“Three-in-one”cathode,achieved via B-site heavy-doping of transition elements(typically Co,Fe)into proton-conductive perovskite,holds promise for enhancing the performance of proton-conducting solid oxide fuel cell(...“Three-in-one”cathode,achieved via B-site heavy-doping of transition elements(typically Co,Fe)into proton-conductive perovskite,holds promise for enhancing the performance of proton-conducting solid oxide fuel cell(H-SOFC)operated below 650℃for electricity generation.However,its electrochemical behavior above 650℃,essential for improving the efficiency of H-SOFC for fuel conversion,remains insufficiently explored.It is still challenging to propose guidance for the design of“threein-one”cathode toward optimal H-SOFC performance below and above 650℃,with the prerequisite of gaining a comprehensive understanding of the roles of Co and Fe in determining the H-SOFC performance.This work is to address this challenge.Through theoretical/experimental studies,Co is identified to play a role in improving the oxygen reduction reaction(ORR)activity while Fe plays a role in facilitating the cathode/electrolyte interfacial proton conduction.Therefore,if the operating temperature is above 650℃,lowering the Co/Fe ratio in“three-in-one”cathode becomes crucial since the limiting factor shifts from ORR activity to proton conduction.Implementing this strategy,the SOFC using BaCo_(0.15)-Fe_(0.55)Zr_(0.1)Y_(0.1)Yb_(0.1)O_(3−δ)cathode achieves peak power densities of 1.67Wcm^(−2)under H-SOFC mode at 700℃and 2.32Wcm^(−2)under dual ion-conducting SOFC mode at 750℃,which are the highest reported values so far.展开更多
BACKGROUND Non-suicidal self-injury(NSSI)is common among adolescents with depressive disorders and poses a major public health challenge.Rumination,a key cognitive feature of depression,includes different subtypes tha...BACKGROUND Non-suicidal self-injury(NSSI)is common among adolescents with depressive disorders and poses a major public health challenge.Rumination,a key cognitive feature of depression,includes different subtypes that may relate to NSSI through distinct psychological mechanisms.However,how these subtypes interact with specific NSSI behaviors remains unclear.AIM To examine associations between rumination subtypes and specific NSSI behaviors in adolescents.METHODS We conducted a cross-sectional study with 305 hospitalized adolescents diagnosed with depressive disorders.The subjects ranged from 12-18 years in age.Rumi-nation subtypes were assessed using the Ruminative Response Scale,and 12 NSSI behaviors were evaluated using a validated questionnaire.Network analysis was applied to explore symptom-level associations and identify central symptoms.RESULTS The network analysis revealed close connections between rumination subtypes and NSSI behaviors.Brooding was linked to behaviors such as hitting objects and burning.Scratching emerged as the most influential NSSI symptom.Symptomfocused rumination served as a key bridge connecting rumination and NSSI.CONCLUSION Symptom-focused rumination and scratching were identified as potential intervention targets.These findings highlight the psychological significance of specific cognitive-behavioral links in adolescent depression and suggest directions for tailored prevention and treatment.However,the cross-sectional,single-site design limits causal inference and generalizability.Future longitudinal and multi-center studies are needed to confirm causal pathways and verify the generalizability of the findings to broader adolescent populations.展开更多
Background:Wenqing Yin(WQY)is a classic prescription used to treat skin diseases like atopic dermatitis(AD)in China,and the aim of this study is to investigate the therapeutic effects and molecular mechanisms of WQY o...Background:Wenqing Yin(WQY)is a classic prescription used to treat skin diseases like atopic dermatitis(AD)in China,and the aim of this study is to investigate the therapeutic effects and molecular mechanisms of WQY on AD.Methods:The DNFB-induced mouse models of AD were established to investigate the therapeutic effects of WQY on AD.The symptoms of AD in the ears and backs of the mice were assessed,while inflammatory factors in the ear were quantified using quantitative real-time-polymerase chain reaction(qRT-PCR),and the percentages of CD4^(+)and CD8^(+)cells in the spleen were analyzed through flow cytometry.The compounds in WQY were identified using ultra-performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS)analysis and the key targets and pathways of WQY to treat AD were predicted by network pharmacology.Subsequently,the key genes were tested and verified by qRT-PCR,and the potential active components and target proteins were verified by molecular docking.Results:WQY relieved the AD symptoms and histopathological injuries in the ear and back skin of mice with AD.Meanwhile,WQY significantly reduced the levels of inflammatory factors IL-6 and IL-1βin ear tissue,as well as the ratio of CD4^(+)/CD8^(+)cells in spleen.Additionally,a total of 142 compounds were identified from the water extract of WQY by UPLC-Orbitrap-MS/MS.39 key targets related to AD were screened out by network pharmacology methods.The KEGG analysis indicated that the effects of WQY were primarily mediated through pathways associated with Toll-like receptor signaling and T cell receptor signaling.Moreover,the results of qRT-PCR demonstrated that WQY significantly reduced the mRNA expressions of IL-4,IL-10,GATA3 and FOXP3,and molecular docking simulation verified that the active components of WQY had excellent binding abilities with IL-4,IL-10,GATA3 and FOXP3 proteins.Conclusion:The present study demonstrated that WQY effectively relieved AD symptoms in mice,decreased the inflammatory factors levels,regulated the balance of CD4^(+)and CD8^(+)cells,and the mechanism may be associated with the suppression of Th2 and Treg cell immune responses.展开更多
