Statistical Data Mining with Slime Mould Optimization for Intelligent Rainfall Classification

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.

Statistical data of Printing and Printing Equipment Industries and materials of China 2007

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

Global Statistical Study of Ionospheric Waves Based on COSMIC GPS Radio Occultation Data

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）.

Comparisons of Three-Dimensional Variational Data Assimilation and Model Output Statistics in Improving Atmospheric Chemistry Forecasts

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.

Series Statistics Data of Non-ferrous Metals of China──Ⅰ Surveying

ＳｅｒｉｅｓＳｔａｔｉｓｔｉｃｓＤａｔａｏｆＮｏｎ－ｆｅｒｒｏｕｓＭｅｔａｌｓｏｆＣｈｉｎａⅠ２ＡＳｕｒｖｅｙｉｎｇｏｆＴｈｅＣｈｉｎａＮａｔｉｏｎａｌＮｏｎ－ｆｅｒｒｏｕｓＭｅｔａｌｓＩｎｄｕｓｔｒｙＣｏｒｐｃｒａｔｉｏｎ（ＣＮＮＣ）Ｂｙｃｏｕｒｔ...

Statistical analysis of geomagnetic field variations during the partial solar eclipse on 2011 January 4 in Turkey

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.

Cryo-EM Data Statistics and Theoretical Analysis of KaiC Hexamer

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.

On the non-Gaussian errors in high-z supernovae type Ia data

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.

Statistical Analysis of Raw Materials and Products in China’s Surfactant Industry in 2021

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.

Association of serum lipids and severity of epithelial ovarian cancer：an observational cohort study of 349 Chinese patients

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.

Learning Vector Quantization for Classifying Astronomical Objects

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.

MERGING FUZZY STATISTICAL DATA WITH IMPRECISE PRIOR INFORMATION

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.

Identifying Carbon stars from the LAMOST pilot survey with the efficient manifold ranking algorithm

Carbon stars are excellent kinematic tracers of galaxies and can serve as a viable standard candle, so it is worthwhile to automatically search for them in a large amount of spectra. In this paper, we apply the efficient manifold ranking algorithm to search for carbon stars from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope （LAMOST） pilot survey, whose performance and robustness are verified comprehensively with four test experiments. Using this algorithm, we find a total of 183 carbon stars, and 158 of them are new findings. According to different spectral features, our carbon stars are classified as 58 C-H stars, 11 C-H star candidates, 56 C-R stars, ten C-R star candidates, 30 C-N stars, three C-N star candidates, and four C-J stars. There are also ten objects which have no spectral type because of low spec- tral quality, and a composite spectrum consisting of a white dwarf and a carbon star. Applying the support vector machine algorithm, we obtain the linear optimum clas- sification plane in the J - H versus/-/- Ks color diagram which can be used to distinguish C-H from C-N stars with their J - H and H - Ks colors. In addition, we identify 18 dwarf carbon stars with their relatively high proper motions, and find three carbon stars with FUV detections likely have optical invisible companions by cross matching with data from the Galaxy Evolution Explorer. In the end, we detect four variable carbon stars with the Northern Sky Variability Survey, the Catalina Sky Survey and the LINEAR variability databases. According to their periods and ampli- tudes derived by fitting light curves with a sinusoidal function, three of them are likely semiregular variable stars and one is likely a Mira variable star.

Investigation of intergranular bright points from the New Vacuum Solar Telescope

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.

Investigating Evacuation Trends and Restoration Processes from Serious Earthquake Damage in Japan

Japan has experienced many large-scale natural disasters, such as earthquakes, typhoons accompanied by heavy rain, and landslides. Based on data for the damage caused by four recent major earthquakes in Japan, we investigate the trends in the number of evacuees and evacuation centres after these disasters as well as the restoration processes for public utilities such as electricity, gas, water, and communication lines. We compare the restoration speeds and trends of the damaged infrastructure systems. We also propose various mathematical models to approximate the recovery trends using both evacuee-related data and damage recovery process data. These results can be used to design natural disaster mitigation policies not only in Japan, but also in other countries. The results of various statistical data analyses and mathematical modelling techniques are applied to provide policy suggestions.

Commercial Credit and Corporate Productivity

As an important form of informal finance,commercial credit is widely used among enterprises.Does commercial credit promote the total factor productivity of enterprises?According to the theoretical literature and the reality,using the large sample data of Chinese industrial enterprises,the paper empirically tests the impact of commercial credit on the productivity of enterprises from three aspects:the provision and acquisition of commercial credit and the net commercial credit.The study finds that the provision of commercial credit reduces the productivity level of enterprises;the acquisition of commercial credit fails to promote productivity;while the net commercial credit as a short-term financial buffer for enterprises can alleviate the financing constraints,faced by enterprises,especially private enterprises,which help to increase their productivity levels.In addition,the study found that the higher the marketization process in the region,the more favorable the commercial credit is to the improvement of the production efficiency of private enterprises.

A STATISTICAL ANALYSIS OF SODAR DATA DETECTED IN YANSHAN MOUNTAIN AREA,BEIJING

According to the characteristics of sodar echo,a classified method for temperature stratification is given. By using sodar data observed in Yanshan Mountain area in Beijing,the statistical characteristics for the height of inversion layer,thermal plume,and the depth of mixed layer are compared.Finally,the appearance frequency for stable,unstable and neutral stratification is analyzed.

A convergent mean shift algorithm to select targets for LAMOST

This paper firstly finds that the Mean Shift Algorithm used by the Observation Control System （OCS） Research Group of the University of Science and Technology of China in Survey Strategy System 2.10 （SSS2.10） to select targets for the Large Sky Area Multi-Object Fiber Spectroscopic Telescope （LAMOST） is not convergent in theory. By carefully studying the mathematical formulation of the Mean Shift Algorithm, we find that it tries to find a point where some objective function achieves its maximum value; the Mean Shift Vector can be regarded as the ascension direction for the objective function. If we regard the objective function as the numerical description for the imaging quality of all targets covered by the focal panel, then the Mean Shift Algorithm can find the place where the imaging quality is the best. So, the problem of selecting targets is equal to the problem of finding the place where the imaging quality is the best. In addition, we also give some effective heuristics to improve computational speed and propose an effective method to assign point sources to the respective fibers. As a result, our program runs fast, and it costs only several seconds to generate an observation.

A fast Stokes inversion technique based on quadratic regression

Stokes inversion calculation is a key process in resolving polarization information on radiation from the Sun and obtaining the associated vector magnetic fields. Even in the cases of simple local thermo- dynamic equilibrium （LTE） and where the Milne-Eddington approximation is valid, the inversion problem may not be easy to solve. The initial values for the iterations are important in handling the case with mul- tiple minima. In this paper, we develop a fast inversion technique without iterations. The time taken for computation is only 1/100 the time that the iterative algorithm takes. In addition, it can provide available initial values even in cases with lower spectral resolutions. This strategy is useful for a filter-type Stokes spectrograph, such as SDO/HMI and the developed two-dimensional real-time spectrograph （2DS）.