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
Modern business information systems face significant challenges in managing heterogeneous data sources,integrating disparate systems,and providing real-time decision support in complex enterprise environments.Contempo...Modern business information systems face significant challenges in managing heterogeneous data sources,integrating disparate systems,and providing real-time decision support in complex enterprise environments.Contemporary enterprises typically operate 200+interconnected systems,with research indicating that 52% of organizations manage three or more enterprise content management systems,creating information silos that reduce operational efficiency by up to 35%.While attention mechanisms have demonstrated remarkable success in natural language processing and computer vision,their systematic application to business information systems remains largely unexplored.This paper presents the theoretical foundation for a Hierarchical Attention-Based Business Information System(HABIS)framework that applies multi-level attention mechanisms to enterprise environments.We provide a comprehensive mathematical formulation of the framework,analyze its computational complexity,and present a proof-of-concept implementation with simulation-based validation that demonstrates a 42% reduction in crosssystem query latency compared to legacy ERP modules and 70% improvement in prediction accuracy over baseline methods.The theoretical framework introduces four hierarchical attention levels:system-level attention for dynamic weighting of business systems,process-level attention for business process prioritization,data-level attention for critical information selection,and temporal attention for time-sensitive pattern recognition.Our complexity analysis demonstrates that the framework achieves O(n log n)computational complexity for attention computation,making it scalable to large enterprise environments including retail supply chains with 200+system-scale deployments.The proof-of-concept implementation validates the theoretical framework’s feasibility withMSE loss of 0.439 and response times of 0.000120 s per query,demonstrating its potential for addressing key challenges in business information systems.This work establishes a foundation for future empirical research and practical implementation of attention-driven enterprise systems.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
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
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
A group optimal penetration strategy in complex attack and defense confrontation situation is proposed in this paper to solve the coordinated penetration decision-making problem of endo-atmospheric gliding simultaneou...A group optimal penetration strategy in complex attack and defense confrontation situation is proposed in this paper to solve the coordinated penetration decision-making problem of endo-atmospheric gliding simultaneous multi-missile penetration of interceptors.First,the problem of large search space of multi-missile coordinated penetration maneuvers is fully considered,and the flight corridor of multi-missile coordinated penetration is designed to constrain search space of multi-agent coordinated strategy,comprehensively considering path constraints and anticollision constraints of gliding multi-missile flight.Then,a multi-missile hierarchical coordinated decision-making mechanism based on confrontation situation is proposed,and the swarm penetration strategy is optimized with the goal of maximizing swarm penetration effectiveness.The upper layer plans the swarm penetration formation according to confrontation situation,and generates the swarm coordinated penetration trajectory based on Multi-Agent Deep Deterministic Policy Gradient(MADDPG)method.The lower layer interpolates and smooths penetration trajectory,and generates the penetration guidance command based on Soft Actor-Critic and Extended Proportional Guidance(SAC-EPG)method.Simulation results verify that the proposed multi-missile cooperative penetration method based on hierarchical reinforcement learning converges faster than the penetration method based on MADDPG,and can quickly learn multi-missile cooperative penetration skills.In addition,multi-missile coordination can give full play to the group's detection and maneuverability,and occupy favorable penetration time and space through coordinated ballistic maneuvers.Thus the success rate of group penetration can be improved.展开更多
Dear Editor,This letter presents a new secure hierarchical control strategy for steering tracking of in-wheel motor driven(IWMD)electric vehicle(EV)subject to limited network resources,hybrid cyber-attacks,model nonli...Dear Editor,This letter presents a new secure hierarchical control strategy for steering tracking of in-wheel motor driven(IWMD)electric vehicle(EV)subject to limited network resources,hybrid cyber-attacks,model nonlinearities,actuator redundancy and airflow disturbance.A hierarchical control architecture is proposed specifically for solving the problems of nonlinear system modeling and actuator redundancy.By utilizing the advantages of fully actuated system(FAS)approach,a nonlinear virtual controller against airflow disturbance is constructed in upper layer system and an event-triggered nonlinear distributed controller is proposed in lower layer system under stochastic hybrid cyber-attacks.A case study of overtaking task is carried out to validate the FAS-based hierarchical control strategy.展开更多
