Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay di...Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay differential equations(DDEs)and delay integrodifferential equations(DIDEs)with constant delays,primarily due to their low regularity at delayinduced breaking points.In this paper,a DNN method that combines multi-task learning(MTL)which is proposed to solve both the forward and inverse problems of DIDEs.The core idea of this approach is to divide the original equation into multiple tasks based on the delay,using auxiliary outputs to represent the integral terms,followed by the use of MTL to seamlessly incorporate the properties at the breaking points into the loss function.Furthermore,given the increased training dificulty associated with multiple tasks and outputs,we employ a sequential training scheme to reduce training complexity and provide reference solutions for subsequent tasks.This approach significantly enhances the approximation accuracy of solving DIDEs with DNNs,as demonstrated by comparisons with traditional DNN methods.We validate the effectiveness of this method through several numerical experiments,test various parameter sharing structures in MTL and compare the testing results of these structures.Finally,this method is implemented to solve the inverse problem of nonlinear DIDE and the results show that the unknown parameters of DIDE can be discovered with sparse or noisy data.展开更多
For the diagnostics and health management of lithium-ion batteries,numerous models have been developed to understand their degradation characteristics.These models typically fall into two categories:data-driven models...For the diagnostics and health management of lithium-ion batteries,numerous models have been developed to understand their degradation characteristics.These models typically fall into two categories:data-driven models and physical models,each offering unique advantages but also facing limitations.Physics-informed neural networks(PINNs)provide a robust framework to integrate data-driven models with physical principles,ensuring consistency with underlying physics while enabling generalization across diverse operational conditions.This study introduces a PINN-based approach to reconstruct open circuit voltage(OCV)curves and estimate key ageing parameters at both the cell and electrode levels.These parameters include available capacity,electrode capacities,and lithium inventory capacity.The proposed method integrates OCV reconstruction models as functional components into convolutional neural networks(CNNs)and is validated using a public dataset.The results reveal that the estimated ageing parameters closely align with those obtained through offline OCV tests,with errors in reconstructed OCV curves remaining within 15 mV.This demonstrates the ability of the method to deliver fast and accurate degradation diagnostics at the electrode level,advancing the potential for precise and efficient battery health management.展开更多
The increasing popularity of the Internet and the widespread use of information technology have led to a rise in the number and sophistication of network attacks and security threats.Intrusion detection systems are cr...The increasing popularity of the Internet and the widespread use of information technology have led to a rise in the number and sophistication of network attacks and security threats.Intrusion detection systems are crucial to network security,playing a pivotal role in safeguarding networks from potential threats.However,in the context of an evolving landscape of sophisticated and elusive attacks,existing intrusion detection methodologies often overlook critical aspects such as changes in network topology over time and interactions between hosts.To address these issues,this paper proposes a real-time network intrusion detection method based on graph neural networks.The proposedmethod leverages the advantages of graph neural networks and employs a straightforward graph construction method to represent network traffic as dynamic graph-structured data.Additionally,a graph convolution operation with a multi-head attention mechanism is utilized to enhance the model’s ability to capture the intricate relationships within the graph structure comprehensively.Furthermore,it uses an integrated graph neural network to address dynamic graphs’structural and topological changes at different time points and the challenges of edge embedding in intrusion detection data.The edge classification problem is effectively transformed into node classification by employing a line graph data representation,which facilitates fine-grained intrusion detection tasks on dynamic graph node feature representations.The efficacy of the proposed method is evaluated using two commonly used intrusion detection datasets,UNSW-NB15 and NF-ToN-IoT-v2,and results are compared with previous studies in this field.The experimental results demonstrate that our proposed method achieves 99.3%and 99.96%accuracy on the two datasets,respectively,and outperforms the benchmark model in several evaluation metrics.展开更多
This study investigates photonuclear reaction(γ,n)cross-sections using Bayesian neural network(BNN)analysis.After determining the optimal network architecture,which features two hidden layers,each with 50 hidden node...This study investigates photonuclear reaction(γ,n)cross-sections using Bayesian neural network(BNN)analysis.After determining the optimal network architecture,which features two hidden layers,each with 50 hidden nodes,training was conducted for 30,000 iterations to ensure comprehensive data capture.By analyzing the distribution of absolute errors positively correlated with the cross-section for the isotope 159Tb,as well as the relative errors unrelated to the cross-section,we confirmed that the network effectively captured the data features without overfitting.Comparison with the TENDL-2021 Database demonstrated the BNN's reliability in fitting photonuclear cross-sections with lower average errors.The predictions for nuclei with single and double giant dipole resonance peak cross-sections,the accurate determination of the photoneutron reaction threshold in the low-energy region,and the precise description of trends in the high-energy cross-sections further demonstrate the network's generalization ability on the validation set.This can be attributed to the consistency of the training data.By using consistent training sets from different laboratories,Bayesian neural networks can predict nearby unknown cross-sections based on existing laboratory data,thereby estimating the potential differences between other laboratories'existing data and their own measurement results.Experimental measurements of photonuclear reactions on the newly constructed SLEGS beamline will contribute to clarifying the differences in cross-sections within the existing data.展开更多
