Agricultural land development is a pivotal strategy for addressing the global food security crisis.Barren grassland,especially those in mountainous regions,constitutes critical areas where cultivation can substantiall...Agricultural land development is a pivotal strategy for addressing the global food security crisis.Barren grassland,especially those in mountainous regions,constitutes critical areas where cultivation can substantially enhance land resources.This study highlights the necessity for a precise correlation between land development initiatives and constraints in order to optimize efficiency and enhance the effectiveness of such projects,with the core being the seamless integration of land development engineering and techniques to eliminate agricultural constraints.This study employs a systems engineering approach to classify improvement factors into mobile and fixed categories,elucidating the integration methods of constraint factors.Adhering to the Wooden Barrel Principle,these constraints were rigorously analyzed based on soil quality,land topography,water availability,and agricultural infrastructure.An innovative method of engineering type combination is proposed,which effectively explains the correlation between natural factors combination,project type combination,and target factors combination.It provides a convenient way for the selection of barren grassland development projects and lays a foundation for land planning,development project establishment,program selection,engineering design,and budget preparation.Taking Tang County of China as an example,it is divided into 19 factor improvement areas,a quick reference table of engineering types is established,and 14 main types of engineering combinations are obtained,which lays a foundation for the application of theoretical framework in practice.展开更多
A Tibetan art form bridges the past and present and connects cultures around the world.THANGKA,a unique form of Tibetan sacred painting,is gaining prominence globally due to its vibrant colors,exquisite craftsmanship,...A Tibetan art form bridges the past and present and connects cultures around the world.THANGKA,a unique form of Tibetan sacred painting,is gaining prominence globally due to its vibrant colors,exquisite craftsmanship,and profound religious and cultural significance.With the acceleration of globalization,this symbol of Tibetan culture that combines artistic expression with spirituality has become a bridge for cultural exchange between the East and the West.Recently,China Today spoke to Yixi Puncog,art collector and council member of the China Association for Preservation and Development of Tibetan Culture,to learn more about Thangka art,its role in international exchange,and how it is enhancing China’s cultural soft power.展开更多
Guided by the significant theoretical principle of the“Two Integrations”and grounded in Marxist cultural theory as its methodological basis,this paper constructs a bidirectional interpretative model linking“Yellow ...Guided by the significant theoretical principle of the“Two Integrations”and grounded in Marxist cultural theory as its methodological basis,this paper constructs a bidirectional interpretative model linking“Yellow River Culture”with“Cultural Confidence”.It proposes an integrated“Objective-Content-Path-Support”framework.Through the synergy of three-dimensional objectives,adaptation of stratified content,innovation in four-dimensional pathways,and support from a three-dimensional guarantee system,this framework establishes a closed-loop operational mechanism of“Curriculum-Practice-Evaluation-Feedback”.The study focuses on core issues in integrating Yellow River culture into university education practices,such as content construction,methodological pathways,and institutional guarantees.It aims to provide a systematic reference for universities to fulfill their fundamental task of“fostering virtue and cultivating talent”and to serve the national strategies for ecological protection and high-quality development in the Yellow River Basin.展开更多
Inside the exhibition hall of the 2025 Osaka World Expo in Japan,crowds gathered before a cloisonne enamel painting titled The Pomegranate of Ethnic Unity.The pomegranate,outlined with gold filigree,appears full and r...Inside the exhibition hall of the 2025 Osaka World Expo in Japan,crowds gathered before a cloisonne enamel painting titled The Pomegranate of Ethnic Unity.The pomegranate,outlined with gold filigree,appears full and ripe;Tibetan auspicious motifs intertwine with Han-style cloud patterns,and the enamel glaze reflects brilliant colors under the lights.Created by teachers and students from Nyingchi Vocational and Technical School(hereafter“Nyingchi Vocational School”),this artwork not only drew admiration from countless visitors but also became a“silent envoy”of dialogue between Tibetan culture and the world.展开更多
The Nuo mask is the core visual vehicle within China’s Nuo culture and constitutes a complex symbolic system through facial features(icons),patterns(indexes),and colors(symbols).Within the context of intangible cultu...The Nuo mask is the core visual vehicle within China’s Nuo culture and constitutes a complex symbolic system through facial features(icons),patterns(indexes),and colors(symbols).Within the context of intangible cultural heritage(ICH)preservation,its value lies in the continuation and revitalization of its dynamic symbolic functions.Grounded in semiotic theory,this paper elaborates on three core dimensions of symbolic value manifested by Nuo masks in ICH preservation:a ritual symbol for living transmission,a symbol of identity for collective memory,and an aesthetic symbol as a source of creativity.展开更多
The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduce...The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduces significant vulnerabilities,including fraud,money laundering,and market manipulation.Traditional anomaly detection techniques often fail to capture the relational and dynamic characteristics of financial data.Graph Neural Networks(GNNs),capable of modeling intricate interdependencies among entities,have emerged as a powerful framework for detecting subtle and sophisticated anomalies.However,the high-dimensionality and inherent noise of FinTech datasets demand robust feature selection strategies to improve model scalability,performance,and interpretability.This paper presents a comprehensive survey of GNN-based approaches for anomaly detection in FinTech,with an emphasis on the synergistic role of feature selection.We examine the theoretical foundations of GNNs,review state-of-the-art feature selection techniques,analyze their integration with GNNs,and categorize prevalent anomaly types in FinTech applications.In addition,we discuss practical implementation challenges,highlight representative case studies,and propose future research directions to advance the field of graph-based anomaly detection in financial systems.展开更多
