The oxygen distribution and evolution within the oxygen carrier exert significant influence on chemical looping processes.This paper describes the influence of oxygen bulk diffusion within FeVO4 oxygen carrier pellets...The oxygen distribution and evolution within the oxygen carrier exert significant influence on chemical looping processes.This paper describes the influence of oxygen bulk diffusion within FeVO4 oxygen carrier pellets on the chemical looping oxidative propane dehydrogenation(CL-ODH).During CL-ODH,the oxygen concentration at the pellet surface initially decreased and then maintained stable before the final decrease.At the stage with the stable surface oxygen concentration,the reaction showed a stable C3H6 formation rate and high C3H6 selectivity.Therefore,based on Fick’s second law,the oxygen distribution and evolution in the oxygen carrier at this stage were further analyzed.It was found that main reactions of selective oxidation and over-oxidation were controlled by the oxygen bulk diffusion.C3H8 conversion rate kept decreasing during this stage due to the decrease of the oxygen flux caused by the decline of oxygen gradient within the oxygen carrier,while C3H6 selectivity increased due to the decrease of overoxidation.In addition,reaction rates could increase with the propane partial pressure due to the increase of the oxygen gradient within the oxygen carrier until the bulk transfer reached its limit at higher propane partial pressure.This study provides fundamental insights for the diffusion-controlled chemical looping reactions.展开更多
This paper investigates the problem of data scarcity in spectrum prediction.A cognitive radio equipment may frequently switch the target frequency as the electromagnetic environment changes.The previously trained mode...This paper investigates the problem of data scarcity in spectrum prediction.A cognitive radio equipment may frequently switch the target frequency as the electromagnetic environment changes.The previously trained model for prediction often cannot maintain a good performance when facing small amount of historical data of the new target frequency.Moreover,the cognitive radio equipment usually implements the dynamic spectrum access in real time which means the time to recollect the data of the new task frequency band and retrain the model is very limited.To address the above issues,we develop a crossband data augmentation framework for spectrum prediction by leveraging the recent advances of generative adversarial network(GAN)and deep transfer learning.Firstly,through the similarity measurement,we pre-train a GAN model using the historical data of the frequency band that is the most similar to the target frequency band.Then,through the data augmentation by feeding the small amount of the target data into the pre-trained GAN,temporal-spectral residual network is further trained using deep transfer learning and the generated data with high similarity from GAN.Finally,experiment results demonstrate the effectiveness of the proposed framework.展开更多
Spectrum prediction is a promising technology to infer future spectrum state by exploiting inherent patterns of historical spectrum data.In practice,for a given spectrum band of interest,when facing relatively scarce ...Spectrum prediction is a promising technology to infer future spectrum state by exploiting inherent patterns of historical spectrum data.In practice,for a given spectrum band of interest,when facing relatively scarce historical data,spectrum prediction based on traditional learning methods does not work well.Thus,this paper proposes a cross-band spectrum prediction model based on transfer learning.Firstly,by analysing service activities and computing the distances between various frequency points based on Dynamic Time Warping,the similarity between spectrum bands has been verified.Next,the features,which mainly affect the performance of transfer learning in the crossband spectrum prediction,are explored by leveraging transfer component analysis.Then,the effectiveness of transfer learning for the cross-band spectrum prediction has been demonstrated.Further,experimental results with real-world spectrum data demonstrate that the performance of the proposed model is better than the state-of-theart models when the historical spectrum data is limited.展开更多
