Correction to:Nano-Micro Letters(2025)17:191 https://doi.org/10.1007/s40820-025-01702-7 Following the publication of the original article[1],the authors reported an error in Fig.3(b),and the figure legend was reversed...Correction to:Nano-Micro Letters(2025)17:191 https://doi.org/10.1007/s40820-025-01702-7 Following the publication of the original article[1],the authors reported an error in Fig.3(b),and the figure legend was reversed.The correct Fig.3 has been provided in this orrection.展开更多
To address the persistent challenge of dynamic mismatch between wellbore lifting capacity and reservoir fluid supply,and to establish a robust optimization framework for drainage operations in high-water-cut tight san...To address the persistent challenge of dynamic mismatch between wellbore lifting capacity and reservoir fluid supply,and to establish a robust optimization framework for drainage operations in high-water-cut tight sandstone gas reservoirs,this study systematically investigates the graded optimization and dynamic adaptation of drainage gas recovery technologies.Production data from a representative tight gas field were first employed to forecast reservoir performance.The predictive reliability was rigorously validated through high-precision history matching,thereby providing a quantitatively consistent foundation for subsequent wellbore optimization.Building on this characterization,a coupled simulation framework was developed that integrates wellbore multiphase flow modeling with nodal analysis based on the Inflow Performance Relationship,IPR,and the Vertical Lift Performance,VLP.This coordinated approach enables comprehensive evaluation of process adaptability and dynamic optimization of foam-assisted drainage,mechanical pumping,and jet pumping systems under evolving water-gas ratio,WGR conditions.The results reveal that a progressively increasing water-gas ratio is the dominant factor driving the transition from chemically assisted drainage methods to mechanically enhanced lifting technologies.A distinct quantitative threshold is identified at WGR≈0.002,beyond which mechanical intervention becomes more effective and economically justified.For mechanical pumping and jet pumping systems,a parameter inversion optimization strategy constrained by the target bottomhole flowing pressure,Pwf,is proposed to ensure stable production while maintaining reservoir drawdown control.In particular,the nozzle-to-throat area ratio of the jet pump is identified as the key governing parameter influencing entrainment capacity and lifting efficiency.Moreover,a configuration characterized by small pump diameter,long stroke length,and low operating speed is demonstrated to satisfy drainage requirements while mitigating torque fluctuations,enhancing volumetric efficiency,and improving pump fillage stability.展开更多
Identifying influential nodes in complex networks is of both theoretical and practical importance. Existing methods identify influential nodes based on their positions in the network and assume that the nodes are homo...Identifying influential nodes in complex networks is of both theoretical and practical importance. Existing methods identify influential nodes based on their positions in the network and assume that the nodes are homogeneous. However, node heterogeneity （i.e., different attributes such as interest, energy, age, and so on ） ubiquitously exists and needs to be taken into consideration. In this paper, we conduct an investigation into node attributes and propose a graph signal pro- cessing based centrality （GSPC） method to identify influential nodes considering both the node attributes and the network topology. We first evaluate our GSPC method using two real-world datasets. The results show that our GSPC method effectively identifies influential nodes, which correspond well with the underlying ground truth. This is compatible to the previous eigenvector centrality and principal component centrality methods under circumstances where the nodes are homogeneous. In addition, spreading analysis shows that the GSPC method has a positive effect on the spreading dynamics.展开更多
Suspicious mass traffic constantly evolves,making network behaviour tracing and structure more complex.Neural networks yield promising results by considering a sufficient number of processing elements with strong inte...Suspicious mass traffic constantly evolves,making network behaviour tracing and structure more complex.Neural networks yield promising results by considering a sufficient number of processing elements with strong interconnections between them.They offer efficient computational Hopfield neural networks models and optimization constraints used by undergoing a good amount of parallelism to yield optimal results.Artificial neural network(ANN)offers optimal solutions in classifying and clustering the various reels of data,and the results obtained purely depend on identifying a problem.In this research work,the design of optimized applications is presented in an organized manner.In addition,this research work examines theoretical approaches to achieving optimized results using ANN.It mainly focuses on designing rules.The optimizing design approach of neural networks analyzes the internal process of the neural networks.Practices in developing the network are based on the interconnections among the hidden nodes and their learning parameters.The methodology is proven best for nonlinear resource allocation problems with a suitable design and complex issues.The ANN proposed here considers more or less 46k nodes hidden inside 49 million connections employed on full-fledged parallel processors.The proposed ANN offered optimal results in real-world application problems,and the results were obtained using MATLAB.展开更多