With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy...With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy.However,efficient client selection and adaptive weight allocation in heterogeneous and non-IID environments remain challenging.To address these issues,we propose Federated Learning with Client Selection and Adaptive Weighting(FedCW),a novel algorithm that leverages adaptive client selection and dynamic weight allocation for optimizing model convergence in real-time vehicular networks.FedCW selects clients based on their Euclidean distance from the global model and dynamically adjusts aggregation weights to optimize both data diversity and model convergence.Experimental results show that FedCW significantly outperforms existing FL algorithms such as FedAvg,FedProx,and SCAFFOLD,particularly in non-IID settings,achieving faster convergence,higher accuracy,and reduced communication overhead.These findings demonstrate that FedCW provides an effective solution for enhancing the performance of FL in heterogeneous,edge-based computing environments.展开更多
High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements.In particular,in amulti-label environment,higher complexity is required asmuch as the number of ...High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements.In particular,in amulti-label environment,higher complexity is required asmuch as the number of labels.Moreover,an optimization problem that fully considers all dependencies between features and labels is difficult to solve.In this study,we propose a novel regression-basedmulti-label feature selectionmethod that integrates mutual information to better exploit the underlying data structure.By incorporating mutual information into the regression formulation,the model captures not only linear relationships but also complex non-linear dependencies.The proposed objective function simultaneously considers three types of relationships:(1)feature redundancy,(2)featurelabel relevance,and(3)inter-label dependency.These three quantities are computed usingmutual information,allowing the proposed formulation to capture nonlinear dependencies among variables.These three types of relationships are key factors in multi-label feature selection,and our method expresses them within a unified formulation,enabling efficient optimization while simultaneously accounting for all of them.To efficiently solve the proposed optimization problem under non-negativity constraints,we develop a gradient-based optimization algorithm with fast convergence.Theexperimental results on sevenmulti-label datasets show that the proposed method outperforms existingmulti-label feature selection techniques.展开更多
Populus species,important economic species combining rapid growth with broad ecological adaptability,play a critical role in sustainable forestry and bioenergy production.In this study,we performed whole-genome resequ...Populus species,important economic species combining rapid growth with broad ecological adaptability,play a critical role in sustainable forestry and bioenergy production.In this study,we performed whole-genome resequencing of 707 individuals from a full-sib family to develop comprehensive single nucleotide polymorphism(SNP)markers and constructed a high-density genetic linkage map of 19 linkage groups.The total genetic length of the map reached 3623.65 cM with an average marker interval of 0.34 cM.By integrating multidimensional phenotypic data,89 quantitative trait loci(QTL)associated with growth,wood physical and chemical properties,disease resistance,and leaf morphology traits were identified,with logarithm of odds(LOD)scores ranging from 3.13 to 21.72 Notably,pleiotropic analysis revealed significant colocaliza and phenotypic variance explained between 1.7% and 11.6%.-tion hotspots on chromosomes LG1,LG5,LG6,LG8,and LG14,with epistatic interaction network analysis confirming genetic basis of coordinated regulation across multiple traits.Functional annotation of 207 candidate genes showed that R2R3-MYB and bHLH transcription factors and pyruvate kinase-encoding genes were significantly enriched,suggesting crucial roles in lignin biosynthesis and carbon metabolic pathways.Allelic effect analysis indicated that the frequency of favorable alleles associated with target traits ranged from 0.20 to 0.55.Incorporation of QTL-derived favorable alleles as random effects into Bayesian-based genomic selection models led to an increase in prediction accuracy ranging from 1% to 21%,with Bayesian ridge regression as the best predictive model.This study provides valuable genomic resources and genetic insights for deciphering complex trait architecture and advancing molecular breeding in poplar.展开更多
As a representative surviving example of residences associated with officials and merchants in the southwestern Shandong region of the lower Yellow River Basin,spanning the Ming and Qing Dynasties to the modern period...As a representative surviving example of residences associated with officials and merchants in the southwestern Shandong region of the lower Yellow River Basin,spanning the Ming and Qing Dynasties to the modern period,the Zhujia Courtyard in Shan County functions as a built medium through which commercial capital and clan culture were closely intertwined.Drawing on field investigation and a review of the literature,this study examines its architectural and cultural characteristics across five dimensions:an overview of the courtyard,spatial layout,structural system,decorative arts,and cultural meanings.The analysis points to four interrelated cores,including the spatial embodiment of Confucian ethics,the status aspirations of canal-based merchant groups,adaptive strategies shaped by Yellow River conditions,and the symbolic articulation of folk beliefs.展开更多
The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects acc...The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects accurately.Machine learning models have demonstrated remarkable potential in addressing these challenges.In this study,we introduced the concept of mixed kernel functions to explore the performance of support vector machine regression(SVR) in GS.Six single kernel functions(SVR_L,SVR_C,SVR_G,SVR_P,SVR_S,SVR_L) and four mixed kernel functions(SVR_GS,SVR_GP,SVR_LS,SVR_LP) were used to predict genome breeding values.The prediction accuracy,mean squared error(MSE) and mean absolute error(MAE) were used as evaluation indicators to compare with two traditional parametric models(GBLUP,BayesB) and two popular machine learning models(RF,KcRR).The results indicate that in most cases,the performance of the mixed kernel function model significantly outperforms that of GBLUP,BayesB and single kernel function.For instance,for T1 in the pig dataset,the predictive accuracy of SVR_GS is improved by 10% compared to GBLUP,and by approximately 4.4 and 18.6% compared to SVR_G and SVR_S respectively.For E1 in the wheat dataset,SVR_GS achieves 13.3% higher prediction accuracy than GBLUP.Among single kernel functions,the Laplacian and Gaussian kernel functions yield similar results,with the Gaussian kernel function performing better.The mixed kernel function notably reduces the MSE and MAE when compared to all single kernel functions.Furthermore,regarding runtime,SVR_GS and SVR_GP mixed kernel functions run approximately three times faster than GBLUP in the pig dataset,with only a slight increase in runtime compared to the single kernel function model.In summary,the mixed kernel function model of SVR demonstrates speed and accuracy competitiveness,and the model such as SVR_GS has important application potential for GS.展开更多
