Graph neural networks(GNN)have shown strong performance in node classification tasks,yet most existing models rely on uniform or shared weight aggregation,lacking flexibility in modeling the varying strength of relati...Graph neural networks(GNN)have shown strong performance in node classification tasks,yet most existing models rely on uniform or shared weight aggregation,lacking flexibility in modeling the varying strength of relationships among nodes.This paper proposes a novel graph coupling convolutional model that introduces an adaptive weighting mechanism to assign distinct importance to neighboring nodes based on their similarity to the central node.Unlike traditional methods,the proposed coupling strategy enhances the interpretability of node interactions while maintaining competitive classification performance.The model operates in the spatial domain,utilizing adjacency list structures for efficient convolution and addressing the limitations of weight sharing through a coupling-based similarity computation.Extensive experiments are conducted on five graph-structured datasets,including Cora,Citeseer,PubMed,Reddit,and BlogCatalog,as well as a custom topology dataset constructed from the Open University Learning Analytics Dataset(OULAD)educational platform.Results demonstrate that the proposed model achieves good classification accuracy,while significantly reducing training time through direct second-order neighbor fusion and data preprocessing.Moreover,analysis of neighborhood order reveals that considering third-order neighbors offers limited accuracy gains but introduces considerable computational overhead,confirming the efficiency of first-and second-order convolution in practical applications.Overall,the proposed graph coupling model offers a lightweight,interpretable,and effective framework for multi-label node classification in complex networks.展开更多
It is well known that aluminum and copper exhibit structural phase transformations in quasi-static and dynamic measurements,including shock wave loading.However,the dependence of phase transformations in a wide range ...It is well known that aluminum and copper exhibit structural phase transformations in quasi-static and dynamic measurements,including shock wave loading.However,the dependence of phase transformations in a wide range of crystallographic directions of shock loading has not been revealed.In this work,we calculated the shock Hugoniot for aluminum and copper in different crystallographic directions([100],[110],[111],[112],[102],[114],[123],[134],[221]and[401])of shock compression using molecular dynamics(MD)simulations.The results showed a high pressure(>160 GPa for Cu and>40 GPa for Al)of the FCC-to-BCC transition.In copper,different characteristics of the phase transition are observed depending on the loading direction with the[100]compression direction being the weakest.The FCC-to-BCC transition for copper is in the range of 150–220 GPa,which is consistent with the existing experimental data.Due to the high transition pressure,the BCC phase transition in copper competes with melting.In aluminum,the FCC-to-BCC transition is observed for all studied directions at pressures between 40 and 50 GPa far beyond the melting.In all considered cases we observe the coexistence of HCP and BCC phases during the FCC-to-BCC transition,which is consistent with the experimental data and atomistic calculations;this HCP phase forms in the course of accompanying plastic deformation with dislocation activity in the parent FCC phase.The plasticity incipience is also anisotropic in bothmetals,which is due to the difference in the projections of stress on the slip plane for different orientations of the FCC crystal.MD modeling results demonstrate a strong dependence of the FCC-to-BCC transition on the crystallographic direction,in which the material is loaded in the copper crystals.However,MD simulations data can only be obtained for specific points in the stereographic direction space;therefore,for more comprehensive understanding of the phase transition process,a feed-forward neural network was trained using MD modeling data.The trained machine learning model allowed us to construct continuous stereographic maps of phase transitions as a function of stress in the shock-compressed state of metal.Due to appearance and growth of multiple centers of new phase,the FCC-to-BCC transition leads to formation of a polycrystalline structure from the parent single crystal.展开更多
Modeling the dynamics of flapping wing aerial vehicle is challenging due to the complexity of aerodynamic effects and mechanical structures.The aim of this work is to develop an accurate dynamics model of flapping win...Modeling the dynamics of flapping wing aerial vehicle is challenging due to the complexity of aerodynamic effects and mechanical structures.The aim of this work is to develop an accurate dynamics model of flapping wing aerial vehicle based on real flight data.We propose a modeling framework that combines rigid body dynamics with a neural network to predict aerodynamic effects.By incorporating the concept of flapping phase,we significantly enhance the network’s ability to analyze transient aerodynamic behavior.We design and utilize a phase-functioned neural network structure for aerodynamic predictions and train the network using real flight data.Evaluation results show that the network can predict aerodynamic effects and demonstrate clear physical significance.We verify that the framework can be used for dynamic propagation and is expected to be utilized for building simulators for flapping wing aerial vehicles.展开更多
The pH-sensitive hydrogels play a crucial role in applications such as soft robotics,drug delivery,and biomedical sensors,as they require precise control of swelling behaviors and stress distributions.Traditional expe...The pH-sensitive hydrogels play a crucial role in applications such as soft robotics,drug delivery,and biomedical sensors,as they require precise control of swelling behaviors and stress distributions.Traditional experimental methods struggle to capture stress distributions due to technical limitations,while numerical approaches are often computationally intensive.This study presents a hybrid framework combining analytical modeling and machine learning(ML)to overcome these challenges.An analytical model is used to simulate transient swelling behaviors and stress distributions,and is confirmed to be viable through the comparison of the obtained simulation results with the existing experimental swelling data.The predictions from this model are used to train neural networks,including a two-step augmented architecture.The initial neural network predicts hydration values,which are then fed into a second network to predict stress distributions,effectively capturing nonlinear interdependencies.This approach achieves mean absolute errors(MAEs)as low as 0.031,with average errors of 1.9%for the radial stress and 2.55%for the hoop stress.This framework significantly enhances the predictive accuracy and reduces the computational complexity,offering actionable insights for optimizing hydrogel-based systems.展开更多