This paper investigates the reliability of internal marine combustion engines using an integrated approach that combines Fault Tree Analysis(FTA)and Bayesian Networks(BN).FTA provides a structured,top-down method for ...This paper investigates the reliability of internal marine combustion engines using an integrated approach that combines Fault Tree Analysis(FTA)and Bayesian Networks(BN).FTA provides a structured,top-down method for identifying critical failure modes and their root causes,while BN introduces flexibility in probabilistic reasoning,enabling dynamic updates based on new evidence.This dual methodology overcomes the limitations of static FTA models,offering a comprehensive framework for system reliability analysis.Critical failures,including External Leakage(ELU),Failure to Start(FTS),and Overheating(OHE),were identified as key risks.By incorporating redundancy into high-risk components such as pumps and batteries,the likelihood of these failures was significantly reduced.For instance,redundant pumps reduced the probability of ELU by 31.88%,while additional batteries decreased the occurrence of FTS by 36.45%.The results underscore the practical benefits of combining FTA and BN for enhancing system reliability,particularly in maritime applications where operational safety and efficiency are critical.This research provides valuable insights for maintenance planning and highlights the importance of redundancy in critical systems,especially as the industry transitions toward more autonomous vessels.展开更多
The development of flame retardant or nonflammable electrolytes is the key to improve the safety of lithium batteries,owing to inflammable organic solvents and polymer matrix in common liquid and polymer electrolytes ...The development of flame retardant or nonflammable electrolytes is the key to improve the safety of lithium batteries,owing to inflammable organic solvents and polymer matrix in common liquid and polymer electrolytes regarded as the main cause of battery fire.Herein,a series of solid-state polyphosphate oligomers(SPPO)as a three-in-one electrolyte that integrated the roles of lithium salt,dissociation matrix,and flame retardant were synthesized.The well-designed SPPO electrolytes showed an optimal ionic conductivity of 5.5×10^(-4)S cm-1at 30℃,an acceptable electrochemical window up to 4.0 V vs.Li/Li+,and lithium ion transference number of 0.547.Stable Li-ion stripping/plating behavior for 500 h of charge-discharge cycles without internal short-circuit in a Li|SPPO|Li cell was confirmed,together with outstanding interface compatibility between the SPPO electrolyte and lithium foil.The optimal Li|SPPO|LiFePO4cell presented good reversible discharge capacity of 149.4 mA h g-1at 0.1 C and Coulombic efficiency of 96.4%after 120 cycles.More importantly,the prepared SPPO cannot be ignited by the lighter fire and show a limited-oxygen-index value as high as 35.5%,indicating splendid nonflammable nature.The SPPO could be a promising candidate as a three-in-one solid-state electrolyte for the improved safety of rechargeable lithium batteries.展开更多
Black phosphorus(BP)nano-materials,especially BP quantum dots(BPQDs),performs outstanding photothermal antitumor effects,excellent biocompatibility and biodegradability.However,there are several challenges to overcome...Black phosphorus(BP)nano-materials,especially BP quantum dots(BPQDs),performs outstanding photothermal antitumor effects,excellent biocompatibility and biodegradability.However,there are several challenges to overcome before offering real benefits,such as poor stability,poor dispersibility as well as difficulty in tailoring other functions.Here,a“three-in-one”mitochondria-targeted BP nano-platform,called as BPQD-PEG-TPP,was designed.In this nano-platform,BPQDs were covalently grafted with a heterobifunctional PEG,in which one end was an aryl diazo group capable of reacting with BPQDs to form a covalent bond and the other end was a mitochondria-targeted triphenylphosphine(TPP)group.In addition to its excellent near-infrared photothermal properties,BPQD-PEG-TPP had much enhanced stability and dispersibility under physiological conditions,efficient mitochondria targeting and promoted ROS production through a photothermal effect.Both in vitro and in vivo experiments demonstrated that BPQD-PEG-TPP performed much superior photothermal cytotoxicity than BPQDs and BPQD-PEG as the mitochondria targeted PTT.Thus this“three-in-one”nanoplatform fabricated through polymer grafting,with excellent stability,dispersibility and negligible side effects,might be a promising strategy for mitochondria-targeted photothermal cancer therapy.展开更多
Using porous carbon hosts in cathodes of Li-S cells can disperse S actives and offset their poor electrical conductivity.However,such reservoirs would in turn absorb excess electrolyte solvents to S-unfilled regions,c...Using porous carbon hosts in cathodes of Li-S cells can disperse S actives and offset their poor electrical conductivity.However,such reservoirs would in turn absorb excess electrolyte solvents to S-unfilled regions,causing the electrolyte overconsumption,specific energy decline,and even safety hazards for battery devices.To build better cathodes,we propose to substitute carbons by In-doped SnO_(2)(ITO)nano ceramics that own three-in-one functionalities:1)using conductive ITO enables minimizing the total carbon content to an extremely low mass ratio(~3%)in cathodes,elevating the electrode tap density and averting the electrolyte overuse;2)polar ITO nanoclusters can serve as robust anchors toward Li polysulfide(LiPS)by electrostatic adsorption or chemical bond interactions;3)they offer catalysis centers for liquid–solid phase conversions of S-based actives.Also,such ceramics are intrinsically nonflammable,preventing S cathodes away from thermal runaway or explosion.These merits entail our configured cathodes with high tap density(1.54 g cm^(−3)),less electrolyte usage,good security for flame retardance,and decent Li-storage behaviors.With lean and LiNO_(3)-free electrolyte,packed full cells exhibit excellent redox kinetics,suppressed LiPS shuttling,and excellent cyclability.This may trigger great research enthusiasm in rational design of low-carbon and safer S cathodes.展开更多
