The wireless signals emitted by base stations serve as a vital link connecting people in today’s society and have been occupying an increasingly important role in real life.The development of the Internet of Things(I...The wireless signals emitted by base stations serve as a vital link connecting people in today’s society and have been occupying an increasingly important role in real life.The development of the Internet of Things(IoT)relies on the support of base stations,which provide a solid foundation for achieving a more intelligent way of living.In a specific area,achieving higher signal coverage with fewer base stations has become an urgent problem.Therefore,this article focuses on the effective coverage area of base station signals and proposes a novel Evolutionary Particle Swarm Optimization(EPSO)algorithm based on collective prediction,referred to herein as ECPPSO.Introducing a new strategy called neighbor-based evolution prediction(NEP)addresses the issue of premature convergence often encountered by PSO.ECPPSO also employs a strengthening evolution(SE)strategy to enhance the algorithm’s global search capability and efficiency,ensuring enhanced robustness and a faster convergence speed when solving complex optimization problems.To better adapt to the actual communication needs of base stations,this article conducts simulation experiments by changing the number of base stations.The experimental results demonstrate thatunder the conditionof 50 ormore base stations,ECPPSOconsistently achieves the best coverage rate exceeding 95%,peaking at 99.4400%when the number of base stations reaches 80.These results validate the optimization capability of the ECPPSO algorithm,proving its feasibility and effectiveness.Further ablative experiments and comparisons with other algorithms highlight the advantages of ECPPSO.展开更多
Lake Taihu,the largest shallow freshwater lake in eastern China,is a vital ecological and economic resource in the Yangtze River Delta.However,the region faces substantial environmental challenges from emerging contam...Lake Taihu,the largest shallow freshwater lake in eastern China,is a vital ecological and economic resource in the Yangtze River Delta.However,the region faces substantial environmental challenges from emerging contaminants(ECs),such as per-and polyfluoroalkyl substances(PFAS)and neonicotinoid insecticides(NEOs),driven by its dense industrial activities and aquaculture and agriculture sectors.A comprehensive literature analysis of the two ECs revealed that PFAS and NEOs have become recent hotspots both globally and in the Taihu Basin.The occurrence and distribution of PFAS and NEOs were summarized to show their high detection frequency and concentrations in the Taihu Basin.Risk assessment indicated that PFAS,NEOs,and other ECs posed considerable ecological risks within the Taihu Basin.Treatment techniques for PFAS and NEOs were systematically reviewed.However,many of these techniques face difficulties in scaling up in the Taihu Basin because of their strict conditions and high energy consumption.Ecological engineering treatment technologies are applied in the Taihu Basin to address emerging agricultural contaminants.Ecological engineering treatment technologies have limitations such as low removal efficiency and toxicity inhibition.Thus,it is necessary to develop more effective technologies for treating ECs in the Taihu Basin.A flowchart for identifying priority controlled ECs is presented and a future for the priority controlled emerging contaminants in the Taihu Basin is discussed.This study provides scientific insights for the sustainable control of ECs.展开更多
The first research and experimental results obtained in China of high-accuracy radiometric calibration based on cryogenic radiometer are reported. Uncertainties of cryogenic radiometer and trap detectors at 7 waveleng...The first research and experimental results obtained in China of high-accuracy radiometric calibration based on cryogenic radiometer are reported. Uncertainties of cryogenic radiometer and trap detectors at 7 wavelengths in the visible spectrum (488-786 nm) were less than 0.023% and 0.035% respectively, which proved the reasonability and possibility of establishing and transferring high-accuracy radiometric standards based on detectors.展开更多
Battery fault diagnosis is essential for ensuring the reliability and safety of electric vehicles(EVs).The existing battery fault diagnosis methods are difficult to detect faults at an early stage based on the real-wo...Battery fault diagnosis is essential for ensuring the reliability and safety of electric vehicles(EVs).The existing battery fault diagnosis methods are difficult to detect faults at an early stage based on the real-world vehicle data since lithium-ion battery systems are usually accompanied by inconsistencies,which are difficult to distinguish from faults.A fault diagnosis method based on signal decomposition and two-dimensional feature clustering is introduced in this paper.Symplectic geometry mode decomposition(SGMD)is introduced to obtain the components characterizing battery states,and distance-based similarity measures with the normalized extended average voltage and dynamic time warping distances are established to evaluate the state of batteries.The 2-dimensional feature clustering based on DBSCAN is developed to reduce the number of feature thresholds and differentiate flaw cells from the battery pack with only one parameter under a wide range of values.The proposed method can achieve fault diagnosis and voltage anomaly identification as early as 43 days ahead of the thermal runaway.And the results of four electric vehicles and the comparison with other traditional methods validated the proposed method with strong robustness,high reliability,and long time scale warning,and the method is easy to implement online.展开更多
