Legato公司的NetWorker Business Edition产品包支持8个客户端，支持26个槽位以下的磁带库和多个磁带驱动器．并提供对集群服务器支持等先进的备份功能。NetWorker Business Edition可升级到Network Edition或Power Edition，以支持更...Legato公司的NetWorker Business Edition产品包支持8个客户端，支持26个槽位以下的磁带库和多个磁带驱动器．并提供对集群服务器支持等先进的备份功能。NetWorker Business Edition可升级到Network Edition或Power Edition，以支持更多的备份客户端和SAN的应用，可平滑迁移到Unix或Linux的备份环境而无需对原有备份数据作任何改变。展开更多
Optimizing routing and resource allocation in decentralized unmanned aerial vehicle(UAV)networks remains challenging due to interference and rapidly changing topologies.The authors introduce a novel framework combinin...Optimizing routing and resource allocation in decentralized unmanned aerial vehicle(UAV)networks remains challenging due to interference and rapidly changing topologies.The authors introduce a novel framework combining double deep Q-networks(DDQNs)and graph neural networks(GNNs)for joint routing and resource allocation.The framework uses GNNs to model the network topology and DDQNs to adaptively control routing and resource allocation,addressing interference and improving network performance.Simulation results show that the proposed approach outperforms traditional methods such as Closest-to-Destination(c2Dst),Max-SINR(mSINR),and Multi-Layer Perceptron(MLP)-based models,achieving approximately 23.5% improvement in throughput,50% increase in connection probability,and 17.6% reduction in number of hops,demonstrating its effectiveness in dynamic UAV networks.展开更多
The key parameters that characterize the morphological quality of multi-layer and multi-pass metal laser deposited parts are the surface roughness and the error between the actual printing height and the theoretical m...The key parameters that characterize the morphological quality of multi-layer and multi-pass metal laser deposited parts are the surface roughness and the error between the actual printing height and the theoretical model height.The Taguchi method was employed to establish the correlations between process parameter combinations and multi-objective characterization of metal deposition morphology(height error and roughness).Results show that using the signal-to-noise ratio and grey relational analysis,the optimal parameter combination for multi-layer and multi-pass deposition is determined as follows:laser power of 800 W,powder feeding rate of 0.3 r/min,step distance of 1.6 mm,and scanning speed of 20 mm/s.Subsequently,a Genetic Bayesian-back propagation(GB-BP)network is constructed to predict multi-objective responses.Compared with the traditional back propagation network,the GB-back propagation network improves the prediction accuracy of height error and surface roughness by 43.14%and 71.43%,respectively.This network can accurately predict the multi-objective characterization of morphological quality of multi-layer and multi-pass metal deposited parts.展开更多
In modern ZnO varistors,traditional aging mechanisms based on increased power consumption are no longer relevant due to reduced power consumption during DC aging.Prolonged exposure to both AC and DC voltages results i...In modern ZnO varistors,traditional aging mechanisms based on increased power consumption are no longer relevant due to reduced power consumption during DC aging.Prolonged exposure to both AC and DC voltages results in increased leakage current,decreased breakdown voltage,and lower nonlinearity,ultimately compromising their protective performance.To investigate the evolution in electrical properties during DC aging,this work developed a finite element model based on Voronoi networks and conducted accelerated aging tests on commercial varistors.Throughout the aging process,current-voltage characteristics and Schottky barrier parameters were measured and analyzed.The results indicate that when subjected to constant voltage,current flows through regions with larger grain sizes,forming discharge channels.As aging progresses,the current focus increases on these channels,leading to a decline in the varistor’s overall performance.Furthermore,analysis of the Schottky barrier parameters shows that the changes in electrical performance during aging are non-monotonic.These findings offer theoretical support for understanding the aging mechanisms and condition assessment of modern stable ZnO varistors.展开更多
BACKGROUND:Breast hyperplasia is a common benign breast disease mainly caused by endocrine disorders,manifested as abnormal hyperplasia of breast tissue.In recent years,traditional Chinese medicine compounds and probi...BACKGROUND:Breast hyperplasia is a common benign breast disease mainly caused by endocrine disorders,manifested as abnormal hyperplasia of breast tissue.In recent years,traditional Chinese medicine compounds and probiotics have shown good potential in regulating the endocrine system and improving the intestinal microecology,providing new ideas for the treatment of breast hyperplasia.OBJECTIVE:To explore the effects and mechanisms of traditional Chinese medicine compounds and fermented probiotic compounds on breast hyperplasia in mice,providing new theoretical and experimental bases for the clinical treatment and prevention of breast hyperplasia.METHODS:(1)Network pharmacology tools were used to predict the anti-breast-hyperplasia activity of Herba Gueldenstaedtiae(Euphorbia humifusa),as well as its potential targets and signaling pathways.The databases included:TCMSP,OMIM,GeneCards database,UniProt website,Venny2.1.0 website,Metascape,HERB website,and STRING database,all of which are open-access databases.Network pharmacology can predict and screen key information such as the targets corresponding to the active ingredients of traditional Chinese medicine,disease targets,and action pathways through network analysis and computer-system analysis.Therefore,it has been increasingly widely used in the research of traditional Chinese medicine.