Firms are embedded in complex networks,where diverse ideas combine and generate new ideas.Shareholders of firms are of-ten seen as critical external resources that have significant influence on firm innovation.The cur...Firms are embedded in complex networks,where diverse ideas combine and generate new ideas.Shareholders of firms are of-ten seen as critical external resources that have significant influence on firm innovation.The current literature tends to focus on the rela-tionship between firms and their shareholders,while paying less attention to the connections between firms with the same shareholders.This article identifies two types of network spillover effects,intra-city network effect and inter-city network effect,by visualizing the co-ownership networks in China’s electric vehicle(EV)industry.We find that firms with the same shareholders,which are defined as co-owned EV firms,are more innovative than non-co-owned ones.Furthermore,there are two dominant types of firm co-ownership ties formed by corporate and financial institution shareholders.While corporate shareholders help exploiting local tacit knowledge,financial institutions are more active in bridging inter-city connections.The conclusion is confirmed at both firm and city levels.This paper theor-izes the firm co-ownership network as a new form of institutional proximity and tested the result empirically.For policy consideration,we have emphasized the importance of building formal or informal inter-firm network,and the government should further enhance the knowledge flow channel by institutional construction.展开更多
Clarifying China’s position in the global system is an important logical basis for developing national diplomacy.Although much research has been done on China’s development status,most studies have been based on cou...Clarifying China’s position in the global system is an important logical basis for developing national diplomacy.Although much research has been done on China’s development status,most studies have been based on country comparisons or institutional en-vironment.In today’s networked era in which the global economy,trade,personnel,and information are closely connected,studies on China’s global position and its status changes and influencing factors in multiple contact networks are still insufficient.In this study,from the perspective of diverse global contact networks,we constructed economic,cultural,and political influence indices to explore the changes and influencing factors on China’s status in the global system from 2005 to 2018.The results show that during the study period,China’s global influence in the fields of economic ties,cultural exchanges,and political contacts increased significantly,but its influ-ence in the fields of cultural exchanges and political contacts lagged far economic ties.The pattern of China’s economic influence on various economies around the world has shown a transformation from an‘upright pyramid’to an‘inverted pyramid’structure.The proportion of these economies in low-influence zones has decreased from more than 60%in 2005 to less than 20%in 2018.China’s cultural and political influence on various economies around the world has increased significantly;however,for the former,the percentage of high-influence areas is still less than 20%,whereas for the latter the percentage of these economies in medium-and high-influence areas is still less than 50%.Analyses such as a scatter plot matrix show that geographical proximity,economic globalization,close cooperation with developing countries,and a proactive and peaceful foreign policy are important factors in improving China’s status in the diverse global network system.展开更多
With the increasing dimensionality of network traffic,extracting effective traffic features and improving the identification accuracy of different intrusion traffic have become critical in intrusion detection systems(...With the increasing dimensionality of network traffic,extracting effective traffic features and improving the identification accuracy of different intrusion traffic have become critical in intrusion detection systems(IDS).However,both unsupervised and semisupervised anomalous traffic detection methods suffer from the drawback of ignoring potential correlations between features,resulting in an analysis that is not an optimal set.Therefore,in order to extract more representative traffic features as well as to improve the accuracy of traffic identification,this paper proposes a feature dimensionality reduction method combining principal component analysis and Hotelling’s T^(2) and a multilayer convolutional bidirectional long short-term memory(MSC_BiLSTM)classifier model for network traffic intrusion detection.This method reduces the parameters and redundancy of the model by feature extraction and extracts the dependent features between the data by a bidirectional long short-term memory(BiLSTM)network,which fully considers the influence between the before and after features.The network traffic is first characteristically downscaled by principal component analysis(PCA),and then the downscaled principal components are used as input to Hotelling’s T^(2) to compare the differences between groups.For datasets with outliers,Hotelling’s T^(2) can help identify the groups where the outliers are located and quantitatively measure the extent of the outliers.Finally,a multilayer convolutional neural network and a BiLSTM network are used to extract the spatial and temporal features of network traffic data.The empirical consequences exhibit that the suggested approach in this manuscript attains superior outcomes in precision,recall and F1-score juxtaposed with the prevailing techniques.The results show that the intrusion detection accuracy,precision,and F1-score of the proposed MSC_BiLSTM model for the CIC-IDS 2017 dataset are 98.71%,95.97%,and 90.22%.展开更多
Hyperspectral image classification stands as a pivotal task within the field of remote sensing,yet achieving highprecision classification remains a significant challenge.In response to this challenge,a Spectral Convol...Hyperspectral image classification stands as a pivotal task within the field of remote sensing,yet achieving highprecision classification remains a significant challenge.In response to this challenge,a Spectral Convolutional Neural Network model based on Adaptive Fick’s Law Algorithm(AFLA-SCNN)is proposed.The Adaptive Fick’s Law Algorithm(AFLA)constitutes a novel metaheuristic algorithm introduced herein,encompassing three new strategies:Adaptive weight factor,Gaussian mutation,and probability update policy.With adaptive weight factor,the algorithmcan adjust theweights according to the change in the number of iterations to improve the performance of the algorithm.Gaussianmutation helps the algorithm avoid falling into local optimal solutions and improves the searchability of the algorithm.The probability update strategy helps to improve the exploitability and adaptability of the algorithm.Within the AFLA-SCNN model,AFLA is employed to optimize two hyperparameters in the SCNN model,namely,“numEpochs”and“miniBatchSize”,to attain their optimal values.AFLA’s performance is initially validated across 28 functions in 10D,30D,and 50D for CEC2013 and 29 functions in 10D,30D,and 50D for CEC2017.Experimental results indicate AFLA’s marked performance superiority over nine other prominent optimization algorithms.Subsequently,the AFLA-SCNN model was compared with the Spectral Convolutional Neural Network model based on Fick’s Law Algorithm(FLA-SCNN),Spectral Convolutional Neural Network model based on Harris Hawks Optimization(HHO-SCNN),Spectral Convolutional Neural Network model based onDifferential Evolution(DE-SCNN),SpectralConvolutionalNeuralNetwork(SCNN)model,and SupportVector Machines(SVM)model using the Indian Pines dataset and PaviaUniversity dataset.The experimental results show that the AFLA-SCNN model outperforms other models in terms of Accuracy,Precision,Recall,and F1-score on Indian Pines and Pavia University.Among them,the Accuracy of the AFLA-SCNN model on Indian Pines reached 99.875%,and the Accuracy on PaviaUniversity reached 98.022%.In conclusion,our proposed AFLA-SCNN model is deemed to significantly enhance the precision of hyperspectral image classification.展开更多