Partial Differential Equations(PDEs)are model candidates of soft sensing for aero-engine health management units.The existing Physics-Informed Neural Networks(PINNs)have made achievements.However,unmeasurable aero-eng...Partial Differential Equations(PDEs)are model candidates of soft sensing for aero-engine health management units.The existing Physics-Informed Neural Networks(PINNs)have made achievements.However,unmeasurable aero-engine driving sources lead to unknown PDE driving terms,which weaken PINNs feasibility.To this end,Physically Informed Hierarchical Learning followed by Recurrent-Prediction Term(PIHL-RPT)is proposed.First,PIHL is proposed for learning nonhomogeneous PDE solutions,in which two networks NetU and NetG are constructed.NetU is for learning solutions satisfying PDEs;NetG is for learning driving terms to regularize NetU training.Then,we propose a hierarchical learning strategy to optimize and couple NetU and NetG,which are integrated into a data-physics-hybrid loss function.Besides,we prove PIHL-RPT can iteratively generate a series of networks converging to a function,which can approximate a solution to well-posed PDE.Furthermore,RPT is proposed for prediction improvement of PIHL,in which network NetU-RP is constructed to compensate for information loss caused by data sampling and driving sources’immeasurability.Finally,artificial datasets and practical vibration process datasets from our wear experiment platform are used to verify the feasibility and effectiveness of PIHL-RPT based soft sensing.Meanwhile,comparisons with relevant methods,discussions,and PIHL-RPT based health monitoring example are given.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
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
The construction of hierarchical thermoplastic polyurethane(TPU)composites with superior flame retardant and electromagnetic shielding capabilities hold significant practical importance.In this work,TPU composites loa...The construction of hierarchical thermoplastic polyurethane(TPU)composites with superior flame retardant and electromagnetic shielding capabilities hold significant practical importance.In this work,TPU composites loaded with a multilayer core-shell flame retardant(APP@CoAl-LDH@Si)and a modified conductive nanofiller(MWCNT-NH_(2)-PA)were firstly prepared through the melt blending method,acting as surface layer.Additionally,multilayered MXene films functionalized by bacterial cellulose(BC)and dopamine hydrochloride(DA)were fabricated via a facile and efficient vacuum filtration approach.Finally,a PBM film was utilized as an intermediate layer to construct hierarchical TPU composites.The results indicated that the introduction of 10 wt%APP@CoAl-LDH@Si hybrid,the peak heat release rate,total heat release,peak smoke production rate,and total smoke release of the TPU composites were decreased by 83.0%,61.3%,48.5%and 66.9%,respectively,compared with those of pure TPU due to the free radicals capture effect of APP,and the flame-retardant functions of LDH and silane.Moreover,the hierarchical TPU/APP@CoAl-LDH@Si/CP1-PBM exhibited excellent electromagnetic shielding performance,achieving 43.6 dB in the X-band because of multiple reflection losses,interface polarization losses,and charge carrier movement-induced thermal dissipation.Extraordinarily,the A and R coefficients were reversed in the X and K bands.This phenomenon was attributed to the different degrees of confinement of the multilayer structure to electromagnetic waves with different wavelengths.This work presents a novel model for the design and preparation of high-performance polymer composites with multiple properties and regulation mechanism.展开更多
As modern communication and detection technologies advance at a swift pace,multifunctional electromagnetic interference(EMI)shielding materials with active/positive infrared stealth,hydrophobicity,and electric-thermal...As modern communication and detection technologies advance at a swift pace,multifunctional electromagnetic interference(EMI)shielding materials with active/positive infrared stealth,hydrophobicity,and electric-thermal conversion ability have received extensive attention.Meeting the aforesaid requirements simultaneously remains a huge challenge.In this research,the melamine foam(MF)/polypyrrole(PPy)nanowire arrays(MF@PPy)were fabricated via one-step electrochemical polymerization.The hierarchical MF@PPy foam was composed of three-dimensional PPy micro-skeleton and ordered PPy nanowire arrays.Due to the upwardly grown PPy nanowire arrays,the MF@PPy foam possessed good hydrophobicity ability with a water contact angle of 142.00°and outstanding stability under various harsh environments.Meanwhile,the MF@PPy foam showed excellent thermal insulation property on account of the low thermal conductivity and elongated ligament characteristic of PPy nanowire arrays.Furthermore,taking advantage of the high conductivity(128.2 S m^(-1)),the MF@PPy foam exhibited rapid Joule heating under 3 V,resulting in dynamic infrared stealth and thermal camouflage effects.More importantly,the MF@PPy foam exhibited remarkable EMI shielding effectiveness values of 55.77 dB and 19,928.57 dB cm^(2)g^(-1).Strong EMI shielding was put down to the hierarchically porous PPy structure,which offered outstanding impedance matching,conduction loss,and multiple attenuations.This innovative approach provides significant insights to the development of advanced multifunctional EMI shielding foams by constructing PPy nanowire arrays,showing great applications in both military and civilian fields.展开更多
