All strong earthquakes are preceded by branching structures having different durations whose development scheme is partly largely predictable because it follows a well organized and recognizable pattern. By using a se...All strong earthquakes are preceded by branching structures having different durations whose development scheme is partly largely predictable because it follows a well organized and recognizable pattern. By using a seismic sequence hierarchization method, this study graphically explains the preparation process of an earthquake, called “branching structure”. In addition, criteria apt to distinguish the structures that will produce shocks of average magnitude from strong earthquakes’ will be defined. Based on the temporal oscillations of the magnitude values, we explain the procedure for identifying the developmental stages that characterize the energy accumulation stage of the branching structure, in order to early detect the energy release stage’s trigger point and obtain information on how it will develop over time. The study identifies also some pre-signals (trigger points) of various magnitudes in the energy release stage, which allows us to early predict the foreshocks and mainshock time position. The method we developed constitutes a truly innovative approach for the earthquake forecasting analysis, which dramatically differs from those developed so far, as it considers the structure of the seismic sequence not only as a magnitude values’ oscillation, but also as a sequence of developmental stages that may begin much earlier.展开更多
Extreme cold weather seriously harms human thermoregulatory system,necessitating high-performance insulating garments to maintain body temperature.However,as the core insulating layer,advanced fibrous materials always...Extreme cold weather seriously harms human thermoregulatory system,necessitating high-performance insulating garments to maintain body temperature.However,as the core insulating layer,advanced fibrous materials always struggle to balance mechanical properties and thermal insulation,resulting in their inability to meet the demands for both washing resistance and personal protection.Herein,inspired by the natural spring-like structures of cucumber tendrils,a superelastic and washable micro/nanofibrous sponge(MNFS)based on biomimetic helical fibers is directly prepared utilizing multiple-jet electrospinning technology for high-performance thermal insulation.By regulating the conductivity of polyvinylidene fluoride solution,multiple-jet ejection and multiple-stage whipping of jets are achieved,and further control of phase separation rates enables the rapid solidification of jets to form spring-like helical fibers,which are directly entangled to assemble MNFS.The resulting MNFS exhibits superelasticity that can withstand large tensile strain(200%),1000 cyclic tensile or compression deformations,and retain good resilience even in liquid nitrogen(-196℃).Furthermore,the MNFS shows efficient thermal insulation with low thermal conductivity(24.85 mW m^(-1)K^(-1)),close to the value of dry air,and remains structural stability even after cyclic washing.This work offers new possibilities for advanced fibrous sponges in transportation,environmental,and energy applications.展开更多
Digital twin technology brings more opportunities and challenges to chemical engineering in both academic and industry.A complex process could have multiple digitalization needs,including simulation,monitoring,operato...Digital twin technology brings more opportunities and challenges to chemical engineering in both academic and industry.A complex process could have multiple digitalization needs,including simulation,monitoring,operator training,etc.;thus,a hierarchical digital twin would be a comprehensive solution to that.In this study,a novel and general framework of the digital twin is proposed for operations in process industry.With the hierarchical structure,the framework can handle various tasks driven by different roles in process industry,including managers,engineers,and operators.To complete these tasks,the framework consists of three modules:OAS(Operation Analysis System),OMS(Operation Monitoring System),and OTS(Operator Training System).Each module focuses on one unique type of demand from the staff,as well as interactions among them enabling efficient data sharing.Based on the hierarchical framework,a digital twin system is applied for one complex industrial nitration process,which successfully enhances the operation efficiency and safety in several industrial scenarios with different demands.展开更多
Message structure reconstruction is a critical task in protocol reverse engineering,aiming to recover protocol field structures without access to source code.It enables important applications in network security,inclu...Message structure reconstruction is a critical task in protocol reverse engineering,aiming to recover protocol field structures without access to source code.It enables important applications in network security,including malware analysis and protocol fuzzing.However,existing methods suffer from inaccurate field boundary delineation and lack hierarchical relationship recovery,resulting in imprecise and incomplete reconstructions.In this paper,we propose ProRE,a novel method for reconstructing protocol field structures based on program execution slice embedding.ProRE extracts code slices from protocol parsing at runtime,converts them into embedding vectors using a data flow-sensitive assembly language model,and performs hierarchical clustering to recover complete protocol field structures.Evaluation on two datasets containing 12 protocols shows that ProRE achieves an average F1 score of 0.85 and a cophenetic correlation coefficient of 0.189,improving by 19%and 0.126%respectively over state-of-the-art methods(including BinPRE,Tupni,Netlifter,and QwQ-32B-preview),demonstrating significant superiority in both accuracy and completeness of field structure recovery.Case studies further validate the effectiveness of ProRE in practical malware analysis scenarios.展开更多
As an important resource in data link,time slots should be strategically allocated to enhance transmission efficiency and resist eavesdropping,especially considering the tremendous increase in the number of nodes and ...As an important resource in data link,time slots should be strategically allocated to enhance transmission efficiency and resist eavesdropping,especially considering the tremendous increase in the number of nodes and diverse communication needs.It is crucial to design control sequences with robust randomness and conflict-freeness to properly address differentiated access control in data link.In this paper,we propose a hierarchical access control scheme based on control sequences to achieve high utilization of time slots and differentiated access control.A theoretical bound of the hierarchical control sequence set is derived to characterize the constraints on the parameters of the sequence set.Moreover,two classes of optimal hierarchical control sequence sets satisfying the theoretical bound are constructed,both of which enable the scheme to achieve maximum utilization of time slots.Compared with the fixed time slot allocation scheme,our scheme reduces the symbol error rate by up to 9%,which indicates a significant improvement in anti-interference and eavesdropping capabilities.展开更多