Realizing a lithium sulfide(Li_(2)S)cathode with both high energy density and a long lifespan requires an innovative cathode design that maximizes electrochemical performance and resists electrode deterioration.Herein...Realizing a lithium sulfide(Li_(2)S)cathode with both high energy density and a long lifespan requires an innovative cathode design that maximizes electrochemical performance and resists electrode deterioration.Herein,a high-loading Li_(2)S-based cathode with micrometric Li_(2)S particles composed of two-dimensional graphene(Gr)and one-dimensional carbon nanotubes(CNTs)in a compact geometry is developed,and the role of CNTs in stable cycling of high-capacity Li–S batteries is emphasized.In a dimensionally combined carbon matrix,CNTs embedded within the Gr sheets create robust and sustainable electron diffusion pathways while suppressing the passivation of the active carbon surface.As a unique point,during the first charging process,the proposed cathode is fully activated through the direct conversion of Li_(2)S into S_(8) without inducing lithium polysulfide formation.The direct conversion of Li_(2)S into S_(8) in the composite cathode is ubiquitously investigated using the combined study of in situ Raman spectroscopy,in situ optical microscopy,and cryogenic transmission electron microscopy.The composite cathode demonstrates unprecedented electrochemical properties even with a high Li_(2)S loading of 10 mg cm^(–2);in particular,the practical and safe Li–S full cell coupled with a graphite anode shows ultra-long-term cycling stability over 800 cycles.展开更多
OBJECTIVE To explore the new indications and key mechanism of Bazi Bushen capsule(BZBS)by network pharmacology and in vitro experiment.METHODS The potential tar⁃get profiles of the components of BZBS were pre⁃dicted.S...OBJECTIVE To explore the new indications and key mechanism of Bazi Bushen capsule(BZBS)by network pharmacology and in vitro experiment.METHODS The potential tar⁃get profiles of the components of BZBS were pre⁃dicted.Subsequently,new indications for BZBS were predicted by disease ontology(DO)enrich⁃ment analysis and initially validated by GO and KEGG pathway enrichment analysis.Further⁃more,the therapeutic target of BZBS acting on AD signaling pathway were identified by intersec⁃tion analysis.Two Alzheimer′s disease(AD)cell models,BV-2 and SH-SY5Y,were used to pre⁃liminarily verify the anti-AD efficacy and mecha⁃nism of BZBS in vitro.RESULTS In total,1499 non-repeated ingredients were obtained from 16 herbs in BZBS formula,and 1320 BZBS targets with high confidence were predicted.Disease enrichment results strongly suggested that BZBS formula has the potential to be used in the treat⁃ment of AD.In vitro experiments showed that BZ⁃BS could significantly reduce the release of TNF-αand IL-6 and the expression of COX-2 and PSEN1 in Aβ25-35-induced BV-2 cells.BZBS reduced the apoptosis rate of Aβ25-35 induced SH-SY5Y cells,significantly increased mitochon⁃drial membrane potential,reduced the expres⁃sion of Caspase3 active fragment and PSEN1,and increased the expression of IDE.CONCLU⁃SIONS BZBS formula has a potential use in the treatment of AD,which is achieved through regu⁃lation of ERK1/2,NF-κB signaling pathways,and GSK-3β/β-catenin signaling pathway.Further⁃more,the network pharmacology technology is a feasible drug repurposing strategy to reposition new clinical use of approved TCM and explore the mechanism of action.The study lays a foun⁃dation for the subsequent in-depth study of BZBS in the treatment of AD and provides a basis for its application in the clinical treatment of AD.展开更多
Many properties of fruit are influenced by plant nutrition. Fruit firmness is one of the most important fruit characteristics and determines post-harvest life of the fruit, in recent decades, artificial intelligence s...Many properties of fruit are influenced by plant nutrition. Fruit firmness is one of the most important fruit characteristics and determines post-harvest life of the fruit, in recent decades, artificial intelligence systems were employed for developing predictive models to estimate and predict many agriculture processes. In the present study, the predictive capabilities of multiple linear regressions (MLR) and artificial neural networks (ANNs) are evaluated to estimate fruit firmness in six months, including each of nutrients concentrations (nitrogen (N), potassium (K), calcium (Ca) and magnesium (Mg)) alone (P1), com- bination of nutrients concentrations (P2), nutrient concentration ratios alone (P3), and combination of nutrient concentrations and nutrient concentration ratios (P4). The results showed that MLR model estimated fruit firmness more accuracy than ANN model in three datasets (P1, P2 and P4). However, the application of P3 (N/Ca ratio) as the input dataset in ANN model improved the prediction of fruit firmness than the MLR model. Correlation coefficient and root mean squared error (RMSE) were 0.850 and 0.539 between the measured and the estimated data by the ANN model, respectively. Generally, the ANN model showed greater potential in determining the relationship between 6-mon-fruit firmness and nutrients concentration.展开更多
The increasing globalization of the Chinese economy has been enabled by both Chinese financial institutions operating globally as well as international firms operating within China. In geographical terms, this has bee...The increasing globalization of the Chinese economy has been enabled by both Chinese financial institutions operating globally as well as international firms operating within China. In geographical terms, this has been organized through a number of strategic cities serving as gateways for the exchange of financial functions, products and practices between China and the global economy. Drawing on location data of financial service firms in China listed on stock exchanges in Shenzhen, Shanghai and Hong Kong, this paper shows that Chinese financial firms are expanding globally and how Chinese financial centers are positioned and connected in the urban networks shaped by these financial service firms. It is found that Hong Kong, China, holds strategic positions in the integration of Chinese cities into global financial center networks, and that establishing a foothold in global financial centers such as New York and London has been a priority for Chinese financial institutions. The increasing capital flows directed by Chinese financial institutionssuggests a shifting global financial geography, with numerous Chinese cities playing increasingly important roles within global financial center networks.展开更多
