Atmospheric chemistry models usually perform badly in forecasting wintertime air pollution because of their uncertainties. Generally, such uncertainties can be decreased effectively by techniques such as data assimila...Atmospheric chemistry models usually perform badly in forecasting wintertime air pollution because of their uncertainties. Generally, such uncertainties can be decreased effectively by techniques such as data assimilation（DA） and model output statistics（MOS）. However, the relative importance and combined effects of the two techniques have not been clarified. Here,a one-month air quality forecast with the Weather Research and Forecasting-Chemistry（WRF-Chem） model was carried out in a virtually operational setup focusing on Hebei Province, China. Meanwhile, three-dimensional variational（3 DVar） DA and MOS based on one-dimensional Kalman filtering were implemented separately and simultaneously to investigate their performance in improving the model forecast. Comparison with observations shows that the chemistry forecast with MOS outperforms that with 3 DVar DA, which could be seen in all the species tested over the whole 72 forecast hours. Combined use of both techniques does not guarantee a better forecast than MOS only, with the improvements and degradations being small and appearing rather randomly. Results indicate that the implementation of MOS is more suitable than 3 DVar DA in improving the operational forecasting ability of WRF-Chem.展开更多
Statistics are most crucial than ever due to the accessibility of huge counts of data from several domains such as finance,medicine,science,engineering,and so on.Statistical data mining(SDM)is an interdisciplinary dom...Statistics are most crucial than ever due to the accessibility of huge counts of data from several domains such as finance,medicine,science,engineering,and so on.Statistical data mining(SDM)is an interdisciplinary domain that examines huge existing databases to discover patterns and connections from the data.It varies in classical statistics on the size of datasets and on the detail that the data could not primarily be gathered based on some experimental strategy but conversely for other resolves.Thus,this paper introduces an effective statistical Data Mining for Intelligent Rainfall Prediction using Slime Mould Optimization with Deep Learning(SDMIRPSMODL)model.In the presented SDMIRP-SMODL model,the feature subset selection process is performed by the SMO algorithm,which in turn minimizes the computation complexity.For rainfall prediction.Convolution neural network with long short-term memory(CNN-LSTM)technique is exploited.At last,this study involves the pelican optimization algorithm(POA)as a hyperparameter optimizer.The experimental evaluation of the SDMIRP-SMODL approach is tested utilizing a rainfall dataset comprising 23682 samples in the negative class and 1865 samples in the positive class.The comparative outcomes reported the supremacy of the SDMIRP-SMODL model compared to existing techniques.展开更多
Cryo-electron microscopy(cryo-EM) provides a powerful tool to resolve the structure of biological macromolecules in natural state. One advantage of cryo-EM technology is that different conformation states of a protein...Cryo-electron microscopy(cryo-EM) provides a powerful tool to resolve the structure of biological macromolecules in natural state. One advantage of cryo-EM technology is that different conformation states of a protein complex structure can be simultaneously built, and the distribution of different states can be measured. This provides a tool to push cryo-EM technology beyond just to resolve protein structures, but to obtain the thermodynamic properties of protein machines. Here, we used a deep manifold learning framework to get the conformational landscape of Kai C proteins, and further obtained the thermodynamic properties of this central oscillator component in the circadian clock by means of statistical physics.展开更多
Extracting and parameterizing ionospheric waves globally and statistically is a longstanding problem. Based on the multichannel maximum entropy method（MMEM） used for studying ionospheric waves by previous work, we c...Extracting and parameterizing ionospheric waves globally and statistically is a longstanding problem. Based on the multichannel maximum entropy method（MMEM） used for studying ionospheric waves by previous work, we calculate the parameters of ionospheric waves by applying the MMEM to numerously temporally approximate and spatially close global-positioning-system radio occultation total electron content profile triples provided by the unique clustered satellites flight between years 2006 and 2007 right after the constellation observing system for meteorology, ionosphere, and climate（COSMIC） mission launch. The results show that the amplitude of ionospheric waves increases at the low and high latitudes（~0.15 TECU） and decreases in the mid-latitudes（~0.05 TECU）. The vertical wavelength of the ionospheric waves increases in the mid-latitudes（e.g., ~50 km at altitudes of 200–250 km） and decreases at the low and high latitudes（e.g., ~35 km at altitudes of 200–250 km）.The horizontal wavelength shows a similar result（e.g., ~1400 km in the mid-latitudes and ~800 km at the low and high latitudes）.展开更多