The distinctive landscape of Zhujiayu Ancient Village in Zhangqiu District,Ji’nan,together with its long-established cultural traditions,has shaped a unique vernacular settlement character,within which traditional dw...The distinctive landscape of Zhujiayu Ancient Village in Zhangqiu District,Ji’nan,together with its long-established cultural traditions,has shaped a unique vernacular settlement character,within which traditional dwellings serve as a primary medium of regional culture.This study examines the residence of Zhu Fengyin,a jinshi degree holder,and adopts field investigation and measured drawing-based analysis.Discussion is organized around three dimensions:an overview of the dwelling,architectural form,and cultural expression.Particular attention is given to the multi-stage,multi-bay compound configuration developed in a mountainous setting,terrain-adaptive courtyard forms,and construction techniques.The analysis further interprets the concepts of ritual order and auspicious symbolism embedded in architectural decoration.By using this representative case,the study aims to deepen understanding of the architectural culture of gentry dwellings in the Ji’nan region and to provide an academic basis for the conservation and inheritance of traditional dwellings in the Ji’nan area.展开更多
Human-modified landscapes serve as ecological filters,determining species distributions and persistence.Energy-efficient technologies,while crucial for climate change mitigation,represent novel filters whose impacts o...Human-modified landscapes serve as ecological filters,determining species distributions and persistence.Energy-efficient technologies,while crucial for climate change mitigation,represent novel filters whose impacts on synanthropic biodiversity are poorly understood.We investigated how attached sunspaces,a widely adopted energy-saving technology in rural China,filter the distribution of two ecologically important aerial insectivores,the Barn Swallow(Hirundo rustica)and Red-rumped Swallow(Cecropis daurica).We surveyed 106 villages during the 2024 and 2025 breeding seasons and recorded a total of 2323 nests(612 Barn Swallow,1711 Red-rumped Swallow).Using Generalized Linear Models,we assessed their responses to building characteristics,landscape composition and the prevalence of sunspaces.Barn Swallow nests preferred perches at the base and single attachment faces,while Red-rumped Swallow nests favored multiple attachment faces and avoided long shelters.The proportion of buildings with sunspaces acted as a strong positive filter for Barn Swallow nest abundance(+24%)but as a significant negative filter for Red-rumped Swallow(-51%).Other landscape variables(e.g.,human population density,NDVI,Human Footprint Index)were not significant.This study demonstrates that specific architectural innovations can act as powerful ecological filters,leading to divergent distributional outcomes for sympatric species reliant on anthropogenic structures.Our findings reveal a critical trade-off in sustainable development:energy efficiency gains may inadvertently reduce habitat suitability for certain species.To reconcile climate and biodiversity goals in rural landscapes,we advocate integrating species-specific habitat requirements into building design.We propose actionable modifications to sunspaces to support swallows without compromising energy savings.These principles provide a template for mitigating the distributional impacts of green infrastructure globally.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
Feature selection serves as a critical preprocessing step inmachine learning,focusing on identifying and preserving the most relevant features to improve the efficiency and performance of classification algorithms.Par...Feature selection serves as a critical preprocessing step inmachine learning,focusing on identifying and preserving the most relevant features to improve the efficiency and performance of classification algorithms.Particle Swarm Optimization has demonstrated significant potential in addressing feature selection challenges.However,there are inherent limitations in Particle Swarm Optimization,such as the delicate balance between exploration and exploitation,susceptibility to local optima,and suboptimal convergence rates,hinder its performance.To tackle these issues,this study introduces a novel Leveraged Opposition-Based Learning method within Fitness Landscape Particle Swarm Optimization,tailored for wrapper-based feature selection.The proposed approach integrates:(1)a fitness-landscape adaptive strategy to dynamically balance exploration and exploitation,(2)the lever principle within Opposition-Based Learning to improve search efficiency,and(3)a Local Selection and Re-optimization mechanism combined with random perturbation to expedite convergence and enhance the quality of the optimal feature subset.The effectiveness of is rigorously evaluated on 24 benchmark datasets and compared against 13 advancedmetaheuristic algorithms.Experimental results demonstrate that the proposed method outperforms the compared algorithms in classification accuracy on over half of the datasets,whilst also significantly reducing the number of selected features.These findings demonstrate its effectiveness and robustness in feature selection tasks.展开更多
Against the backdrop of the Healthy China strategy and the vigorous development of the cultural tourism industry,the integration of healthcare and cultural tourism has emerged as a highly promising direction for innov...Against the backdrop of the Healthy China strategy and the vigorous development of the cultural tourism industry,the integration of healthcare and cultural tourism has emerged as a highly promising direction for innovation.This paper takes the Dap eng Hospital of the Shenzhen Second People's Hospital as the research object,and analyzes how to organically combine medical academic activities,clinical medical services with the cultural and tourism resources of Dapeng New District in Shenzhen within the policy fr amework.By constructing a compliance-oriented,professional,and distinctive integr ation standards system,this paper explores an innovative development path that integrates academic exchanges,medical services,spiritual healing,and cultural experiences,providing a practical example and theoretical support for the integration of healthcare and cultural tourism nationwide.展开更多
Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from...Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy.This paper proposes an industrial Internet ofThings intrusion detection feature selection algorithm based on an improved whale optimization algorithm(GSLDWOA).The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to,such as local optimality,long detection time,and reduced accuracy.First,the initial population’s diversity is increased using the Gaussian Mutation mechanism.Then,Non-linear Shrinking Factor balances global exploration and local development,avoiding premature convergence.Lastly,Variable-step Levy Flight operator and Dynamic Differential Evolution strategy are introduced to improve the algorithm’s search efficiency and convergence accuracy in highdimensional feature space.Experiments on the NSL-KDD and WUSTL-IIoT-2021 datasets demonstrate that the feature subset selected by GSLDWOA significantly improves detection performance.Compared to the traditional WOA algorithm,the detection rate and F1-score increased by 3.68%and 4.12%.On the WUSTL-IIoT-2021 dataset,accuracy,recall,and F1-score all exceed 99.9%.展开更多
Unconfined Compressive Strength(UCS)is a key parameter for the assessment of the stability and performance of stabilized soils,yet traditional laboratory testing is both time and resource intensive.In this study,an in...Unconfined Compressive Strength(UCS)is a key parameter for the assessment of the stability and performance of stabilized soils,yet traditional laboratory testing is both time and resource intensive.In this study,an interpretable machine learning approach to UCS prediction is presented,pairing five models(Random Forest(RF),Gradient Boosting(GB),Extreme Gradient Boosting(XGB),CatBoost,and K-Nearest Neighbors(KNN))with SHapley Additive exPlanations(SHAP)for enhanced interpretability and to guide feature removal.A complete dataset of 12 geotechnical and chemical parameters,i.e.,Atterberg limits,compaction properties,stabilizer chemistry,dosage,curing time,was used to train and test the models.R2,RMSE,MSE,and MAE were used to assess performance.Initial results with all 12 features indicated that boosting-based models(GB,XGB,CatBoost)exhibited the highest predictive accuracy(R^(2)=0.93)with satisfactory generalization on test data,followed by RF and KNN.SHAP analysis consistently picked CaO content,curing time,stabilizer dosage,and compaction parameters as the most important features,aligning with established soil stabilization mechanisms.Models were then re-trained on the top 8 and top 5 SHAP-ranked features.Interestingly,GB,XGB,and CatBoost maintained comparable accuracy with reduced input sets,while RF was moderately sensitive and KNN was somewhat better owing to reduced dimensionality.The findings confirm that feature reduction through SHAP enables cost-effective UCS prediction through the reduction of laboratory test requirements without significant accuracy loss.The suggested hybrid approach offers an explainable,interpretable,and cost-effective tool for geotechnical engineering practice.展开更多
Automated essay scoring(AES)systems have gained significant importance in educational settings,offering a scalable,efficient,and objective method for evaluating student essays.However,developing AES systems for Arabic...Automated essay scoring(AES)systems have gained significant importance in educational settings,offering a scalable,efficient,and objective method for evaluating student essays.However,developing AES systems for Arabic poses distinct challenges due to the language’s complex morphology,diglossia,and the scarcity of annotated datasets.This paper presents a hybrid approach to Arabic AES by combining text-based,vector-based,and embeddingbased similarity measures to improve essay scoring accuracy while minimizing the training data required.Using a large Arabic essay dataset categorized into thematic groups,the study conducted four experiments to evaluate the impact of feature selection,data size,and model performance.Experiment 1 established a baseline using a non-machine learning approach,selecting top-N correlated features to predict essay scores.The subsequent experiments employed 5-fold cross-validation.Experiment 2 showed that combining embedding-based,text-based,and vector-based features in a Random Forest(RF)model achieved an R2 of 88.92%and an accuracy of 83.3%within a 0.5-point tolerance.Experiment 3 further refined the feature selection process,demonstrating that 19 correlated features yielded optimal results,improving R2 to 88.95%.In Experiment 4,an optimal data efficiency training approach was introduced,where training data portions increased from 5%to 50%.The study found that using just 10%of the data achieved near-peak performance,with an R2 of 85.49%,emphasizing an effective trade-off between performance and computational costs.These findings highlight the potential of the hybrid approach for developing scalable Arabic AES systems,especially in low-resource environments,addressing linguistic challenges while ensuring efficient data usage.展开更多
Advanced intensity measures(IMs)based on an inelastic deformation spectrum improved the evaluation of the median engineering demand parameters(EDPs)and reduced dispersion.In this regard,an optimized two-degreefreedom(...Advanced intensity measures(IMs)based on an inelastic deformation spectrum improved the evaluation of the median engineering demand parameters(EDPs)and reduced dispersion.In this regard,an optimized two-degreefreedom(2DOF)modal pushover-based scaling procedure(2DMPS)has been developed for a nonlinear dynamic analysis of asymmetric in-plan buildings.The 2DMPS procedure scales ground motions to approach close enough to a target value of the inelastic displacement of the first-mode inelastic 2DOF modal stick,extended for structures with significant contributions of higher modes.Further,4-,6-and 13-story RC SMRF buildings were selected for analyses using ground motion records scaled by the 2DMPS procedure,the modal pushover-based scaling method(MPS),and ASCE/SEI 7-16 scaling procedures.The median values of EDPs on scaled records closely matched the benchmark results.The bias in the EDP values due to the scaled records in every group regarding their median value was lower than the dispersion of the 21 unscaled records.These results generally demonstrate the accuracy and efficiency of the 2DMPS method.Additionally,the 2DOF modal stick’s inelastic response spectra are better suited for calculating seismic demands for one-way asymmetric-plan structures than the SDOF inelastic response spectra.展开更多
基金funded by Science and Technology Project of Hebei Education Department[QN2023085].