Real-time applications based on Wireless Sensor Network(WSN)tech-nologies are quickly increasing due to intelligent surroundings.Among the most significant resources in the WSN are battery power and security.Clustering...Real-time applications based on Wireless Sensor Network(WSN)tech-nologies are quickly increasing due to intelligent surroundings.Among the most significant resources in the WSN are battery power and security.Clustering stra-tegies improve the power factor and secure the WSN environment.It takes more electricity to forward data in a WSN.Though numerous clustering methods have been developed to provide energy consumption,there is indeed a risk of unequal load balancing,resulting in a decrease in the network’s lifetime due to network inequalities and less security.These possibilities arise due to the cluster head’s limited life span.These cluster heads(CH)are in charge of all activities and con-trol intra-cluster and inter-cluster interactions.The proposed method uses Lifetime centric load balancing mechanisms(LCLBM)and Cluster-based energy optimiza-tion using a mobile sink algorithm(CEOMS).LCLBM emphasizes the selection of CH,system architectures,and optimal distribution of CH.In addition,the LCLBM was added with an assistant cluster head(ACH)for load balancing.Power consumption,communications latency,the frequency of failing nodes,high security,and one-way delay are essential variables to consider while evaluating LCLBM.CEOMS will choose a cluster leader based on the influence of the fol-lowing parameters on the energy balance of WSNs.According to simulatedfind-ings,the suggested LCLBM-CEOMS method increases cluster head selection self-adaptability,improves the network’s lifetime,decreases data latency,and bal-ances network capacity.展开更多
Wireless Sensor Network(WSN)consists of a group of limited energy source sensors that are installed in a particular region to collect data from the environment.Designing the energy-efficient data collection methods in...Wireless Sensor Network(WSN)consists of a group of limited energy source sensors that are installed in a particular region to collect data from the environment.Designing the energy-efficient data collection methods in largescale wireless sensor networks is considered to be a difficult area in the research.Sensor node clustering is a popular approach for WSN.Moreover,the sensor nodes are grouped to form clusters in a cluster-based WSN environment.The battery performance of the sensor nodes is likewise constrained.As a result,the energy efficiency of WSNs is critical.In specific,the energy usage is influenced by the loads on the sensor node as well as it ranges from the Base Station(BS).Therefore,energy efficiency and load balancing are very essential in WSN.In the proposed method,a novel Grey Wolf Improved Particle Swarm Optimization with Tabu Search Techniques(GW-IPSO-TS)was used.The selection of Cluster Heads(CHs)and routing path of every CH from the base station is enhanced by the proposed method.It provides the best routing path and increases the lifetime and energy efficiency of the network.End-to-end delay and packet loss rate have also been improved.The proposed GW-IPSO-TS method enhances the evaluation of alive nodes,dead nodes,network survival index,convergence rate,and standard deviation of sensor nodes.Compared to the existing algorithms,the proposed method outperforms better and improves the lifetime of the network.展开更多
[Objectives]Based on UPLC-Q-TOF-MS/MS,network pharmacology and molecular docking techniques,the mechanism of Euphorbia peplus in the treatment of Alzheimer s disease(AD)was studied.[Methods]The UPLC-Q-TOF-MS/MS techni...[Objectives]Based on UPLC-Q-TOF-MS/MS,network pharmacology and molecular docking techniques,the mechanism of Euphorbia peplus in the treatment of Alzheimer s disease(AD)was studied.[Methods]The UPLC-Q-TOF-MS/MS technique was used to rapidly analyze the chemical components of E.peplus.Active components and potential targets of E.peplus were retrieved from TCMSP and Swiss Target Prediction database,and AD targets were screened using GeneCards database.The targets of E.peplus in the treatment of AD were obtained.The PPI network was constructed using String platform,and the network topology of Cytoscape software was used to compute and screen key targets,and the GO and KEGG pathway enrichment analysis was carried out in Metascape database to construct the"component-target-pathway-disease"network.Molecular docking was used to predict the binding properties of active ingredients and targets.[Results]The results of UPLC-Q-TOF-MS/MS showed that 83 compounds were identified from E.peplus,including 19 terpenoids,10 phenolic acids and phenols,16 flavonoids,2 phenylpropanoids,4 coumarins,1 alkaloid,1 anthraquinone and 30 other compounds.The results of network pharmacological analysis showed that 82 active ingredients were screened,and 279 common targets were identified for the treatment of AD,among which the key targets were ALB(albumin),GAPDH(glyceraldehyde triphosphate dehydrogenase),TNF(tumor necrosis factor),AKT1(serine/threonine protein kinase 1),and IL6(interleukin-6).KEGG enrichment analysis showed that key signaling pathways include cancer pathways,lipid and atherosclerosis,Alzheimer s disease,insulin resistance,serotonergic synapses,calcium signaling pathway,cAMP signaling pathway and other signaling pathways.Molecular docking results showed that 14-deoxyandrographolide,dehydroandrographolide,licochalcone B,apigenin and naringenin may be the key components of E.peplus in the treatment of AD.[Conclusions]The results suggest that E.peplus can be used to treat Alzheimer s disease through multi-component,multi-target and multi-pathway.展开更多
Wireless sensor Mobile ad hoc networks have excellent potential in moving and monitoring disaster area networks on real-time basis.The recent challenges faced in Mobile Ad Hoc Networks(MANETs)include scalability,local...Wireless sensor Mobile ad hoc networks have excellent potential in moving and monitoring disaster area networks on real-time basis.The recent challenges faced in Mobile Ad Hoc Networks(MANETs)include scalability,localization,heterogeneous network,self-organization,and self-sufficient operation.In this background,the current study focuses on specially-designed communication link establishment for high connection stability of wireless mobile sensor networks,especially in disaster area network.Existing protocols focus on location-dependent communications and use networks based on typically-used Internet Protocol(IP)architecture.However,IP-based communications have a few limitations such as inefficient bandwidth utilization,high processing,less transfer speeds,and excessive memory intake.To overcome these challenges,the number of neighbors(Node Density)is minimized and high Mobility Nodes(Node Speed)are avoided.The proposed Geographic Drone