue”of shadowing could be achieved between students and teacher is even more significant than that of other courses.This paper intends to present the dialogic ethical triggering of fusion of horizons in class.In another word,students’knowing could be guided by teacher’s dialogic ethical triggering in English News classes.In voicing out the language,knowing in listening and speaking could help students have confidence in not only their language skills but in conquering their difficulties in their life.Teaching English News Listening at Northeastern University(NEU)in this way since 2013 has turned out to be good for students’growth and maturation in life.
文摘Letϕbe a smooth radial weight that decays faster than the class Gaussian ones.We obtain certain estimates for the reproducing kernels and the Lp-estimates for solutions of theδ-equation on the weighted Fock spaces F_(ϕ)^(p)(1≤p≤∞),which extends the classical Hörmander Theorem.Furthermore,for a suitable f,we completely characterize the boundedness and compactness of the Hankel operator H_(f):F_(ϕ)^(p)→L^(q)(C,e^(qϕ(·))dm)for all possible 1≤p,q<∞and also characterize the Schatten-p class Hankel operator Hf from F_(ϕ)^(2)to L^(2)(C,E^(-2ϕ)dm) for all 0<p<∞. As an application, we give a complete characterization of the simultaneously bounded, compact and Schatten-p classes Hankel operators H_(f) and h_(f)^(-) on F_(ϕ)^(2).
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.
文摘Abs As a crucial vehicle for young children’s artistic enlightenment,music appreciation holds an irreplaceable value in cognitive development,emotional edification,and the cultivation of aesthetic abilities.Currently,in music appreciation activities for senior kindergarten classes,there is a widespread phenomenon of homogenized teaching content and mechanized teaching methods,which results in insufficient enthusiasm for participation among young children and a superficial understanding of music.The situational teaching method,by constructing concrete and immersive learning scenarios,can effectively activate young children’s multi-dimensional sensory experiences.Its characteristics of intuitiveness and interactivity are highly consistent with the traits of young children’s concrete thinking,thus providing a new approach to resolving the current predicament.The research focuses on the practical pain points in music appreciation activities for senior kindergarten classes and proposes targeted solutions from four dimensions:content design,method innovation,resource integration,and teacher training,aiming to reconstruct a child-centered,in-depth music learning model.Practice has shown that the situational teaching method can not only enhance young children’s perceptual sensitivity to musical elements but also guide them to achieve emotional resonance through role-playing and life-related associations,laying a foundation for the sustainable development of young children’s musical literacy.
基金supported by the National Natural Science Foundation of China(12061035)the Research Foundation of Jiangxi Science and Technology Normal University of China(2021QNBJRC003)supported by the Graduate Innovation Fund of Jiangxi Science and Technology Normal University(YC2024-X10).
文摘In this paper,the class of starlike functions of complex order γ(γ∈ℂ−{0})is extended from the case on unit disk U=(z∈C:|z|<1)to the case on the unit ball B in a complex Banach space or the unit polydisk U^(n) in C^(n).Let g be a convex function in U. We mainly establish the sharp bounds of all terms of homogeneous polynomial expansions for a subclass of g-parametric starlike mappings of complex order γ on B (resp.U^(n))when the mappings f are k-fold symmetric, k ∈ N. Our results partly solve the Bieberbach conjecture in several complex variables and generalize some prior works.
文摘Let M(u) be an N function, A=D r+∑r-1k=0a k(x)D k a linear differential operator and W M(A) the Sobolev Orlicz class defined by M(u) and A. In this paper we give the asymptotic estimates of the n K width d n(W M(A),L 2[0,1]) .
文摘Teaching large,heterogeneous English class is now a teaching fact in China.It brings a lot of practical problems to English teachers who teach that kind of class because of the existence of differences among learners.This paper aims to provide practical principles illustrated by some scholars to address problems appeared in those classes and achieve the aim of better learning for all members of the class.