LiNi_(0.5)Co_(0.2)Mn_(0.3)O_(2) is extensively researched as one of the most widely used commercially materials for Li-ion batteries at present.However,the poor high-voltage performance(≥4.3 V)with low reversible cap...LiNi_(0.5)Co_(0.2)Mn_(0.3)O_(2) is extensively researched as one of the most widely used commercially materials for Li-ion batteries at present.However,the poor high-voltage performance(≥4.3 V)with low reversible capacity limits its replacement for LiCoO_(2) in high-end digital field.Herein,three-in-one modification,Na-doping and Al_(2)O_(3)@Li_(3)BO_(3) dual-coating simultaneously,is explored for single-crystalline LiNi_(0.5)Co_(0.2)Mn_(0.3)O_(2)(N-NCM@AB),which exhibits excellent high-voltage performance.N-NCM@AB displays a discharge-specific capacity of 201.8 mAh g^(−1) at 0.2 C with a high upper voltage of 4.6 V and maintains 158.9 mAh g^(−1) discharge capacity at 1 C over 200 cycles with the corresponding capacity retention of 87.8%.Remarkably,the N-NCM@AB||graphite pouch-type full cell retains 81.2% of its initial capacity with high working voltage of 4.4 V over 1600 cycles.More importantly,the fundamental understandings of three-in-one modification on surface morphology,crystal structure,and phase transformation of N-NCM@AB are clearly revealed.The Na+doped into the Li–O slab can enhance the bond energy,stabilize the crystal structure,and facilitate Li+transport.Additionally,the interior surface layer of Li^(+)-ions conductor Li_(3)BO_(3) relieves the charge transfer resistance with surface coating,whereas the outer surface Al_(2)O_(3) coating layer is beneficial for reducing the active materials loss and alleviating the electrode/electrolyte parasite reaction.This three-in-one strategy provides a reference for the further research on the performance attenuation mechanism of NCM,paving a new avenue to boost the high-voltage performance of NCM cathode in Li-ion batteries.展开更多
Medical procedures are inherently invasive and carry the risk of inducing pain to the mind and body.Recently,efforts have been made to alleviate the discomfort associated with invasive medical procedures through the u...Medical procedures are inherently invasive and carry the risk of inducing pain to the mind and body.Recently,efforts have been made to alleviate the discomfort associated with invasive medical procedures through the use of virtual reality(VR)technology.VR has been demonstrated to be an effective treatment for pain associated with medical procedures,as well as for chronic pain conditions for which no effective treatment has been established.The precise mechanism by which the diversion from reality facilitated by VR contributes to the diminution of pain and anxiety has yet to be elucidated.However,the provision of positive images through VR-based visual stimulation may enhance the functionality of brain networks.The salience network is diminished,while the default mode network is enhanced.Additionally,the medial prefrontal cortex may establish a stronger connection with the default mode network,which could result in a reduction of pain and anxiety.Further research into the potential of VR technology to alleviate pain could lead to a reduction in the number of individuals who overdose on painkillers and contribute to positive change in the medical field.展开更多
Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently...Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.展开更多
Satellite edge computing has garnered significant attention from researchers;however,processing a large volume of tasks within multi-node satellite networks still poses considerable challenges.The sharp increase in us...Satellite edge computing has garnered significant attention from researchers;however,processing a large volume of tasks within multi-node satellite networks still poses considerable challenges.The sharp increase in user demand for latency-sensitive tasks has inevitably led to offloading bottlenecks and insufficient computational capacity on individual satellite edge servers,making it necessary to implement effective task offloading scheduling to enhance user experience.In this paper,we propose a priority-based task scheduling strategy based on a Software-Defined Network(SDN)framework for satellite-terrestrial integrated networks,which clarifies the execution order of tasks based on their priority.Subsequently,we apply a Dueling-Double Deep Q-Network(DDQN)algorithm enhanced with prioritized experience replay to derive a computation offloading strategy,improving the experience replay mechanism within the Dueling-DDQN framework.Next,we utilize the Deep Deterministic Policy Gradient(DDPG)algorithm to determine the optimal resource allocation strategy to reduce the processing latency of sub-tasks.Simulation results demonstrate that the proposed d3-DDPG algorithm outperforms other approaches,effectively reducing task processing latency and thus improving user experience and system efficiency.展开更多
Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical robots.This st...Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical robots.This study proposes a novel end-to-end disparity estimation model to address these challenges.Our approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting interferences.This study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and efficiency.The model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video data.Experimental results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing parameters.Moreover,the model exhibited faster convergence during training,contributing to overall performance enhancement.This study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.展开更多
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
Network pharmacology has gained widespread application in drug discovery,particularly in traditional Chinese medicine(TCM)research,which is characterized by its“multi-component,multi-target,and multi-pathway”nature....Network pharmacology has gained widespread application in drug discovery,particularly in traditional Chinese medicine(TCM)research,which is characterized by its“multi-component,multi-target,and multi-pathway”nature.Through the integration of network biology,TCM network pharmacology enables systematic evaluation of therapeutic efficacy and detailed elucidation of action mechanisms,establishing a novel research paradigm for TCM modernization.The rapid advancement of machine learning,particularly revolutionary deep learning methods,has substantially enhanced artificial intelligence(AI)technology,offering significant potential to advance TCM network pharmacology research.This paper describes the methodology of TCM network pharmacology,encompassing ingredient identification,network construction,network analysis,and experimental validation.Furthermore,it summarizes key strategies for constructing various networks and analyzing constructed networks using AI methods.Finally,it addresses challenges and future directions regarding cell-cell communication(CCC)-based network construction,analysis,and validation,providing valuable insights for TCM network pharmacology.展开更多
