Application of Statistical Tools for Data Analysis and Interpretation in Rice Plant Pathology

There has been a significant advancement in the application of statistical tools in plant pathology during the past four decades. These tools include multivariate analysis of disease dynamics involving principal component analysis, cluster analysis, factor analysis, pattern analysis, discriminant analysis, multivariate analysis of variance, correspondence analysis, canonical correlation analysis, redundancy analysis, genetic diversity analysis, and stability analysis, which involve in joint regression, additive main effects and multiplicative interactions, and genotype-by-environment interaction biplot analysis. The advanced statistical tools, such as non-parametric analysis of disease association, meta-analysis, Bayesian analysis, and decision theory, take an important place in analysis of disease dynamics. Disease forecasting methods by simulation models for plant diseases have a great potentiality in practical disease control strategies. Common mathematical tools such as monomolecular, exponential, logistic, Gompertz and linked differential equations take an important place in growth curve analysis of disease epidemics. The highly informative means of displaying a range of numerical data through construction of box and whisker plots has been suggested. The probable applications of recent advanced tools of linear and non-linear mixed models like the linear mixed model, generalized linear model, and generalized linear mixed models have been presented. The most recent technologies such as micro-array analysis, though cost effective, provide estimates of gene expressions for thousands of genes simultaneously and need attention by the molecular biologists. Some of these advanced tools can be well applied in different branches of rice research, including crop improvement, crop production, crop protection, social sciences as well as agricultural engineering. The rice research scientists should take advantage of these new opportunities adequately in adoption of the new highly potential advanced technologies while planning experimental designs, data collection, analysis and interpretation of their research data sets.

Identification of Sewage Monitoring Data Based on Mathematical Statistics

In order to reduce the enormous pressure to environmental monitoring work brought by the false sewage monitoring data, Grubbs method, box plot, t test and other methods are used to make depth analysis to the data, providing a set of technological process to identify the sewage monitoring data, which is convenient and simple.

Alternative 3D Modeling Approaches Based on Complex Multi-Source Geological Data Interpretation

Due to the complex nature of multi-source geological data, it is difficult to rebuild every geological structure through a single 3D modeling method. The multi-source data interpretation method put forward in this analysis is based on a database-driven pattern and focuses on the discrete and irregular features of geological data. The geological data from a variety of sources covering a range of accuracy, resolution, quantity and quality are classified and integrated according to their reliability and consistency for 3D modeling. The new interpolation-approximation fitting construction algorithm of geological surfaces with the non-uniform rational B-spline(NURBS) technique is then presented. The NURBS technique can retain the balance among the requirements for accuracy, surface continuity and data storage of geological structures. Finally, four alternative 3D modeling approaches are demonstrated with reference to some examples, which are selected according to the data quantity and accuracy specification. The proposed approaches offer flexible modeling patterns for different practical engineering demands.

Application of Catastrophe Theory in 3D Seismic Data Interpretation of Coal Mine

In order to detect fault exactly and quickly, cusp catastrophe theory is used to interpret 3D coal seismic data in this paper. By establishing a cusp model, seismic signal is transformed into standard form of cusp catastrophe and catastrophe parameters, including time-domain catastrophe potential, time-domain catastrophe time, frequency-domain catastrophe potential and frequency- domain degree, are calculated. Catastrophe theory is used in 3D seismic structural interpretation in coal mine. The results show that the position of abnormality of the catastrophe parameter profile or curve is related to the location of fault, and the cusp catastrophe theory is effective to automatically pick up geology information and improve the interpretation precision in 3D seismic data.

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.

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）.

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

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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.

Visualising data distributions with kernel density estimation and reduced chi-squared statistic

The application of frequency distribution statistics to data provides objective means to assess the nature of the data distribution and viability of numerical models that are used to visualize and interpret data.Two commonly used tools are the kernel density estimation and reduced chi-squared statistic used in combination with a weighted mean.Due to the wide applicability of these tools,we present a Java-based computer application called KDX to facilitate the visualization of data and the utilization of these numerical tools.

