Based on the implementation of NNSPC (Neural NetWork Synchronous Parallel Computer) developed by NJU, this paper discusses two schemes for implementing artificial neural network computer withdistributed memories: One ...Based on the implementation of NNSPC (Neural NetWork Synchronous Parallel Computer) developed by NJU, this paper discusses two schemes for implementing artificial neural network computer withdistributed memories: One is Switch Network Structure; the other is Ring Topology Structure. This papergives a comparison betWeen the two schemes and the principles of scheme selection.展开更多
This paper presents a hierarchical dynamic routing protocol (HDRP) based on the discrete dynamic programming principle. The proposed protocol can adapt to the dynamic and large computer networks (DLCN) with clustering...This paper presents a hierarchical dynamic routing protocol (HDRP) based on the discrete dynamic programming principle. The proposed protocol can adapt to the dynamic and large computer networks (DLCN) with clustering topology. The procedures for realizing routing update and decision are presented in this paper. The proof of correctness and complexity analysis of the protocol are also made. The performance measures of the HDRP including throughput and average message delay are evaluated by using of simulation. The study shows that the HDRP provides a new available approach to the routing decision for DLCN or high speed networks with clustering topology.展开更多
The optimal transmission lines assignment with maximal reliabilities (OTLAMR) in the multi-source multi-sink multi-state computer network (MMMCN) was investigated. The OTLAMR problem contains two sub-problems: the MMM...The optimal transmission lines assignment with maximal reliabilities (OTLAMR) in the multi-source multi-sink multi-state computer network (MMMCN) was investigated. The OTLAMR problem contains two sub-problems: the MMMCN reliabilities evaluation and multi-objective transmission lines assignment optimization. First, a reliability evaluation with a transmission line assignment (RETLA) algorithm is proposed to calculate the MMMCN reliabilities under the cost constraint for a certain transmission lines configuration. Second, the non-dominated sorting genetic algorithm II (NSGA-II) is adopted to find the non-dominated set of the transmission lines assignments based on the reliabilities obtained from the RETLA algorithm. By combining the RETLA and the NSGA-II algorithms together, the RETLA-NSGA II algorithm is proposed to solve the OTLAMR problem. The experiments result show that the RETLA-NSGA II algorithm can provide efficient solutions in a reasonable time, from which the decision makers can choose the best solution based on their preferences and experiences.展开更多
A dist ributed optimal local double loop (DOLDL) network is presented. Emphasis is laid on the topology and distributed routing algorithms for the DOLDL. On the basis of building an abstract model, a set of definition...A dist ributed optimal local double loop (DOLDL) network is presented. Emphasis is laid on the topology and distributed routing algorithms for the DOLDL. On the basis of building an abstract model, a set of definitions and theorems are described and proved. An algorithm which can optimize the double loop networks is presented. The optimal values of the topologic parameters for the DOLDL have been obtained by the algorithm, and these numerical results are analyzed. The study shows that the bounds of the optimal diameter d and average hop distance a for this class of networks are [3N- 2]≤d≤[3N ] and (5N/9 (N-1))-(3N -1.8)<a<(5N/9(N-1)) (3N -0.9),respectively (N is the number of nodes in the network ). A class of the distributed routing algorithms for the DOLDL and the implementation procedure of an adaptive fault-tolerant algorithm are proposed and analyzed. The correctness of the algorithm has also been verified by simulating.展开更多
A routing algorithm for distributed optimal double loop computer networks is proposed and analyzed. In this paper, the routing algorithm rule is described, and the procedures realizing the algorithm are given. The pr...A routing algorithm for distributed optimal double loop computer networks is proposed and analyzed. In this paper, the routing algorithm rule is described, and the procedures realizing the algorithm are given. The proposed algorithm is shown to be optimal and robust for optimal double loop. In the absence of failures,the algorithm can send a packet along the shortest path to destination; when there are failures,the packet can bypasss failed nodes and links.展开更多
August 4-August 7, 2014 Shanghai, China http:/www.icccn.org/icccn14/ICCCN is one of the leading international conferences for presenting noel ideas and fundamental advances in the fields of computer communications and...August 4-August 7, 2014 Shanghai, China http:/www.icccn.org/icccn14/ICCCN is one of the leading international conferences for presenting noel ideas and fundamental advances in the fields of computer communications and networks. ICCCN serves to foster communication among researchers and practitioners with展开更多
Low Earth orbit(LEO)satellite networks exhibit distinct characteristics,e.g.,limited resources of individual satellite nodes and dynamic network topology,which have brought many challenges for routing algorithms.To sa...Low Earth orbit(LEO)satellite networks exhibit distinct characteristics,e.g.,limited resources of individual satellite nodes and dynamic network topology,which have brought many challenges for routing algorithms.To satisfy quality of service(QoS)requirements of various users,it is critical to research efficient routing strategies to fully utilize satellite resources.This paper proposes a multi-QoS information optimized routing algorithm based on reinforcement learning for LEO satellite networks,which guarantees high level assurance demand services to be prioritized under limited satellite resources while considering the load balancing performance of the satellite networks for low level assurance demand services to ensure the full and effective utilization of satellite resources.An auxiliary path search algorithm is proposed to accelerate the convergence of satellite routing algorithm.Simulation results show that the generated routing strategy can timely process and fully meet the QoS demands of high assurance services while effectively improving the load balancing performance of the link.展开更多
Human disturbance activities is one of the main reasons for inducing geohazards.Ecological impact assessment metrics of roads are inconsistent criteria and multiple.From the perspective of visual observation,the envir...Human disturbance activities is one of the main reasons for inducing geohazards.Ecological impact assessment metrics of roads are inconsistent criteria and multiple.From the perspective of visual observation,the environment damage can be shown through detecting the uncovered area of vegetation in the images along road.To realize this,an end-to-end environment damage detection model based on convolutional neural network is proposed.A 50-layer residual network is used to extract feature map.The initial parameters are optimized by transfer learning.An example is shown by this method.The dataset including cliff and landslide damage are collected by us along road in Shennongjia national forest park.Results show 0.4703 average precision(AP)rating for cliff damage and 0.4809 average precision(AP)rating for landslide damage.Compared with YOLOv3,our model shows a better accuracy in cliff and landslide detection although a certain amount of speed is sacrificed.展开更多
