The 2nd Sino-German Workshop on Computational and Applied Mathematics took place in Hangzhou, China, from October 9-13, 2007. The long list of senior Chinese numerical analysts who had spent a year or more somewhere i...The 2nd Sino-German Workshop on Computational and Applied Mathematics took place in Hangzhou, China, from October 9-13, 2007. The long list of senior Chinese numerical analysts who had spent a year or more somewhere in Germany as Humboldt fellows had led to the first Sino-German Workshop in Berlin held at the Humboldt-Universitat zu Berlin in 2005. The particular purpose of the second German-Chinese Workshop on Computational and Applied Mathematics was to attract more junior Chinese scientists to the actual research activities in Germany. A summer school in Beijing on adaptive finite element methods with Carsten Carstensen and Roll Rannacher piror to the Hangzhou workshop underlined this activity to foster the collaboration of the new generations in the fields of computational and applied mathematics. This special issue reflects the present topics therein in both countries and can be summarised under five headings （i）-（v）.展开更多
In March of 1979, Chinese Academy of Sciences （CAS） established, with the approval of the State Council of China, an office for promoting the application of mathematics and Interdisciplinary studies in practice. Lat...In March of 1979, Chinese Academy of Sciences （CAS） established, with the approval of the State Council of China, an office for promoting the application of mathematics and Interdisciplinary studies in practice. Later in October of 1979, based on this office CAS established the Institute of Applied Mathematics （IAM）. The first director of IAM was the world-wide famous mathematician, Professor HUA Loo-Keng, and most faculty members of IAM came from Institute of Mathematics within CAS, which was founded in July of 1952 and was also directed by Prof. HUA.展开更多
Gauss radial basis functions(GRBF)are frequently employed in data fitting and machine learning.Their linear independence property can theoretically guarantee the avoidance of data redundancy.In this paper,one of the m...Gauss radial basis functions(GRBF)are frequently employed in data fitting and machine learning.Their linear independence property can theoretically guarantee the avoidance of data redundancy.In this paper,one of the main contributions is proving this property using linear algebra instead of profound knowledge.This makes it easy to read and understand this fundamental fact.The proof of linear independence of a set of Gauss functions relies on the constructing method for one-dimensional space and on the deducing method for higher dimensions.Additionally,under the condition of preserving the same moments between the original function and interpolating function,both the interpolating existence and uniqueness are proven for GRBF in one-dimensional space.The final work demonstrates the application of the GRBF method to locate lunar olivine.By combining preprocessed data using GRBF with the removing envelope curve method,a program is created to find the position of lunar olivine based on spectrum data,and the numerical experiment shows that it is an effective scheme.展开更多
Automated Program Repair(APR)techniques have shown significant potential in mitigating the cost and complexity associated with debugging by automatically generating corrective patches for software defects.Despite cons...Automated Program Repair(APR)techniques have shown significant potential in mitigating the cost and complexity associated with debugging by automatically generating corrective patches for software defects.Despite considerable progress in APR methodologies,existing approaches frequently lack contextual awareness of runtime behaviors and structural intricacies inherent in buggy source code.In this paper,we propose a novel APR approach that integrates attention mechanisms within an autoencoder-based framework,explicitly utilizing structural code affinity and execution context correlation derived from stack trace analysis.Our approach begins with an innovative preprocessing pipeline,where code segments and stack traces are transformed into tokenized representations.Subsequently,the BM25 ranking algorithm is employed to quantitatively measure structural code affinity and execution context correlation,identifying syntactically and semantically analogous buggy code snippets and relevant runtime error contexts from extensive repositories.These extracted features are then encoded via an attention-enhanced autoencoder model,specifically designed to capture significant patterns and correlations essential for effective patch generation.To assess the efficacy and generalizability of our proposed method,we conducted rigorous experimental comparisons against DeepFix,a state-of-the-art APR system,using a substantial dataset comprising 53,478 studentdeveloped C programs.Experimental outcomes indicate that our model achieves a notable bug repair success rate of approximately 62.36%,representing a statistically significant performance improvement of over 6%compared to the baseline.Furthermore,a thorough K-fold cross-validation reinforced the consistency,robustness,and reliability of our method across diverse subsets of the dataset.Our findings present the critical advantage of integrating attentionbased learning with code structural and execution context features in APR tasks,leading to improved accuracy and practical applicability.Future work aims to extend the model’s applicability across different programming languages,systematically optimize hyperparameters,and explore alternative feature representation methods to further enhance debugging efficiency and effectiveness.展开更多
Convex feasibility problems are widely used in image reconstruction, sparse signal recovery, and other areas. This paper is devoted to considering a class of convex feasibility problem arising from sparse signal recov...Convex feasibility problems are widely used in image reconstruction, sparse signal recovery, and other areas. This paper is devoted to considering a class of convex feasibility problem arising from sparse signal recovery. We first derive the projection formulas for a vector onto the feasible sets. The centralized circumcentered-reflection method is designed to solve the convex feasibility problem. Some numerical experiments demonstrate the feasibility and effectiveness of the proposed algorithm, showing superior performance compared to conventional alternating projection methods.展开更多
In this paper,we first give a sufficient condition for a graph being fractional ID-[a,b]-factor-critical covered in terms of its independence number and minimum degree,which partially answers the problem posed by Sizh...In this paper,we first give a sufficient condition for a graph being fractional ID-[a,b]-factor-critical covered in terms of its independence number and minimum degree,which partially answers the problem posed by Sizhong Zhou,Hongxia Liu and Yang Xu(2022).Then,an A_(α)-spectral condition is given to ensure that G is a fractional ID-[a,b]-factor-critical covered graph and an(a,b,k)-factor-critical graph,respectively.In fact,(a,b,k)-factor-critical graph is a graph which has an[a,b]-factor for k=0.Thus,these above results extend the results of Jia Wei and Shenggui Zhang(2023)and Ao Fan,Ruifang Liu and Guoyan Ao(2023)in some sense.展开更多