Accurate purchase prediction in e-commerce critically depends on the quality of behavioral features.This paper proposes a layered and interpretable feature engineering framework that organizes user signals into three ...Accurate purchase prediction in e-commerce critically depends on the quality of behavioral features.This paper proposes a layered and interpretable feature engineering framework that organizes user signals into three layers:Basic,Conversion&Stability(efficiency and volatility across actions),and Advanced Interactions&Activity(crossbehavior synergies and intensity).Using real Taobao(Alibaba’s primary e-commerce platform)logs(57,976 records for 10,203 users;25 November–03 December 2017),we conducted a hierarchical,layer-wise evaluation that holds data splits and hyperparameters fixed while varying only the feature set to quantify each layer’s marginal contribution.Across logistic regression(LR),decision tree,random forest,XGBoost,and CatBoost models with stratified 5-fold cross-validation,the performance improvedmonotonically fromBasic to Conversion&Stability to Advanced features.With LR,F1 increased from 0.613(Basic)to 0.962(Advanced);boosted models achieved high discrimination(0.995 AUC Score)and an F1 score up to 0.983.Calibration and precision–recall analyses indicated strong ranking quality and acknowledged potential dataset and period biases given the short(9-day)window.By making feature contributions measurable and reproducible,the framework complements model-centric advances and offers a transparent blueprint for production-grade behavioralmodeling.The code and processed artifacts are publicly available,and future work will extend the validation to longer,seasonal datasets and hybrid approaches that combine automated feature learning with domain-driven design.展开更多
The spatial organization of urban-rural systems is fundamentally shaped by the agglomeration and diffusion effects inherent in human-Earth processes,giving rise to distinct gradient-based and hierarchical structures.U...The spatial organization of urban-rural systems is fundamentally shaped by the agglomeration and diffusion effects inherent in human-Earth processes,giving rise to distinct gradient-based and hierarchical structures.Understanding the complexity of these interactions and their multidimensional drivers is essential for deciphering the mechanisms of integrated urban-rural development.Here,we apply a novel hierarchical spatial system framework based on the human-Earth system,combining social network analysis and multi-level modeling,to examine the evolution of the socio-spatial structure in the Beijing-Tianjin-Hebei region from 2000 to 2020.We developed a comprehensive evaluation system spanning economic,social,environmental,and infrastructural dimensions to characterize spatial patterns across multiple network levels,including city clusters,metropolitan areas,municipal-counties,towns,and villages.Our analysis reveals three key findings:First,the density of foundational network connections increased significantly,reflecting a trend toward spatial concentration driven by policy-led regional integration.Second,network structures at the city-cluster and metropolitan scales exhibited a pattern of“initial expansion followed by convergence”,accompanied by notable shifts in their spatial centers of gravity.In parallel,differentiated patterns of agglomeration and expansion were evident in the township-and village-level networks of Baoding,Tangshan,and Handan,while village-level networks in Anxin,Quyang,and other locations demonstrated distinct developmental trends.Third,community structures demonstrated strong functional homophily and interactive cohesion across multiple dimensions,with metropolitan and township communities undergoing restructuring that reflects a reconfiguration of cross-level influence and functional coupling.Spatially,the system manifests as a gradient structure of interwoven point,line,and area networks,establishing a mechanism for functional differentiation and transmission from rural to urban areas.This study provides theoretical foundations and methodological support for understanding the spatial organization logic of integrated urban-rural development,offering practical reference value for advancing regional coordination and rural revitalization in a scientifically informed manner.展开更多
In the context of modern software development characterized by increasing complexity and compressed development cycles,traditional static vulnerability detection methods face prominent challenges including high false ...In the context of modern software development characterized by increasing complexity and compressed development cycles,traditional static vulnerability detection methods face prominent challenges including high false positive rates and missed detections of complex logic due to their over-reliance on rule templates.This paper proposes a Syntax-Aware Hierarchical Attention Network(SAHAN)model,which achieves high-precision vulnerability detection through grammar-rule-driven multi-granularity code slicing and hierarchical semantic fusion mechanisms.The SAHAN model first generates Syntax Independent Units(SIUs),which slices the code based on Abstract Syntax Tree(AST)and predefined grammar rules,retaining vulnerability-sensitive contexts.Following this,through a hierarchical attention mechanism,the local syntax-aware layer encodes fine-grained patterns within SIUs,while the global semantic correlation layer captures vulnerability chains across SIUs,achieving synergistic modeling of syntax and semantics.Experiments show that on benchmark datasets like QEMU,SAHAN significantly improves detection performance by 4.8%to 13.1%on average compared to baseline models such as Devign and VulDeePecker.展开更多
Soft grippers research is gaining increasing attention for their flexibility.However,the conventional soft gripper primar-ily focuses on soft fingers,without considering the palm.This makes grasping forces concentrate...Soft grippers research is gaining increasing attention for their flexibility.However,the conventional soft gripper primar-ily focuses on soft fingers,without considering the palm.This makes grasping forces concentrated in the fingertip areas,resulting in objects being prone to damage and instability during handling,especially for delicate items.Additionally,pre-transportation classification faces challenges:tactile methods are complex,visual methods are environment-sensitive,and both struggle with similar objects.To address these problems,inspired by the human hand's transition between finger grasp and palm support and the lotus's hierarchical structure,this paper proposes a dual-layer gripper,named IOSGrip-per.It features four pneumatic soft fingers and a rotational soft-rigid palm.Through coordinated control of the fingers and palm,it transitions concentrated fingertip squeeze force to distributed palm support force,reducing squeeze force and squeeze duration.Moreover,it integrates a range sensor and four load cells,enabling stable and accurate measurements of the object's height and weight.Furthermore,a classifier is developed based on K-nearest neighbor algorithm,allowing real-time object classification.Experiments demonstrate that compared to only using soft fingers,the IOSGripper signifi-cantly reduces the squeeze force on the objects(with 0 N squeeze force during palm support)and damage on the delicate object,while improving grasping stability.Its height and weight measurement errors are within 2 mm and 1 g,respectively.And it achieves high accuracy in three test scenarios,including classifying similar objects.This study provides useful insights for the design of soft grippers capable of human-like grasping and sorting tasks.展开更多