Accurate prediction of molecular properties is crucial for selecting compounds with ideal properties and reducing the costs and risks of trials.Traditional methods based on manually crafted features and graph-based me...Accurate prediction of molecular properties is crucial for selecting compounds with ideal properties and reducing the costs and risks of trials.Traditional methods based on manually crafted features and graph-based methods have shown promising results in molecular property prediction.However,traditional methods rely on expert knowledge and often fail to capture the complex structures and interactions within molecules.Similarly,graph-based methods typically overlook the chemical structure and function hidden in molecular motifs and struggle to effectively integrate global and local molecular information.To address these limitations,we propose a novel fingerprint-enhanced hierarchical graph neural network(FH-GNN)for molecular property prediction that simultaneously learns information from hierarchical molecular graphs and fingerprints.The FH-GNN captures diverse hierarchical chemical information by applying directed message-passing neural networks(D-MPNN)on a hierarchical molecular graph that integrates atomic-level,motif-level,and graph-level information along with their relationships.Addi-tionally,we used an adaptive attention mechanism to balance the importance of hierarchical graphs and fingerprint features,creating a comprehensive molecular embedding that integrated hierarchical mo-lecular structures with domain knowledge.Experiments on eight benchmark datasets from MoleculeNet showed that FH-GNN outperformed the baseline models in both classification and regression tasks for molecular property prediction,validating its capability to comprehensively capture molecular informa-tion.By integrating molecular structure and chemical knowledge,FH-GNN provides a powerful tool for the accurate prediction of molecular properties and aids in the discovery of potential drug candidates.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
Exo-atmospheric vehicles are constrained by limited maneuverability,which leads to the contradiction between evasive maneuver and precision strike.To address the problem of Integrated Evasion and Impact(IEI)decision u...Exo-atmospheric vehicles are constrained by limited maneuverability,which leads to the contradiction between evasive maneuver and precision strike.To address the problem of Integrated Evasion and Impact(IEI)decision under multi-constraint conditions,a hierarchical intelligent decision-making method based on Deep Reinforcement Learning(DRL)was proposed.First,an intelligent decision-making framework of“DRL evasion decision”+“impact prediction guidance decision”was established:it takes the impact point deviation correction ability as the constraint and the maximum miss distance as the objective,and effectively solves the problem of poor decisionmaking effect caused by the large IEI decision space.Second,to solve the sparse reward problem faced by evasion decision-making,a hierarchical decision-making method consisting of maneuver timing decision and maneuver duration decision was proposed,and the corresponding Markov Decision Process(MDP)was designed.A detailed simulation experiment was designed to analyze the advantages and computational complexity of the proposed method.Simulation results show that the proposed model has good performance and low computational resource requirement.The minimum miss distance is 21.3 m under the condition of guaranteeing the impact point accuracy,and the single decision-making time is 4.086 ms on an STM32F407 single-chip microcomputer,which has engineering application value.展开更多
The increase in the utilization of infrared heat detection technology in military applications necessitates research on composites with improved thermal transmission performance and microwave absorption capabilities.T...The increase in the utilization of infrared heat detection technology in military applications necessitates research on composites with improved thermal transmission performance and microwave absorption capabilities.This study satisfactorily fabricated a series of MoS_(2)/BN-xyz composites(which were characterized by the weight ratio of MoS_(2)to BN,denoted by xy:z)through chemical vapor depos-ition,which resulted in their improved thermal stability and thermal transmission performance.The results show that the remaining mass of MoS_(2)/BN-101 was as high as 69.25wt%at 800℃under air atmosphere,and a temperature difference of 31.7℃was maintained between the surface temperature and the heating source at a heating temperature of 200℃.Furthermore,MoS_(2)/BN-301 exhibited an im-pressive minimum reflection loss value of-32.21 dB at 4.0 mm and a wide effective attenuation bandwidth ranging from 9.32 to 18.00 GHz(8.68 GHz).Therefore,these simplified synthesized MoS_(2)/BN-xyz composites demonstrate great potential as highly efficient con-tenders for the enhancement of microwave absorption performance and thermal conductance.展开更多
Listeria monocytogenes(LM)is a dangerous foodborne pathogen for humans.One emerging and validated method of indirectly assessing LM in food is detecting 3-hydroxy-2-butanone(3H2B)gas.In this study,the synthesis of 3-(...Listeria monocytogenes(LM)is a dangerous foodborne pathogen for humans.One emerging and validated method of indirectly assessing LM in food is detecting 3-hydroxy-2-butanone(3H2B)gas.In this study,the synthesis of 3-(2-aminoethylamino)propyltrimethoxysilane(AAPTMS)functionalized hierarchical hollow TiO_(2)nanospheres was achieved via precise controlling of solvothermal reaction temperature and post-grafting route.The sensors based on as-prepared materials exhibited excellent sensitivity(480 Hz@50 ppm),low detection limit(100 ppb),and outstanding selectivity.Moreover,the evaluation of LM with high sensitivity and specificity was achieved using the sensors.Such stable three-dimensional spheres,whose distinctive hierarchical and hollow nanostructure simultaneously improved both sensitivity and response/recovery speed dramatically,were spontaneously assembled by nanosheets.Meanwhile,the moderate loadings of AAPTMS significantly improved the selectivity of sensors.Then,the gas-sensing mechanism was explored by utilizing thermodynamic investigation,Gaussian 16 software,and in situ diffuse reflectance infrared transform spectroscopy,illustrating the weak chemisorption between the-NHgroup and 3H2B molecules.These portable sensors are promising for real-time assessment of LM at room temperature,which will make a magnificent contribution to food safety.展开更多