In Saharan climates,greenhouses face extreme diurnal temperature fluctuations that generate thermal stress,reduce crop productivity,and hinder sustainable agricultural practices.Passive thermal storage using Phase Cha...In Saharan climates,greenhouses face extreme diurnal temperature fluctuations that generate thermal stress,reduce crop productivity,and hinder sustainable agricultural practices.Passive thermal storage using Phase Change Materials(PCM)is a promising solution to stabilize microclimatic conditions.This study aims to evaluate experimentally and numerically the effectiveness of PCM integration for moderating greenhouse temperature fluctuations under Saharan climatic conditions.Two identical greenhouse prototypes were constructed in Ghardaia,Algeria:a reference greenhouse and a PCM-integrated greenhouse using calcium chloride hexahydrate(CaCl_(2)⋅6H_(2)O).Thermal performance was assessed during a five-day experimental period(7–11May 2025)under severe ambient conditions.To complement this,a Nonlinear Auto-Regressive with eXogenous inputs(NARX)neural network model was developed and trained using a larger dataset(7–25 May 2025)to predict greenhouse thermal dynamics.The PCM greenhouse reduced peak daytime air temperature by an average of 8.14℃and decreased the diurnal temperature amplitude by 53.6%compared to the reference greenhouse.The NARX model achieved high predictive accuracy(R^(2)=0.990,RMSE=0.425℃,MAE=0.223℃,MBE=0.008℃),capturing both sensible and latent heat transfer mechanisms,including PCM melting and solidification.The combined experimental and predictive modeling results confirm the potential of PCM integration as an effective passive thermal regulation strategy for greenhouses in arid regions.This approach enhances microclimatic stability,improves energy efficiency,and supports the sustainability of protected agriculture under extreme climatic conditions.展开更多
The temperature-humidity models of wood drying were developed based on Time-delay neural network and the identification structures of Time-delay neural network were given. The controlling model and the schedule model,...The temperature-humidity models of wood drying were developed based on Time-delay neural network and the identification structures of Time-delay neural network were given. The controlling model and the schedule model, which revealed the relation between controlling signal and temperature-humidity and the relation between wood moisture content and temperature-humidity of wood drying, were separately presented. The models were simulated by using the measured data of the experimental drying kiln. The numerical simulation results showed that the modeling method was feasible, and the models were effective.展开更多
A novel behavioral model using three-layer time-delay feed-forward neural networks （TDFFNN）is adopted to model radio frequency （RF）power amplifiers exhibiting memory nonlinearities. In order to extract the paramet...A novel behavioral model using three-layer time-delay feed-forward neural networks （TDFFNN）is adopted to model radio frequency （RF）power amplifiers exhibiting memory nonlinearities. In order to extract the parameters, the back- propagation algorithm is applied to train the proposed neural networks. The proposed model is verified by the typical odd- order-only memory polynomial model in simulation, and the performance is compared with different numbers of taped delay lines（TDLs） and perceptrons of the hidden layer. For validating the TDFFNN model by experiments, a digital test bench is set up to collect input and output data of power amplifiers at a 60 × 10^6 sample/s sampling rate. The 3.75 MHz 16-QAM signal generated in the vector signal generator（VSG） is chosen as the input signal, when measuring the dynamic AM/AM and AM/PM characteristics of power amplifiers. By comparisons and analyses, the presented model provides a good performance in convergence, accuracy and efficiency, which is approved by simulation results and experimental results in the time domain and frequency domain.展开更多
Accelerated aging tests are widely used to rapidly evaluate the durability of materials,of which thermal-oxidative aging is the most common approach.To quantitatively predict the effects of multiple coupled factors,th...Accelerated aging tests are widely used to rapidly evaluate the durability of materials,of which thermal-oxidative aging is the most common approach.To quantitatively predict the effects of multiple coupled factors,this study takes polyamide66 reinforced with glass fiber(PA66-GF)as a model system and proposed a high-precision paradigm for coupled thermal-oxidative aging.By integrating Arrhenius-type reaction kinetics with oxygen diffusion,a predictive formula that holistically captures the nonlinear synergistic effects of multiple factors was developed,thereby overcoming the limitations of traditional single-variable models.A systematic evaluation of the stepwise improved formulas through nonlinear fitting showed that the coefficient of determination(R^(2))increased from 0.223 to 0.803,elucidating the fundamental reason why conventional approaches fail in quantitative prediction.These formulae were further embedded as physical constraints into a physics-informed neural network(PINN),which further enhanced the predictive performance,with the proposed formula achieving a peak R^(2)of 0.946.The results highlight that robust data fitting alone is insufficient;the decisive factor for the success of PINN lies in whether the embedded formula faithfully reflects the underlying physical mechanisms.When applied to polyamide 6 reinforced with glass fiber(PA6-GF),the Formula-constrained PINN maintained a high level of accuracy(R^(2)=0.916),demonstrating its strong cross-system generalizability.In summary,this work establishes a robust hybrid physics-machine learning framework that combines high accuracy with transferability for predicting the thermal-oxidative aging behavior of composite material systems.展开更多
Timely and accurate forecasting of storm surges can effectively prevent typhoon storm surges from causing large economic losses and casualties in coastal areas.At present,numerical model forecasting consumes too many ...Timely and accurate forecasting of storm surges can effectively prevent typhoon storm surges from causing large economic losses and casualties in coastal areas.At present,numerical model forecasting consumes too many resources and takes too long to compute,while neural network forecasting lacks regional data to train regional forecasting models.In this study,we used the DUAL wind model to build typhoon wind fields,and constructed a typhoon database of 75 processes in the northern South China Sea using the coupled Advanced Circulation-Simulating Waves Nearshore(ADCIRC-SWAN)model.Then,a neural network with a Res-U-Net structure was trained using the typhoon database to forecast the typhoon processes in the validation dataset,and an excellent storm surge forecasting effect was achieved in the Pearl River Estuary region.The storm surge forecasting effect of stronger typhoons was improved by adding a branch structure and transfer learning.展开更多
Landslide susceptibility mapping(LSM)plays a crucial role in assessing geological risks.The current LSM techniques face a significant challenge in achieving accurate results due to uncertainties associated with region...Landslide susceptibility mapping(LSM)plays a crucial role in assessing geological risks.The current LSM techniques face a significant challenge in achieving accurate results due to uncertainties associated with regional-scale geotechnical parameters.To explore rainfall-induced LSM,this study proposes a hybrid model that combines the physically-based probabilistic model(PPM)with convolutional neural network(CNN).The PPM is capable of effectively capturing the spatial distribution of landslides by incorporating the probability of failure(POF)considering the slope stability mechanism under rainfall conditions.This significantly characterizes the variation of POF caused by parameter uncertainties.CNN was used as a binary classifier to capture the spatial and channel correlation between landslide conditioning factors and the probability of landslide occurrence.OpenCV image enhancement technique was utilized to extract non-landslide points based on the POF of landslides.The proposed model comprehensively considers physical mechanics when selecting non-landslide samples,effectively filtering out samples that do not adhere to physical principles and reduce the risk of overfitting.The results indicate that the proposed PPM-CNN hybrid model presents a higher prediction accuracy,with an area under the curve(AUC)value of 0.85 based on the landslide case of the Niangniangba area of Gansu Province,China compared with the individual CNN model(AUC=0.61)and the PPM(AUC=0.74).This model can also consider the statistical correlation and non-normal probability distributions of model parameters.These results offer practical guidance for future research on rainfall-induced LSM at the regional scale.展开更多