Understory plants are an integral part of forests,serving a variety of functions that help maintain healthy ecosystems.The structure and composition of the understory are influenced by numerous biotic and abiotic fact...Understory plants are an integral part of forests,serving a variety of functions that help maintain healthy ecosystems.The structure and composition of the understory are influenced by numerous biotic and abiotic factors,with light being critical.The introduction of the pathogen Cronartium ribicola,which causes white pine blister rust,into North America in the early 20 th century led to the near total loss of western white pine(Pinus monticola)from moist forests of the Northern Rockies.Management is reintroducing blister rust-resistant western white pine across the landscape,but the effects on the understory are unknown.We examined the effects of stand structure and proportion of western white pine in the overstory on understory diversity of vascular plants in closed canopy stands dominated by blister rust-resistant western white pine across northern Idaho.Habitat series explained the greatest amount of variation(34%)in species presence-absence,while canopy cover accounted for 25%,basal area of all trees for 18%,and the proportion of western white pine composition by 14%.Our analysis revealed positive relationships between the proportion of western white pine in the overstory and both the presence of understory plants and the cover of several understory species.For both the presence and cover,separate sets of thirteen species were found to have a positive relationship with the proportion of western white pine in the overstory,with eight species in common.This research fills a knowledge gap by using data from a range of stands across northern Idaho with varying abundance of western white pine in the overstory to evaluate the relationship between the understory and overstory composition.As land managers plant more western white pine trees,we are likely to see the concomitant increase in understory plant diversity across the landscape,in addition to numerous other benefits,including disturbance resistance and resilience.展开更多
As joint operations have become a key trend in modern military development,unmanned aerial vehicles(UAVs)play an increasingly important role in enhancing the intelligence and responsiveness of combat systems.However,t...As joint operations have become a key trend in modern military development,unmanned aerial vehicles(UAVs)play an increasingly important role in enhancing the intelligence and responsiveness of combat systems.However,the heterogeneity of aircraft,partial observability,and dynamic uncertainty in operational airspace pose significant challenges to autonomous collision avoidance using traditional methods.To address these issues,this paper proposes an adaptive collision avoidance approach for UAVs based on deep reinforcement learning.First,a unified uncertainty model incorporating dynamic wind fields is constructed to capture the complexity of joint operational environments.Then,to effectively handle the heterogeneity between manned and unmanned aircraft and the limitations of dynamic observations,a sector-based partial observation mechanism is designed.A Dynamic Threat Prioritization Assessment algorithm is also proposed to evaluate potential collision threats from multiple dimensions,including time to closest approach,minimum separation distance,and aircraft type.Furthermore,a Hierarchical Prioritized Experience Replay(HPER)mechanism is introduced,which classifies experience samples into high,medium,and low priority levels to preferentially sample critical experiences,thereby improving learning efficiency and accelerating policy convergence.Simulation results show that the proposed HPER-D3QN algorithm outperforms existing methods in terms of learning speed,environmental adaptability,and robustness,significantly enhancing collision avoidance performance and convergence rate.Finally,transfer experiments on a high-fidelity battlefield airspace simulation platform validate the proposed method's deployment potential and practical applicability in complex,real-world joint operational scenarios.展开更多
In this work,the Hierarchical Quadrature Element Method(HQEM)formulation of geometrically exact shells is proposed and applied for geometrically nonlinear analyses of both isotropic and laminated shells.The stress res...In this work,the Hierarchical Quadrature Element Method(HQEM)formulation of geometrically exact shells is proposed and applied for geometrically nonlinear analyses of both isotropic and laminated shells.The stress resultant formulation is developed within the HQEM framework,consequently significantly simplifying the computations of residual force and stiffness matrix.The present formulation inherently avoids shear and membrane locking,benefiting from its high-order approximation property.Furthermore,HQEM’s independent nodal distribution capability conveniently supports local p-refinement and flexibly facilitates mesh generation in various structural configurations through the combination of quadrilateral and triangular elements.Remarkably,in lateral buckling analysis,the HQEM outperforms the weak-form quadrilateral element(QEM)in accuracy with identical nodal degrees of freedom(three displacements and two rotations).Under high-load nonlinear response,the QEM exhibits a maximum relative deviation of approximately 9.5%from the reference,while the HQEM remains closely aligned with the benchmark results.In addition,for the cantilever beam under tip moment,HQEM produces virtually no out-of-plane deviation,compared to a slight deviation of 0.00001 with QEM,confirming its superior numerical reliability.In summary,the method demonstrates high accuracy,superior convergence,and robustness in handling large rotations and complex post-buckling behaviors across a series of benchmark problems.展开更多
AIM:To evaluate long-term visual field(VF)prediction using K-means clustering in patients with primary open angle glaucoma(POAG).METHODS:Patients who underwent 24-2 VF tests≥10 were included in this study.Using 52 to...AIM:To evaluate long-term visual field(VF)prediction using K-means clustering in patients with primary open angle glaucoma(POAG).METHODS:Patients who underwent 24-2 VF tests≥10 were included in this study.Using 52 total deviation values(TDVs)from the first 10 VF tests of the training dataset,VF points were clustered into several regions using the hierarchical ordered partitioning and collapsing hybrid(HOPACH)and K-means clustering.Based on the clustering results,a linear regression analysis was applied to each clustered region of the testing dataset to predict the TDVs of the 10th VF test.Three to nine VF tests were used to predict the 10th VF test,and the prediction errors(root mean square error,RMSE)of each clustering method and pointwise linear regression(PLR)were compared.RESULTS:The training group consisted of 228 patients(mean age,54.20±14.38y;123 males and 105 females),and the testing group included 81 patients(mean age,54.88±15.22y;43 males and 38 females).All subjects were diagnosed with POAG.Fifty-two VF points were clustered into 11 and nine regions using HOPACH and K-means clustering,respectively.K-means clustering had a lower prediction error than PLR when n=1:3 and 1:4(both P≤0.003).The prediction errors of K-means clustering were lower than those of HOPACH in all sections(n=1:4 to 1:9;all P≤0.011),except for n=1:3(P=0.680).PLR outperformed K-means clustering only when n=1:8 and 1:9(both P≤0.020).CONCLUSION:K-means clustering can predict longterm VF test results more accurately in patients with POAG with limited VF data.展开更多
With vigorous developments in nanotechnology,the elaborate regulation of microstructure shows attractive potential in the design of electromagnetic wave absorbers.Herein,a hierarchical porous structure and composite h...With vigorous developments in nanotechnology,the elaborate regulation of microstructure shows attractive potential in the design of electromagnetic wave absorbers.Herein,a hierarchical porous structure and composite heterogeneous interface are constructed successfully to optimize the electromagnetic loss capacity.The macro–micro-synergistic graphene aerogel formed by the ice template‑assisted 3D printing strategy is cut by silicon carbide nanowires(SiC_(nws))grown in situ,while boron nitride(BN)interfacial structure is introduced on graphene nanoplates.The unique composite structure forces multiple scattering of incident EMWs,ensuring the combined effects of interfacial polarization,conduction networks,and magnetic-dielectric synergy.Therefore,the as-prepared composites present a minimum reflection loss value of−37.8 dB and a wide effective absorption bandwidth(EAB)of 9.2 GHz(from 8.8 to 18.0 GHz)at 2.5 mm.Besides,relying on the intrinsic high-temperature resistance of SiC_(nws) and BN,the EAB also remains above 5.0 GHz after annealing in air environment at 600℃ for 10 h.展开更多