The newly formulated non-Newtonian rivulet flows streaming down an inclined planar surface,with additional periodic perturbations arising from the application of the 2nd Stokes problem to the investigation of rivulet ...The newly formulated non-Newtonian rivulet flows streaming down an inclined planar surface,with additional periodic perturbations arising from the application of the 2nd Stokes problem to the investigation of rivulet dynamics,are demonstrated in the current research.Hereby,the 2nd Stokes problem assumes that the surface,with a thin shared layer of the fluid on it,oscillates in a harmonic manner along the x-axis of the rivulet flow,which coincides with the main flow direction streaming down the underlying surface.We obtain the exact extension of the rivulet flow family,clarifying the structure of the pressure field,which fully absorbs the arising perturbation.The profile of the velocity field is assumed to be Gaussian-type with a non-zero level of plasticity.Hence,the absolutely non-Newtonian case of the viscoplastic flow solution,which satisfies the motion and continuity equations,is considered(with particular cases of exact solutions for pressure).The perturbed governing equations of motion for rivulet flows then result in the Riccati-type ordinary differential equation(ODE),describing the dynamics of the coordinate x(t).The approximated schematic dynamics are presented in graphical plots.展开更多
This paper investigates the distributed continuoustime aggregative optimization problem for second-order multiagent systems,where the local cost function is not only related to its own decision variables,but also to t...This paper investigates the distributed continuoustime aggregative optimization problem for second-order multiagent systems,where the local cost function is not only related to its own decision variables,but also to the aggregation of the decision variables of all the agents.By using the gradient descent method,the distributed average tracking(DAT)technique and the time-base generator(TBG)technique,a distributed continuous-time aggregative optimization algorithm is proposed.Subsequently,the optimality of the system's equilibrium point is analyzed,and the convergence of the closed-loop system is proved using the Lyapunov stability theory.Finally,the effectiveness of the proposed algorithm is validated through case studies on multirobot systems and power generation systems.展开更多
Uncertain parameters are widespread in engineering systems.This study investigates the modal analysis of a fluid-conveying pipe subjected to elastic supports with unknown-but-bound parameters.The governing equation fo...Uncertain parameters are widespread in engineering systems.This study investigates the modal analysis of a fluid-conveying pipe subjected to elastic supports with unknown-but-bound parameters.The governing equation for the elastically supported fluid-conveying pipe is transformed into ordinary differential equations using the Galerkin truncation method.The Chebyshev interval approach,integrated with the assumed mode method is then used to investigate the effects of uncertainties of support stiffness,fluid speed,and pipe length on the natural frequencies and mode shapes of the pipe.Additionally,both symmetrical and asymmetrical support stiffnesses are discussed.The accuracy and effectiveness of the Chebyshev interval approach are verified through comparison with the Monte Carlo method.The results reveal that,for the same deviation coefficient,uncertainties in symmetrical support stiffness have a greater impact on the first four natural frequencies than those of the asymmetrical one.There may be significant differences in the sensitivity of natural frequencies and mode shapes of the same order to uncertain parameters.Notably,mode shapes susceptible to uncertain parameters exhibit wider fluctuation intervals near the elastic supports,requiring more attention.展开更多
This study demonstrates a novel integration of large language models,machine learning,and multicriteria decision-making to investigate self-moderation in small online communities,a topic under-explored compared to use...This study demonstrates a novel integration of large language models,machine learning,and multicriteria decision-making to investigate self-moderation in small online communities,a topic under-explored compared to user behavior and platform-driven moderation on social media.The proposed methodological framework(1)utilizes large language models for social media post analysis and categorization,(2)employs k-means clustering for content characterization,and(3)incorporates the TODIM(Tomada de Decisão Interativa Multicritério)method to determine moderation strategies based on expert judgments.In general,the fully integrated framework leverages the strengths of these intelligent systems in a more systematic evaluation of large-scale decision problems.When applied in social media moderation,this approach promotes nuanced and context-sensitive self-moderation by taking into account factors such as cultural background and geographic location.The application of this framework is demonstrated within Facebook groups.Eight distinct content clusters encompassing safety,harassment,diversity,and misinformation are identified.Analysis revealed a preference for content removal across all clusters,suggesting a cautious approach towards potentially harmful content.However,the framework also highlights the use of other moderation actions,like account suspension,depending on the content category.These findings contribute to the growing body of research on self-moderation and offer valuable insights for creating safer and more inclusive online spaces within smaller communities.展开更多
With the growing complexity and decentralization of network systems,the attack surface has expanded,which has led to greater concerns over network threats.In this context,artificial intelligence(AI)-based network intr...With the growing complexity and decentralization of network systems,the attack surface has expanded,which has led to greater concerns over network threats.In this context,artificial intelligence(AI)-based network intrusion detection systems(NIDS)have been extensively studied,and recent efforts have shifted toward integrating distributed learning to enable intelligent and scalable detection mechanisms.However,most existing works focus on individual distributed learning frameworks,and there is a lack of systematic evaluations that compare different algorithms under consistent conditions.In this paper,we present a comprehensive evaluation of representative distributed learning frameworks—Federated Learning(FL),Split Learning(SL),hybrid collaborative learning(SFL),and fully distributed learning—in the context of AI-driven NIDS.Using recent benchmark intrusion detection datasets,a unified model backbone,and controlled distributed scenarios,we assess these frameworks across multiple criteria,including detection performance,communication cost,computational efficiency,and convergence behavior.Our findings highlight distinct trade-offs among the distributed learning frameworks,demonstrating that the optimal choice depends strongly on systemconstraints such as bandwidth availability,node resources,and data distribution.This work provides the first holistic analysis of distributed learning approaches for AI-driven NIDS and offers practical guidelines for designing secure and efficient intrusion detection systems in decentralized environments.展开更多