High frequency(HF) communication is widely spread due to some merits like easy deployment and wide communication coverage. Spectrum prediction is a promising technique to facilitate the working frequency selection and...High frequency(HF) communication is widely spread due to some merits like easy deployment and wide communication coverage. Spectrum prediction is a promising technique to facilitate the working frequency selection and enhance the function of automatic link establishment. Most of the existing spectrum prediction algorithms focus on predicting spectrum values in a slot-by-slot manner and therefore are lack of timeliness. Deep learning based spectrum prediction is developed in this paper by simultaneously predicting multi-slot ahead states of multiple spectrum points within a period of time. Specifically, we first employ supervised learning and construct samples depending on longterm and short-term HF spectrum data. Then, advanced residual units are introduced to build multiple residual network modules to respectively capture characteristics in these data with diverse time scales. Further, convolution neural network fuses the outputs of residual network modules above for temporal-spectral prediction, which is combined with residual network modules to construct the deep temporal-spectral residual network. Experiments have demonstrated that the approach proposed in this paper has a significant advantage over the benchmark schemes.展开更多
Green tea and its bioactive components possess many health-promoting and disease-preventing benefits,especially anti-inflammatory,antioxidant,anticancer,and metabolic modulation effects with multi-target modes of acti...Green tea and its bioactive components possess many health-promoting and disease-preventing benefits,especially anti-inflammatory,antioxidant,anticancer,and metabolic modulation effects with multi-target modes of action.In contrast,the effects and mechanisms of tea and its components on the immune system are rarely reviewed.The study aimed to review the most potent compounds in tea that affect the immune systems and mechanisms associated with it.As a result of in vitro studies,animal models,and human trials,researchers have found that green tea extracts and compounds have the possibility of modulating the innate immune system,adaptive immune system,and intestinal immune system.In immune-related diseases,tea polyphenols are the most significant compounds that modify immune functions,though other compounds are being investigated and cannot be ruled out.The review provides a new perspective on how the immune-regulatory effects of tea and its components are exerted on immune systems,as well as how they affect the emergence and treatment of diseases.展开更多
Camellia sinensis(tea),one of the most popular commercial crops,is commonly applied in all parts of the world.The main active ingredients of tea include polyphenols,alkaloids,polysaccharides,amino acids,aroma and vola...Camellia sinensis(tea),one of the most popular commercial crops,is commonly applied in all parts of the world.The main active ingredients of tea include polyphenols,alkaloids,polysaccharides,amino acids,aroma and volatile constitutes,all of which are potentially responsible for the activities of tea.Stem cells(SCs)are the immature and undifferentiated cells by a varying capacity for proliferation,self-renewal and the capability to differentiate into one or more different derivatives with specialized function or maintain their stem cell phenotype.Herein,a thorough review is conducted of the functional mechanism on SCs by tea bioactive compounds.展开更多
During skin aging,the degeneration of epidermal stem cells(EpiSCs)leads to diminished wound healing capabilities and epidermal disintegration.This study tackles this issue through a comprehensive analysis combining tr...During skin aging,the degeneration of epidermal stem cells(EpiSCs)leads to diminished wound healing capabilities and epidermal disintegration.This study tackles this issue through a comprehensive analysis combining transcriptomics and untargeted metabolomics,revealing age-dependent alterations in the Gpx gene family and arachidonic acid(AA)metabolic networks,resulting in enhanced ferroptosis.Selenomethionine(Se-Met)could enhance GPX4 expression,thereby assisting EpiSCs in countering AA-induced mitochondrial damage and ferroptosis.Additionally,Se-Met demonstrates antioxidative characteristics and extensive ultraviolet absorption.For the sustained and controllable release of Se-Met,it was covalently grafted to UV-responsive GelMA hydrogels via AC-PEG-NHS tethers.The Se-Met@GelMA hydrogel effectively accelerated wound healing in a chronological aging mice model,by inhibiting lipid peroxidation and ferroptosis with augmented GPX4 expression.Moreover,in a photoaging model,this hydrogel significantly mitigated inflammatory responses,extracellular matrix remodeling,and ferroptosis in UV-exposed mice.These characteristics render Se-Met@GelMA hydrogel valuable in practical clinical applications.展开更多
