With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance s...With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance strategies often struggle to accurately predict the degradation process of equipment,leading to excessive maintenance costs or potential failure risks.However,existing prediction methods based on statistical models are difficult to adapt to nonlinear degradation processes.To address these challenges,this study proposes a novel condition-based maintenance framework for planetary gearboxes.A comprehensive full-lifecycle degradation experiment was conducted to collect raw vibration signals,which were then processed using a temporal convolutional network autoencoder with multi-scale perception capability to extract deep temporal degradation features,enabling the collaborative extraction of longperiod meshing frequencies and short-term impact features from the vibration signals.Kernel principal component analysis was employed to fuse and normalize these features,enhancing the characterization of degradation progression.A nonlinear Wiener process was used to model the degradation trajectory,with a threshold decay function introduced to dynamically adjust maintenance strategies,and model parameters optimized through maximum likelihood estimation.Meanwhile,the maintenance strategy was optimized to minimize costs per unit time,determining the optimal maintenance timing and preventive maintenance threshold.The comprehensive indicator of degradation trends extracted by this method reaches 0.756,which is 41.2%higher than that of traditional time-domain features;the dynamic threshold strategy reduces the maintenance cost per unit time to 55.56,which is 8.9%better than that of the static threshold optimization.Experimental results demonstrate significant reductions in maintenance costs while enhancing system reliability and safety.This study realizes the organic integration of deep learning and reliability theory in the maintenance of planetary gearboxes,provides an interpretable solution for the predictive maintenance of complex mechanical systems,and promotes the development of condition-based maintenance strategies for planetary gearboxes.展开更多
Growing demand for sustainable,high-performance materials is driving research to replace petroleumbased plastics with abundant biomass,especially cellulose.However,the effective modification and functionalization of c...Growing demand for sustainable,high-performance materials is driving research to replace petroleumbased plastics with abundant biomass,especially cellulose.However,the effective modification and functionalization of cellulose is often impeded by complex processing requirements and limited performance tunability.Here,an innovative“active”green medium strategy based on an ethyl cellulose/thymol eutectic system is reported,enabling in situ chemical modification of eutectic components and the construction of dynamic self-adaptive networks without external catalysts or initiators.Through precise molecular design,dynamic boroxine networks and acrylate crosslinking networks are synergistically integrated into the cellulosic bioplastic(CBP)matrix.The resulting CBP-A2B8 exhibits exceptional optical transparency(~85%),superior mechanical properties(tensile strength~30 MPa),facile thermal processability,and closed-loop recyclability.Its chemical structure and mechanical performance remain highly stable even after 20 hot-compression recycling cycles.Complete biodegradation occurs under natural environmental conditions within approximately 100 days.Furthermore,the bioplastic,when combined with silver nanowires,forms high-performance flexible transparent conductive films successfully applied in customizable electroluminescent devices.Post-lifecycle,device components(silver nanowires and CBP matrix)are efficiently separated and recycled using a straightforward solvent-based method.This eutectic system-mediated strategy offers a novel pathway for the development of sustainable,high-performance bioplastics with a closed-loop lifecycle.展开更多
In this paper,we propose a novel graph signal processing convolution recurrent network(GSP CRN)for signal enhancement against high suppressive interference(HSI)in wireless communications.GSPCRN consists of the short-t...In this paper,we propose a novel graph signal processing convolution recurrent network(GSP CRN)for signal enhancement against high suppressive interference(HSI)in wireless communications.GSPCRN consists of the short-time graph signal processing(SGSP)approach and a modified convolution recurrent network.Similar to the traditional shorttime time-frequency transformation,SGSP frames the complex-valued communication signal and transforms it to the graph-domain representations,where the connection and weight flexibility of each vertex are fully taken into account.In the presence of HSI,SGSP can extract signal features from new graph-domain dimensions and empower neural networks for weak signal enhancement.Two SGSP methods,adjacency singular value decomposition and implicit graph transformation,are designed to capture relationships among the sampling points in the segmented signals.Simulation results demonstrate that our proposed GSPCRN outperforms existing classic methods in extracting weak signals from the HSI environment.When the interference-to-signal ratio exceeds 27dB,only our proposed GSPCRN can achieve the interference mitigation.展开更多
Shape memory polymers used in 4D printing only had one permanent shape after molding,which limited their applications in requiring multiple reconstructions and multifunctional shapes.Furthermore,the inherent stability...Shape memory polymers used in 4D printing only had one permanent shape after molding,which limited their applications in requiring multiple reconstructions and multifunctional shapes.Furthermore,the inherent stability of the triazine ring structure within cyanate ester(CE)crosslinked networks after molding posed significant challenges for both recycling,repairing,and degradation of resin.To address these obstacles,dynamic thiocyanate ester(TCE)bonds and photocurable group were incorporated into CE,obtaining the recyclable and 3D printable CE covalent adaptable networks(CANs),denoted as PTCE1.5.This material exhibits a Young's modulus of 810 MPa and a tensile strength of 50.8 MPa.Notably,damaged printed PTCE1.5 objects can be readily repaired through reprinting and interface rejoining by thermal treatment.Leveraging the solid-state plasticity,PTCE1.5 also demonstrated attractive shape memory ability and permanent shape reconfigurability,enabling its reconfigurable 4D printing.The printed PTCE1.5 hinges and a main body were assembled into a deployable and retractable satellite model,validating its potential application as a controllable component in the aerospace field.Moreover,printed PTCE1.5 can be fully degraded into thiol-modified intermediate products.Overall,this material not only enriches the application range of CE resin,but also provides a reliable approach to addressing environmental issue.展开更多
一、作为哲学的AI for Process(一)郭为的哲学思想1.郭为是谁郭为是谁?他是一位哲学家。顺便说,他同时还领导着神州数码。为什么说郭为是哲学家呢?因为他在著作中谈到高深的哲学,如“数据如水,奔流不息,无界融合”。他引述古希腊哲学家...一、作为哲学的AI for Process(一)郭为的哲学思想1.郭为是谁郭为是谁?他是一位哲学家。顺便说,他同时还领导着神州数码。为什么说郭为是哲学家呢?因为他在著作中谈到高深的哲学,如“数据如水,奔流不息,无界融合”。他引述古希腊哲学家赫拉克利特所说的“万物流转”,又说“你不能两次踏进同一条河流,因为新的水不断地流过你的身旁”,他所表达的意思是“世界上唯一不变的就是变化”。展开更多