Based on reanalysis data from 1979 to 2021,this study explores the spatial distribution of the Southern Indian Ocean Dipole(SIOD)and its individual and synergistic effects with the El Niño-Southern Oscillation(EN...Based on reanalysis data from 1979 to 2021,this study explores the spatial distribution of the Southern Indian Ocean Dipole(SIOD)and its individual and synergistic effects with the El Niño-Southern Oscillation(ENSO)on summer precipitation in China.The inverse phase spatial distribution of sea surface temperature anomalies(SSTAs)in the southwest and northeast of the southern Indian Ocean is defined as the SIOD.Positive SIOD events(positive SSTAs in the southwest,negative SSTAs in the northeast)are associated with La Niña events(Central Pacific(CP)type),while negative SIOD events(negative SSTAs in the southwest,positive SSTAs in the northeast)are associated with El Niño events(Eastern Pacific(EP)type).Both SIOD and ENSO have certain impacts on summer precipitation in China.Precipitation in the Yangtze River basin decreases,while precipitation in southern China increases during pure positive SIOD(P_PSIOD)events.During pure negative SIOD(P_NSIOD)events,the changes in precipitation are exactly the opposite of those during P_PSIOD events,which may be due to differences in the cross-equatorial flow in the southern Indian Ocean,particularly in low-level Australian cross-equatorial flow.When positive SIOD and CP-type La Niña events occur simultaneously(PSIOD+La_Niña),precipitation increases in the Yangtze-Huaihe River basin,while it decreases in northern China.When negative SIOD and EP-type El Niño events occur simultaneously(NSIOD+El_Niño),precipitation in the Yangtze-Huaihe River basin is significantly lower than during P_NSIOD events.This is caused by differences in water vapor originating from the Pacific Ocean during different events.展开更多
The node labels collected from real-world applications are often accompanied by the occurrence of in-distribution noise(seen class nodes with wrong labels) and out-of-distribution noise(unseen class nodes with seen cl...The node labels collected from real-world applications are often accompanied by the occurrence of in-distribution noise(seen class nodes with wrong labels) and out-of-distribution noise(unseen class nodes with seen class labels), which significantly degrade the superior performance of recently emerged open-set graph neural networks(GNN). Nowadays, only a few researchers have attempted to introduce sample selection strategies developed in non-graph areas to limit the influence of noisy node labels. These studies often neglect the impact of inaccurate graph structure relationships, invalid utilization of noisy nodes and unlabeled nodes self-supervision information for noisy node labels constraint. More importantly, simply enhancing the accuracy of graph structure relationships or the utilization of nodes' self-supervision information still cannot minimize the influence of noisy node labels for open-set GNN. In this paper, we propose a novel RT-OGNN(robust training of open-set GNN) framework to solve the above-mentioned issues. Specifically, an effective graph structure learning module is proposed to weaken the impact of structure noise and extend the receptive field of nodes. Then, the augmented graph is sent to a pair of peer GNNs to accurately distinguish noisy node labels of labeled nodes. Third, the label propagation and multilayer perceptron-based decoder modules are simultaneously introduced to discover more supervision information from remaining nodes apart from clean nodes. Finally, we jointly optimize the above modules and open-set GNN in an end-to-end way via consistency regularization loss and cross-entropy loss, which minimizes the influence of noisy node labels and provides more supervision guidance for open-set GNN optimization.Extensive experiments on three benchmarks and various noise rates validate the superiority of RT-OGNN over state-of-the-art models.展开更多
Near-Earth objects are important not only in studying the early formation of the Solar System,but also because they pose a serious hazard to humanity when they make close approaches to the Earth.Study of their physica...Near-Earth objects are important not only in studying the early formation of the Solar System,but also because they pose a serious hazard to humanity when they make close approaches to the Earth.Study of their physical properties can provide useful information on their origin,evolution,and hazard to human beings.However,it remains challenging to investigate small,newly discovered,near-Earth objects because of our limited observational window.This investigation seeks to determine the visible colors of near-Earth asteroids(NEAs),perform an initial taxonomic classification based on visible colors and analyze possible correlations between the distribution of taxonomic classification and asteroid size or orbital parameters.Observations were performed in the broadband BVRI Johnson−Cousins photometric system,applied to images from the Yaoan High Precision Telescope and the 1.88 m telescope at the Kottamia Astronomical Observatory.We present new photometric observations of 84 near-Earth asteroids,and classify 80 of them taxonomically,based on their photometric colors.We find that nearly half(46.3%)of the objects in our sample can be classified as S-complex,26.3%as C-complex,6%as D-complex,and 15.0%as X-complex;the remaining belong to the A-or V-types.Additionally,we identify three P-type NEAs in our sample,according to the Tholen scheme.The fractional abundances of the C/X-complex members with absolute magnitude H≥17.0 were more than twice as large as those with H<17.0.However,the fractions of C-and S-complex members with diameters≤1 km and>1 km are nearly equal,while X-complex members tend to have sub-kilometer diameters.In our sample,the C/D-complex objects are predominant among those with a Jovian Tisserand parameter of T_(J)<3.1.These bodies could have a cometary origin.C-and S-complex members account for a considerable proportion of the asteroids that are potentially hazardous.展开更多
Colorectal cancer is the third most diagnosed cancer worldwide,and immune checkpoint inhibitors have shown promising therapeutic outcomes in selected patient groups.This study performed a comprehensive analysis of mul...Colorectal cancer is the third most diagnosed cancer worldwide,and immune checkpoint inhibitors have shown promising therapeutic outcomes in selected patient groups.This study performed a comprehensive analysis of multi-omics data from The Cancer Genome Atlas colorectal adenocarcinoma cohort(TCGA-COADREAD),accessed through cBioPortal,to develop machine learning models for predicting progression-free survival(PFS)following immunotherapy.The dataset included clinical variables,genomic alterations in Kirsten Rat Sarcoma Viral Oncogene Homolog(KRAS),B-Raf Proto-Oncogene(BRAF),and Neuroblastoma RAS Viral Oncogene Homolog(NRAS),microsatellite instability(MSI)status,tumor mutation burden(TMB),and expression of immune checkpoint genes.Kaplan–Meier analysis showed that KRAS mutations were significantly associated with reduced PFS,while BRAF and NRAS mutations had no significant impact.MSI-high tumors exhibited elevated TMB and increased immune checkpoint expression,reflecting their immunologically active phenotype.We developed both survival and classification models,with the Extra Trees classifier achieving the best performance(accuracy=0.86,precision=0.67,recall=0.70,F1-score=0.68,AUC=0.84).These findings highlight the potential of combining genomic and immune biomarkers with machine learning to improve patient stratification and guide personalized immunotherapy decisions.An interactive web application was also developed to enable clinicians to input patient-specific molecular and clinical data and visualize individualized PFS predictions,supporting timely,data-driven treatment planning.展开更多