The disordered macroporous-mesoporous La1-xCexCoO3 catalysts were prepared by complexcombustion method with ethylene glycol as complexing agent at relatively low calcination temperature.The samples were characterized ...The disordered macroporous-mesoporous La1-xCexCoO3 catalysts were prepared by complexcombustion method with ethylene glycol as complexing agent at relatively low calcination temperature.The samples were characterized by means of X-ray diffraction,N2 adsorption-ndash;desorption,Xray photoelectron spectroscopy,transmission electron microscopy,hydrogen temperature-programmed reduction and soot temperature-programmed reduction,and so on.The results show that the use of complexing agent and relatively low calcination temperature increase the specific surface area of the catalyst and have abundant pore structure.The Ce ions introduced into lattice of LaCoO3 mainly exist in the form of tetravalent.At the same time,Ce ions enhance the redox performance of the catalyst and the mobility of active oxygen species,which enhances the catalytic activity of the catalyst for soot combustion.The results of activity test show that La0.9Ce0.1CoO3 catalyst exhibits the highest activity in the absence of NO and NO2,and its T10,T50 and T90 are 371,444,and 497℃,respectively.At the same time,a possible reaction mechanism is proposed in this study based on the turnover frequency(TOF) calculated by isothermal anaerobic titrations,XPS and XRD results.展开更多
Cell-based tissue engineering is one of the optimistic approaches to replace current treatments for bone defects.Urine-derived stem cells(USCs)are obtained non-invasively and become one of the promising seed cells for...Cell-based tissue engineering is one of the optimistic approaches to replace current treatments for bone defects.Urine-derived stem cells(USCs)are obtained non-invasively and become one of the promising seed cells for bone regeneration.An injectable BMP2-releasing chitosan microspheres/type I collagen hydrogel(BMP2-CSM/Col I hydrogel)was fabricated.USCs proliferated in a time-dependent fashion,spread with good extension and interconnected with each other in different hydrogels both for 2D and 3D models.BMP2 was released in a sustained mode for more than 28 days.Sustained-released BMP2 increased the ALP activities and mineral depositions of USCs in 2D culture,and enhanced the expression of osteogenic genes and proteins in 3D culture.In vivo,the mixture of USCs and BMP2-CSM/Col I hydrogels effectively enhanced bone regeneration,and the ratio of new bone volume to total bone volume was 38%after 8weeks of implantation.Our results suggested that BMP2-CSM/Col I hydrogels promoted osteogenic differentiation of USCs in 2D and 3D culture in vitro and USCs provided a promising cell source for bone tissue engineering in vivo.As such,USCs-seeded hydrogel scaffolds are regarded as an alternative approach in the repair of bone defects.展开更多
Wireless network is the communication foundation that supports the intelligentization of Unmanned Aerial Vehicle(UAV) swarm. The topology of UAV communication network is the key to understanding and analyzing the beha...Wireless network is the communication foundation that supports the intelligentization of Unmanned Aerial Vehicle(UAV) swarm. The topology of UAV communication network is the key to understanding and analyzing the behavior of UAV swarm, thus supporting the further prediction of UAV operations. However, the UAV swarm network topology varies over time due to the high mobility and diversified mission requirements of UAVs. Therefore, it is important but challenging to research dynamic topology inference for tracking the topology changes of the UAV network,especially in non-cooperative manner. In this paper, we study the problem of inferring UAV swarm network topology based on external observations, and propose a dynamic topology inference method. First, we establish a sensing framework for acquiring the communication behavior of the target network over time. Then, we expand the multi-dimensional dynamic Hawkes process to model the communication event sequence in a dynamic wireless network. Finally, combining the sliding time window mechanism, the maximum weighted likelihood estimation is applied to inferring the network topology. Extensive simulation results demonstrate the effectiveness of the proposed method.展开更多
Rapid advances in machine learning combined with wide availability of online social media have created considerable research activity in predicting what might be the news of tomorrow based on an analysis of the past.I...Rapid advances in machine learning combined with wide availability of online social media have created considerable research activity in predicting what might be the news of tomorrow based on an analysis of the past.In this work,we present a deep learning forecasting framework which is capable to predict tomorrow’s news topics on Twitter and news feeds based on yesterday’s content and topic-interaction features.The proposed framework starts by generating topics from words using word embeddings and K-means clustering.Then temporal topic-networks are constructed where two topics are linked if the same user has worked on both topics.Structural and dynamic metrics calculated from networks along with content features and past activity,are used as input of a long short-term memory(LSTM)model,which predicts the number of mentions of a specific topic on the subsequent day.Utilizing dependencies among topics,our experiments on two Twitter datasets and the HuffPost news dataset demonstrate that selecting a topic’s historical local neighbors in the topic-network as extra features greatly improves the prediction accuracy and outperforms existing baselines.展开更多