Traditional Chinese medicine(TCM)demonstrates distinctive advantages in disease prevention and treatment.However,analyzing its biological mechanisms through the modern medical research paradigm of“single drug,single ...Traditional Chinese medicine(TCM)demonstrates distinctive advantages in disease prevention and treatment.However,analyzing its biological mechanisms through the modern medical research paradigm of“single drug,single target”presents significant challenges due to its holistic approach.Network pharmacology and its core theory of network targets connect drugs and diseases from a holistic and systematic perspective based on biological networks,overcoming the limitations of reductionist research models and showing considerable value in TCM research.Recent integration of network target computational and experimental methods with artificial intelligence(AI)and multi-modal multi-omics technologies has substantially enhanced network pharmacology methodology.The advancement in computational and experimental techniques provides complementary support for network target theory in decoding TCM principles.This review,centered on network targets,examines the progress of network target methods combined with AI in predicting disease molecular mechanisms and drug-target relationships,alongside the application of multi-modal multi-omics technologies in analyzing TCM formulae,syndromes,and toxicity.Looking forward,network target theory is expected to incorporate emerging technologies while developing novel approaches aligned with its unique characteristics,potentially leading to significant breakthroughs in TCM research and advancing scientific understanding and innovation in TCM.展开更多
Seal authentication is an important task for verifying the authenticity of stamped seals used in various domains to protect legal documents from tampering and counterfeiting.Stamped seal inspection is commonly audited...Seal authentication is an important task for verifying the authenticity of stamped seals used in various domains to protect legal documents from tampering and counterfeiting.Stamped seal inspection is commonly audited manually to ensure document authenticity.However,manual assessment of seal images is tedious and laborintensive due to human errors,inconsistent placement,and completeness of the seal.Traditional image recognition systems are inadequate enough to identify seal types accurately,necessitating a neural network-based method for seal image recognition.However,neural network-based classification algorithms,such as Residual Networks(ResNet)andVisualGeometryGroup with 16 layers(VGG16)yield suboptimal recognition rates on stamp datasets.Additionally,the fixed training data categories make handling new categories to be a challenging task.This paper proposes amulti-stage seal recognition algorithmbased on Siamese network to overcome these limitations.Firstly,the seal image is pre-processed by applying an image rotation correction module based on Histogram of Oriented Gradients(HOG).Secondly,the similarity between input seal image pairs is measured by utilizing a similarity comparison module based on the Siamese network.Finally,we compare the results with the pre-stored standard seal template images in the database to obtain the seal type.To evaluate the performance of the proposed method,we further create a new seal image dataset that contains two subsets with 210,000 valid labeled pairs in total.The proposed work has a practical significance in industries where automatic seal authentication is essential as in legal,financial,and governmental sectors,where automatic seal recognition can enhance document security and streamline validation processes.Furthermore,the experimental results show that the proposed multi-stage method for seal image recognition outperforms state-of-the-art methods on the two established datasets.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
With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based...With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based on GNN can deal with encrypted traffic well. However, existing GNN-based approaches ignore the relationship between client or server packets. In this paper, we design a network traffic topology based on GCN, called Flow Mapping Graph (FMG). FMG establishes sequential edges between vertexes by the arrival order of packets and establishes jump-order edges between vertexes by connecting packets in different bursts with the same direction. It not only reflects the time characteristics of the packet but also strengthens the relationship between the client or server packets. According to FMG, a Traffic Mapping Classification model (TMC-GCN) is designed, which can automatically capture and learn the characteristics and structure information of the top vertex in FMG. The TMC-GCN model is used to classify the encrypted traffic. The encryption stream classification problem is transformed into a graph classification problem, which can effectively deal with data from different data sources and application scenarios. By comparing the performance of TMC-GCN with other classical models in four public datasets, including CICIOT2023, ISCXVPN2016, CICAAGM2017, and GraphDapp, the effectiveness of the FMG algorithm is verified. The experimental results show that the accuracy rate of the TMC-GCN model is 96.13%, the recall rate is 95.04%, and the F1 rate is 94.54%.展开更多
基金supported by the Natural Sciences and Engineering Research Council(NSERC)of Canada,Discovery Grant(GRPIN-2016-05494)Strategic Research Projects of Alberta Innovates Technology Futures(#G2016000655)funding from the Canada First Research Excellence Fund(CFREF-2015-00001).
