This study analyzes the evolution of China's green technology innovation cooperation network from 2011 to 2020,utilizing green patent application data.Employing a Spatial Durbin Model(SDM),we scrutinized the netwo...This study analyzes the evolution of China's green technology innovation cooperation network from 2011 to 2020,utilizing green patent application data.Employing a Spatial Durbin Model(SDM),we scrutinized the network's influence on urban carbon emissions,utilizing panel data encompassing 323 city nodes.Results show network expansion and a shift in central nodes from eastern coastal areas to interior cities,with Beijing,Shenzhen,Nanjing,and Shanghai consistently acting as key innovation hubs.A core-periphery structure emerged,clustering cities into high-and low-cooperation clusters.Core cities,particularly Beijing,which gain informational advantages by bridging non-overlapping nodes and exhibit distinct characteristics in terms of the structural hole indexes,reflecting their multifaceted roles within the network.SDM analysis indicates that the green technology innovation cooperation network has a significant positive impact on urban carbon reduction efforts.Specifically,degree centrality,closeness centrality,effective size,efficiency,and hierarchy of node cities exhibit a negative correlation with carbon emissions,suggesting that higher centrality and efficiency within the network correlate with lower emissions.Conversely,betweenness centrality and constraint have a positive impact on emissions,indicating that cities that act as bridges in the network may paradoxically contribute to higher emissions.Moreover,the network's influence on carbon emissions is nuanced across different green technology sectors.Cooperation in areas such as waste management,alternative energy production,energy conservation,agriculture and forestry,and transportation is found to have a more substantial impact on carbon reduction than cooperation in nuclear power,and administrative,regulatory,and design fields.展开更多
This study explored the therapeutic targets and molecular mechanisms of Huangqi Guizhi Decoction (HGD) in alleviatingpulmonary embolism (PE) by employing network pharmacology and molecular docking techniques. Firstly,...This study explored the therapeutic targets and molecular mechanisms of Huangqi Guizhi Decoction (HGD) in alleviatingpulmonary embolism (PE) by employing network pharmacology and molecular docking techniques. Firstly, the effective activecomponents of the Chinese herbs in HGD were retrieved from the Traditional Chinese Medicine Systems Pharmacology Database(TCMSP), and their potential therapeutic targets were predicted using the Swiss Target Prediction platform. Subsequently, PErelatedtarget genes were obtained from the Online Mendelian Inheritance in Man (OMIM) database and GeneCards database.Then, the Wei Sheng Xin tool was used to generate a Venn diagram for identifying the common targets between the herb-relatedtargets and PE-related targets. After screening these common targets, a “drug-component-target network” and a protein-proteininteraction (PPI) network were constructed. Furthermore, Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia ofGenes and Genomes (KEGG) enrichment analysis were conducted on the intersecting targets, and molecular docking verificationwas performed using AutoDockTools and PyMol software. Finally, 20 active components were screened from Astragali Radix, 7from Cinnamomi Ramulus, 13 from Paeoniae Radix Alba, 5 from Zingiberis Rhizoma Recens, and 29 from Jujubae Fructus, witha total of 983 therapeutic targets. Among these targets, 134 were associated with PE, and protein kinase B1 (AKT1), mitogenactivatedprotein kinase 1 (MAPK1), and transformation-related protein 53 (TP53) served as the core targets. The results of GOand KEGG enrichment analyses indicated that the alleviation of PE by HGD is mainly related to pathways including immuneresponse, regulation of gene expression, atherosclerosis, and tumorigenesis. Molecular docking results showed that the keyactive components in HGD could bind to the core targets spontaneously and stably. This study revealed that HGD may alleviatesymptoms in PE patients by regulating signaling pathways, modulating platelet function to exert anticoagulant effects, andregulating the expression of anti-inflammatory genes, which provided a direction for subsequent experimental research.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
In the sixth generation mobile communication(6G) system,Non-Terrestrial Networks(NTN),as a supplement to terrestrial network,can meet the requirements of wide area intelligent connection and global ubiquitous seamless...In the sixth generation mobile communication(6G) system,Non-Terrestrial Networks(NTN),as a supplement to terrestrial network,can meet the requirements of wide area intelligent connection and global ubiquitous seamless access,establish intelligent connection for wide area objects,and provide intelligent services.Due to issues such as massive access,doppler shift,and limited spectrum resources in NTN,research on resource management is crucial for optimizing NTN performance.In this paper,a comprehensive survey of multi-pattern heterogeneous NTN resource management is provided.Firstly,the key technologies involved in NTN resource management is summarized.Secondly,NTN resource management is discussed from network pattern and resource pattern.The network pattern focuses on the application of different optimization methods to different network dimension communication resource management,and the resource type pattern focuses on the research and application of multi-domain resource management such as computation,cache,communication and sensing.Finally,future research directions and challenges of 6G NTN resource management are discussed.展开更多
The convergence of optical and wireless technologies is driving the evolution of intelligent indoor networks,with Fiber-to-the-Room(FTTR)emerging as a key ar⁃chitecture for delivering gigabit connectivity in both home...The convergence of optical and wireless technologies is driving the evolution of intelligent indoor networks,with Fiber-to-the-Room(FTTR)emerging as a key ar⁃chitecture for delivering gigabit connectivity in both home and enterprise environments.By deploying optical fiber directly to rooms and integrating it with advanced wireless so⁃lutions such as millimeter-wave and Wi-Fi 7,FTTR enables next-generation applications,including immersive Virtual Re⁃ality(VR)/Augmented Reality(AR)and industrial Internet of Things(IoT).Nevertheless,its large-scale deployment pres⁃ents challenges in network management,energy efficiency,in⁃terference mitigation,and intelligent root cause analysis.展开更多