Projective synchronization problems of a drive system and a particular response network were investigated,where the drive system is an arbitrary system with n+1 dimensions;it may be a linear or nonlinear system,and ev...Projective synchronization problems of a drive system and a particular response network were investigated,where the drive system is an arbitrary system with n+1 dimensions;it may be a linear or nonlinear system,and even a chaotic or hyperchaotic system,the response network is complex system coupled by N nodes,and every node is showed by the approximately linear part of the drive system.Only controlling any one node of the response network by designed controller can achieve the projective synchronization.Some numerical examples were employed to verify the effectiveness and correctness of the designed controller.展开更多
The burgeoning development of nanomedicine has provided state-of-the-art technologies and innovative methodologies for contemporary biomedical research,presenting unprecedented opportunities for resolving pivotal biom...The burgeoning development of nanomedicine has provided state-of-the-art technologies and innovative methodologies for contemporary biomedical research,presenting unprecedented opportunities for resolving pivotal biomedical challenges.Nanomaterials possess distinctive structures and properties.Through the exploration of the fabrication of emerging nanomedicines,multiple functions can be integrated to enable more precise diagnosis and treatment,thereby compensating for the limitations of traditional treatment modalities.Among various substances,polyphenols are natural organic compounds classified as plant secondary metabolites and are ubiquitously present in vegetables,teas,and other plants.Polyphenols are rich in active groups,including hydroxyl,carboxyl,amino,and conjugated double bonds.They exhibit robust adhesion,antioxidant,anti-inflammatory,and antibacterial biological activities and are extensively applied in pharmaceutical formulations.Additionally,polyphenols are characterized by their low cost,ready availability,and do not necessitate intricate chemical synthesis processes.Nevertheless,when natural polyphenol-based nanomedicines are utilized in isolation,they encounter several issues.These include poor water solubility,feeble stability,low bioavailability,the requirement for high dosages,and difficulties in precisely reaching the site of action.To address these concerns,researchers have developed nanomedicines by combining metal ions and functional ligands through metal coordination strategies.Nanomaterials,owing to their unique electronic and optical properties,have been successfully introduced into the realm of medical biology.Nano preparations not only enhance the stability of natural products but also endow them with targeting capabilities,thus enabling precise drug delivery.Polyphenols can further synergize with metal ions,anti-cancer drugs,or photosensitizers via supramolecular interactions to achieve multifunctional synergistic therapies,such as targeted drug delivery,efficacy enhancement,and the construction of engineering scaffolds.Metal-Polyphenol Coordination Polymers(MPCPs),composed of metal ions and phenolic ligands,are regarded as ideal nanoplatforms for disease diagnosis and treatment.In recent years,MPCPs have attracted extensive research in the biomedical field on account of their advantages,including facile synthesis,adjustable structure,excellent biocompatibility,and pH responsiveness.In this review,the classification and preparation strategies of MPCPs were systematically presented.Subsequently,their remarkable achievements in biomedical domains,such as bioimaging,biosensing,drug delivery,tumor therapy,and antimicrobial applications were highlighted.Finally,the principal limitations and prospects of MPCPs were comprehensi vely discussed.展开更多
Social interaction with peer pressure is widely studied in social network analysis.Game theory can be utilized to model dynamic social interaction,and one class of game network models assumes that people’s decision p...Social interaction with peer pressure is widely studied in social network analysis.Game theory can be utilized to model dynamic social interaction,and one class of game network models assumes that people’s decision payoff functions hinge on individual covariates and the choices of their friends.However,peer pressure would be misidentified and induce a non-negligible bias when incomplete covariates are involved in the game model.For this reason,we develop a generalized constant peer effects model based on homogeneity structure in dynamic social networks.The new model can effectively avoid bias through homogeneity pursuit and can be applied to a wider range of scenarios.To estimate peer pressure in the model,we first present two algorithms based on the initialize expand merge method and the polynomial-time twostage method to estimate homogeneity parameters.Then we apply the nested pseudo-likelihood method and obtain consistent estimators of peer pressure.Simulation evaluations show that our proposed methodology can achieve desirable and effective results in terms of the community misclassification rate and parameter estimation error.We also illustrate the advantages of our model in the empirical analysis when compared with a benchmark model.展开更多
In this paper,we propose a neural network approach to learn the parameters of a class of stochastic Lotka-Volterra systems.Approximations of the mean and covariance matrix of the observational variables are obtained f...In this paper,we propose a neural network approach to learn the parameters of a class of stochastic Lotka-Volterra systems.Approximations of the mean and covariance matrix of the observational variables are obtained from the Euler-Maruyama discretization of the underlying stochastic differential equations(SDEs),based on which the loss function is built.The stochastic gradient descent method is applied in the neural network training.Numerical experiments demonstrate the effectiveness of our method.展开更多
In the field of image processing,the analysis of Synthetic Aperture Radar(SAR)images is crucial due to its broad range of applications.However,SAR images are often affected by coherent speckle noise,which significantl...In the field of image processing,the analysis of Synthetic Aperture Radar(SAR)images is crucial due to its broad range of applications.However,SAR images are often affected by coherent speckle noise,which significantly degrades image quality.Traditional denoising methods,typically based on filter techniques,often face challenges related to inefficiency and limited adaptability.To address these limitations,this study proposes a novel SAR image denoising algorithm based on an enhanced residual network architecture,with the objective of enhancing the utility of SAR imagery in complex electromagnetic environments.The proposed algorithm integrates residual network modules,which directly process the noisy input images to generate denoised outputs.This approach not only reduces computational complexity but also mitigates the difficulties associated with model training.By combining the Transformer module with the residual block,the algorithm enhances the network's ability to extract global features,offering superior feature extraction capabilities compared to CNN-based residual modules.Additionally,the algorithm employs the adaptive activation function Meta-ACON,which dynamically adjusts the activation patterns of neurons,thereby improving the network's feature extraction efficiency.The effectiveness of the proposed denoising method is empirically validated using real SAR images from the RSOD dataset.The proposed algorithm exhibits remarkable performance in terms of EPI,SSIM,and ENL,while achieving a substantial enhancement in PSNR when compared to traditional and deep learning-based algorithms.The PSNR performance is enhanced by over twofold.Moreover,the evaluation of the MSTAR SAR dataset substantiates the algorithm's robustness and applicability in SAR denoising tasks,with a PSNR of 25.2021 being attained.These findings underscore the efficacy of the proposed algorithm in mitigating speckle noise while preserving critical features in SAR imagery,thereby enhancing its quality and usability in practical scenarios.展开更多