Lassa Fever(LF)is a viral hemorrhagic illness transmitted via rodents and is endemic in West Africa,causing thousands of deaths annually.This study develops a dynamic model of Lassa virus transmission,capturing the pr...Lassa Fever(LF)is a viral hemorrhagic illness transmitted via rodents and is endemic in West Africa,causing thousands of deaths annually.This study develops a dynamic model of Lassa virus transmission,capturing the progression of the disease through susceptible,exposed,infected,and recovered populations.The focus is on simulating this model using the fractional Caputo derivative,allowing both qualitative and quantitative analyses of boundedness,positivity,and solution uniqueness.Fixed-point theory and Lipschitz conditions are employed to confirm the existence and uniqueness of solutions,while Lyapunov functions establish the global stability of both disease-free and endemic equilibria.The study further examines the role of the Caputo operator by solving the generalized power-law kernel via a two-step Lagrange polynomial method.This approach offers practical advantages in handling additional data points in integral forms,though Newton polynomial-based schemes are generally more accurate and can outperform Lagrange-based Adams-Bashforth methods.Graphical simulations validate the proposed numerical approach for different fractional orders(ν)and illustrate the influence of model parameters on disease dynamics.Results indicate that increasing the fractional order accelerates the decline of Lassa fever in both human and rodent populations.Moreover,fractional-order modeling provides more nuanced insights than traditional integer-order models,suggesting potential improvements for medical intervention strategies.The study demonstrates that carefully chosen fractional orders can optimize convergence and enhance the predictive capacity of Lassa fever models,offering a promising direction for future research in epidemiological modeling.展开更多
Environmental problems are intensifying due to the rapid growth of the population,industry,and urban infrastructure.This expansion has resulted in increased air and water pollution,intensified urban heat island effect...Environmental problems are intensifying due to the rapid growth of the population,industry,and urban infrastructure.This expansion has resulted in increased air and water pollution,intensified urban heat island effects,and greater runoff from parks and other green spaces.Addressing these challenges requires prioritizing green infrastructure and other sustainable urban development strategies.This study introduces a novel Integrated Decision Support System that combines Pythagorean Fuzzy Sets with the Advanced Alternative Ranking Order Method allowing for Two-Step Normalization(AAROM-TN),enhanced by a dual weighting strategy.The weighting approach integrates the Criteria Importance Through Intercriteria Correlation(CRITIC)method with the Criteria Importance through Means and Standard Deviation(CIMAS)technique.The originality of the proposed framework lies in its ability to objectively quantify criteria importance using CRITIC,incorporate decision-makers’preferences through CIMAS,and capture the uncertainty and hesitation inherent in human judgment via Pythagorean Fuzzy Sets.A case study evaluating green infrastructure alternatives in metropolitan regions demonstrates the applicability and effectiveness of the framework.A sensitivity analysis is conducted to examine how variations in criteria weights affect the rankings and to evaluate the robustness of the results.Furthermore,a comparative analysis highlights the practical and financial implications of each alternative by assessing their respective strengths and weaknesses.展开更多
This paper focuses on the unsupervised detection of the Higgs boson particle using the most informative features and variables which characterize the“Higgs machine learning challenge 2014”data set.This unsupervised ...This paper focuses on the unsupervised detection of the Higgs boson particle using the most informative features and variables which characterize the“Higgs machine learning challenge 2014”data set.This unsupervised detection goes in this paper analysis through 4 steps:(1)selection of the most informative features from the considered data;(2)definition of the number of clusters based on the elbow criterion.The experimental results showed that the optimal number of clusters that group the considered data in an unsupervised manner corresponds to 2 clusters;(3)proposition of a new approach for hybridization of both hard and fuzzy clustering tuned with Ant Lion Optimization(ALO);(4)comparison with some existing metaheuristic optimizations such as Genetic Algorithm(GA)and Particle Swarm Optimization(PSO).By employing a multi-angle analysis based on the cluster validation indices,the confusion matrix,the efficiencies and purities rates,the average cost variation,the computational time and the Sammon mapping visualization,the results highlight the effectiveness of the improved Gustafson-Kessel algorithm optimized withALO(ALOGK)to validate the proposed approach.Even if the paper gives a complete clustering analysis,its novel contribution concerns only the Steps(1)and(3)considered above.The first contribution lies in the method used for Step(1)to select the most informative features and variables.We used the t-Statistic technique to rank them.Afterwards,a feature mapping is applied using Self-Organizing Map(SOM)to identify the level of correlation between them.Then,Particle Swarm Optimization(PSO),a metaheuristic optimization technique,is used to reduce the data set dimension.The second contribution of thiswork concern the third step,where each one of the clustering algorithms as K-means(KM),Global K-means(GlobalKM),Partitioning AroundMedoids(PAM),Fuzzy C-means(FCM),Gustafson-Kessel(GK)and Gath-Geva(GG)is optimized and tuned with ALO.展开更多
A new idea of using the parabolized stability equation （PSE） method to predict laminar-turbulent transition is proposed. It is tested in the prediction of the location of transition for compressible boundary layers ...A new idea of using the parabolized stability equation （PSE） method to predict laminar-turbulent transition is proposed. It is tested in the prediction of the location of transition for compressible boundary layers on fiat plates, and the results are compared with those obtained by direct numerical simulations （DNS）. The agreement is satisfactory, and the reason for this is that the PSE method faithfully reproduces the mechanism leading to the breakdown process in laminar-turbulent transition, i.e., the modification of mean flow profile leads to a remarkable change in its stability characteristics.展开更多
Using the modified method of multiple scales, the nonlinear stability of a truncated shallow spherical shell of variable thickness with a nondeformable rigid body at the center under compound loads is investigated. Wh...Using the modified method of multiple scales, the nonlinear stability of a truncated shallow spherical shell of variable thickness with a nondeformable rigid body at the center under compound loads is investigated. When the geometrical parameter k is larger, the uniformly valid asymptotic solutions of this problem are obtained and the remainder terms are estimated.展开更多
文摘The 2nd Sino-German Workshop on Computational and Applied Mathematics took place in Hangzhou, China, from October 9-13, 2007. The long list of senior Chinese numerical analysts who had spent a year or more somewhere in Germany as Humboldt fellows had led to the first Sino-German Workshop in Berlin held at the Humboldt-Universitat zu Berlin in 2005. The particular purpose of the second German-Chinese Workshop on Computational and Applied Mathematics was to attract more junior Chinese scientists to the actual research activities in Germany. A summer school in Beijing on adaptive finite element methods with Carsten Carstensen and Roll Rannacher piror to the Hangzhou workshop underlined this activity to foster the collaboration of the new generations in the fields of computational and applied mathematics. This special issue reflects the present topics therein in both countries and can be summarised under five headings （i）-（v）.