COVID-19 has spread globally to over 200 countries with more than 40 million confirmed cases and one million deaths as of November 1,2020.The SARS-CoV-2 virus,leading to COVID-19,shows extremely high rates of infectiv...COVID-19 has spread globally to over 200 countries with more than 40 million confirmed cases and one million deaths as of November 1,2020.The SARS-CoV-2 virus,leading to COVID-19,shows extremely high rates of infectivity and replication,and can result in pneumonia,acute respiratory distress,or even mortality.SARS-CoV-2 has been found to continue to rapidly evolve,with several genomic variants emerging in different regions throughout the world.In addition,despite intensive study of the spike protein,its origin,and molecular mechanisms in mediating host invasion are still only partially resolved.Finally,the repertoire of drugs for COVID-19 treatment is still limited,with several candidates still under clinical trial and no effective therapeutic yet reported.Although vaccines based on either DNA/mRNA or protein have been deployed,their efficacy against emerging variants requires ongoing study,with multivalent vaccines supplanting the first-generation vaccines due to their low efficacy against new strains.Here,we provide a systematic review of studies on the epidemiology,immunological pathogenesis,molecular mechanisms,and structural biology,as well as approaches for drug or vaccine development for SARSCoV-2.展开更多
This paper introduces several algorithms for signal estimation using binary-valued outputsensing.The main idea is derived from the empirical measure approach for quantized identification,which has been shown to be con...This paper introduces several algorithms for signal estimation using binary-valued outputsensing.The main idea is derived from the empirical measure approach for quantized identification,which has been shown to be convergent and asymptotically efficient when the unknown parametersare constants.Signal estimation under binary-valued observations must take into consideration oftime varying variables.Typical empirical measure based algorithms are modified with exponentialweighting and threshold adaptation to accommodate time-varying natures of the signals.Without anyinformation on signal generators,the authors establish estimation algorithms,interaction between noisereduction by averaging and signal tracking,convergence rates,and asymptotic efficiency.A thresholdadaptation algorithm is introduced.Its convergence and convergence rates are analyzed by using theODE method for stochastic approximation problems.展开更多
基金supported by the National Natural Science Foundation of China(Nos.62272418,62102058)Basic Public Welfare Research Program of Zhejiang Province(No.LGG18E050011)the Major Open Project of Key Laboratory for Advanced Design and Intelligent Computing of the Ministry of Education under Grant ADIC2023ZD001,National Undergraduate Training Program on Innovation and Entrepreneurship(No.202410345054).
文摘The wireless signals emitted by base stations serve as a vital link connecting people in today’s society and have been occupying an increasingly important role in real life.The development of the Internet of Things(IoT)relies on the support of base stations,which provide a solid foundation for achieving a more intelligent way of living.In a specific area,achieving higher signal coverage with fewer base stations has become an urgent problem.Therefore,this article focuses on the effective coverage area of base station signals and proposes a novel Evolutionary Particle Swarm Optimization(EPSO)algorithm based on collective prediction,referred to herein as ECPPSO.Introducing a new strategy called neighbor-based evolution prediction(NEP)addresses the issue of premature convergence often encountered by PSO.ECPPSO also employs a strengthening evolution(SE)strategy to enhance the algorithm’s global search capability and efficiency,ensuring enhanced robustness and a faster convergence speed when solving complex optimization problems.To better adapt to the actual communication needs of base stations,this article conducts simulation experiments by changing the number of base stations.The experimental results demonstrate thatunder the conditionof 50 ormore base stations,ECPPSOconsistently achieves the best coverage rate exceeding 95%,peaking at 99.4400%when the number of base stations reaches 80.These results validate the optimization capability of the ECPPSO algorithm,proving its feasibility and effectiveness.Further ablative experiments and comparisons with other algorithms highlight the advantages of ECPPSO.
基金supported by the National Key Research and Development Program of China(No.2023YFE0100900)the National Natural Science Foundation of China(No.42227806).
文摘Lake Taihu,the largest shallow freshwater lake in eastern China,is a vital ecological and economic resource in the Yangtze River Delta.However,the region faces substantial environmental challenges from emerging contaminants(ECs),such as per-and polyfluoroalkyl substances(PFAS)and neonicotinoid insecticides(NEOs),driven by its dense industrial activities and aquaculture and agriculture sectors.A comprehensive literature analysis of the two ECs revealed that PFAS and NEOs have become recent hotspots both globally and in the Taihu Basin.The occurrence and distribution of PFAS and NEOs were summarized to show their high detection frequency and concentrations in the Taihu Basin.Risk assessment indicated that PFAS,NEOs,and other ECs posed considerable ecological risks within the Taihu Basin.Treatment techniques for PFAS and NEOs were systematically reviewed.However,many of these techniques face difficulties in scaling up in the Taihu Basin because of their strict conditions and high energy consumption.Ecological engineering treatment technologies are applied in the Taihu Basin to address emerging agricultural contaminants.Ecological engineering treatment technologies have limitations such as low removal efficiency and toxicity inhibition.Thus,it is necessary to develop more effective technologies for treating ECs in the Taihu Basin.A flowchart for identifying priority controlled ECs is presented and a future for the priority controlled emerging contaminants in the Taihu Basin is discussed.This study provides scientific insights for the sustainable control of ECs.