The anti-hair loss mechanism of Aquilaria sinensis leaf extract(ASE)has been studied by using metabolomics and network pharmacology.Metabolomics was utilized to comprehensively identify the active constituents of ASE,...The anti-hair loss mechanism of Aquilaria sinensis leaf extract(ASE)has been studied by using metabolomics and network pharmacology.Metabolomics was utilized to comprehensively identify the active constituents of ASE,and the network pharmacology was used to elucidate their anti-hair loss mechanism,which was verified by molecular docking technology.572 active compounds were identified from the ASE by metabolomics methods,where there are 1447 corresponding targets and 492 targets related to hair loss,totaling 88 targets.20 core active substances were identified by constructing a network between common targets and active substances,which include vanillic acid,chorionic acid,caffeic acid and apigenin.The five key targets of TNF,TP53,IL6,PPARG,and EGFR were screened out by the PPI network analysis on 88 common targets.The GO and KEGG pathway enrichment analysis showed that the inflammation,hormone balance,cell growth,proliferation,apoptosis,and oxidative stress are involved.Molecular docking studies have confirmed the high binding affinity between core active compounds and key targets.The drug similarity assessment on these core compounds suggested that they have the potential to be used as potential hair loss treatment drugs.This study elucidates the complex molecular mechanism of ASE in treating hair loss,and provides a reference for the future applications in hair care products.展开更多
Objective Traditional Chinese medicine(TCM)constitutes a valuable cultural heritage and an important source of antitumor compounds.Poria(Poria cocos(Schw.)Wolf),the dried sclerotium of a polyporaceae fungus,was first ...Objective Traditional Chinese medicine(TCM)constitutes a valuable cultural heritage and an important source of antitumor compounds.Poria(Poria cocos(Schw.)Wolf),the dried sclerotium of a polyporaceae fungus,was first documented in Shennong’s Classic of Materia Medica and has been used therapeutically and dietarily in China for millennia.Traditionally recognized for its diuretic,spleen-tonifying,and sedative properties,modern pharmacological studies confirm that Poria exhibits antioxidant,anti-inflammatory,antibacterial,and antitumor activities.Pachymic acid(PA;a triterpenoid with the chemical structure 3β-acetyloxy-16α-hydroxy-lanosta-8,24(31)-dien-21-oic acid),isolated from Poria,is a principal bioactive constituent.Emerging evidence indicates PA exerts antitumor effects through multiple mechanisms,though these remain incompletely characterized.Neuroblastoma(NB),a highly malignant pediatric extracranial solid tumor accounting for 15%of childhood cancer deaths,urgently requires safer therapeutics due to the limitations of current treatments.Although PA shows multi-mechanistic antitumor potential,its efficacy against NB remains uncharacterized.This study systematically investigated the potential molecular targets and mechanisms underlying the anti-NB effects of PA by integrating network pharmacology-based target prediction with experimental validation of multi-target interactions through molecular docking,dynamic simulations,and in vitro assays,aimed to establish a novel perspective on PA’s antitumor activity and explore its potential clinical implications for NB treatment by integrating computational predictions with biological assays.Methods This study employed network pharmacology to identify potential targets of PA in NB,followed by validation using molecular docking,molecular dynamics(MD)simulations,MM/PBSA free energy analysis,RT-qPCR and Western blot experiments.Network pharmacology analysis included target screening via TCMSP,GeneCards,DisGeNET,SwissTargetPrediction,SuperPred,and PharmMapper.Subsequently,potential targets were predicted by intersecting the results from these databases via Venn analysis.Following target prediction,topological analysis was performed to identify key targets using Cytoscape software.Molecular docking was conducted using AutoDock Vina,with the binding pocket defined based on crystal structures.MD simulations were performed for 100 ns using GROMACS,and RMSD,RMSF,SASA,and hydrogen bonding dynamics were analyzed.MM/PBSA calculations were carried out to estimate the binding free energy of each protein-ligand complex.In vitro validation included RT-qPCR and Western blot,with GAPDH used as an internal control.Results The CCK-8 assay demonstrated a concentration-dependent inhibitory effect of PA on NB cell viability.GO analysis suggested that the anti-NB activity of PA might involve cellular response to chemical stress,vesicle lumen,and protein tyrosine kinase activity.KEGG pathway enrichment analysis suggested that the anti-NB activity of PA might involve the PI3K/AKT,MAPK,and Ras signaling pathways.Molecular docking and MD simulations revealed stable binding interactions between PA and the core target proteins AKT1,EGFR,SRC,and HSP90AA1.RT-qPCR and Western blot analyses further confirmed that PA treatment significantly decreased the mRNA and protein expression of AKT1,EGFR,and SRC while increasing the HSP90AA1 mRNA and protein levels.Conclusion It was suggested that PA may exert its anti-NB effects by inhibiting AKT1,EGFR,and SRC expression,potentially modulating the PI3K/AKT signaling pathway.These findings provide crucial evidence supporting PA’s development as a therapeutic candidate for NB.展开更多
Objective N6-methyladenosine(m6A),the most prevalent epigenetic modification in eukaryotic RNA,plays a pivotal role in regulating cellular differentiation and developmental processes,with its dysregulation implicated ...Objective N6-methyladenosine(m6A),the most prevalent epigenetic modification in eukaryotic RNA,plays a pivotal role in regulating cellular differentiation and developmental processes,with its dysregulation implicated in diverse pathological conditions.Accurate prediction of m6A sites is critical for elucidating their regulatory mechanisms and informing drug development.However,traditional experimental methods are time-consuming and costly.Although various computational approaches have been proposed,challenges remain in feature learning,predictive accuracy,and generalization.Here,we present m6A-PSRA,a dual-branch residual-network-based predictor that fully exploits RNA sequence information to enhance prediction performance and model generalization.Methods m6A-PSRA adopts a parallel dual-branch network architecture to comprehensively extract RNA sequence features via two independent pathways.The first branch applies one-hot encoding to transform the RNA sequence into a numerical matrix while strictly preserving positional information and sequence continuity.This ensures that the biological context conveyed by nucleotide order is retained.A bidirectional long short-term memory network(BiLSTM)then processes the encoded matrix,capturing both forward and backward dependencies between bases to resolve contextual correlations.The second branch employs a k-mer tokenization strategy(k=3),decomposing the sequence into overlapping 3-mer subsequences to capture local sequence patterns.A pre-trained Doc2vec model maps these subsequences into fixeddimensional vectors,reducing feature dimensionality while extracting latent global semantic information via context learning.Both branches integrate residual networks(ResNet)and a self-attention mechanism:ResNet mitigates vanishing gradients through skip connections,preserving feature integrity,while self-attention adaptively assigns weights to focus on sequence regions most relevant to methylation prediction.This synergy enhances both feature learning and generalization capability.Results Across 11 tissues from humans,mice,and rats,m6A-PSRA consistently outperformed existing methods in accuracy(ACC)and area under the curve(AUC),achieving>90%ACC and>95%AUC in every tissue tested,indicating strong cross-species and cross-tissue adaptability.Validation on independent datasets—including three human cell lines(MOLM1,HEK293,A549)and a long-sequence dataset(m6A_IND,1001 nt)—confirmed stable performance across varied biological contexts and sequence lengths.Ablation studies demonstrated that the dual-branch architecture,residual network,and self-attention mechanism each contribute critically to performance,with their combination reducing interference between pathways.Motif analysis revealed an enrichment of m6A sites in guanine(G)and cytosine(C),consistent with known regulatory patterns,supporting the model’s biological plausibility.Conclusion m6A-PSRA effectively captures RNA sequence features,achieving high prediction accuracy and robust generalization across tissues and species,providing an efficient computational tool for m6A methylation site prediction.展开更多