(2)A breast hyperplasia model was induced in mice by injecting estrogen and progesterone.Mice in the normal blank group were injected intraperitoneally with normal saline every day.Mice in the model group and drugadministration groups were injected intraperitoneally with estradiol benzoate injection at a concentration of 0.5 mg/kg every day for 25 days.From the 26th day,the injection of estradiol benzoate injection was stopped.Mice in the normal blank group were injected intramuscularly with normal saline every day,and mice in the model group and drug-administration groups were injected intramuscularly with progesterone injection at a concentration of 5 mg/kg for 5 days.After the model was established,each group was given drugs respectively.The normal blank group and the model group were gavaged with 0.2 mL/d of normal saline;the positive blank group(Xiaozheng Pill group)was gavaged with an aqueous solution of Xiaozheng Pill at 0.9 mg/g;the low-,medium-and high-dose groups of Compound Herba Gueldenstaedtiae were gavaged with an aqueous solution of the compound medicine at 0.75,1.5,and 3.0 mg/(g·d)respectively;the low-,medium-and high-dose groups of traditional Chinese medicine-bacteria fermentation were gavaged with an aqueous solution of the compound medicine at 0.75,1.5,and 3.0 mg/(g·d)respectively.The administration was continuous for 30 days.RESULTS AND CONCLUSION:(1)The results of network pharmacology research showed that the Compound Herba Gueldenstaedtiae(Euphorbia humifusa)contained 46 active ingredients,which were related to 1213 potential targets.After comparison with 588 known breast-hyperplasia targets,it was speculated that 50 of these targets might be related to the direct effect of the compound on breast hyperplasia.(2)After drug intervention,there was no significant change in the high-dose group of Compound Herba Gueldenstaedtiae compared with the normal blank group.The liver indicators of the other intervention groups all significantly decreased(P<0.05).(3)In terms of kidney and uterine indicators,the medium-dose group of Compound Herba Gueldenstaedtiae decreased significantly compared with the normal blank group(P<0.05).In terms of the uterine index,the model group increased significantly compared with the normal blank group(P<0.01).(4)After 1-month drug treatment,the number of lobules and acini in the breast tissue of the Xiaozheng Pill group,the low,medium,and high-dose group of Compound Herba Gueldenstaedtiae,the low,medium,and highdose groups of traditional Chinese medicine-bacteria fermentation decreased,and the duct openings narrowed.With the increase of drug dose,diffuse hyperplasia of breast tissue was significantly improved.(5)The ELISA results showed that compared with the model group,the estrogen level was lower in the medium-dose group of traditional Chinese medicine-bacteria fermentation after the intervention(P<0.05).In addition,the follicle-stimulating hormone level in the low-dose group of Compound Herba Gueldenstaedtiae was lower than that of the model group(P<0.05).(6)The intervention in the mouse model led to changes in the abundance of short chain fatty acids and intestinal flora in all groups.To conclude,the Compound Herba Gueldenstaedtiae and its probiotic fermentation products significantly improved mammary gland hyperplasia in mice by regulating hormone levels,improving the structure of the gut microbiota,and increasing the content of shortchain fatty acids,providing new ideas and potential sources of drugs for the treatment of breast hyperplasia.展开更多
Network attacks have become a critical issue in the internet security domain.Artificial intelligence technology-based detection methodologies have attracted attention;however,recent studies have struggled to adapt to ...Network attacks have become a critical issue in the internet security domain.Artificial intelligence technology-based detection methodologies have attracted attention;however,recent studies have struggled to adapt to changing attack patterns and complex network environments.In addition,it is difficult to explain the detection results logically using artificial intelligence.We propose a method for classifying network attacks using graph models to explain the detection results.First,we reconstruct the network packet data into a graphical structure.We then use a graph model to predict network attacks using edge classification.To explain the prediction results,we observed numerical changes by randomly masking and calculating the importance of neighbors,allowing us to extract significant subgraphs.Our experiments on six public datasets demonstrate superior performance with an average F1-score of 0.960 and accuracy of 0.964,outperforming traditional machine learning and other graph models.The visual representation of the extracted subgraphs highlights the neighboring nodes that have the greatest impact on the results,thus explaining detection.In conclusion,this study demonstrates that graph-based models are suitable for network attack detection in complex environments,and the importance of graph neighbors can be calculated to efficiently analyze the results.This approach can contribute to real-world network security analyses and provide a new direction in the field.展开更多
