Long-term navigation ability based on consumer-level wearable inertial sensors plays an essential role towards various emerging fields, for instance, smart healthcare, emergency rescue, soldier positioning et al. The ...Long-term navigation ability based on consumer-level wearable inertial sensors plays an essential role towards various emerging fields, for instance, smart healthcare, emergency rescue, soldier positioning et al. The performance of existing long-term navigation algorithm is limited by the cumulative error of inertial sensors, disturbed local magnetic field, and complex motion modes of the pedestrian. This paper develops a robust data and physical model dual-driven based trajectory estimation(DPDD-TE) framework, which can be applied for long-term navigation tasks. A Bi-directional Long Short-Term Memory(Bi-LSTM) based quasi-static magnetic field(QSMF) detection algorithm is developed for extracting useful magnetic observation for heading calibration, and another Bi-LSTM is adopted for walking speed estimation by considering hybrid human motion information under a specific time period. In addition, a data and physical model dual-driven based multi-source fusion model is proposed to integrate basic INS mechanization and multi-level constraint and observations for maintaining accuracy under long-term navigation tasks, and enhanced by the magnetic and trajectory features assisted loop detection algorithm. Real-world experiments indicate that the proposed DPDD-TE outperforms than existing algorithms, and final estimated heading and positioning accuracy indexes reaches 5° and less than 2 m under the time period of 30 min, respectively.展开更多
In this article,we have modeled the astroclimatic parameters using data from the fifth reanalysis series(ERA5)from the European Centre for Medium-Range Weather Forecasts over the period 2010-2020.These data have been ...In this article,we have modeled the astroclimatic parameters using data from the fifth reanalysis series(ERA5)from the European Centre for Medium-Range Weather Forecasts over the period 2010-2020.These data have been projected onto the site of the Roque de los Muchachos Observatory(ORM).The analysis covers the main parameters influencing the quality of astronomical observation,notably the vertical profiles of temperature,potential temperature,and optical turbulence(C_(n)~2),as well as the vertical wind distributions and seasonal behaviors of atmospheric turbulence,which are treated in detail by specifying the wind speed at the 200 hPa level(V200).Around this level,the wind at high altitude presents moderate speeds and a generally stable direction,conditions favorable for adaptive optics.The thermal profiles show good agreement with standard atmospheric models,with a well-defined tropopause and a stable stratosphere,as well as the integrated parameters such as seeing(free and total),isoplanatic angle,and coherence time.The results reveal a stratified and stable atmosphere,with turbulence predominantly concentrated in the lower troposphere.The thermal profiles show a good agreement with standard atmospheric models,with a well-defined tropopause and a stable stratosphere.The seasonality of the astroclimatic parameters indicates conditions particularly favorable for observation during the winter months,with the median of free seeing,total seeing,isoplanatic angle and a coherence time as respectively 0″.48,0″.72,2″,and 6 ms.These results confirm the strong potential of ORM as a reference astronomical site,capable of hosting high-precision observation instruments.The methodological approach employed also provides a replicable basis for the characterization of other candidate sites for the installation of ground-based telescopes.As a preliminary study,this work establishes a scientific basis for further on-site monitoring and detailed characterization of the ORM site.展开更多
Astronomical outliers,such as unusual,rare or unknown types of astronomical objects or phenomena,constantly lead to the discovery of genuinely unforeseen knowledge in astronomy.More unpredictable outliers will be unco...Astronomical outliers,such as unusual,rare or unknown types of astronomical objects or phenomena,constantly lead to the discovery of genuinely unforeseen knowledge in astronomy.More unpredictable outliers will be uncovered in principle with the increment of the coverage and quality of upcoming survey data.However,it is a severe challenge to mine rare and unexpected targets from enormous data with human inspection due to a significant workload.Supervised learning is also unsuitable for this purpose because designing proper training sets for unanticipated signals is unworkable.Motivated by these challenges,we adopt unsupervised machine learning approaches to identify outliers in the data of galaxy images to explore the paths for detecting astronomical outliers.For comparison,we construct three methods,which are built upon the k-nearest neighbors(KNN),Convolutional Auto-Encoder(CAE)+KNN,and CAE+KNN+Attention Mechanism(att CAE_KNN)separately.Testing sets are created based on the Galaxy Zoo image data published online to evaluate the performance of the above methods.Results show that att CAE_KNN achieves the best recall(78%),which is 53%higher than the classical KNN method and 22%higher than CAE+KNN.The efficiency of att CAE_KNN(10 minutes)is also superior to KNN(4 h)and equal to CAE+KNN(10 minutes)for accomplishing the same task.Thus,we believe that it is feasible to detect astronomical outliers in the data of galaxy images in an unsupervised manner.Next,we will apply att CAE_KNN to available survey data sets to assess its applicability and reliability.展开更多