Objective Repetitive transcranial magnetic stimulation(rTMS)has demonstrated efficacy in enhancing neurocognitive performance in Alzheimer’s disease(AD),but the neurobiological mechanisms linking synaptic pathology,n...Objective Repetitive transcranial magnetic stimulation(rTMS)has demonstrated efficacy in enhancing neurocognitive performance in Alzheimer’s disease(AD),but the neurobiological mechanisms linking synaptic pathology,neural oscillatory dynamics,and brain network reorganization remain unclear.This investigation seeks to systematically evaluate the therapeutic potential of rTMS as a non-invasive neuromodulatory intervention through a multimodal framework integrating clinical assessments,molecular profiling,and neurophysiological monitoring.Methods In this prospective double-blind trial,12 AD patients underwent a 14-day protocol of 20 Hz rTMS,with comprehensive multimodal assessments performed pre-and postintervention.Cognitive functioning was quantified using the mini-mental state examination(MMSE)and Montreal cognitive assessment(MOCA),while daily living capacities and neuropsychiatric profiles were respectively evaluated through the activities of daily living(ADL)scale and combined neuropsychiatric inventory(NPI)-Hamilton depression rating scale(HAMD).Peripheral blood biomarkers,specifically Aβ1-40 and phosphorylated tau(p-tau181),were analyzed to investigate the effects of rTMS on molecular metabolism.Spectral power analysis was employed to investigate rTMS-induced modulations of neural rhythms in AD patients,while brain network analyses incorporating topological properties were conducted to examine stimulus-driven network reorganization.Furthermore,systematic assessment of correlations between cognitive scale scores,blood biomarkers,and network characteristics was performed to elucidate cross-modal therapeutic associations.Results Clinically,MMSE and MOCA scores improved significantly(P<0.05).Biomarker showed that Aβ1-40 level increased(P<0.05),contrasting with p-tau181 reduction.Moreover,the levels of Aβ1-40 were positively correlated with MMSE and MOCA scores.Post-intervention analyses revealed significant modulations in oscillatory power,characterized by pronounced reductions in delta(P<0.05)and theta bands(P<0.05),while concurrent enhancements were observed in alpha,beta,and gamma band activities(all P<0.05).Network analysis revealed frequency-specific reorganization:clustering coefficients were significantly decreased in delta,theta,and alpha bands(P<0.05),while global efficiency improvement was exclusively detected in the delta band(P<0.05).The alpha band demonstrated concurrent increases in average nodal degree(P<0.05)and characteristic path length reduction(P<0.05).Further research findings indicate that the changes in the clinical scale HAMD scores before and after rTMS stimulation are negatively correlated with the changes in the blood biomarkers Aβ1-40 and p-tau181.Additionally,the changes in the clinical scales MMSE and MoCA scores were negatively correlated with the changes in the node degree of the alpha frequency band and negatively correlated with the clustering coefficient of the delta frequency band.However,the changes in MMSE scores are positively correlated with the changes in global efficiency of both the delta and alpha frequency bands.Conclusion 20 Hz rTMS targeting dorsolateral prefrontal cortex(DLPFC)significantly improves cognitive function and enhances the metabolic clearance ofβ-amyloid and tau proteins in AD patients.This neurotherapeutic effect is mechanistically associated with rTMS-mediated frequency-selective neuromodulation,which enhances the connectivity of oscillatory networks through improved neuronal synchronization and optimized topological organization of functional brain networks.These findings not only support the efficacy of rTMS as an adjunctive therapy for AD but also underscore the importance of employing multiple assessment methods—including clinical scales,blood biomarkers,and EEG——in understanding and monitoring the progression of AD.This research provides a significant theoretical foundation and empirical evidence for further exploration of rTMS applications in AD treatment.展开更多
In this paper,we propose hierarchical attention dual network(DNet)for fine-grained image classification.The DNet can randomly select pairs of inputs from the dataset and compare the differences between them through hi...In this paper,we propose hierarchical attention dual network(DNet)for fine-grained image classification.The DNet can randomly select pairs of inputs from the dataset and compare the differences between them through hierarchical attention feature learning,which are used simultaneously to remove noise and retain salient features.In the loss function,it considers the losses of difference in paired images according to the intra-variance and inter-variance.In addition,we also collect the disaster scene dataset from remote sensing images and apply the proposed method to disaster scene classification,which contains complex scenes and multiple types of disasters.Compared to other methods,experimental results show that the DNet with hierarchical attention is robust to different datasets and performs better.展开更多
Multi-agent systems often require good interoperability in the process of completing their assigned tasks.This paper first models the static structure and dynamic behavior of multiagent systems based on layered weight...Multi-agent systems often require good interoperability in the process of completing their assigned tasks.This paper first models the static structure and dynamic behavior of multiagent systems based on layered weighted scale-free community network and susceptible-infected-recovered(SIR)model.To solve the problem of difficulty in describing the changes in the structure and collaboration mode of the system under external factors,a two-dimensional Monte Carlo method and an improved dynamic Bayesian network are used to simulate the impact of external environmental factors on multi-agent systems.A collaborative information flow path optimization algorithm for agents under environmental factors is designed based on the Dijkstra algorithm.A method for evaluating system interoperability is designed based on simulation experiments,providing reference for the construction planning and optimization of organizational application of the system.Finally,the feasibility of the method is verified through case studies.展开更多
文摘Based on the implementation of NNSPC (Neural NetWork Synchronous Parallel Computer) developed by NJU, this paper discusses two schemes for implementing artificial neural network computer withdistributed memories: One is Switch Network Structure; the other is Ring Topology Structure. This papergives a comparison betWeen the two schemes and the principles of scheme selection.