Fault diagnosis in industrial process is essential for ensuring production safety and efficiency.However,existing methods exhibit limited capability in recognizing hard samples and struggle to maintain consistency in ...Fault diagnosis in industrial process is essential for ensuring production safety and efficiency.However,existing methods exhibit limited capability in recognizing hard samples and struggle to maintain consistency in feature distributions across domains,resulting in suboptimal performance and robustness.Therefore,this paper proposes a fault diagnosis neural network for hard sample mining and domain adaptive(SmdaNet).First,the method uses deep belief networks(DBN)to build a diagnostic model.Hard samples are mined based on the loss values,dividing the data set into hard and easy samples.Second,elastic weight consolidation(EWC)is used to train the model on hard samples,effectively preventing information forgetting.Finally,the feature space domain adaptation is introduced to optimize the feature space by minimizing the Kullback–Leibler divergence of the feature distributions.Experimental results show that the proposed SmdaNet method outperforms existing approaches in terms of classification accuracy,robustness and interpretability on the penicillin simulation and Tennessee Eastman process datasets.展开更多
Complex industrial processes present typical uncertainty due to fluctuations in the composition of raw materials and frequently changing operating conditions.This poses three challenges for precise fault diagnosis,inc...Complex industrial processes present typical uncertainty due to fluctuations in the composition of raw materials and frequently changing operating conditions.This poses three challenges for precise fault diagnosis,including random noise interference,less distinguishability between multi-class faults,and the new fault emerging.To address these issues,this study formulates fault diagnosis in uncertain industrial processes as a multilevel refined fault diagnosis problem.A hierarchical stochastic network approach is proposed to refine fault diagnosis of multiclass faults.This method considers the augmentation of fault categories as naturally following a hierarchical structure.At each hierarchical stage,stochastic network methods are designed according to the sources of uncertainty.For fault feature extraction,a doubly stochastic attention-based variational graph autoencoder is introduced to suppress noise during the messagepassing process,ensuring the extraction of high-quality fault features and providing the provision of differentiated information.Subsequently,multiple stochastic configuration networks are deployed to realize multi-level fault diagnosis from coarse to fine granularity via a hierarchical structure rather than treating all faults equally.This approach effectively enhances the precision of multi-class fault diagnosis and ensures its robust generalization capability.Finally,the feasibility and effectiveness of the proposed method are validated using two industrial processes.The results demonstrate that the proposed method can effectively suppress the random noise interference and adapt to the emergence of small samples and imbalanced extreme fault-type data,achieving a satisfactory fault diagnosis performance.展开更多
In the electroslag remelting(ESR)process,it mainly relies on thermal experiments or analysis via mechanistic models to realize the physical fields simulation of the electromagnetic field and temperature field coupled ...In the electroslag remelting(ESR)process,it mainly relies on thermal experiments or analysis via mechanistic models to realize the physical fields simulation of the electromagnetic field and temperature field coupled transfer,which has the limitations of high cost,a large amount of calculating data and high computing power requirements.A novel network based on physics-informed neural network(PINN)was designed to realize the fast and high-fidelity prediction of the distribution of electromagnetic field and temperature field in ESR process.The physical laws were combined with the deep learning network through PINN,and physical constraints were embedded to achieve effective solution of partial differential equations(PDEs).PINN was used to minimize the loss function consisting of data error,physical information error and boundary condition error.The physical laws and boundary condition constraints in the ESR process were considered to maintain high PDE solution accuracy under different spatial and temporal resolutions.Automatic differentiation(Autodiff)technique and gradient descent algorithm were used to optimize the network parameters.The experimental results show that compared with the mechanistic models,PINN can effectively replace thermal experiments to realize the physical field simulation of ESR process with only a few experimental data,which can avoid the disadvantages of pure data-driven network simulation that requires a large amount of training data.Moreover,the solution of PINN has good physical interpretability and reliability of simulation results.For simulating electromagnetic field and temperature field distribution,the training time of the network is only 140 and 203 s,and the regression indicators of root mean square error can reach 12.65 and 13.76,respectively.展开更多
The increased accessibility of social networking services(SNSs)has facilitated communication and information sharing among users.However,it has also heightened concerns about digital safety,particularly for children a...The increased accessibility of social networking services(SNSs)has facilitated communication and information sharing among users.However,it has also heightened concerns about digital safety,particularly for children and adolescents who are increasingly exposed to online grooming crimes.Early and accurate identification of grooming conversations is crucial in preventing long-term harm to victims.However,research on grooming detection in South Korea remains limited,as existing models trained primarily on English text and fail to reflect the unique linguistic features of SNS conversations,leading to inaccurate classifications.To address these issues,this study proposes a novel framework that integrates optical character recognition(OCR)technology with KcELECTRA,a deep learning-based natural language processing(NLP)model that shows excellent performance in processing the colloquial Korean language.In the proposed framework,the KcELECTRA model is fine-tuned by an extensive dataset,including Korean social media conversations,Korean ethical verification data from AI-Hub,and Korean hate speech data from Hug-gingFace,to enable more accurate classification of text extracted from social media conversation images.Experimental results show that the proposed framework achieves an accuracy of 0.953,outperforming existing transformer-based models.Furthermore,OCR technology shows high accuracy in extracting text from images,demonstrating that the proposed framework is effective for online grooming detection.The proposed framework is expected to contribute to the more accurate detection of grooming text and the prevention of grooming-related crimes.展开更多
Software-defined satellite networks(SDSNs)play an essential role in future networks.Due to the diverse service scenarios,SDSN faces the demand of packet processing for heterogeneous protocols.Existing packet switching...Software-defined satellite networks(SDSNs)play an essential role in future networks.Due to the diverse service scenarios,SDSN faces the demand of packet processing for heterogeneous protocols.Existing packet switching typically works on one single protocol.For protocol-heterogeneous users,existing packet switch architectures have to construct multiple protocol-specific switching instances,resulting in severe resource waste.In this article,we propose the heterogeneous protocol-independent packet switch architecture(HISA).HISA employs a fast parsing structure to achieve efficient heterogeneous packet parsing and a novel match-action pipeline to achieve shared packet processing among heterogeneous users.HISA can also support the online configuration of switching behaviors.Use cases illustrate the effectiveness of applying HISA in SDSN.Numerical results show that compared to existing packet switching,HISA can significantly improve the resource utilization of SDSN.展开更多