In this study,we explore the potential of using quantum natural language processing(QNLP)for property-guided inverse design of metal-organic frameworks(MOFs)with targeted properties.Specifically,by analyzing 450 hypot...In this study,we explore the potential of using quantum natural language processing(QNLP)for property-guided inverse design of metal-organic frameworks(MOFs)with targeted properties.Specifically,by analyzing 450 hypothetical MOF structures consisting of 3 topologies,10 metal nodes and 15 organic ligands,we categorize these structures into four distinct classes for pore volume and CO_(2)Henry’s constant values.We then compare various QNLP models(i.e.,the bag-of-words,DisCoCat(Distributional Compositional Categorical),and sequence-based models)to identify the most effective approach to process the MOF dataset.Using a classical simulator provided by the IBM Qiskit,the bag-of-words model is identified to be the optimum model,achieving validation accuracies of 88.6%and 78.0%for binary classification tasks on pore volume and CO_(2)Henry’s constant,respectively.Further,we developed multi-class classification models tailored to the probabilistic nature of quantum circuits,with average test accuracies of 92%and 80%across different classes for pore volume and CO_(2)Henry’s constant datasets.Finally,the performance of generating MOF with target properties showed accuracies of 97.75%for pore volume and 90%for CO_(2)Henry’s constant,respectively.Although our investigation covers only a fraction of the vast MOF search space,it marks a promising first step towards using quantum computing for materials design,offering a new perspective through which to explore the complex landscape of MOFs.展开更多
The reversible spreading processes with repeated infection widely exist in nature and human society,such as gonorrhea propagation and meme spreading.Identifying influential spreaders is an important issue in the rever...The reversible spreading processes with repeated infection widely exist in nature and human society,such as gonorrhea propagation and meme spreading.Identifying influential spreaders is an important issue in the reversible spreading dynamics on complex networks,which has been given much attention.Except for structural centrality,the nodes’dynamical states play a significant role in their spreading influence in the reversible spreading processes.By integrating the number of outgoing edges and infection risks of node’s neighbors into structural centrality,a new measure for identifying influential spreaders is articulated which considers the relative importance of structure and dynamics on node influence.The number of outgoing edges and infection risks of neighbors represent the positive effect of the local structural characteristic and the negative effect of the dynamical states of nodes in identifying influential spreaders,respectively.We find that an appropriate combination of these two characteristics can greatly improve the accuracy of the proposed measure in identifying the most influential spreaders.Notably,compared with the positive effect of the local structural characteristic,slightly weakening the negative effect of dynamical states of nodes can make the proposed measure play the best performance.Quantitatively understanding the relative importance of structure and dynamics on node influence provides a significant insight into identifying influential nodes in the reversible spreading processes.展开更多
In a social network analysis the output provided includes many measures and metrics. For each of these measures and metric, the output provides the ability to obtain a rank ordering of the nodes in terms of these meas...In a social network analysis the output provided includes many measures and metrics. For each of these measures and metric, the output provides the ability to obtain a rank ordering of the nodes in terms of these measures. We might use this information in decision making concerning disrupting or deceiving a given network. All is fine when all the measures indicate the same node as the key or influential node. What happens when the measures indicate different key nodes？ Our goal in this paper is to explore two methodologies to identify the key players or nodes in a given network. We apply TOPSIS to analyze these outputs to find the most influential nodes as a function of the decision makers＇ inputs as a process to consider both subjective and objectives inputs through pairwise comparison matrices. We illustrate our results using two common networks from the literature： the Kite network and the Information flow network from Knoke and Wood. We discuss some basic sensitivity analysis can may be applied to the methods. We find the use of TOPSIS as a flexible method to weight the criterion based upon the decision makers＇ inputs or the topology of the network.展开更多