文摘This paper presents a hierarchical dynamic routing protocol (HDRP) based on the discrete dynamic programming principle. The proposed protocol can adapt to the dynamic and large computer networks (DLCN) with clustering topology. The procedures for realizing routing update and decision are presented in this paper. The proof of correctness and complexity analysis of the protocol are also made. The performance measures of the HDRP including throughput and average message delay are evaluated by using of simulation. The study shows that the HDRP provides a new available approach to the routing decision for DLCN or high speed networks with clustering topology.
基金Projects(61004074,61134001,21076179)supported by the National Natural Science Foundation of ChinaProject(2009BAG12A08)supported by the National Key Technology Support Program of China+1 种基金Project(2010QNA5001)supported by the Fundamental Research Funds for the Central Universities of ChinaProjects(2012AA06A404,2006AA04Z184)supported by the National High Technology Research and Development Program of China
文摘The optimal transmission lines assignment with maximal reliabilities (OTLAMR) in the multi-source multi-sink multi-state computer network (MMMCN) was investigated. The OTLAMR problem contains two sub-problems: the MMMCN reliabilities evaluation and multi-objective transmission lines assignment optimization. First, a reliability evaluation with a transmission line assignment (RETLA) algorithm is proposed to calculate the MMMCN reliabilities under the cost constraint for a certain transmission lines configuration. Second, the non-dominated sorting genetic algorithm II (NSGA-II) is adopted to find the non-dominated set of the transmission lines assignments based on the reliabilities obtained from the RETLA algorithm. By combining the RETLA and the NSGA-II algorithms together, the RETLA-NSGA II algorithm is proposed to solve the OTLAMR problem. The experiments result show that the RETLA-NSGA II algorithm can provide efficient solutions in a reasonable time, from which the decision makers can choose the best solution based on their preferences and experiences.
文摘A dist ributed optimal local double loop (DOLDL) network is presented. Emphasis is laid on the topology and distributed routing algorithms for the DOLDL. On the basis of building an abstract model, a set of definitions and theorems are described and proved. An algorithm which can optimize the double loop networks is presented. The optimal values of the topologic parameters for the DOLDL have been obtained by the algorithm, and these numerical results are analyzed. The study shows that the bounds of the optimal diameter d and average hop distance a for this class of networks are [3N- 2]≤d≤[3N ] and (5N/9 (N-1))-(3N -1.8)<a<(5N/9(N-1)) (3N -0.9),respectively (N is the number of nodes in the network ). A class of the distributed routing algorithms for the DOLDL and the implementation procedure of an adaptive fault-tolerant algorithm are proposed and analyzed. The correctness of the algorithm has also been verified by simulating.
文摘A routing algorithm for distributed optimal double loop computer networks is proposed and analyzed. In this paper, the routing algorithm rule is described, and the procedures realizing the algorithm are given. The proposed algorithm is shown to be optimal and robust for optimal double loop. In the absence of failures,the algorithm can send a packet along the shortest path to destination; when there are failures,the packet can bypasss failed nodes and links.
文摘August 4-August 7, 2014 Shanghai, China http:/www.icccn.org/icccn14/ICCCN is one of the leading international conferences for presenting noel ideas and fundamental advances in the fields of computer communications and networks. ICCCN serves to foster communication among researchers and practitioners with
基金National Key Research and Development Program(2021YFB2900604)。
文摘Low Earth orbit(LEO)satellite networks exhibit distinct characteristics,e.g.,limited resources of individual satellite nodes and dynamic network topology,which have brought many challenges for routing algorithms.To satisfy quality of service(QoS)requirements of various users,it is critical to research efficient routing strategies to fully utilize satellite resources.This paper proposes a multi-QoS information optimized routing algorithm based on reinforcement learning for LEO satellite networks,which guarantees high level assurance demand services to be prioritized under limited satellite resources while considering the load balancing performance of the satellite networks for low level assurance demand services to ensure the full and effective utilization of satellite resources.An auxiliary path search algorithm is proposed to accelerate the convergence of satellite routing algorithm.Simulation results show that the generated routing strategy can timely process and fully meet the QoS demands of high assurance services while effectively improving the load balancing performance of the link.
文摘Human disturbance activities is one of the main reasons for inducing geohazards.Ecological impact assessment metrics of roads are inconsistent criteria and multiple.From the perspective of visual observation,the environment damage can be shown through detecting the uncovered area of vegetation in the images along road.To realize this,an end-to-end environment damage detection model based on convolutional neural network is proposed.A 50-layer residual network is used to extract feature map.The initial parameters are optimized by transfer learning.An example is shown by this method.The dataset including cliff and landslide damage are collected by us along road in Shennongjia national forest park.Results show 0.4703 average precision(AP)rating for cliff damage and 0.4809 average precision(AP)rating for landslide damage.Compared with YOLOv3,our model shows a better accuracy in cliff and landslide detection although a certain amount of speed is sacrificed.