文摘Agricultural land development is a pivotal strategy for addressing the global food security crisis.Barren grassland,especially those in mountainous regions,constitutes critical areas where cultivation can substantially enhance land resources.This study highlights the necessity for a precise correlation between land development initiatives and constraints in order to optimize efficiency and enhance the effectiveness of such projects,with the core being the seamless integration of land development engineering and techniques to eliminate agricultural constraints.This study employs a systems engineering approach to classify improvement factors into mobile and fixed categories,elucidating the integration methods of constraint factors.Adhering to the Wooden Barrel Principle,these constraints were rigorously analyzed based on soil quality,land topography,water availability,and agricultural infrastructure.An innovative method of engineering type combination is proposed,which effectively explains the correlation between natural factors combination,project type combination,and target factors combination.It provides a convenient way for the selection of barren grassland development projects and lays a foundation for land planning,development project establishment,program selection,engineering design,and budget preparation.Taking Tang County of China as an example,it is divided into 19 factor improvement areas,a quick reference table of engineering types is established,and 14 main types of engineering combinations are obtained,which lays a foundation for the application of theoretical framework in practice.
文摘A Tibetan art form bridges the past and present and connects cultures around the world.THANGKA,a unique form of Tibetan sacred painting,is gaining prominence globally due to its vibrant colors,exquisite craftsmanship,and profound religious and cultural significance.With the acceleration of globalization,this symbol of Tibetan culture that combines artistic expression with spirituality has become a bridge for cultural exchange between the East and the West.Recently,China Today spoke to Yixi Puncog,art collector and council member of the China Association for Preservation and Development of Tibetan Culture,to learn more about Thangka art,its role in international exchange,and how it is enhancing China’s cultural soft power.
基金Philosophy and Social Sciences Research Project of Shandong Higher Education Institutions:“Research on the Double Helix Mechanism of Yellow River Culture Empowering Ideological and Political Education in Universities from the Perspective of Cultural Confidence Cultivation”(2025ZSYB077)Youth Key Project of Shandong Humanities and Social Sciences Research Project,“Research on Integrating Yellow River Culture into the Cultivation of University Students’Cultural Confidence”Shandong Higher Education Institutions Young Innovation Team Program:“Yellow River Delta Ecological Protection and Governance Innovation Team”(2023RW036).
文摘Guided by the significant theoretical principle of the“Two Integrations”and grounded in Marxist cultural theory as its methodological basis,this paper constructs a bidirectional interpretative model linking“Yellow River Culture”with“Cultural Confidence”.It proposes an integrated“Objective-Content-Path-Support”framework.Through the synergy of three-dimensional objectives,adaptation of stratified content,innovation in four-dimensional pathways,and support from a three-dimensional guarantee system,this framework establishes a closed-loop operational mechanism of“Curriculum-Practice-Evaluation-Feedback”.The study focuses on core issues in integrating Yellow River culture into university education practices,such as content construction,methodological pathways,and institutional guarantees.It aims to provide a systematic reference for universities to fulfill their fundamental task of“fostering virtue and cultivating talent”and to serve the national strategies for ecological protection and high-quality development in the Yellow River Basin.
文摘Inside the exhibition hall of the 2025 Osaka World Expo in Japan,crowds gathered before a cloisonne enamel painting titled The Pomegranate of Ethnic Unity.The pomegranate,outlined with gold filigree,appears full and ripe;Tibetan auspicious motifs intertwine with Han-style cloud patterns,and the enamel glaze reflects brilliant colors under the lights.Created by teachers and students from Nyingchi Vocational and Technical School(hereafter“Nyingchi Vocational School”),this artwork not only drew admiration from countless visitors but also became a“silent envoy”of dialogue between Tibetan culture and the world.
基金National Social Science Fund project“Research on the Narrative of the Sense of Community for the Chinese Nation in Southwest Nuo Opera”(24XMZ063).
文摘The Nuo mask is the core visual vehicle within China’s Nuo culture and constitutes a complex symbolic system through facial features(icons),patterns(indexes),and colors(symbols).Within the context of intangible cultural heritage(ICH)preservation,its value lies in the continuation and revitalization of its dynamic symbolic functions.Grounded in semiotic theory,this paper elaborates on three core dimensions of symbolic value manifested by Nuo masks in ICH preservation:a ritual symbol for living transmission,a symbol of identity for collective memory,and an aesthetic symbol as a source of creativity.
基金supported by Ho Chi Minh City Open University,Vietnam under grant number E2024.02.1CD and Suan Sunandha Rajabhat University,Thailand.
文摘The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduces significant vulnerabilities,including fraud,money laundering,and market manipulation.Traditional anomaly detection techniques often fail to capture the relational and dynamic characteristics of financial data.Graph Neural Networks(GNNs),capable of modeling intricate interdependencies among entities,have emerged as a powerful framework for detecting subtle and sophisticated anomalies.However,the high-dimensionality and inherent noise of FinTech datasets demand robust feature selection strategies to improve model scalability,performance,and interpretability.This paper presents a comprehensive survey of GNN-based approaches for anomaly detection in FinTech,with an emphasis on the synergistic role of feature selection.We examine the theoretical foundations of GNNs,review state-of-the-art feature selection techniques,analyze their integration with GNNs,and categorize prevalent anomaly types in FinTech applications.In addition,we discuss practical implementation challenges,highlight representative case studies,and propose future research directions to advance the field of graph-based anomaly detection in financial systems.
文摘With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy.However,efficient client selection and adaptive weight allocation in heterogeneous and non-IID environments remain challenging.To address these issues,we propose Federated Learning with Client Selection and Adaptive Weighting(FedCW),a novel algorithm that leverages adaptive client selection and dynamic weight allocation for optimizing model convergence in real-time vehicular networks.FedCW selects clients based on their Euclidean distance from the global model and dynamically adjusts aggregation weights to optimize both data diversity and model convergence.Experimental results show that FedCW significantly outperforms existing FL algorithms such as FedAvg,FedProx,and SCAFFOLD,particularly in non-IID settings,achieving faster convergence,higher accuracy,and reduced communication overhead.These findings demonstrate that FedCW provides an effective solution for enhancing the performance of FL in heterogeneous,edge-based computing environments.