With the rapid development of information technology,the scale of the network is expanding,and the complexity is increasing day by day.The traditional network management is facing great challenges.The emergence of sof...With the rapid development of information technology,the scale of the network is expanding,and the complexity is increasing day by day.The traditional network management is facing great challenges.The emergence of software-defined network(SDN)technology has brought revolutionary changes to modern network management.This paper aims to discuss the application and prospects of SDN technology in modern network management.Firstly,the basic principle and architecture of SDN are introduced,including the separation of control plane and data plane,centralized control and open programmable interface.Then,it analyzes the advantages of SDN technology in network management,such as simplifying network configuration,improving network flexibility,optimizing network resource utilization,and realizing fast fault recovery.The application examples of SDN in data center networks and WAN optimization management are analyzed.This paper also discusses the development status and trend of SDN in enterprise networks,including the integration of technologies such as cloud computing,big data,and artificial intelligence,the construction of an intelligent and automated network management platform,the improvement of network management efficiency and quality,and the openness and interoperability of network equipment.Finally,the advantages and challenges of SDN technology are summarized,and its future development direction is provided.展开更多
To investigate the targets and mechanism of Hedysarum Multijugum Maxim(HMM)in treatment of bladder cancer(BC).Based on Traditional Chinese Medicine Systems Pharmacology(TCMSP)and gene databases,active substances and p...To investigate the targets and mechanism of Hedysarum Multijugum Maxim(HMM)in treatment of bladder cancer(BC).Based on Traditional Chinese Medicine Systems Pharmacology(TCMSP)and gene databases,active substances and potential targets of HMM were screened,and the HMM-active substances-targets-BC(HATB)regulatory network and PPI network were constructed.Hub targets were screened by Cytoscape.The main active substances and Hub targets were molecularly docked with AutoDock and visualized by PyMOL.12 Hub targets were screened.Molecular docking showed that active substances mainly acted on MAPK14,MAPK1 and CCND1.The bindings of calycosin to MAPK14,formononetin to MAPK14,and calycosin to CCND1 were stable.展开更多
The 5G-R network is on the verge of entering the construction stage.Given that the dedicated network for railways is closely linked to train operation safety,there are extremely high requirements for network security....The 5G-R network is on the verge of entering the construction stage.Given that the dedicated network for railways is closely linked to train operation safety,there are extremely high requirements for network security.As a result,there is an urgent need to conduct research on 5G-R network security.To comprehensively enhance the end-to-end security protection of the 5G-R network,this study summarized the security requirements of the GSM-R network,analyzed the security risks and requirements faced by the 5G-R network,and proposed an overall 5G-R network security architecture.The security technical schemes were detailed from various aspects:5G-R infrastructure security,terminal access security,networking security,operation and maintenance security,data security,and network boundary security.Additionally,the study proposed leveraging the 5G-R security situation awareness system to achieve a comprehensive upgrade from basic security technologies to endogenous security capabilities within the 5G-R system.展开更多
Security attributes are the premise and foundation for implementing Attribute-Based Access Control(ABAC)mechanisms.However,when dealing with massive volumes of unstructured text big data resources,the current attribut...Security attributes are the premise and foundation for implementing Attribute-Based Access Control(ABAC)mechanisms.However,when dealing with massive volumes of unstructured text big data resources,the current attribute management methods based on manual extraction face several issues,such as high costs for attribute extraction,long processing times,unstable accuracy,and poor scalability.To address these problems,this paper proposes an attribute mining technology for access control institutions based on hybrid capsule networks.This technology leverages transfer learning ideas,utilizing Bidirectional Encoder Representations from Transformers(BERT)pre-trained language models to achieve vectorization of unstructured text data resources.Furthermore,we have designed a novel end-to-end parallel hybrid network structure,where the parallel networks handle global and local information features of the text that they excel at,respectively.By employing techniques such as attention mechanisms,capsule networks,and dynamic routing,effective mining of security attributes for access control resources has been achieved.Finally,we evaluated the performance level of the proposed attribute mining method for access control institutions through experiments on the medical referral text resource dataset.The experimental results show that,compared with baseline algorithms,our method adopts a parallel network structure that can better balance global and local feature information,resulting in improved overall performance.Specifically,it achieves a comprehensive performance enhancement of 2.06%to 8.18%in the F1 score metric.Therefore,this technology can effectively provide attribute support for access control of unstructured text big data resources.展开更多
In order to reveal the multi-target pharmacological mechanism of Zedoary turmeric oil combined with docetaxel in the treatment of breast cancer,we used network pharmacology and molecular docking.The targets of docetax...In order to reveal the multi-target pharmacological mechanism of Zedoary turmeric oil combined with docetaxel in the treatment of breast cancer,we used network pharmacology and molecular docking.The targets of docetaxel were retrieved from the Swiss Target Prediction database.The active components of Curcuma Zedoary turmeric were screened using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP),with criteria set as oral bioavailability OB≥30%and drug-likeness DL≥0.1.Potential targets of these components were subsequently predicted.Breast cancer-related targets were retrieved from the OMIM and GeneCards databases.The Venny tool was used to identify 177 overlapping targets between docetaxel,Zedoary turmeric oil,and breast cancer,followed by protein-protein interaction(PPI)analysis.Gene Ontology(GO)functional enrichment and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analyses were performed using the Metascape database.A drug-breast cancer-KEGG pathway target network was constructed using Cytoscape 3.8.0.Molecular docking was employed to verify the binding ability between drugs and core targets.Results showed that the combined treatment may exert anti-breast cancer effects through key targets such as MAPK1,PIK3CA,and HSP90AA1,primarily implicating the biological process of protein phosphorylation and engaging the PI3K-Akt signaling pathway.This study successfully predicted the key targets and enriched pathways of Zedoary turmeric oil combined with docetaxel for breast cancer treatment,providing new insights for further research and development.展开更多