基金Supported by the National Natural Science Foundation of China (11161027)。
文摘Projective synchronization problems of a drive system and a particular response network were investigated,where the drive system is an arbitrary system with n+1 dimensions;it may be a linear or nonlinear system,and even a chaotic or hyperchaotic system,the response network is complex system coupled by N nodes,and every node is showed by the approximately linear part of the drive system.Only controlling any one node of the response network by designed controller can achieve the projective synchronization.Some numerical examples were employed to verify the effectiveness and correctness of the designed controller.
文摘The burgeoning development of nanomedicine has provided state-of-the-art technologies and innovative methodologies for contemporary biomedical research,presenting unprecedented opportunities for resolving pivotal biomedical challenges.Nanomaterials possess distinctive structures and properties.Through the exploration of the fabrication of emerging nanomedicines,multiple functions can be integrated to enable more precise diagnosis and treatment,thereby compensating for the limitations of traditional treatment modalities.Among various substances,polyphenols are natural organic compounds classified as plant secondary metabolites and are ubiquitously present in vegetables,teas,and other plants.Polyphenols are rich in active groups,including hydroxyl,carboxyl,amino,and conjugated double bonds.They exhibit robust adhesion,antioxidant,anti-inflammatory,and antibacterial biological activities and are extensively applied in pharmaceutical formulations.Additionally,polyphenols are characterized by their low cost,ready availability,and do not necessitate intricate chemical synthesis processes.Nevertheless,when natural polyphenol-based nanomedicines are utilized in isolation,they encounter several issues.These include poor water solubility,feeble stability,low bioavailability,the requirement for high dosages,and difficulties in precisely reaching the site of action.To address these concerns,researchers have developed nanomedicines by combining metal ions and functional ligands through metal coordination strategies.Nanomaterials,owing to their unique electronic and optical properties,have been successfully introduced into the realm of medical biology.Nano preparations not only enhance the stability of natural products but also endow them with targeting capabilities,thus enabling precise drug delivery.Polyphenols can further synergize with metal ions,anti-cancer drugs,or photosensitizers via supramolecular interactions to achieve multifunctional synergistic therapies,such as targeted drug delivery,efficacy enhancement,and the construction of engineering scaffolds.Metal-Polyphenol Coordination Polymers(MPCPs),composed of metal ions and phenolic ligands,are regarded as ideal nanoplatforms for disease diagnosis and treatment.In recent years,MPCPs have attracted extensive research in the biomedical field on account of their advantages,including facile synthesis,adjustable structure,excellent biocompatibility,and pH responsiveness.In this review,the classification and preparation strategies of MPCPs were systematically presented.Subsequently,their remarkable achievements in biomedical domains,such as bioimaging,biosensing,drug delivery,tumor therapy,and antimicrobial applications were highlighted.Finally,the principal limitations and prospects of MPCPs were comprehensi vely discussed.
基金supported by the National Nature Science Foundation of China(71771201,72531009,71973001)the USTC Research Funds of the Double First-Class Initiative(FSSF-A-240202).
文摘Social interaction with peer pressure is widely studied in social network analysis.Game theory can be utilized to model dynamic social interaction,and one class of game network models assumes that people’s decision payoff functions hinge on individual covariates and the choices of their friends.However,peer pressure would be misidentified and induce a non-negligible bias when incomplete covariates are involved in the game model.For this reason,we develop a generalized constant peer effects model based on homogeneity structure in dynamic social networks.The new model can effectively avoid bias through homogeneity pursuit and can be applied to a wider range of scenarios.To estimate peer pressure in the model,we first present two algorithms based on the initialize expand merge method and the polynomial-time twostage method to estimate homogeneity parameters.Then we apply the nested pseudo-likelihood method and obtain consistent estimators of peer pressure.Simulation evaluations show that our proposed methodology can achieve desirable and effective results in terms of the community misclassification rate and parameter estimation error.We also illustrate the advantages of our model in the empirical analysis when compared with a benchmark model.
基金Supported by the National Natural Science Foundation of China(11971458,11471310)。
文摘In this paper,we propose a neural network approach to learn the parameters of a class of stochastic Lotka-Volterra systems.Approximations of the mean and covariance matrix of the observational variables are obtained from the Euler-Maruyama discretization of the underlying stochastic differential equations(SDEs),based on which the loss function is built.The stochastic gradient descent method is applied in the neural network training.Numerical experiments demonstrate the effectiveness of our method.
文摘In the field of image processing,the analysis of Synthetic Aperture Radar(SAR)images is crucial due to its broad range of applications.However,SAR images are often affected by coherent speckle noise,which significantly degrades image quality.Traditional denoising methods,typically based on filter techniques,often face challenges related to inefficiency and limited adaptability.To address these limitations,this study proposes a novel SAR image denoising algorithm based on an enhanced residual network architecture,with the objective of enhancing the utility of SAR imagery in complex electromagnetic environments.The proposed algorithm integrates residual network modules,which directly process the noisy input images to generate denoised outputs.This approach not only reduces computational complexity but also mitigates the difficulties associated with model training.By combining the Transformer module with the residual block,the algorithm enhances the network's ability to extract global features,offering superior feature extraction capabilities compared to CNN-based residual modules.Additionally,the algorithm employs the adaptive activation function Meta-ACON,which dynamically adjusts the activation patterns of neurons,thereby improving the network's feature extraction efficiency.The effectiveness of the proposed denoising method is empirically validated using real SAR images from the RSOD dataset.The proposed algorithm exhibits remarkable performance in terms of EPI,SSIM,and ENL,while achieving a substantial enhancement in PSNR when compared to traditional and deep learning-based algorithms.The PSNR performance is enhanced by over twofold.Moreover,the evaluation of the MSTAR SAR dataset substantiates the algorithm's robustness and applicability in SAR denoising tasks,with a PSNR of 25.2021 being attained.These findings underscore the efficacy of the proposed algorithm in mitigating speckle noise while preserving critical features in SAR imagery,thereby enhancing its quality and usability in practical scenarios.