BACKGROUND Delays in sentinel lymph node(SLN)biopsy may affect the positivity of non-SLNs.For these reasons,effort is being directed at obtaining reliable information regarding SLN positivity prior to surgical excisio...BACKGROUND Delays in sentinel lymph node(SLN)biopsy may affect the positivity of non-SLNs.For these reasons,effort is being directed at obtaining reliable information regarding SLN positivity prior to surgical excision.However,the existing tools,e.g.,dermoscopy,do not recognize statistically significant predictive criteria for SLN positivity in melanomas.AIM To investigate the possible association of computer-assisted objectively obtained color,color texture,sharpness and geometry variables with SLN positivity.METHODS We retrospectively reviewed and analyzed the computerized medical records of all patients diagnosed with cutaneous melanoma in a tertiary hospital in Germany during a 3-year period.The study included patients with histologically confirmed melanomas with Breslow>0.75 mm who underwent lesion excision and SLN biopsy during the study period and who had clinical images shot with a digital camera and a handheld ruler aligned beside the lesion.RESULTS Ninety-nine patients with an equal number of lesions met the inclusion criteria and were included in the analysis.Overall mean(±standard deviation)age was 66(15)years.The study group consisted of 20 patients with tumor-positive SLN(SLN+)biopsy,who were compared to 79 patients with tumor-negative SLN biopsy specimen(control group).The two groups differed significantly in terms of age(61 years vs 68 years)and histological subtype,with the SLN+patients being younger and presenting more often with nodular or secondary nodular tumors(P<0.05).The study group patients showed significantly higher eccentricity(i.e.distance between color and geometrical midpoint)as well as higher sharpness(i.e.these lesions were more discrete from the surrounding normal skin,P<0.05).Regarding color variables,SLN+patients demonstrated higher range in all four color intensities(gray,red,green,blue)and significantly higher skewness in three color intensities(gray,red,blue),P<0.05.Color texture variables,i.e.lacunarity,were comparable in both groups.CONCLUSION SLN+patients demonstrated significantly higher eccentricity,higher sharpness,higher range in all four color intensities(gray,red,green,blue)and significantly higher skewness in three color intensities(gray,red,blue).Further prospective studies are needed to better understand the effectiveness of clinical image processing in SLN+melanoma patients.展开更多
In Wireless Sensor Network(WSN),energy and packet forwarding tendencies of sensor nodes plays a potential role in ensuring a maximum degree of co-operation under data delivery.This quantified level of co-operation sig...In Wireless Sensor Network(WSN),energy and packet forwarding tendencies of sensor nodes plays a potential role in ensuring a maximum degree of co-operation under data delivery.This quantified level of co-operation signifies the performance of the network in terms of increased throughput,packet delivery rate and decreased delay depending on the data being aggregated and level of control overhead.The performance of a sensor network is highly inclined by the selfish behaving nature of sensor nodes that gets revealed when the residual energy ranges below a bearable level of activeness in packet forwarding.The selfish sensor node needs to be identified in future through reliable forecasting mechanism for improving the lifetime and packet delivery rate.Semi Markov Process Inspired Selfish aware Co-operative Scheme(SMPISCS)is propounded for making an attempt to mitigate selfish nodes for prolonging the lifetime of the network and balancing energy consumptions of the network.SMPISCS model provides a kind of sensor node’s behavior for quantifying and future forecasting the probability with which the node could turn into selfish.Simulation experiments are carried out through Network Simulator 2 and the performance are analyzed based on varying the number of selfish sensor nodes,number of sensor nodes and range of detection threshold.展开更多