文摘“Three-in-one”cathode,achieved via B-site heavy-doping of transition elements(typically Co,Fe)into proton-conductive perovskite,holds promise for enhancing the performance of proton-conducting solid oxide fuel cell(H-SOFC)operated below 650℃for electricity generation.However,its electrochemical behavior above 650℃,essential for improving the efficiency of H-SOFC for fuel conversion,remains insufficiently explored.It is still challenging to propose guidance for the design of“threein-one”cathode toward optimal H-SOFC performance below and above 650℃,with the prerequisite of gaining a comprehensive understanding of the roles of Co and Fe in determining the H-SOFC performance.This work is to address this challenge.Through theoretical/experimental studies,Co is identified to play a role in improving the oxygen reduction reaction(ORR)activity while Fe plays a role in facilitating the cathode/electrolyte interfacial proton conduction.Therefore,if the operating temperature is above 650℃,lowering the Co/Fe ratio in“three-in-one”cathode becomes crucial since the limiting factor shifts from ORR activity to proton conduction.Implementing this strategy,the SOFC using BaCo_(0.15)-Fe_(0.55)Zr_(0.1)Y_(0.1)Yb_(0.1)O_(3−δ)cathode achieves peak power densities of 1.67Wcm^(−2)under H-SOFC mode at 700℃and 2.32Wcm^(−2)under dual ion-conducting SOFC mode at 750℃,which are the highest reported values so far.
基金Supported by Key Research and Development Program of Shaanxi Province,China,No.2024SF-YBXM-078.
文摘BACKGROUND Non-suicidal self-injury(NSSI)is common among adolescents with depressive disorders and poses a major public health challenge.Rumination,a key cognitive feature of depression,includes different subtypes that may relate to NSSI through distinct psychological mechanisms.However,how these subtypes interact with specific NSSI behaviors remains unclear.AIM To examine associations between rumination subtypes and specific NSSI behaviors in adolescents.METHODS We conducted a cross-sectional study with 305 hospitalized adolescents diagnosed with depressive disorders.The subjects ranged from 12-18 years in age.Rumi-nation subtypes were assessed using the Ruminative Response Scale,and 12 NSSI behaviors were evaluated using a validated questionnaire.Network analysis was applied to explore symptom-level associations and identify central symptoms.RESULTS The network analysis revealed close connections between rumination subtypes and NSSI behaviors.Brooding was linked to behaviors such as hitting objects and burning.Scratching emerged as the most influential NSSI symptom.Symptomfocused rumination served as a key bridge connecting rumination and NSSI.CONCLUSION Symptom-focused rumination and scratching were identified as potential intervention targets.These findings highlight the psychological significance of specific cognitive-behavioral links in adolescent depression and suggest directions for tailored prevention and treatment.However,the cross-sectional,single-site design limits causal inference and generalizability.Future longitudinal and multi-center studies are needed to confirm causal pathways and verify the generalizability of the findings to broader adolescent populations.
基金supported by grants from the National Natural Science Foundation of China(82004252)the Project of Administration of Traditional Chinese Medicine of Guangdong Province(202405112017596500)the Basic and Applied Basic Research Foundation of Guangzhou Municipal Science and Technology Bureau(202102020533).
文摘Background:Wenqing Yin(WQY)is a classic prescription used to treat skin diseases like atopic dermatitis(AD)in China,and the aim of this study is to investigate the therapeutic effects and molecular mechanisms of WQY on AD.Methods:The DNFB-induced mouse models of AD were established to investigate the therapeutic effects of WQY on AD.The symptoms of AD in the ears and backs of the mice were assessed,while inflammatory factors in the ear were quantified using quantitative real-time-polymerase chain reaction(qRT-PCR),and the percentages of CD4^(+)and CD8^(+)cells in the spleen were analyzed through flow cytometry.The compounds in WQY were identified using ultra-performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS)analysis and the key targets and pathways of WQY to treat AD were predicted by network pharmacology.Subsequently,the key genes were tested and verified by qRT-PCR,and the potential active components and target proteins were verified by molecular docking.Results:WQY relieved the AD symptoms and histopathological injuries in the ear and back skin of mice with AD.Meanwhile,WQY significantly reduced the levels of inflammatory factors IL-6 and IL-1βin ear tissue,as well as the ratio of CD4^(+)/CD8^(+)cells in spleen.Additionally,a total of 142 compounds were identified from the water extract of WQY by UPLC-Orbitrap-MS/MS.39 key targets related to AD were screened out by network pharmacology methods.The KEGG analysis indicated that the effects of WQY were primarily mediated through pathways associated with Toll-like receptor signaling and T cell receptor signaling.Moreover,the results of qRT-PCR demonstrated that WQY significantly reduced the mRNA expressions of IL-4,IL-10,GATA3 and FOXP3,and molecular docking simulation verified that the active components of WQY had excellent binding abilities with IL-4,IL-10,GATA3 and FOXP3 proteins.Conclusion:The present study demonstrated that WQY effectively relieved AD symptoms in mice,decreased the inflammatory factors levels,regulated the balance of CD4^(+)and CD8^(+)cells,and the mechanism may be associated with the suppression of Th2 and Treg cell immune responses.
基金supported by Istanbul Technical University(Project No.45698)supported through the“Young Researchers’Career Development Project-training of doctoral students”of the Croatian Science Foundation.