In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The e...In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The essence of cross-view geo-localization resides in matching images containing the same geographical targets from disparate platforms,such as UAV-view and satellite-view images.However,images of the same geographical targets may suffer from occlusions and geometric distortions due to variations in the capturing platform,view,and timing.The existing methods predominantly extract features by segmenting feature maps,which overlook the holistic semantic distribution and structural information of objects,resulting in loss of image information.To address these challenges,dilated neighborhood attention Transformer is employed as the feature extraction backbone,and Multi-feature representations based on Multi-scale Hierarchical Contextual Aggregation(MMHCA)is proposed.In the proposed MMHCA method,the multiscale hierarchical contextual aggregation method is utilized to extract contextual information from local to global across various granularity levels,establishing feature associations of contextual information with global and local information in the image.Subsequently,the multi-feature representations method is utilized to obtain rich discriminative feature information,bolstering the robustness of model in scenarios characterized by positional shifts,varying distances,and scale ambiguities.Comprehensive experiments conducted on the extensively utilized University-1652 and SUES-200 benchmarks indicate that the MMHCA method surpasses the existing techniques.showing outstanding results in UAV localization and navigation.展开更多
To predict the endpoint carbon content and temperature in basic oxygen furnace (BOF), the industrial parameters of BOF steelmaking are taken as input values. Firstly, a series of preprocessing works such as the Pauta ...To predict the endpoint carbon content and temperature in basic oxygen furnace (BOF), the industrial parameters of BOF steelmaking are taken as input values. Firstly, a series of preprocessing works such as the Pauta criterion, hierarchical clustering, and principal component analysis on the original data were performed. Secondly, the prediction results of classic machine learning models of ridge regression, support vector machine, gradient boosting regression (GBR), random forest regression, back-propagation (BP) neural network models, and multi-layer perceptron (MLP) were compared before and after data preprocessing. An improved model was established based on the improved sparrow algorithm and BP using tent chaotic mapping (CSSA-BP). The CSSA-BP model showed the best performance for endpoint carbon prediction with the lowest mean absolute error (MAE) and root mean square error (RMSE) values of 0.01124 and 0.01345 mass% among seven models, respectively. And the lowest MAE and RMSE values of 8.9839 and 10.9321 ℃ for endpoint temperature prediction were obtained among seven models, respectively. Furthermore, the CSSA-BP and GBR models have the smallest error fluctuation range in both endpoint carbon content and temperature predictions. Finally, in order to improve the interpretability of the model, SHapley additive interpretation (SHAP) was used to analyze the results.展开更多
In machine vision,elliptical targets frequently appear within the camera's region of interest(ROI).Ellipse detection is essential for shape detection and geometric measurements in machine vision.However,existing e...In machine vision,elliptical targets frequently appear within the camera's region of interest(ROI).Ellipse detection is essential for shape detection and geometric measurements in machine vision.However,existing ellipse detection algorithms often face issues such as high computational complexity,strong dependence on initial conditions,sensitivity to noise,and lack of robustness to occlusions.In this paper,we propose a fast and robust ellipse detection method to address these challenges.This method first utilizes edge gradient and curvature information to segment the curve into circular arcs.Next,based on the convexity of the arcs,it divides them into different quadrants of the ellipse,groups and fits the arcs according to multiple geometric constraints at a low computational cost.Finally,it reduces the parameter space for hierarchical clustering and then segments the complete ellipse into several sectors for verification.We compare our method across seven datasets,including five public image datasets and two from industrial camera scenes.Experimental results show that our method achieves a precision ranging from 67.1%to 98.9%,a recall ranging from 48.1%to 92.9%,and an F-measure ranging from 58.0%to 95.8%.The average execution time per image ranges from 25 ms to 192 ms,demonstrating both high accuracy and efficiency.展开更多
K–Se batteries have been identified as promising energy storage systems owing to their high energy density and cost-effectiveness.However,challenges such as substantial volume changes and low Se utilization require f...K–Se batteries have been identified as promising energy storage systems owing to their high energy density and cost-effectiveness.However,challenges such as substantial volume changes and low Se utilization require further investigation.In this study,novel N-doped multichannel carbon nanofibers(h-NMCNFs)with hierarchical porous structures were successfully synthesized as efficient cathode hosts for K–Se batteries through the carbonization of two electrospun immiscible polymer nanofibers and subsequent chemical activation.Mesopores originated from the decomposition of the polymer embedded in the carbon nanofibers,and micropores were introduced via KOH activation.During the activation step,hierarchical porous carbon nanofibers with enhanced pore volumes were formed because of the micropores in the carbon nanofibers.Owing to the mesopores that enabled easy access to the electrolyte and the high utilization of chain-like Se within the micropores,the Se-loaded hierarchical porous carbon nanofibers(60 wt%Se)exhibited a high discharge capacity and excellent rate performance.The discharge capacity of the nanofibers at the 1,000th cycle was 210.8 mA.h.g^(-1)at a current density of 0.5C.The capacity retention after the initial activation was 64%.In addition,a discharge capacity of 165 mA.h.g^(-1)was obtained at an extremely high current density of 3.0C.展开更多
文摘All strong earthquakes are preceded by branching structures having different durations whose development scheme is partly largely predictable because it follows a well organized and recognizable pattern. By using a seismic sequence hierarchization method, this study graphically explains the preparation process of an earthquake, called “branching structure”. In addition, criteria apt to distinguish the structures that will produce shocks of average magnitude from strong earthquakes’ will be defined. Based on the temporal oscillations of the magnitude values, we explain the procedure for identifying the developmental stages that characterize the energy accumulation stage of the branching structure, in order to early detect the energy release stage’s trigger point and obtain information on how it will develop over time. The study identifies also some pre-signals (trigger points) of various magnitudes in the energy release stage, which allows us to early predict the foreshocks and mainshock time position. The method we developed constitutes a truly innovative approach for the earthquake forecasting analysis, which dramatically differs from those developed so far, as it considers the structure of the seismic sequence not only as a magnitude values’ oscillation, but also as a sequence of developmental stages that may begin much earlier.