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 Key Project of Joint Fund of the National Natural Science Foundation of China“Research on Key Technologies and Demonstration Applications for Trusted and Secure Data Circulation and Trading”(U24A20241)the National Natural Science Foundation of China“Research on Trusted Theories and Key Technologies of Data Security Trading Based on Blockchain”(62202118)+4 种基金the Major Scientific and Technological Special Project of Guizhou Province([2024]014)Scientific and Technological Research Projects from the Guizhou Education Department(Qian jiao ji[2023]003)the Hundred-Level Innovative Talent Project of the Guizhou Provincial Science and Technology Department(Qiankehe Platform Talent-GCC[2023]018)the Major Project of Guizhou Province“Research and Application of Key Technologies for Trusted Large Models Oriented to Public Big Data”(Qiankehe Major Project[2024]003)the Guizhou Province Computational Power Network Security Protection Science and Technology Innovation Talent Team(Qiankehe Talent CXTD[2025]029).
文摘As blockchain technology rapidly evolves,smart contracts have seen widespread adoption in financial transactions and beyond.However,the growing prevalence of malicious Ponzi scheme contracts presents serious security threats to blockchain ecosystems.Although numerous detection techniques have been proposed,existing methods suffer from significant limitations,such as class imbalance and insufficient modeling of transaction-related semantic features.To address these challenges,this paper proposes an oversampling-based detection framework for Ponzi smart contracts.We enhance the Adaptive Synthetic Sampling(ADASYN)algorithm by incorporating sample proximity to decision boundaries and ensuring realistic sample distributions.This enhancement facilitates the generation of high-quality minority class samples and effectively mitigates class imbalance.In addition,we design a Contract Transaction Graph(CTG)construction algorithm to preserve key transactional semantics through feature extraction from contract code.A graph neural network(GNN)is then applied for classification.This study employs a publicly available dataset from the XBlock platform,consisting of 318 verified Ponzi contracts and 6498 benign contracts.Sourced from real Ethereum deployments,the dataset reflects diverse application scenarios and captures the varied characteristics of Ponzi schemes.Experimental results demonstrate that our approach achieves an accuracy of 96%,a recall of 92%,and an F1-score of 94%in detecting Ponzi contracts,outperforming state-of-the-art methods.
基金funding from the China National Key Research and Development Program(No.2023YFC3603104)the National Natural Science Foundation of China(Nos.82472243 and 82272180)+6 种基金the Fundamental Research Funds for the Central Universities(No.226-2025-00024)the Huadong Medicine Joint Funds of the Zhejiang Provincial Natural Science Foundation of China(No.LHDMD24H150001)the Key Research&Development Project of Zhejiang Province(No.2024C03240)a collaborative scientific project co-established by the Science and Technology Department of the National Administration of Traditional Chinese Medicine and the Zhejiang Provincial Administration of Traditional Chinese Medicine(No.GZY-ZJ-KJ-24082)he General Health Science and Technology Program of Zhejiang Province(No.2024KY1099)the Project of Zhejiang University Longquan Innovation Center(No.ZJDXLQCXZCJBGS2024016)Wu Jieping Medical Foundation Special Research Grant(No.320.6750.2024-23-07).
文摘Objective:Sepsis exhibits remarkable heterogeneity in disease progression trajectories,and accurate identificationof distinct trajectory-based phenotypes is critical for implementing personalized therapeutic strategies and prognostic assessment.However,trajectory clustering analysis of time-series clinical data poses substantial methodological challenges for researchers.This study provides a comprehensive tutorial framework demonstrating six trajectory modeling approaches integrated with proteomic analysis to guide researchers in identifying sepsis subtypes after laparoscopic surgery.Methods:This study employs simulated longitudinal data from 300 septic patients after laparoscopic surgery to demonstrate six trajectory modeling methods(group-based trajectory modeling,latent growth mixture modeling,latent transition analysis,time-varying effect modeling,K-means for longitudinal data,agglomerative hierarchical clustering)for identifying associations between predefinedsequential organ failure assessment trajectories and 25 proteomic biomarkers.Clustering performance was evaluated via multiple metrics,and a biomarker discovery pipeline integrating principal component analysis,random forests,feature selection,and receiver operating characteristic analysis was developed.Results:The six methods demonstrated varying performance in identifying trajectory structures,with each approach exhibiting distinct analytical characteristics.The performance metrics revealed differences across methods,which may inform context-specificmethod selection and interpretation strategies.Conclusion:This study illustrates practical implementations of trajectory modeling approaches under controlled conditions,facilitating informed method selection for clinical researchers.The inclusion of complete R code and integrated proteomics workflows offers a reproducible analytical framework connecting temporal pattern recognition to biomarker discovery.Beyond sepsis,this pipeline-oriented approach may be adapted to diverse clinical scenarios requiring longitudinal disease characterization and precision medicine applications.The comparative analysis reveals that each method has distinct strengths,providing a practical guide for clinical researchers in selecting appropriate methods based on their specificstudy goals and data characteristics.