We report the properties of more than 600 bursts(including cluster-bursts)detected from the repeating fast radio burst(FRB)source FRB 20201124A with the Five-hundred-meter Aperture Spherical radio Telescope during an ...We report the properties of more than 600 bursts(including cluster-bursts)detected from the repeating fast radio burst(FRB)source FRB 20201124A with the Five-hundred-meter Aperture Spherical radio Telescope during an extremely active episode on UTC 2021 September 25–28,in a series of four papers.The observations were carried out in the band of 1.0–1.5 GHz by using the center beam of the L-band 19-beam receiver.We monitored the source in sixteen1 hr sessions and one 3 hr session spanning 23 days.All the bursts were detected during the first four days.In this first paper of the series,we perform a detailed morphological study of 624 bursts using the two-dimensional frequencytime“waterfall”plots,with a burst(or cluster-burst)defined as an emission episode during which the adjacent emission peaks have a separation shorter than 400 ms.The duration of a burst is therefore always longer than 1 ms,with the longest up to more than 120 ms.The emission spectra of the sub-bursts are typically narrow within the observing band with a characteristic width of~277 MHz.The center frequency distribution has a dominant peak at about 1091.9 MHz and a secondary weak peak around 1327.9 MHz.Most bursts show a frequencydownwarddrifting pattern.Based on the drifting patterns,we classify the bursts into five main categories:downward drifting(263)bursts,upward drifting(3)bursts,complex(203),no drifting(35)bursts,and no evidence for drifting(121)bursts.Subtypes are introduced based on the emission frequency range in the band(low,middle,high and wide)as well as the number of components in one burst(1,2,or multiple).We measured a varying scintillation bandwidth from about 0.5 MHz at 1.0 GHz to 1.4 MHz at 1.5 GHz with a spectral index of 3.0.展开更多
Considering features of stellar spectral radiation and sky surveys, we established a computational model for stellar effective temperatures, detected angular parameters and gray rates. Using known stellar flux data in...Considering features of stellar spectral radiation and sky surveys, we established a computational model for stellar effective temperatures, detected angular parameters and gray rates. Using known stellar flux data in some bands, we estimated stellar effective temperatures and detected angular parameters using stochastic particle swarm optimization （SPSO）. We first verified the reliability of SPSO, and then determined reasonable parameters that produced highly accurate estimates under certain gray deviation levels. Finally, we calculated 177 860 stellar effective temperatures and detected angular parameters using data from the Midcourse Space Experiment （MSX） catalog. These derived stellar effective temperatures were accurate when we compared them to known values from literatures. This research makes full use of catalog data and presents an original technique for studying stellar characteristics. It proposes a novel method for calculating stellar effective temperatures and detecting angular parameters, and provides theoretical and practical data for finding information about radiation in any band.展开更多
Based on the shell-model Monte Carlo method and random phase approximation theory, the neutrino energy spectrum （NES） and the electron capture （EC） of ^56Fe, ^56Co, ^56Ni, ^56Mn, ^56Cr and ^56V are investigated in...Based on the shell-model Monte Carlo method and random phase approximation theory, the neutrino energy spectrum （NES） and the electron capture （EC） of ^56Fe, ^56Co, ^56Ni, ^56Mn, ^56Cr and ^56V are investigated in presupemova surroundings. The results show that the EC rates are affected greatly at different densities and temperatures. The rates increase greatly and even exceed six orders of magnitude at lower temperature. On the other hand, the NES is very sensitive to stellar temperature and electron energy. The higher the temperature and the lower the electron energy, the larger the influence on NES is. For example, the maxima of NES in the ground state are 9.02, 160, 80, 24.01, 0.44, 1.42 me c^2 for ^56Fe, ^56Co, ^56Ni, ^56Mn, ^56Cr and ^56V respectively at ρ7 = 10.7, Ye = 0.45 and T9 = 15. Furthermore, the influence on NES due to EC for different nuclei has some otherness because of different Qo-values. For example, the spectrum of ^56Co shows a double bump structure.展开更多
In a study that attempted to relate solar and human activity to Earth's recent temperature change,Connolly et al.committed a basic error in the choice of statistical methods and so overreported the effect of the S...In a study that attempted to relate solar and human activity to Earth's recent temperature change,Connolly et al.committed a basic error in the choice of statistical methods and so overreported the effect of the Sun.A major theme of their study was that there are many data sets of past solar activity,and some of these allegedly provide statistical evidence of“most of the recent global warming being due to changes in solar activity.”We avoid methods that are known to give inaccurate results and show that for 1970–2005 Northern Hemisphere land the corrected solar attribution fraction is-7%to+5%,compared with values of up to 64%reported in Connolly et al.Their higher values are entirely due to mistaken application of statistics.Unfortunately,we cannot test truly“recent”global warming since most of their solar data sets end before 2015,and two finish in the 1990s,but all tested post-1970 periods show similarly small solar contributions.The solar-climate linkage is an area of fascinating and ongoing research with rigorous technical discussion.We argue that instead of repeating errors,they should be acknowledged and corrected so that the debate can focus on areas of legitimate scientific uncertainty.展开更多