Under the paradigm of Industry 5.0,intelligent manufacturing transcends mere efficiency enhancement by emphasizing human-machine collaboration,where human expertise plays a central role in assembly processes.Despite a...Under the paradigm of Industry 5.0,intelligent manufacturing transcends mere efficiency enhancement by emphasizing human-machine collaboration,where human expertise plays a central role in assembly processes.Despite advancements in intelligent and digital technologies,assembly process design still heavily relies on manual knowledge reuse,and inefficiencies and inconsistent quality in process documentation are caused.To address the aforementioned issues,this paper proposes a knowledge push method of complex product assembly process design based on distillation model-based dynamically enhanced graph and Bayesian network.First,an initial knowledge graph is constructed using a BERT-BiLSTM-CRF model trained with integrated human expertise and a fine-tuned large language model.Then,a confidence-based dynamic weighted fusion strategy is employed to achieve dynamic incremental construction of the knowledge graph with low resource consumption.Subsequently,a Bayesian network model is constructed based on the relationships between assembly components,assembly features,and operations.Bayesian network reasoning is used to push assembly process knowledge under different design requirements.Finally,the feasibility of the Bayesian network construction method and the effectiveness of Bayesian network reasoning are verified through a specific example,significantly improving the utilization of assembly process knowledge and the efficiency of assembly process design.展开更多
Agricultural Products Processing and Storage(ISSN 3059-4510,Owner:Hunan Academy of Agricultural Sciences,China.Production and hosting:Springer Nature)is an international,peer-reviewed open access journal with the aim ...Agricultural Products Processing and Storage(ISSN 3059-4510,Owner:Hunan Academy of Agricultural Sciences,China.Production and hosting:Springer Nature)is an international,peer-reviewed open access journal with the aim to offer a platform for the rapid dissemination of significant,novel,and high-impact research in the fields of agricultural product processing science,technology,engineering,and nutrition.Additionally,supplemental issues are curated and published to facilitate in-depth discussions on special topics.展开更多
This study systematically investigates the hot deformation behavior and microstructural evolution of CoNiV medium-entropy alloy(MEA)in the temperature range of 950-1100℃ and strain rates of 0.001-1 s^(-1).The Arrheni...This study systematically investigates the hot deformation behavior and microstructural evolution of CoNiV medium-entropy alloy(MEA)in the temperature range of 950-1100℃ and strain rates of 0.001-1 s^(-1).The Arrhenius model and machine learning model were developed to forecast flow stresses at various conditions.The predictive capability of both models was assessed using the coefficients of determination(R^(2)),average absolute relative error(AARE),and root mean square error(RMSE).The findings show that the osprey optimization algorithm convolutional neural network(OOA-CNN)model outperforms the Arrhenius model,achieving a high R^(2) value of 0.99959 and lower AARE and RMSE values.The flow stress that the OOA-CNN model predicted was used to generate power dissipation maps and instability maps under different strains.Finally,combining the processing map and microstructure characterization,the ideal processing domain was identified as 1100℃ at strain rates of 0.01-0.1 s^(-1).This study provided key insights into optimizing the hot working process of CoNiV MEA.展开更多
Accurate state of health(SOH)estimation is essential for the safe and reliable operation of lithium-ion batteries.However,existing methods face significant challenges,primarily because they rely on complete charge–di...Accurate state of health(SOH)estimation is essential for the safe and reliable operation of lithium-ion batteries.However,existing methods face significant challenges,primarily because they rely on complete charge–discharge cycles and fixed-form physical constraints,which limit adaptability to different chemistries and real-world conditions.To address these issues,this study proposes an approach that extracts features from segmented state of charge(SOC)intervals and integrates them into an enhanced physics-informed neural network(PINN).Specifically,voltage data within the 25%–75%SOC range during charging are used to derive statistical,time–frequency,and mechanism-based features that capture degradation trends.A hybrid PINN-Lasso-Transformer-BiLSTM architecture is developed,where Lasso regression enables sparse feature selection,and a nonlinear empirical degradation model is embedded as a learnable physical term within a dynamically scaled composite loss.This design adaptively balances data-driven accuracy with physical consistency,thereby enhancing estimation precision,robustness,and generalization.The results show that the proposed method outperforms conventional neural networks across four battery chemistries,achieving root mean square error and mean absolute error below 1%.Notably,features from partial charging segments exhibit higher robustness than those from full cycles.Furthermore,the model maintains strong performance under high temperatures and demonstrates excellent generalization capacity in transfer learning across chemistries,temperatures,and C-rates.This work establishes a scalable and interpretable solution for accurate SOH estimation under diverse practical operating conditions.展开更多
OBJECTIVE:To elucidate the potential mechanisms and identify bioactive components of Suanzaoren(Ziziphi Spinosae Semen)(ZSS)and it's processed products for insomnia treatment.METHODS:The principal components of ZS...OBJECTIVE:To elucidate the potential mechanisms and identify bioactive components of Suanzaoren(Ziziphi Spinosae Semen)(ZSS)and it's processed products for insomnia treatment.METHODS:The principal components of ZSS and its processed products were analyzed using UltraPerformance liquid chromatography quadrupole time-offlight mass spectrometry(UPLC-Q-TOF-MS).The effects of ZSS on insomnia were assessed in parachlorophenylalanine(PCPA)-induced rats.Additionally,to investigate the mechanisms of action in insomnia,serum and hippocampal non-targeted metabolomics were performed alongside network pharmacology and molecular docking.RESULTS:UPLC-Q-TOF-MS was used to analyze the main components of ZSS and its processed products.In the insomnia rat model induced by PCPA,both ZSS and its processed products demonstrated therapeutic effects,with the processed products showing particularly significant effects,significantly increasing the levels of the Gamma-Aminobutyric Acid and melatonin.Non-targeted metabolomics identified 23 and 10 potential differential metabolites in serum and hippocampus,respectively,involving pathways such as lipid metabolism and amino acid metabolism.Network pharmacology identified three core targets,mainly associated with pathways such as Transient Receptor Potential channel regulation,Advanced Glycation End-products-Receptor for Advanced Glycation End-products signaling pathway,and neuroactive ligand-receptor interaction.Molecular docking confirmed that the main active components of ZSS can stably bind to these core targets,thereby exerting an effect in improving insomnia.CONCLUSIONS:Combining in vivo experiments,metabolomics,and network pharmacology,we have identified potential pharmacological mechanisms by which ZSS and its derivatives mitigate insomnia.These findings provide a theoretical foundation for the further development and utilization of ZSS,supporting the innovation of new therapeutic agents for insomnia treatment.展开更多