In this editorial,we comment on the article by Micucci et al published in the recent issue.We focus on the heterogenous nature of gastric cancer(GC)and the potential benefits of integrating traditional Chinese medicin...In this editorial,we comment on the article by Micucci et al published in the recent issue.We focus on the heterogenous nature of gastric cancer(GC)and the potential benefits of integrating traditional Chinese medicine(TCM)with the modern technology of network pharmacology(NP)and omics sequencing.GC is a heterogenous disease,as it incorporates several biochemical pathways that contribute to pathogenesis.TCM acknowledges the multifactorial,heterogenous nature of disease and utilizes an integrative approach to medicine.NP,a modern philosophy within drug development,integrates traditional knowledge of nutraceuticals and modern technologies to address the complex interactions of pathways within the body.Omics technologies,which is at the core of precision medicine,has allowed for this newfound principle of drug development.Metabolic pathways are better distinguished,leading to more targeted drug development.However,the use of omics technology needs to be employed to better characterize the subtypes of GC.This will allow TCM’s use of nutraceuticals in the application of NP to better target metabolic pathways that may aid in the prevention of GC as well as enhance treatment.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
To explore the material basis and mechanisms of the anti-inflammatory effects of Hibiscus mutabilis L..The active ingredients and potential targets of Hibiscus mutabilis L.were obtained through the literature review a...To explore the material basis and mechanisms of the anti-inflammatory effects of Hibiscus mutabilis L..The active ingredients and potential targets of Hibiscus mutabilis L.were obtained through the literature review and SwissADME platform.Genes related to the inflammation were collected using Genecards and OMIM databases,and the intersection genes were submitted on STRING and DAVID websites.Then,the protein interaction network(PPI),gene ontology(GO)and pathway(KEGG)were analyzed.Cytoscape 3.7.2 software was used to construct the“Hibiscus mutabilis L.-active ingredient-target-inflammation”network diagram,and AutoDockTools-1.5.6 software was used for the molecular docking verification.The antiinflammatory effect of Hibiscus mutabilis L.active ingredient was verified by the RAW264.7 inflammatory cell model.The results showed that 11 active components and 94 potential targets,1029 inflammatory targets and 24 intersection targets were obtained from Hibiscus mutabilis L..The key anti-inflammatory active ingredients of Hibiscus mutabilis L.are quercetin,apigenin and luteolin.Its action pathway is mainly related to NF-κB,cancer pathway and TNF signaling pathway.Cell experiments showed that total flavonoids of Hibiscus mutabilis L.could effectively inhibit the expression of tumor necrosis factor(TNF-α),interleukin 8(IL-8)and epidermal growth factor receptor(EGFR)in LPS-induced RAW 264.7 inflammatory cells.It also downregulates the phosphorylation of human nuclear factor ĸB inhibitory protein α(IĸBα)and NF-κB p65 subunit protein(p65).Overall,the anti-inflammatory effect of Hibiscus mutabilis L.is related to many active components,many signal pathways and targets,which provides a theoretical basis for its further development and application.展开更多
The existence of absorption and reflection of light underwater leads to problems such as color distortion and blue-green bias in underwater images.In this study,a depthwise separable convolution-based generative adver...The existence of absorption and reflection of light underwater leads to problems such as color distortion and blue-green bias in underwater images.In this study,a depthwise separable convolution-based generative adversarial network(GAN)algorithm was proposed.Taking GAN as the basic framework,it combined a depthwise separable convolution module,attention mechanism,and reconstructed convolution module to realize the enhancement of underwater degraded images.Multi-scale features were captured by the depthwise separable convolution module,and the attention mechanism was utilized to enhance attention to important features.The reconstructed convolution module further extracts and fuses local and global features.Experimental results showed that the algorithm performs well in improving the color bias and blurring of underwater images,with PSNR reaching 27.835,SSIM reaching 0.883,UIQM reaching 3.205,and UCIQE reaching 0.713.The enhanced image outperforms the comparison algorithm in both subjective and objective metrics.展开更多
Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)t...Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)techniques for DDoS attack diagnosis normally apply network traffic statistical features such as packet sizes and inter-arrival times.However,such techniques sometimes fail to capture complicated relations among various traffic flows.In this paper,we present a new multi-scale ensemble strategy given the Graph Neural Networks(GNNs)for improving DDoS detection.Our technique divides traffic into macro-and micro-level elements,letting various GNN models to get the two corase-scale anomalies and subtle,stealthy attack models.Through modeling network traffic as graph-structured data,GNNs efficiently learn intricate relations among network entities.The proposed ensemble learning algorithm combines the results of several GNNs to improve generalization,robustness,and scalability.Extensive experiments on three benchmark datasets—UNSW-NB15,CICIDS2017,and CICDDoS2019—show that our approach outperforms traditional machine learning and deep learning models in detecting both high-rate and low-rate(stealthy)DDoS attacks,with significant improvements in accuracy and recall.These findings demonstrate the suggested method’s applicability and robustness for real-world implementation in contexts where several DDoS patterns coexist.展开更多
Networked predictive control(NPC) has gained significant attention in recent years for its ability to effectively and actively address communication constraints in networked control systems(NCSs),such as network-induc...Networked predictive control(NPC) has gained significant attention in recent years for its ability to effectively and actively address communication constraints in networked control systems(NCSs),such as network-induced delays,packet dropouts,and packet disorders.Despite significant advancements,the increasing complexity and dynamism of network environments,along with the growing complexity of systems,pose new challenges for NPC.These challenges include difficulties in system modeling,cyber attacks,component faults,limited network bandwidth,and the necessity for distributed collaboration.This survey aims to provide a comprehensive review of NPC strategies.It begins with a summary of the primary challenges faced by NCSs,followed by an introduction to the control structure and core concepts of NPC.The survey then discusses several typical NPC schemes and examines their extensions in the areas of secure control,fault-tolerant control,distributed coordinated control,and event-triggered control.Moreover,it reviews notable works that have implemented these schemes.Finally,the survey concludes by exploring typical applications of NPC schemes and highlighting several challenging issues that could guide future research efforts.展开更多