基金supported by Young Elite Scientists Sponsorship Program by China Association for Science and Technology(No.2022QNRC001)the National Natural Science Foundation of China(No.52273053)the Chenguang Program of Shanghai Education Development Foundation and Shanghai Municipal Education Commission(No.21CGA41)。
文摘Extreme cold weather seriously harms human thermoregulatory system,necessitating high-performance insulating garments to maintain body temperature.However,as the core insulating layer,advanced fibrous materials always struggle to balance mechanical properties and thermal insulation,resulting in their inability to meet the demands for both washing resistance and personal protection.Herein,inspired by the natural spring-like structures of cucumber tendrils,a superelastic and washable micro/nanofibrous sponge(MNFS)based on biomimetic helical fibers is directly prepared utilizing multiple-jet electrospinning technology for high-performance thermal insulation.By regulating the conductivity of polyvinylidene fluoride solution,multiple-jet ejection and multiple-stage whipping of jets are achieved,and further control of phase separation rates enables the rapid solidification of jets to form spring-like helical fibers,which are directly entangled to assemble MNFS.The resulting MNFS exhibits superelasticity that can withstand large tensile strain(200%),1000 cyclic tensile or compression deformations,and retain good resilience even in liquid nitrogen(-196℃).Furthermore,the MNFS shows efficient thermal insulation with low thermal conductivity(24.85 mW m^(-1)K^(-1)),close to the value of dry air,and remains structural stability even after cyclic washing.This work offers new possibilities for advanced fibrous sponges in transportation,environmental,and energy applications.
基金support of the“Pioneer”and“Leading Goose”Research&Development Program of Zhejiang(2024C01028)the State Key Laboratory of Industrial Control Technology,China(ICT2024C04)are gratefully acknowledged.
文摘Digital twin technology brings more opportunities and challenges to chemical engineering in both academic and industry.A complex process could have multiple digitalization needs,including simulation,monitoring,operator training,etc.;thus,a hierarchical digital twin would be a comprehensive solution to that.In this study,a novel and general framework of the digital twin is proposed for operations in process industry.With the hierarchical structure,the framework can handle various tasks driven by different roles in process industry,including managers,engineers,and operators.To complete these tasks,the framework consists of three modules:OAS(Operation Analysis System),OMS(Operation Monitoring System),and OTS(Operator Training System).Each module focuses on one unique type of demand from the staff,as well as interactions among them enabling efficient data sharing.Based on the hierarchical framework,a digital twin system is applied for one complex industrial nitration process,which successfully enhances the operation efficiency and safety in several industrial scenarios with different demands.
文摘Message structure reconstruction is a critical task in protocol reverse engineering,aiming to recover protocol field structures without access to source code.It enables important applications in network security,including malware analysis and protocol fuzzing.However,existing methods suffer from inaccurate field boundary delineation and lack hierarchical relationship recovery,resulting in imprecise and incomplete reconstructions.In this paper,we propose ProRE,a novel method for reconstructing protocol field structures based on program execution slice embedding.ProRE extracts code slices from protocol parsing at runtime,converts them into embedding vectors using a data flow-sensitive assembly language model,and performs hierarchical clustering to recover complete protocol field structures.Evaluation on two datasets containing 12 protocols shows that ProRE achieves an average F1 score of 0.85 and a cophenetic correlation coefficient of 0.189,improving by 19%and 0.126%respectively over state-of-the-art methods(including BinPRE,Tupni,Netlifter,and QwQ-32B-preview),demonstrating significant superiority in both accuracy and completeness of field structure recovery.Case studies further validate the effectiveness of ProRE in practical malware analysis scenarios.
基金supported by the National Science Foundation of China(No.62171387)the Science and Technology Program of Sichuan Province(No.2024NSFSC0468)the China Postdoctoral Science Foundation(No.2019M663475).
文摘As an important resource in data link,time slots should be strategically allocated to enhance transmission efficiency and resist eavesdropping,especially considering the tremendous increase in the number of nodes and diverse communication needs.It is crucial to design control sequences with robust randomness and conflict-freeness to properly address differentiated access control in data link.In this paper,we propose a hierarchical access control scheme based on control sequences to achieve high utilization of time slots and differentiated access control.A theoretical bound of the hierarchical control sequence set is derived to characterize the constraints on the parameters of the sequence set.Moreover,two classes of optimal hierarchical control sequence sets satisfying the theoretical bound are constructed,both of which enable the scheme to achieve maximum utilization of time slots.Compared with the fixed time slot allocation scheme,our scheme reduces the symbol error rate by up to 9%,which indicates a significant improvement in anti-interference and eavesdropping capabilities.
基金supported by the research fund of Hanyang University(HY-202500000001616).
文摘Accurate purchase prediction in e-commerce critically depends on the quality of behavioral features.This paper proposes a layered and interpretable feature engineering framework that organizes user signals into three layers:Basic,Conversion&Stability(efficiency and volatility across actions),and Advanced Interactions&Activity(crossbehavior synergies and intensity).Using real Taobao(Alibaba’s primary e-commerce platform)logs(57,976 records for 10,203 users;25 November–03 December 2017),we conducted a hierarchical,layer-wise evaluation that holds data splits and hyperparameters fixed while varying only the feature set to quantify each layer’s marginal contribution.Across logistic regression(LR),decision tree,random forest,XGBoost,and CatBoost models with stratified 5-fold cross-validation,the performance improvedmonotonically fromBasic to Conversion&Stability to Advanced features.With LR,F1 increased from 0.613(Basic)to 0.962(Advanced);boosted models achieved high discrimination(0.995 AUC Score)and an F1 score up to 0.983.Calibration and precision–recall analyses indicated strong ranking quality and acknowledged potential dataset and period biases given the short(9-day)window.By making feature contributions measurable and reproducible,the framework complements model-centric advances and offers a transparent blueprint for production-grade behavioralmodeling.The code and processed artifacts are publicly available,and future work will extend the validation to longer,seasonal datasets and hybrid approaches that combine automated feature learning with domain-driven design.