The organization of biological neuronal networks into functional modules has intrigued scientists and inspired engineers to develop artificial systems.These networks are characterized by two key properties.First,they ...The organization of biological neuronal networks into functional modules has intrigued scientists and inspired engineers to develop artificial systems.These networks are characterized by two key properties.First,they exhibit dense interconnectivity(Braitenburg and Schüz,1998;Campagnola et al.,2022).The strength and probability of connectivity depend on cell type,inter-neuronal distance,and species.Still,every cortical neuron receives input from thousands of other neurons while transmitting output to a similar number of neurons.Second,communication between neurons occurs primarily via chemical or electrical synapses.展开更多
The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-gener...The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-generation(5G)networks transformed mobile broadband and machine-type communications at massive scales,their properties of scaling,interference management,and latency remain a limitation in dense high mobility settings.To overcome these limitations,artificial intelligence(AI)and unmanned aerial vehicles(UAVs)have emerged as potential solutions to develop versatile,dynamic,and energy-efficient communication systems.The study proposes an AI-based UAV architecture that utilizes cooperative reinforcement learning(CoRL)to manage an autonomous network.The UAVs collaborate by sharing local observations and real-time state exchanges to optimize user connectivity,movement directions,allocate power,and resource distribution.Unlike conventional centralized or autonomous methods,CoRL involves joint state sharing and conflict-sensitive reward shaping,which ensures fair coverage,less interference,and enhanced adaptability in a dynamic urban environment.Simulations conducted in smart city scenarios with 10 UAVs and 50 ground users demonstrate that the proposed CoRL-based UAV system increases user coverage by up to 10%,achieves convergence 40%faster,and reduces latency and energy consumption by 30%compared with centralized and decentralized baselines.Furthermore,the distributed nature of the algorithm ensures scalability and flexibility,making it well-suited for future large-scale 6G deployments.The results highlighted that AI-enabled UAV systems enhance connectivity,support ultra-reliable low-latency communications(URLLC),and improve 6G network efficiency.Future work will extend the framework with adaptive modulation,beamforming-aware positioning,and real-world testbed deployment.展开更多
文摘Legato公司的NetWorker Business Edition产品包支持8个客户端，支持26个槽位以下的磁带库和多个磁带驱动器．并提供对集群服务器支持等先进的备份功能。NetWorker Business Edition可升级到Network Edition或Power Edition，以支持更多的备份客户端和SAN的应用，可平滑迁移到Unix或Linux的备份环境而无需对原有备份数据作任何改变。
文摘Optimizing routing and resource allocation in decentralized unmanned aerial vehicle(UAV)networks remains challenging due to interference and rapidly changing topologies.The authors introduce a novel framework combining double deep Q-networks(DDQNs)and graph neural networks(GNNs)for joint routing and resource allocation.The framework uses GNNs to model the network topology and DDQNs to adaptively control routing and resource allocation,addressing interference and improving network performance.Simulation results show that the proposed approach outperforms traditional methods such as Closest-to-Destination(c2Dst),Max-SINR(mSINR),and Multi-Layer Perceptron(MLP)-based models,achieving approximately 23.5% improvement in throughput,50% increase in connection probability,and 17.6% reduction in number of hops,demonstrating its effectiveness in dynamic UAV networks.
基金National Natural Science Foundation of China(52175237)。
文摘The key parameters that characterize the morphological quality of multi-layer and multi-pass metal laser deposited parts are the surface roughness and the error between the actual printing height and the theoretical model height.The Taguchi method was employed to establish the correlations between process parameter combinations and multi-objective characterization of metal deposition morphology(height error and roughness).Results show that using the signal-to-noise ratio and grey relational analysis,the optimal parameter combination for multi-layer and multi-pass deposition is determined as follows:laser power of 800 W,powder feeding rate of 0.3 r/min,step distance of 1.6 mm,and scanning speed of 20 mm/s.Subsequently,a Genetic Bayesian-back propagation(GB-BP)network is constructed to predict multi-objective responses.Compared with the traditional back propagation network,the GB-back propagation network improves the prediction accuracy of height error and surface roughness by 43.14%and 71.43%,respectively.This network can accurately predict the multi-objective characterization of morphological quality of multi-layer and multi-pass metal deposited parts.