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(RS-2020-NR049579).
文摘High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements.In particular,in amulti-label environment,higher complexity is required asmuch as the number of labels.Moreover,an optimization problem that fully considers all dependencies between features and labels is difficult to solve.In this study,we propose a novel regression-basedmulti-label feature selectionmethod that integrates mutual information to better exploit the underlying data structure.By incorporating mutual information into the regression formulation,the model captures not only linear relationships but also complex non-linear dependencies.The proposed objective function simultaneously considers three types of relationships:(1)feature redundancy,(2)featurelabel relevance,and(3)inter-label dependency.These three quantities are computed usingmutual information,allowing the proposed formulation to capture nonlinear dependencies among variables.These three types of relationships are key factors in multi-label feature selection,and our method expresses them within a unified formulation,enabling efficient optimization while simultaneously accounting for all of them.To efficiently solve the proposed optimization problem under non-negativity constraints,we develop a gradient-based optimization algorithm with fast convergence.Theexperimental results on sevenmulti-label datasets show that the proposed method outperforms existingmulti-label feature selection techniques.
基金supported by the National Key Research and Development Plan of China(2021YFD2200202)the Key Research and Development Project of Jiangsu Province,China(BE2021366).
文摘Populus species,important economic species combining rapid growth with broad ecological adaptability,play a critical role in sustainable forestry and bioenergy production.In this study,we performed whole-genome resequencing of 707 individuals from a full-sib family to develop comprehensive single nucleotide polymorphism(SNP)markers and constructed a high-density genetic linkage map of 19 linkage groups.The total genetic length of the map reached 3623.65 cM with an average marker interval of 0.34 cM.By integrating multidimensional phenotypic data,89 quantitative trait loci(QTL)associated with growth,wood physical and chemical properties,disease resistance,and leaf morphology traits were identified,with logarithm of odds(LOD)scores ranging from 3.13 to 21.72 Notably,pleiotropic analysis revealed significant colocaliza and phenotypic variance explained between 1.7% and 11.6%.-tion hotspots on chromosomes LG1,LG5,LG6,LG8,and LG14,with epistatic interaction network analysis confirming genetic basis of coordinated regulation across multiple traits.Functional annotation of 207 candidate genes showed that R2R3-MYB and bHLH transcription factors and pyruvate kinase-encoding genes were significantly enriched,suggesting crucial roles in lignin biosynthesis and carbon metabolic pathways.Allelic effect analysis indicated that the frequency of favorable alleles associated with target traits ranged from 0.20 to 0.55.Incorporation of QTL-derived favorable alleles as random effects into Bayesian-based genomic selection models led to an increase in prediction accuracy ranging from 1% to 21%,with Bayesian ridge regression as the best predictive model.This study provides valuable genomic resources and genetic insights for deciphering complex trait architecture and advancing molecular breeding in poplar.
基金Major Project of the National Social Science Foundation of China“Historical Collation and Research of Cultural Exchanges Between Chinese and Foreign Craftsmen Along the Silk Road”(Approval No.22&ZD227)the Planning Project of Shaanxi Art Institute“Research on National Cultural Park Design”(Project No.YG2025001).
文摘As a representative surviving example of residences associated with officials and merchants in the southwestern Shandong region of the lower Yellow River Basin,spanning the Ming and Qing Dynasties to the modern period,the Zhujia Courtyard in Shan County functions as a built medium through which commercial capital and clan culture were closely intertwined.Drawing on field investigation and a review of the literature,this study examines its architectural and cultural characteristics across five dimensions:an overview of the courtyard,spatial layout,structural system,decorative arts,and cultural meanings.The analysis points to four interrelated cores,including the spatial embodiment of Confucian ethics,the status aspirations of canal-based merchant groups,adaptive strategies shaped by Yellow River conditions,and the symbolic articulation of folk beliefs.
基金supported by the China Agriculture Research System of MOF and MARAthe National Natural Science Foundation of China (31872337 and 31501919)the Agricultural Science and Technology Innovation Project,China (ASTIP-IAS02)。
文摘The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects accurately.Machine learning models have demonstrated remarkable potential in addressing these challenges.In this study,we introduced the concept of mixed kernel functions to explore the performance of support vector machine regression(SVR) in GS.Six single kernel functions(SVR_L,SVR_C,SVR_G,SVR_P,SVR_S,SVR_L) and four mixed kernel functions(SVR_GS,SVR_GP,SVR_LS,SVR_LP) were used to predict genome breeding values.The prediction accuracy,mean squared error(MSE) and mean absolute error(MAE) were used as evaluation indicators to compare with two traditional parametric models(GBLUP,BayesB) and two popular machine learning models(RF,KcRR).The results indicate that in most cases,the performance of the mixed kernel function model significantly outperforms that of GBLUP,BayesB and single kernel function.For instance,for T1 in the pig dataset,the predictive accuracy of SVR_GS is improved by 10% compared to GBLUP,and by approximately 4.4 and 18.6% compared to SVR_G and SVR_S respectively.For E1 in the wheat dataset,SVR_GS achieves 13.3% higher prediction accuracy than GBLUP.Among single kernel functions,the Laplacian and Gaussian kernel functions yield similar results,with the Gaussian kernel function performing better.The mixed kernel function notably reduces the MSE and MAE when compared to all single kernel functions.Furthermore,regarding runtime,SVR_GS and SVR_GP mixed kernel functions run approximately three times faster than GBLUP in the pig dataset,with only a slight increase in runtime compared to the single kernel function model.In summary,the mixed kernel function model of SVR demonstrates speed and accuracy competitiveness,and the model such as SVR_GS has important application potential for GS.