Spectrum-based fault localization (SBFL) generates a ranked list of suspicious elements by using the program execution spectrum, but the excessive number of elements ranked in parallel results in low localization accu...Spectrum-based fault localization (SBFL) generates a ranked list of suspicious elements by using the program execution spectrum, but the excessive number of elements ranked in parallel results in low localization accuracy. Most researchers consider intra-class dependencies to improve localization accuracy. However, some studies show that inter-class method call type faults account for more than 20%, which means such methods still have certain limitations. To solve the above problems, this paper proposes a two-phase software fault localization based on relational graph convolutional neural networks (Two-RGCNFL). Firstly, in Phase 1, the method call dependence graph (MCDG) of the program is constructed, the intra-class and inter-class dependencies in MCDG are extracted by using the relational graph convolutional neural network, and the classifier is used to identify the faulty methods. Then, the GraphSMOTE algorithm is improved to alleviate the impact of class imbalance on classification accuracy. Aiming at the problem of parallel ranking of element suspicious values in traditional SBFL technology, in Phase 2, Doc2Vec is used to learn static features, while spectrum information serves as dynamic features. A RankNet model based on siamese multi-layer perceptron is constructed to score and rank statements in the faulty method. This work conducts experiments on 5 real projects of Defects4J benchmark. Experimental results show that, compared with the traditional SBFL technique and two baseline methods, our approach improves the Top-1 accuracy by 262.86%, 29.59% and 53.01%, respectively, which verifies the effectiveness of Two-RGCNFL. Furthermore, this work verifies the importance of inter-class dependencies through ablation experiments.展开更多
Real-time prediction and precise control of sinter quality are pivotal for energy saving,cost reduction,quality improvement and efficiency enhancement in the ironmaking process.To advance,the accuracy and comprehensiv...Real-time prediction and precise control of sinter quality are pivotal for energy saving,cost reduction,quality improvement and efficiency enhancement in the ironmaking process.To advance,the accuracy and comprehensiveness of sinter quality prediction,an intelligent flare monitoring system for sintering machine tails that combines hybrid neural networks integrating convolutional neural network with long short-term memory(CNN-LSTM)networks was proposed.The system utilized a high-temperature thermal imager for image acquisition at the sintering machine tail and employed a zone-triggered method to accurately capture dynamic feature images under challenging conditions of high-temperature,high dust,and occlusion.The feature images were then segmented through a triple-iteration multi-thresholding approach based on the maximum between-class variance method to minimize detail loss during the segmentation process.Leveraging the advantages of CNN and LSTM networks in capturing temporal and spatial information,a comprehensive model for sinter quality prediction was constructed,with inputs including the proportion of combustion layer,porosity rate,temperature distribution,and image features obtained from the convolutional neural network,and outputs comprising quality indicators such as underburning index,uniformity index,and FeO content of the sinter.The accuracy is notably increased,achieving a 95.8%hit rate within an error margin of±1.0.After the system is applied,the average qualified rate of FeO content increases from 87.24%to 89.99%,representing an improvement of 2.75%.The average monthly solid fuel consumption is reduced from 49.75 to 46.44 kg/t,leading to a 6.65%reduction and underscoring significant energy saving and cost reduction effects.展开更多
Severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)mutations are influenced by random and uncontrollable factors,and the risk of the next widespread epidemic remains.Dual-target drugs that synergistically act ...Severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)mutations are influenced by random and uncontrollable factors,and the risk of the next widespread epidemic remains.Dual-target drugs that synergistically act on two targets exhibit strong therapeutic effects and advantages against mutations.In this study,a novel computational workflow was developed to design dual-target SARS-CoV-2 candidate inhibitors with the Envelope protein and Main protease selected as the two target proteins.The drug-like molecules of our self-constructed 3D scaffold database were used as high-throughput molecular docking probes for feature extraction of two target protein pockets.A multi-layer perceptron(MLP)was employed to embed the binding affinities into a latent space as conditional vectors to control conditional distribution.Utilizing a conditional generative neural network,cG-SchNet,with 3D Euclidean group(E3)symmetries,the conditional probability distributions of molecular 3D structures were acquired and a set of novel SARS-CoV-2 dual-target candidate inhibitors were generated.The 1D probability,2D joint probability,and 2D cumulative probability distribution results indicate that the generated sets are significantly enhanced compared to the training set in the high binding affinity area.Among the 201 generated molecules,42 molecules exhibited a sum binding affinity exceeding 17.0 kcal/mol while 9 of them having a sum binding affinity exceeding 19.0 kcal/mol,demonstrating structure diversity along with strong dual-target affinities,good absorption,distribution,metabolism,excretion,and toxicity(ADMET)properties,and ease of synthesis.Dual-target drugs are rare and difficult to find,and our“high-throughput docking-multi-conditional generation”workflow offers a wide range of options for designing or optimizing potent dual-target SARS-CoV-2 inhibitors.展开更多
Accurate estimation of mineralogy from geophysical well logs is crucial for characterizing geological formations,particularly in hydrocarbon exploration,CO_(2) sequestration,and geothermal energy development.Current t...Accurate estimation of mineralogy from geophysical well logs is crucial for characterizing geological formations,particularly in hydrocarbon exploration,CO_(2) sequestration,and geothermal energy development.Current techniques,such as multimineral petrophysical analysis,offer details into mineralogical distribution.However,it is inherently time-intensive and demands substantial geological expertise for accurate model evaluation.Furthermore,traditional machine learning techniques often struggle to predict mineralogy accurately and sometimes produce estimations that violate fundamental physical principles.To address this,we present a new approach using Physics-Integrated Neural Networks(PINNs),that combines data-driven learning with domain-specific physical constraints,embedding petrophysical relationships directly into the neural network architecture.This approach enforces that predictions adhere to physical laws.The methodology is applied to the Broom Creek Deep Saline aquifer,a CO_(2) sequestration site in the Williston Basin,to predict the volumes of key mineral constituents—quartz,dolomite,feldspar,anhydrite,illite—along with porosity.Compared to traditional artificial neural networks (ANN),the PINN approach demonstrates higher accuracy and better generalizability,significantly enhancing predictive performance on unseen well datasets.The average mean error across the three blind wells is 0.123 for ANN and 0.042 for PINN,highlighting the superior accuracy of the PINN approach.This method reduces uncertainties in reservoir characterization by improving the reliability of mineralogy and porosity predictions,providing a more robust tool for decision-making in various subsurface geoscience applications.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