文摘The first research and experimental results obtained in China of high-accuracy radiometric calibration based on cryogenic radiometer are reported. Uncertainties of cryogenic radiometer and trap detectors at 7 wavelengths in the visible spectrum (488-786 nm) were less than 0.023% and 0.035% respectively, which proved the reasonability and possibility of establishing and transferring high-accuracy radiometric standards based on detectors.
基金the National Natural Science Foundation of China[No.51977007,No.52007006]the Natural Science Foundation of Beijing under grant 3212033.
文摘Battery fault diagnosis is essential for ensuring the reliability and safety of electric vehicles(EVs).The existing battery fault diagnosis methods are difficult to detect faults at an early stage based on the real-world vehicle data since lithium-ion battery systems are usually accompanied by inconsistencies,which are difficult to distinguish from faults.A fault diagnosis method based on signal decomposition and two-dimensional feature clustering is introduced in this paper.Symplectic geometry mode decomposition(SGMD)is introduced to obtain the components characterizing battery states,and distance-based similarity measures with the normalized extended average voltage and dynamic time warping distances are established to evaluate the state of batteries.The 2-dimensional feature clustering based on DBSCAN is developed to reduce the number of feature thresholds and differentiate flaw cells from the battery pack with only one parameter under a wide range of values.The proposed method can achieve fault diagnosis and voltage anomaly identification as early as 43 days ahead of the thermal runaway.And the results of four electric vehicles and the comparison with other traditional methods validated the proposed method with strong robustness,high reliability,and long time scale warning,and the method is easy to implement online.
基金This research was funded by Hunan Provincial Innovation Platform and Talents Program(No.2018RS3105)the Natural Science Foundation of China(No.61803151)+2 种基金the Natural Science Foundation of Hunan province(No.2018JJ3570)the Project of Scientific Research Fund of Hunan Provincial Education Department(No 19A060 and 19C0185)the project to introduce intelligence from oversea experts to the Changsha City(Grant No.2089901).
文摘COVID-19 has spread globally to over 200 countries with more than 40 million confirmed cases and one million deaths as of November 1,2020.The SARS-CoV-2 virus,leading to COVID-19,shows extremely high rates of infectivity and replication,and can result in pneumonia,acute respiratory distress,or even mortality.SARS-CoV-2 has been found to continue to rapidly evolve,with several genomic variants emerging in different regions throughout the world.In addition,despite intensive study of the spike protein,its origin,and molecular mechanisms in mediating host invasion are still only partially resolved.Finally,the repertoire of drugs for COVID-19 treatment is still limited,with several candidates still under clinical trial and no effective therapeutic yet reported.Although vaccines based on either DNA/mRNA or protein have been deployed,their efficacy against emerging variants requires ongoing study,with multivalent vaccines supplanting the first-generation vaccines due to their low efficacy against new strains.Here,we provide a systematic review of studies on the epidemiology,immunological pathogenesis,molecular mechanisms,and structural biology,as well as approaches for drug or vaccine development for SARSCoV-2.
基金supported in part by the National Science Foundation under ECS-0329597 and DMS-0624849in part by the Air Force Office of Scientific Research under FA9550-10-1-0210+2 种基金supported by the National Science Foundation under DMS-0907753 and DMS-0624849in part by the Air Force Office of Scientific Research under FA9550-10-1-0210supported in part by a research grant from the Australian Research Council
文摘This paper introduces several algorithms for signal estimation using binary-valued outputsensing.The main idea is derived from the empirical measure approach for quantized identification,which has been shown to be convergent and asymptotically efficient when the unknown parametersare constants.Signal estimation under binary-valued observations must take into consideration oftime varying variables.Typical empirical measure based algorithms are modified with exponentialweighting and threshold adaptation to accommodate time-varying natures of the signals.Without anyinformation on signal generators,the authors establish estimation algorithms,interaction between noisereduction by averaging and signal tracking,convergence rates,and asymptotic efficiency.A thresholdadaptation algorithm is introduced.Its convergence and convergence rates are analyzed by using theODE method for stochastic approximation problems.