The integration of a single III-V semiconductor quantum dot with a plasmonic nanoantenna as a means toward efficient single-photon sources(SPEs)is limited due to its weak,wide-angle emission,and low emission rate.Thes...The integration of a single III-V semiconductor quantum dot with a plasmonic nanoantenna as a means toward efficient single-photon sources(SPEs)is limited due to its weak,wide-angle emission,and low emission rate.These limitations can be overcome by designing a unique linear array of plasmonic antenna structures coupled to nanowire-based quantum dot(NWQD)emitters.A linear array of a coupled device composed of multiple plasmonic antennas at an optimum distance from the quantum dot emitter can be designed to enhance the directionality and the spontaneous emission rate of an integrated single-photon emitter.Finite element modeling has been used to design these compact structures with high quantum efficiencies and directionality of single-photon emission while retaining the advantages of NWQDs.The Purcell enhancement factor of these structures approaches 66.1 and 145.8,respectively.Compared to a single NWQD of the same diameter,the fluorescence was enhanced by 1054 and 2916times.The predicted collection efficiencies approach 85%(numerical aperture,NA=0.5)and 80%(NA=0.5),respectively.Unlike single-photon emitters based on bulky conventional optics,this is a unique nanophotonic single-emission photon source based on a line-array configuration that uses a surface plasmon-enhanced design with minimum dissipation.The designs presented in this work will facilitate the development of SPEs with potential integration with semiconductor optoelectronics.展开更多
基金the National Key Research and Development Program of China (2021YFA1501302)the National Natural Science Foundation of China (22122808, U20B6002)+1 种基金the Haihe Laboratory of Sustainable Chemical Transformations and the Program of Introducing Talents of Discipline to Universities (BP0618007) for financial supportsupported by the XPLORER PRIZE by Tencent Foundation
文摘The oxygen distribution and evolution within the oxygen carrier exert significant influence on chemical looping processes.This paper describes the influence of oxygen bulk diffusion within FeVO4 oxygen carrier pellets on the chemical looping oxidative propane dehydrogenation(CL-ODH).During CL-ODH,the oxygen concentration at the pellet surface initially decreased and then maintained stable before the final decrease.At the stage with the stable surface oxygen concentration,the reaction showed a stable C3H6 formation rate and high C3H6 selectivity.Therefore,based on Fick’s second law,the oxygen distribution and evolution in the oxygen carrier at this stage were further analyzed.It was found that main reactions of selective oxidation and over-oxidation were controlled by the oxygen bulk diffusion.C3H8 conversion rate kept decreasing during this stage due to the decrease of the oxygen flux caused by the decline of oxygen gradient within the oxygen carrier,while C3H6 selectivity increased due to the decrease of overoxidation.In addition,reaction rates could increase with the propane partial pressure due to the increase of the oxygen gradient within the oxygen carrier until the bulk transfer reached its limit at higher propane partial pressure.This study provides fundamental insights for the diffusion-controlled chemical looping reactions.
基金This work was supported by the Science and Technology Innovation 2030-Key Project of“New Generation Artificial Intelligence”of China under Grant 2018AAA0102303the Natural Science Foundation for Distinguished Young Scholars of Jiangsu Province(No.BK20190030)the National Natural Science Foundation of China(No.61631020,No.61871398,No.61931011 and No.U20B2038).
文摘This paper investigates the problem of data scarcity in spectrum prediction.A cognitive radio equipment may frequently switch the target frequency as the electromagnetic environment changes.The previously trained model for prediction often cannot maintain a good performance when facing small amount of historical data of the new target frequency.Moreover,the cognitive radio equipment usually implements the dynamic spectrum access in real time which means the time to recollect the data of the new task frequency band and retrain the model is very limited.To address the above issues,we develop a crossband data augmentation framework for spectrum prediction by leveraging the recent advances of generative adversarial network(GAN)and deep transfer learning.Firstly,through the similarity measurement,we pre-train a GAN model using the historical data of the frequency band that is the most similar to the target frequency band.Then,through the data augmentation by feeding the small amount of the target data into the pre-trained GAN,temporal-spectral residual network is further trained using deep transfer learning and the generated data with high similarity from GAN.Finally,experiment results demonstrate the effectiveness of the proposed framework.