BACKGROUND Mitochondrial genes are involved in tumor metabolism in ovarian cancer(OC)and affect immune cell infiltration and treatment responses.AIM To predict prognosis and immunotherapy response in patients diagnose...BACKGROUND Mitochondrial genes are involved in tumor metabolism in ovarian cancer(OC)and affect immune cell infiltration and treatment responses.AIM To predict prognosis and immunotherapy response in patients diagnosed with OC using mitochondrial genes and neural networks.METHODS Prognosis,immunotherapy efficacy,and next-generation sequencing data of patients with OC were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus.Mitochondrial genes were sourced from the MitoCarta3.0 database.The discovery cohort for model construction was created from 70% of the patients,whereas the remaining 30% constituted the validation cohort.Using the expression of mitochondrial genes as the predictor variable and based on neural network algorithm,the overall survival time and immunotherapy efficacy(complete or partial response)of patients were predicted.RESULTS In total,375 patients with OC were included to construct the prognostic model,and 26 patients were included to construct the immune efficacy model.The average area under the receiver operating characteristic curve of the prognostic model was 0.7268[95% confidence interval(CI):0.7258-0.7278]in the discovery cohort and 0.6475(95%CI:0.6466-0.6484)in the validation cohort.The average area under the receiver operating characteristic curve of the immunotherapy efficacy model was 0.9444(95%CI:0.8333-1.0000)in the discovery cohort and 0.9167(95%CI:0.6667-1.0000)in the validation cohort.CONCLUSION The application of mitochondrial genes and neural networks has the potential to predict prognosis and immunotherapy response in patients with OC,providing valuable insights into personalized treatment strategies.展开更多
Human disturbance activities is one of the main reasons for inducing geohazards.Ecological impact assessment metrics of roads are inconsistent criteria and multiple.From the perspective of visual observation,the envir...Human disturbance activities is one of the main reasons for inducing geohazards.Ecological impact assessment metrics of roads are inconsistent criteria and multiple.From the perspective of visual observation,the environment damage can be shown through detecting the uncovered area of vegetation in the images along road.To realize this,an end-to-end environment damage detection model based on convolutional neural network is proposed.A 50-layer residual network is used to extract feature map.The initial parameters are optimized by transfer learning.An example is shown by this method.The dataset including cliff and landslide damage are collected by us along road in Shennongjia national forest park.Results show 0.4703 average precision(AP)rating for cliff damage and 0.4809 average precision(AP)rating for landslide damage.Compared with YOLOv3,our model shows a better accuracy in cliff and landslide detection although a certain amount of speed is sacrificed.展开更多
In this paper,we propose a neural network approach to learn the parameters of a class of stochastic Lotka-Volterra systems.Approximations of the mean and covariance matrix of the observational variables are obtained f...In this paper,we propose a neural network approach to learn the parameters of a class of stochastic Lotka-Volterra systems.Approximations of the mean and covariance matrix of the observational variables are obtained from the Euler-Maruyama discretization of the underlying stochastic differential equations(SDEs),based on which the loss function is built.The stochastic gradient descent method is applied in the neural network training.Numerical experiments demonstrate the effectiveness of our method.展开更多
The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and hist...The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and historical context,DL offers a powerful complement by enhancing the speed,objectivity,and precision of the classification process.This research explores the significance of image data augmentation techniques in optimizing the performance of convolutional neural networks(CNNs)for geological image analysis,particularly in the classification of igneous,metamorphic,and sedimentary rock types from rock thin section(RTS)images.This study primarily focuses on classic image augmentation techniques and evaluates their impact on model accuracy and precision.Results demonstrate that augmentation techniques like Equalize significantly enhance the model's classification capabilities,achieving an F1-Score of 0.9869 for igneous rocks,0.9884 for metamorphic rocks,and 0.9929 for sedimentary rocks,representing improvements compared to the baseline original results.Moreover,the weighted average F1-Score across all classes and techniques is 0.9886,indicating an enhancement.Conversely,methods like Distort lead to decreased accuracy and F1-Score,with an F1-Score of 0.949 for igneous rocks,0.954 for metamorphic rocks,and 0.9416 for sedimentary rocks,exacerbating the performance compared to the baseline.The study underscores the practicality of image data augmentation in geological image classification and advocates for the adoption of DL methods in this domain for automation and improved results.The findings of this study can benefit various fields,including remote sensing,mineral exploration,and environmental monitoring,by enhancing the accuracy of geological image analysis both for scientific research and industrial applications.展开更多
Reverse design of highly GeO2-doped silica optical fibers with broadband and flat dispersion profiles is proposed using a neural network(NN) combined with a particle swarm optimization(PSO) algorithm.Firstly,the NN mo...Reverse design of highly GeO2-doped silica optical fibers with broadband and flat dispersion profiles is proposed using a neural network(NN) combined with a particle swarm optimization(PSO) algorithm.Firstly,the NN model designed to predict optical fiber dispersion is trained with an appropriate choice of hyperparameters,achieving a root mean square error(RMSE) of 9.47×10-7on the test dataset,with a determination coefficient(R2) of 0.999.Secondly,the NN is combined with the PSO algorithm for the inverse design of dispersion-flattened optical fibers.To expand the search space and avoid particles becoming trapped in local optimal solutions,the PSO algorithm incorporates adaptive inertia weight updating and a simulated annealing algorithm.Finally,by using a suitable fitness function,the designed fibers exhibit flat group velocity dispersion(GVD) profiles at 1 400—2 400 nm,where the GVD fluctuations and minimum absolute GVD values are below 18 ps·nm-1·km-1and 7 ps·nm-1·km-1,respectively.展开更多
Computing free energy is a fundamental problem in statistical physics.Recently,two distinct methods have been developed and have demonstrated remarkable success:the tensor-network-based contraction method and the neur...Computing free energy is a fundamental problem in statistical physics.Recently,two distinct methods have been developed and have demonstrated remarkable success:the tensor-network-based contraction method and the neural-network-based variational method.Tensor networks are accurate,but their application is often limited to low-dimensional systems due to the high computational complexity in high-dimensional systems.The neural network method applies to systems with general topology.However,as a variational method,it is not as accurate as tensor networks.In this work,we propose an integrated approach,tensor-network-based variational autoregressive networks(TNVAN),that leverages the strengths of both tensor networks and neural networks:combining the variational autoregressive neural network’s ability to compute an upper bound on free energy and perform unbiased sampling from the variational distribution with the tensor network’s power to accurately compute the partition function for small sub-systems,resulting in a robust method for precisely estimating free energy.To evaluate the proposed approach,we conducted numerical experiments on spin glass systems with various topologies,including two-dimensional lattices,fully connected graphs,and random graphs.Our numerical results demonstrate the superior accuracy of our method compared to existing approaches.In particular,it effectively handles systems with longrange interactions and leverages GPU efficiency without requiring singular value decomposition,indicating great potential in tackling statistical mechanics problems and simulating high-dimensional complex systems through both tensor networks and neural networks.展开更多