By introducing the Dirac δ-function and Pauli exclusion principle in the presence of superstrong magnetic fields （SMFs）, we investigate the influence of SMFs on beta decay and the change rates of electron fraction ...By introducing the Dirac δ-function and Pauli exclusion principle in the presence of superstrong magnetic fields （SMFs）, we investigate the influence of SMFs on beta decay and the change rates of electron fraction （CREF） of nuclides 56Fe, 62Ni, 64Ni and 6SNi in magnetars, which are powered by magnetic field energy. We find that the magnetic fields have a great influence on the beta decay rates, and the beta decay rates can decrease by more than six orders of magnitude in the presence of SMFs. The CREF also decreases by more than seven orders of magnitude in the presence of SMFs.展开更多
Electron screening has strong effects on electron energy and threshold energy of the beta decay reaction.In this paper,we study β~-decay rates of some iron isotopes.The beta decay rates increase by about two orders o...Electron screening has strong effects on electron energy and threshold energy of the beta decay reaction.In this paper,we study β~-decay rates of some iron isotopes.The beta decay rates increase by about two orders of magnitude due to electron screening.Strongly screened beta decay rates due to Q-value correction are more than one order of magnitude higher than those without Q-value correction.展开更多
Based on the theory of relativistic superstrong magnetic fields （SMFs）, by using the method of Thomas-Fermi-Dirac approximations, we investigate the problem of strong electron screening （SES） in SMFs and the influ...Based on the theory of relativistic superstrong magnetic fields （SMFs）, by using the method of Thomas-Fermi-Dirac approximations, we investigate the problem of strong electron screening （SES） in SMFs and the influence of SES on the nuclear reaction of 23Mg （p, Y）24A1. Our calculations show that the nuclear reaction will be markedly effected by the SES in SMFs in the surface of magnetars. Our calculated screening rates can increase two orders of magnitude due to SES in SMFs.展开更多
Using the Shell-Model Monte Carlo method and the Random Phase Approximation theory, we carry out an estimation of neutrino energy loss （NEL） for 55Co and 56Ni by electron capture. We find that the NEL rates increase...Using the Shell-Model Monte Carlo method and the Random Phase Approximation theory, we carry out an estimation of neutrino energy loss （NEL） for 55Co and 56Ni by electron capture. We find that the NEL rates increase greatly at some typical stellar conditions, and can even exceed five orders of magnitude （e.g. T9 = 38.5, Ye = 0.42 for 56Ni）. On the other hand, the error factor C shows that the fit is fairly good for two results at higher density and lower temperature, and the max- imum error is - 1.2%. However, the maximum error is ,- 55.60% （e.g. T9 = 18.5, Ye = 0.45） at lower density and higher temperature.展开更多
Following the theory of relativity, in the presence of an ultrastrong magnetic field （UMF） and utilizing a nuclear shell model, we carry out an estimation of the neutrino energy loss （NEL） rates of nuclides ^53-60...Following the theory of relativity, in the presence of an ultrastrong magnetic field （UMF） and utilizing a nuclear shell model, we carry out an estimation of the neutrino energy loss （NEL） rates of nuclides ^53-60Cr, which occur by electron capture in magnetars. The results show that the NEL rates greatly increase when a UMF is present, and can even exceed nine orders of magnitude at relatively lower density and temperature （e.g. ρ7 = 5.86, Ye = 0.47, T9 = 7.33） in the range from 1013 G to 1018 G. However, the increase in rates was no more than six orders of magnitude at relatively higher density and temperature （e.g.ρ7 = 4.86 × 10^8, Ye = 0.39, T9 = 14.35）.展开更多
Cataract is a very common eye disease and the most significant cause of blindness.In consideration of its burden on society,the focus was put on testing the risk factors of cataract and building robust machine learnin...Cataract is a very common eye disease and the most significant cause of blindness.In consideration of its burden on society,the focus was put on testing the risk factors of cataract and building robust machine learning models in which these factors can be utilized to predict the risk of cataract.The data used herein was collected by a Chinese physical examination center located in Shanghai.It contains more than 120,000 examinees and about 500 physical examination metrics.Firstly,association rules were adopted to filter 39 abnormalities which are more likely to incur the risk of cataract,and the significance of these abnormalities was tested with univariate analysis and multivariate analysis.The test results indicate that age,diabetes,refractive error,retinal arteriosclerosis,thyroid nodules,and incomplete mammary gland degeneration significantly increase the possibility of cataract.Various machine learning models were compared in terms of their performance in predicting the risk of cataract based on these six factors,among which the logistic regression model and the decision-tree based ensemble methods outperform others.The test set A U C of these models can reach 0.84.展开更多
A novel physical layer data encryption scheme using two-level constellation masking in three-dimensional(3D)carrier-less amplitude and phase modulation(CAP)passive optical network(PON)is proposed in this Letter.The ch...A novel physical layer data encryption scheme using two-level constellation masking in three-dimensional(3D)carrier-less amplitude and phase modulation(CAP)passive optical network(PON)is proposed in this Letter.The chaotic sequence generated by Chua’s circuit model realizes two-level encryption of displacement masking and constellation rotation for3 D constellations.We successfully conduct an experiment demonstrating 8.7 Gb/s 3 D-CAP-8 data transmission over25 km standard single-mode fiber.With two-level constellation masking,a key space size of 2.1×1085 is achieved to bring about high security and good encryption performance,suggesting broad application prospects in future short-range secure communications.展开更多