A virtual node placement strategy based on service-aware is proposed for an information acquisition platform. The performance preferences and types of services in the information acquisition platform are analyzed as w...A virtual node placement strategy based on service-aware is proposed for an information acquisition platform. The performance preferences and types of services in the information acquisition platform are analyzed as well as a comparison of the running time of services both in virtual node centralized and decentralized placing. All physical hosts are divided into different sub-clusters by using the analytic hierarchy process( AHP),in order to fit service of different performance preferences. In the sub-cluster,both load balance and quality of service are taken into account. Comparing with the heuristic algorithm,the experiment results show that the proposed placement strategy is running for a shorter time. And comparing with the virtual node placement strategy provided by OpenStack,the experiment results show that the proposed placement strategy can improve the execution speed of service in the information acquisition platform,and also can balance the load which improves resources utilization.展开更多
In Wireless Sensor Network(WSN),energy and packet forwarding tendencies of sensor nodes plays a potential role in ensuring a maximum degree of co-operation under data delivery.This quantified level of co-operation sig...In Wireless Sensor Network(WSN),energy and packet forwarding tendencies of sensor nodes plays a potential role in ensuring a maximum degree of co-operation under data delivery.This quantified level of co-operation signifies the performance of the network in terms of increased throughput,packet delivery rate and decreased delay depending on the data being aggregated and level of control overhead.The performance of a sensor network is highly inclined by the selfish behaving nature of sensor nodes that gets revealed when the residual energy ranges below a bearable level of activeness in packet forwarding.The selfish sensor node needs to be identified in future through reliable forecasting mechanism for improving the lifetime and packet delivery rate.Semi Markov Process Inspired Selfish aware Co-operative Scheme(SMPISCS)is propounded for making an attempt to mitigate selfish nodes for prolonging the lifetime of the network and balancing energy consumptions of the network.SMPISCS model provides a kind of sensor node’s behavior for quantifying and future forecasting the probability with which the node could turn into selfish.Simulation experiments are carried out through Network Simulator 2 and the performance are analyzed based on varying the number of selfish sensor nodes,number of sensor nodes and range of detection threshold.展开更多
A Dark Network is a network that cannot be accessed through tradition means. Once uncovered, to any degree, dark network analysis can be accomplished using the SNA software. The output of SNA software includes many me...A Dark Network is a network that cannot be accessed through tradition means. Once uncovered, to any degree, dark network analysis can be accomplished using the SNA software. The output of SNA software includes many measures and metrics. For each of these measures and metric, the output in ORA additionally provides the ability to obtain a rank ordering of the nodes in terms of these measures. We might use this information in decision making concerning best methods to disrupt or deceive a given dark network. In the Noordin Dark network, different nodes were identified as key nodes based upon the metric used. Our goal in this paper is to use methodologies to identify the key players or nodes in a Dark Network in a similar manner as we previously proposed in social networks. We apply two multi-attribute decision making methods, a hybrid AHP & TOPSIS and an average weighted ranks scheme, to analyze these outputs to find the most influential nodes as a function of the decision makers’ inputs. We compare these methods by illustration using the Noordin Dark Network with seventy-nine nodes. We discuss sensitivity analysis that is applied to the criteria weights in order to measure the change in the ranking of the nodes.展开更多
基金supported in part by STI 2030-Major Projects under Grant 2022ZD0209200in part by Beijing Natural Science Foundation-Xiaomi Innovation Joint Fund (L233009)+4 种基金in part by National Natural Science Foundation of China under Grant No. 62374099in part by the Tsinghua-Toyota Joint Research Fundin part by the Daikin Tsinghua Union Programin part by Independent Research Program of School of Integrated Circuits,Tsinghua Universitysponsored by CIE-Tencent Robotics X Rhino-Bird Focused Research Program
文摘Correction to:Nano-Micro Letters(2025)17:191 https://doi.org/10.1007/s40820-025-01702-7 Following the publication of the original article[1],the authors reported an error in Fig.3(b),and the figure legend was reversed.The correct Fig.3 has been provided in this orrection.
基金supported by the Major Science and Technology Project of PetroChina Company Limited“Research on Key Technologies for Enhancing Recovery in Tight Sandstone Gas Reservoirs”,specifically under its third sub-project:“Research on Integrated Fracturing,Drainage,and Production Technology to Enhance Single-Well Production in Water-Bearing Gas Reservoirs”(Grant number:2023ZZ25YJ03).