In this paper,a new study concerning the usage of artificial neural networks in the control application is given.It is shown,that the data gathered during proper operation of a given control plant can be used in the l...In this paper,a new study concerning the usage of artificial neural networks in the control application is given.It is shown,that the data gathered during proper operation of a given control plant can be used in the learning process to fully embrace the control pattern.Interestingly,the instances driven by neural networks have the ability to outperform the original analytically driven scenarios.Three different control schemes,namely perfect,linear-quadratic,and generalized predictive controllers were used in the theoretical study.In addition,the nonlinear recurrent neural network-based generalized predictive controller with the radial basis function-originated predictor was obtained to exemplify the main results of the paper regarding the real-world application.展开更多
Recently,a novel type of neural networks,known as liquid neural networks(LNNs),has been designed from first principles to address robustness and interpretability challenges facing artificial intelligence(AI)solutions....Recently,a novel type of neural networks,known as liquid neural networks(LNNs),has been designed from first principles to address robustness and interpretability challenges facing artificial intelligence(AI)solutions.The potential of LNNs in telecommunications is explored in this paper.First,we illustrate the mechanisms of LNNs and highlight their unique advantages over traditional networks.Then we explore the opportunities that LNNs bring to future wireless networks.Furthermore,we discuss the challenges and design directions for the implementation of LNNs.Finally,we summarize the performance of LNNs in two case studies.展开更多
Schizophrenia(SZ)stands as a severe psychiatric disorder.This study applied diffusion tensor imaging(DTI)data in conjunction with graph neural networks to distinguish SZ patients from normal controls(NCs)and showcases...Schizophrenia(SZ)stands as a severe psychiatric disorder.This study applied diffusion tensor imaging(DTI)data in conjunction with graph neural networks to distinguish SZ patients from normal controls(NCs)and showcases the superior performance of a graph neural network integrating combined fractional anisotropy and fiber number brain network features,achieving an accuracy of 73.79%in distinguishing SZ patients from NCs.Beyond mere discrimination,our study delved deeper into the advantages of utilizing white matter brain network features for identifying SZ patients through interpretable model analysis and gene expression analysis.These analyses uncovered intricate interrelationships between brain imaging markers and genetic biomarkers,providing novel insights into the neuropathological basis of SZ.In summary,our findings underscore the potential of graph neural networks applied to multimodal DTI data for enhancing SZ detection through an integrated analysis of neuroimaging and genetic features.展开更多
Spiking neural networks(SNNs)represent a biologically-inspired computational framework that bridges neuroscience and artificial intelligence,offering unique advantages in temporal data processing,energy efficiency,and...Spiking neural networks(SNNs)represent a biologically-inspired computational framework that bridges neuroscience and artificial intelligence,offering unique advantages in temporal data processing,energy efficiency,and real-time decision-making.This paper explores the evolution of SNN technologies,emphasizing their integration with advanced learning mechanisms such as spike-timing-dependent plasticity(STDP)and hybridization with deep learning architectures.Leveraging memristors as nanoscale synaptic devices,we demonstrate significant enhancements in energy efficiency,adaptability,and scalability,addressing key challenges in neuromorphic computing.Through phase portraits and nonlinear dynamics analysis,we validate the system’s stability and robustness under diverse workloads.These advancements position SNNs as a transformative technology for applications in robotics,IoT,and adaptive low-power AI systems,paving the way for future innovations in neuromorphic hardware and hybrid learning paradigms.展开更多
This study investigates the load-bearing capacity of open-ended pipe piles in sandy soil, with a specific focus on the impact of soil plug constraints at four levels(no plug, 25% plug, 50% plug, and full plug). Levera...This study investigates the load-bearing capacity of open-ended pipe piles in sandy soil, with a specific focus on the impact of soil plug constraints at four levels(no plug, 25% plug, 50% plug, and full plug). Leveraging a dataset comprising open-ended pipe piles with varying geometrical and geotechnical properties, this research employs shallow neural network(SNN) and deep neural network(DNN) models to predict plugging conditions for both driven and pressed installation types. This paper underscores the importance of key parameters such as the settlement value,applied load, installation type, and soil configuration(loose, medium, and dense) in accurately predicting pile settlement. These findings offer valuable insights for optimizing pile design and construction in geotechnical engineering,addressing a longstanding challenge in the field. The study demonstrates the potential of the SNN and DNN models in precisely identifying plugging conditions before pile driving, with the SNN achieving R2 values ranging from0.444 to 0.711 and RMSPE values ranging from 24.621% to 48.663%, whereas the DNN exhibits superior performance, with R2 values ranging from 0.815 to 0.942 and RMSPE values ranging from 4.419% to 10.325%. These results have significant implications for enhancing construction practices and reducing uncertainties associated with pile foundation projects in addition to leveraging artificial intelligence tools to avoid long experimental procedures.展开更多
文摘Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay differential equations(DDEs)and delay integrodifferential equations(DIDEs)with constant delays,primarily due to their low regularity at delayinduced breaking points.In this paper,a DNN method that combines multi-task learning(MTL)which is proposed to solve both the forward and inverse problems of DIDEs.The core idea of this approach is to divide the original equation into multiple tasks based on the delay,using auxiliary outputs to represent the integral terms,followed by the use of MTL to seamlessly incorporate the properties at the breaking points into the loss function.Furthermore,given the increased training dificulty associated with multiple tasks and outputs,we employ a sequential training scheme to reduce training complexity and provide reference solutions for subsequent tasks.This approach significantly enhances the approximation accuracy of solving DIDEs with DNNs,as demonstrated by comparisons with traditional DNN methods.We validate the effectiveness of this method through several numerical experiments,test various parameter sharing structures in MTL and compare the testing results of these structures.Finally,this method is implemented to solve the inverse problem of nonlinear DIDE and the results show that the unknown parameters of DIDE can be discovered with sparse or noisy data.