Hierarchical porous carbon(HPC)materials exhibit superior performance profiles in various applications due to their well-developed multiscale interconnected pore structures.The synthesis of HPC from natural biomass pr...Hierarchical porous carbon(HPC)materials exhibit superior performance profiles in various applications due to their well-developed multiscale interconnected pore structures.The synthesis of HPC from natural biomass precursors instead of fossil fuel-based precursors has gained considerable attention in recent decades.Rice husk,a globally abundant agricultural waste,offers a sustainable and cost-effective precursor for HPC production.The structural components and inherent silica content of rice husk act as a natural self-template for forming hierarchical pore structures with superior characteristics.In this review,recent studies on preparing rice husk-based HPC are summarized,and synthesis techniques are evaluated.In addition,recent advancements in activation methods and the effect of silica templates are reviewed while comparing these with traditional activated carbon production methods.Potential future directions for research and development activities are also discussed.Rice husk is a highly promising candidate for producing high-performance HPC materials.展开更多
Dear Editor,This letter investigates the problem of multi-dimension formation tracking(MDFT)for the cross-domain unmanned systems,including several interconnected agents,namely,unmanned aerial vehicles(UAVs)and unmann...Dear Editor,This letter investigates the problem of multi-dimension formation tracking(MDFT)for the cross-domain unmanned systems,including several interconnected agents,namely,unmanned aerial vehicles(UAVs)and unmanned surface vehicles(USVs).We assume that each agent suffers from by the mixed constraints on its velocity,control input and Euler angle.Solving the MDFT problem implies that 1)The virtual state of each USV is determined in the earth coordinate by expanding its 2D work space to the 3D space.展开更多
Introduction It is necessary for an ideal bioceramic scaffold to have a suitable structure.The structure can affect the mechanical properties of the scaffold(i.e.,elastic modulus and compressive strength)and the biolo...Introduction It is necessary for an ideal bioceramic scaffold to have a suitable structure.The structure can affect the mechanical properties of the scaffold(i.e.,elastic modulus and compressive strength)and the biological properties of the scaffold(i.e.,degradability and cell growth rate).Lattice structure is a kind of periodic porous structure,which has some advantages of light weight and high strength,and is widely used in the preparation of bioceramic scaffolders.For the structure of the scaffold,high porosity and large pore size are important for bone growth,bone integration and promoting good mechanical interlocking between neighboring bones and the scaffold.However,scaffolds with a high porosity often lack mechanical strength.In addition,different parts of the bone have different structural requirements.In this paper,scaffolds with a non-uniform structure or a hierarchical structure were designed,with loose and porous exterior to facilitate cell adhesion,osteogenic differentiation and vascularization as well as relatively dense interior to provide sufficient mechanical support for bone repair.Methods In this work,composite ceramics scaffolds with 10%akermanite content were prepared by DLP technology.The scaffold had a high porosity outside to promote the growth of bone tissue,and a low porosity inside to withstand external forces.The compressive strength,fracture form,in-vitro degradation performance and bioactivity of graded bioceramic scaffolds were investigated.The models of scaffolds were imported into the DLP printer with a 405 nm light.The samples were printed with the intensity of 8 mJ/cm^(2)and a layer thickness of 50μm.Finally,the ceramic samples were sintered at 1100℃.The degradability of the hierarchical gyroid bioceramic scaffolds was evaluated through immersion in Tris-HCl solution and SBF solution at a ratio of 200 mL/g.The bioactivity of bioceramic was obtained via immersing them in SBF solution for two weeks.The concentrations of calcium,phosphate,silicon,and magnesium ions in the soaking solution were determined by an inductively coupled plasma optical emission spectrometer.Results and discussion In this work,a hierarchical Gyroid structure HA-AK10 scaffold(sintered at 1100℃)with a radial internal porosity of 50%and an external porosity of 70%is prepared,and the influence of structural form on the compressive strength and degradation performance of the scaffold is investigated.The biological activity of the bioceramics in vitro is also verified.The mechanical simulation results show that the stress distribution corresponds to the porosity distribution of the structure,and the low porosity is larger and the overall stress concentration phenomenon does not appear.After soaking in SBF solution,Si—OH is firstly formed on the surface of bioceramics,and then silicon gel layer is produced due to the presence of calcium and silicon ions.The silicon gel layer is dissociated into negatively charged groups under alkaline environment secondary adsorption of calcium ions and phosphate ions,forming amorphous calcium phosphate,and finally amorphous calcium phosphate crystals and adsorption of carbonate ions,forming carbonate hydroxyapatite.This indicates that the composite bioceramics have a good biological activity in-vitro and can provide a good environment for the growth of bone cells.A hierarchical Gyroid ceramic scaffold with a bone geometry is prepared via applying the hierarchical structure to the bone contour scaffold.The maximum load capacity of the hierarchical Gyroid ceramic scaffold is 8 times that of the uniform structure.Conclusions The hierarchical structure scaffold designed had good overall compressive performance,good degradation performance,and still maintained a good mechanical stability during degradation.In addition,in-vitro biological experimental results showed that the surface graded composite scaffold could have a good in-vitro biological activity and provide a good environment for bone cells.Compared to the heterosexual structure,the graded scaffold had greater mechanical properties.展开更多