基金supported by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia Grant No.KFU253765.
文摘Most predictive maintenance studies have emphasized accuracy but provide very little focus on Interpretability or deployment readiness.This study improves on prior methods by developing a small yet robust system that can predict when turbofan engines will fail.It uses the NASA CMAPSS dataset,which has over 200,000 engine cycles from260 engines.The process begins with systematic preprocessing,which includes imputation,outlier removal,scaling,and labelling of the remaining useful life.Dimensionality is reduced using a hybrid selection method that combines variance filtering,recursive elimination,and gradient-boosted importance scores,yielding a stable set of 10 informative sensors.To mitigate class imbalance,minority cases are oversampled,and class-weighted losses are applied during training.Benchmarking is carried out with logistic regression,gradient boosting,and a recurrent design that integrates gated recurrent units with long short-term memory networks.The Long Short-Term Memory–Gated Recurrent Unit(LSTM–GRU)hybrid achieved the strongest performance with an F1 score of 0.92,precision of 0.93,recall of 0.91,ReceiverOperating Characteristic–AreaUnder the Curve(ROC-AUC)of 0.97,andminority recall of 0.75.Interpretability testing using permutation importance and Shapley values indicates that sensors 13,15,and 11 are the most important indicators of engine wear.The proposed system combines imbalance handling,feature reduction,and Interpretability into a practical design suitable for real industrial settings.
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.
文摘In recent years, the scale of enrollment of college students has been expanded. As a result, in colleges teachers are badly needed for the basic course of college English. To solve this problem, big classes are adopted for college English teaching in some universities. And scientific management can guarantee the quality of college English teaching of big classes. Finally, this article introduces four tactics to manage college English teaching of big classes.
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 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 High-Level Talent Foundation of Jinling Institute of Technology(grant number.JIT-B-202413).
文摘With the increasing severity of network security threats,Network Intrusion Detection(NID)has become a key technology to ensure network security.To address the problem of low detection rate of traditional intrusion detection models,this paper proposes a Dual-Attention model for NID,which combines Convolutional Neural Network(CNN)and Bidirectional Long Short-Term Memory(BiLSTM)to design two modules:the FocusConV and the TempoNet module.The FocusConV module,which automatically adjusts and weights CNN extracted local features,focuses on local features that are more important for intrusion detection.The TempoNet module focuses on global information,identifies more important features in time steps or sequences,and filters and weights the information globally to further improve the accuracy and robustness of NID.Meanwhile,in order to solve the class imbalance problem in the dataset,the EQL v2 method is used to compute the class weights of each class and to use them in the loss computation,which optimizes the performance of the model on the class imbalance problem.Extensive experiments were conducted on the NSL-KDD,UNSW-NB15,and CIC-DDos2019 datasets,achieving average accuracy rates of 99.66%,87.47%,and 99.39%,respectively,demonstrating excellent detection accuracy and robustness.The model also improves the detection performance of minority classes in the datasets.On the UNSW-NB15 dataset,the detection rates for Analysis,Exploits,and Shellcode attacks increased by 7%,7%,and 10%,respectively,demonstrating the Dual-Attention CNN-BiLSTM model’s excellent performance in NID.
文摘With more and more people who are jumping into the line of learning the second language (especially English),and also due to the limited resources in language teaching,many foreign language teachers are nowadays confronting the large classes when teaching.So how to teach foreign language effectively in large classes has become an issue as well as a special field to make researches in.
文摘Let M(u) be an N function, A=D r+∑r-1k=0a k(x)D k a linear differential operator and W M(A) the Sobolev Orlicz class defined by M(u) and A. In this paper we give the asymptotic estimates of the n K width d n(W M(A),L 2[0,1]) .
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.