基金supported by the Beijing Natural Science Foundation(Grant No.L223013)。
文摘For the diagnostics and health management of lithium-ion batteries,numerous models have been developed to understand their degradation characteristics.These models typically fall into two categories:data-driven models and physical models,each offering unique advantages but also facing limitations.Physics-informed neural networks(PINNs)provide a robust framework to integrate data-driven models with physical principles,ensuring consistency with underlying physics while enabling generalization across diverse operational conditions.This study introduces a PINN-based approach to reconstruct open circuit voltage(OCV)curves and estimate key ageing parameters at both the cell and electrode levels.These parameters include available capacity,electrode capacities,and lithium inventory capacity.The proposed method integrates OCV reconstruction models as functional components into convolutional neural networks(CNNs)and is validated using a public dataset.The results reveal that the estimated ageing parameters closely align with those obtained through offline OCV tests,with errors in reconstructed OCV curves remaining within 15 mV.This demonstrates the ability of the method to deliver fast and accurate degradation diagnostics at the electrode level,advancing the potential for precise and efficient battery health management.
文摘The increasing popularity of the Internet and the widespread use of information technology have led to a rise in the number and sophistication of network attacks and security threats.Intrusion detection systems are crucial to network security,playing a pivotal role in safeguarding networks from potential threats.However,in the context of an evolving landscape of sophisticated and elusive attacks,existing intrusion detection methodologies often overlook critical aspects such as changes in network topology over time and interactions between hosts.To address these issues,this paper proposes a real-time network intrusion detection method based on graph neural networks.The proposedmethod leverages the advantages of graph neural networks and employs a straightforward graph construction method to represent network traffic as dynamic graph-structured data.Additionally,a graph convolution operation with a multi-head attention mechanism is utilized to enhance the model’s ability to capture the intricate relationships within the graph structure comprehensively.Furthermore,it uses an integrated graph neural network to address dynamic graphs’structural and topological changes at different time points and the challenges of edge embedding in intrusion detection data.The edge classification problem is effectively transformed into node classification by employing a line graph data representation,which facilitates fine-grained intrusion detection tasks on dynamic graph node feature representations.The efficacy of the proposed method is evaluated using two commonly used intrusion detection datasets,UNSW-NB15 and NF-ToN-IoT-v2,and results are compared with previous studies in this field.The experimental results demonstrate that our proposed method achieves 99.3%and 99.96%accuracy on the two datasets,respectively,and outperforms the benchmark model in several evaluation metrics.
基金supported by National key research and development program(No.2022YFA1602404)the National Natural Science Foundation of China(Nos.12388102,12275338,12005280)the Key Laboratory of Nuclear Data foundation(No.JCKY2022201C152)。
文摘This study investigates photonuclear reaction(γ,n)cross-sections using Bayesian neural network(BNN)analysis.After determining the optimal network architecture,which features two hidden layers,each with 50 hidden nodes,training was conducted for 30,000 iterations to ensure comprehensive data capture.By analyzing the distribution of absolute errors positively correlated with the cross-section for the isotope 159Tb,as well as the relative errors unrelated to the cross-section,we confirmed that the network effectively captured the data features without overfitting.Comparison with the TENDL-2021 Database demonstrated the BNN's reliability in fitting photonuclear cross-sections with lower average errors.The predictions for nuclei with single and double giant dipole resonance peak cross-sections,the accurate determination of the photoneutron reaction threshold in the low-energy region,and the precise description of trends in the high-energy cross-sections further demonstrate the network's generalization ability on the validation set.This can be attributed to the consistency of the training data.By using consistent training sets from different laboratories,Bayesian neural networks can predict nearby unknown cross-sections based on existing laboratory data,thereby estimating the potential differences between other laboratories'existing data and their own measurement results.Experimental measurements of photonuclear reactions on the newly constructed SLEGS beamline will contribute to clarifying the differences in cross-sections within the existing data.
基金funded by the Youth Fund of the National Natural Science Foundation of China(Grant No.42261070).