文摘In March of 1979, Chinese Academy of Sciences （CAS） established, with the approval of the State Council of China, an office for promoting the application of mathematics and Interdisciplinary studies in practice. Later in October of 1979, based on this office CAS established the Institute of Applied Mathematics （IAM）. The first director of IAM was the world-wide famous mathematician, Professor HUA Loo-Keng, and most faculty members of IAM came from Institute of Mathematics within CAS, which was founded in July of 1952 and was also directed by Prof. HUA.
基金Supported by the National Basic Research Program of China(2012CB025904)Zhengzhou Shengda University of Economics,Business and Management(SD-YB2025085)。
文摘Gauss radial basis functions(GRBF)are frequently employed in data fitting and machine learning.Their linear independence property can theoretically guarantee the avoidance of data redundancy.In this paper,one of the main contributions is proving this property using linear algebra instead of profound knowledge.This makes it easy to read and understand this fundamental fact.The proof of linear independence of a set of Gauss functions relies on the constructing method for one-dimensional space and on the deducing method for higher dimensions.Additionally,under the condition of preserving the same moments between the original function and interpolating function,both the interpolating existence and uniqueness are proven for GRBF in one-dimensional space.The final work demonstrates the application of the GRBF method to locate lunar olivine.By combining preprocessed data using GRBF with the removing envelope curve method,a program is created to find the position of lunar olivine based on spectrum data,and the numerical experiment shows that it is an effective scheme.
文摘Automated Program Repair(APR)techniques have shown significant potential in mitigating the cost and complexity associated with debugging by automatically generating corrective patches for software defects.Despite considerable progress in APR methodologies,existing approaches frequently lack contextual awareness of runtime behaviors and structural intricacies inherent in buggy source code.In this paper,we propose a novel APR approach that integrates attention mechanisms within an autoencoder-based framework,explicitly utilizing structural code affinity and execution context correlation derived from stack trace analysis.Our approach begins with an innovative preprocessing pipeline,where code segments and stack traces are transformed into tokenized representations.Subsequently,the BM25 ranking algorithm is employed to quantitatively measure structural code affinity and execution context correlation,identifying syntactically and semantically analogous buggy code snippets and relevant runtime error contexts from extensive repositories.These extracted features are then encoded via an attention-enhanced autoencoder model,specifically designed to capture significant patterns and correlations essential for effective patch generation.To assess the efficacy and generalizability of our proposed method,we conducted rigorous experimental comparisons against DeepFix,a state-of-the-art APR system,using a substantial dataset comprising 53,478 studentdeveloped C programs.Experimental outcomes indicate that our model achieves a notable bug repair success rate of approximately 62.36%,representing a statistically significant performance improvement of over 6%compared to the baseline.Furthermore,a thorough K-fold cross-validation reinforced the consistency,robustness,and reliability of our method across diverse subsets of the dataset.Our findings present the critical advantage of integrating attentionbased learning with code structural and execution context features in APR tasks,leading to improved accuracy and practical applicability.Future work aims to extend the model’s applicability across different programming languages,systematically optimize hyperparameters,and explore alternative feature representation methods to further enhance debugging efficiency and effectiveness.
基金Supported by the Natural Science Foundation of Guangxi Province(Grant Nos.2023GXNSFAA026067,2024GXN SFAA010521)the National Natural Science Foundation of China(Nos.12361079,12201149,12261026).
文摘Convex feasibility problems are widely used in image reconstruction, sparse signal recovery, and other areas. This paper is devoted to considering a class of convex feasibility problem arising from sparse signal recovery. We first derive the projection formulas for a vector onto the feasible sets. The centralized circumcentered-reflection method is designed to solve the convex feasibility problem. Some numerical experiments demonstrate the feasibility and effectiveness of the proposed algorithm, showing superior performance compared to conventional alternating projection methods.
基金Supported by the National Natural Science Foundation of China(Grant Nos.11961041,12261055)the Key Project of Natural Science Foundation of Gansu Province(Grant No.24JRRA222)the Foundation for Innovative Fundamental Research Group Project of Gansu Province(Grant No.25JRRA805).
文摘In this paper,we first give a sufficient condition for a graph being fractional ID-[a,b]-factor-critical covered in terms of its independence number and minimum degree,which partially answers the problem posed by Sizhong Zhou,Hongxia Liu and Yang Xu(2022).Then,an A_(α)-spectral condition is given to ensure that G is a fractional ID-[a,b]-factor-critical covered graph and an(a,b,k)-factor-critical graph,respectively.In fact,(a,b,k)-factor-critical graph is a graph which has an[a,b]-factor for k=0.Thus,these above results extend the results of Jia Wei and Shenggui Zhang(2023)and Ao Fan,Ruifang Liu and Guoyan Ao(2023)in some sense.