Detecting sophisticated cyberattacks,mainly Distributed Denial of Service(DDoS)attacks,with unexpected patterns remains challenging in modern networks.Traditional detection systems often struggle to mitigate such atta...Detecting sophisticated cyberattacks,mainly Distributed Denial of Service(DDoS)attacks,with unexpected patterns remains challenging in modern networks.Traditional detection systems often struggle to mitigate such attacks in conventional and software-defined networking(SDN)environments.While Machine Learning(ML)models can distinguish between benign and malicious traffic,their limited feature scope hinders the detection of new zero-day or low-rate DDoS attacks requiring frequent retraining.In this paper,we propose a novel DDoS detection framework that combines Machine Learning(ML)and Ensemble Learning(EL)techniques to improve DDoS attack detection and mitigation in SDN environments.Our model leverages the“DDoS SDN”dataset for training and evaluation and employs a dynamic feature selection mechanism that enhances detection accuracy by focusing on the most relevant features.This adaptive approach addresses the limitations of conventional ML models and provides more accurate detection of various DDoS attack scenarios.Our proposed ensemble model introduces an additional layer of detection,increasing reliability through the innovative application of ensemble techniques.The proposed solution significantly enhances the model’s ability to identify and respond to dynamic threats in SDNs.It provides a strong foundation for proactive DDoS detection and mitigation,enhancing network defenses against evolving threats.Our comprehensive runtime analysis of Simultaneous Multi-Threading(SMT)on identical configurations shows superior accuracy and efficiency,with significantly reduced computational time,making it ideal for real-time DDoS detection in dynamic,rapidly changing SDNs.Experimental results demonstrate that our model achieves outstanding performance,outperforming traditional algorithms with 99%accuracy using Random Forest(RF)and K-Nearest Neighbors(KNN)and 98%accuracy using XGBoost.展开更多
Radar data, which have incomparably high temporal and spatial resolution, and lightning data, which are great indicators of severe convection, have been used to improve the initial field and increase the accuracies of...Radar data, which have incomparably high temporal and spatial resolution, and lightning data, which are great indicators of severe convection, have been used to improve the initial field and increase the accuracies of nowcasting and short-term forecasting. Physical initialization combined with the three-dimensional variational data assimilation method(PI3 DVarrh) is used in this study to assimilate two kinds of observation data simultaneously, in which radar data are dominant and lightning data are introduced as constraint conditions. In this way, the advantages of dual observations are adopted. To verify the effect of assimilating radar and lightning data using the PI3 DVarrh method, a severe convective activity that occurred on 5 June 2009 is utilized, and five assimilation experiments are designed based on the Weather Research and Forecasting(WRF) model. The assimilation of radar and lightning data results in moister conditions below cloud top, where severe convection occurs;thus, wet forecasts are generated in this study.The results show that the control experiment has poor prediction accuracy. Radar data assimilation using the PI3 DVarrh method improves the location prediction of reflectivity and precipitation, especially in the last 3-h prediction, although the reflectivity and precipitation are notably overestimated. The introduction of lightning data effectively thins the radar data, reduces the overestimates in radar data assimilation, and results in better spatial pattern and intensity predictions. The predicted graupel mixing ratio is closer to the distribution of the observed lightning,which can provide more accurate lightning warning information.展开更多
文摘Long-term navigation ability based on consumer-level wearable inertial sensors plays an essential role towards various emerging fields, for instance, smart healthcare, emergency rescue, soldier positioning et al. The performance of existing long-term navigation algorithm is limited by the cumulative error of inertial sensors, disturbed local magnetic field, and complex motion modes of the pedestrian. This paper develops a robust data and physical model dual-driven based trajectory estimation(DPDD-TE) framework, which can be applied for long-term navigation tasks. A Bi-directional Long Short-Term Memory(Bi-LSTM) based quasi-static magnetic field(QSMF) detection algorithm is developed for extracting useful magnetic observation for heading calibration, and another Bi-LSTM is adopted for walking speed estimation by considering hybrid human motion information under a specific time period. In addition, a data and physical model dual-driven based multi-source fusion model is proposed to integrate basic INS mechanization and multi-level constraint and observations for maintaining accuracy under long-term navigation tasks, and enhanced by the magnetic and trajectory features assisted loop detection algorithm. Real-world experiments indicate that the proposed DPDD-TE outperforms than existing algorithms, and final estimated heading and positioning accuracy indexes reaches 5° and less than 2 m under the time period of 30 min, respectively.