This study directs the discussion of HIV disease with a novel kind of complex dynamical generalized and piecewise operator in the sense of classical and Atangana Baleanu(AB)derivatives having arbitrary order.The HIV i...This study directs the discussion of HIV disease with a novel kind of complex dynamical generalized and piecewise operator in the sense of classical and Atangana Baleanu(AB)derivatives having arbitrary order.The HIV infection model has a susceptible class,a recovered class,along with a case of infection divided into three sub-different levels or categories and the recovered class.The total time interval is converted into two,which are further investigated for ordinary and fractional order operators of the AB derivative,respectively.The proposed model is tested separately for unique solutions and existence on bi intervals.The numerical solution of the proposed model is treated by the piece-wise numerical iterative scheme of Newtons Polynomial.The proposed method is established for piece-wise derivatives under natural order and non-singular Mittag-Leffler Law.The cross-over or bending characteristics in the dynamical system of HIV are easily examined by the aspect of this research having a memory effect for controlling the said disease.This study uses the neural network(NN)technique to obtain a better set of weights with low residual errors,and the epochs number is considered 1000.The obtained figures represent the approximate solution and absolute error which are tested with NN to train the data accurately.展开更多
In recent years,discrete neuron and discrete neural network models have played an important role in the development of neural dynamics.This paper reviews the theoretical advantages of well-known discrete neuron models...In recent years,discrete neuron and discrete neural network models have played an important role in the development of neural dynamics.This paper reviews the theoretical advantages of well-known discrete neuron models,some existing discretized continuous neuron models,and discrete neural networks in simulating complex neural dynamics.It places particular emphasis on the importance of memristors in the composition of neural networks,especially their unique memory and nonlinear characteristics.The integration of memristors into discrete neural networks,including Hopfield networks and their fractional-order variants,cellular neural networks and discrete neuron models has enabled the study and construction of various neural models with memory.These models exhibit complex dynamic behaviors,including superchaotic attractors,hidden attractors,multistability,and synchronization transitions.Furthermore,the present paper undertakes an analysis of more complex dynamical properties,including synchronization,speckle patterns,and chimera states in discrete coupled neural networks.This research provides new theoretical foundations and potential applications in the fields of brain-inspired computing,artificial intelligence,image encryption,and biological modeling.展开更多
System design and optimization problems require large-scale chemical kinetic models. Pure kinetic models of naphtha pyrolysis need to solve a complete set of stiff ODEs and is therefore too computational expensive. On...System design and optimization problems require large-scale chemical kinetic models. Pure kinetic models of naphtha pyrolysis need to solve a complete set of stiff ODEs and is therefore too computational expensive. On the other hand, artificial neural networks that completely neglect the topology of the reaction networks often have poor generalization. In this paper, a framework is proposed for learning local representations from largescale chemical reaction networks. At first, the features of naphtha pyrolysis reactions are extracted by applying complex network characterization methods. The selected features are then used as inputs in convolutional architectures. Different CNN models are established and compared to optimize the neural network structure.After the pre-training and fine-tuning step, the ultimate CNN model reduces the computational cost of the previous kinetic model by over 300 times and predicts the yields of main products with the average error of less than 3%. The obtained results demonstrate the high efficiency of the proposed framework.展开更多
The topology structure of the artificial neural network is an intelligent control model,which is used for the intelligent vehicle control system and household sweeping robot.When setting the intelligent control system...The topology structure of the artificial neural network is an intelligent control model,which is used for the intelligent vehicle control system and household sweeping robot.When setting the intelligent control system,the connection point of each network is regarded as a neuron in the nervous system,and each connection point has input and output functions.Only when the input of nodes reaches a certain threshold can the output function of nodes be stimulated.Using the networking mode of the artificial neural network model,the mobile node can output in multiple directions.If the input direction of a certain path is the same as that of other nodes,it can choose to avoid and choose another path.The weighted value of each path between nodes is different,which means that the influence of the front node on the current node varies.The control method based on the artificial neural network model can be applied to vehicle control,household sweeping robots,and other fields,and a relatively optimized scheme can be obtained from the aspect of time and energy consumption.展开更多
The constitutive models of shape memory alloys(SMAs)play an important role in facilitating the widespread application of such types of alloys in various engineering fields.However,to accurately describe the deformatio...The constitutive models of shape memory alloys(SMAs)play an important role in facilitating the widespread application of such types of alloys in various engineering fields.However,to accurately describe the deformation behaviors of SMAs,the concepts in classical plasticity are employed in the existing constitutive models,and a series of complex mathematical equations are involved.Such complexity brings inconvenience for the construction,implementation,and application of the constitutive models.To overcome these shortcomings,a data-driven constitutive model of SMAs is developed in this work based on the artificial neural network(ANN).In the proposed model,the components of the strain tensor in principal space,ambient temperature,and the maximum equivalent strain in the deformation history from the initial state to the current loading state are chosen as the input features,and the components of the stress tensor in principal space are set as the output.The proposed ANN-based constitutive model is implemented into the finite element program ABAQUS by deriving its consistent tangent modulus and writing a user-defined material subroutine.The stress-strain responses of SMA material under various loading paths and at different ambient temperatures are used to train the ANN model,which is generated from the existing constitutive model(numerical experiments).To validate the capability of the proposed model,the predicted stress-strain responses of SMA material,and the global and local responses of two typical SMA structures are compared with the corresponding numerical experiments.This work demonstrates a good potential to obtain the constitutive model of SMAs by pure data and avoid the need for vast stores of knowledge for the construction of constitutive models.展开更多