文摘The newly formulated non-Newtonian rivulet flows streaming down an inclined planar surface,with additional periodic perturbations arising from the application of the 2nd Stokes problem to the investigation of rivulet dynamics,are demonstrated in the current research.Hereby,the 2nd Stokes problem assumes that the surface,with a thin shared layer of the fluid on it,oscillates in a harmonic manner along the x-axis of the rivulet flow,which coincides with the main flow direction streaming down the underlying surface.We obtain the exact extension of the rivulet flow family,clarifying the structure of the pressure field,which fully absorbs the arising perturbation.The profile of the velocity field is assumed to be Gaussian-type with a non-zero level of plasticity.Hence,the absolutely non-Newtonian case of the viscoplastic flow solution,which satisfies the motion and continuity equations,is considered(with particular cases of exact solutions for pressure).The perturbed governing equations of motion for rivulet flows then result in the Riccati-type ordinary differential equation(ODE),describing the dynamics of the coordinate x(t).The approximated schematic dynamics are presented in graphical plots.
基金supported by the National Key Research and Development Program of China(2025YFE0213100)the National Natural Science Foundation of China(62422315,62573348)+1 种基金the Natural Science Basic Research Program of Shaanxi(2025JC-YBMS-667)the“Shuang Yi Liu”Construction Foundation(25GH02010366)。
文摘This paper investigates the distributed continuoustime aggregative optimization problem for second-order multiagent systems,where the local cost function is not only related to its own decision variables,but also to the aggregation of the decision variables of all the agents.By using the gradient descent method,the distributed average tracking(DAT)technique and the time-base generator(TBG)technique,a distributed continuous-time aggregative optimization algorithm is proposed.Subsequently,the optimality of the system's equilibrium point is analyzed,and the convergence of the closed-loop system is proved using the Lyapunov stability theory.Finally,the effectiveness of the proposed algorithm is validated through case studies on multirobot systems and power generation systems.
基金supported by the National Natural Science Foundation of China(Grant Nos.12272211,12072181,and 12121002).
文摘Uncertain parameters are widespread in engineering systems.This study investigates the modal analysis of a fluid-conveying pipe subjected to elastic supports with unknown-but-bound parameters.The governing equation for the elastically supported fluid-conveying pipe is transformed into ordinary differential equations using the Galerkin truncation method.The Chebyshev interval approach,integrated with the assumed mode method is then used to investigate the effects of uncertainties of support stiffness,fluid speed,and pipe length on the natural frequencies and mode shapes of the pipe.Additionally,both symmetrical and asymmetrical support stiffnesses are discussed.The accuracy and effectiveness of the Chebyshev interval approach are verified through comparison with the Monte Carlo method.The results reveal that,for the same deviation coefficient,uncertainties in symmetrical support stiffness have a greater impact on the first four natural frequencies than those of the asymmetrical one.There may be significant differences in the sensitivity of natural frequencies and mode shapes of the same order to uncertain parameters.Notably,mode shapes susceptible to uncertain parameters exhibit wider fluctuation intervals near the elastic supports,requiring more attention.
基金funded by the Office of the Vice-President for Research and Development of Cebu Technological University.
文摘This study demonstrates a novel integration of large language models,machine learning,and multicriteria decision-making to investigate self-moderation in small online communities,a topic under-explored compared to user behavior and platform-driven moderation on social media.The proposed methodological framework(1)utilizes large language models for social media post analysis and categorization,(2)employs k-means clustering for content characterization,and(3)incorporates the TODIM(Tomada de Decisão Interativa Multicritério)method to determine moderation strategies based on expert judgments.In general,the fully integrated framework leverages the strengths of these intelligent systems in a more systematic evaluation of large-scale decision problems.When applied in social media moderation,this approach promotes nuanced and context-sensitive self-moderation by taking into account factors such as cultural background and geographic location.The application of this framework is demonstrated within Facebook groups.Eight distinct content clusters encompassing safety,harassment,diversity,and misinformation are identified.Analysis revealed a preference for content removal across all clusters,suggesting a cautious approach towards potentially harmful content.However,the framework also highlights the use of other moderation actions,like account suspension,depending on the content category.These findings contribute to the growing body of research on self-moderation and offer valuable insights for creating safer and more inclusive online spaces within smaller communities.
基金supported by the Research year project of the KongjuNational University in 2025 and the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.RS-2024-00444170,Research and International Collaboration on Trust Model-Based Intelligent Incident Response Technologies in 6G Open Network Environment).
文摘With the growing complexity and decentralization of network systems,the attack surface has expanded,which has led to greater concerns over network threats.In this context,artificial intelligence(AI)-based network intrusion detection systems(NIDS)have been extensively studied,and recent efforts have shifted toward integrating distributed learning to enable intelligent and scalable detection mechanisms.However,most existing works focus on individual distributed learning frameworks,and there is a lack of systematic evaluations that compare different algorithms under consistent conditions.In this paper,we present a comprehensive evaluation of representative distributed learning frameworks—Federated Learning(FL),Split Learning(SL),hybrid collaborative learning(SFL),and fully distributed learning—in the context of AI-driven NIDS.Using recent benchmark intrusion detection datasets,a unified model backbone,and controlled distributed scenarios,we assess these frameworks across multiple criteria,including detection performance,communication cost,computational efficiency,and convergence behavior.Our findings highlight distinct trade-offs among the distributed learning frameworks,demonstrating that the optimal choice depends strongly on systemconstraints such as bandwidth availability,node resources,and data distribution.This work provides the first holistic analysis of distributed learning approaches for AI-driven NIDS and offers practical guidelines for designing secure and efficient intrusion detection systems in decentralized environments.