A Statistical Analysis of China＇s Traffic Tunnel Development Data

Traffic tunnels include tunnel works for traffic and transport in the areas of railway, highway, and rail transit. With many mountains and nearly one fifth of the global population, China possesses numerous large cities and megapolises with rapidly growing economies and huge traffic demands. As a result, a great deal of railway, highway, and rail transit facilities are required in this country. In the past, the construction of these facilities mainly involved subgrade and bridge works; in recent years.

APPLICATION OF STATISTICAL INTERPRETATION TECHNIQUES WITH NWP PRODUCTS FOR OBJECTIVE FORECASTING OF TROPICAL CYCLONE MOTION

Statistical study is first performed of the efficiency of the technique of statistical interpretation using the products of NWP. The result shows that the application of the technique has improved the predictabilily of predictors in objective forecasting of tropical cyclone motion, increased the forecasting skill of models and extended the valid period of forecast. Then a discussion is made of some technical problems in the application in the motion forecasting, suggesting: a large sample of data and perfect forecast method be employed in constructing objective forecast models for tropical cyclone motion, predictors be included that are so finely built that they reflect all synoptic features and physical quantity fields and NWP products be used and corrected that are available at multiple times. It is one of the effective ways to improve the skill and stability of the forecast by composite use of outcomes from various forecasting models.

SMT: A SPATIAL MAPPING TOOL FOR STATISTICAL DATA SET

The statistical map is usually used to indicate the quantitative features of various socio economic phenomena among regions on the base map of administrative divisions or on other base maps which connected with statistical unit. Making use of geographic information system (GIS) techniques, and supported by Auto CAD software, the author of this paper has put forward a practical method for making statistical map and developed a software (SMT) for the making of small scale statistical map using C language.

Seismic Data Processing and Interpretation on the Loess Plateau, Part 2: Seismic Data Interpretation

The loess plateau covering the North Shaanxi slope and Tianhuan depression consists of a regional monocline, high in the east and low in the west, with dips of less than 1^0, Structural movement in this region was weak so that faults and local structures were not well developed. As a result, numerous wide and gentle noses and small traps with magnitudes less than 50 m were developed on the large westward-dipping monocline. Reservoirs, including Mesozoic oil reservoirs and Paleozoic gas reservoirs in the Ordos Basin, are dominantly lithologic with a small number of structural reservoirs. Single reservoirs are characterized as thin with large lateral variations, strong anisotropy, low porosity, low permeability, and low richness. A series of approaches for predicting reservoir thickness, physical properties, and hydrocarbon potential of subtle lithologic reservoirs was established based on the interpretation of erosion surfaces.

Collecting Statistical Methods for the Analysis of Climate Data as Service for Adaptation Projects

The development of adaptation measures to climate change relies on data from climate models or impact models. In order to analyze these large data sets or an ensemble of these data sets, the use of statistical methods is required. In this paper, the methodological approach to collecting, structuring and publishing the methods, which have been used or developed by former or present adaptation initiatives, is described. The intention is to communicate achieved knowledge and thus support future users. A key component is the participation of users in the development process. Main elements of the approach are standardized, template-based descriptions of the methods including the specific applications, references, and method assessment. All contributions have been quality checked, sorted, and placed in a larger context. The result is a report on statistical methods which is freely available as printed or online version. Examples of how to use the methods are presented in this paper and are also included in the brochure.

A Novel Approach to Disqualify Datasets Using Accumulative Statistical Spread Map with Neural Networks (ASSM-NN)

A novel approach to detect and filter out an unhealthy dataset from a matrix of datasets is developed, tested, and proved. The technique employs a new type of self organizing map called Accumulative Statistical Spread Map (ASSM) to establish the destructive and negative effect a dataset will have on the rest of the matrix if stayed within that matrix. The ASSM is supported by training a neural network engine, which will determine which dataset is responsible for its inability to learn, classify and predict. The carried out experiments proved that a neural system was not able to learn in the presence of such an unhealthy dataset that possessed some deviated characteristics, even though it was produced under the same conditions and through the same process as the rest of the datasets in the matrix, and hence, it should be disqualified, and either removed completely or transferred to another matrix. Such novel approach is very useful in pattern recognition of datasets and features that do not belong to their source and could be used as an effective tool to detect suspicious activities in many areas of secure filing, communication and data storage.