According to statistics of Printing and Printing Equipment Industries Association of China (PEIAC), the total output value of printing industry of China in 2007 reached 440 billion RMB , the total output value of prin...According to statistics of Printing and Printing Equipment Industries Association of China (PEIAC), the total output value of printing industry of China in 2007 reached 440 billion RMB , the total output value of printing equipment was展开更多
1 Production and Running Status of China’s Refractories Industry in 20241.1 Production and Running Status In 2024,according to the statistical data from The Association of China Refractories Industry,China’s refract...1 Production and Running Status of China’s Refractories Industry in 20241.1 Production and Running Status In 2024,according to the statistical data from The Association of China Refractories Industry,China’s refractories output was 22.0711million tons,decreasing by 3.73%YOY;in which the outputs of dense shaped refractory products,insulating refractory products and monolithic refractories were 11.3163 million tons decreasing by 6.07%YOY,83.77 thousand tons increasing by 11.17%YOY,and 9.9971 million tons decreasing by 2.07%YOY,respectively.The outputs of the main varieties are shown in Fig.1.展开更多
Given the rise of artificial intelligence,big data analytics has emerged as an important tool for processing and assimilating the enormous volume of data available on social media.It is of great theoretical and practi...Given the rise of artificial intelligence,big data analytics has emerged as an important tool for processing and assimilating the enormous volume of data available on social media.It is of great theoretical and practical significance to explore the public opinion diffusion process and characteristics,and users’emotions of mega sports events based on big data statistics in the social media environment.This paper takes the Jakarta Asian Games,Russian World Cup and PyeongChang Winter Olympics held in 2018 as cases,uses text mining and social network analysis methods to analyze the dissemination process of social media users’data,presents the semantic words disseminated in sports events through high-frequency word cloud diagrams,and summarizes the general rules of public opinion dissemination.The results show that the more users’participation,the greater diffusion volume,and the diffusion process shows fast increasing,short duration,scattered topics,diversified contents,and strong guidance and weak continuity of attention.The high-frequency words,except for the names of the events,such as“cheer”,“win the game”and“must win”,have obvious concentration of emotional words.展开更多
Some geophysical parameters, such as those related to gravitation and the geomagnetic field, could change during solar eclipses. In order to observe geomagnetic fluctuations, geomagnetic measurements were carded out i...Some geophysical parameters, such as those related to gravitation and the geomagnetic field, could change during solar eclipses. In order to observe geomagnetic fluctuations, geomagnetic measurements were carded out in a limited time frame during the partial solar eclipse that occurred on 2011 January 4 and was observed in Canakkale and Ankara, Turkey. Additionally, records of the geomagnetic field spanning 24 hours, obtained from another observatory （in Iznik, Turkey）, were also analyzed to check for any peculiar variations. In the data processing stage, a polynomial fit, following the application of a running average routine, was applied to the geomagnetic field data sets. Geomagnetic field data sets indicated there was a characteristic decrease at the beginning of the solar eclipse and this decrease can be well-correlated with previous geomagnetic field measurements that were taken during the total solar eclipse that was observed in Turkey on 2006 March 29. The behavior of the geomagnetic field is also consistent with previous observations in the literature. As a result of these analyses, it can be suggested that eclipses can cause a shielding effect on the geomagnetic field of the Earth.展开更多
This paper focuses on how to extract physically meaningful information from climate data,with emphases placed on adaptive and local analysis. It is argued that many traditional statistical analysis methods with rigoro...This paper focuses on how to extract physically meaningful information from climate data,with emphases placed on adaptive and local analysis. It is argued that many traditional statistical analysis methods with rigorous mathematical footing may not be efficient in extracting essential physical information from climate data;rather,adaptive and local analysis methods that agree well with fundamental physical principles are more capable of capturing key information of climate data. To illustrate the improved power of adaptive and local analysis of climate data,we also introduce briefly the empirical mode decomposition and its later developments.展开更多