Based Route Optimization(GDRO)method reduces the entire overhead to a considerable level in an efficient manner and significantly improves the overall performance by identifying the disaster region.This drone communicates with anchor node periodically and shares the information to it so as to introduce a drone-based disaster network in an area.Geographic routing is a promising approach to enhance the routing efficiency in MANET.This algorithm helps in reaching the anchor(target)node with the help of Geographical Graph-Based Mapping(GGM).Global Positioning System(GPS)is enabled on mobile network of the anchor node which regularly broadcasts its location information that helps in finding the location.In first step,the node searches for local and remote anticipated Expected Transmission Count(ETX),thereby calculating the estimated distance.Received Signal Strength Indicator(RSSI)results are stored in the local memory of the node.Then,the node calculates the least remote anticipated ETX,Link Loss Rate,and information to the new location.Freeway Heuristic algorithm improves the data speed,efficiency and determines the path and optimization problem.In comparison with other models,the proposed method yielded an efficient communication,increased the throughput,and reduced the end-to-end delay,energy consumption and packet loss performance in disaster area networks.展开更多
Objective To reveal the mechanism of Huangjing pill in treating Alzheimer’s disease(AD)based on network pharmacology and molecular docking technology.Methods We obtained the active ingredients and targets of Huangjin...Objective To reveal the mechanism of Huangjing pill in treating Alzheimer’s disease(AD)based on network pharmacology and molecular docking technology.Methods We obtained the active ingredients and targets of Huangjing pill through Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform,and supplemented the effective components by consulting literature and predicted targets through the PharmMapper database.We used DrugBank,the GeneCards,the TTD,and the OMIM database to collect targets of AD.The Venn diagram was drawn and the key targets of Huangjing pill in the treatment of AD were obtained by Venny 2.1 platform.The Cytoscape 3.8.1 software was used to construct a network diagram of“drugs-active ingredients-key targets-disease”.The protein-protein interaction(PPI)network diagram was constructed through the STRING 11.5 database.DAVID database was used for Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis.AutoDock Vina1.1.2 software was used for molecular docking of the active components and core targets,and PyMOL 1.7.2.1 software was used for visual processing.Results After screening,we obtained 13 active ingredients and 116 targets of Huangjing Pill,1438 related targets for AD,and 75 common targets.566 items by GO enrichment analysis and 149 items related to KEGG pathway enrichment were obtained.Molecular docking results showed that there is a strong affinity between the key active ingredients and the core targets.Conclusion This study revealed that Huangjing pill could treat AD through multiple components,multiple targets and multiple pathways.展开更多
Backgroud:Parkinson’s disease(PD)is a neurodegenerative disorder with an increasing global prevalence.However,the development of drugs for PD treatment has not kept pace with the continuously growing number of patien...Backgroud:Parkinson’s disease(PD)is a neurodegenerative disorder with an increasing global prevalence.However,the development of drugs for PD treatment has not kept pace with the continuously growing number of patients.Currently,the search for new effective substances from natural drugs is a major research direction.Two Chinese medicinal materials,Saposhnikoviae Radix(Fangfeng)and Chuanxiong Rhizoma(Chuanxiong),are commonly used in the treatment of PD in China.However,the mechanism of their combination is not clear,and further research is needed.Methods:Data were collected from publicly available databases:TCMSP,UnitProt,GeneCards OMIM,PharmGKB,Therapeutic Target Database and DrugBank.Network pharmacology and molecular docking methods was used to analyze the data to discover the possible pharmacological effects of the two drugs in the treatment of PD.Results:Beta-sitosterol,Mandenol and Wallichilide were the active components of Saposhnikoviae Radix and Chuanxiong Rhizoma(FC),and they stably bonded with PD targets,including PTGS2,CASP3,AKT1 and JUN.The target genes of FC were significantly enriched in PD-associated pathways,including calcium signaling and apoptosis pathways.Moreover,the study revealed that the active components of FC may affect cellular structures,such as membrane rafts,membrane microdomains,membrane regions,and postsynaptic membranes,which,in turn,affect a variety of molecular functions and biological processes.Conclusion:The results of this study indicate the direction for clarifying the pharmacodynamic substances of FC,the extraction method of pharmacodynamic substances,as well as the mechanism and efficacy of pharmacodynamic substances.Importantly,this study provides a strategy for developing new therapeutic drugs for PD.展开更多
F_(10.7)指数是太阳活动的重要指标,准确预测F_(10.7)指数有助于预防和缓解太阳活动对无线电通信、导航和卫星通信等领域的影响.基于F_(10.7)射电流量的特性,在双向长短时记忆网络(Bidirectional Long Short-Term Memory Network,BiLSTM...F_(10.7)指数是太阳活动的重要指标,准确预测F_(10.7)指数有助于预防和缓解太阳活动对无线电通信、导航和卫星通信等领域的影响.基于F_(10.7)射电流量的特性,在双向长短时记忆网络(Bidirectional Long Short-Term Memory Network,BiLSTM)基础上融入注意力机制(Attention),提出了一种基于BiLSTM-Attention的F_(10.7)预报模型.在加拿大DRAO数据集上其平均绝对误差(MAE)为5.38,平均绝对百分比误差(MAPE)控制在5%以内,相关系数(R)高达0.987,与其他RNN模型相比拥有优越的预测性能.针对中国廊坊L&S望远镜观测的F_(10.7)数据集,提出了一种转换平均校准(Conversion Average Calibration,CAC)方法进行数据预处理,处理后的数据与DRAO数据集具有较高的相关性.基于该数据集对比分析了RNN系列模型的预报效果,实验结果表明,BiLSTM-Attention和BiLSTM两种模型在预测F_(10.7)指数方面具有较好的优势,表现出较好的预测性能和稳定性.展开更多
基金Under the auspices of Natural Science Foundation of China(No.42122006,41971154)。
文摘Firms are embedded in complex networks,where diverse ideas combine and generate new ideas.Shareholders of firms are of-ten seen as critical external resources that have significant influence on firm innovation.The current literature tends to focus on the rela-tionship between firms and their shareholders,while paying less attention to the connections between firms with the same shareholders.This article identifies two types of network spillover effects,intra-city network effect and inter-city network effect,by visualizing the co-ownership networks in China’s electric vehicle(EV)industry.We find that firms with the same shareholders,which are defined as co-owned EV firms,are more innovative than non-co-owned ones.Furthermore,there are two dominant types of firm co-ownership ties formed by corporate and financial institution shareholders.While corporate shareholders help exploiting local tacit knowledge,financial institutions are more active in bridging inter-city connections.The conclusion is confirmed at both firm and city levels.This paper theor-izes the firm co-ownership network as a new form of institutional proximity and tested the result empirically.For policy consideration,we have emphasized the importance of building formal or informal inter-firm network,and the government should further enhance the knowledge flow channel by institutional construction.