文摘This paper investigates the reliability of internal marine combustion engines using an integrated approach that combines Fault Tree Analysis(FTA)and Bayesian Networks(BN).FTA provides a structured,top-down method for identifying critical failure modes and their root causes,while BN introduces flexibility in probabilistic reasoning,enabling dynamic updates based on new evidence.This dual methodology overcomes the limitations of static FTA models,offering a comprehensive framework for system reliability analysis.Critical failures,including External Leakage(ELU),Failure to Start(FTS),and Overheating(OHE),were identified as key risks.By incorporating redundancy into high-risk components such as pumps and batteries,the likelihood of these failures was significantly reduced.For instance,redundant pumps reduced the probability of ELU by 31.88%,while additional batteries decreased the occurrence of FTS by 36.45%.The results underscore the practical benefits of combining FTA and BN for enhancing system reliability,particularly in maritime applications where operational safety and efficiency are critical.This research provides valuable insights for maintenance planning and highlights the importance of redundancy in critical systems,especially as the industry transitions toward more autonomous vessels.
基金the financial support of the National Natural Science Foundation of China(21961044,22169024)the Yunnan Fundamental Research Projects(202105AC160072,202101BC070001-019,202101AT070280,202102AB080017)the Yunnan University’s Research Innovation Fund for graduate students(2021Y394)。
文摘The development of flame retardant or nonflammable electrolytes is the key to improve the safety of lithium batteries,owing to inflammable organic solvents and polymer matrix in common liquid and polymer electrolytes regarded as the main cause of battery fire.Herein,a series of solid-state polyphosphate oligomers(SPPO)as a three-in-one electrolyte that integrated the roles of lithium salt,dissociation matrix,and flame retardant were synthesized.The well-designed SPPO electrolytes showed an optimal ionic conductivity of 5.5×10^(-4)S cm-1at 30℃,an acceptable electrochemical window up to 4.0 V vs.Li/Li+,and lithium ion transference number of 0.547.Stable Li-ion stripping/plating behavior for 500 h of charge-discharge cycles without internal short-circuit in a Li|SPPO|Li cell was confirmed,together with outstanding interface compatibility between the SPPO electrolyte and lithium foil.The optimal Li|SPPO|LiFePO4cell presented good reversible discharge capacity of 149.4 mA h g-1at 0.1 C and Coulombic efficiency of 96.4%after 120 cycles.More importantly,the prepared SPPO cannot be ignited by the lighter fire and show a limited-oxygen-index value as high as 35.5%,indicating splendid nonflammable nature.The SPPO could be a promising candidate as a three-in-one solid-state electrolyte for the improved safety of rechargeable lithium batteries.
基金We are grateful for the financial support from National Natural Science Foundation of China(51703258,81772449 and 81971081)Guangzhou science technology and innovation commission(201804010309 and 201803010090)Science,Technology&Innovation Commission of Shenzhen Municipality(JCYJ20180307154606793 and JCYJ20180507181654186).
文摘Black phosphorus(BP)nano-materials,especially BP quantum dots(BPQDs),performs outstanding photothermal antitumor effects,excellent biocompatibility and biodegradability.However,there are several challenges to overcome before offering real benefits,such as poor stability,poor dispersibility as well as difficulty in tailoring other functions.Here,a“three-in-one”mitochondria-targeted BP nano-platform,called as BPQD-PEG-TPP,was designed.In this nano-platform,BPQDs were covalently grafted with a heterobifunctional PEG,in which one end was an aryl diazo group capable of reacting with BPQDs to form a covalent bond and the other end was a mitochondria-targeted triphenylphosphine(TPP)group.In addition to its excellent near-infrared photothermal properties,BPQD-PEG-TPP had much enhanced stability and dispersibility under physiological conditions,efficient mitochondria targeting and promoted ROS production through a photothermal effect.Both in vitro and in vivo experiments demonstrated that BPQD-PEG-TPP performed much superior photothermal cytotoxicity than BPQDs and BPQD-PEG as the mitochondria targeted PTT.Thus this“three-in-one”nanoplatform fabricated through polymer grafting,with excellent stability,dispersibility and negligible side effects,might be a promising strategy for mitochondria-targeted photothermal cancer therapy.
基金support by the National Natural Science Foundation of China(51802269,21773138)Fundamental Research Funds for the Central Universities(XDJK2019AA002)+1 种基金the Venture&Innovation Support Program for Chongqing Overseas Returnees(cx2018027)the innovation platform for academicians of Hainan province.
文摘Using porous carbon hosts in cathodes of Li-S cells can disperse S actives and offset their poor electrical conductivity.However,such reservoirs would in turn absorb excess electrolyte solvents to S-unfilled regions,causing the electrolyte overconsumption,specific energy decline,and even safety hazards for battery devices.To build better cathodes,we propose to substitute carbons by In-doped SnO_(2)(ITO)nano ceramics that own three-in-one functionalities:1)using conductive ITO enables minimizing the total carbon content to an extremely low mass ratio(~3%)in cathodes,elevating the electrode tap density and averting the electrolyte overuse;2)polar ITO nanoclusters can serve as robust anchors toward Li polysulfide(LiPS)by electrostatic adsorption or chemical bond interactions;3)they offer catalysis centers for liquid–solid phase conversions of S-based actives.Also,such ceramics are intrinsically nonflammable,preventing S cathodes away from thermal runaway or explosion.These merits entail our configured cathodes with high tap density(1.54 g cm^(−3)),less electrolyte usage,good security for flame retardance,and decent Li-storage behaviors.With lean and LiNO_(3)-free electrolyte,packed full cells exhibit excellent redox kinetics,suppressed LiPS shuttling,and excellent cyclability.This may trigger great research enthusiasm in rational design of low-carbon and safer S cathodes.