文摘In this article,we have modeled the astroclimatic parameters using data from the fifth reanalysis series(ERA5)from the European Centre for Medium-Range Weather Forecasts over the period 2010-2020.These data have been projected onto the site of the Roque de los Muchachos Observatory(ORM).The analysis covers the main parameters influencing the quality of astronomical observation,notably the vertical profiles of temperature,potential temperature,and optical turbulence(C_(n)~2),as well as the vertical wind distributions and seasonal behaviors of atmospheric turbulence,which are treated in detail by specifying the wind speed at the 200 hPa level(V200).Around this level,the wind at high altitude presents moderate speeds and a generally stable direction,conditions favorable for adaptive optics.The thermal profiles show good agreement with standard atmospheric models,with a well-defined tropopause and a stable stratosphere,as well as the integrated parameters such as seeing(free and total),isoplanatic angle,and coherence time.The results reveal a stratified and stable atmosphere,with turbulence predominantly concentrated in the lower troposphere.The thermal profiles show a good agreement with standard atmospheric models,with a well-defined tropopause and a stable stratosphere.The seasonality of the astroclimatic parameters indicates conditions particularly favorable for observation during the winter months,with the median of free seeing,total seeing,isoplanatic angle and a coherence time as respectively 0″.48,0″.72,2″,and 6 ms.These results confirm the strong potential of ORM as a reference astronomical site,capable of hosting high-precision observation instruments.The methodological approach employed also provides a replicable basis for the characterization of other candidate sites for the installation of ground-based telescopes.As a preliminary study,this work establishes a scientific basis for further on-site monitoring and detailed characterization of the ORM site.
基金the science research grants from the China Manned Space Project with Nos.CMS-CSST-2021-A01 and CMS-CSST-2021-B05the funding and technical support from the Jiangsu Key Laboratory of Big Data Security and Intelligent Processing。
文摘Astronomical outliers,such as unusual,rare or unknown types of astronomical objects or phenomena,constantly lead to the discovery of genuinely unforeseen knowledge in astronomy.More unpredictable outliers will be uncovered in principle with the increment of the coverage and quality of upcoming survey data.However,it is a severe challenge to mine rare and unexpected targets from enormous data with human inspection due to a significant workload.Supervised learning is also unsuitable for this purpose because designing proper training sets for unanticipated signals is unworkable.Motivated by these challenges,we adopt unsupervised machine learning approaches to identify outliers in the data of galaxy images to explore the paths for detecting astronomical outliers.For comparison,we construct three methods,which are built upon the k-nearest neighbors(KNN),Convolutional Auto-Encoder(CAE)+KNN,and CAE+KNN+Attention Mechanism(att CAE_KNN)separately.Testing sets are created based on the Galaxy Zoo image data published online to evaluate the performance of the above methods.Results show that att CAE_KNN achieves the best recall(78%),which is 53%higher than the classical KNN method and 22%higher than CAE+KNN.The efficiency of att CAE_KNN(10 minutes)is also superior to KNN(4 h)and equal to CAE+KNN(10 minutes)for accomplishing the same task.Thus,we believe that it is feasible to detect astronomical outliers in the data of galaxy images in an unsupervised manner.Next,we will apply att CAE_KNN to available survey data sets to assess its applicability and reliability.