基金supported by the National Key R&D Program of China under Grant 2018AAA0102303 and Grant 2018YFB1801103the National Natural Science Foundation of China (No. 61871398 and No. 61931011)+1 种基金the Natural Science Foundation for Distinguished Young Scholars of Jiangsu Province (No. BK20190030)the Equipment Advanced Research Field Foundation (No. 61403120304)
文摘Spectrum prediction is a promising technology to infer future spectrum state by exploiting inherent patterns of historical spectrum data.In practice,for a given spectrum band of interest,when facing relatively scarce historical data,spectrum prediction based on traditional learning methods does not work well.Thus,this paper proposes a cross-band spectrum prediction model based on transfer learning.Firstly,by analysing service activities and computing the distances between various frequency points based on Dynamic Time Warping,the similarity between spectrum bands has been verified.Next,the features,which mainly affect the performance of transfer learning in the crossband spectrum prediction,are explored by leveraging transfer component analysis.Then,the effectiveness of transfer learning for the cross-band spectrum prediction has been demonstrated.Further,experimental results with real-world spectrum data demonstrate that the performance of the proposed model is better than the state-of-theart models when the historical spectrum data is limited.
基金supported in part by the National Natural Science Foundation of China (Grants No. 61501510 and No. 61631020)Natural Science Foundation of Jiangsu Province (Grant No. BK20150717)+2 种基金China Postdoctoral Science Foundation Funded Project (Grant No. 2016M590398 and No.2018T110426)Jiangsu Planned Projects for Postdoctoral Research Funds (Grant No. 1501009A)Natural Science Foundation for Distinguished Young Scholars of Jiangsu Province (Grant No. BK20160034)
文摘High frequency(HF) communication is widely spread due to some merits like easy deployment and wide communication coverage. Spectrum prediction is a promising technique to facilitate the working frequency selection and enhance the function of automatic link establishment. Most of the existing spectrum prediction algorithms focus on predicting spectrum values in a slot-by-slot manner and therefore are lack of timeliness. Deep learning based spectrum prediction is developed in this paper by simultaneously predicting multi-slot ahead states of multiple spectrum points within a period of time. Specifically, we first employ supervised learning and construct samples depending on longterm and short-term HF spectrum data. Then, advanced residual units are introduced to build multiple residual network modules to respectively capture characteristics in these data with diverse time scales. Further, convolution neural network fuses the outputs of residual network modules above for temporal-spectral prediction, which is combined with residual network modules to construct the deep temporal-spectral residual network. Experiments have demonstrated that the approach proposed in this paper has a significant advantage over the benchmark schemes.
基金supported by College Student Innovation and Entrepreneurship Training(202110069122)Tianjin Key R&D Plan-Key Projects Supported by Science and Technology(19YFZCSN00010)Tianjin Agricultural Science and Technology Achievements Transformation and Promotion Project(202101120)。
文摘Green tea and its bioactive components possess many health-promoting and disease-preventing benefits,especially anti-inflammatory,antioxidant,anticancer,and metabolic modulation effects with multi-target modes of action.In contrast,the effects and mechanisms of tea and its components on the immune system are rarely reviewed.The study aimed to review the most potent compounds in tea that affect the immune systems and mechanisms associated with it.As a result of in vitro studies,animal models,and human trials,researchers have found that green tea extracts and compounds have the possibility of modulating the innate immune system,adaptive immune system,and intestinal immune system.In immune-related diseases,tea polyphenols are the most significant compounds that modify immune functions,though other compounds are being investigated and cannot be ruled out.The review provides a new perspective on how the immune-regulatory effects of tea and its components are exerted on immune systems,as well as how they affect the emergence and treatment of diseases.
基金supported by National College Students Innovation and Entrepreneurship Training Program(201910069007,201910069102)Tianjin Key R&D Plan-Key Projects Supported by Science and Technology(19YFZCSN00010)。
文摘Camellia sinensis(tea),one of the most popular commercial crops,is commonly applied in all parts of the world.The main active ingredients of tea include polyphenols,alkaloids,polysaccharides,amino acids,aroma and volatile constitutes,all of which are potentially responsible for the activities of tea.Stem cells(SCs)are the immature and undifferentiated cells by a varying capacity for proliferation,self-renewal and the capability to differentiate into one or more different derivatives with specialized function or maintain their stem cell phenotype.Herein,a thorough review is conducted of the functional mechanism on SCs by tea bioactive compounds.