基金We gratefully acknowledge the financial support from the National Natural Science Foundation of China(52070194,51902347,51908555,and 51822812)Natural Science Foundation of Hunan Province(2020JJ5741)the Graduate Innovation Project of Central South University(2020zzts093).
文摘LiNi_(0.5)Co_(0.2)Mn_(0.3)O_(2) is extensively researched as one of the most widely used commercially materials for Li-ion batteries at present.However,the poor high-voltage performance(≥4.3 V)with low reversible capacity limits its replacement for LiCoO_(2) in high-end digital field.Herein,three-in-one modification,Na-doping and Al_(2)O_(3)@Li_(3)BO_(3) dual-coating simultaneously,is explored for single-crystalline LiNi_(0.5)Co_(0.2)Mn_(0.3)O_(2)(N-NCM@AB),which exhibits excellent high-voltage performance.N-NCM@AB displays a discharge-specific capacity of 201.8 mAh g^(−1) at 0.2 C with a high upper voltage of 4.6 V and maintains 158.9 mAh g^(−1) discharge capacity at 1 C over 200 cycles with the corresponding capacity retention of 87.8%.Remarkably,the N-NCM@AB||graphite pouch-type full cell retains 81.2% of its initial capacity with high working voltage of 4.4 V over 1600 cycles.More importantly,the fundamental understandings of three-in-one modification on surface morphology,crystal structure,and phase transformation of N-NCM@AB are clearly revealed.The Na+doped into the Li–O slab can enhance the bond energy,stabilize the crystal structure,and facilitate Li+transport.Additionally,the interior surface layer of Li^(+)-ions conductor Li_(3)BO_(3) relieves the charge transfer resistance with surface coating,whereas the outer surface Al_(2)O_(3) coating layer is beneficial for reducing the active materials loss and alleviating the electrode/electrolyte parasite reaction.This three-in-one strategy provides a reference for the further research on the performance attenuation mechanism of NCM,paving a new avenue to boost the high-voltage performance of NCM cathode in Li-ion batteries.
文摘Medical procedures are inherently invasive and carry the risk of inducing pain to the mind and body.Recently,efforts have been made to alleviate the discomfort associated with invasive medical procedures through the use of virtual reality(VR)technology.VR has been demonstrated to be an effective treatment for pain associated with medical procedures,as well as for chronic pain conditions for which no effective treatment has been established.The precise mechanism by which the diversion from reality facilitated by VR contributes to the diminution of pain and anxiety has yet to be elucidated.However,the provision of positive images through VR-based visual stimulation may enhance the functionality of brain networks.The salience network is diminished,while the default mode network is enhanced.Additionally,the medial prefrontal cortex may establish a stronger connection with the default mode network,which could result in a reduction of pain and anxiety.Further research into the potential of VR technology to alleviate pain could lead to a reduction in the number of individuals who overdose on painkillers and contribute to positive change in the medical field.
基金National Natural Science Foundation of China(11971211,12171388).
文摘Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.
文摘Satellite edge computing has garnered significant attention from researchers;however,processing a large volume of tasks within multi-node satellite networks still poses considerable challenges.The sharp increase in user demand for latency-sensitive tasks has inevitably led to offloading bottlenecks and insufficient computational capacity on individual satellite edge servers,making it necessary to implement effective task offloading scheduling to enhance user experience.In this paper,we propose a priority-based task scheduling strategy based on a Software-Defined Network(SDN)framework for satellite-terrestrial integrated networks,which clarifies the execution order of tasks based on their priority.Subsequently,we apply a Dueling-Double Deep Q-Network(DDQN)algorithm enhanced with prioritized experience replay to derive a computation offloading strategy,improving the experience replay mechanism within the Dueling-DDQN framework.Next,we utilize the Deep Deterministic Policy Gradient(DDPG)algorithm to determine the optimal resource allocation strategy to reduce the processing latency of sub-tasks.Simulation results demonstrate that the proposed d3-DDPG algorithm outperforms other approaches,effectively reducing task processing latency and thus improving user experience and system efficiency.
基金Supported by Sichuan Science and Technology Program(2023YFSY0026,2023YFH0004)Supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korean government(MSIT)(No.RS-2022-00155885,Artificial Intelligence Convergence Innovation Human Resources Development(Hanyang University ERICA)).
文摘Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical robots.This study proposes a novel end-to-end disparity estimation model to address these challenges.Our approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting interferences.This study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and efficiency.The model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video data.Experimental results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing parameters.Moreover,the model exhibited faster convergence during training,contributing to overall performance enhancement.This study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.