基金supported by the National Natural Science Foundation of China(NSFC,Nos.11988101 and 11833009)the Key Research Program of the Chinese Academy of Sciences(Grant No.QYZDJ-SSWSLH021)+6 种基金supported by the Cultivation Project for the FAST scientific Payoff and Research Achievement of CAMS-CASsupported by the Key Research Project of Zhejiang Lab no.2021PE0AC0supported by the National SKA Program of China(2020SKA0120100)the National Key R&D Program of China(2017YFA0402602)the National Natural Science Foundation of China(No.12041303)the CAS-MPG LEGACY projectfunding from the MaxPlanck Partner Group。
文摘We report the properties of more than 600 bursts(including cluster-bursts)detected from the repeating fast radio burst(FRB)source FRB 20201124A with the Five-hundred-meter Aperture Spherical radio Telescope during an extremely active episode on UTC 2021 September 25–28,in a series of four papers.The observations were carried out in the band of 1.0–1.5 GHz by using the center beam of the L-band 19-beam receiver.We monitored the source in sixteen1 hr sessions and one 3 hr session spanning 23 days.All the bursts were detected during the first four days.In this first paper of the series,we perform a detailed morphological study of 624 bursts using the two-dimensional frequencytime“waterfall”plots,with a burst(or cluster-burst)defined as an emission episode during which the adjacent emission peaks have a separation shorter than 400 ms.The duration of a burst is therefore always longer than 1 ms,with the longest up to more than 120 ms.The emission spectra of the sub-bursts are typically narrow within the observing band with a characteristic width of~277 MHz.The center frequency distribution has a dominant peak at about 1091.9 MHz and a secondary weak peak around 1327.9 MHz.Most bursts show a frequencydownwarddrifting pattern.Based on the drifting patterns,we classify the bursts into five main categories:downward drifting(263)bursts,upward drifting(3)bursts,complex(203),no drifting(35)bursts,and no evidence for drifting(121)bursts.Subtypes are introduced based on the emission frequency range in the band(low,middle,high and wide)as well as the number of components in one burst(1,2,or multiple).We measured a varying scintillation bandwidth from about 0.5 MHz at 1.0 GHz to 1.4 MHz at 1.5 GHz with a spectral index of 3.0.
基金supported by the National Natural Science Foundation of China (Grant Nos. 51327803 and 51406041)the Fundamental Research Funds for the Central Universities (Grant No. HIT. NSRIF.2014090)
文摘Considering features of stellar spectral radiation and sky surveys, we established a computational model for stellar effective temperatures, detected angular parameters and gray rates. Using known stellar flux data in some bands, we estimated stellar effective temperatures and detected angular parameters using stochastic particle swarm optimization （SPSO）. We first verified the reliability of SPSO, and then determined reasonable parameters that produced highly accurate estimates under certain gray deviation levels. Finally, we calculated 177 860 stellar effective temperatures and detected angular parameters using data from the Midcourse Space Experiment （MSX） catalog. These derived stellar effective temperatures were accurate when we compared them to known values from literatures. This research makes full use of catalog data and presents an original technique for studying stellar characteristics. It proposes a novel method for calculating stellar effective temperatures and detecting angular parameters, and provides theoretical and practical data for finding information about radiation in any band.
基金supported by the Natural Science Foundation of Hainan province (Grant No.114012)
文摘Based on the shell-model Monte Carlo method and random phase approximation theory, the neutrino energy spectrum （NES） and the electron capture （EC） of ^56Fe, ^56Co, ^56Ni, ^56Mn, ^56Cr and ^56V are investigated in presupemova surroundings. The results show that the EC rates are affected greatly at different densities and temperatures. The rates increase greatly and even exceed six orders of magnitude at lower temperature. On the other hand, the NES is very sensitive to stellar temperature and electron energy. The higher the temperature and the lower the electron energy, the larger the influence on NES is. For example, the maxima of NES in the ground state are 9.02, 160, 80, 24.01, 0.44, 1.42 me c^2 for ^56Fe, ^56Co, ^56Ni, ^56Mn, ^56Cr and ^56V respectively at ρ7 = 10.7, Ye = 0.45 and T9 = 15. Furthermore, the influence on NES due to EC for different nuclei has some otherness because of different Qo-values. For example, the spectrum of ^56Co shows a double bump structure.
文摘In a study that attempted to relate solar and human activity to Earth's recent temperature change,Connolly et al.committed a basic error in the choice of statistical methods and so overreported the effect of the Sun.A major theme of their study was that there are many data sets of past solar activity,and some of these allegedly provide statistical evidence of“most of the recent global warming being due to changes in solar activity.”We avoid methods that are known to give inaccurate results and show that for 1970–2005 Northern Hemisphere land the corrected solar attribution fraction is-7%to+5%,compared with values of up to 64%reported in Connolly et al.Their higher values are entirely due to mistaken application of statistics.Unfortunately,we cannot test truly“recent”global warming since most of their solar data sets end before 2015,and two finish in the 1990s,but all tested post-1970 periods show similarly small solar contributions.The solar-climate linkage is an area of fascinating and ongoing research with rigorous technical discussion.We argue that instead of repeating errors,they should be acknowledged and corrected so that the debate can focus on areas of legitimate scientific uncertainty.