Elevation patterns and assembly processes of soil microbial community structures are essential for understanding biogeo-chemical processes in mountain systems.Differences in soil properties caused by elevation gradien...Elevation patterns and assembly processes of soil microbial community structures are essential for understanding biogeo-chemical processes in mountain systems.Differences in soil properties caused by elevation gradients can regulate the spatial distribu-tion and network complexity of the community structure.To explore the variations in soil microbial community structures and their as-sembly mechanisms across different elevations of the Changbai Mountains,as well as their responses to environmental factors,we col-lected microbial samples along an elevational gradient(seven elevations containing four vegetation zones)on the western slope of the Changbai Mountains using the method of metagenomic sequencing.The results showed a significant difference(P<0.05)for the Chao1 index across different elevations,but no significant difference was observed for the Shannon and Simpson indices.With increasing elev-ation,the number of nodes and links in the microbial network gradually decreased.Acidobacteria were highly connected to many nodes.The microbial communities indicated a significant distance-decay relationship(P<0.001)and were affected more by stochastic pro-cesses along the elevation gradient.The results of the Structural Equation Model(SEM)showed that elevation had direct significant ef-fect on carbon(C,P<0.01),nitrogen(N,P<0.01),and phosphorus(P,P<0.05)and weak negative effect on their ecological stoi-chiometry.Elevation was one of the major variables contributing to microbial network topology.The contribution of C and N to micro-bial network complexity was higher than that of P.Our study provides valuable insights into the responses of soil microbial communit-ies to elevation variations.展开更多
Artificial intelligence(AI)is a revolutionizing problem-solver across various domains,including scientific research.Its application to chemical processes holds remarkable potential for rapid optimization of protocols ...Artificial intelligence(AI)is a revolutionizing problem-solver across various domains,including scientific research.Its application to chemical processes holds remarkable potential for rapid optimization of protocols and methods.A notable application of AI is in the photoFenton degradation of organic compounds.Despite the high novelty and recent surge of interest in this area,a comprehensive synthesis of existing literature on AI applications in the photo-Fenton process is lacking.This review aims to bridge this gap by providing an in-depth summary of the state-of-the-art use of artificial neural networks(ANN)in the photo-Fenton process,with the goal of aiding researchers in the water treatment field to identify the most crucial and relevant variables.It examines the types and architectures of ANNs,input and output variables,and the efficiency of these networks.The findings reveal a rapidly expanding field with increasing publications highlighting AI's potential to optimize the photo-Fenton process.This review also discusses the benefits and drawbacks of using ANNs,emphasizing the need for further research to advance this promising area.展开更多
The in-flight heating process of cerium dioxide(CeO_(2))powders was investigated through experiments and numerical simulations.In the experiment,CeO_(2)powder(average size of 30μm)was injected into radio-frequency(RF...The in-flight heating process of cerium dioxide(CeO_(2))powders was investigated through experiments and numerical simulations.In the experiment,CeO_(2)powder(average size of 30μm)was injected into radio-frequency(RF)argon plasma,and the temperatures were measured using a DPV-2000 monitor.A model combining the electromagnetism,thermal flow,and heat transfer characteristics of powder during in-flight heating in argon plasma was proposed.The melting processes of CeO_(2)powders of different diameters,with and without thermal resistance effect,were investigated.Results show that the heating process of CeO_(2)powder particles consists of three main stages,one of which is relevant to a dimensionless parameter known as the Biot number.When the Biot value≥0.1,thermal resistance increases significantly,especially for the larger powders.The predicted temperature of the particles at the outlet(1800–2880 K)is in good agreement with the experimental result.展开更多
The hot compression deformation behavior of Mg-6Zn-1Mn-0.5Ca(ZM61-0.5Ca)and Mg-6Zn-1Mn-2Sn-0.5Ca(ZMT612-0.5Ca)alloys was investigated at deformation temperatures ranging from 250℃to 400℃and strain rates varying from...The hot compression deformation behavior of Mg-6Zn-1Mn-0.5Ca(ZM61-0.5Ca)and Mg-6Zn-1Mn-2Sn-0.5Ca(ZMT612-0.5Ca)alloys was investigated at deformation temperatures ranging from 250℃to 400℃and strain rates varying from 0.001 s^(-1) to 1 s^(-1).The results show that the addition of Sn promotes dynamic recrystallization(DRX),and CaMgSn phases can act as nucleation sites during the compression deformation.Flow stress increases with increasing the strain rate and decreasing the temperature.Both the ZM61-0.5Ca and ZMT612-0.5Ca alloys exhibit obvious DRX characteristics.CaMgSn phases can effectively inhibit dislocation motion with the addition of Sn,thus increasing the peak fl ow stress of the alloy.The addition of Sn increases the hot deformation activation energy of the ZM61-0.5Ca alloy from 199.654 kJ/mol to 276.649 kJ/mol,thus improving the thermal stability of the alloy.For the ZMT612-0.5Ca alloy,the optimal hot deformation parameters are determined to be a deformation temperature range of 350–400℃and a strain rate range of 0.001–0.01 s^(-1).展开更多
Optimizing routing and resource allocation in decentralized unmanned aerial vehicle(UAV)networks remains challenging due to interference and rapidly changing topologies.The authors introduce a novel framework combinin...Optimizing routing and resource allocation in decentralized unmanned aerial vehicle(UAV)networks remains challenging due to interference and rapidly changing topologies.The authors introduce a novel framework combining double deep Q-networks(DDQNs)and graph neural networks(GNNs)for joint routing and resource allocation.The framework uses GNNs to model the network topology and DDQNs to adaptively control routing and resource allocation,addressing interference and improving network performance.Simulation results show that the proposed approach outperforms traditional methods such as Closest-to-Destination(c2Dst),Max-SINR(mSINR),and Multi-Layer Perceptron(MLP)-based models,achieving approximately 23.5% improvement in throughput,50% increase in connection probability,and 17.6% reduction in number of hops,demonstrating its effectiveness in dynamic UAV networks.展开更多
基金funded by scientific research projects under Grant JY2024B011.