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“Pioneer”and“Leading Goose”R&D Program of Zhejiang(No.2024C03106,X.F.)the National Natural Science Foundation of China(No.82474160,X.S.)+2 种基金the Joint Funds of the Zhejiang Provincial Natural Science Foundation of China(No.LBZ24H270001,X.P.)the Major Joint Projects Supported by the National Administration of TCM and Zhejiang Province(No.GZY-ZI-KJ-23037,X.P.)the Ningbo Top Medical and Health Research Program(No.2022030309,X.P.)。
文摘Network pharmacology has gained widespread application in drug discovery,particularly in traditional Chinese medicine(TCM)research,which is characterized by its“multi-component,multi-target,and multi-pathway”nature.Through the integration of network biology,TCM network pharmacology enables systematic evaluation of therapeutic efficacy and detailed elucidation of action mechanisms,establishing a novel research paradigm for TCM modernization.The rapid advancement of machine learning,particularly revolutionary deep learning methods,has substantially enhanced artificial intelligence(AI)technology,offering significant potential to advance TCM network pharmacology research.This paper describes the methodology of TCM network pharmacology,encompassing ingredient identification,network construction,network analysis,and experimental validation.Furthermore,it summarizes key strategies for constructing various networks and analyzing constructed networks using AI methods.Finally,it addresses challenges and future directions regarding cell-cell communication(CCC)-based network construction,analysis,and validation,providing valuable insights for TCM network pharmacology.
文摘Traditional Chinese medicine(TCM)demonstrates distinctive advantages in disease prevention and treatment.However,analyzing its biological mechanisms through the modern medical research paradigm of“single drug,single target”presents significant challenges due to its holistic approach.Network pharmacology and its core theory of network targets connect drugs and diseases from a holistic and systematic perspective based on biological networks,overcoming the limitations of reductionist research models and showing considerable value in TCM research.Recent integration of network target computational and experimental methods with artificial intelligence(AI)and multi-modal multi-omics technologies has substantially enhanced network pharmacology methodology.The advancement in computational and experimental techniques provides complementary support for network target theory in decoding TCM principles.This review,centered on network targets,examines the progress of network target methods combined with AI in predicting disease molecular mechanisms and drug-target relationships,alongside the application of multi-modal multi-omics technologies in analyzing TCM formulae,syndromes,and toxicity.Looking forward,network target theory is expected to incorporate emerging technologies while developing novel approaches aligned with its unique characteristics,potentially leading to significant breakthroughs in TCM research and advancing scientific understanding and innovation in TCM.
基金the National Natural Science Foundation of China(Grant No.62172132)Public Welfare Technology Research Project of Zhejiang Province(Grant No.LGF21F020014)the Opening Project of Key Laboratory of Public Security Information Application Based on Big-Data Architecture,Ministry of Public Security of Zhejiang Police College(Grant No.2021DSJSYS002).
文摘Seal authentication is an important task for verifying the authenticity of stamped seals used in various domains to protect legal documents from tampering and counterfeiting.Stamped seal inspection is commonly audited manually to ensure document authenticity.However,manual assessment of seal images is tedious and laborintensive due to human errors,inconsistent placement,and completeness of the seal.Traditional image recognition systems are inadequate enough to identify seal types accurately,necessitating a neural network-based method for seal image recognition.However,neural network-based classification algorithms,such as Residual Networks(ResNet)andVisualGeometryGroup with 16 layers(VGG16)yield suboptimal recognition rates on stamp datasets.Additionally,the fixed training data categories make handling new categories to be a challenging task.This paper proposes amulti-stage seal recognition algorithmbased on Siamese network to overcome these limitations.Firstly,the seal image is pre-processed by applying an image rotation correction module based on Histogram of Oriented Gradients(HOG).Secondly,the similarity between input seal image pairs is measured by utilizing a similarity comparison module based on the Siamese network.Finally,we compare the results with the pre-stored standard seal template images in the database to obtain the seal type.To evaluate the performance of the proposed method,we further create a new seal image dataset that contains two subsets with 210,000 valid labeled pairs in total.The proposed work has a practical significance in industries where automatic seal authentication is essential as in legal,financial,and governmental sectors,where automatic seal recognition can enhance document security and streamline validation processes.Furthermore,the experimental results show that the proposed multi-stage method for seal image recognition outperforms state-of-the-art methods on the two established datasets.
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.
基金supported by the National Key Research and Development Program of China No.2023YFA1009500.
文摘With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based on GNN can deal with encrypted traffic well. However, existing GNN-based approaches ignore the relationship between client or server packets. In this paper, we design a network traffic topology based on GCN, called Flow Mapping Graph (FMG). FMG establishes sequential edges between vertexes by the arrival order of packets and establishes jump-order edges between vertexes by connecting packets in different bursts with the same direction. It not only reflects the time characteristics of the packet but also strengthens the relationship between the client or server packets. According to FMG, a Traffic Mapping Classification model (TMC-GCN) is designed, which can automatically capture and learn the characteristics and structure information of the top vertex in FMG. The TMC-GCN model is used to classify the encrypted traffic. The encryption stream classification problem is transformed into a graph classification problem, which can effectively deal with data from different data sources and application scenarios. By comparing the performance of TMC-GCN with other classical models in four public datasets, including CICIOT2023, ISCXVPN2016, CICAAGM2017, and GraphDapp, the effectiveness of the FMG algorithm is verified. The experimental results show that the accuracy rate of the TMC-GCN model is 96.13%, the recall rate is 95.04%, and the F1 rate is 94.54%.