The nature of random errors in any data set is Gaussian, which is a well established fact according to the Central Limit Theorem. Supernovae type Ia data have played a crucial role in major discoveries in cosmology. U...The nature of random errors in any data set is Gaussian, which is a well established fact according to the Central Limit Theorem. Supernovae type Ia data have played a crucial role in major discoveries in cosmology. Unlike in laboratory experiments, astronomical measurements cannot be performed in controlled situations. Thus, errors in astronomical data can be more severe in terms of systematics and non-Gaussianity compared to those of laboratory experiments. In this paper, we use the Kolmogorov-Smiruov statistic to test non-Gaussianity in high-z supernovae data. We apply this statistic to four data sets, i.e., Gold data （2004）, Gold data （2007）, the Union2 catalog and the Union2.1 data set for our analysis. Our results show that in all four data sets the errors are consistent with a Gaussian distribution.展开更多
Some recent developments(accelerated expansion)in the Universe cannot be explained by the conventional formulation of general relativity.We apply the recently proposed f(T,B)gravity to investigate the accelerated expa...Some recent developments(accelerated expansion)in the Universe cannot be explained by the conventional formulation of general relativity.We apply the recently proposed f(T,B)gravity to investigate the accelerated expansion of the Universe.By parametrizing the Hubble parameter and estimating the best fit values of the model parameters b_(0),b_(1),and b_(2)imposed from Supernovae type la,Cosmic Microwave Background,B aryon Acoustic Oscillation,and Hubble data using the Markov Chain Monte Carlo method,we propose a method to determine the precise solutions to the field equations.We then observe that the model appears to be in good agreement with the observations.A change from the deceleration to the acceleration phase of the Universe is shown by the evolution of the deceleration parameter.In addition,we investigate the behavior of the statefinder analysis,equation of state(EoS)parameters,along with the energy conditions.Furthermore,to discuss other cosmological parameters,we consider some wellknown f(T,B)gravity models,specifically,f(T,B)=aT^(b)+cB^(d).Lastly,we find that the considered f(T,B)gravity models predict that the present Universe is accelerating and the EoS parameter behaves like the ACDM model.展开更多
According to the statistics of production and marketing data of major enterprises in 2021 by Surfac-tant Committee of China Cleaning Industry Association(CCIA),the development trend of main raw materials and products ...According to the statistics of production and marketing data of major enterprises in 2021 by Surfac-tant Committee of China Cleaning Industry Association(CCIA),the development trend of main raw materials and products of surfactant industry is analyzed in detail.The main trend development trend and characteristics of product structure is discussed under the influence of COVID-19,crude oil price fluctuation and economic structure adjustment.It pointed that the direction and planning of high-quality industry and the demand of new market in the 14th Five-Year.Product structure innovation and technological innovation is important.展开更多
AIM：To conduct a multicentre retrospective review of virological response rates in Asians infected with genotype 1 chronic hepatitis C（CHC） treated with combination interferon and ribavirin and then to compare thei...AIM：To conduct a multicentre retrospective review of virological response rates in Asians infected with genotype 1 chronic hepatitis C（CHC） treated with combination interferon and ribavirin and then to compare their responses to that among Caucasians.METHODS：Asian patients infected with genotype 1 CHC treated at 4 Australian centres between 2001 to 2005 were identified through hospital databases.Baseline demographic characteristics,biochemical,virological and histological data and details of treatment were collected.Sustained virological responses（SVR） in this cohort were then compared to that in Caucasian subjects,matched by genotype,age,gender and the stage of hepatic fibrosis.RESULTS：A total of 108 Asians with genotype 1 CHC were identified.The end of treatment response（ETR） for the cohort was 79% while the SVR was 67%.Due to the relatively advanced age of the Asian cohort,only sixty-four subjects could be matched with Caucasians.The ETR among matched Asians and Caucasians was 81% and 56% respectively（P=0.003）,while the SVR rates were 73% and 36%（P 〈0.001） respectively.This difference remained significant after adjusting for other predictive variables. CONCLUSION： Genotype 1 CHC in Asian subjects is associated with higher rates of virological response compared to that in Caucasians.展开更多