文摘With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance strategies often struggle to accurately predict the degradation process of equipment,leading to excessive maintenance costs or potential failure risks.However,existing prediction methods based on statistical models are difficult to adapt to nonlinear degradation processes.To address these challenges,this study proposes a novel condition-based maintenance framework for planetary gearboxes.A comprehensive full-lifecycle degradation experiment was conducted to collect raw vibration signals,which were then processed using a temporal convolutional network autoencoder with multi-scale perception capability to extract deep temporal degradation features,enabling the collaborative extraction of longperiod meshing frequencies and short-term impact features from the vibration signals.Kernel principal component analysis was employed to fuse and normalize these features,enhancing the characterization of degradation progression.A nonlinear Wiener process was used to model the degradation trajectory,with a threshold decay function introduced to dynamically adjust maintenance strategies,and model parameters optimized through maximum likelihood estimation.Meanwhile,the maintenance strategy was optimized to minimize costs per unit time,determining the optimal maintenance timing and preventive maintenance threshold.The comprehensive indicator of degradation trends extracted by this method reaches 0.756,which is 41.2%higher than that of traditional time-domain features;the dynamic threshold strategy reduces the maintenance cost per unit time to 55.56,which is 8.9%better than that of the static threshold optimization.Experimental results demonstrate significant reductions in maintenance costs while enhancing system reliability and safety.This study realizes the organic integration of deep learning and reliability theory in the maintenance of planetary gearboxes,provides an interpretable solution for the predictive maintenance of complex mechanical systems,and promotes the development of condition-based maintenance strategies for planetary gearboxes.
基金supported by the Jiangsu Provincial Natural Science Foundation(BK20240685)the Opening Project of Key Laboratory of Optoelectronic Chemical Materials and Devices,Ministry of Education,Jianghan University(JDGD202309)。
文摘Growing demand for sustainable,high-performance materials is driving research to replace petroleumbased plastics with abundant biomass,especially cellulose.However,the effective modification and functionalization of cellulose is often impeded by complex processing requirements and limited performance tunability.Here,an innovative“active”green medium strategy based on an ethyl cellulose/thymol eutectic system is reported,enabling in situ chemical modification of eutectic components and the construction of dynamic self-adaptive networks without external catalysts or initiators.Through precise molecular design,dynamic boroxine networks and acrylate crosslinking networks are synergistically integrated into the cellulosic bioplastic(CBP)matrix.The resulting CBP-A2B8 exhibits exceptional optical transparency(~85%),superior mechanical properties(tensile strength~30 MPa),facile thermal processability,and closed-loop recyclability.Its chemical structure and mechanical performance remain highly stable even after 20 hot-compression recycling cycles.Complete biodegradation occurs under natural environmental conditions within approximately 100 days.Furthermore,the bioplastic,when combined with silver nanowires,forms high-performance flexible transparent conductive films successfully applied in customizable electroluminescent devices.Post-lifecycle,device components(silver nanowires and CBP matrix)are efficiently separated and recycled using a straightforward solvent-based method.This eutectic system-mediated strategy offers a novel pathway for the development of sustainable,high-performance bioplastics with a closed-loop lifecycle.
基金supported by he National Social Science Found of China(2022-SKJJ-B-112).
文摘In this paper,we propose a novel graph signal processing convolution recurrent network(GSP CRN)for signal enhancement against high suppressive interference(HSI)in wireless communications.GSPCRN consists of the short-time graph signal processing(SGSP)approach and a modified convolution recurrent network.Similar to the traditional shorttime time-frequency transformation,SGSP frames the complex-valued communication signal and transforms it to the graph-domain representations,where the connection and weight flexibility of each vertex are fully taken into account.In the presence of HSI,SGSP can extract signal features from new graph-domain dimensions and empower neural networks for weak signal enhancement.Two SGSP methods,adjacency singular value decomposition and implicit graph transformation,are designed to capture relationships among the sampling points in the segmented signals.Simulation results demonstrate that our proposed GSPCRN outperforms existing classic methods in extracting weak signals from the HSI environment.When the interference-to-signal ratio exceeds 27dB,only our proposed GSPCRN can achieve the interference mitigation.
基金supported by the National Natural Science Foundation of China(Nos.52473080,52403167 and 52173079)the Fundamental Research Funds for the Central Universities(Nos.xtr052023001 and xzy012023037)+1 种基金the Postdoctoral Research Project of Shaanxi Province(No.2024BSHSDZZ054)the Shaanxi Laboratory of Advanced Materials(No.2024ZY-JCYJ-04-12).
文摘Shape memory polymers used in 4D printing only had one permanent shape after molding,which limited their applications in requiring multiple reconstructions and multifunctional shapes.Furthermore,the inherent stability of the triazine ring structure within cyanate ester(CE)crosslinked networks after molding posed significant challenges for both recycling,repairing,and degradation of resin.To address these obstacles,dynamic thiocyanate ester(TCE)bonds and photocurable group were incorporated into CE,obtaining the recyclable and 3D printable CE covalent adaptable networks(CANs),denoted as PTCE1.5.This material exhibits a Young's modulus of 810 MPa and a tensile strength of 50.8 MPa.Notably,damaged printed PTCE1.5 objects can be readily repaired through reprinting and interface rejoining by thermal treatment.Leveraging the solid-state plasticity,PTCE1.5 also demonstrated attractive shape memory ability and permanent shape reconfigurability,enabling its reconfigurable 4D printing.The printed PTCE1.5 hinges and a main body were assembled into a deployable and retractable satellite model,validating its potential application as a controllable component in the aerospace field.Moreover,printed PTCE1.5 can be fully degraded into thiol-modified intermediate products.Overall,this material not only enriches the application range of CE resin,but also provides a reliable approach to addressing environmental issue.
文摘一、作为哲学的AI for Process(一)郭为的哲学思想1.郭为是谁郭为是谁?他是一位哲学家。顺便说,他同时还领导着神州数码。为什么说郭为是哲学家呢?因为他在著作中谈到高深的哲学,如“数据如水,奔流不息,无界融合”。他引述古希腊哲学家赫拉克利特所说的“万物流转”,又说“你不能两次踏进同一条河流,因为新的水不断地流过你的身旁”,他所表达的意思是“世界上唯一不变的就是变化”。
基金support from the following foundations:the National Natural Science Foundation of China(62322309,62433004)Shanghai Science and Technology Innovation Action Plan(23S41900500)Shanghai Pilot Program for Basic Research(22TQ1400100-16).