基金Major Project of the National Social Science Foundation of China“Historical Collation and Research of Cultural Exchanges Between Chinese and Foreign Craftsmen Along the Silk Road”(Approval No.22&ZD227)the Planning Project of Shaanxi Art Institute“Research on National Cultural Park Design”(Project No.YG2025001)part of the phased outcomes of the“Shaanxi Special Support Program for High-Level Talents”(Project Approval No.21HH02).
文摘The distinctive landscape of Zhujiayu Ancient Village in Zhangqiu District,Ji’nan,together with its long-established cultural traditions,has shaped a unique vernacular settlement character,within which traditional dwellings serve as a primary medium of regional culture.This study examines the residence of Zhu Fengyin,a jinshi degree holder,and adopts field investigation and measured drawing-based analysis.Discussion is organized around three dimensions:an overview of the dwelling,architectural form,and cultural expression.Particular attention is given to the multi-stage,multi-bay compound configuration developed in a mountainous setting,terrain-adaptive courtyard forms,and construction techniques.The analysis further interprets the concepts of ritual order and auspicious symbolism embedded in architectural decoration.By using this representative case,the study aims to deepen understanding of the architectural culture of gentry dwellings in the Ji’nan region and to provide an academic basis for the conservation and inheritance of traditional dwellings in the Ji’nan area.
基金funded by the National Natural Science Foundation of China(No.32201304)the Fundamental Research Funds for the Central Universities(2412022QD026)。
文摘Human-modified landscapes serve as ecological filters,determining species distributions and persistence.Energy-efficient technologies,while crucial for climate change mitigation,represent novel filters whose impacts on synanthropic biodiversity are poorly understood.We investigated how attached sunspaces,a widely adopted energy-saving technology in rural China,filter the distribution of two ecologically important aerial insectivores,the Barn Swallow(Hirundo rustica)and Red-rumped Swallow(Cecropis daurica).We surveyed 106 villages during the 2024 and 2025 breeding seasons and recorded a total of 2323 nests(612 Barn Swallow,1711 Red-rumped Swallow).Using Generalized Linear Models,we assessed their responses to building characteristics,landscape composition and the prevalence of sunspaces.Barn Swallow nests preferred perches at the base and single attachment faces,while Red-rumped Swallow nests favored multiple attachment faces and avoided long shelters.The proportion of buildings with sunspaces acted as a strong positive filter for Barn Swallow nest abundance(+24%)but as a significant negative filter for Red-rumped Swallow(-51%).Other landscape variables(e.g.,human population density,NDVI,Human Footprint Index)were not significant.This study demonstrates that specific architectural innovations can act as powerful ecological filters,leading to divergent distributional outcomes for sympatric species reliant on anthropogenic structures.Our findings reveal a critical trade-off in sustainable development:energy efficiency gains may inadvertently reduce habitat suitability for certain species.To reconcile climate and biodiversity goals in rural landscapes,we advocate integrating species-specific habitat requirements into building design.We propose actionable modifications to sunspaces to support swallows without compromising energy savings.These principles provide a template for mitigating the distributional impacts of green infrastructure globally.
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 National Natural Science Foundation of China(62106092)Natural Science Foundation of Fujian Province(2024J01822,2024J01820,2022J01916)Natural Science Foundation of Zhangzhou City(ZZ2024J28).
文摘Feature selection serves as a critical preprocessing step inmachine learning,focusing on identifying and preserving the most relevant features to improve the efficiency and performance of classification algorithms.Particle Swarm Optimization has demonstrated significant potential in addressing feature selection challenges.However,there are inherent limitations in Particle Swarm Optimization,such as the delicate balance between exploration and exploitation,susceptibility to local optima,and suboptimal convergence rates,hinder its performance.To tackle these issues,this study introduces a novel Leveraged Opposition-Based Learning method within Fitness Landscape Particle Swarm Optimization,tailored for wrapper-based feature selection.The proposed approach integrates:(1)a fitness-landscape adaptive strategy to dynamically balance exploration and exploitation,(2)the lever principle within Opposition-Based Learning to improve search efficiency,and(3)a Local Selection and Re-optimization mechanism combined with random perturbation to expedite convergence and enhance the quality of the optimal feature subset.The effectiveness of is rigorously evaluated on 24 benchmark datasets and compared against 13 advancedmetaheuristic algorithms.Experimental results demonstrate that the proposed method outperforms the compared algorithms in classification accuracy on over half of the datasets,whilst also significantly reducing the number of selected features.These findings demonstrate its effectiveness and robustness in feature selection tasks.
基金funded by the Guangdong Provincial Science and Technology Program Project(2023B0101200003)the National Health Commission Hospital Management Research Institute Project(JCWJ2024ZD01)+2 种基金the Guangxi Graduate Education Innovation Program Project(JGY2023168)the Shenzhen Philosophy and Social Sciences Planning 2025 Annual Research Topic(SZ2025D040)the 2025 Shenzhen Health Economics Society Project(202506)。
文摘Against the backdrop of the Healthy China strategy and the vigorous development of the cultural tourism industry,the integration of healthcare and cultural tourism has emerged as a highly promising direction for innovation.This paper takes the Dap eng Hospital of the Shenzhen Second People's Hospital as the research object,and analyzes how to organically combine medical academic activities,clinical medical services with the cultural and tourism resources of Dapeng New District in Shenzhen within the policy fr amework.By constructing a compliance-oriented,professional,and distinctive integr ation standards system,this paper explores an innovative development path that integrates academic exchanges,medical services,spiritual healing,and cultural experiences,providing a practical example and theoretical support for the integration of healthcare and cultural tourism nationwide.