文摘To address the persistent challenge of dynamic mismatch between wellbore lifting capacity and reservoir fluid supply,and to establish a robust optimization framework for drainage operations in high-water-cut tight sandstone gas reservoirs,this study systematically investigates the graded optimization and dynamic adaptation of drainage gas recovery technologies.Production data from a representative tight gas field were first employed to forecast reservoir performance.The predictive reliability was rigorously validated through high-precision history matching,thereby providing a quantitatively consistent foundation for subsequent wellbore optimization.Building on this characterization,a coupled simulation framework was developed that integrates wellbore multiphase flow modeling with nodal analysis based on the Inflow Performance Relationship,IPR,and the Vertical Lift Performance,VLP.This coordinated approach enables comprehensive evaluation of process adaptability and dynamic optimization of foam-assisted drainage,mechanical pumping,and jet pumping systems under evolving water-gas ratio,WGR conditions.The results reveal that a progressively increasing water-gas ratio is the dominant factor driving the transition from chemically assisted drainage methods to mechanically enhanced lifting technologies.A distinct quantitative threshold is identified at WGR≈0.002,beyond which mechanical intervention becomes more effective and economically justified.For mechanical pumping and jet pumping systems,a parameter inversion optimization strategy constrained by the target bottomhole flowing pressure,Pwf,is proposed to ensure stable production while maintaining reservoir drawdown control.In particular,the nozzle-to-throat area ratio of the jet pump is identified as the key governing parameter influencing entrainment capacity and lifting efficiency.Moreover,a configuration characterized by small pump diameter,long stroke length,and low operating speed is demonstrated to satisfy drainage requirements while mitigating torque fluctuations,enhancing volumetric efficiency,and improving pump fillage stability.
基金supported by the National Natural Science Foundation of China(Grant No.61231010)the Fundamental Research Funds for the Central Universities,China(Grant No.HUST No.2012QN076)
文摘Identifying influential nodes in complex networks is of both theoretical and practical importance. Existing methods identify influential nodes based on their positions in the network and assume that the nodes are homogeneous. However, node heterogeneity （i.e., different attributes such as interest, energy, age, and so on ） ubiquitously exists and needs to be taken into consideration. In this paper, we conduct an investigation into node attributes and propose a graph signal pro- cessing based centrality （GSPC） method to identify influential nodes considering both the node attributes and the network topology. We first evaluate our GSPC method using two real-world datasets. The results show that our GSPC method effectively identifies influential nodes, which correspond well with the underlying ground truth. This is compatible to the previous eigenvector centrality and principal component centrality methods under circumstances where the nodes are homogeneous. In addition, spreading analysis shows that the GSPC method has a positive effect on the spreading dynamics.
基金This research is funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R 151)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Suspicious mass traffic constantly evolves,making network behaviour tracing and structure more complex.Neural networks yield promising results by considering a sufficient number of processing elements with strong interconnections between them.They offer efficient computational Hopfield neural networks models and optimization constraints used by undergoing a good amount of parallelism to yield optimal results.Artificial neural network(ANN)offers optimal solutions in classifying and clustering the various reels of data,and the results obtained purely depend on identifying a problem.In this research work,the design of optimized applications is presented in an organized manner.In addition,this research work examines theoretical approaches to achieving optimized results using ANN.It mainly focuses on designing rules.The optimizing design approach of neural networks analyzes the internal process of the neural networks.Practices in developing the network are based on the interconnections among the hidden nodes and their learning parameters.The methodology is proven best for nonlinear resource allocation problems with a suitable design and complex issues.The ANN proposed here considers more or less 46k nodes hidden inside 49 million connections employed on full-fledged parallel processors.The proposed ANN offered optimal results in real-world application problems,and the results were obtained using MATLAB.
基金National Research Foundation of Korea(Project Number RS-2024-00337004)for the financial support.
文摘In this study,we explore the potential of using quantum natural language processing(QNLP)for property-guided inverse design of metal-organic frameworks(MOFs)with targeted properties.Specifically,by analyzing 450 hypothetical MOF structures consisting of 3 topologies,10 metal nodes and 15 organic ligands,we categorize these structures into four distinct classes for pore volume and CO_(2)Henry’s constant values.We then compare various QNLP models(i.e.,the bag-of-words,DisCoCat(Distributional Compositional Categorical),and sequence-based models)to identify the most effective approach to process the MOF dataset.Using a classical simulator provided by the IBM Qiskit,the bag-of-words model is identified to be the optimum model,achieving validation accuracies of 88.6%and 78.0%for binary classification tasks on pore volume and CO_(2)Henry’s constant,respectively.Further,we developed multi-class classification models tailored to the probabilistic nature of quantum circuits,with average test accuracies of 92%and 80%across different classes for pore volume and CO_(2)Henry’s constant datasets.Finally,the performance of generating MOF with target properties showed accuracies of 97.75%for pore volume and 90%for CO_(2)Henry’s constant,respectively.Although our investigation covers only a fraction of the vast MOF search space,it marks a promising first step towards using quantum computing for materials design,offering a new perspective through which to explore the complex landscape of MOFs.