A multi-stimuli-responsive hydrogel,P(VI-co-MAAC-NE),was successfully constructed by covalently integrating the aggregation-induced emission(AIE)moiety(Z)-N-(4-(1-cyano-2-(4-(diethylamino)phenyl)vinyl)-phenyl)methacry...A multi-stimuli-responsive hydrogel,P(VI-co-MAAC-NE),was successfully constructed by covalently integrating the aggregation-induced emission(AIE)moiety(Z)-N-(4-(1-cyano-2-(4-(diethylamino)phenyl)vinyl)-phenyl)methacrylamide(NE)into a dynamic hydrogen-bonding network composed of 1-vinylimidazole(VI)and methacrylic acid(MAAC)groups.The dense hydrogen-bonding network not only provides enhanced mechanical robustness,but also significantly enhances the AIE effect of NE by restricting its molecular motion.Under various external stimuli,the hydrogen bonds within the hydrogel network undergo reversible dissociation and reformation,thus enabling synergistic modulation of the hydrogel’s mechanical properties and luminescence behavior.Specifically,organic solvents disrupt the hydrogen-bonding network and the aggregation of the AIE moiety NE,resulting in macroscopic swelling and fluorescence quenching of the hydrogel.In strongly acidic conditions,protonation of NE molecules suppresses the intramolecular charge transfer(ICT)process,yielding a blue-shifted emission band accompanied by intense blue fluorescence;in highly alkaline environments,deprotonation of carboxyl groups induces hydrogel swelling and disperses NE aggregates,leading to pronounced fluorescence quenching.Moreover,the system exhibits thermally activated shape-memory behavior:heating above the glass transition temperature(T_(g):ca.62℃)softens the hydrogel to allow programmable reshaping,and subsequent hydrogen bond reformation at ambient conditions locks in the resultant geometries without sacrificing the hydrogel’s fluorescence performance.By capitalizing on these multi-stimuli-responsive characteristics and shape-memory behavior,the potential of hydrogel P(VI-co-MAAC-NE)for advanced information encryption and anti-counterfeiting applications is demonstrated.This work not only provides a versatile material platform for sensing and information storage,but also offers new insights into the design of intelligent soft materials integrating AIE features with dynamically regulated supramolecular network structures.展开更多
Urbanization is a significant driver of the loss of biodiversity and the disruption of ecosystems.Amphibians are especially vulnerable to the negative impact of urbanization as their life cycles and habitat requiremen...Urbanization is a significant driver of the loss of biodiversity and the disruption of ecosystems.Amphibians are especially vulnerable to the negative impact of urbanization as their life cycles and habitat requirements are complex.The present study investigated the effects of urbanization on amphibian predation networks in suburban Kunming in Yunnan,China and aimed to understand how predation network structure and stability vary with urbanization level.We constructed predation networks by analyzing the stomach contents of amphibians from 12d istinct urbanization gradients.We used the bipartite package in R to evaluate network robustness metrics such as modularity,nestedness,connectivity,and average shortest path length(ASPL).We found that urbanization level is negatively correlated with predation network connectivity(R=−0.67,Ρ=0.02),but there were no significant correlations between urbanization level and nestedness,modularity,or ASPL.Removal of the keystone species destabilized the predation networks at certain locations.The present work highlighted that maintaining prey quantity and diversity preserves predation network connectivity and stabilizes the overall network in urbanizing landscapes.It also underscored the critical role that keystone species play in sustaining network robustness.The results of this research provided insights into the ecological consequences of urbanization.They also suggested that conservation measures should protect the key species and habitats of amphibian predation networks and mitigate the negative impact of urban development on them.展开更多
In the process of programmable networks simplifying network management and increasing network flexibility through custom packet behavior,security incidents caused by human logic errors are seriously threatening their ...In the process of programmable networks simplifying network management and increasing network flexibility through custom packet behavior,security incidents caused by human logic errors are seriously threatening their safe operation,robust verificationmethods are required to ensure their correctness.As one of the formalmethods,symbolic execution offers a viable approach for verifying programmable networks by systematically exploring all possible paths within a program.However,its application in this field encounters scalability issues due to path explosion and complex constraint-solving.Therefore,in this paper,we propose NetVerifier,a scalable verification system for programmable networks.Tomitigate the path explosion issue,we developmultiple pruning strategies that strategically eliminate irrelevant execution paths while preserving verification integrity by precisely identifying the execution paths related to the verification purpose.To address the complex constraint-solving problem,we introduce an execution results reuse solution to avoid redundant computation of the same constraints.To apply these solutions intelligently,a matching algorithm is implemented to automatically select appropriate solutions based on the characteristics of the verification requirement.Moreover,Language Aided Verification(LAV),an assertion language,is designed to express verification intentions in a concise form.Experimental results on diverse open-source programs of varying scales demonstrate NetVerifier’s improvement in scalability and effectiveness in identifying potential network errors.In the best scenario,compared with ASSERT-P4,NetVerifier reduced the execution path,verification time,and memory occupation of the verification process by 99.92%,94.76%,and 65.19%,respectively.展开更多
Near-infrared image sensors are widely used in fields such as material identification,machine vision,and autonomous driving.Lead sulfide colloidal quantum dot-based infrared photodiodes can be integrated with sil⁃icon...Near-infrared image sensors are widely used in fields such as material identification,machine vision,and autonomous driving.Lead sulfide colloidal quantum dot-based infrared photodiodes can be integrated with sil⁃icon-based readout circuits in a single step.Based on this,we propose a photodiode based on an n-i-p structure,which removes the buffer layer and further simplifies the manufacturing process of quantum dot image sensors,thus reducing manufacturing costs.Additionally,for the noise complexity in quantum dot image sensors when capturing images,traditional denoising and non-uniformity methods often do not achieve optimal denoising re⁃sults.For the noise and stripe-type non-uniformity commonly encountered in infrared quantum dot detector imag⁃es,a network architecture has been developed that incorporates multiple key modules.This network combines channel attention and spatial attention mechanisms,dynamically adjusting the importance of feature maps to en⁃hance the ability to distinguish between noise and details.Meanwhile,the residual dense feature fusion module further improves the network's ability to process complex image structures through hierarchical feature extraction and fusion.Furthermore,the pyramid pooling module effectively captures information at different scales,improv⁃ing the network's multi-scale feature representation ability.Through the collaborative effect of these modules,the network can better handle various mixed noise and image non-uniformity issues.Experimental results show that it outperforms the traditional U-Net network in denoising and image correction tasks.展开更多
基金supported by the National Natural Science Foundation of China(72573020,72103022).