文摘Fault diagnosis in industrial process is essential for ensuring production safety and efficiency.However,existing methods exhibit limited capability in recognizing hard samples and struggle to maintain consistency in feature distributions across domains,resulting in suboptimal performance and robustness.Therefore,this paper proposes a fault diagnosis neural network for hard sample mining and domain adaptive(SmdaNet).First,the method uses deep belief networks(DBN)to build a diagnostic model.Hard samples are mined based on the loss values,dividing the data set into hard and easy samples.Second,elastic weight consolidation(EWC)is used to train the model on hard samples,effectively preventing information forgetting.Finally,the feature space domain adaptation is introduced to optimize the feature space by minimizing the Kullback–Leibler divergence of the feature distributions.Experimental results show that the proposed SmdaNet method outperforms existing approaches in terms of classification accuracy,robustness and interpretability on the penicillin simulation and Tennessee Eastman process datasets.
基金supported in part by the National Key Research and Development Program of China(2022YFB3304900)the Science and Technology Innovation Program of Hunan Province(2022RC1089)+1 种基金the Central South University Innovation Driven Research Programme(2023CXQD040)the Fundamental Research Funds for the Central Universities of Central South University(2025ZZTS0213).
文摘Complex industrial processes present typical uncertainty due to fluctuations in the composition of raw materials and frequently changing operating conditions.This poses three challenges for precise fault diagnosis,including random noise interference,less distinguishability between multi-class faults,and the new fault emerging.To address these issues,this study formulates fault diagnosis in uncertain industrial processes as a multilevel refined fault diagnosis problem.A hierarchical stochastic network approach is proposed to refine fault diagnosis of multiclass faults.This method considers the augmentation of fault categories as naturally following a hierarchical structure.At each hierarchical stage,stochastic network methods are designed according to the sources of uncertainty.For fault feature extraction,a doubly stochastic attention-based variational graph autoencoder is introduced to suppress noise during the messagepassing process,ensuring the extraction of high-quality fault features and providing the provision of differentiated information.Subsequently,multiple stochastic configuration networks are deployed to realize multi-level fault diagnosis from coarse to fine granularity via a hierarchical structure rather than treating all faults equally.This approach effectively enhances the precision of multi-class fault diagnosis and ensures its robust generalization capability.Finally,the feasibility and effectiveness of the proposed method are validated using two industrial processes.The results demonstrate that the proposed method can effectively suppress the random noise interference and adapt to the emergence of small samples and imbalanced extreme fault-type data,achieving a satisfactory fault diagnosis performance.
基金supported by National Natural Science Foundation of China(52274323 and 524743495)the Postdoctoral Fellowship Program of CPSF under Grant Number GZC20240231.
文摘In the electroslag remelting(ESR)process,it mainly relies on thermal experiments or analysis via mechanistic models to realize the physical fields simulation of the electromagnetic field and temperature field coupled transfer,which has the limitations of high cost,a large amount of calculating data and high computing power requirements.A novel network based on physics-informed neural network(PINN)was designed to realize the fast and high-fidelity prediction of the distribution of electromagnetic field and temperature field in ESR process.The physical laws were combined with the deep learning network through PINN,and physical constraints were embedded to achieve effective solution of partial differential equations(PDEs).PINN was used to minimize the loss function consisting of data error,physical information error and boundary condition error.The physical laws and boundary condition constraints in the ESR process were considered to maintain high PDE solution accuracy under different spatial and temporal resolutions.Automatic differentiation(Autodiff)technique and gradient descent algorithm were used to optimize the network parameters.The experimental results show that compared with the mechanistic models,PINN can effectively replace thermal experiments to realize the physical field simulation of ESR process with only a few experimental data,which can avoid the disadvantages of pure data-driven network simulation that requires a large amount of training data.Moreover,the solution of PINN has good physical interpretability and reliability of simulation results.For simulating electromagnetic field and temperature field distribution,the training time of the network is only 140 and 203 s,and the regression indicators of root mean square error can reach 12.65 and 13.76,respectively.
基金supported by the IITP(Institute of Information&Communications Technology Planning&Evaluation)-ITRC(Information Technology Research Center)grant funded by the Korean government(Ministry of Science and ICT)(IITP-2025-RS-2024-00438056).
文摘The increased accessibility of social networking services(SNSs)has facilitated communication and information sharing among users.However,it has also heightened concerns about digital safety,particularly for children and adolescents who are increasingly exposed to online grooming crimes.Early and accurate identification of grooming conversations is crucial in preventing long-term harm to victims.However,research on grooming detection in South Korea remains limited,as existing models trained primarily on English text and fail to reflect the unique linguistic features of SNS conversations,leading to inaccurate classifications.To address these issues,this study proposes a novel framework that integrates optical character recognition(OCR)technology with KcELECTRA,a deep learning-based natural language processing(NLP)model that shows excellent performance in processing the colloquial Korean language.In the proposed framework,the KcELECTRA model is fine-tuned by an extensive dataset,including Korean social media conversations,Korean ethical verification data from AI-Hub,and Korean hate speech data from Hug-gingFace,to enable more accurate classification of text extracted from social media conversation images.Experimental results show that the proposed framework achieves an accuracy of 0.953,outperforming existing transformer-based models.Furthermore,OCR technology shows high accuracy in extracting text from images,demonstrating that the proposed framework is effective for online grooming detection.The proposed framework is expected to contribute to the more accurate detection of grooming text and the prevention of grooming-related crimes.
基金supported by the National Natural Science Foundation of China(62101300,62341130)the Youth Fund Program of the Beijing National Research Center for Information Science and Technology under Grant BNR2021RC01012the Open Research Fund Program of the Beijing National Research Center for Information Science and Technology under Grant BNR2021KF02001.
文摘Software-defined satellite networks(SDSNs)play an essential role in future networks.Due to the diverse service scenarios,SDSN faces the demand of packet processing for heterogeneous protocols.Existing packet switching typically works on one single protocol.For protocol-heterogeneous users,existing packet switch architectures have to construct multiple protocol-specific switching instances,resulting in severe resource waste.In this article,we propose the heterogeneous protocol-independent packet switch architecture(HISA).HISA employs a fast parsing structure to achieve efficient heterogeneous packet parsing and a novel match-action pipeline to achieve shared packet processing among heterogeneous users.HISA can also support the online configuration of switching behaviors.Use cases illustrate the effectiveness of applying HISA in SDSN.Numerical results show that compared to existing packet switching,HISA can significantly improve the resource utilization of SDSN.