While obesity and fat intake have been associated with the risk and prognosis of epithelial ovarian cancer, the association between the lipid levels and epithelial ovarian cancer phenotype remains controversial. We co...While obesity and fat intake have been associated with the risk and prognosis of epithelial ovarian cancer, the association between the lipid levels and epithelial ovarian cancer phenotype remains controversial. We conducted a retrospective study of 349 epithelial ovarian cancer patients who received treatment at Jiangsu Cancer Hospital, China between 2011 and 2017. We analyzed age at diagnosis, blood pressure, plasma glucose content, body mass index（BMI）, lipid levels and clinical parameters. Severity of epithelial ovarian cancer was classified according to the International Federation of Gynecology and Obstetrics（FIGO） grading system. Univariate analysis of the clinical factors according to the severity of epithelial ovarian cancer was followed by logistic regression analysis to identify clinical factors significantly associated with epithelial ovarian cancer severity. Univariate analysis indicated that age,BMI, triglyceride（TG）, and high density lipoproteins（HDL） differed significantly among different stages of epithelial ovarian cancer（P〈0.05）. In the logistic regression model, elevated TG（OR： 1.883; 95% CI= 1.207-2.937）, and low HDL（OR： 0.497; 95% CI = 0.298-0.829） levels were significantly associated with the high severity epithelial ovarian cancer. Our data indicate that high TG and low HDL levels correlate with a high severity of epithelial ovarian cancer. These data provide important insight into the potential relationship between the lipid pathway and epithelial ovarian cancer phenotype and development.展开更多
The sizes of astronomical surveys in different wavebands are increasing rapidly. Therefore, automatic classification of objects is becoming ever more important. We explore the performance of learning vector quantizati...The sizes of astronomical surveys in different wavebands are increasing rapidly. Therefore, automatic classification of objects is becoming ever more important. We explore the performance of learning vector quantization (LVQ) in classifying multi-wavelength data. Our analysis concentrates on separating active sources from non-active ones. Different classes of X-ray emitters populate distinct regions of a multidimensional parameter space. In order to explore the distribution of various objects in a multidimensional parameter space, we positionally cross-correlate the data of quasars, BL Lacs, active galaxies, stars and normal galaxies in the optical, X-ray and infrared bands. We then apply LVQ to classify them with the obtained data. Our results show that LVQ is an effective method for separating AGNs from stars and normal galaxies with multi-wavelength data.展开更多
Six high-resolution TiO-band image sequences from the New Vacuum Solar Telescope （NVST） are used to investigate the properties of intergranular bright points （igBPs）. We detect the igBPs using a Laplacian and morp...Six high-resolution TiO-band image sequences from the New Vacuum Solar Telescope （NVST） are used to investigate the properties of intergranular bright points （igBPs）. We detect the igBPs using a Laplacian and morphological dilation algorithm （LMD） and automatically track them using a three- dimensional segmentation algorithm, and then investigate the morphologic, photometric and dynamic prop- erties of igBPs in terms of equivalent diameter, intensity contrast, lifetime, horizontal velocity, diffusion index, motion range and motion type. The statistical results confirm previous studies based on G-band or TiO-band igBPs from other telescopes. These results illustrate that TiO data from the NVST are stable and reliable, and are suitable for studying igBPs. In addition, our method is feasible for detecting and track- ing igBPs with TiO data from the NVST. With the aid of vector magnetograms obtained from the Solar Dynamics Observatory/Helioseismic and Magnetic Imager, the properties of igBPs are found to be strongly influenced by their embedded magnetic environments. The areal coverage, size and intensity contrast values of igBPs are generally larger in regions with higher magnetic flux. However, the dynamics of igBPs, includ- ing the horizontal velocity, diffusion index, ratio of motion range and index of motion type are generally larger in the regions with lower magnetic flux. This suggests that the absence of strong magnetic fields in the medium makes it possible for the igBPs to look smaller and weaker, diffuse faster, and move faster and further along a straighter path.展开更多