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Modern business information systems face significant challenges in managing heterogeneous data sources,integrating disparate systems,and providing real-time decision support in complex enterprise environments.Contemporary enterprises typically operate 200+interconnected systems,with research indicating that 52% of organizations manage three or more enterprise content management systems,creating information silos that reduce operational efficiency by up to 35%.While attention mechanisms have demonstrated remarkable success in natural language processing and computer vision,their systematic application to business information systems remains largely unexplored.This paper presents the theoretical foundation for a Hierarchical Attention-Based Business Information System(HABIS)framework that applies multi-level attention mechanisms to enterprise environments.We provide a comprehensive mathematical formulation of the framework,analyze its computational complexity,and present a proof-of-concept implementation with simulation-based validation that demonstrates a 42% reduction in crosssystem query latency compared to legacy ERP modules and 70% improvement in prediction accuracy over baseline methods.The theoretical framework introduces four hierarchical attention levels:system-level attention for dynamic weighting of business systems,process-level attention for business process prioritization,data-level attention for critical information selection,and temporal attention for time-sensitive pattern recognition.Our complexity analysis demonstrates that the framework achieves O(n log n)computational complexity for attention computation,making it scalable to large enterprise environments including retail supply chains with 200+system-scale deployments.The proof-of-concept implementation validates the theoretical framework’s feasibility withMSE loss of 0.439 and response times of 0.000120 s per query,demonstrating its potential for addressing key challenges in business information systems.This work establishes a foundation for future empirical research and practical implementation of attention-driven enterprise systems.
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 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.
文摘A group optimal penetration strategy in complex attack and defense confrontation situation is proposed in this paper to solve the coordinated penetration decision-making problem of endo-atmospheric gliding simultaneous multi-missile penetration of interceptors.First,the problem of large search space of multi-missile coordinated penetration maneuvers is fully considered,and the flight corridor of multi-missile coordinated penetration is designed to constrain search space of multi-agent coordinated strategy,comprehensively considering path constraints and anticollision constraints of gliding multi-missile flight.Then,a multi-missile hierarchical coordinated decision-making mechanism based on confrontation situation is proposed,and the swarm penetration strategy is optimized with the goal of maximizing swarm penetration effectiveness.The upper layer plans the swarm penetration formation according to confrontation situation,and generates the swarm coordinated penetration trajectory based on Multi-Agent Deep Deterministic Policy Gradient(MADDPG)method.The lower layer interpolates and smooths penetration trajectory,and generates the penetration guidance command based on Soft Actor-Critic and Extended Proportional Guidance(SAC-EPG)method.Simulation results verify that the proposed multi-missile cooperative penetration method based on hierarchical reinforcement learning converges faster than the penetration method based on MADDPG,and can quickly learn multi-missile cooperative penetration skills.In addition,multi-missile coordination can give full play to the group's detection and maneuverability,and occupy favorable penetration time and space through coordinated ballistic maneuvers.Thus the success rate of group penetration can be improved.
基金supported by the National Natural Science Foundation of China(62173209,61773238)the Science Center Program of National Natural Science Foundation of China(62188101).
文摘Dear Editor,This letter presents a new secure hierarchical control strategy for steering tracking of in-wheel motor driven(IWMD)electric vehicle(EV)subject to limited network resources,hybrid cyber-attacks,model nonlinearities,actuator redundancy and airflow disturbance.A hierarchical control architecture is proposed specifically for solving the problems of nonlinear system modeling and actuator redundancy.By utilizing the advantages of fully actuated system(FAS)approach,a nonlinear virtual controller against airflow disturbance is constructed in upper layer system and an event-triggered nonlinear distributed controller is proposed in lower layer system under stochastic hybrid cyber-attacks.A case study of overtaking task is carried out to validate the FAS-based hierarchical control strategy.
基金supported in part by the National Science and Technology Major Project of China(No.2019-I-0019-0018)the National Natural Science Foundation of China(Nos.61890920,61890921,12302065 and 12172073).