文摘The anti-hair loss mechanism of Aquilaria sinensis leaf extract(ASE)has been studied by using metabolomics and network pharmacology.Metabolomics was utilized to comprehensively identify the active constituents of ASE,and the network pharmacology was used to elucidate their anti-hair loss mechanism,which was verified by molecular docking technology.572 active compounds were identified from the ASE by metabolomics methods,where there are 1447 corresponding targets and 492 targets related to hair loss,totaling 88 targets.20 core active substances were identified by constructing a network between common targets and active substances,which include vanillic acid,chorionic acid,caffeic acid and apigenin.The five key targets of TNF,TP53,IL6,PPARG,and EGFR were screened out by the PPI network analysis on 88 common targets.The GO and KEGG pathway enrichment analysis showed that the inflammation,hormone balance,cell growth,proliferation,apoptosis,and oxidative stress are involved.Molecular docking studies have confirmed the high binding affinity between core active compounds and key targets.The drug similarity assessment on these core compounds suggested that they have the potential to be used as potential hair loss treatment drugs.This study elucidates the complex molecular mechanism of ASE in treating hair loss,and provides a reference for the future applications in hair care products.
文摘Objective Traditional Chinese medicine(TCM)constitutes a valuable cultural heritage and an important source of antitumor compounds.Poria(Poria cocos(Schw.)Wolf),the dried sclerotium of a polyporaceae fungus,was first documented in Shennong’s Classic of Materia Medica and has been used therapeutically and dietarily in China for millennia.Traditionally recognized for its diuretic,spleen-tonifying,and sedative properties,modern pharmacological studies confirm that Poria exhibits antioxidant,anti-inflammatory,antibacterial,and antitumor activities.Pachymic acid(PA;a triterpenoid with the chemical structure 3β-acetyloxy-16α-hydroxy-lanosta-8,24(31)-dien-21-oic acid),isolated from Poria,is a principal bioactive constituent.Emerging evidence indicates PA exerts antitumor effects through multiple mechanisms,though these remain incompletely characterized.Neuroblastoma(NB),a highly malignant pediatric extracranial solid tumor accounting for 15%of childhood cancer deaths,urgently requires safer therapeutics due to the limitations of current treatments.Although PA shows multi-mechanistic antitumor potential,its efficacy against NB remains uncharacterized.This study systematically investigated the potential molecular targets and mechanisms underlying the anti-NB effects of PA by integrating network pharmacology-based target prediction with experimental validation of multi-target interactions through molecular docking,dynamic simulations,and in vitro assays,aimed to establish a novel perspective on PA’s antitumor activity and explore its potential clinical implications for NB treatment by integrating computational predictions with biological assays.Methods This study employed network pharmacology to identify potential targets of PA in NB,followed by validation using molecular docking,molecular dynamics(MD)simulations,MM/PBSA free energy analysis,RT-qPCR and Western blot experiments.Network pharmacology analysis included target screening via TCMSP,GeneCards,DisGeNET,SwissTargetPrediction,SuperPred,and PharmMapper.Subsequently,potential targets were predicted by intersecting the results from these databases via Venn analysis.Following target prediction,topological analysis was performed to identify key targets using Cytoscape software.Molecular docking was conducted using AutoDock Vina,with the binding pocket defined based on crystal structures.MD simulations were performed for 100 ns using GROMACS,and RMSD,RMSF,SASA,and hydrogen bonding dynamics were analyzed.MM/PBSA calculations were carried out to estimate the binding free energy of each protein-ligand complex.In vitro validation included RT-qPCR and Western blot,with GAPDH used as an internal control.Results The CCK-8 assay demonstrated a concentration-dependent inhibitory effect of PA on NB cell viability.GO analysis suggested that the anti-NB activity of PA might involve cellular response to chemical stress,vesicle lumen,and protein tyrosine kinase activity.KEGG pathway enrichment analysis suggested that the anti-NB activity of PA might involve the PI3K/AKT,MAPK,and Ras signaling pathways.Molecular docking and MD simulations revealed stable binding interactions between PA and the core target proteins AKT1,EGFR,SRC,and HSP90AA1.RT-qPCR and Western blot analyses further confirmed that PA treatment significantly decreased the mRNA and protein expression of AKT1,EGFR,and SRC while increasing the HSP90AA1 mRNA and protein levels.Conclusion It was suggested that PA may exert its anti-NB effects by inhibiting AKT1,EGFR,and SRC expression,potentially modulating the PI3K/AKT signaling pathway.These findings provide crucial evidence supporting PA’s development as a therapeutic candidate for NB.
基金supported by grants from The National Natural Science Foundation of China(12361104)Yunnan Fundamental Research Projects(202301AT070016,202401AT070036)+2 种基金the Youth Talent Program of Xingdian Talent Support Plan(XDYC-QNRC-2022-0514)the Yunnan Province International Joint Laboratory for Intelligent Integration and Application of Ethnic Multilingualism(202403AP140014)the Open Research Fund of Yunnan Key Laboratory of Statistical Modeling and Data Analysis,Yunnan University(SMDAYB2023004)。
文摘Objective N6-methyladenosine(m6A),the most prevalent epigenetic modification in eukaryotic RNA,plays a pivotal role in regulating cellular differentiation and developmental processes,with its dysregulation implicated in diverse pathological conditions.Accurate prediction of m6A sites is critical for elucidating their regulatory mechanisms and informing drug development.However,traditional experimental methods are time-consuming and costly.Although various computational approaches have been proposed,challenges remain in feature learning,predictive accuracy,and generalization.Here,we present m6A-PSRA,a dual-branch residual-network-based predictor that fully exploits RNA sequence information to enhance prediction performance and model generalization.Methods m6A-PSRA adopts a parallel dual-branch network architecture to comprehensively extract RNA sequence features via two independent pathways.The first branch applies one-hot encoding to transform the RNA sequence into a numerical matrix while strictly preserving positional information and sequence continuity.This ensures that the biological context conveyed by nucleotide order is retained.A bidirectional long short-term memory network(BiLSTM)then processes the encoded matrix,capturing both forward and backward dependencies between bases to resolve contextual correlations.The second branch employs a k-mer tokenization strategy(k=3),decomposing the sequence into overlapping 3-mer subsequences to capture local sequence patterns.A pre-trained Doc2vec model maps these subsequences into fixeddimensional vectors,reducing feature dimensionality while extracting latent global semantic information via context learning.Both branches integrate residual networks(ResNet)and a self-attention mechanism:ResNet mitigates vanishing gradients through skip connections,preserving feature integrity,while self-attention adaptively assigns weights to focus on sequence regions most relevant to methylation prediction.This synergy enhances both feature learning and generalization capability.Results Across 11 tissues from humans,mice,and rats,m6A-PSRA consistently outperformed existing methods in accuracy(ACC)and area under the curve(AUC),achieving>90%ACC and>95%AUC in every tissue tested,indicating strong cross-species and cross-tissue adaptability.Validation on independent datasets—including three human cell lines(MOLM1,HEK293,A549)and a long-sequence dataset(m6A_IND,1001 nt)—confirmed stable performance across varied biological contexts and sequence lengths.Ablation studies demonstrated that the dual-branch architecture,residual network,and self-attention mechanism each contribute critically to performance,with their combination reducing interference between pathways.Motif analysis revealed an enrichment of m6A sites in guanine(G)and cytosine(C),consistent with known regulatory patterns,supporting the model’s biological plausibility.Conclusion m6A-PSRA effectively captures RNA sequence features,achieving high prediction accuracy and robust generalization across tissues and species,providing an efficient computational tool for m6A methylation site prediction.