Solving complex decision problems requires the usage of information from different sources. Usually this information is uncertain and statistical or probabilistic methods are needed for its processing. However, in man...Solving complex decision problems requires the usage of information from different sources. Usually this information is uncertain and statistical or probabilistic methods are needed for its processing. However, in many cases a decision maker faces not only uncertainty of a random nature but also imprecision in the description of input data that is rather of linguistic nature. Therefore, there is a need to merge uncertainties of both types into one mathematical model. In the paper we present methodology of merging information from imprecisely reported statistical data and imprecisely formulated fuzzy prior information. Moreover, we also consider the case of imprecisely defined loss functions. The proposed methodology may be considered as the application of fuzzy statistical methods for the decision making in the systems analysis.展开更多
基金supported by the State Key Research and Development Program (Grant Nos. 2017YFC0209803, 2016YFC0208504, 2016YFC0203303 and 2017YFC0210106)the National Natural Science Foundation of China (Grant Nos. 91544230, 41575145, 41621005 and 41275128)
文摘Atmospheric chemistry models usually perform badly in forecasting wintertime air pollution because of their uncertainties. Generally, such uncertainties can be decreased effectively by techniques such as data assimilation（DA） and model output statistics（MOS）. However, the relative importance and combined effects of the two techniques have not been clarified. Here,a one-month air quality forecast with the Weather Research and Forecasting-Chemistry（WRF-Chem） model was carried out in a virtually operational setup focusing on Hebei Province, China. Meanwhile, three-dimensional variational（3 DVar） DA and MOS based on one-dimensional Kalman filtering were implemented separately and simultaneously to investigate their performance in improving the model forecast. Comparison with observations shows that the chemistry forecast with MOS outperforms that with 3 DVar DA, which could be seen in all the species tested over the whole 72 forecast hours. Combined use of both techniques does not guarantee a better forecast than MOS only, with the improvements and degradations being small and appearing rather randomly. Results indicate that the implementation of MOS is more suitable than 3 DVar DA in improving the operational forecasting ability of WRF-Chem.
基金This research was partly supported by the Technology Development Program of MSS[No.S3033853]by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.2021R1A4A1031509).
文摘Statistics are most crucial than ever due to the accessibility of huge counts of data from several domains such as finance,medicine,science,engineering,and so on.Statistical data mining(SDM)is an interdisciplinary domain that examines huge existing databases to discover patterns and connections from the data.It varies in classical statistics on the size of datasets and on the detail that the data could not primarily be gathered based on some experimental strategy but conversely for other resolves.Thus,this paper introduces an effective statistical Data Mining for Intelligent Rainfall Prediction using Slime Mould Optimization with Deep Learning(SDMIRPSMODL)model.In the presented SDMIRP-SMODL model,the feature subset selection process is performed by the SMO algorithm,which in turn minimizes the computation complexity.For rainfall prediction.Convolution neural network with long short-term memory(CNN-LSTM)technique is exploited.At last,this study involves the pelican optimization algorithm(POA)as a hyperparameter optimizer.The experimental evaluation of the SDMIRP-SMODL approach is tested utilizing a rainfall dataset comprising 23682 samples in the negative class and 1865 samples in the positive class.The comparative outcomes reported the supremacy of the SDMIRP-SMODL model compared to existing techniques.
基金supported by the National Natural Science Foundation of China (Grant No. 12090054)。
文摘Cryo-electron microscopy(cryo-EM) provides a powerful tool to resolve the structure of biological macromolecules in natural state. One advantage of cryo-EM technology is that different conformation states of a protein complex structure can be simultaneously built, and the distribution of different states can be measured. This provides a tool to push cryo-EM technology beyond just to resolve protein structures, but to obtain the thermodynamic properties of protein machines. Here, we used a deep manifold learning framework to get the conformational landscape of Kai C proteins, and further obtained the thermodynamic properties of this central oscillator component in the circadian clock by means of statistical physics.