Recognizing essential proteins within bacteriophages is fundamental to uncovering their replication and survival mechanisms and contributes to advances in phage-based antibacterial therapies.Despite notable progress,e...Recognizing essential proteins within bacteriophages is fundamental to uncovering their replication and survival mechanisms and contributes to advances in phage-based antibacterial therapies.Despite notable progress,existing computational techniques struggle to represent the interplay between sequence-derived and structuredependent protein features.To overcome this limitation,we introduce GLM-EP,a unified framework that fuses protein language models with equivariant graph neural networks.Bymerging semantic embeddings extracted from amino acid sequences with geometry-aware graph representations,GLM-EP enables an in-depth depiction of phage proteins and enhances essential protein identification.Evaluation on diverse benchmark datasets confirms that GLM-EP surpasses conventional sequence-based and independent deep-learning methods,yielding higher F1 and AUROC outcomes.Component-wise analysis demonstrates that GCNII,EGNN,and the gated multi-head attention mechanism function in a complementary manner to encode complex molecular attributes.In summary,GLM-EP serves as a robust and efficient tool for bacteriophage genomic analysis and provides valuable methodological perspectives for the discovery of antibiotic-resistance therapeutic targets.The corresponding code repository is available at:https://github.com/MiJia-ID/GLM-EP(accessed on 01 November 2025).展开更多
Traditional steganography conceals information by modifying cover data,but steganalysis tools easily detect such alterations.While deep learning-based steganography often involves high training costs and complex deplo...Traditional steganography conceals information by modifying cover data,but steganalysis tools easily detect such alterations.While deep learning-based steganography often involves high training costs and complex deployment.Diffusion model-based methods face security vulnerabilities,particularly due to potential information leakage during generation.We propose a fixed neural network image steganography framework based on secure diffu-sion models to address these challenges.Unlike conventional approaches,our method minimizes cover modifications through neural network optimization,achieving superior steganographic performance in human visual perception and computer vision analyses.The cover images are generated in an anime style using state-of-the-art diffusion models,ensuring the transmitted images appear more natural.This study introduces fixed neural network technology that allows senders to transmit only minimal critical information alongside stego-images.Recipients can accurately reconstruct secret images using this compact data,significantly reducing transmission overhead compared to conventional deep steganography.Furthermore,our framework innovatively integrates ElGamal,a cryptographic algorithm,to protect critical information during transmission,enhancing overall system security and ensuring end-to-end information protection.This dual optimization of payload reduction and cryptographic reinforcement establishes a new paradigm for secure and efficient image steganography.展开更多
Deep neural networks(DNNs)have found extensive applications in safety-critical artificial intelligence systems,such as autonomous driving and facial recognition systems.However,recent research has revealed their susce...Deep neural networks(DNNs)have found extensive applications in safety-critical artificial intelligence systems,such as autonomous driving and facial recognition systems.However,recent research has revealed their susceptibility to backdoors maliciously injected by adversaries.This vulnerability arises due to the intricate architecture and opacity of DNNs,resulting in numerous redundant neurons embedded within the models.Adversaries exploit these vulnerabilities to conceal malicious backdoor information within DNNs,thereby causing erroneous outputs and posing substantial threats to the efficacy of DNN-based applications.This article presents a comprehensive survey of backdoor attacks against DNNs and the countermeasure methods employed to mitigate them.Initially,we trace the evolution of the concept from traditional backdoor attacks to backdoor attacks against DNNs,highlighting the feasibility and practicality of generating backdoor attacks against DNNs.Subsequently,we provide an overview of notable works encompassing various attack and defense strategies,facilitating a comparative analysis of their approaches.Through these discussions,we offer constructive insights aimed at refining these techniques.Finally,we extend our research perspective to the domain of large language models(LLMs)and synthesize the characteristics and developmental trends of backdoor attacks and defense methods targeting LLMs.Through a systematic review of existing studies on backdoor vulnerabilities in LLMs,we identify critical open challenges in this field and propose actionable directions for future research.展开更多
As a critical material in construction engineering,concrete requires accurate prediction of its outlet temperature to ensure structural quality and enhance construction efficiency.This study proposes a novel hybrid pr...As a critical material in construction engineering,concrete requires accurate prediction of its outlet temperature to ensure structural quality and enhance construction efficiency.This study proposes a novel hybrid prediction method that integrates a heat conduction physical model with a multilayer perceptron(MLP)neural network,dynamically fused via a weighted strategy to achieve high-precision temperature estimation.Experimental results on an independent test set demonstrated the superior performance of the fused model,with a root mean square error(RMSE)of 1.59℃ and a mean absolute error(MAE)of 1.23℃,representing a 25.3%RMSE reduction compared to conventional physical models.Ambient temperature and coarse aggregate temperature were identified as the most influential variables.Furthermore,the model-based temperature control strategy reduced costs by 0.81 CNY/m^(3),showing significant potential for improving resource efficiency and supporting sustainable construction practices.展开更多
In this work,a frame work for time-varying channel modeling and simulation is proposed by using neural network(NN)to overcome the shortcomings in geometry based stochastic model(GBSM)and simulation approach.Two NN mod...In this work,a frame work for time-varying channel modeling and simulation is proposed by using neural network(NN)to overcome the shortcomings in geometry based stochastic model(GBSM)and simulation approach.Two NN models are developed for modeling of path loss together with shadow fading(SF)and joint small scale channel parameters.The NN models can predict path loss plus SF and small scale channel parameters accurately compared with measurement at 26 GHz performed in an outdoor microcell.The time-varying path loss and small scale channel parameters generated by the NN models are proposed to replace the empirical path loss and channel parameter random numbers in GBSM-based framework to playback the measured channel and match with its environment.Moreover,the sparse feature of clusters,delay and angular spread,channel capacity are investigated by a virtual array measurement at 28 GHz in a large waiting hall.展开更多
基金Support by Sichuan Science and Technology Program[2023YFSY0026,2023YFH0004]Guangzhou Huashang University[2024HSZD01,HS2023JYSZH01].