文摘Partial Differential Equations(PDEs)are model candidates of soft sensing for aero-engine health management units.The existing Physics-Informed Neural Networks(PINNs)have made achievements.However,unmeasurable aero-engine driving sources lead to unknown PDE driving terms,which weaken PINNs feasibility.To this end,Physically Informed Hierarchical Learning followed by Recurrent-Prediction Term(PIHL-RPT)is proposed.First,PIHL is proposed for learning nonhomogeneous PDE solutions,in which two networks NetU and NetG are constructed.NetU is for learning solutions satisfying PDEs;NetG is for learning driving terms to regularize NetU training.Then,we propose a hierarchical learning strategy to optimize and couple NetU and NetG,which are integrated into a data-physics-hybrid loss function.Besides,we prove PIHL-RPT can iteratively generate a series of networks converging to a function,which can approximate a solution to well-posed PDE.Furthermore,RPT is proposed for prediction improvement of PIHL,in which network NetU-RP is constructed to compensate for information loss caused by data sampling and driving sources’immeasurability.Finally,artificial datasets and practical vibration process datasets from our wear experiment platform are used to verify the feasibility and effectiveness of PIHL-RPT based soft sensing.Meanwhile,comparisons with relevant methods,discussions,and PIHL-RPT based health monitoring example are given.
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.
基金financially supported by the National Natural Science Foundation of China(No.52173070)the Key research and development projects of Baoying County(No.BY202205).
文摘The construction of hierarchical thermoplastic polyurethane(TPU)composites with superior flame retardant and electromagnetic shielding capabilities hold significant practical importance.In this work,TPU composites loaded with a multilayer core-shell flame retardant(APP@CoAl-LDH@Si)and a modified conductive nanofiller(MWCNT-NH_(2)-PA)were firstly prepared through the melt blending method,acting as surface layer.Additionally,multilayered MXene films functionalized by bacterial cellulose(BC)and dopamine hydrochloride(DA)were fabricated via a facile and efficient vacuum filtration approach.Finally,a PBM film was utilized as an intermediate layer to construct hierarchical TPU composites.The results indicated that the introduction of 10 wt%APP@CoAl-LDH@Si hybrid,the peak heat release rate,total heat release,peak smoke production rate,and total smoke release of the TPU composites were decreased by 83.0%,61.3%,48.5%and 66.9%,respectively,compared with those of pure TPU due to the free radicals capture effect of APP,and the flame-retardant functions of LDH and silane.Moreover,the hierarchical TPU/APP@CoAl-LDH@Si/CP1-PBM exhibited excellent electromagnetic shielding performance,achieving 43.6 dB in the X-band because of multiple reflection losses,interface polarization losses,and charge carrier movement-induced thermal dissipation.Extraordinarily,the A and R coefficients were reversed in the X and K bands.This phenomenon was attributed to the different degrees of confinement of the multilayer structure to electromagnetic waves with different wavelengths.This work presents a novel model for the design and preparation of high-performance polymer composites with multiple properties and regulation mechanism.
基金supported by the Key Research and Development Program of Sichuan Province(Grant No.2023ZHCG0050)the Fundamental Research Funds for the Central Universities of China(Grant No.2682024QZ006 and 2682024ZTPY042)the Analytic and Testing Center of Southwest Jiaotong University.
文摘As modern communication and detection technologies advance at a swift pace,multifunctional electromagnetic interference(EMI)shielding materials with active/positive infrared stealth,hydrophobicity,and electric-thermal conversion ability have received extensive attention.Meeting the aforesaid requirements simultaneously remains a huge challenge.In this research,the melamine foam(MF)/polypyrrole(PPy)nanowire arrays(MF@PPy)were fabricated via one-step electrochemical polymerization.The hierarchical MF@PPy foam was composed of three-dimensional PPy micro-skeleton and ordered PPy nanowire arrays.Due to the upwardly grown PPy nanowire arrays,the MF@PPy foam possessed good hydrophobicity ability with a water contact angle of 142.00°and outstanding stability under various harsh environments.Meanwhile,the MF@PPy foam showed excellent thermal insulation property on account of the low thermal conductivity and elongated ligament characteristic of PPy nanowire arrays.Furthermore,taking advantage of the high conductivity(128.2 S m^(-1)),the MF@PPy foam exhibited rapid Joule heating under 3 V,resulting in dynamic infrared stealth and thermal camouflage effects.More importantly,the MF@PPy foam exhibited remarkable EMI shielding effectiveness values of 55.77 dB and 19,928.57 dB cm^(2)g^(-1).Strong EMI shielding was put down to the hierarchically porous PPy structure,which offered outstanding impedance matching,conduction loss,and multiple attenuations.This innovative approach provides significant insights to the development of advanced multifunctional EMI shielding foams by constructing PPy nanowire arrays,showing great applications in both military and civilian fields.
基金supported by Macao Science and Technology Development Fund,Macao SAR,China(Grant No.:0043/2023/AFJ)the National Natural Science Foundation of China(Grant No.:22173038)Macao Polytechnic University,Macao SAR,China(Grant No.:RP/FCA-01/2022).