基金Supported by the National Natural Science Foundation of China under Grant Nos 41774158,41474129 and 41704148the Chinese Meridian Projectthe Youth Innovation Promotion Association of the Chinese Academy of Sciences under Grant No2011324
文摘Extracting and parameterizing ionospheric waves globally and statistically is a longstanding problem. Based on the multichannel maximum entropy method（MMEM） used for studying ionospheric waves by previous work, we calculate the parameters of ionospheric waves by applying the MMEM to numerously temporally approximate and spatially close global-positioning-system radio occultation total electron content profile triples provided by the unique clustered satellites flight between years 2006 and 2007 right after the constellation observing system for meteorology, ionosphere, and climate（COSMIC） mission launch. The results show that the amplitude of ionospheric waves increases at the low and high latitudes（~0.15 TECU） and decreases in the mid-latitudes（~0.05 TECU）. The vertical wavelength of the ionospheric waves increases in the mid-latitudes（e.g., ~50 km at altitudes of 200–250 km） and decreases at the low and high latitudes（e.g., ~35 km at altitudes of 200–250 km）.The horizontal wavelength shows a similar result（e.g., ~1400 km in the mid-latitudes and ~800 km at the low and high latitudes）.
文摘According to statistics of Printing and Printing Equipment Industries Association of China (PEIAC), the total output value of printing industry of China in 2007 reached 440 billion RMB , the total output value of printing equipment was
文摘1 Production and Running Status of China’s Refractories Industry in 20241.1 Production and Running Status In 2024,according to the statistical data from The Association of China Refractories Industry,China’s refractories output was 22.0711million tons,decreasing by 3.73%YOY;in which the outputs of dense shaped refractory products,insulating refractory products and monolithic refractories were 11.3163 million tons decreasing by 6.07%YOY,83.77 thousand tons increasing by 11.17%YOY,and 9.9971 million tons decreasing by 2.07%YOY,respectively.The outputs of the main varieties are shown in Fig.1.
基金Supported by National Natural Science Foundation of China(72302230)Shandong Provincial Natural Science Foundation Youth Project(ZR2023QG068)。
文摘Given the rise of artificial intelligence,big data analytics has emerged as an important tool for processing and assimilating the enormous volume of data available on social media.It is of great theoretical and practical significance to explore the public opinion diffusion process and characteristics,and users’emotions of mega sports events based on big data statistics in the social media environment.This paper takes the Jakarta Asian Games,Russian World Cup and PyeongChang Winter Olympics held in 2018 as cases,uses text mining and social network analysis methods to analyze the dissemination process of social media users’data,presents the semantic words disseminated in sports events through high-frequency word cloud diagrams,and summarizes the general rules of public opinion dissemination.The results show that the more users’participation,the greater diffusion volume,and the diffusion process shows fast increasing,short duration,scattered topics,diversified contents,and strong guidance and weak continuity of attention.The high-frequency words,except for the names of the events,such as“cheer”,“win the game”and“must win”,have obvious concentration of emotional words.
文摘Some geophysical parameters, such as those related to gravitation and the geomagnetic field, could change during solar eclipses. In order to observe geomagnetic fluctuations, geomagnetic measurements were carded out in a limited time frame during the partial solar eclipse that occurred on 2011 January 4 and was observed in Canakkale and Ankara, Turkey. Additionally, records of the geomagnetic field spanning 24 hours, obtained from another observatory （in Iznik, Turkey）, were also analyzed to check for any peculiar variations. In the data processing stage, a polynomial fit, following the application of a running average routine, was applied to the geomagnetic field data sets. Geomagnetic field data sets indicated there was a characteristic decrease at the beginning of the solar eclipse and this decrease can be well-correlated with previous geomagnetic field measurements that were taken during the total solar eclipse that was observed in Turkey on 2006 March 29. The behavior of the geomagnetic field is also consistent with previous observations in the literature. As a result of these analyses, it can be suggested that eclipses can cause a shielding effect on the geomagnetic field of the Earth.
基金US National Science Foundation Grant(No.AGS-1139479)
文摘This paper focuses on how to extract physically meaningful information from climate data,with emphases placed on adaptive and local analysis. It is argued that many traditional statistical analysis methods with rigorous mathematical footing may not be efficient in extracting essential physical information from climate data;rather,adaptive and local analysis methods that agree well with fundamental physical principles are more capable of capturing key information of climate data. To illustrate the improved power of adaptive and local analysis of climate data,we also introduce briefly the empirical mode decomposition and its later developments.