基金Under the auspices of National Natural Science Foundation of China(No.42201181,42171181)Fundamental Research Funds for the Central Universities(No.2412022QD002)The Medium and Long-term Major Training Foundation of Philosophy and Social Sciences of Northeast Normal University(No.22FR006)。
文摘Clarifying China’s position in the global system is an important logical basis for developing national diplomacy.Although much research has been done on China’s development status,most studies have been based on country comparisons or institutional en-vironment.In today’s networked era in which the global economy,trade,personnel,and information are closely connected,studies on China’s global position and its status changes and influencing factors in multiple contact networks are still insufficient.In this study,from the perspective of diverse global contact networks,we constructed economic,cultural,and political influence indices to explore the changes and influencing factors on China’s status in the global system from 2005 to 2018.The results show that during the study period,China’s global influence in the fields of economic ties,cultural exchanges,and political contacts increased significantly,but its influ-ence in the fields of cultural exchanges and political contacts lagged far economic ties.The pattern of China’s economic influence on various economies around the world has shown a transformation from an‘upright pyramid’to an‘inverted pyramid’structure.The proportion of these economies in low-influence zones has decreased from more than 60%in 2005 to less than 20%in 2018.China’s cultural and political influence on various economies around the world has increased significantly;however,for the former,the percentage of high-influence areas is still less than 20%,whereas for the latter the percentage of these economies in medium-and high-influence areas is still less than 50%.Analyses such as a scatter plot matrix show that geographical proximity,economic globalization,close cooperation with developing countries,and a proactive and peaceful foreign policy are important factors in improving China’s status in the diverse global network system.
基金supported by Tianshan Talent Training Project-Xinjiang Science and Technology Innovation Team Program(2023TSYCTD).
文摘With the increasing dimensionality of network traffic,extracting effective traffic features and improving the identification accuracy of different intrusion traffic have become critical in intrusion detection systems(IDS).However,both unsupervised and semisupervised anomalous traffic detection methods suffer from the drawback of ignoring potential correlations between features,resulting in an analysis that is not an optimal set.Therefore,in order to extract more representative traffic features as well as to improve the accuracy of traffic identification,this paper proposes a feature dimensionality reduction method combining principal component analysis and Hotelling’s T^(2) and a multilayer convolutional bidirectional long short-term memory(MSC_BiLSTM)classifier model for network traffic intrusion detection.This method reduces the parameters and redundancy of the model by feature extraction and extracts the dependent features between the data by a bidirectional long short-term memory(BiLSTM)network,which fully considers the influence between the before and after features.The network traffic is first characteristically downscaled by principal component analysis(PCA),and then the downscaled principal components are used as input to Hotelling’s T^(2) to compare the differences between groups.For datasets with outliers,Hotelling’s T^(2) can help identify the groups where the outliers are located and quantitatively measure the extent of the outliers.Finally,a multilayer convolutional neural network and a BiLSTM network are used to extract the spatial and temporal features of network traffic data.The empirical consequences exhibit that the suggested approach in this manuscript attains superior outcomes in precision,recall and F1-score juxtaposed with the prevailing techniques.The results show that the intrusion detection accuracy,precision,and F1-score of the proposed MSC_BiLSTM model for the CIC-IDS 2017 dataset are 98.71%,95.97%,and 90.22%.
基金Natural Science Foundation of Shandong Province,China(Grant No.ZR202111230202).