基金Supported by National Key Research and Development Program(Grant No.2024YFB3312700)National Natural Science Foundation of China(Grant No.52405541)the Changzhou Municipal Sci&Tech Program(Grant No.CJ20241131)。
文摘Under the paradigm of Industry 5.0,intelligent manufacturing transcends mere efficiency enhancement by emphasizing human-machine collaboration,where human expertise plays a central role in assembly processes.Despite advancements in intelligent and digital technologies,assembly process design still heavily relies on manual knowledge reuse,and inefficiencies and inconsistent quality in process documentation are caused.To address the aforementioned issues,this paper proposes a knowledge push method of complex product assembly process design based on distillation model-based dynamically enhanced graph and Bayesian network.First,an initial knowledge graph is constructed using a BERT-BiLSTM-CRF model trained with integrated human expertise and a fine-tuned large language model.Then,a confidence-based dynamic weighted fusion strategy is employed to achieve dynamic incremental construction of the knowledge graph with low resource consumption.Subsequently,a Bayesian network model is constructed based on the relationships between assembly components,assembly features,and operations.Bayesian network reasoning is used to push assembly process knowledge under different design requirements.Finally,the feasibility of the Bayesian network construction method and the effectiveness of Bayesian network reasoning are verified through a specific example,significantly improving the utilization of assembly process knowledge and the efficiency of assembly process design.
文摘Agricultural Products Processing and Storage(ISSN 3059-4510,Owner:Hunan Academy of Agricultural Sciences,China.Production and hosting:Springer Nature)is an international,peer-reviewed open access journal with the aim to offer a platform for the rapid dissemination of significant,novel,and high-impact research in the fields of agricultural product processing science,technology,engineering,and nutrition.Additionally,supplemental issues are curated and published to facilitate in-depth discussions on special topics.
基金supported by the National Natural Science Foundation of China(NSFC)(Grant No.51901078)the Central Guidance for Local Scientific and Technological Development Funding Project(Grant No.236Z1003G)+3 种基金the Science and Technology Plan Project of Tangshan City(Grant No.24130207C)the Natural Science Foundation of Hebei Province(Grant No.E2022209070)the High-level Talent Project of Hebei(Grant No.E2019100007)the Open Project Program of Key Laboratory of Ministry of Education for Modern Metallurgy Technology(Grant No.2024YJKF02).
文摘This study systematically investigates the hot deformation behavior and microstructural evolution of CoNiV medium-entropy alloy(MEA)in the temperature range of 950-1100℃ and strain rates of 0.001-1 s^(-1).The Arrhenius model and machine learning model were developed to forecast flow stresses at various conditions.The predictive capability of both models was assessed using the coefficients of determination(R^(2)),average absolute relative error(AARE),and root mean square error(RMSE).The findings show that the osprey optimization algorithm convolutional neural network(OOA-CNN)model outperforms the Arrhenius model,achieving a high R^(2) value of 0.99959 and lower AARE and RMSE values.The flow stress that the OOA-CNN model predicted was used to generate power dissipation maps and instability maps under different strains.Finally,combining the processing map and microstructure characterization,the ideal processing domain was identified as 1100℃ at strain rates of 0.01-0.1 s^(-1).This study provided key insights into optimizing the hot working process of CoNiV MEA.
基金supported by the Shanghai Pilot Program for Basic Research(22T01400100-18)the National Natural Science Foundation of China(22278127 and 12447149)+1 种基金the Fundamental Research Funds for the Central Universities(2022ZFJH004)the Postdoctoral Fellowship Program of CPSF(GZB20250159).
文摘Accurate state of health(SOH)estimation is essential for the safe and reliable operation of lithium-ion batteries.However,existing methods face significant challenges,primarily because they rely on complete charge–discharge cycles and fixed-form physical constraints,which limit adaptability to different chemistries and real-world conditions.To address these issues,this study proposes an approach that extracts features from segmented state of charge(SOC)intervals and integrates them into an enhanced physics-informed neural network(PINN).Specifically,voltage data within the 25%–75%SOC range during charging are used to derive statistical,time–frequency,and mechanism-based features that capture degradation trends.A hybrid PINN-Lasso-Transformer-BiLSTM architecture is developed,where Lasso regression enables sparse feature selection,and a nonlinear empirical degradation model is embedded as a learnable physical term within a dynamically scaled composite loss.This design adaptively balances data-driven accuracy with physical consistency,thereby enhancing estimation precision,robustness,and generalization.The results show that the proposed method outperforms conventional neural networks across four battery chemistries,achieving root mean square error and mean absolute error below 1%.Notably,features from partial charging segments exhibit higher robustness than those from full cycles.Furthermore,the model maintains strong performance under high temperatures and demonstrates excellent generalization capacity in transfer learning across chemistries,temperatures,and C-rates.This work establishes a scalable and interpretable solution for accurate SOH estimation under diverse practical operating conditions.
文摘OBJECTIVE:To elucidate the potential mechanisms and identify bioactive components of Suanzaoren(Ziziphi Spinosae Semen)(ZSS)and it's processed products for insomnia treatment.METHODS:The principal components of ZSS and its processed products were analyzed using UltraPerformance liquid chromatography quadrupole time-offlight mass spectrometry(UPLC-Q-TOF-MS).The effects of ZSS on insomnia were assessed in parachlorophenylalanine(PCPA)-induced rats.Additionally,to investigate the mechanisms of action in insomnia,serum and hippocampal non-targeted metabolomics were performed alongside network pharmacology and molecular docking.RESULTS:UPLC-Q-TOF-MS was used to analyze the main components of ZSS and its processed products.In the insomnia rat model induced by PCPA,both ZSS and its processed products demonstrated therapeutic effects,with the processed products showing particularly significant effects,significantly increasing the levels of the Gamma-Aminobutyric Acid and melatonin.Non-targeted metabolomics identified 23 and 10 potential differential metabolites in serum and hippocampus,respectively,involving pathways such as lipid metabolism and amino acid metabolism.Network pharmacology identified three core targets,mainly associated with pathways such as Transient Receptor Potential channel regulation,Advanced Glycation End-products-Receptor for Advanced Glycation End-products signaling pathway,and neuroactive ligand-receptor interaction.Molecular docking confirmed that the main active components of ZSS can stably bind to these core targets,thereby exerting an effect in improving insomnia.CONCLUSIONS:Combining in vivo experiments,metabolomics,and network pharmacology,we have identified potential pharmacological mechanisms by which ZSS and its derivatives mitigate insomnia.These findings provide a theoretical foundation for the further development and utilization of ZSS,supporting the innovation of new therapeutic agents for insomnia treatment.