文摘In modern ZnO varistors,traditional aging mechanisms based on increased power consumption are no longer relevant due to reduced power consumption during DC aging.Prolonged exposure to both AC and DC voltages results in increased leakage current,decreased breakdown voltage,and lower nonlinearity,ultimately compromising their protective performance.To investigate the evolution in electrical properties during DC aging,this work developed a finite element model based on Voronoi networks and conducted accelerated aging tests on commercial varistors.Throughout the aging process,current-voltage characteristics and Schottky barrier parameters were measured and analyzed.The results indicate that when subjected to constant voltage,current flows through regions with larger grain sizes,forming discharge channels.As aging progresses,the current focus increases on these channels,leading to a decline in the varistor’s overall performance.Furthermore,analysis of the Schottky barrier parameters shows that the changes in electrical performance during aging are non-monotonic.These findings offer theoretical support for understanding the aging mechanisms and condition assessment of modern stable ZnO varistors.
文摘BACKGROUND:Breast hyperplasia is a common benign breast disease mainly caused by endocrine disorders,manifested as abnormal hyperplasia of breast tissue.In recent years,traditional Chinese medicine compounds and probiotics have shown good potential in regulating the endocrine system and improving the intestinal microecology,providing new ideas for the treatment of breast hyperplasia.OBJECTIVE:To explore the effects and mechanisms of traditional Chinese medicine compounds and fermented probiotic compounds on breast hyperplasia in mice,providing new theoretical and experimental bases for the clinical treatment and prevention of breast hyperplasia.METHODS:(1)Network pharmacology tools were used to predict the anti-breast-hyperplasia activity of Herba Gueldenstaedtiae(Euphorbia humifusa),as well as its potential targets and signaling pathways.The databases included:TCMSP,OMIM,GeneCards database,UniProt website,Venny2.1.0 website,Metascape,HERB website,and STRING database,all of which are open-access databases.Network pharmacology can predict and screen key information such as the targets corresponding to the active ingredients of traditional Chinese medicine,disease targets,and action pathways through network analysis and computer-system analysis.Therefore,it has been increasingly widely used in the research of traditional Chinese medicine.(2)A breast hyperplasia model was induced in mice by injecting estrogen and progesterone.Mice in the normal blank group were injected intraperitoneally with normal saline every day.Mice in the model group and drugadministration groups were injected intraperitoneally with estradiol benzoate injection at a concentration of 0.5 mg/kg every day for 25 days.From the 26th day,the injection of estradiol benzoate injection was stopped.Mice in the normal blank group were injected intramuscularly with normal saline every day,and mice in the model group and drug-administration groups were injected intramuscularly with progesterone injection at a concentration of 5 mg/kg for 5 days.After the model was established,each group was given drugs respectively.The normal blank group and the model group were gavaged with 0.2 mL/d of normal saline;the positive blank group(Xiaozheng Pill group)was gavaged with an aqueous solution of Xiaozheng Pill at 0.9 mg/g;the low-,medium-and high-dose groups of Compound Herba Gueldenstaedtiae were gavaged with an aqueous solution of the compound medicine at 0.75,1.5,and 3.0 mg/(g·d)respectively;the low-,medium-and high-dose groups of traditional Chinese medicine-bacteria fermentation were gavaged with an aqueous solution of the compound medicine at 0.75,1.5,and 3.0 mg/(g·d)respectively.The administration was continuous for 30 days.RESULTS AND CONCLUSION:(1)The results of network pharmacology research showed that the Compound Herba Gueldenstaedtiae(Euphorbia humifusa)contained 46 active ingredients,which were related to 1213 potential targets.After comparison with 588 known breast-hyperplasia targets,it was speculated that 50 of these targets might be related to the direct effect of the compound on breast hyperplasia.(2)After drug intervention,there was no significant change in the high-dose group of Compound Herba Gueldenstaedtiae compared with the normal blank group.The liver indicators of the other intervention groups all significantly decreased(P<0.05).(3)In terms of kidney and uterine indicators,the medium-dose group of Compound Herba Gueldenstaedtiae decreased significantly compared with the normal blank group(P<0.05).In terms of the uterine index,the model group increased significantly compared with the normal blank group(P<0.01).(4)After 1-month drug treatment,the number of lobules and acini in the breast tissue of the Xiaozheng Pill group,the low,medium,and high-dose group of Compound Herba Gueldenstaedtiae,the low,medium,and highdose groups of traditional Chinese medicine-bacteria fermentation decreased,and the duct openings narrowed.With the increase of drug dose,diffuse hyperplasia of breast tissue was significantly improved.(5)The ELISA results showed that compared with the model group,the estrogen level was lower in the medium-dose group of traditional Chinese medicine-bacteria fermentation after the intervention(P<0.05).In addition,the follicle-stimulating hormone level in the low-dose group of Compound Herba Gueldenstaedtiae was lower than that of the model group(P<0.05).(6)The intervention in the mouse model led to changes in the abundance of short chain fatty acids and intestinal flora in all groups.To conclude,the Compound Herba Gueldenstaedtiae and its probiotic fermentation products significantly improved mammary gland hyperplasia in mice by regulating hormone levels,improving the structure of the gut microbiota,and increasing the content of shortchain fatty acids,providing new ideas and potential sources of drugs for the treatment of breast hyperplasia.