A statistical analysis of geomechanical data and its effect on rock mass numerical modeling:a case study

Geomechanical data are never sufficient in quantity or adequately precise and accurate for design purposes in mining and civil engineering.The objective of this paper is to show the variability of rock properties at the sampled point in the roadway's roof,and then,how the statistical processing of the available geomechanical data can affect the results of numerical modelling of the roadway's stability.Four cases were applied in the numerical analysis,using average values(the most common in geomechanical data analysis),average minus standard deviation,median,and average value minus statistical error.The study show that different approach to the same geomechanical data set can change the modelling results considerably.The case shows that average minus standard deviation is the most conservative and least risky.It gives the displacements and yielded elements zone in four times broader range comparing to the average values scenario,which is the least conservative option.The two other cases need to be studied further.The results obtained from them are placed between most favorable and most adverse values.Taking the average values corrected by statistical error for the numerical analysis seems to be the best solution.Moreover,the confidence level can be adjusted depending on the object importance and the assumed risk level.

Data-driven Seeing Prediction for Optics Telescope:from Statistical Modeling,Machine Learning to Deep Learning Techniques

Predicting seeing of astronomical observations can provide hints of the quality of optical imaging in the near future,and facilitate flexible scheduling of observation tasks to maximize the use of astronomical observatories.Traditional approaches to seeing prediction mostly rely on regional weather models to capture the in-dome optical turbulence patterns.Thanks to the developing of data gathering and aggregation facilities of astronomical observatories in recent years,data-driven approaches are becoming increasingly feasible and attractive to predict astronomical seeing.This paper systematically investigates data-driven approaches to seeing prediction by leveraging various big data techniques,from traditional statistical modeling,machine learning to new emerging deep learning methods,on the monitoring data of the Large sky Area Multi-Object fiber Spectroscopic Telescope(LAMOST).The raw monitoring data are preprocessed to allow for big data modeling.Then we formulate the seeing prediction task under each type of modeling framework and develop seeing prediction models through using representative big data techniques,including ARIMA and Prophet for statistical modeling,MLP and XGBoost for machine learning,and LSTM,GRU and Transformer for deep learning.We perform empirical studies on the developed models with a variety of feature configurations,yielding notable insights into the applicability of big data techniques to the seeing prediction task.

Analysis and Visualization of Marketing, Statistical and Macroeconomic Data With GIS

The most common way to analyze economics data is to use statistics software and spreadsheets.The paper presents opportunities of modern Geographical Information System （GIS） for analysis of marketing, statistical, and macroeconomic data. It considers existing tools and models and their applications in various sectors. The advantage is that the statistical data could be combined with geographic views, maps and also additional data derived from the GIS. As a result, a programming system is developed, using GIS for analysis of marketing, statistical, macroeconomic data, and risk assessment in real time and prevention. The system has been successfully implemented as web-based software application designed for use with a variety of hardware platforms （mobile devices, laptops, and desktop computers）. The software is mainly written in the programming language Python, which offers a better structure and supports for the development of large applications. Optimization of the analysis, visualization of macroeconomic, and statistical data by region for different business research are achieved. The system is designed with Geographical Information System for settlements in their respective countries and regions. Information system integration with external software packages for statistical calculations and analysis is implemented in order to share data analyzing, processing, and forecasting. Technologies and processes for loading data from different sources and tools for data analysis are developed. The successfully developed system allows implementation of qualitative data analysis.

A Study on the Application of Statistical Analysis Method of Big Data in Economic Management

This paper analyzes the application value of statistical analysis method of big data in economic management from the macro and micro perspectives,and analyzes its specific application from three aspects such as economic trends,industrial operations and marketing strategies.