文摘This study analyzes the evolution of China's green technology innovation cooperation network from 2011 to 2020,utilizing green patent application data.Employing a Spatial Durbin Model(SDM),we scrutinized the network's influence on urban carbon emissions,utilizing panel data encompassing 323 city nodes.Results show network expansion and a shift in central nodes from eastern coastal areas to interior cities,with Beijing,Shenzhen,Nanjing,and Shanghai consistently acting as key innovation hubs.A core-periphery structure emerged,clustering cities into high-and low-cooperation clusters.Core cities,particularly Beijing,which gain informational advantages by bridging non-overlapping nodes and exhibit distinct characteristics in terms of the structural hole indexes,reflecting their multifaceted roles within the network.SDM analysis indicates that the green technology innovation cooperation network has a significant positive impact on urban carbon reduction efforts.Specifically,degree centrality,closeness centrality,effective size,efficiency,and hierarchy of node cities exhibit a negative correlation with carbon emissions,suggesting that higher centrality and efficiency within the network correlate with lower emissions.Conversely,betweenness centrality and constraint have a positive impact on emissions,indicating that cities that act as bridges in the network may paradoxically contribute to higher emissions.Moreover,the network's influence on carbon emissions is nuanced across different green technology sectors.Cooperation in areas such as waste management,alternative energy production,energy conservation,agriculture and forestry,and transportation is found to have a more substantial impact on carbon reduction than cooperation in nuclear power,and administrative,regulatory,and design fields.
基金supported by Research Project on Traditional Chinese Medicine in Heilongjiang Province in 2025(Research on the pharmacological substance basis of Huangqi Guizhi decoction in improving acute pulmonary embolism and lung injury based on the theory of“Diaphoresis and expanding meridian”No.ZHY2025-043).
文摘This study explored the therapeutic targets and molecular mechanisms of Huangqi Guizhi Decoction (HGD) in alleviatingpulmonary embolism (PE) by employing network pharmacology and molecular docking techniques. Firstly, the effective activecomponents of the Chinese herbs in HGD were retrieved from the Traditional Chinese Medicine Systems Pharmacology Database(TCMSP), and their potential therapeutic targets were predicted using the Swiss Target Prediction platform. Subsequently, PErelatedtarget genes were obtained from the Online Mendelian Inheritance in Man (OMIM) database and GeneCards database.Then, the Wei Sheng Xin tool was used to generate a Venn diagram for identifying the common targets between the herb-relatedtargets and PE-related targets. After screening these common targets, a “drug-component-target network” and a protein-proteininteraction (PPI) network were constructed. Furthermore, Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia ofGenes and Genomes (KEGG) enrichment analysis were conducted on the intersecting targets, and molecular docking verificationwas performed using AutoDockTools and PyMol software. Finally, 20 active components were screened from Astragali Radix, 7from Cinnamomi Ramulus, 13 from Paeoniae Radix Alba, 5 from Zingiberis Rhizoma Recens, and 29 from Jujubae Fructus, witha total of 983 therapeutic targets. Among these targets, 134 were associated with PE, and protein kinase B1 (AKT1), mitogenactivatedprotein kinase 1 (MAPK1), and transformation-related protein 53 (TP53) served as the core targets. The results of GOand KEGG enrichment analyses indicated that the alleviation of PE by HGD is mainly related to pathways including immuneresponse, regulation of gene expression, atherosclerosis, and tumorigenesis. Molecular docking results showed that the keyactive components in HGD could bind to the core targets spontaneously and stably. This study revealed that HGD may alleviatesymptoms in PE patients by regulating signaling pathways, modulating platelet function to exert anticoagulant effects, andregulating the expression of anti-inflammatory genes, which provided a direction for subsequent experimental research.
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supported in part by the National Natural Science Foundation of China under Grant 62225103,U22B2003,U2441227,and U24A20211the Beijing Natural Science Foundation under Grant L241008+3 种基金the Defense Industrial Technology Development Program JCKY2022110C010the National Key Laboratory of Wireless Communications Foundation under Grant IFN20230201the Fundamental Research Funds for the Central Universities under Grant FRFTP-22-002C2the Xiaomi Fund of Young Scholar。
文摘In the sixth generation mobile communication(6G) system,Non-Terrestrial Networks(NTN),as a supplement to terrestrial network,can meet the requirements of wide area intelligent connection and global ubiquitous seamless access,establish intelligent connection for wide area objects,and provide intelligent services.Due to issues such as massive access,doppler shift,and limited spectrum resources in NTN,research on resource management is crucial for optimizing NTN performance.In this paper,a comprehensive survey of multi-pattern heterogeneous NTN resource management is provided.Firstly,the key technologies involved in NTN resource management is summarized.Secondly,NTN resource management is discussed from network pattern and resource pattern.The network pattern focuses on the application of different optimization methods to different network dimension communication resource management,and the resource type pattern focuses on the research and application of multi-domain resource management such as computation,cache,communication and sensing.Finally,future research directions and challenges of 6G NTN resource management are discussed.