基金supported by the Major Science and Technology Programs in Henan Province(No.241100210100)Henan Provincial Science and Technology Research Project(No.252102211085,No.252102211105)+3 种基金Endogenous Security Cloud Network Convergence R&D Center(No.602431011PQ1)The Special Project for Research and Development in Key Areas of Guangdong Province(No.2021ZDZX1098)The Stabilization Support Program of Science,Technology and Innovation Commission of Shenzhen Municipality(No.20231128083944001)The Key scientific research projects of Henan higher education institutions(No.24A520042).
文摘Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy.This paper proposes an industrial Internet ofThings intrusion detection feature selection algorithm based on an improved whale optimization algorithm(GSLDWOA).The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to,such as local optimality,long detection time,and reduced accuracy.First,the initial population’s diversity is increased using the Gaussian Mutation mechanism.Then,Non-linear Shrinking Factor balances global exploration and local development,avoiding premature convergence.Lastly,Variable-step Levy Flight operator and Dynamic Differential Evolution strategy are introduced to improve the algorithm’s search efficiency and convergence accuracy in highdimensional feature space.Experiments on the NSL-KDD and WUSTL-IIoT-2021 datasets demonstrate that the feature subset selected by GSLDWOA significantly improves detection performance.Compared to the traditional WOA algorithm,the detection rate and F1-score increased by 3.68%and 4.12%.On the WUSTL-IIoT-2021 dataset,accuracy,recall,and F1-score all exceed 99.9%.
文摘Unconfined Compressive Strength(UCS)is a key parameter for the assessment of the stability and performance of stabilized soils,yet traditional laboratory testing is both time and resource intensive.In this study,an interpretable machine learning approach to UCS prediction is presented,pairing five models(Random Forest(RF),Gradient Boosting(GB),Extreme Gradient Boosting(XGB),CatBoost,and K-Nearest Neighbors(KNN))with SHapley Additive exPlanations(SHAP)for enhanced interpretability and to guide feature removal.A complete dataset of 12 geotechnical and chemical parameters,i.e.,Atterberg limits,compaction properties,stabilizer chemistry,dosage,curing time,was used to train and test the models.R2,RMSE,MSE,and MAE were used to assess performance.Initial results with all 12 features indicated that boosting-based models(GB,XGB,CatBoost)exhibited the highest predictive accuracy(R^(2)=0.93)with satisfactory generalization on test data,followed by RF and KNN.SHAP analysis consistently picked CaO content,curing time,stabilizer dosage,and compaction parameters as the most important features,aligning with established soil stabilization mechanisms.Models were then re-trained on the top 8 and top 5 SHAP-ranked features.Interestingly,GB,XGB,and CatBoost maintained comparable accuracy with reduced input sets,while RF was moderately sensitive and KNN was somewhat better owing to reduced dimensionality.The findings confirm that feature reduction through SHAP enables cost-effective UCS prediction through the reduction of laboratory test requirements without significant accuracy loss.The suggested hybrid approach offers an explainable,interpretable,and cost-effective tool for geotechnical engineering practice.
基金funded by Deanship of Graduate studies and Scientific Research at Jouf University under grant No.(DGSSR-2024-02-01264).
文摘Automated essay scoring(AES)systems have gained significant importance in educational settings,offering a scalable,efficient,and objective method for evaluating student essays.However,developing AES systems for Arabic poses distinct challenges due to the language’s complex morphology,diglossia,and the scarcity of annotated datasets.This paper presents a hybrid approach to Arabic AES by combining text-based,vector-based,and embeddingbased similarity measures to improve essay scoring accuracy while minimizing the training data required.Using a large Arabic essay dataset categorized into thematic groups,the study conducted four experiments to evaluate the impact of feature selection,data size,and model performance.Experiment 1 established a baseline using a non-machine learning approach,selecting top-N correlated features to predict essay scores.The subsequent experiments employed 5-fold cross-validation.Experiment 2 showed that combining embedding-based,text-based,and vector-based features in a Random Forest(RF)model achieved an R2 of 88.92%and an accuracy of 83.3%within a 0.5-point tolerance.Experiment 3 further refined the feature selection process,demonstrating that 19 correlated features yielded optimal results,improving R2 to 88.95%.In Experiment 4,an optimal data efficiency training approach was introduced,where training data portions increased from 5%to 50%.The study found that using just 10%of the data achieved near-peak performance,with an R2 of 85.49%,emphasizing an effective trade-off between performance and computational costs.These findings highlight the potential of the hybrid approach for developing scalable Arabic AES systems,especially in low-resource environments,addressing linguistic challenges while ensuring efficient data usage.
文摘Advanced intensity measures(IMs)based on an inelastic deformation spectrum improved the evaluation of the median engineering demand parameters(EDPs)and reduced dispersion.In this regard,an optimized two-degreefreedom(2DOF)modal pushover-based scaling procedure(2DMPS)has been developed for a nonlinear dynamic analysis of asymmetric in-plan buildings.The 2DMPS procedure scales ground motions to approach close enough to a target value of the inelastic displacement of the first-mode inelastic 2DOF modal stick,extended for structures with significant contributions of higher modes.Further,4-,6-and 13-story RC SMRF buildings were selected for analyses using ground motion records scaled by the 2DMPS procedure,the modal pushover-based scaling method(MPS),and ASCE/SEI 7-16 scaling procedures.The median values of EDPs on scaled records closely matched the benchmark results.The bias in the EDP values due to the scaled records in every group regarding their median value was lower than the dispersion of the 21 unscaled records.These results generally demonstrate the accuracy and efficiency of the 2DMPS method.Additionally,the 2DOF modal stick’s inelastic response spectra are better suited for calculating seismic demands for one-way asymmetric-plan structures than the SDOF inelastic response spectra.