基金This work was supported by the National Natural Science Foundation of China(Grant Nos.11875132,11975099,82161148012,11835003,and 61802321)the Natural Science Foundation of Shanghai(Grant No.18ZR1411800)the Science and Technology Commission of Shanghai Municipality(Grant No.14DZ2260800).
文摘The reversible spreading processes with repeated infection widely exist in nature and human society,such as gonorrhea propagation and meme spreading.Identifying influential spreaders is an important issue in the reversible spreading dynamics on complex networks,which has been given much attention.Except for structural centrality,the nodes’dynamical states play a significant role in their spreading influence in the reversible spreading processes.By integrating the number of outgoing edges and infection risks of node’s neighbors into structural centrality,a new measure for identifying influential spreaders is articulated which considers the relative importance of structure and dynamics on node influence.The number of outgoing edges and infection risks of neighbors represent the positive effect of the local structural characteristic and the negative effect of the dynamical states of nodes in identifying influential spreaders,respectively.We find that an appropriate combination of these two characteristics can greatly improve the accuracy of the proposed measure in identifying the most influential spreaders.Notably,compared with the positive effect of the local structural characteristic,slightly weakening the negative effect of dynamical states of nodes can make the proposed measure play the best performance.Quantitatively understanding the relative importance of structure and dynamics on node influence provides a significant insight into identifying influential nodes in the reversible spreading processes.
文摘In a social network analysis the output provided includes many measures and metrics. For each of these measures and metric, the output provides the ability to obtain a rank ordering of the nodes in terms of these measures. We might use this information in decision making concerning disrupting or deceiving a given network. All is fine when all the measures indicate the same node as the key or influential node. What happens when the measures indicate different key nodes？ Our goal in this paper is to explore two methodologies to identify the key players or nodes in a given network. We apply TOPSIS to analyze these outputs to find the most influential nodes as a function of the decision makers＇ inputs as a process to consider both subjective and objectives inputs through pairwise comparison matrices. We illustrate our results using two common networks from the literature： the Kite network and the Information flow network from Knoke and Wood. We discuss some basic sensitivity analysis can may be applied to the methods. We find the use of TOPSIS as a flexible method to weight the criterion based upon the decision makers＇ inputs or the topology of the network.
文摘BACKGROUND Delays in sentinel lymph node(SLN)biopsy may affect the positivity of non-SLNs.For these reasons,effort is being directed at obtaining reliable information regarding SLN positivity prior to surgical excision.However,the existing tools,e.g.,dermoscopy,do not recognize statistically significant predictive criteria for SLN positivity in melanomas.AIM To investigate the possible association of computer-assisted objectively obtained color,color texture,sharpness and geometry variables with SLN positivity.METHODS We retrospectively reviewed and analyzed the computerized medical records of all patients diagnosed with cutaneous melanoma in a tertiary hospital in Germany during a 3-year period.The study included patients with histologically confirmed melanomas with Breslow>0.75 mm who underwent lesion excision and SLN biopsy during the study period and who had clinical images shot with a digital camera and a handheld ruler aligned beside the lesion.RESULTS Ninety-nine patients with an equal number of lesions met the inclusion criteria and were included in the analysis.Overall mean(±standard deviation)age was 66(15)years.The study group consisted of 20 patients with tumor-positive SLN(SLN+)biopsy,who were compared to 79 patients with tumor-negative SLN biopsy specimen(control group).The two groups differed significantly in terms of age(61 years vs 68 years)and histological subtype,with the SLN+patients being younger and presenting more often with nodular or secondary nodular tumors(P<0.05).The study group patients showed significantly higher eccentricity(i.e.distance between color and geometrical midpoint)as well as higher sharpness(i.e.these lesions were more discrete from the surrounding normal skin,P<0.05).Regarding color variables,SLN+patients demonstrated higher range in all four color intensities(gray,red,green,blue)and significantly higher skewness in three color intensities(gray,red,blue),P<0.05.Color texture variables,i.e.lacunarity,were comparable in both groups.CONCLUSION SLN+patients demonstrated significantly higher eccentricity,higher sharpness,higher range in all four color intensities(gray,red,green,blue)and significantly higher skewness in three color intensities(gray,red,blue).Further prospective studies are needed to better understand the effectiveness of clinical image processing in SLN+melanoma patients.