文摘Objective Repetitive transcranial magnetic stimulation(rTMS)has demonstrated efficacy in enhancing neurocognitive performance in Alzheimer’s disease(AD),but the neurobiological mechanisms linking synaptic pathology,neural oscillatory dynamics,and brain network reorganization remain unclear.This investigation seeks to systematically evaluate the therapeutic potential of rTMS as a non-invasive neuromodulatory intervention through a multimodal framework integrating clinical assessments,molecular profiling,and neurophysiological monitoring.Methods In this prospective double-blind trial,12 AD patients underwent a 14-day protocol of 20 Hz rTMS,with comprehensive multimodal assessments performed pre-and postintervention.Cognitive functioning was quantified using the mini-mental state examination(MMSE)and Montreal cognitive assessment(MOCA),while daily living capacities and neuropsychiatric profiles were respectively evaluated through the activities of daily living(ADL)scale and combined neuropsychiatric inventory(NPI)-Hamilton depression rating scale(HAMD).Peripheral blood biomarkers,specifically Aβ1-40 and phosphorylated tau(p-tau181),were analyzed to investigate the effects of rTMS on molecular metabolism.Spectral power analysis was employed to investigate rTMS-induced modulations of neural rhythms in AD patients,while brain network analyses incorporating topological properties were conducted to examine stimulus-driven network reorganization.Furthermore,systematic assessment of correlations between cognitive scale scores,blood biomarkers,and network characteristics was performed to elucidate cross-modal therapeutic associations.Results Clinically,MMSE and MOCA scores improved significantly(P<0.05).Biomarker showed that Aβ1-40 level increased(P<0.05),contrasting with p-tau181 reduction.Moreover,the levels of Aβ1-40 were positively correlated with MMSE and MOCA scores.Post-intervention analyses revealed significant modulations in oscillatory power,characterized by pronounced reductions in delta(P<0.05)and theta bands(P<0.05),while concurrent enhancements were observed in alpha,beta,and gamma band activities(all P<0.05).Network analysis revealed frequency-specific reorganization:clustering coefficients were significantly decreased in delta,theta,and alpha bands(P<0.05),while global efficiency improvement was exclusively detected in the delta band(P<0.05).The alpha band demonstrated concurrent increases in average nodal degree(P<0.05)and characteristic path length reduction(P<0.05).Further research findings indicate that the changes in the clinical scale HAMD scores before and after rTMS stimulation are negatively correlated with the changes in the blood biomarkers Aβ1-40 and p-tau181.Additionally,the changes in the clinical scales MMSE and MoCA scores were negatively correlated with the changes in the node degree of the alpha frequency band and negatively correlated with the clustering coefficient of the delta frequency band.However,the changes in MMSE scores are positively correlated with the changes in global efficiency of both the delta and alpha frequency bands.Conclusion 20 Hz rTMS targeting dorsolateral prefrontal cortex(DLPFC)significantly improves cognitive function and enhances the metabolic clearance ofβ-amyloid and tau proteins in AD patients.This neurotherapeutic effect is mechanistically associated with rTMS-mediated frequency-selective neuromodulation,which enhances the connectivity of oscillatory networks through improved neuronal synchronization and optimized topological organization of functional brain networks.These findings not only support the efficacy of rTMS as an adjunctive therapy for AD but also underscore the importance of employing multiple assessment methods—including clinical scales,blood biomarkers,and EEG——in understanding and monitoring the progression of AD.This research provides a significant theoretical foundation and empirical evidence for further exploration of rTMS applications in AD treatment.
基金Supported by the National Natural Science Foundation of China(61601176)。
文摘In this paper,we propose hierarchical attention dual network(DNet)for fine-grained image classification.The DNet can randomly select pairs of inputs from the dataset and compare the differences between them through hierarchical attention feature learning,which are used simultaneously to remove noise and retain salient features.In the loss function,it considers the losses of difference in paired images according to the intra-variance and inter-variance.In addition,we also collect the disaster scene dataset from remote sensing images and apply the proposed method to disaster scene classification,which contains complex scenes and multiple types of disasters.Compared to other methods,experimental results show that the DNet with hierarchical attention is robust to different datasets and performs better.
基金supported by the Key R&D Projects in Jiangsu Province(BE2021729)the Key Primary Research Project of Primary Strengthening Program(KYZYJKKCJC23001).
文摘Multi-agent systems often require good interoperability in the process of completing their assigned tasks.This paper first models the static structure and dynamic behavior of multiagent systems based on layered weighted scale-free community network and susceptible-infected-recovered(SIR)model.To solve the problem of difficulty in describing the changes in the structure and collaboration mode of the system under external factors,a two-dimensional Monte Carlo method and an improved dynamic Bayesian network are used to simulate the impact of external environmental factors on multi-agent systems.A collaborative information flow path optimization algorithm for agents under environmental factors is designed based on the Dijkstra algorithm.A method for evaluating system interoperability is designed based on simulation experiments,providing reference for the construction planning and optimization of organizational application of the system.Finally,the feasibility of the method is verified through case studies.