基金Under the auspices of the National Natural Science Foundation of China(No.42430511,U20A2083)the National Key Research and Development Program of China(No.2022YFF1300900)the Science and Technology Development Program of Jilin Province(No.20210509037RQ,20230101348JC)。
文摘Elevation patterns and assembly processes of soil microbial community structures are essential for understanding biogeo-chemical processes in mountain systems.Differences in soil properties caused by elevation gradients can regulate the spatial distribu-tion and network complexity of the community structure.To explore the variations in soil microbial community structures and their as-sembly mechanisms across different elevations of the Changbai Mountains,as well as their responses to environmental factors,we col-lected microbial samples along an elevational gradient(seven elevations containing four vegetation zones)on the western slope of the Changbai Mountains using the method of metagenomic sequencing.The results showed a significant difference(P<0.05)for the Chao1 index across different elevations,but no significant difference was observed for the Shannon and Simpson indices.With increasing elev-ation,the number of nodes and links in the microbial network gradually decreased.Acidobacteria were highly connected to many nodes.The microbial communities indicated a significant distance-decay relationship(P<0.001)and were affected more by stochastic pro-cesses along the elevation gradient.The results of the Structural Equation Model(SEM)showed that elevation had direct significant ef-fect on carbon(C,P<0.01),nitrogen(N,P<0.01),and phosphorus(P,P<0.05)and weak negative effect on their ecological stoi-chiometry.Elevation was one of the major variables contributing to microbial network topology.The contribution of C and N to micro-bial network complexity was higher than that of P.Our study provides valuable insights into the responses of soil microbial communit-ies to elevation variations.
基金financial support provided by the Valencian Regional Governement(Grant No.CIPROM2023/037)Davide Palma and Alessandra Bianco Prevot acknowledge support from the Project CH4.0 under the MUR program"Dipartimenti di Eccellenza 2023-2027"(Grant No.CUP:D13C22003520001).
文摘Artificial intelligence(AI)is a revolutionizing problem-solver across various domains,including scientific research.Its application to chemical processes holds remarkable potential for rapid optimization of protocols and methods.A notable application of AI is in the photoFenton degradation of organic compounds.Despite the high novelty and recent surge of interest in this area,a comprehensive synthesis of existing literature on AI applications in the photo-Fenton process is lacking.This review aims to bridge this gap by providing an in-depth summary of the state-of-the-art use of artificial neural networks(ANN)in the photo-Fenton process,with the goal of aiding researchers in the water treatment field to identify the most crucial and relevant variables.It examines the types and architectures of ANNs,input and output variables,and the efficiency of these networks.The findings reveal a rapidly expanding field with increasing publications highlighting AI's potential to optimize the photo-Fenton process.This review also discusses the benefits and drawbacks of using ANNs,emphasizing the need for further research to advance this promising area.
基金National Natural Science Foundation of China(11875039)Shanxi Scholarship Council of China(2023-033)+2 种基金Fundamental Research Program of Shanxi Province(202303021221071)China Baowu Low Carbon Metallurgical Innovation Foundation(2022)2023 Anhui Major Industrial Innovation Plan Project。
文摘The in-flight heating process of cerium dioxide(CeO_(2))powders was investigated through experiments and numerical simulations.In the experiment,CeO_(2)powder(average size of 30μm)was injected into radio-frequency(RF)argon plasma,and the temperatures were measured using a DPV-2000 monitor.A model combining the electromagnetism,thermal flow,and heat transfer characteristics of powder during in-flight heating in argon plasma was proposed.The melting processes of CeO_(2)powders of different diameters,with and without thermal resistance effect,were investigated.Results show that the heating process of CeO_(2)powder particles consists of three main stages,one of which is relevant to a dimensionless parameter known as the Biot number.When the Biot value≥0.1,thermal resistance increases significantly,especially for the larger powders.The predicted temperature of the particles at the outlet(1800–2880 K)is in good agreement with the experimental result.
基金Sichuan Science and Technology Program(2025ZNSFSC1341)Fundamental Research Funds for the Central Universities(J2022-090,25CAFUC04087)。
文摘The hot compression deformation behavior of Mg-6Zn-1Mn-0.5Ca(ZM61-0.5Ca)and Mg-6Zn-1Mn-2Sn-0.5Ca(ZMT612-0.5Ca)alloys was investigated at deformation temperatures ranging from 250℃to 400℃and strain rates varying from 0.001 s^(-1) to 1 s^(-1).The results show that the addition of Sn promotes dynamic recrystallization(DRX),and CaMgSn phases can act as nucleation sites during the compression deformation.Flow stress increases with increasing the strain rate and decreasing the temperature.Both the ZM61-0.5Ca and ZMT612-0.5Ca alloys exhibit obvious DRX characteristics.CaMgSn phases can effectively inhibit dislocation motion with the addition of Sn,thus increasing the peak fl ow stress of the alloy.The addition of Sn increases the hot deformation activation energy of the ZM61-0.5Ca alloy from 199.654 kJ/mol to 276.649 kJ/mol,thus improving the thermal stability of the alloy.For the ZMT612-0.5Ca alloy,the optimal hot deformation parameters are determined to be a deformation temperature range of 350–400℃and a strain rate range of 0.001–0.01 s^(-1).
文摘Optimizing routing and resource allocation in decentralized unmanned aerial vehicle(UAV)networks remains challenging due to interference and rapidly changing topologies.The authors introduce a novel framework combining double deep Q-networks(DDQNs)and graph neural networks(GNNs)for joint routing and resource allocation.The framework uses GNNs to model the network topology and DDQNs to adaptively control routing and resource allocation,addressing interference and improving network performance.Simulation results show that the proposed approach outperforms traditional methods such as Closest-to-Destination(c2Dst),Max-SINR(mSINR),and Multi-Layer Perceptron(MLP)-based models,achieving approximately 23.5% improvement in throughput,50% increase in connection probability,and 17.6% reduction in number of hops,demonstrating its effectiveness in dynamic UAV networks.