文摘Graph neural networks(GNN)have shown strong performance in node classification tasks,yet most existing models rely on uniform or shared weight aggregation,lacking flexibility in modeling the varying strength of relationships among nodes.This paper proposes a novel graph coupling convolutional model that introduces an adaptive weighting mechanism to assign distinct importance to neighboring nodes based on their similarity to the central node.Unlike traditional methods,the proposed coupling strategy enhances the interpretability of node interactions while maintaining competitive classification performance.The model operates in the spatial domain,utilizing adjacency list structures for efficient convolution and addressing the limitations of weight sharing through a coupling-based similarity computation.Extensive experiments are conducted on five graph-structured datasets,including Cora,Citeseer,PubMed,Reddit,and BlogCatalog,as well as a custom topology dataset constructed from the Open University Learning Analytics Dataset(OULAD)educational platform.Results demonstrate that the proposed model achieves good classification accuracy,while significantly reducing training time through direct second-order neighbor fusion and data preprocessing.Moreover,analysis of neighborhood order reveals that considering third-order neighbors offers limited accuracy gains but introduces considerable computational overhead,confirming the efficiency of first-and second-order convolution in practical applications.Overall,the proposed graph coupling model offers a lightweight,interpretable,and effective framework for multi-label node classification in complex networks.
基金founded by the Ministry of Science and Higher Education of the Russian Federation,State assignments for research,registration No.1024032600084-8-1.3.2Study of the grain growth and the formation of polycrystalline structure as a result of phase transition(Section 6)was founded by the Russian Science Foundation,Project No.24-71-00078+3 种基金https://rscf.ru/en/project/24-71-00078/(accessed on 01 December 2025).Study of the orientation dependence of the phase transition of aluminum in Section 3 was founded by the Russian Science Foundation,Project No.24-19-00684https://rscf.ru/en/project/24-19-00684/(accessed on 01 December 2025).
文摘It is well known that aluminum and copper exhibit structural phase transformations in quasi-static and dynamic measurements,including shock wave loading.However,the dependence of phase transformations in a wide range of crystallographic directions of shock loading has not been revealed.In this work,we calculated the shock Hugoniot for aluminum and copper in different crystallographic directions([100],[110],[111],[112],[102],[114],[123],[134],[221]and[401])of shock compression using molecular dynamics(MD)simulations.The results showed a high pressure(>160 GPa for Cu and>40 GPa for Al)of the FCC-to-BCC transition.In copper,different characteristics of the phase transition are observed depending on the loading direction with the[100]compression direction being the weakest.The FCC-to-BCC transition for copper is in the range of 150–220 GPa,which is consistent with the existing experimental data.Due to the high transition pressure,the BCC phase transition in copper competes with melting.In aluminum,the FCC-to-BCC transition is observed for all studied directions at pressures between 40 and 50 GPa far beyond the melting.In all considered cases we observe the coexistence of HCP and BCC phases during the FCC-to-BCC transition,which is consistent with the experimental data and atomistic calculations;this HCP phase forms in the course of accompanying plastic deformation with dislocation activity in the parent FCC phase.The plasticity incipience is also anisotropic in bothmetals,which is due to the difference in the projections of stress on the slip plane for different orientations of the FCC crystal.MD modeling results demonstrate a strong dependence of the FCC-to-BCC transition on the crystallographic direction,in which the material is loaded in the copper crystals.However,MD simulations data can only be obtained for specific points in the stereographic direction space;therefore,for more comprehensive understanding of the phase transition process,a feed-forward neural network was trained using MD modeling data.The trained machine learning model allowed us to construct continuous stereographic maps of phase transitions as a function of stress in the shock-compressed state of metal.Due to appearance and growth of multiple centers of new phase,the FCC-to-BCC transition leads to formation of a polycrystalline structure from the parent single crystal.
基金supported by National Natural Science Foundation of China under Grant No.62236007the specialized research projects of Huanjiang Laboratory.
文摘Modeling the dynamics of flapping wing aerial vehicle is challenging due to the complexity of aerodynamic effects and mechanical structures.The aim of this work is to develop an accurate dynamics model of flapping wing aerial vehicle based on real flight data.We propose a modeling framework that combines rigid body dynamics with a neural network to predict aerodynamic effects.By incorporating the concept of flapping phase,we significantly enhance the network’s ability to analyze transient aerodynamic behavior.We design and utilize a phase-functioned neural network structure for aerodynamic predictions and train the network using real flight data.Evaluation results show that the network can predict aerodynamic effects and demonstrate clear physical significance.We verify that the framework can be used for dynamic propagation and is expected to be utilized for building simulators for flapping wing aerial vehicles.
文摘The pH-sensitive hydrogels play a crucial role in applications such as soft robotics,drug delivery,and biomedical sensors,as they require precise control of swelling behaviors and stress distributions.Traditional experimental methods struggle to capture stress distributions due to technical limitations,while numerical approaches are often computationally intensive.This study presents a hybrid framework combining analytical modeling and machine learning(ML)to overcome these challenges.An analytical model is used to simulate transient swelling behaviors and stress distributions,and is confirmed to be viable through the comparison of the obtained simulation results with the existing experimental swelling data.The predictions from this model are used to train neural networks,including a two-step augmented architecture.The initial neural network predicts hydration values,which are then fed into a second network to predict stress distributions,effectively capturing nonlinear interdependencies.This approach achieves mean absolute errors(MAEs)as low as 0.031,with average errors of 1.9%for the radial stress and 2.55%for the hoop stress.This framework significantly enhances the predictive accuracy and reduces the computational complexity,offering actionable insights for optimizing hydrogel-based systems.