文摘The convergence of optical and wireless technologies is driving the evolution of intelligent indoor networks,with Fiber-to-the-Room(FTTR)emerging as a key ar⁃chitecture for delivering gigabit connectivity in both home and enterprise environments.By deploying optical fiber directly to rooms and integrating it with advanced wireless so⁃lutions such as millimeter-wave and Wi-Fi 7,FTTR enables next-generation applications,including immersive Virtual Re⁃ality(VR)/Augmented Reality(AR)and industrial Internet of Things(IoT).Nevertheless,its large-scale deployment pres⁃ents challenges in network management,energy efficiency,in⁃terference mitigation,and intelligent root cause analysis.
文摘With the rapid development of information technology,the scale of the network is expanding,and the complexity is increasing day by day.The traditional network management is facing great challenges.The emergence of software-defined network(SDN)technology has brought revolutionary changes to modern network management.This paper aims to discuss the application and prospects of SDN technology in modern network management.Firstly,the basic principle and architecture of SDN are introduced,including the separation of control plane and data plane,centralized control and open programmable interface.Then,it analyzes the advantages of SDN technology in network management,such as simplifying network configuration,improving network flexibility,optimizing network resource utilization,and realizing fast fault recovery.The application examples of SDN in data center networks and WAN optimization management are analyzed.This paper also discusses the development status and trend of SDN in enterprise networks,including the integration of technologies such as cloud computing,big data,and artificial intelligence,the construction of an intelligent and automated network management platform,the improvement of network management efficiency and quality,and the openness and interoperability of network equipment.Finally,the advantages and challenges of SDN technology are summarized,and its future development direction is provided.
基金2025 Open Experimental Special Fund of Beijing Institute of Technology, “Applications and Practices of R Language in Bioinformatics”。
文摘To investigate the targets and mechanism of Hedysarum Multijugum Maxim(HMM)in treatment of bladder cancer(BC).Based on Traditional Chinese Medicine Systems Pharmacology(TCMSP)and gene databases,active substances and potential targets of HMM were screened,and the HMM-active substances-targets-BC(HATB)regulatory network and PPI network were constructed.Hub targets were screened by Cytoscape.The main active substances and Hub targets were molecularly docked with AutoDock and visualized by PyMOL.12 Hub targets were screened.Molecular docking showed that active substances mainly acted on MAPK14,MAPK1 and CCND1.The bindings of calycosin to MAPK14,formononetin to MAPK14,and calycosin to CCND1 were stable.
文摘The 5G-R network is on the verge of entering the construction stage.Given that the dedicated network for railways is closely linked to train operation safety,there are extremely high requirements for network security.As a result,there is an urgent need to conduct research on 5G-R network security.To comprehensively enhance the end-to-end security protection of the 5G-R network,this study summarized the security requirements of the GSM-R network,analyzed the security risks and requirements faced by the 5G-R network,and proposed an overall 5G-R network security architecture.The security technical schemes were detailed from various aspects:5G-R infrastructure security,terminal access security,networking security,operation and maintenance security,data security,and network boundary security.Additionally,the study proposed leveraging the 5G-R security situation awareness system to achieve a comprehensive upgrade from basic security technologies to endogenous security capabilities within the 5G-R system.
基金supported by National Natural Science Foundation of China(No.62102449).
文摘Security attributes are the premise and foundation for implementing Attribute-Based Access Control(ABAC)mechanisms.However,when dealing with massive volumes of unstructured text big data resources,the current attribute management methods based on manual extraction face several issues,such as high costs for attribute extraction,long processing times,unstable accuracy,and poor scalability.To address these problems,this paper proposes an attribute mining technology for access control institutions based on hybrid capsule networks.This technology leverages transfer learning ideas,utilizing Bidirectional Encoder Representations from Transformers(BERT)pre-trained language models to achieve vectorization of unstructured text data resources.Furthermore,we have designed a novel end-to-end parallel hybrid network structure,where the parallel networks handle global and local information features of the text that they excel at,respectively.By employing techniques such as attention mechanisms,capsule networks,and dynamic routing,effective mining of security attributes for access control resources has been achieved.Finally,we evaluated the performance level of the proposed attribute mining method for access control institutions through experiments on the medical referral text resource dataset.The experimental results show that,compared with baseline algorithms,our method adopts a parallel network structure that can better balance global and local feature information,resulting in improved overall performance.Specifically,it achieves a comprehensive performance enhancement of 2.06%to 8.18%in the F1 score metric.Therefore,this technology can effectively provide attribute support for access control of unstructured text big data resources.
基金supported by Key Cultivation Project of Qiqihar Academy of Medical Sciences(No.2022-ZDPY-003).
文摘In order to reveal the multi-target pharmacological mechanism of Zedoary turmeric oil combined with docetaxel in the treatment of breast cancer,we used network pharmacology and molecular docking.The targets of docetaxel were retrieved from the Swiss Target Prediction database.The active components of Curcuma Zedoary turmeric were screened using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP),with criteria set as oral bioavailability OB≥30%and drug-likeness DL≥0.1.Potential targets of these components were subsequently predicted.Breast cancer-related targets were retrieved from the OMIM and GeneCards databases.The Venny tool was used to identify 177 overlapping targets between docetaxel,Zedoary turmeric oil,and breast cancer,followed by protein-protein interaction(PPI)analysis.Gene Ontology(GO)functional enrichment and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analyses were performed using the Metascape database.A drug-breast cancer-KEGG pathway target network was constructed using Cytoscape 3.8.0.Molecular docking was employed to verify the binding ability between drugs and core targets.Results showed that the combined treatment may exert anti-breast cancer effects through key targets such as MAPK1,PIK3CA,and HSP90AA1,primarily implicating the biological process of protein phosphorylation and engaging the PI3K-Akt signaling pathway.This study successfully predicted the key targets and enriched pathways of Zedoary turmeric oil combined with docetaxel for breast cancer treatment,providing new insights for further research and development.