文摘Spectrum-based fault localization (SBFL) generates a ranked list of suspicious elements by using the program execution spectrum, but the excessive number of elements ranked in parallel results in low localization accuracy. Most researchers consider intra-class dependencies to improve localization accuracy. However, some studies show that inter-class method call type faults account for more than 20%, which means such methods still have certain limitations. To solve the above problems, this paper proposes a two-phase software fault localization based on relational graph convolutional neural networks (Two-RGCNFL). Firstly, in Phase 1, the method call dependence graph (MCDG) of the program is constructed, the intra-class and inter-class dependencies in MCDG are extracted by using the relational graph convolutional neural network, and the classifier is used to identify the faulty methods. Then, the GraphSMOTE algorithm is improved to alleviate the impact of class imbalance on classification accuracy. Aiming at the problem of parallel ranking of element suspicious values in traditional SBFL technology, in Phase 2, Doc2Vec is used to learn static features, while spectrum information serves as dynamic features. A RankNet model based on siamese multi-layer perceptron is constructed to score and rank statements in the faulty method. This work conducts experiments on 5 real projects of Defects4J benchmark. Experimental results show that, compared with the traditional SBFL technique and two baseline methods, our approach improves the Top-1 accuracy by 262.86%, 29.59% and 53.01%, respectively, which verifies the effectiveness of Two-RGCNFL. Furthermore, this work verifies the importance of inter-class dependencies through ablation experiments.
基金founded by the Open Project Program of Anhui Province Key Laboratory of Metallurgical Engineering and Resources Recycling(Anhui University of Technology)(No.SKF21-06)Research Fund for Young Teachers of Anhui University of Technology in 2020(No.QZ202001).
文摘Real-time prediction and precise control of sinter quality are pivotal for energy saving,cost reduction,quality improvement and efficiency enhancement in the ironmaking process.To advance,the accuracy and comprehensiveness of sinter quality prediction,an intelligent flare monitoring system for sintering machine tails that combines hybrid neural networks integrating convolutional neural network with long short-term memory(CNN-LSTM)networks was proposed.The system utilized a high-temperature thermal imager for image acquisition at the sintering machine tail and employed a zone-triggered method to accurately capture dynamic feature images under challenging conditions of high-temperature,high dust,and occlusion.The feature images were then segmented through a triple-iteration multi-thresholding approach based on the maximum between-class variance method to minimize detail loss during the segmentation process.Leveraging the advantages of CNN and LSTM networks in capturing temporal and spatial information,a comprehensive model for sinter quality prediction was constructed,with inputs including the proportion of combustion layer,porosity rate,temperature distribution,and image features obtained from the convolutional neural network,and outputs comprising quality indicators such as underburning index,uniformity index,and FeO content of the sinter.The accuracy is notably increased,achieving a 95.8%hit rate within an error margin of±1.0.After the system is applied,the average qualified rate of FeO content increases from 87.24%to 89.99%,representing an improvement of 2.75%.The average monthly solid fuel consumption is reduced from 49.75 to 46.44 kg/t,leading to a 6.65%reduction and underscoring significant energy saving and cost reduction effects.
基金supported by Interdisciplinary Innova-tion Project of“Bioarchaeology Laboratory”of Jilin University,China,and“MedicineþX”Interdisciplinary Innovation Team of Norman Bethune Health Science Center of Jilin University,China(Grant No.:2022JBGS05).
文摘Severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)mutations are influenced by random and uncontrollable factors,and the risk of the next widespread epidemic remains.Dual-target drugs that synergistically act on two targets exhibit strong therapeutic effects and advantages against mutations.In this study,a novel computational workflow was developed to design dual-target SARS-CoV-2 candidate inhibitors with the Envelope protein and Main protease selected as the two target proteins.The drug-like molecules of our self-constructed 3D scaffold database were used as high-throughput molecular docking probes for feature extraction of two target protein pockets.A multi-layer perceptron(MLP)was employed to embed the binding affinities into a latent space as conditional vectors to control conditional distribution.Utilizing a conditional generative neural network,cG-SchNet,with 3D Euclidean group(E3)symmetries,the conditional probability distributions of molecular 3D structures were acquired and a set of novel SARS-CoV-2 dual-target candidate inhibitors were generated.The 1D probability,2D joint probability,and 2D cumulative probability distribution results indicate that the generated sets are significantly enhanced compared to the training set in the high binding affinity area.Among the 201 generated molecules,42 molecules exhibited a sum binding affinity exceeding 17.0 kcal/mol while 9 of them having a sum binding affinity exceeding 19.0 kcal/mol,demonstrating structure diversity along with strong dual-target affinities,good absorption,distribution,metabolism,excretion,and toxicity(ADMET)properties,and ease of synthesis.Dual-target drugs are rare and difficult to find,and our“high-throughput docking-multi-conditional generation”workflow offers a wide range of options for designing or optimizing potent dual-target SARS-CoV-2 inhibitors.
基金the North Dakota Industrial Commission (NDIC) for their financial supportprovided by the University of North Dakota Computational Research Center。
文摘Accurate estimation of mineralogy from geophysical well logs is crucial for characterizing geological formations,particularly in hydrocarbon exploration,CO_(2) sequestration,and geothermal energy development.Current techniques,such as multimineral petrophysical analysis,offer details into mineralogical distribution.However,it is inherently time-intensive and demands substantial geological expertise for accurate model evaluation.Furthermore,traditional machine learning techniques often struggle to predict mineralogy accurately and sometimes produce estimations that violate fundamental physical principles.To address this,we present a new approach using Physics-Integrated Neural Networks(PINNs),that combines data-driven learning with domain-specific physical constraints,embedding petrophysical relationships directly into the neural network architecture.This approach enforces that predictions adhere to physical laws.The methodology is applied to the Broom Creek Deep Saline aquifer,a CO_(2) sequestration site in the Williston Basin,to predict the volumes of key mineral constituents—quartz,dolomite,feldspar,anhydrite,illite—along with porosity.Compared to traditional artificial neural networks (ANN),the PINN approach demonstrates higher accuracy and better generalizability,significantly enhancing predictive performance on unseen well datasets.The average mean error across the three blind wells is 0.123 for ANN and 0.042 for PINN,highlighting the superior accuracy of the PINN approach.This method reduces uncertainties in reservoir characterization by improving the reliability of mineralogy and porosity predictions,providing a more robust tool for decision-making in various subsurface geoscience applications.