基金supported by the National Natural Science Foundation of China(Grant Nos.42293270,42530712)the Young Scientists Fund of the National Natural Science Foundation of China(Grant No.42401334).
文摘The spatial organization of urban-rural systems is fundamentally shaped by the agglomeration and diffusion effects inherent in human-Earth processes,giving rise to distinct gradient-based and hierarchical structures.Understanding the complexity of these interactions and their multidimensional drivers is essential for deciphering the mechanisms of integrated urban-rural development.Here,we apply a novel hierarchical spatial system framework based on the human-Earth system,combining social network analysis and multi-level modeling,to examine the evolution of the socio-spatial structure in the Beijing-Tianjin-Hebei region from 2000 to 2020.We developed a comprehensive evaluation system spanning economic,social,environmental,and infrastructural dimensions to characterize spatial patterns across multiple network levels,including city clusters,metropolitan areas,municipal-counties,towns,and villages.Our analysis reveals three key findings:First,the density of foundational network connections increased significantly,reflecting a trend toward spatial concentration driven by policy-led regional integration.Second,network structures at the city-cluster and metropolitan scales exhibited a pattern of“initial expansion followed by convergence”,accompanied by notable shifts in their spatial centers of gravity.In parallel,differentiated patterns of agglomeration and expansion were evident in the township-and village-level networks of Baoding,Tangshan,and Handan,while village-level networks in Anxin,Quyang,and other locations demonstrated distinct developmental trends.Third,community structures demonstrated strong functional homophily and interactive cohesion across multiple dimensions,with metropolitan and township communities undergoing restructuring that reflects a reconfiguration of cross-level influence and functional coupling.Spatially,the system manifests as a gradient structure of interwoven point,line,and area networks,establishing a mechanism for functional differentiation and transmission from rural to urban areas.This study provides theoretical foundations and methodological support for understanding the spatial organization logic of integrated urban-rural development,offering practical reference value for advancing regional coordination and rural revitalization in a scientifically informed manner.
基金supported by the research start-up funds for invited doctor of Lanzhou University of Technology under Grant 14/062402。
文摘In the context of modern software development characterized by increasing complexity and compressed development cycles,traditional static vulnerability detection methods face prominent challenges including high false positive rates and missed detections of complex logic due to their over-reliance on rule templates.This paper proposes a Syntax-Aware Hierarchical Attention Network(SAHAN)model,which achieves high-precision vulnerability detection through grammar-rule-driven multi-granularity code slicing and hierarchical semantic fusion mechanisms.The SAHAN model first generates Syntax Independent Units(SIUs),which slices the code based on Abstract Syntax Tree(AST)and predefined grammar rules,retaining vulnerability-sensitive contexts.Following this,through a hierarchical attention mechanism,the local syntax-aware layer encodes fine-grained patterns within SIUs,while the global semantic correlation layer captures vulnerability chains across SIUs,achieving synergistic modeling of syntax and semantics.Experiments show that on benchmark datasets like QEMU,SAHAN significantly improves detection performance by 4.8%to 13.1%on average compared to baseline models such as Devign and VulDeePecker.
基金the Major research program of national natural science foundation of China(91848206).
文摘Soft grippers research is gaining increasing attention for their flexibility.However,the conventional soft gripper primar-ily focuses on soft fingers,without considering the palm.This makes grasping forces concentrated in the fingertip areas,resulting in objects being prone to damage and instability during handling,especially for delicate items.Additionally,pre-transportation classification faces challenges:tactile methods are complex,visual methods are environment-sensitive,and both struggle with similar objects.To address these problems,inspired by the human hand's transition between finger grasp and palm support and the lotus's hierarchical structure,this paper proposes a dual-layer gripper,named IOSGrip-per.It features four pneumatic soft fingers and a rotational soft-rigid palm.Through coordinated control of the fingers and palm,it transitions concentrated fingertip squeeze force to distributed palm support force,reducing squeeze force and squeeze duration.Moreover,it integrates a range sensor and four load cells,enabling stable and accurate measurements of the object's height and weight.Furthermore,a classifier is developed based on K-nearest neighbor algorithm,allowing real-time object classification.Experiments demonstrate that compared to only using soft fingers,the IOSGripper signifi-cantly reduces the squeeze force on the objects(with 0 N squeeze force during palm support)and damage on the delicate object,while improving grasping stability.Its height and weight measurement errors are within 2 mm and 1 g,respectively.And it achieves high accuracy in three test scenarios,including classifying similar objects.This study provides useful insights for the design of soft grippers capable of human-like grasping and sorting tasks.
基金supported by the United States Department of Agriculture,Forest Service,Rocky Mountain Research Station through Research Joint Venture Agreement 17–098Funding was provided by the USDA Forest Service Northern Region。
文摘Understory plants are an integral part of forests,serving a variety of functions that help maintain healthy ecosystems.The structure and composition of the understory are influenced by numerous biotic and abiotic factors,with light being critical.The introduction of the pathogen Cronartium ribicola,which causes white pine blister rust,into North America in the early 20 th century led to the near total loss of western white pine(Pinus monticola)from moist forests of the Northern Rockies.Management is reintroducing blister rust-resistant western white pine across the landscape,but the effects on the understory are unknown.We examined the effects of stand structure and proportion of western white pine in the overstory on understory diversity of vascular plants in closed canopy stands dominated by blister rust-resistant western white pine across northern Idaho.Habitat series explained the greatest amount of variation(34%)in species presence-absence,while canopy cover accounted for 25%,basal area of all trees for 18%,and the proportion of western white pine composition by 14%.Our analysis revealed positive relationships between the proportion of western white pine in the overstory and both the presence of understory plants and the cover of several understory species.For both the presence and cover,separate sets of thirteen species were found to have a positive relationship with the proportion of western white pine in the overstory,with eight species in common.This research fills a knowledge gap by using data from a range of stands across northern Idaho with varying abundance of western white pine in the overstory to evaluate the relationship between the understory and overstory composition.As land managers plant more western white pine trees,we are likely to see the concomitant increase in understory plant diversity across the landscape,in addition to numerous other benefits,including disturbance resistance and resilience.