文摘In Wireless Sensor Network(WSN),energy and packet forwarding tendencies of sensor nodes plays a potential role in ensuring a maximum degree of co-operation under data delivery.This quantified level of co-operation signifies the performance of the network in terms of increased throughput,packet delivery rate and decreased delay depending on the data being aggregated and level of control overhead.The performance of a sensor network is highly inclined by the selfish behaving nature of sensor nodes that gets revealed when the residual energy ranges below a bearable level of activeness in packet forwarding.The selfish sensor node needs to be identified in future through reliable forecasting mechanism for improving the lifetime and packet delivery rate.Semi Markov Process Inspired Selfish aware Co-operative Scheme(SMPISCS)is propounded for making an attempt to mitigate selfish nodes for prolonging the lifetime of the network and balancing energy consumptions of the network.SMPISCS model provides a kind of sensor node’s behavior for quantifying and future forecasting the probability with which the node could turn into selfish.Simulation experiments are carried out through Network Simulator 2 and the performance are analyzed based on varying the number of selfish sensor nodes,number of sensor nodes and range of detection threshold.
基金Supported by the National Natural Science Foundation of China(No.61100189,61370215,61370211,61402137)the National Key Technology R&D Program(No.2012BAH45B01)the Open Project Foundation of Information Security Evaluation Center of Civil Aviation,Civil Aviation University of China(No.CAAC-ISECCA-201703)
文摘A virtual node placement strategy based on service-aware is proposed for an information acquisition platform. The performance preferences and types of services in the information acquisition platform are analyzed as well as a comparison of the running time of services both in virtual node centralized and decentralized placing. All physical hosts are divided into different sub-clusters by using the analytic hierarchy process( AHP),in order to fit service of different performance preferences. In the sub-cluster,both load balance and quality of service are taken into account. Comparing with the heuristic algorithm,the experiment results show that the proposed placement strategy is running for a shorter time. And comparing with the virtual node placement strategy provided by OpenStack,the experiment results show that the proposed placement strategy can improve the execution speed of service in the information acquisition platform,and also can balance the load which improves resources utilization.
文摘In Wireless Sensor Network(WSN),energy and packet forwarding tendencies of sensor nodes plays a potential role in ensuring a maximum degree of co-operation under data delivery.This quantified level of co-operation signifies the performance of the network in terms of increased throughput,packet delivery rate and decreased delay depending on the data being aggregated and level of control overhead.The performance of a sensor network is highly inclined by the selfish behaving nature of sensor nodes that gets revealed when the residual energy ranges below a bearable level of activeness in packet forwarding.The selfish sensor node needs to be identified in future through reliable forecasting mechanism for improving the lifetime and packet delivery rate.Semi Markov Process Inspired Selfish aware Co-operative Scheme(SMPISCS)is propounded for making an attempt to mitigate selfish nodes for prolonging the lifetime of the network and balancing energy consumptions of the network.SMPISCS model provides a kind of sensor node’s behavior for quantifying and future forecasting the probability with which the node could turn into selfish.Simulation experiments are carried out through Network Simulator 2 and the performance are analyzed based on varying the number of selfish sensor nodes,number of sensor nodes and range of detection threshold.
文摘A Dark Network is a network that cannot be accessed through tradition means. Once uncovered, to any degree, dark network analysis can be accomplished using the SNA software. The output of SNA software includes many measures and metrics. For each of these measures and metric, the output in ORA additionally provides the ability to obtain a rank ordering of the nodes in terms of these measures. We might use this information in decision making concerning best methods to disrupt or deceive a given dark network. In the Noordin Dark network, different nodes were identified as key nodes based upon the metric used. Our goal in this paper is to use methodologies to identify the key players or nodes in a Dark Network in a similar manner as we previously proposed in social networks. We apply two multi-attribute decision making methods, a hybrid AHP & TOPSIS and an average weighted ranks scheme, to analyze these outputs to find the most influential nodes as a function of the decision makers’ inputs. We compare these methods by illustration using the Noordin Dark Network with seventy-nine nodes. We discuss sensitivity analysis that is applied to the criteria weights in order to measure the change in the ranking of the nodes.