文摘In Saharan climates,greenhouses face extreme diurnal temperature fluctuations that generate thermal stress,reduce crop productivity,and hinder sustainable agricultural practices.Passive thermal storage using Phase Change Materials(PCM)is a promising solution to stabilize microclimatic conditions.This study aims to evaluate experimentally and numerically the effectiveness of PCM integration for moderating greenhouse temperature fluctuations under Saharan climatic conditions.Two identical greenhouse prototypes were constructed in Ghardaia,Algeria:a reference greenhouse and a PCM-integrated greenhouse using calcium chloride hexahydrate(CaCl_(2)⋅6H_(2)O).Thermal performance was assessed during a five-day experimental period(7–11May 2025)under severe ambient conditions.To complement this,a Nonlinear Auto-Regressive with eXogenous inputs(NARX)neural network model was developed and trained using a larger dataset(7–25 May 2025)to predict greenhouse thermal dynamics.The PCM greenhouse reduced peak daytime air temperature by an average of 8.14℃and decreased the diurnal temperature amplitude by 53.6%compared to the reference greenhouse.The NARX model achieved high predictive accuracy(R^(2)=0.990,RMSE=0.425℃,MAE=0.223℃,MBE=0.008℃),capturing both sensible and latent heat transfer mechanisms,including PCM melting and solidification.The combined experimental and predictive modeling results confirm the potential of PCM integration as an effective passive thermal regulation strategy for greenhouses in arid regions.This approach enhances microclimatic stability,improves energy efficiency,and supports the sustainability of protected agriculture under extreme climatic conditions.
基金This study was supported by the Key Program of Ministry of Education of China (01066)
文摘The temperature-humidity models of wood drying were developed based on Time-delay neural network and the identification structures of Time-delay neural network were given. The controlling model and the schedule model, which revealed the relation between controlling signal and temperature-humidity and the relation between wood moisture content and temperature-humidity of wood drying, were separately presented. The models were simulated by using the measured data of the experimental drying kiln. The numerical simulation results showed that the modeling method was feasible, and the models were effective.
基金The National Natural Science Foundation of China(No.60621002)the National High Technology Research and Development Pro-gram of China(863 Program)(No.2007AA01Z2B4).
文摘A novel behavioral model using three-layer time-delay feed-forward neural networks （TDFFNN）is adopted to model radio frequency （RF）power amplifiers exhibiting memory nonlinearities. In order to extract the parameters, the back- propagation algorithm is applied to train the proposed neural networks. The proposed model is verified by the typical odd- order-only memory polynomial model in simulation, and the performance is compared with different numbers of taped delay lines（TDLs） and perceptrons of the hidden layer. For validating the TDFFNN model by experiments, a digital test bench is set up to collect input and output data of power amplifiers at a 60 × 10^6 sample/s sampling rate. The 3.75 MHz 16-QAM signal generated in the vector signal generator（VSG） is chosen as the input signal, when measuring the dynamic AM/AM and AM/PM characteristics of power amplifiers. By comparisons and analyses, the presented model provides a good performance in convergence, accuracy and efficiency, which is approved by simulation results and experimental results in the time domain and frequency domain.
基金financially supported by the National Natural Science Foundation of China(No.22473032)。
文摘Accelerated aging tests are widely used to rapidly evaluate the durability of materials,of which thermal-oxidative aging is the most common approach.To quantitatively predict the effects of multiple coupled factors,this study takes polyamide66 reinforced with glass fiber(PA66-GF)as a model system and proposed a high-precision paradigm for coupled thermal-oxidative aging.By integrating Arrhenius-type reaction kinetics with oxygen diffusion,a predictive formula that holistically captures the nonlinear synergistic effects of multiple factors was developed,thereby overcoming the limitations of traditional single-variable models.A systematic evaluation of the stepwise improved formulas through nonlinear fitting showed that the coefficient of determination(R^(2))increased from 0.223 to 0.803,elucidating the fundamental reason why conventional approaches fail in quantitative prediction.These formulae were further embedded as physical constraints into a physics-informed neural network(PINN),which further enhanced the predictive performance,with the proposed formula achieving a peak R^(2)of 0.946.The results highlight that robust data fitting alone is insufficient;the decisive factor for the success of PINN lies in whether the embedded formula faithfully reflects the underlying physical mechanisms.When applied to polyamide 6 reinforced with glass fiber(PA6-GF),the Formula-constrained PINN maintained a high level of accuracy(R^(2)=0.916),demonstrating its strong cross-system generalizability.In summary,this work establishes a robust hybrid physics-machine learning framework that combines high accuracy with transferability for predicting the thermal-oxidative aging behavior of composite material systems.
基金supported by the National Natural Science Foundation of China(Grant No.42076214)Natural Science Foundation of Shandong Province(Grant No.ZR2024QD057).
文摘Timely and accurate forecasting of storm surges can effectively prevent typhoon storm surges from causing large economic losses and casualties in coastal areas.At present,numerical model forecasting consumes too many resources and takes too long to compute,while neural network forecasting lacks regional data to train regional forecasting models.In this study,we used the DUAL wind model to build typhoon wind fields,and constructed a typhoon database of 75 processes in the northern South China Sea using the coupled Advanced Circulation-Simulating Waves Nearshore(ADCIRC-SWAN)model.Then,a neural network with a Res-U-Net structure was trained using the typhoon database to forecast the typhoon processes in the validation dataset,and an excellent storm surge forecasting effect was achieved in the Pearl River Estuary region.The storm surge forecasting effect of stronger typhoons was improved by adding a branch structure and transfer learning.
基金funding support from the National Natural Science Foundation of China(Grant Nos.U22A20594,52079045)Hong-Zhi Cui acknowledges the financial support of the China Scholarship Council(Grant No.CSC:202206710014)for his research at Universitat Politecnica de Catalunya,Barcelona.