基金supported by the National Natural Science Foundation of China (Grant No. 11565020)the Counterpart Foundation of Sanya (Grant No. 2016PT43)+2 种基金the Special Foundation of Science and Technology Cooperation for Advanced Academy and Regional of Sanya (Grant No. 2016YD28)the Natural Science Foundation of Hainan province (Grant No. 114012)the Natural Science Foundation of Jiangxi Province (Grant No. 20132BAB212005)
文摘By introducing the Dirac δ-function and Pauli exclusion principle in the presence of superstrong magnetic fields （SMFs）, we investigate the influence of SMFs on beta decay and the change rates of electron fraction （CREF） of nuclides 56Fe, 62Ni, 64Ni and 6SNi in magnetars, which are powered by magnetic field energy. We find that the magnetic fields have a great influence on the beta decay rates, and the beta decay rates can decrease by more than six orders of magnitude in the presence of SMFs. The CREF also decreases by more than seven orders of magnitude in the presence of SMFs.
基金supported in part by the National Natural Science Foundation of China(Grant 11565020)the Counterpart Foundation of Sanya(Grant 2016PT43)+2 种基金the Special Foundation of Science and Technology Cooperation for Advanced Academy and Region of Sanya(Grant 2016YD28)the Scientific Research Starting Foundation for 515 Talented Project of Hainan Tropical Ocean University(Grant RHDRC201701)the Natural Science Foundation of Hainan Province(Grant 114012)
文摘Electron screening has strong effects on electron energy and threshold energy of the beta decay reaction.In this paper,we study β~-decay rates of some iron isotopes.The beta decay rates increase by about two orders of magnitude due to electron screening.Strongly screened beta decay rates due to Q-value correction are more than one order of magnitude higher than those without Q-value correction.
基金supported in part by the National Natural Science Foundation of China through grant No. 11565020the Natural Science Foundation of Hainan province under grant No. 114012the Undergraduate Innovation Program of Hainan province under grant No. 20130139
文摘Based on the theory of relativistic superstrong magnetic fields （SMFs）, by using the method of Thomas-Fermi-Dirac approximations, we investigate the problem of strong electron screening （SES） in SMFs and the influence of SES on the nuclear reaction of 23Mg （p, Y）24A1. Our calculations show that the nuclear reaction will be markedly effected by the SES in SMFs in the surface of magnetars. Our calculated screening rates can increase two orders of magnitude due to SES in SMFs.
基金supported by the Advanced Academy Special Foundation of Sanya under Grant No. 2011YD14
文摘Using the Shell-Model Monte Carlo method and the Random Phase Approximation theory, we carry out an estimation of neutrino energy loss （NEL） for 55Co and 56Ni by electron capture. We find that the NEL rates increase greatly at some typical stellar conditions, and can even exceed five orders of magnitude （e.g. T9 = 38.5, Ye = 0.42 for 56Ni）. On the other hand, the error factor C shows that the fit is fairly good for two results at higher density and lower temperature, and the max- imum error is - 1.2%. However, the maximum error is ,- 55.60% （e.g. T9 = 18.5, Ye = 0.45） at lower density and higher temperature.
基金supported by the Advanced Academy Special Foundation of Sanya (Grant No. 2011YD14)
文摘Following the theory of relativity, in the presence of an ultrastrong magnetic field （UMF） and utilizing a nuclear shell model, we carry out an estimation of the neutrino energy loss （NEL） rates of nuclides ^53-60Cr, which occur by electron capture in magnetars. The results show that the NEL rates greatly increase when a UMF is present, and can even exceed nine orders of magnitude at relatively lower density and temperature （e.g. ρ7 = 5.86, Ye = 0.47, T9 = 7.33） in the range from 1013 G to 1018 G. However, the increase in rates was no more than six orders of magnitude at relatively higher density and temperature （e.g.ρ7 = 4.86 × 10^8, Ye = 0.39, T9 = 14.35）.
基金the National Key R&D Program of China under Grant No.2020AAA0103800.