基金supported by the National Natural Science Foundation of China[grant numbers 41975087,U2242212,and 41975085]supported by the National Natural Science Foundation of China[grant number U2242212]。
文摘Based on reanalysis data from 1979 to 2021,this study explores the spatial distribution of the Southern Indian Ocean Dipole(SIOD)and its individual and synergistic effects with the El Niño-Southern Oscillation(ENSO)on summer precipitation in China.The inverse phase spatial distribution of sea surface temperature anomalies(SSTAs)in the southwest and northeast of the southern Indian Ocean is defined as the SIOD.Positive SIOD events(positive SSTAs in the southwest,negative SSTAs in the northeast)are associated with La Niña events(Central Pacific(CP)type),while negative SIOD events(negative SSTAs in the southwest,positive SSTAs in the northeast)are associated with El Niño events(Eastern Pacific(EP)type).Both SIOD and ENSO have certain impacts on summer precipitation in China.Precipitation in the Yangtze River basin decreases,while precipitation in southern China increases during pure positive SIOD(P_PSIOD)events.During pure negative SIOD(P_NSIOD)events,the changes in precipitation are exactly the opposite of those during P_PSIOD events,which may be due to differences in the cross-equatorial flow in the southern Indian Ocean,particularly in low-level Australian cross-equatorial flow.When positive SIOD and CP-type La Niña events occur simultaneously(PSIOD+La_Niña),precipitation increases in the Yangtze-Huaihe River basin,while it decreases in northern China.When negative SIOD and EP-type El Niño events occur simultaneously(NSIOD+El_Niño),precipitation in the Yangtze-Huaihe River basin is significantly lower than during P_NSIOD events.This is caused by differences in water vapor originating from the Pacific Ocean during different events.
基金supported by the General Program of the National Natural Science Foundation of China (Grant No.62575116)the National Natural Science Foundation of China (Grant No.62262005)+1 种基金the High-level Innovative Talents in Guizhou Province (Grant No.GCC[2023]033)the Open Project of the Text Computing and Cognitive Intelligence Ministry of Education Engineering Research Center(Grant No.TCCI250208)。
文摘The node labels collected from real-world applications are often accompanied by the occurrence of in-distribution noise(seen class nodes with wrong labels) and out-of-distribution noise(unseen class nodes with seen class labels), which significantly degrade the superior performance of recently emerged open-set graph neural networks(GNN). Nowadays, only a few researchers have attempted to introduce sample selection strategies developed in non-graph areas to limit the influence of noisy node labels. These studies often neglect the impact of inaccurate graph structure relationships, invalid utilization of noisy nodes and unlabeled nodes self-supervision information for noisy node labels constraint. More importantly, simply enhancing the accuracy of graph structure relationships or the utilization of nodes' self-supervision information still cannot minimize the influence of noisy node labels for open-set GNN. In this paper, we propose a novel RT-OGNN(robust training of open-set GNN) framework to solve the above-mentioned issues. Specifically, an effective graph structure learning module is proposed to weaken the impact of structure noise and extend the receptive field of nodes. Then, the augmented graph is sent to a pair of peer GNNs to accurately distinguish noisy node labels of labeled nodes. Third, the label propagation and multilayer perceptron-based decoder modules are simultaneously introduced to discover more supervision information from remaining nodes apart from clean nodes. Finally, we jointly optimize the above modules and open-set GNN in an end-to-end way via consistency regularization loss and cross-entropy loss, which minimizes the influence of noisy node labels and provides more supervision guidance for open-set GNN optimization.Extensive experiments on three benchmarks and various noise rates validate the superiority of RT-OGNN over state-of-the-art models.
基金funded by the China National Space Administration(KJSP2023020105)supported by the National Key R&D Program of China(Grant No.2023YFA1608100)+2 种基金the NSFC(Grant No.62227901)the Minor Planet Foundationsupported by the Egyptian Science,Technology&Innovation Funding Authority(STDF)under Grant No.48102.
文摘Near-Earth objects are important not only in studying the early formation of the Solar System,but also because they pose a serious hazard to humanity when they make close approaches to the Earth.Study of their physical properties can provide useful information on their origin,evolution,and hazard to human beings.However,it remains challenging to investigate small,newly discovered,near-Earth objects because of our limited observational window.This investigation seeks to determine the visible colors of near-Earth asteroids(NEAs),perform an initial taxonomic classification based on visible colors and analyze possible correlations between the distribution of taxonomic classification and asteroid size or orbital parameters.Observations were performed in the broadband BVRI Johnson−Cousins photometric system,applied to images from the Yaoan High Precision Telescope and the 1.88 m telescope at the Kottamia Astronomical Observatory.We present new photometric observations of 84 near-Earth asteroids,and classify 80 of them taxonomically,based on their photometric colors.We find that nearly half(46.3%)of the objects in our sample can be classified as S-complex,26.3%as C-complex,6%as D-complex,and 15.0%as X-complex;the remaining belong to the A-or V-types.Additionally,we identify three P-type NEAs in our sample,according to the Tholen scheme.The fractional abundances of the C/X-complex members with absolute magnitude H≥17.0 were more than twice as large as those with H<17.0.However,the fractions of C-and S-complex members with diameters≤1 km and>1 km are nearly equal,while X-complex members tend to have sub-kilometer diameters.In our sample,the C/D-complex objects are predominant among those with a Jovian Tisserand parameter of T_(J)<3.1.These bodies could have a cometary origin.C-and S-complex members account for a considerable proportion of the asteroids that are potentially hazardous.
基金funded by the Research,Development,and Innovation Authority(RDIA)—Kingdom of Saudi Arabia(Grant No.13292-psu-2023-PSNU-R-3-1-EF-).
文摘Colorectal cancer is the third most diagnosed cancer worldwide,and immune checkpoint inhibitors have shown promising therapeutic outcomes in selected patient groups.This study performed a comprehensive analysis of multi-omics data from The Cancer Genome Atlas colorectal adenocarcinoma cohort(TCGA-COADREAD),accessed through cBioPortal,to develop machine learning models for predicting progression-free survival(PFS)following immunotherapy.The dataset included clinical variables,genomic alterations in Kirsten Rat Sarcoma Viral Oncogene Homolog(KRAS),B-Raf Proto-Oncogene(BRAF),and Neuroblastoma RAS Viral Oncogene Homolog(NRAS),microsatellite instability(MSI)status,tumor mutation burden(TMB),and expression of immune checkpoint genes.Kaplan–Meier analysis showed that KRAS mutations were significantly associated with reduced PFS,while BRAF and NRAS mutations had no significant impact.MSI-high tumors exhibited elevated TMB and increased immune checkpoint expression,reflecting their immunologically active phenotype.We developed both survival and classification models,with the Extra Trees classifier achieving the best performance(accuracy=0.86,precision=0.67,recall=0.70,F1-score=0.68,AUC=0.84).These findings highlight the potential of combining genomic and immune biomarkers with machine learning to improve patient stratification and guide personalized immunotherapy decisions.An interactive web application was also developed to enable clinicians to input patient-specific molecular and clinical data and visualize individualized PFS predictions,supporting timely,data-driven treatment planning.