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 Key Technology Research and Developmental Program of China,No.2022YFC2704400 and No.2022YFC2704405.
文摘BACKGROUND Mitochondrial genes are involved in tumor metabolism in ovarian cancer(OC)and affect immune cell infiltration and treatment responses.AIM To predict prognosis and immunotherapy response in patients diagnosed with OC using mitochondrial genes and neural networks.METHODS Prognosis,immunotherapy efficacy,and next-generation sequencing data of patients with OC were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus.Mitochondrial genes were sourced from the MitoCarta3.0 database.The discovery cohort for model construction was created from 70% of the patients,whereas the remaining 30% constituted the validation cohort.Using the expression of mitochondrial genes as the predictor variable and based on neural network algorithm,the overall survival time and immunotherapy efficacy(complete or partial response)of patients were predicted.RESULTS In total,375 patients with OC were included to construct the prognostic model,and 26 patients were included to construct the immune efficacy model.The average area under the receiver operating characteristic curve of the prognostic model was 0.7268[95% confidence interval(CI):0.7258-0.7278]in the discovery cohort and 0.6475(95%CI:0.6466-0.6484)in the validation cohort.The average area under the receiver operating characteristic curve of the immunotherapy efficacy model was 0.9444(95%CI:0.8333-1.0000)in the discovery cohort and 0.9167(95%CI:0.6667-1.0000)in the validation cohort.CONCLUSION The application of mitochondrial genes and neural networks has the potential to predict prognosis and immunotherapy response in patients with OC,providing valuable insights into personalized treatment strategies.
文摘Human disturbance activities is one of the main reasons for inducing geohazards.Ecological impact assessment metrics of roads are inconsistent criteria and multiple.From the perspective of visual observation,the environment damage can be shown through detecting the uncovered area of vegetation in the images along road.To realize this,an end-to-end environment damage detection model based on convolutional neural network is proposed.A 50-layer residual network is used to extract feature map.The initial parameters are optimized by transfer learning.An example is shown by this method.The dataset including cliff and landslide damage are collected by us along road in Shennongjia national forest park.Results show 0.4703 average precision(AP)rating for cliff damage and 0.4809 average precision(AP)rating for landslide damage.Compared with YOLOv3,our model shows a better accuracy in cliff and landslide detection although a certain amount of speed is sacrificed.
基金Supported by the National Natural Science Foundation of China(11971458,11471310)。
文摘In this paper,we propose a neural network approach to learn the parameters of a class of stochastic Lotka-Volterra systems.Approximations of the mean and covariance matrix of the observational variables are obtained from the Euler-Maruyama discretization of the underlying stochastic differential equations(SDEs),based on which the loss function is built.The stochastic gradient descent method is applied in the neural network training.Numerical experiments demonstrate the effectiveness of our method.
文摘The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and historical context,DL offers a powerful complement by enhancing the speed,objectivity,and precision of the classification process.This research explores the significance of image data augmentation techniques in optimizing the performance of convolutional neural networks(CNNs)for geological image analysis,particularly in the classification of igneous,metamorphic,and sedimentary rock types from rock thin section(RTS)images.This study primarily focuses on classic image augmentation techniques and evaluates their impact on model accuracy and precision.Results demonstrate that augmentation techniques like Equalize significantly enhance the model's classification capabilities,achieving an F1-Score of 0.9869 for igneous rocks,0.9884 for metamorphic rocks,and 0.9929 for sedimentary rocks,representing improvements compared to the baseline original results.Moreover,the weighted average F1-Score across all classes and techniques is 0.9886,indicating an enhancement.Conversely,methods like Distort lead to decreased accuracy and F1-Score,with an F1-Score of 0.949 for igneous rocks,0.954 for metamorphic rocks,and 0.9416 for sedimentary rocks,exacerbating the performance compared to the baseline.The study underscores the practicality of image data augmentation in geological image classification and advocates for the adoption of DL methods in this domain for automation and improved results.The findings of this study can benefit various fields,including remote sensing,mineral exploration,and environmental monitoring,by enhancing the accuracy of geological image analysis both for scientific research and industrial applications.
基金supported by the Fundamental Research Funds for the Central Universities (No.2024JBZY021)the National Natural Science Foundation of China (No.61575018)。
文摘Reverse design of highly GeO2-doped silica optical fibers with broadband and flat dispersion profiles is proposed using a neural network(NN) combined with a particle swarm optimization(PSO) algorithm.Firstly,the NN model designed to predict optical fiber dispersion is trained with an appropriate choice of hyperparameters,achieving a root mean square error(RMSE) of 9.47×10-7on the test dataset,with a determination coefficient(R2) of 0.999.Secondly,the NN is combined with the PSO algorithm for the inverse design of dispersion-flattened optical fibers.To expand the search space and avoid particles becoming trapped in local optimal solutions,the PSO algorithm incorporates adaptive inertia weight updating and a simulated annealing algorithm.Finally,by using a suitable fitness function,the designed fibers exhibit flat group velocity dispersion(GVD) profiles at 1 400—2 400 nm,where the GVD fluctuations and minimum absolute GVD values are below 18 ps·nm-1·km-1and 7 ps·nm-1·km-1,respectively.