文摘Hyperspectral image classification stands as a pivotal task within the field of remote sensing,yet achieving highprecision classification remains a significant challenge.In response to this challenge,a Spectral Convolutional Neural Network model based on Adaptive Fick’s Law Algorithm(AFLA-SCNN)is proposed.The Adaptive Fick’s Law Algorithm(AFLA)constitutes a novel metaheuristic algorithm introduced herein,encompassing three new strategies:Adaptive weight factor,Gaussian mutation,and probability update policy.With adaptive weight factor,the algorithmcan adjust theweights according to the change in the number of iterations to improve the performance of the algorithm.Gaussianmutation helps the algorithm avoid falling into local optimal solutions and improves the searchability of the algorithm.The probability update strategy helps to improve the exploitability and adaptability of the algorithm.Within the AFLA-SCNN model,AFLA is employed to optimize two hyperparameters in the SCNN model,namely,“numEpochs”and“miniBatchSize”,to attain their optimal values.AFLA’s performance is initially validated across 28 functions in 10D,30D,and 50D for CEC2013 and 29 functions in 10D,30D,and 50D for CEC2017.Experimental results indicate AFLA’s marked performance superiority over nine other prominent optimization algorithms.Subsequently,the AFLA-SCNN model was compared with the Spectral Convolutional Neural Network model based on Fick’s Law Algorithm(FLA-SCNN),Spectral Convolutional Neural Network model based on Harris Hawks Optimization(HHO-SCNN),Spectral Convolutional Neural Network model based onDifferential Evolution(DE-SCNN),SpectralConvolutionalNeuralNetwork(SCNN)model,and SupportVector Machines(SVM)model using the Indian Pines dataset and PaviaUniversity dataset.The experimental results show that the AFLA-SCNN model outperforms other models in terms of Accuracy,Precision,Recall,and F1-score on Indian Pines and Pavia University.Among them,the Accuracy of the AFLA-SCNN model on Indian Pines reached 99.875%,and the Accuracy on PaviaUniversity reached 98.022%.In conclusion,our proposed AFLA-SCNN model is deemed to significantly enhance the precision of hyperspectral image classification.
基金Korea Institute of Energy Technology Evaluation and Planning,Grant/Award Number:20214000000320Samsung Research Funding&Incubation Center of Samsung Electronics,Grant/Award Number:SRFC-MA1901-06。
文摘Realizing a lithium sulfide(Li_(2)S)cathode with both high energy density and a long lifespan requires an innovative cathode design that maximizes electrochemical performance and resists electrode deterioration.Herein,a high-loading Li_(2)S-based cathode with micrometric Li_(2)S particles composed of two-dimensional graphene(Gr)and one-dimensional carbon nanotubes(CNTs)in a compact geometry is developed,and the role of CNTs in stable cycling of high-capacity Li–S batteries is emphasized.In a dimensionally combined carbon matrix,CNTs embedded within the Gr sheets create robust and sustainable electron diffusion pathways while suppressing the passivation of the active carbon surface.As a unique point,during the first charging process,the proposed cathode is fully activated through the direct conversion of Li_(2)S into S_(8) without inducing lithium polysulfide formation.The direct conversion of Li_(2)S into S_(8) in the composite cathode is ubiquitously investigated using the combined study of in situ Raman spectroscopy,in situ optical microscopy,and cryogenic transmission electron microscopy.The composite cathode demonstrates unprecedented electrochemical properties even with a high Li_(2)S loading of 10 mg cm^(–2);in particular,the practical and safe Li–S full cell coupled with a graphite anode shows ultra-long-term cycling stability over 800 cycles.
基金Chinese Academy of Engi⁃neering Strategic Consulting Project(2022-XY-45)S&T Program of Hebei(22372502D)+1 种基金Scien⁃tific Research Project of Hebei Provincial Admin⁃istration of Traditional Chinese Medicine(023172)and Scientific Research Project of Hebei Provincial Administration of Traditional Chinese Medicine(2021273)。
文摘OBJECTIVE To explore the new indications and key mechanism of Bazi Bushen capsule(BZBS)by network pharmacology and in vitro experiment.METHODS The potential tar⁃get profiles of the components of BZBS were pre⁃dicted.Subsequently,new indications for BZBS were predicted by disease ontology(DO)enrich⁃ment analysis and initially validated by GO and KEGG pathway enrichment analysis.Further⁃more,the therapeutic target of BZBS acting on AD signaling pathway were identified by intersec⁃tion analysis.Two Alzheimer′s disease(AD)cell models,BV-2 and SH-SY5Y,were used to pre⁃liminarily verify the anti-AD efficacy and mecha⁃nism of BZBS in vitro.RESULTS In total,1499 non-repeated ingredients were obtained from 16 herbs in BZBS formula,and 1320 BZBS targets with high confidence were predicted.Disease enrichment results strongly suggested that BZBS formula has the potential to be used in the treat⁃ment of AD.In vitro experiments showed that BZ⁃BS could significantly reduce the release of TNF-αand IL-6 and the expression of COX-2 and PSEN1 in Aβ25-35-induced BV-2 cells.BZBS reduced the apoptosis rate of Aβ25-35 induced SH-SY5Y cells,significantly increased mitochon⁃drial membrane potential,reduced the expres⁃sion of Caspase3 active fragment and PSEN1,and increased the expression of IDE.CONCLU⁃SIONS BZBS formula has a potential use in the treatment of AD,which is achieved through regu⁃lation of ERK1/2,NF-κB signaling pathways,and GSK-3β/β-catenin signaling pathway.Further⁃more,the network pharmacology technology is a feasible drug repurposing strategy to reposition new clinical use of approved TCM and explore the mechanism of action.The study lays a foun⁃dation for the subsequent in-depth study of BZBS in the treatment of AD and provides a basis for its application in the clinical treatment of AD.
文摘Many properties of fruit are influenced by plant nutrition. Fruit firmness is one of the most important fruit characteristics and determines post-harvest life of the fruit, in recent decades, artificial intelligence systems were employed for developing predictive models to estimate and predict many agriculture processes. In the present study, the predictive capabilities of multiple linear regressions (MLR) and artificial neural networks (ANNs) are evaluated to estimate fruit firmness in six months, including each of nutrients concentrations (nitrogen (N), potassium (K), calcium (Ca) and magnesium (Mg)) alone (P1), com- bination of nutrients concentrations (P2), nutrient concentration ratios alone (P3), and combination of nutrient concentrations and nutrient concentration ratios (P4). The results showed that MLR model estimated fruit firmness more accuracy than ANN model in three datasets (P1, P2 and P4). However, the application of P3 (N/Ca ratio) as the input dataset in ANN model improved the prediction of fruit firmness than the MLR model. Correlation coefficient and root mean squared error (RMSE) were 0.850 and 0.539 between the measured and the estimated data by the ANN model, respectively. Generally, the ANN model showed greater potential in determining the relationship between 6-mon-fruit firmness and nutrients concentration.