文摘Cataract is a very common eye disease and the most significant cause of blindness.In consideration of its burden on society,the focus was put on testing the risk factors of cataract and building robust machine learning models in which these factors can be utilized to predict the risk of cataract.The data used herein was collected by a Chinese physical examination center located in Shanghai.It contains more than 120,000 examinees and about 500 physical examination metrics.Firstly,association rules were adopted to filter 39 abnormalities which are more likely to incur the risk of cataract,and the significance of these abnormalities was tested with univariate analysis and multivariate analysis.The test results indicate that age,diabetes,refractive error,retinal arteriosclerosis,thyroid nodules,and incomplete mammary gland degeneration significantly increase the possibility of cataract.Various machine learning models were compared in terms of their performance in predicting the risk of cataract based on these six factors,among which the logistic regression model and the decision-tree based ensemble methods outperform others.The test set A U C of these models can reach 0.84.
基金the National Key Research and Development Program of China(No.2018YFB1801302)National Natural Science Foundation of China(Nos.61835005,61822507,61522501,61475024,61675004,61705107,61727817,61775098,61720106015,and 61875248)+2 种基金Beijing Young Talent(No.2016000026833ZK15)Open Fund of IPOC(BUPT)(No.IPOC2019A011)Jiangsu Talent of Innovation and Entrepreneurship,Jiangsu Team of Innovation and Entrepreneurship,and Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX200963)。
文摘A novel physical layer data encryption scheme using two-level constellation masking in three-dimensional(3D)carrier-less amplitude and phase modulation(CAP)passive optical network(PON)is proposed in this Letter.The chaotic sequence generated by Chua’s circuit model realizes two-level encryption of displacement masking and constellation rotation for3 D constellations.We successfully conduct an experiment demonstrating 8.7 Gb/s 3 D-CAP-8 data transmission over25 km standard single-mode fiber.With two-level constellation masking,a key space size of 2.1×1085 is achieved to bring about high security and good encryption performance,suggesting broad application prospects in future short-range secure communications.
文摘Detecting sophisticated cyberattacks,mainly Distributed Denial of Service(DDoS)attacks,with unexpected patterns remains challenging in modern networks.Traditional detection systems often struggle to mitigate such attacks in conventional and software-defined networking(SDN)environments.While Machine Learning(ML)models can distinguish between benign and malicious traffic,their limited feature scope hinders the detection of new zero-day or low-rate DDoS attacks requiring frequent retraining.In this paper,we propose a novel DDoS detection framework that combines Machine Learning(ML)and Ensemble Learning(EL)techniques to improve DDoS attack detection and mitigation in SDN environments.Our model leverages the“DDoS SDN”dataset for training and evaluation and employs a dynamic feature selection mechanism that enhances detection accuracy by focusing on the most relevant features.This adaptive approach addresses the limitations of conventional ML models and provides more accurate detection of various DDoS attack scenarios.Our proposed ensemble model introduces an additional layer of detection,increasing reliability through the innovative application of ensemble techniques.The proposed solution significantly enhances the model’s ability to identify and respond to dynamic threats in SDNs.It provides a strong foundation for proactive DDoS detection and mitigation,enhancing network defenses against evolving threats.Our comprehensive runtime analysis of Simultaneous Multi-Threading(SMT)on identical configurations shows superior accuracy and efficiency,with significantly reduced computational time,making it ideal for real-time DDoS detection in dynamic,rapidly changing SDNs.Experimental results demonstrate that our model achieves outstanding performance,outperforming traditional algorithms with 99%accuracy using Random Forest(RF)and K-Nearest Neighbors(KNN)and 98%accuracy using XGBoost.
基金the National Key Research and Development Program of China (2017YFC1502102)National Natural Science Youth Fund of China (41905089)。
文摘Radar data, which have incomparably high temporal and spatial resolution, and lightning data, which are great indicators of severe convection, have been used to improve the initial field and increase the accuracies of nowcasting and short-term forecasting. Physical initialization combined with the three-dimensional variational data assimilation method(PI3 DVarrh) is used in this study to assimilate two kinds of observation data simultaneously, in which radar data are dominant and lightning data are introduced as constraint conditions. In this way, the advantages of dual observations are adopted. To verify the effect of assimilating radar and lightning data using the PI3 DVarrh method, a severe convective activity that occurred on 5 June 2009 is utilized, and five assimilation experiments are designed based on the Weather Research and Forecasting(WRF) model. The assimilation of radar and lightning data results in moister conditions below cloud top, where severe convection occurs;thus, wet forecasts are generated in this study.The results show that the control experiment has poor prediction accuracy. Radar data assimilation using the PI3 DVarrh method improves the location prediction of reflectivity and precipitation, especially in the last 3-h prediction, although the reflectivity and precipitation are notably overestimated. The introduction of lightning data effectively thins the radar data, reduces the overestimates in radar data assimilation, and results in better spatial pattern and intensity predictions. The predicted graupel mixing ratio is closer to the distribution of the observed lightning,which can provide more accurate lightning warning information.