文摘Landslide susceptibility mapping(LSM)plays a crucial role in assessing geological risks.The current LSM techniques face a significant challenge in achieving accurate results due to uncertainties associated with regional-scale geotechnical parameters.To explore rainfall-induced LSM,this study proposes a hybrid model that combines the physically-based probabilistic model(PPM)with convolutional neural network(CNN).The PPM is capable of effectively capturing the spatial distribution of landslides by incorporating the probability of failure(POF)considering the slope stability mechanism under rainfall conditions.This significantly characterizes the variation of POF caused by parameter uncertainties.CNN was used as a binary classifier to capture the spatial and channel correlation between landslide conditioning factors and the probability of landslide occurrence.OpenCV image enhancement technique was utilized to extract non-landslide points based on the POF of landslides.The proposed model comprehensively considers physical mechanics when selecting non-landslide samples,effectively filtering out samples that do not adhere to physical principles and reduce the risk of overfitting.The results indicate that the proposed PPM-CNN hybrid model presents a higher prediction accuracy,with an area under the curve(AUC)value of 0.85 based on the landslide case of the Niangniangba area of Gansu Province,China compared with the individual CNN model(AUC=0.61)and the PPM(AUC=0.74).This model can also consider the statistical correlation and non-normal probability distributions of model parameters.These results offer practical guidance for future research on rainfall-induced LSM at the regional scale.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-RP23066).
文摘This study directs the discussion of HIV disease with a novel kind of complex dynamical generalized and piecewise operator in the sense of classical and Atangana Baleanu(AB)derivatives having arbitrary order.The HIV infection model has a susceptible class,a recovered class,along with a case of infection divided into three sub-different levels or categories and the recovered class.The total time interval is converted into two,which are further investigated for ordinary and fractional order operators of the AB derivative,respectively.The proposed model is tested separately for unique solutions and existence on bi intervals.The numerical solution of the proposed model is treated by the piece-wise numerical iterative scheme of Newtons Polynomial.The proposed method is established for piece-wise derivatives under natural order and non-singular Mittag-Leffler Law.The cross-over or bending characteristics in the dynamical system of HIV are easily examined by the aspect of this research having a memory effect for controlling the said disease.This study uses the neural network(NN)technique to obtain a better set of weights with low residual errors,and the epochs number is considered 1000.The obtained figures represent the approximate solution and absolute error which are tested with NN to train the data accurately.
基金supported by the Natural Science Foundation of Hunan Province(Grant No.2025JJ50368)the Scientific Research Fund of Hunan Provincial Education Department(Grant No.24A0248)the Guiding Science and Technology Plan Project of Changsha City(Grant No.kzd2501129)。
文摘In recent years,discrete neuron and discrete neural network models have played an important role in the development of neural dynamics.This paper reviews the theoretical advantages of well-known discrete neuron models,some existing discretized continuous neuron models,and discrete neural networks in simulating complex neural dynamics.It places particular emphasis on the importance of memristors in the composition of neural networks,especially their unique memory and nonlinear characteristics.The integration of memristors into discrete neural networks,including Hopfield networks and their fractional-order variants,cellular neural networks and discrete neuron models has enabled the study and construction of various neural models with memory.These models exhibit complex dynamic behaviors,including superchaotic attractors,hidden attractors,multistability,and synchronization transitions.Furthermore,the present paper undertakes an analysis of more complex dynamical properties,including synchronization,speckle patterns,and chimera states in discrete coupled neural networks.This research provides new theoretical foundations and potential applications in the fields of brain-inspired computing,artificial intelligence,image encryption,and biological modeling.
基金Supported by the National Natural Science Foundation of China(U1462206)
文摘System design and optimization problems require large-scale chemical kinetic models. Pure kinetic models of naphtha pyrolysis need to solve a complete set of stiff ODEs and is therefore too computational expensive. On the other hand, artificial neural networks that completely neglect the topology of the reaction networks often have poor generalization. In this paper, a framework is proposed for learning local representations from largescale chemical reaction networks. At first, the features of naphtha pyrolysis reactions are extracted by applying complex network characterization methods. The selected features are then used as inputs in convolutional architectures. Different CNN models are established and compared to optimize the neural network structure.After the pre-training and fine-tuning step, the ultimate CNN model reduces the computational cost of the previous kinetic model by over 300 times and predicts the yields of main products with the average error of less than 3%. The obtained results demonstrate the high efficiency of the proposed framework.
文摘The topology structure of the artificial neural network is an intelligent control model,which is used for the intelligent vehicle control system and household sweeping robot.When setting the intelligent control system,the connection point of each network is regarded as a neuron in the nervous system,and each connection point has input and output functions.Only when the input of nodes reaches a certain threshold can the output function of nodes be stimulated.Using the networking mode of the artificial neural network model,the mobile node can output in multiple directions.If the input direction of a certain path is the same as that of other nodes,it can choose to avoid and choose another path.The weighted value of each path between nodes is different,which means that the influence of the front node on the current node varies.The control method based on the artificial neural network model can be applied to vehicle control,household sweeping robots,and other fields,and a relatively optimized scheme can be obtained from the aspect of time and energy consumption.
基金supported by the National Natural Science Foundation of China(NSFC)(Grant No.12322203).
文摘The constitutive models of shape memory alloys(SMAs)play an important role in facilitating the widespread application of such types of alloys in various engineering fields.However,to accurately describe the deformation behaviors of SMAs,the concepts in classical plasticity are employed in the existing constitutive models,and a series of complex mathematical equations are involved.Such complexity brings inconvenience for the construction,implementation,and application of the constitutive models.To overcome these shortcomings,a data-driven constitutive model of SMAs is developed in this work based on the artificial neural network(ANN).In the proposed model,the components of the strain tensor in principal space,ambient temperature,and the maximum equivalent strain in the deformation history from the initial state to the current loading state are chosen as the input features,and the components of the stress tensor in principal space are set as the output.The proposed ANN-based constitutive model is implemented into the finite element program ABAQUS by deriving its consistent tangent modulus and writing a user-defined material subroutine.The stress-strain responses of SMA material under various loading paths and at different ambient temperatures are used to train the ANN model,which is generated from the existing constitutive model(numerical experiments).To validate the capability of the proposed model,the predicted stress-strain responses of SMA material,and the global and local responses of two typical SMA structures are compared with the corresponding numerical experiments.This work demonstrates a good potential to obtain the constitutive model of SMAs by pure data and avoid the need for vast stores of knowledge for the construction of constitutive models.
基金supported in part by funds from the Ministry of Science and Technology(2022FY101104).