基金Under the auspices of the National Natural Science Foundation of China(No.41201107)the Fundamental Research Funds for the Central Universities(No.2015KJJCB30)
文摘The increasing globalization of the Chinese economy has been enabled by both Chinese financial institutions operating globally as well as international firms operating within China. In geographical terms, this has been organized through a number of strategic cities serving as gateways for the exchange of financial functions, products and practices between China and the global economy. Drawing on location data of financial service firms in China listed on stock exchanges in Shenzhen, Shanghai and Hong Kong, this paper shows that Chinese financial firms are expanding globally and how Chinese financial centers are positioned and connected in the urban networks shaped by these financial service firms. It is found that Hong Kong, China, holds strategic positions in the integration of Chinese cities into global financial center networks, and that establishing a foothold in global financial centers such as New York and London has been a priority for Chinese financial institutions. The increasing capital flows directed by Chinese financial institutionssuggests a shifting global financial geography, with numerous Chinese cities playing increasingly important roles within global financial center networks.
文摘Real-time applications based on Wireless Sensor Network(WSN)tech-nologies are quickly increasing due to intelligent surroundings.Among the most significant resources in the WSN are battery power and security.Clustering stra-tegies improve the power factor and secure the WSN environment.It takes more electricity to forward data in a WSN.Though numerous clustering methods have been developed to provide energy consumption,there is indeed a risk of unequal load balancing,resulting in a decrease in the network’s lifetime due to network inequalities and less security.These possibilities arise due to the cluster head’s limited life span.These cluster heads(CH)are in charge of all activities and con-trol intra-cluster and inter-cluster interactions.The proposed method uses Lifetime centric load balancing mechanisms(LCLBM)and Cluster-based energy optimiza-tion using a mobile sink algorithm(CEOMS).LCLBM emphasizes the selection of CH,system architectures,and optimal distribution of CH.In addition,the LCLBM was added with an assistant cluster head(ACH)for load balancing.Power consumption,communications latency,the frequency of failing nodes,high security,and one-way delay are essential variables to consider while evaluating LCLBM.CEOMS will choose a cluster leader based on the influence of the fol-lowing parameters on the energy balance of WSNs.According to simulatedfind-ings,the suggested LCLBM-CEOMS method increases cluster head selection self-adaptability,improves the network’s lifetime,decreases data latency,and bal-ances network capacity.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Larg Groups project Under Grant Number(71/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R238)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:22UQU4340237DSR20.
文摘Wireless Sensor Network(WSN)consists of a group of limited energy source sensors that are installed in a particular region to collect data from the environment.Designing the energy-efficient data collection methods in largescale wireless sensor networks is considered to be a difficult area in the research.Sensor node clustering is a popular approach for WSN.Moreover,the sensor nodes are grouped to form clusters in a cluster-based WSN environment.The battery performance of the sensor nodes is likewise constrained.As a result,the energy efficiency of WSNs is critical.In specific,the energy usage is influenced by the loads on the sensor node as well as it ranges from the Base Station(BS).Therefore,energy efficiency and load balancing are very essential in WSN.In the proposed method,a novel Grey Wolf Improved Particle Swarm Optimization with Tabu Search Techniques(GW-IPSO-TS)was used.The selection of Cluster Heads(CHs)and routing path of every CH from the base station is enhanced by the proposed method.It provides the best routing path and increases the lifetime and energy efficiency of the network.End-to-end delay and packet loss rate have also been improved.The proposed GW-IPSO-TS method enhances the evaluation of alive nodes,dead nodes,network survival index,convergence rate,and standard deviation of sensor nodes.Compared to the existing algorithms,the proposed method outperforms better and improves the lifetime of the network.
基金Supported by Science and Technology Fund of the Health Commission of Guizhou Province (gzwkj2021-440).
文摘[Objectives]Based on UPLC-Q-TOF-MS/MS,network pharmacology and molecular docking techniques,the mechanism of Euphorbia peplus in the treatment of Alzheimer s disease(AD)was studied.[Methods]The UPLC-Q-TOF-MS/MS technique was used to rapidly analyze the chemical components of E.peplus.Active components and potential targets of E.peplus were retrieved from TCMSP and Swiss Target Prediction database,and AD targets were screened using GeneCards database.The targets of E.peplus in the treatment of AD were obtained.The PPI network was constructed using String platform,and the network topology of Cytoscape software was used to compute and screen key targets,and the GO and KEGG pathway enrichment analysis was carried out in Metascape database to construct the"component-target-pathway-disease"network.Molecular docking was used to predict the binding properties of active ingredients and targets.[Results]The results of UPLC-Q-TOF-MS/MS showed that 83 compounds were identified from E.peplus,including 19 terpenoids,10 phenolic acids and phenols,16 flavonoids,2 phenylpropanoids,4 coumarins,1 alkaloid,1 anthraquinone and 30 other compounds.The results of network pharmacological analysis showed that 82 active ingredients were screened,and 279 common targets were identified for the treatment of AD,among which the key targets were ALB(albumin),GAPDH(glyceraldehyde triphosphate dehydrogenase),TNF(tumor necrosis factor),AKT1(serine/threonine protein kinase 1),and IL6(interleukin-6).KEGG enrichment analysis showed that key signaling pathways include cancer pathways,lipid and atherosclerosis,Alzheimer s disease,insulin resistance,serotonergic synapses,calcium signaling pathway,cAMP signaling pathway and other signaling pathways.Molecular docking results showed that 14-deoxyandrographolide,dehydroandrographolide,licochalcone B,apigenin and naringenin may be the key components of E.peplus in the treatment of AD.[Conclusions]The results suggest that E.peplus can be used to treat Alzheimer s disease through multi-component,multi-target and multi-pathway.