基金supported by Projects 12325501,12047503,and 12247104 of the National Natural Science Foundation of ChinaProject ZDRW-XX-2022-3-02 of the Chinese Academy of Sciencessupported by the Innovation Program for Quantum Science and Technology project 2021ZD0301900。
文摘Computing free energy is a fundamental problem in statistical physics.Recently,two distinct methods have been developed and have demonstrated remarkable success:the tensor-network-based contraction method and the neural-network-based variational method.Tensor networks are accurate,but their application is often limited to low-dimensional systems due to the high computational complexity in high-dimensional systems.The neural network method applies to systems with general topology.However,as a variational method,it is not as accurate as tensor networks.In this work,we propose an integrated approach,tensor-network-based variational autoregressive networks(TNVAN),that leverages the strengths of both tensor networks and neural networks:combining the variational autoregressive neural network’s ability to compute an upper bound on free energy and perform unbiased sampling from the variational distribution with the tensor network’s power to accurately compute the partition function for small sub-systems,resulting in a robust method for precisely estimating free energy.To evaluate the proposed approach,we conducted numerical experiments on spin glass systems with various topologies,including two-dimensional lattices,fully connected graphs,and random graphs.Our numerical results demonstrate the superior accuracy of our method compared to existing approaches.In particular,it effectively handles systems with longrange interactions and leverages GPU efficiency without requiring singular value decomposition,indicating great potential in tackling statistical mechanics problems and simulating high-dimensional complex systems through both tensor networks and neural networks.
文摘In this paper,a new study concerning the usage of artificial neural networks in the control application is given.It is shown,that the data gathered during proper operation of a given control plant can be used in the learning process to fully embrace the control pattern.Interestingly,the instances driven by neural networks have the ability to outperform the original analytically driven scenarios.Three different control schemes,namely perfect,linear-quadratic,and generalized predictive controllers were used in the theoretical study.In addition,the nonlinear recurrent neural network-based generalized predictive controller with the radial basis function-originated predictor was obtained to exemplify the main results of the paper regarding the real-world application.
基金supported by the China National Key R&D Program under Grant Nos.2021YFA1000500 and 2023YFB2904804National Natural Science Foundation of China under Grant Nos.62331023,62101492,62394292 and U20A20158+1 种基金Zhejiang Provincial Natural Science Foundation of China under Grant No.LR22F010002Zhejiang Provincial Science and Technology Plan Project under Grant No.2024C01033。
文摘Recently,a novel type of neural networks,known as liquid neural networks(LNNs),has been designed from first principles to address robustness and interpretability challenges facing artificial intelligence(AI)solutions.The potential of LNNs in telecommunications is explored in this paper.First,we illustrate the mechanisms of LNNs and highlight their unique advantages over traditional networks.Then we explore the opportunities that LNNs bring to future wireless networks.Furthermore,we discuss the challenges and design directions for the implementation of LNNs.Finally,we summarize the performance of LNNs in two case studies.
基金supported by the National Natural Science Foundation of China(62276049,61701078,61872068,and 62006038)the Natural Science Foundation of Sichuan Province(2025ZNSFSC0487)+3 种基金the Science and Technology Innovation 2030-Brain Science and Brain-Inspired Intelligence Project(2021ZD0200200)the National Key R&D Program of China(2023YFE0118600)Sichuan Province Science and Technology Support Program(2019YJ0193,2021YFG0126,2021YFG0366,and 2022YFS0180)Medico-Engineering Cooperation Funds from the University of Electronic Science and Technology of China(ZYGX2021YGLH014).
文摘Schizophrenia(SZ)stands as a severe psychiatric disorder.This study applied diffusion tensor imaging(DTI)data in conjunction with graph neural networks to distinguish SZ patients from normal controls(NCs)and showcases the superior performance of a graph neural network integrating combined fractional anisotropy and fiber number brain network features,achieving an accuracy of 73.79%in distinguishing SZ patients from NCs.Beyond mere discrimination,our study delved deeper into the advantages of utilizing white matter brain network features for identifying SZ patients through interpretable model analysis and gene expression analysis.These analyses uncovered intricate interrelationships between brain imaging markers and genetic biomarkers,providing novel insights into the neuropathological basis of SZ.In summary,our findings underscore the potential of graph neural networks applied to multimodal DTI data for enhancing SZ detection through an integrated analysis of neuroimaging and genetic features.
基金Supported by CUP(J53C22003010006,J43C24000230007)ICREA2019.
文摘Spiking neural networks(SNNs)represent a biologically-inspired computational framework that bridges neuroscience and artificial intelligence,offering unique advantages in temporal data processing,energy efficiency,and real-time decision-making.This paper explores the evolution of SNN technologies,emphasizing their integration with advanced learning mechanisms such as spike-timing-dependent plasticity(STDP)and hybridization with deep learning architectures.Leveraging memristors as nanoscale synaptic devices,we demonstrate significant enhancements in energy efficiency,adaptability,and scalability,addressing key challenges in neuromorphic computing.Through phase portraits and nonlinear dynamics analysis,we validate the system’s stability and robustness under diverse workloads.These advancements position SNNs as a transformative technology for applications in robotics,IoT,and adaptive low-power AI systems,paving the way for future innovations in neuromorphic hardware and hybrid learning paradigms.
文摘This study investigates the load-bearing capacity of open-ended pipe piles in sandy soil, with a specific focus on the impact of soil plug constraints at four levels(no plug, 25% plug, 50% plug, and full plug). Leveraging a dataset comprising open-ended pipe piles with varying geometrical and geotechnical properties, this research employs shallow neural network(SNN) and deep neural network(DNN) models to predict plugging conditions for both driven and pressed installation types. This paper underscores the importance of key parameters such as the settlement value,applied load, installation type, and soil configuration(loose, medium, and dense) in accurately predicting pile settlement. These findings offer valuable insights for optimizing pile design and construction in geotechnical engineering,addressing a longstanding challenge in the field. The study demonstrates the potential of the SNN and DNN models in precisely identifying plugging conditions before pile driving, with the SNN achieving R2 values ranging from0.444 to 0.711 and RMSPE values ranging from 24.621% to 48.663%, whereas the DNN exhibits superior performance, with R2 values ranging from 0.815 to 0.942 and RMSPE values ranging from 4.419% to 10.325%. These results have significant implications for enhancing construction practices and reducing uncertainties associated with pile foundation projects in addition to leveraging artificial intelligence tools to avoid long experimental procedures.