文摘The nature of random errors in any data set is Gaussian, which is a well established fact according to the Central Limit Theorem. Supernovae type Ia data have played a crucial role in major discoveries in cosmology. Unlike in laboratory experiments, astronomical measurements cannot be performed in controlled situations. Thus, errors in astronomical data can be more severe in terms of systematics and non-Gaussianity compared to those of laboratory experiments. In this paper, we use the Kolmogorov-Smiruov statistic to test non-Gaussianity in high-z supernovae data. We apply this statistic to four data sets, i.e., Gold data （2004）, Gold data （2007）, the Union2 catalog and the Union2.1 data set for our analysis. Our results show that in all four data sets the errors are consistent with a Gaussian distribution.
基金the Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan provided funding for this study(Grant No.AP09058240)。
文摘Some recent developments(accelerated expansion)in the Universe cannot be explained by the conventional formulation of general relativity.We apply the recently proposed f(T,B)gravity to investigate the accelerated expansion of the Universe.By parametrizing the Hubble parameter and estimating the best fit values of the model parameters b_(0),b_(1),and b_(2)imposed from Supernovae type la,Cosmic Microwave Background,B aryon Acoustic Oscillation,and Hubble data using the Markov Chain Monte Carlo method,we propose a method to determine the precise solutions to the field equations.We then observe that the model appears to be in good agreement with the observations.A change from the deceleration to the acceleration phase of the Universe is shown by the evolution of the deceleration parameter.In addition,we investigate the behavior of the statefinder analysis,equation of state(EoS)parameters,along with the energy conditions.Furthermore,to discuss other cosmological parameters,we consider some wellknown f(T,B)gravity models,specifically,f(T,B)=aT^(b)+cB^(d).Lastly,we find that the considered f(T,B)gravity models predict that the present Universe is accelerating and the EoS parameter behaves like the ACDM model.
文摘According to the statistics of production and marketing data of major enterprises in 2021 by Surfac-tant Committee of China Cleaning Industry Association(CCIA),the development trend of main raw materials and products of surfactant industry is analyzed in detail.The main trend development trend and characteristics of product structure is discussed under the influence of COVID-19,crude oil price fluctuation and economic structure adjustment.It pointed that the direction and planning of high-quality industry and the demand of new market in the 14th Five-Year.Product structure innovation and technological innovation is important.
文摘AIM：To conduct a multicentre retrospective review of virological response rates in Asians infected with genotype 1 chronic hepatitis C（CHC） treated with combination interferon and ribavirin and then to compare their responses to that among Caucasians.METHODS：Asian patients infected with genotype 1 CHC treated at 4 Australian centres between 2001 to 2005 were identified through hospital databases.Baseline demographic characteristics,biochemical,virological and histological data and details of treatment were collected.Sustained virological responses（SVR） in this cohort were then compared to that in Caucasian subjects,matched by genotype,age,gender and the stage of hepatic fibrosis.RESULTS：A total of 108 Asians with genotype 1 CHC were identified.The end of treatment response（ETR） for the cohort was 79% while the SVR was 67%.Due to the relatively advanced age of the Asian cohort,only sixty-four subjects could be matched with Caucasians.The ETR among matched Asians and Caucasians was 81% and 56% respectively（P=0.003）,while the SVR rates were 73% and 36%（P 〈0.001） respectively.This difference remained significant after adjusting for other predictive variables. CONCLUSION： Genotype 1 CHC in Asian subjects is associated with higher rates of virological response compared to that in Caucasians.