文摘Accurate prediction of molecular properties is crucial for selecting compounds with ideal properties and reducing the costs and risks of trials.Traditional methods based on manually crafted features and graph-based methods have shown promising results in molecular property prediction.However,traditional methods rely on expert knowledge and often fail to capture the complex structures and interactions within molecules.Similarly,graph-based methods typically overlook the chemical structure and function hidden in molecular motifs and struggle to effectively integrate global and local molecular information.To address these limitations,we propose a novel fingerprint-enhanced hierarchical graph neural network(FH-GNN)for molecular property prediction that simultaneously learns information from hierarchical molecular graphs and fingerprints.The FH-GNN captures diverse hierarchical chemical information by applying directed message-passing neural networks(D-MPNN)on a hierarchical molecular graph that integrates atomic-level,motif-level,and graph-level information along with their relationships.Addi-tionally,we used an adaptive attention mechanism to balance the importance of hierarchical graphs and fingerprint features,creating a comprehensive molecular embedding that integrated hierarchical mo-lecular structures with domain knowledge.Experiments on eight benchmark datasets from MoleculeNet showed that FH-GNN outperformed the baseline models in both classification and regression tasks for molecular property prediction,validating its capability to comprehensively capture molecular informa-tion.By integrating molecular structure and chemical knowledge,FH-GNN provides a powerful tool for the accurate prediction of molecular properties and aids in the discovery of potential drug candidates.
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co-supported by the National Natural Science Foundation of China(No.62103432)the China Postdoctoral Science Foundation(No.284881)the Young Talent fund of University Association for Science and Technology in Shaanxi,China(No.20210108)。
文摘Exo-atmospheric vehicles are constrained by limited maneuverability,which leads to the contradiction between evasive maneuver and precision strike.To address the problem of Integrated Evasion and Impact(IEI)decision under multi-constraint conditions,a hierarchical intelligent decision-making method based on Deep Reinforcement Learning(DRL)was proposed.First,an intelligent decision-making framework of“DRL evasion decision”+“impact prediction guidance decision”was established:it takes the impact point deviation correction ability as the constraint and the maximum miss distance as the objective,and effectively solves the problem of poor decisionmaking effect caused by the large IEI decision space.Second,to solve the sparse reward problem faced by evasion decision-making,a hierarchical decision-making method consisting of maneuver timing decision and maneuver duration decision was proposed,and the corresponding Markov Decision Process(MDP)was designed.A detailed simulation experiment was designed to analyze the advantages and computational complexity of the proposed method.Simulation results show that the proposed model has good performance and low computational resource requirement.The minimum miss distance is 21.3 m under the condition of guaranteeing the impact point accuracy,and the single decision-making time is 4.086 ms on an STM32F407 single-chip microcomputer,which has engineering application value.
基金supported by the Science and Technology Department of Qinghai Province,China(No.2022-ZJ-932Q).
文摘The increase in the utilization of infrared heat detection technology in military applications necessitates research on composites with improved thermal transmission performance and microwave absorption capabilities.This study satisfactorily fabricated a series of MoS_(2)/BN-xyz composites(which were characterized by the weight ratio of MoS_(2)to BN,denoted by xy:z)through chemical vapor depos-ition,which resulted in their improved thermal stability and thermal transmission performance.The results show that the remaining mass of MoS_(2)/BN-101 was as high as 69.25wt%at 800℃under air atmosphere,and a temperature difference of 31.7℃was maintained between the surface temperature and the heating source at a heating temperature of 200℃.Furthermore,MoS_(2)/BN-301 exhibited an im-pressive minimum reflection loss value of-32.21 dB at 4.0 mm and a wide effective attenuation bandwidth ranging from 9.32 to 18.00 GHz(8.68 GHz).Therefore,these simplified synthesized MoS_(2)/BN-xyz composites demonstrate great potential as highly efficient con-tenders for the enhancement of microwave absorption performance and thermal conductance.
基金supported by the National Natural Science Foundation of China(No.32272399)the Shanghai Natural Science Foundation(No.21ZR1427500).
文摘Listeria monocytogenes(LM)is a dangerous foodborne pathogen for humans.One emerging and validated method of indirectly assessing LM in food is detecting 3-hydroxy-2-butanone(3H2B)gas.In this study,the synthesis of 3-(2-aminoethylamino)propyltrimethoxysilane(AAPTMS)functionalized hierarchical hollow TiO_(2)nanospheres was achieved via precise controlling of solvothermal reaction temperature and post-grafting route.The sensors based on as-prepared materials exhibited excellent sensitivity(480 Hz@50 ppm),low detection limit(100 ppb),and outstanding selectivity.Moreover,the evaluation of LM with high sensitivity and specificity was achieved using the sensors.Such stable three-dimensional spheres,whose distinctive hierarchical and hollow nanostructure simultaneously improved both sensitivity and response/recovery speed dramatically,were spontaneously assembled by nanosheets.Meanwhile,the moderate loadings of AAPTMS significantly improved the selectivity of sensors.Then,the gas-sensing mechanism was explored by utilizing thermodynamic investigation,Gaussian 16 software,and in situ diffuse reflectance infrared transform spectroscopy,illustrating the weak chemisorption between the-NHgroup and 3H2B molecules.These portable sensors are promising for real-time assessment of LM at room temperature,which will make a magnificent contribution to food safety.