文摘In this editorial,we comment on the article by Micucci et al published in the recent issue.We focus on the heterogenous nature of gastric cancer(GC)and the potential benefits of integrating traditional Chinese medicine(TCM)with the modern technology of network pharmacology(NP)and omics sequencing.GC is a heterogenous disease,as it incorporates several biochemical pathways that contribute to pathogenesis.TCM acknowledges the multifactorial,heterogenous nature of disease and utilizes an integrative approach to medicine.NP,a modern philosophy within drug development,integrates traditional knowledge of nutraceuticals and modern technologies to address the complex interactions of pathways within the body.Omics technologies,which is at the core of precision medicine,has allowed for this newfound principle of drug development.Metabolic pathways are better distinguished,leading to more targeted drug development.However,the use of omics technology needs to be employed to better characterize the subtypes of GC.This will allow TCM’s use of nutraceuticals in the application of NP to better target metabolic pathways that may aid in the prevention of GC as well as enhance treatment.
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.
文摘To explore the material basis and mechanisms of the anti-inflammatory effects of Hibiscus mutabilis L..The active ingredients and potential targets of Hibiscus mutabilis L.were obtained through the literature review and SwissADME platform.Genes related to the inflammation were collected using Genecards and OMIM databases,and the intersection genes were submitted on STRING and DAVID websites.Then,the protein interaction network(PPI),gene ontology(GO)and pathway(KEGG)were analyzed.Cytoscape 3.7.2 software was used to construct the“Hibiscus mutabilis L.-active ingredient-target-inflammation”network diagram,and AutoDockTools-1.5.6 software was used for the molecular docking verification.The antiinflammatory effect of Hibiscus mutabilis L.active ingredient was verified by the RAW264.7 inflammatory cell model.The results showed that 11 active components and 94 potential targets,1029 inflammatory targets and 24 intersection targets were obtained from Hibiscus mutabilis L..The key anti-inflammatory active ingredients of Hibiscus mutabilis L.are quercetin,apigenin and luteolin.Its action pathway is mainly related to NF-κB,cancer pathway and TNF signaling pathway.Cell experiments showed that total flavonoids of Hibiscus mutabilis L.could effectively inhibit the expression of tumor necrosis factor(TNF-α),interleukin 8(IL-8)and epidermal growth factor receptor(EGFR)in LPS-induced RAW 264.7 inflammatory cells.It also downregulates the phosphorylation of human nuclear factor ĸB inhibitory protein α(IĸBα)and NF-κB p65 subunit protein(p65).Overall,the anti-inflammatory effect of Hibiscus mutabilis L.is related to many active components,many signal pathways and targets,which provides a theoretical basis for its further development and application.
文摘The existence of absorption and reflection of light underwater leads to problems such as color distortion and blue-green bias in underwater images.In this study,a depthwise separable convolution-based generative adversarial network(GAN)algorithm was proposed.Taking GAN as the basic framework,it combined a depthwise separable convolution module,attention mechanism,and reconstructed convolution module to realize the enhancement of underwater degraded images.Multi-scale features were captured by the depthwise separable convolution module,and the attention mechanism was utilized to enhance attention to important features.The reconstructed convolution module further extracts and fuses local and global features.Experimental results showed that the algorithm performs well in improving the color bias and blurring of underwater images,with PSNR reaching 27.835,SSIM reaching 0.883,UIQM reaching 3.205,and UCIQE reaching 0.713.The enhanced image outperforms the comparison algorithm in both subjective and objective metrics.
文摘Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)techniques for DDoS attack diagnosis normally apply network traffic statistical features such as packet sizes and inter-arrival times.However,such techniques sometimes fail to capture complicated relations among various traffic flows.In this paper,we present a new multi-scale ensemble strategy given the Graph Neural Networks(GNNs)for improving DDoS detection.Our technique divides traffic into macro-and micro-level elements,letting various GNN models to get the two corase-scale anomalies and subtle,stealthy attack models.Through modeling network traffic as graph-structured data,GNNs efficiently learn intricate relations among network entities.The proposed ensemble learning algorithm combines the results of several GNNs to improve generalization,robustness,and scalability.Extensive experiments on three benchmark datasets—UNSW-NB15,CICIDS2017,and CICDDoS2019—show that our approach outperforms traditional machine learning and deep learning models in detecting both high-rate and low-rate(stealthy)DDoS attacks,with significant improvements in accuracy and recall.These findings demonstrate the suggested method’s applicability and robustness for real-world implementation in contexts where several DDoS patterns coexist.
基金supported by the National Natural Science Foundation of China(62173002,62403235,62403010,52301408,62173255)the Beijing Natural Science Foundation(L241015,4222045)+2 种基金the Yuxiu Innovation Project of NCUT(2024NCUTYXCX111)the China Postdoctoral Science Foundation(2025T180466)the Beijing Postdoctoral Research Foundation(2025-ZZ-70)。
文摘Networked predictive control(NPC) has gained significant attention in recent years for its ability to effectively and actively address communication constraints in networked control systems(NCSs),such as network-induced delays,packet dropouts,and packet disorders.Despite significant advancements,the increasing complexity and dynamism of network environments,along with the growing complexity of systems,pose new challenges for NPC.These challenges include difficulties in system modeling,cyber attacks,component faults,limited network bandwidth,and the necessity for distributed collaboration.This survey aims to provide a comprehensive review of NPC strategies.It begins with a summary of the primary challenges faced by NCSs,followed by an introduction to the control structure and core concepts of NPC.The survey then discusses several typical NPC schemes and examines their extensions in the areas of secure control,fault-tolerant control,distributed coordinated control,and event-triggered control.Moreover,it reviews notable works that have implemented these schemes.Finally,the survey concludes by exploring typical applications of NPC schemes and highlighting several challenging issues that could guide future research efforts.