基金supported by the National Key Research and Development Program of China(No.2022YFB4300902).
文摘As joint operations have become a key trend in modern military development,unmanned aerial vehicles(UAVs)play an increasingly important role in enhancing the intelligence and responsiveness of combat systems.However,the heterogeneity of aircraft,partial observability,and dynamic uncertainty in operational airspace pose significant challenges to autonomous collision avoidance using traditional methods.To address these issues,this paper proposes an adaptive collision avoidance approach for UAVs based on deep reinforcement learning.First,a unified uncertainty model incorporating dynamic wind fields is constructed to capture the complexity of joint operational environments.Then,to effectively handle the heterogeneity between manned and unmanned aircraft and the limitations of dynamic observations,a sector-based partial observation mechanism is designed.A Dynamic Threat Prioritization Assessment algorithm is also proposed to evaluate potential collision threats from multiple dimensions,including time to closest approach,minimum separation distance,and aircraft type.Furthermore,a Hierarchical Prioritized Experience Replay(HPER)mechanism is introduced,which classifies experience samples into high,medium,and low priority levels to preferentially sample critical experiences,thereby improving learning efficiency and accelerating policy convergence.Simulation results show that the proposed HPER-D3QN algorithm outperforms existing methods in terms of learning speed,environmental adaptability,and robustness,significantly enhancing collision avoidance performance and convergence rate.Finally,transfer experiments on a high-fidelity battlefield airspace simulation platform validate the proposed method's deployment potential and practical applicability in complex,real-world joint operational scenarios.
基金supported by the National Natural Science Foundation of China(Grant Nos.12472194,12002018,11972004,11772031,11402015).
文摘In this work,the Hierarchical Quadrature Element Method(HQEM)formulation of geometrically exact shells is proposed and applied for geometrically nonlinear analyses of both isotropic and laminated shells.The stress resultant formulation is developed within the HQEM framework,consequently significantly simplifying the computations of residual force and stiffness matrix.The present formulation inherently avoids shear and membrane locking,benefiting from its high-order approximation property.Furthermore,HQEM’s independent nodal distribution capability conveniently supports local p-refinement and flexibly facilitates mesh generation in various structural configurations through the combination of quadrilateral and triangular elements.Remarkably,in lateral buckling analysis,the HQEM outperforms the weak-form quadrilateral element(QEM)in accuracy with identical nodal degrees of freedom(three displacements and two rotations).Under high-load nonlinear response,the QEM exhibits a maximum relative deviation of approximately 9.5%from the reference,while the HQEM remains closely aligned with the benchmark results.In addition,for the cantilever beam under tip moment,HQEM produces virtually no out-of-plane deviation,compared to a slight deviation of 0.00001 with QEM,confirming its superior numerical reliability.In summary,the method demonstrates high accuracy,superior convergence,and robustness in handling large rotations and complex post-buckling behaviors across a series of benchmark problems.
基金Supported by the Korea Health Technology R&D Project through the Korea Health Industry Development Institute(KHIDI),the Ministry of Health&Welfare,Republic of Korea(No.RS-2020-KH088726)the Patient-Centered Clinical Research Coordinating Center(PACEN),the Ministry of Health and Welfare,Republic of Korea(No.HC19C0276)the National Research Foundation of Korea(NRF),the Korea Government(MSIT)(No.RS-2023-00247504).
文摘AIM:To evaluate long-term visual field(VF)prediction using K-means clustering in patients with primary open angle glaucoma(POAG).METHODS:Patients who underwent 24-2 VF tests≥10 were included in this study.Using 52 total deviation values(TDVs)from the first 10 VF tests of the training dataset,VF points were clustered into several regions using the hierarchical ordered partitioning and collapsing hybrid(HOPACH)and K-means clustering.Based on the clustering results,a linear regression analysis was applied to each clustered region of the testing dataset to predict the TDVs of the 10th VF test.Three to nine VF tests were used to predict the 10th VF test,and the prediction errors(root mean square error,RMSE)of each clustering method and pointwise linear regression(PLR)were compared.RESULTS:The training group consisted of 228 patients(mean age,54.20±14.38y;123 males and 105 females),and the testing group included 81 patients(mean age,54.88±15.22y;43 males and 38 females).All subjects were diagnosed with POAG.Fifty-two VF points were clustered into 11 and nine regions using HOPACH and K-means clustering,respectively.K-means clustering had a lower prediction error than PLR when n=1:3 and 1:4(both P≤0.003).The prediction errors of K-means clustering were lower than those of HOPACH in all sections(n=1:4 to 1:9;all P≤0.011),except for n=1:3(P=0.680).PLR outperformed K-means clustering only when n=1:8 and 1:9(both P≤0.020).CONCLUSION:K-means clustering can predict longterm VF test results more accurately in patients with POAG with limited VF data.
基金sponsored by National Natural Science Foundation of China(No.52302121,No.52203386)Shanghai Sailing Program(No.23YF1454700)+1 种基金Shanghai Natural Science Foundation(No.23ZR1472700)Shanghai Post-doctoral Excellent Program(No.2022664).
文摘With vigorous developments in nanotechnology,the elaborate regulation of microstructure shows attractive potential in the design of electromagnetic wave absorbers.Herein,a hierarchical porous structure and composite heterogeneous interface are constructed successfully to optimize the electromagnetic loss capacity.The macro–micro-synergistic graphene aerogel formed by the ice template‑assisted 3D printing strategy is cut by silicon carbide nanowires(SiC_(nws))grown in situ,while boron nitride(BN)interfacial structure is introduced on graphene nanoplates.The unique composite structure forces multiple scattering of incident EMWs,ensuring the combined effects of interfacial polarization,conduction networks,and magnetic-dielectric synergy.Therefore,the as-prepared composites present a minimum reflection loss value of−37.8 dB and a wide effective absorption bandwidth(EAB)of 9.2 GHz(from 8.8 to 18.0 GHz)at 2.5 mm.Besides,relying on the intrinsic high-temperature resistance of SiC_(nws) and BN,the EAB also remains above 5.0 GHz after annealing in air environment at 600℃ for 10 h.