基金supported by the MSIT(Ministry of Science and ICT),Republic of Korea,under the ICAN(ICT Challenge and Advanced Network of HRD)support program(IITP-2025-RS-2023-00259497)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation)and was supported by Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Republic of Korea government(MSIT)(No.IITP-2025-RS-2023-00254129+1 种基金Graduate School of Metaverse Convergence(Sungkyunkwan University))was supported by the Basic Science Research Program of the National Research Foundation(NRF)funded by the Republic of Korean government(MSIT)(No.RS-2024-00346737).
文摘Network attacks have become a critical issue in the internet security domain.Artificial intelligence technology-based detection methodologies have attracted attention;however,recent studies have struggled to adapt to changing attack patterns and complex network environments.In addition,it is difficult to explain the detection results logically using artificial intelligence.We propose a method for classifying network attacks using graph models to explain the detection results.First,we reconstruct the network packet data into a graphical structure.We then use a graph model to predict network attacks using edge classification.To explain the prediction results,we observed numerical changes by randomly masking and calculating the importance of neighbors,allowing us to extract significant subgraphs.Our experiments on six public datasets demonstrate superior performance with an average F1-score of 0.960 and accuracy of 0.964,outperforming traditional machine learning and other graph models.The visual representation of the extracted subgraphs highlights the neighboring nodes that have the greatest impact on the results,thus explaining detection.In conclusion,this study demonstrates that graph-based models are suitable for network attack detection in complex environments,and the importance of graph neighbors can be calculated to efficiently analyze the results.This approach can contribute to real-world network security analyses and provide a new direction in the field.
基金supported in part by the Rosetrees Trust(#CF-2023-I-2_113)by the Israel Ministry of Innovation,Science,and Technology(#7393)(to ES).
文摘The organization of biological neuronal networks into functional modules has intrigued scientists and inspired engineers to develop artificial systems.These networks are characterized by two key properties.First,they exhibit dense interconnectivity(Braitenburg and Schüz,1998;Campagnola et al.,2022).The strength and probability of connectivity depend on cell type,inter-neuronal distance,and species.Still,every cortical neuron receives input from thousands of other neurons while transmitting output to a similar number of neurons.Second,communication between neurons occurs primarily via chemical or electrical synapses.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(RS-2025-00559546)supported by the IITP(Institute of Information&Coummunications Technology Planning&Evaluation)-ITRC(Information Technology Research Center)grant funded by the Korea government(Ministry of Science and ICT)(IITP-2025-RS-2023-00259004).
文摘The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-generation(5G)networks transformed mobile broadband and machine-type communications at massive scales,their properties of scaling,interference management,and latency remain a limitation in dense high mobility settings.To overcome these limitations,artificial intelligence(AI)and unmanned aerial vehicles(UAVs)have emerged as potential solutions to develop versatile,dynamic,and energy-efficient communication systems.The study proposes an AI-based UAV architecture that utilizes cooperative reinforcement learning(CoRL)to manage an autonomous network.The UAVs collaborate by sharing local observations and real-time state exchanges to optimize user connectivity,movement directions,allocate power,and resource distribution.Unlike conventional centralized or autonomous methods,CoRL involves joint state sharing and conflict-sensitive reward shaping,which ensures fair coverage,less interference,and enhanced adaptability in a dynamic urban environment.Simulations conducted in smart city scenarios with 10 UAVs and 50 ground users demonstrate that the proposed CoRL-based UAV system increases user coverage by up to 10%,achieves convergence 40%faster,and reduces latency and energy consumption by 30%compared with centralized and decentralized baselines.Furthermore,the distributed nature of the algorithm ensures scalability and flexibility,making it well-suited for future large-scale 6G deployments.The results highlighted that AI-enabled UAV systems enhance connectivity,support ultra-reliable low-latency communications(URLLC),and improve 6G network efficiency.Future work will extend the framework with adaptive modulation,beamforming-aware positioning,and real-world testbed deployment.