文摘A multi-stimuli-responsive hydrogel,P(VI-co-MAAC-NE),was successfully constructed by covalently integrating the aggregation-induced emission(AIE)moiety(Z)-N-(4-(1-cyano-2-(4-(diethylamino)phenyl)vinyl)-phenyl)methacrylamide(NE)into a dynamic hydrogen-bonding network composed of 1-vinylimidazole(VI)and methacrylic acid(MAAC)groups.The dense hydrogen-bonding network not only provides enhanced mechanical robustness,but also significantly enhances the AIE effect of NE by restricting its molecular motion.Under various external stimuli,the hydrogen bonds within the hydrogel network undergo reversible dissociation and reformation,thus enabling synergistic modulation of the hydrogel’s mechanical properties and luminescence behavior.Specifically,organic solvents disrupt the hydrogen-bonding network and the aggregation of the AIE moiety NE,resulting in macroscopic swelling and fluorescence quenching of the hydrogel.In strongly acidic conditions,protonation of NE molecules suppresses the intramolecular charge transfer(ICT)process,yielding a blue-shifted emission band accompanied by intense blue fluorescence;in highly alkaline environments,deprotonation of carboxyl groups induces hydrogel swelling and disperses NE aggregates,leading to pronounced fluorescence quenching.Moreover,the system exhibits thermally activated shape-memory behavior:heating above the glass transition temperature(T_(g):ca.62℃)softens the hydrogel to allow programmable reshaping,and subsequent hydrogen bond reformation at ambient conditions locks in the resultant geometries without sacrificing the hydrogel’s fluorescence performance.By capitalizing on these multi-stimuli-responsive characteristics and shape-memory behavior,the potential of hydrogel P(VI-co-MAAC-NE)for advanced information encryption and anti-counterfeiting applications is demonstrated.This work not only provides a versatile material platform for sensing and information storage,but also offers new insights into the design of intelligent soft materials integrating AIE features with dynamically regulated supramolecular network structures.
基金supported by Yunnan Fundamental Research Projects(202501BD070001-081).
文摘Urbanization is a significant driver of the loss of biodiversity and the disruption of ecosystems.Amphibians are especially vulnerable to the negative impact of urbanization as their life cycles and habitat requirements are complex.The present study investigated the effects of urbanization on amphibian predation networks in suburban Kunming in Yunnan,China and aimed to understand how predation network structure and stability vary with urbanization level.We constructed predation networks by analyzing the stomach contents of amphibians from 12d istinct urbanization gradients.We used the bipartite package in R to evaluate network robustness metrics such as modularity,nestedness,connectivity,and average shortest path length(ASPL).We found that urbanization level is negatively correlated with predation network connectivity(R=−0.67,Ρ=0.02),but there were no significant correlations between urbanization level and nestedness,modularity,or ASPL.Removal of the keystone species destabilized the predation networks at certain locations.The present work highlighted that maintaining prey quantity and diversity preserves predation network connectivity and stabilizes the overall network in urbanizing landscapes.It also underscored the critical role that keystone species play in sustaining network robustness.The results of this research provided insights into the ecological consequences of urbanization.They also suggested that conservation measures should protect the key species and habitats of amphibian predation networks and mitigate the negative impact of urban development on them.
基金supported by the National Key Research and Development Program of China under Grant 2023YFB2903902in part by the Science and Technology Innovation Leading Talents Subsidy Project of Central Plains under Grant 244200510038.
文摘In the process of programmable networks simplifying network management and increasing network flexibility through custom packet behavior,security incidents caused by human logic errors are seriously threatening their safe operation,robust verificationmethods are required to ensure their correctness.As one of the formalmethods,symbolic execution offers a viable approach for verifying programmable networks by systematically exploring all possible paths within a program.However,its application in this field encounters scalability issues due to path explosion and complex constraint-solving.Therefore,in this paper,we propose NetVerifier,a scalable verification system for programmable networks.Tomitigate the path explosion issue,we developmultiple pruning strategies that strategically eliminate irrelevant execution paths while preserving verification integrity by precisely identifying the execution paths related to the verification purpose.To address the complex constraint-solving problem,we introduce an execution results reuse solution to avoid redundant computation of the same constraints.To apply these solutions intelligently,a matching algorithm is implemented to automatically select appropriate solutions based on the characteristics of the verification requirement.Moreover,Language Aided Verification(LAV),an assertion language,is designed to express verification intentions in a concise form.Experimental results on diverse open-source programs of varying scales demonstrate NetVerifier’s improvement in scalability and effectiveness in identifying potential network errors.In the best scenario,compared with ASSERT-P4,NetVerifier reduced the execution path,verification time,and memory occupation of the verification process by 99.92%,94.76%,and 65.19%,respectively.
基金Supported by the National key research and development program in the 14th five year plan 2021YFA1200700)the National Natural Science Foundation of China(62535018,62431025,62561160113)the Natural Science Foundation of Shanghai(23ZR1473400).
文摘Near-infrared image sensors are widely used in fields such as material identification,machine vision,and autonomous driving.Lead sulfide colloidal quantum dot-based infrared photodiodes can be integrated with sil⁃icon-based readout circuits in a single step.Based on this,we propose a photodiode based on an n-i-p structure,which removes the buffer layer and further simplifies the manufacturing process of quantum dot image sensors,thus reducing manufacturing costs.Additionally,for the noise complexity in quantum dot image sensors when capturing images,traditional denoising and non-uniformity methods often do not achieve optimal denoising re⁃sults.For the noise and stripe-type non-uniformity commonly encountered in infrared quantum dot detector imag⁃es,a network architecture has been developed that incorporates multiple key modules.This network combines channel attention and spatial attention mechanisms,dynamically adjusting the importance of feature maps to en⁃hance the ability to distinguish between noise and details.Meanwhile,the residual dense feature fusion module further improves the network's ability to process complex image structures through hierarchical feature extraction and fusion.Furthermore,the pyramid pooling module effectively captures information at different scales,improv⁃ing the network's multi-scale feature representation ability.Through the collaborative effect of these modules,the network can better handle various mixed noise and image non-uniformity issues.Experimental results show that it outperforms the traditional U-Net network in denoising and image correction tasks.