文摘Recognizing essential proteins within bacteriophages is fundamental to uncovering their replication and survival mechanisms and contributes to advances in phage-based antibacterial therapies.Despite notable progress,existing computational techniques struggle to represent the interplay between sequence-derived and structuredependent protein features.To overcome this limitation,we introduce GLM-EP,a unified framework that fuses protein language models with equivariant graph neural networks.Bymerging semantic embeddings extracted from amino acid sequences with geometry-aware graph representations,GLM-EP enables an in-depth depiction of phage proteins and enhances essential protein identification.Evaluation on diverse benchmark datasets confirms that GLM-EP surpasses conventional sequence-based and independent deep-learning methods,yielding higher F1 and AUROC outcomes.Component-wise analysis demonstrates that GCNII,EGNN,and the gated multi-head attention mechanism function in a complementary manner to encode complex molecular attributes.In summary,GLM-EP serves as a robust and efficient tool for bacteriophage genomic analysis and provides valuable methodological perspectives for the discovery of antibiotic-resistance therapeutic targets.The corresponding code repository is available at:https://github.com/MiJia-ID/GLM-EP(accessed on 01 November 2025).
基金supported in part by the National Natural Science Foundation of China under Grants 62102450,62272478 and the Independent Research Project of a Certain Unit under Grant ZZKY20243127。
文摘Traditional steganography conceals information by modifying cover data,but steganalysis tools easily detect such alterations.While deep learning-based steganography often involves high training costs and complex deployment.Diffusion model-based methods face security vulnerabilities,particularly due to potential information leakage during generation.We propose a fixed neural network image steganography framework based on secure diffu-sion models to address these challenges.Unlike conventional approaches,our method minimizes cover modifications through neural network optimization,achieving superior steganographic performance in human visual perception and computer vision analyses.The cover images are generated in an anime style using state-of-the-art diffusion models,ensuring the transmitted images appear more natural.This study introduces fixed neural network technology that allows senders to transmit only minimal critical information alongside stego-images.Recipients can accurately reconstruct secret images using this compact data,significantly reducing transmission overhead compared to conventional deep steganography.Furthermore,our framework innovatively integrates ElGamal,a cryptographic algorithm,to protect critical information during transmission,enhancing overall system security and ensuring end-to-end information protection.This dual optimization of payload reduction and cryptographic reinforcement establishes a new paradigm for secure and efficient image steganography.
基金supported in part by the National Natural Science Foundation of China under Grants No.62372087 and No.62072076the Research Fund of State Key Laboratory of Processors under Grant No.CLQ202310the CSC scholarship.
文摘Deep neural networks(DNNs)have found extensive applications in safety-critical artificial intelligence systems,such as autonomous driving and facial recognition systems.However,recent research has revealed their susceptibility to backdoors maliciously injected by adversaries.This vulnerability arises due to the intricate architecture and opacity of DNNs,resulting in numerous redundant neurons embedded within the models.Adversaries exploit these vulnerabilities to conceal malicious backdoor information within DNNs,thereby causing erroneous outputs and posing substantial threats to the efficacy of DNN-based applications.This article presents a comprehensive survey of backdoor attacks against DNNs and the countermeasure methods employed to mitigate them.Initially,we trace the evolution of the concept from traditional backdoor attacks to backdoor attacks against DNNs,highlighting the feasibility and practicality of generating backdoor attacks against DNNs.Subsequently,we provide an overview of notable works encompassing various attack and defense strategies,facilitating a comparative analysis of their approaches.Through these discussions,we offer constructive insights aimed at refining these techniques.Finally,we extend our research perspective to the domain of large language models(LLMs)and synthesize the characteristics and developmental trends of backdoor attacks and defense methods targeting LLMs.Through a systematic review of existing studies on backdoor vulnerabilities in LLMs,we identify critical open challenges in this field and propose actionable directions for future research.
基金funded by National Key Research and Development Plan(2018YFC0406703)Supported by the National Natural Science Foundation of China(51779277)+4 种基金Chinese Academy of Water Sciences(SD0145B072021)Supported by the State Key Laboratory of Flow Water Cycle Simulation and Regulation,SKL2022ZD05Support provided by the fund of State Key Laboratory of Water Cycle and Water Security,IWHR(Grant No.SKL2024YJZD05)Support provided by the fund of Power China(DJ-ZDXM-2020-50)Support provided by the fund of Research and Application of Intelligent Simulation and Intelligent Control Technology for Structural States of Gravity DAMS in Jingling Reservoir Project,Zhejiang Province(JLSKFW-2024113).
文摘As a critical material in construction engineering,concrete requires accurate prediction of its outlet temperature to ensure structural quality and enhance construction efficiency.This study proposes a novel hybrid prediction method that integrates a heat conduction physical model with a multilayer perceptron(MLP)neural network,dynamically fused via a weighted strategy to achieve high-precision temperature estimation.Experimental results on an independent test set demonstrated the superior performance of the fused model,with a root mean square error(RMSE)of 1.59℃ and a mean absolute error(MAE)of 1.23℃,representing a 25.3%RMSE reduction compared to conventional physical models.Ambient temperature and coarse aggregate temperature were identified as the most influential variables.Furthermore,the model-based temperature control strategy reduced costs by 0.81 CNY/m^(3),showing significant potential for improving resource efficiency and supporting sustainable construction practices.
基金supported by the National Nature Science Foundation of China(NSFC)under grant No.61771194supported by Key Program of Beijing Municipal Natural Science Foundation with No.17L20052
文摘In this work,a frame work for time-varying channel modeling and simulation is proposed by using neural network(NN)to overcome the shortcomings in geometry based stochastic model(GBSM)and simulation approach.Two NN models are developed for modeling of path loss together with shadow fading(SF)and joint small scale channel parameters.The NN models can predict path loss plus SF and small scale channel parameters accurately compared with measurement at 26 GHz performed in an outdoor microcell.The time-varying path loss and small scale channel parameters generated by the NN models are proposed to replace the empirical path loss and channel parameter random numbers in GBSM-based framework to playback the measured channel and match with its environment.Moreover,the sparse feature of clusters,delay and angular spread,channel capacity are investigated by a virtual array measurement at 28 GHz in a large waiting hall.