基金supported by the National Natural Science Foundation of China(Nos.12072027,62103052,61603346 and 62103379)the Henan Key Laboratory of General Aviation Technology,China(No.ZHKF-230201)+3 种基金the Funding for the Open Research Project of the Rotor Aerodynamics Key Laboratory,China(No.RAL20200101)the Key Research and Development Program of Henan Province,China(Nos.241111222000 and 241111222900)the Key Science and Technology Program of Henan Province,China(No.232102220067)the Scholarship Funding from the China Scholarship Council(No.202206030079).
文摘In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The essence of cross-view geo-localization resides in matching images containing the same geographical targets from disparate platforms,such as UAV-view and satellite-view images.However,images of the same geographical targets may suffer from occlusions and geometric distortions due to variations in the capturing platform,view,and timing.The existing methods predominantly extract features by segmenting feature maps,which overlook the holistic semantic distribution and structural information of objects,resulting in loss of image information.To address these challenges,dilated neighborhood attention Transformer is employed as the feature extraction backbone,and Multi-feature representations based on Multi-scale Hierarchical Contextual Aggregation(MMHCA)is proposed.In the proposed MMHCA method,the multiscale hierarchical contextual aggregation method is utilized to extract contextual information from local to global across various granularity levels,establishing feature associations of contextual information with global and local information in the image.Subsequently,the multi-feature representations method is utilized to obtain rich discriminative feature information,bolstering the robustness of model in scenarios characterized by positional shifts,varying distances,and scale ambiguities.Comprehensive experiments conducted on the extensively utilized University-1652 and SUES-200 benchmarks indicate that the MMHCA method surpasses the existing techniques.showing outstanding results in UAV localization and navigation.
基金supported by the National Natural Science Foundation of China(Grant No.U1960202)the Science and Technology Commission of Shanghai Municipality(No.19DZ2270200).
文摘To predict the endpoint carbon content and temperature in basic oxygen furnace (BOF), the industrial parameters of BOF steelmaking are taken as input values. Firstly, a series of preprocessing works such as the Pauta criterion, hierarchical clustering, and principal component analysis on the original data were performed. Secondly, the prediction results of classic machine learning models of ridge regression, support vector machine, gradient boosting regression (GBR), random forest regression, back-propagation (BP) neural network models, and multi-layer perceptron (MLP) were compared before and after data preprocessing. An improved model was established based on the improved sparrow algorithm and BP using tent chaotic mapping (CSSA-BP). The CSSA-BP model showed the best performance for endpoint carbon prediction with the lowest mean absolute error (MAE) and root mean square error (RMSE) values of 0.01124 and 0.01345 mass% among seven models, respectively. And the lowest MAE and RMSE values of 8.9839 and 10.9321 ℃ for endpoint temperature prediction were obtained among seven models, respectively. Furthermore, the CSSA-BP and GBR models have the smallest error fluctuation range in both endpoint carbon content and temperature predictions. Finally, in order to improve the interpretability of the model, SHapley additive interpretation (SHAP) was used to analyze the results.
基金supported by National Major Scientific Research Instrument Development Project of China(No.51927804)Science Fund for Shaanxi Provincial Department of Education's Youth Innovation Team Research Plan under Grant(No.23JP169).
文摘In machine vision,elliptical targets frequently appear within the camera's region of interest(ROI).Ellipse detection is essential for shape detection and geometric measurements in machine vision.However,existing ellipse detection algorithms often face issues such as high computational complexity,strong dependence on initial conditions,sensitivity to noise,and lack of robustness to occlusions.In this paper,we propose a fast and robust ellipse detection method to address these challenges.This method first utilizes edge gradient and curvature information to segment the curve into circular arcs.Next,based on the convexity of the arcs,it divides them into different quadrants of the ellipse,groups and fits the arcs according to multiple geometric constraints at a low computational cost.Finally,it reduces the parameter space for hierarchical clustering and then segments the complete ellipse into several sectors for verification.We compare our method across seven datasets,including five public image datasets and two from industrial camera scenes.Experimental results show that our method achieves a precision ranging from 67.1%to 98.9%,a recall ranging from 48.1%to 92.9%,and an F-measure ranging from 58.0%to 95.8%.The average execution time per image ranges from 25 ms to 192 ms,demonstrating both high accuracy and efficiency.
基金financially supported by the Materials/Parts Technology Development Program(No.RS-202400456324)funded by the Ministry of Trade,Industry&Energy(MOTIE,Korea)by the National Research Foundation(NRF)of Korea grant(No.RS-2024-00454367)funded by the Ministry of Science and ICT(MSIT,Korea)。
文摘K–Se batteries have been identified as promising energy storage systems owing to their high energy density and cost-effectiveness.However,challenges such as substantial volume changes and low Se utilization require further investigation.In this study,novel N-doped multichannel carbon nanofibers(h-NMCNFs)with hierarchical porous structures were successfully synthesized as efficient cathode hosts for K–Se batteries through the carbonization of two electrospun immiscible polymer nanofibers and subsequent chemical activation.Mesopores originated from the decomposition of the polymer embedded in the carbon nanofibers,and micropores were introduced via KOH activation.During the activation step,hierarchical porous carbon nanofibers with enhanced pore volumes were formed because of the micropores in the carbon nanofibers.Owing to the mesopores that enabled easy access to the electrolyte and the high utilization of chain-like Se within the micropores,the Se-loaded hierarchical porous carbon nanofibers(60 wt%Se)exhibited a high discharge capacity and excellent rate performance.The discharge capacity of the nanofibers at the 1,000th cycle was 210.8 mA.h.g^(-1)at a current density of 0.5C.The capacity retention after the initial activation was 64%.In addition,a discharge capacity of 165 mA.h.g^(-1)was obtained at an extremely high current density of 3.0C.