文摘Wireless sensor Mobile ad hoc networks have excellent potential in moving and monitoring disaster area networks on real-time basis.The recent challenges faced in Mobile Ad Hoc Networks(MANETs)include scalability,localization,heterogeneous network,self-organization,and self-sufficient operation.In this background,the current study focuses on specially-designed communication link establishment for high connection stability of wireless mobile sensor networks,especially in disaster area network.Existing protocols focus on location-dependent communications and use networks based on typically-used Internet Protocol(IP)architecture.However,IP-based communications have a few limitations such as inefficient bandwidth utilization,high processing,less transfer speeds,and excessive memory intake.To overcome these challenges,the number of neighbors(Node Density)is minimized and high Mobility Nodes(Node Speed)are avoided.The proposed Geographic Drone Based Route Optimization(GDRO)method reduces the entire overhead to a considerable level in an efficient manner and significantly improves the overall performance by identifying the disaster region.This drone communicates with anchor node periodically and shares the information to it so as to introduce a drone-based disaster network in an area.Geographic routing is a promising approach to enhance the routing efficiency in MANET.This algorithm helps in reaching the anchor(target)node with the help of Geographical Graph-Based Mapping(GGM).Global Positioning System(GPS)is enabled on mobile network of the anchor node which regularly broadcasts its location information that helps in finding the location.In first step,the node searches for local and remote anticipated Expected Transmission Count(ETX),thereby calculating the estimated distance.Received Signal Strength Indicator(RSSI)results are stored in the local memory of the node.Then,the node calculates the least remote anticipated ETX,Link Loss Rate,and information to the new location.Freeway Heuristic algorithm improves the data speed,efficiency and determines the path and optimization problem.In comparison with other models,the proposed method yielded an efficient communication,increased the throughput,and reduced the end-to-end delay,energy consumption and packet loss performance in disaster area networks.
基金supported by National Key Research and Development Program of China(No.2018YFC1707000).
文摘Objective To reveal the mechanism of Huangjing pill in treating Alzheimer’s disease(AD)based on network pharmacology and molecular docking technology.Methods We obtained the active ingredients and targets of Huangjing pill through Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform,and supplemented the effective components by consulting literature and predicted targets through the PharmMapper database.We used DrugBank,the GeneCards,the TTD,and the OMIM database to collect targets of AD.The Venn diagram was drawn and the key targets of Huangjing pill in the treatment of AD were obtained by Venny 2.1 platform.The Cytoscape 3.8.1 software was used to construct a network diagram of“drugs-active ingredients-key targets-disease”.The protein-protein interaction(PPI)network diagram was constructed through the STRING 11.5 database.DAVID database was used for Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis.AutoDock Vina1.1.2 software was used for molecular docking of the active components and core targets,and PyMOL 1.7.2.1 software was used for visual processing.Results After screening,we obtained 13 active ingredients and 116 targets of Huangjing Pill,1438 related targets for AD,and 75 common targets.566 items by GO enrichment analysis and 149 items related to KEGG pathway enrichment were obtained.Molecular docking results showed that there is a strong affinity between the key active ingredients and the core targets.Conclusion This study revealed that Huangjing pill could treat AD through multiple components,multiple targets and multiple pathways.
文摘Backgroud:Parkinson’s disease(PD)is a neurodegenerative disorder with an increasing global prevalence.However,the development of drugs for PD treatment has not kept pace with the continuously growing number of patients.Currently,the search for new effective substances from natural drugs is a major research direction.Two Chinese medicinal materials,Saposhnikoviae Radix(Fangfeng)and Chuanxiong Rhizoma(Chuanxiong),are commonly used in the treatment of PD in China.However,the mechanism of their combination is not clear,and further research is needed.Methods:Data were collected from publicly available databases:TCMSP,UnitProt,GeneCards OMIM,PharmGKB,Therapeutic Target Database and DrugBank.Network pharmacology and molecular docking methods was used to analyze the data to discover the possible pharmacological effects of the two drugs in the treatment of PD.Results:Beta-sitosterol,Mandenol and Wallichilide were the active components of Saposhnikoviae Radix and Chuanxiong Rhizoma(FC),and they stably bonded with PD targets,including PTGS2,CASP3,AKT1 and JUN.The target genes of FC were significantly enriched in PD-associated pathways,including calcium signaling and apoptosis pathways.Moreover,the study revealed that the active components of FC may affect cellular structures,such as membrane rafts,membrane microdomains,membrane regions,and postsynaptic membranes,which,in turn,affect a variety of molecular functions and biological processes.Conclusion:The results of this study indicate the direction for clarifying the pharmacodynamic substances of FC,the extraction method of pharmacodynamic substances,as well as the mechanism and efficacy of pharmacodynamic substances.Importantly,this study provides a strategy for developing new therapeutic drugs for PD.
文摘F_(10.7)指数是太阳活动的重要指标,准确预测F_(10.7)指数有助于预防和缓解太阳活动对无线电通信、导航和卫星通信等领域的影响.基于F_(10.7)射电流量的特性,在双向长短时记忆网络(Bidirectional Long Short-Term Memory Network,BiLSTM)基础上融入注意力机制(Attention),提出了一种基于BiLSTM-Attention的F_(10.7)预报模型.在加拿大DRAO数据集上其平均绝对误差(MAE)为5.38,平均绝对百分比误差(MAPE)控制在5%以内,相关系数(R)高达0.987,与其他RNN模型相比拥有优越的预测性能.针对中国廊坊L&S望远镜观测的F_(10.7)数据集,提出了一种转换平均校准(Conversion Average Calibration,CAC)方法进行数据预处理,处理后的数据与DRAO数据集具有较高的相关性.基于该数据集对比分析了RNN系列模型的预报效果,实验结果表明,BiLSTM-Attention和BiLSTM两种模型在预测F_(10.7)指数方面具有较好的优势,表现出较好的预测性能和稳定性.