基金supported from the Program of National Natural Science Foundation of China(82272279,82072169).
文摘During skin aging,the degeneration of epidermal stem cells(EpiSCs)leads to diminished wound healing capabilities and epidermal disintegration.This study tackles this issue through a comprehensive analysis combining transcriptomics and untargeted metabolomics,revealing age-dependent alterations in the Gpx gene family and arachidonic acid(AA)metabolic networks,resulting in enhanced ferroptosis.Selenomethionine(Se-Met)could enhance GPX4 expression,thereby assisting EpiSCs in countering AA-induced mitochondrial damage and ferroptosis.Additionally,Se-Met demonstrates antioxidative characteristics and extensive ultraviolet absorption.For the sustained and controllable release of Se-Met,it was covalently grafted to UV-responsive GelMA hydrogels via AC-PEG-NHS tethers.The Se-Met@GelMA hydrogel effectively accelerated wound healing in a chronological aging mice model,by inhibiting lipid peroxidation and ferroptosis with augmented GPX4 expression.Moreover,in a photoaging model,this hydrogel significantly mitigated inflammatory responses,extracellular matrix remodeling,and ferroptosis in UV-exposed mice.These characteristics render Se-Met@GelMA hydrogel valuable in practical clinical applications.
基金supported by the National Key R&D Program of China(2021YFA1501302)the National Natural Science Foundation of China(22121004,U1862207,and 22122808)+1 种基金Haihe Laboratory of Sustainable Chemical Transformations,the Program of Introducing Talents of Discipline to Universities(BP0618007)the XPLORER PRIZE.
基金National Natural Science Foundation of China(21761162016)Key R&D Planning Research Project of Liaoning Province(2107229008)Science and Technology Research Planning Project of Shenyang City(Z17-5-056)。
文摘The disordered macroporous-mesoporous La1-xCexCoO3 catalysts were prepared by complexcombustion method with ethylene glycol as complexing agent at relatively low calcination temperature.The samples were characterized by means of X-ray diffraction,N2 adsorption-ndash;desorption,Xray photoelectron spectroscopy,transmission electron microscopy,hydrogen temperature-programmed reduction and soot temperature-programmed reduction,and so on.The results show that the use of complexing agent and relatively low calcination temperature increase the specific surface area of the catalyst and have abundant pore structure.The Ce ions introduced into lattice of LaCoO3 mainly exist in the form of tetravalent.At the same time,Ce ions enhance the redox performance of the catalyst and the mobility of active oxygen species,which enhances the catalytic activity of the catalyst for soot combustion.The results of activity test show that La0.9Ce0.1CoO3 catalyst exhibits the highest activity in the absence of NO and NO2,and its T10,T50 and T90 are 371,444,and 497℃,respectively.At the same time,a possible reaction mechanism is proposed in this study based on the turnover frequency(TOF) calculated by isothermal anaerobic titrations,XPS and XRD results.
基金supported by National Natural Science Foundation of China[31870961,81874027]Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions[312200102]+3 种基金Science&Technology Department of Sichuan Province[2020YFS0140,2021YFSY0003]Health Commission of Sichuan Province[19PJ104]Clinical Research Incubation project of West China Hospital of Sichuan University[2019HXFH041,2021HXFH036]the‘1.3.5 Program for Disciplines of Excellence’West China Hospital,Sichuan University。
文摘Cell-based tissue engineering is one of the optimistic approaches to replace current treatments for bone defects.Urine-derived stem cells(USCs)are obtained non-invasively and become one of the promising seed cells for bone regeneration.An injectable BMP2-releasing chitosan microspheres/type I collagen hydrogel(BMP2-CSM/Col I hydrogel)was fabricated.USCs proliferated in a time-dependent fashion,spread with good extension and interconnected with each other in different hydrogels both for 2D and 3D models.BMP2 was released in a sustained mode for more than 28 days.Sustained-released BMP2 increased the ALP activities and mineral depositions of USCs in 2D culture,and enhanced the expression of osteogenic genes and proteins in 3D culture.In vivo,the mixture of USCs and BMP2-CSM/Col I hydrogels effectively enhanced bone regeneration,and the ratio of new bone volume to total bone volume was 38%after 8weeks of implantation.Our results suggested that BMP2-CSM/Col I hydrogels promoted osteogenic differentiation of USCs in 2D and 3D culture in vitro and USCs provided a promising cell source for bone tissue engineering in vivo.As such,USCs-seeded hydrogel scaffolds are regarded as an alternative approach in the repair of bone defects.