文摘Hierarchical porous carbon(HPC)materials exhibit superior performance profiles in various applications due to their well-developed multiscale interconnected pore structures.The synthesis of HPC from natural biomass precursors instead of fossil fuel-based precursors has gained considerable attention in recent decades.Rice husk,a globally abundant agricultural waste,offers a sustainable and cost-effective precursor for HPC production.The structural components and inherent silica content of rice husk act as a natural self-template for forming hierarchical pore structures with superior characteristics.In this review,recent studies on preparing rice husk-based HPC are summarized,and synthesis techniques are evaluated.In addition,recent advancements in activation methods and the effect of silica templates are reviewed while comparing these with traditional activated carbon production methods.Potential future directions for research and development activities are also discussed.Rice husk is a highly promising candidate for producing high-performance HPC materials.
基金supported in part by the National Natural Science Foundation of China(62073301,62373162,62473349,U24A20268,62233007)the Shenzhen Science and Technology Program(JCYJ20240813114007010).
文摘Dear Editor,This letter investigates the problem of multi-dimension formation tracking(MDFT)for the cross-domain unmanned systems,including several interconnected agents,namely,unmanned aerial vehicles(UAVs)and unmanned surface vehicles(USVs).We assume that each agent suffers from by the mixed constraints on its velocity,control input and Euler angle.Solving the MDFT problem implies that 1)The virtual state of each USV is determined in the earth coordinate by expanding its 2D work space to the 3D space.
文摘Introduction It is necessary for an ideal bioceramic scaffold to have a suitable structure.The structure can affect the mechanical properties of the scaffold(i.e.,elastic modulus and compressive strength)and the biological properties of the scaffold(i.e.,degradability and cell growth rate).Lattice structure is a kind of periodic porous structure,which has some advantages of light weight and high strength,and is widely used in the preparation of bioceramic scaffolders.For the structure of the scaffold,high porosity and large pore size are important for bone growth,bone integration and promoting good mechanical interlocking between neighboring bones and the scaffold.However,scaffolds with a high porosity often lack mechanical strength.In addition,different parts of the bone have different structural requirements.In this paper,scaffolds with a non-uniform structure or a hierarchical structure were designed,with loose and porous exterior to facilitate cell adhesion,osteogenic differentiation and vascularization as well as relatively dense interior to provide sufficient mechanical support for bone repair.Methods In this work,composite ceramics scaffolds with 10%akermanite content were prepared by DLP technology.The scaffold had a high porosity outside to promote the growth of bone tissue,and a low porosity inside to withstand external forces.The compressive strength,fracture form,in-vitro degradation performance and bioactivity of graded bioceramic scaffolds were investigated.The models of scaffolds were imported into the DLP printer with a 405 nm light.The samples were printed with the intensity of 8 mJ/cm^(2)and a layer thickness of 50μm.Finally,the ceramic samples were sintered at 1100℃.The degradability of the hierarchical gyroid bioceramic scaffolds was evaluated through immersion in Tris-HCl solution and SBF solution at a ratio of 200 mL/g.The bioactivity of bioceramic was obtained via immersing them in SBF solution for two weeks.The concentrations of calcium,phosphate,silicon,and magnesium ions in the soaking solution were determined by an inductively coupled plasma optical emission spectrometer.Results and discussion In this work,a hierarchical Gyroid structure HA-AK10 scaffold(sintered at 1100℃)with a radial internal porosity of 50%and an external porosity of 70%is prepared,and the influence of structural form on the compressive strength and degradation performance of the scaffold is investigated.The biological activity of the bioceramics in vitro is also verified.The mechanical simulation results show that the stress distribution corresponds to the porosity distribution of the structure,and the low porosity is larger and the overall stress concentration phenomenon does not appear.After soaking in SBF solution,Si—OH is firstly formed on the surface of bioceramics,and then silicon gel layer is produced due to the presence of calcium and silicon ions.The silicon gel layer is dissociated into negatively charged groups under alkaline environment secondary adsorption of calcium ions and phosphate ions,forming amorphous calcium phosphate,and finally amorphous calcium phosphate crystals and adsorption of carbonate ions,forming carbonate hydroxyapatite.This indicates that the composite bioceramics have a good biological activity in-vitro and can provide a good environment for the growth of bone cells.A hierarchical Gyroid ceramic scaffold with a bone geometry is prepared via applying the hierarchical structure to the bone contour scaffold.The maximum load capacity of the hierarchical Gyroid ceramic scaffold is 8 times that of the uniform structure.Conclusions The hierarchical structure scaffold designed had good overall compressive performance,good degradation performance,and still maintained a good mechanical stability during degradation.In addition,in-vitro biological experimental results showed that the surface graded composite scaffold could have a good in-vitro biological activity and provide a good environment for bone cells.Compared to the heterosexual structure,the graded scaffold had greater mechanical properties.