基金supported by Jiangsu Cancer Hospital (ZK201606ZK201610)
文摘While obesity and fat intake have been associated with the risk and prognosis of epithelial ovarian cancer, the association between the lipid levels and epithelial ovarian cancer phenotype remains controversial. We conducted a retrospective study of 349 epithelial ovarian cancer patients who received treatment at Jiangsu Cancer Hospital, China between 2011 and 2017. We analyzed age at diagnosis, blood pressure, plasma glucose content, body mass index（BMI）, lipid levels and clinical parameters. Severity of epithelial ovarian cancer was classified according to the International Federation of Gynecology and Obstetrics（FIGO） grading system. Univariate analysis of the clinical factors according to the severity of epithelial ovarian cancer was followed by logistic regression analysis to identify clinical factors significantly associated with epithelial ovarian cancer severity. Univariate analysis indicated that age,BMI, triglyceride（TG）, and high density lipoproteins（HDL） differed significantly among different stages of epithelial ovarian cancer（P〈0.05）. In the logistic regression model, elevated TG（OR： 1.883; 95% CI= 1.207-2.937）, and low HDL（OR： 0.497; 95% CI = 0.298-0.829） levels were significantly associated with the high severity epithelial ovarian cancer. Our data indicate that high TG and low HDL levels correlate with a high severity of epithelial ovarian cancer. These data provide important insight into the potential relationship between the lipid pathway and epithelial ovarian cancer phenotype and development.
基金Supported by the National Natural Science Foundation of China.
文摘The sizes of astronomical surveys in different wavebands are increasing rapidly. Therefore, automatic classification of objects is becoming ever more important. We explore the performance of learning vector quantization (LVQ) in classifying multi-wavelength data. Our analysis concentrates on separating active sources from non-active ones. Different classes of X-ray emitters populate distinct regions of a multidimensional parameter space. In order to explore the distribution of various objects in a multidimensional parameter space, we positionally cross-correlate the data of quasars, BL Lacs, active galaxies, stars and normal galaxies in the optical, X-ray and infrared bands. We then apply LVQ to classify them with the obtained data. Our results show that LVQ is an effective method for separating AGNs from stars and normal galaxies with multi-wavelength data.
基金the support received from the National Natural Science Foundation of China (Nos. 11573012, 11303011, 11263004, 11163004 and U1231205)the Open Research Program of the Key Laboratory of Solar Activity of the Chinese Academy of Sciences (Nos. KLSA201414 and KLSA201505)
文摘Six high-resolution TiO-band image sequences from the New Vacuum Solar Telescope （NVST） are used to investigate the properties of intergranular bright points （igBPs）. We detect the igBPs using a Laplacian and morphological dilation algorithm （LMD） and automatically track them using a three- dimensional segmentation algorithm, and then investigate the morphologic, photometric and dynamic prop- erties of igBPs in terms of equivalent diameter, intensity contrast, lifetime, horizontal velocity, diffusion index, motion range and motion type. The statistical results confirm previous studies based on G-band or TiO-band igBPs from other telescopes. These results illustrate that TiO data from the NVST are stable and reliable, and are suitable for studying igBPs. In addition, our method is feasible for detecting and track- ing igBPs with TiO data from the NVST. With the aid of vector magnetograms obtained from the Solar Dynamics Observatory/Helioseismic and Magnetic Imager, the properties of igBPs are found to be strongly influenced by their embedded magnetic environments. The areal coverage, size and intensity contrast values of igBPs are generally larger in regions with higher magnetic flux. However, the dynamics of igBPs, includ- ing the horizontal velocity, diffusion index, ratio of motion range and index of motion type are generally larger in the regions with lower magnetic flux. This suggests that the absence of strong magnetic fields in the medium makes it possible for the igBPs to look smaller and weaker, diffuse faster, and move faster and further along a straighter path.
基金The original version was presented at the congress of the IFSR2005.
文摘Solving complex decision problems requires the usage of information from different sources. Usually this information is uncertain and statistical or probabilistic methods are needed for its processing. However, in many cases a decision maker faces not only uncertainty of a random nature but also imprecision in the description of input data that is rather of linguistic nature. Therefore, there is a need to merge uncertainties of both types into one mathematical model. In the paper we present methodology of merging information from imprecisely reported statistical data and imprecisely formulated fuzzy prior information. Moreover, we also consider the case of imprecisely defined loss functions. The proposed methodology may be considered as the application of fuzzy statistical methods for the decision making in the systems analysis.