基金supported by the National Natural Science Foundation of China(Nos.U20B2038,61871398,61901520 and 61931011)the Natural Science Foundation for Distinguished Young Scholars of Jiangsu Province,China(No.BK20190030)。
文摘Wireless network is the communication foundation that supports the intelligentization of Unmanned Aerial Vehicle(UAV) swarm. The topology of UAV communication network is the key to understanding and analyzing the behavior of UAV swarm, thus supporting the further prediction of UAV operations. However, the UAV swarm network topology varies over time due to the high mobility and diversified mission requirements of UAVs. Therefore, it is important but challenging to research dynamic topology inference for tracking the topology changes of the UAV network,especially in non-cooperative manner. In this paper, we study the problem of inferring UAV swarm network topology based on external observations, and propose a dynamic topology inference method. First, we establish a sensing framework for acquiring the communication behavior of the target network over time. Then, we expand the multi-dimensional dynamic Hawkes process to model the communication event sequence in a dynamic wireless network. Finally, combining the sliding time window mechanism, the maximum weighted likelihood estimation is applied to inferring the network topology. Extensive simulation results demonstrate the effectiveness of the proposed method.
基金supported in part by the China Scholarship Council Program,under grant No.201906380135.
文摘Rapid advances in machine learning combined with wide availability of online social media have created considerable research activity in predicting what might be the news of tomorrow based on an analysis of the past.In this work,we present a deep learning forecasting framework which is capable to predict tomorrow’s news topics on Twitter and news feeds based on yesterday’s content and topic-interaction features.The proposed framework starts by generating topics from words using word embeddings and K-means clustering.Then temporal topic-networks are constructed where two topics are linked if the same user has worked on both topics.Structural and dynamic metrics calculated from networks along with content features and past activity,are used as input of a long short-term memory(LSTM)model,which predicts the number of mentions of a specific topic on the subsequent day.Utilizing dependencies among topics,our experiments on two Twitter datasets and the HuffPost news dataset demonstrate that selecting a topic’s historical local neighbors in the topic-network as extra features greatly improves the prediction accuracy and outperforms existing baselines.
基金National Natural Science Foundation of China(62005037)Innovation Group Project of Sichuan Province(20CXTD0090)+2 种基金111 Project(B20030)National Key Research and Development Program of China(2019YFB2203400)Spanish Ministerio de Ciencia e Innovacción(PID2020-118282RA-I00)。
文摘The integration of a single III-V semiconductor quantum dot with a plasmonic nanoantenna as a means toward efficient single-photon sources(SPEs)is limited due to its weak,wide-angle emission,and low emission rate.These limitations can be overcome by designing a unique linear array of plasmonic antenna structures coupled to nanowire-based quantum dot(NWQD)emitters.A linear array of a coupled device composed of multiple plasmonic antennas at an optimum distance from the quantum dot emitter can be designed to enhance the directionality and the spontaneous emission rate of an integrated single-photon emitter.Finite element modeling has been used to design these compact structures with high quantum efficiencies and directionality of single-photon emission while retaining the advantages of NWQDs.The Purcell enhancement factor of these structures approaches 66.1 and 145.8,respectively.Compared to a single NWQD of the same diameter,the fluorescence was enhanced by 1054 and 2916times.The predicted collection efficiencies approach 85%(numerical aperture,NA=0.5)and 80%(NA=0.5),respectively.Unlike single-photon emitters based on bulky conventional optics,this is a unique nanophotonic single-emission photon source based on a line-array configuration that uses a surface plasmon-enhanced design with minimum dissipation.The designs presented in this work will facilitate the development of SPEs with potential integration with semiconductor optoelectronics.