文摘Lassa Fever(LF)is a viral hemorrhagic illness transmitted via rodents and is endemic in West Africa,causing thousands of deaths annually.This study develops a dynamic model of Lassa virus transmission,capturing the progression of the disease through susceptible,exposed,infected,and recovered populations.The focus is on simulating this model using the fractional Caputo derivative,allowing both qualitative and quantitative analyses of boundedness,positivity,and solution uniqueness.Fixed-point theory and Lipschitz conditions are employed to confirm the existence and uniqueness of solutions,while Lyapunov functions establish the global stability of both disease-free and endemic equilibria.The study further examines the role of the Caputo operator by solving the generalized power-law kernel via a two-step Lagrange polynomial method.This approach offers practical advantages in handling additional data points in integral forms,though Newton polynomial-based schemes are generally more accurate and can outperform Lagrange-based Adams-Bashforth methods.Graphical simulations validate the proposed numerical approach for different fractional orders(ν)and illustrate the influence of model parameters on disease dynamics.Results indicate that increasing the fractional order accelerates the decline of Lassa fever in both human and rodent populations.Moreover,fractional-order modeling provides more nuanced insights than traditional integer-order models,suggesting potential improvements for medical intervention strategies.The study demonstrates that carefully chosen fractional orders can optimize convergence and enhance the predictive capacity of Lassa fever models,offering a promising direction for future research in epidemiological modeling.
基金supported by the Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2026R259)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.Ashit Kumar Dutta would like to thank AlMaarefa University for supporting this research under project number MHIRSP2025017.
文摘Environmental problems are intensifying due to the rapid growth of the population,industry,and urban infrastructure.This expansion has resulted in increased air and water pollution,intensified urban heat island effects,and greater runoff from parks and other green spaces.Addressing these challenges requires prioritizing green infrastructure and other sustainable urban development strategies.This study introduces a novel Integrated Decision Support System that combines Pythagorean Fuzzy Sets with the Advanced Alternative Ranking Order Method allowing for Two-Step Normalization(AAROM-TN),enhanced by a dual weighting strategy.The weighting approach integrates the Criteria Importance Through Intercriteria Correlation(CRITIC)method with the Criteria Importance through Means and Standard Deviation(CIMAS)technique.The originality of the proposed framework lies in its ability to objectively quantify criteria importance using CRITIC,incorporate decision-makers’preferences through CIMAS,and capture the uncertainty and hesitation inherent in human judgment via Pythagorean Fuzzy Sets.A case study evaluating green infrastructure alternatives in metropolitan regions demonstrates the applicability and effectiveness of the framework.A sensitivity analysis is conducted to examine how variations in criteria weights affect the rankings and to evaluate the robustness of the results.Furthermore,a comparative analysis highlights the practical and financial implications of each alternative by assessing their respective strengths and weaknesses.
文摘This paper focuses on the unsupervised detection of the Higgs boson particle using the most informative features and variables which characterize the“Higgs machine learning challenge 2014”data set.This unsupervised detection goes in this paper analysis through 4 steps:(1)selection of the most informative features from the considered data;(2)definition of the number of clusters based on the elbow criterion.The experimental results showed that the optimal number of clusters that group the considered data in an unsupervised manner corresponds to 2 clusters;(3)proposition of a new approach for hybridization of both hard and fuzzy clustering tuned with Ant Lion Optimization(ALO);(4)comparison with some existing metaheuristic optimizations such as Genetic Algorithm(GA)and Particle Swarm Optimization(PSO).By employing a multi-angle analysis based on the cluster validation indices,the confusion matrix,the efficiencies and purities rates,the average cost variation,the computational time and the Sammon mapping visualization,the results highlight the effectiveness of the improved Gustafson-Kessel algorithm optimized withALO(ALOGK)to validate the proposed approach.Even if the paper gives a complete clustering analysis,its novel contribution concerns only the Steps(1)and(3)considered above.The first contribution lies in the method used for Step(1)to select the most informative features and variables.We used the t-Statistic technique to rank them.Afterwards,a feature mapping is applied using Self-Organizing Map(SOM)to identify the level of correlation between them.Then,Particle Swarm Optimization(PSO),a metaheuristic optimization technique,is used to reduce the data set dimension.The second contribution of thiswork concern the third step,where each one of the clustering algorithms as K-means(KM),Global K-means(GlobalKM),Partitioning AroundMedoids(PAM),Fuzzy C-means(FCM),Gustafson-Kessel(GK)and Gath-Geva(GG)is optimized and tuned with ALO.
基金Project supported by the National Natural Science Foundation of China (Nos.10632050,90716007)the Science Foundation of LIU Hui Center of Applied Mathematics of Nankai University and Tianjin university.
文摘A new idea of using the parabolized stability equation （PSE） method to predict laminar-turbulent transition is proposed. It is tested in the prediction of the location of transition for compressible boundary layers on fiat plates, and the results are compared with those obtained by direct numerical simulations （DNS）. The agreement is satisfactory, and the reason for this is that the PSE method faithfully reproduces the mechanism leading to the breakdown process in laminar-turbulent transition, i.e., the modification of mean flow profile leads to a remarkable change in its stability characteristics.
文摘Using the modified method of multiple scales, the nonlinear stability of a truncated shallow spherical shell of variable thickness with a nondeformable rigid body at the center under compound loads is investigated. When the geometrical parameter k is larger, the uniformly valid asymptotic solutions of this problem are obtained and the remainder terms are estimated.
