Statistic Analysis on Quantitative Characteristics for Developing the DUS Test Guideline of Ranunculus asiaticus L.

Selection of quantitative characteristics, division of their expression ranges, and selection of example varieties are key issues on developing DUS Test Guidelines, which are more crucial for quantitative characteristics since their expressions vary in different degrees. Taking the development of DUS Test Guideline of Ranunculus asiaticus L. as an example, this paper applied statistic-based approaches for the analyses of quantitative characteristics. We selected 9 quantitative characteristics from 18 pre-selected characteristics, based on within-variety uniformity, stability between different growing cycles, and correlation among characteristics, by the analyses of coefficient of variation, paired-samples t-test and partial correlation. The expression ranges of the 9 selected quantitative characteristics were divided into different states using descriptive statistics and distribution frequency of varieties. Eight of the 9 selected quantitative characteristics were categorized as standard characteristics as they showed one peak in distribution frequency of 120 varieties in various expressions of the characteristics, whereas, plant height can be categorized as grouping characteristic since it gave two peaks, and can group the varieties into pot and cut varieties. Finally, box-plot was applied to visually select the example varieties, and varieties 7, 12, and 28 were determined as the example varieties for plant height. The methods described in this paper are effective for the selection of quantitative characteristics, division of expression ranges, and selection of example varieties in Ranunculus asiaticus L. for DUS test, and may also be interest for other plant genera.

Customized Optimization for Vehicle Acoustic Statistical Energy Analysis

Statistical Energy Analysis(SEA) is one of the conventional tools for predicting vehicle high-frequency acoustic responses.This study proposes a new method that can provide customized optimization solutions to meet NVH targets based on the specific needs of different project teams during the initial project stages.This approach innovatively integrates dynamic optimization,Radial Basis Function(RBF),and Fuzzy Design Variables Genetic Algorithm(FDVGA) into the optimization process of Statistical Energy Analysis(SEA),and also takes vehicle sheet metal into account in the optimization of sound packages.In the implementation process,a correlation model is established through Python scripts to link material density with acoustic parameters,weight,and cost.By combining Optimus and VaOne software,an optimization design workflow is constructed and the optimization design process is successfully executed.Under various constraints related to acoustic performance,weight and cost,a globally optimal design is achieved.This technology has been effectively applied in the field of Battery Electric Vehicle(BEV).

Quantitative analysis and prediction of the sound field convergence zone in mesoscale eddy environment based on data mining methods

The mesoscale eddy(ME)has a significant influence on the convergence effect in deep-sea acoustic propagation.This paper use statistical approaches to express quantitative relationships between the ME conditions and convergence zone(CZ)characteristics.Based on the Gaussian vortex model,we construct various sound propagation scenarios under different eddy conditions,and carry out sound propagation experiments to obtain simulation samples.With a large number of samples,we first adopt the unified regression to set up analytic relationships between eddy conditions and CZ parameters.The sensitivity of eddy indicators to the CZ is quantitatively analyzed.Then,we adopt the machine learning(ML)algorithms to establish prediction models of CZ parameters by exploring the nonlinear relationships between multiple ME indicators and CZ parameters.Through the research,we can express the influence of ME on the CZ quantitatively,and achieve the rapid prediction of CZ parameters in ocean eddies.The prediction accuracy(R)of the CZ distance(mean R:0.9815)is obviously better than that of the CZ width(mean R:0.8728).Among the three ML algorithms,Gradient Boosting Decision Tree has the best prediction ability(root mean square error(RMSE):0.136),followed by Random Forest(RMSE:0.441)and Extreme Learning Machine(RMSE:0.518).

Statistical analysis on the influence of mechanical parameters in the vibration of pylons

We present a statistical investigation of the degree of influence that assumptions made in relation to the mechanical parameters of a pylon have on its ground-induced vibrations.The study is set up by using as a key kinematic variable the displacement at the top of a reference,a stand-alone pylon with a uniform cross-section and fixity at its base.Next,statistics are produced using a dimensionless displacement ratio defined between the‘parental’and the‘subsidiary’cases,the latter defined for the pylon(a)resting on compliant soil,(b)having an attached top mass,and(c)being non-uniform with height.Furthermore,two materials are examined,namely,steel and reinforced concrete(R/C).More specifically,this displacement ratio is independent of the excitation and plays the role of a transfer function between the base and the top of the pylon.Both horizontal and vertical motions are considered,and the equations of motion are solved in the frequency domain.The ensuing statistical analysis is conducted for the following parameter combinations:(a)pylon founded on soft,intermediate,and stiff soil;(b)low,intermediate,and high-mass ratios of the attached mass to the pylon′s mass;(c)a constant and quadratic degree of pylon tapering with height.Spearman correlation coefficients are calculated for all the above combinations to arrive at statistical results that establish validity bounds and quantify the degree of influence of each assumption on the pylon′s response.

Effect of Jianpi Bushen formula for colon cancer patients who underwent adjuvant chemotherapy:Statistical analysis plan for a multicenter trial

Background:Patients with colon cancer who receive chemotherapy usually experience various gastrointestinal adverse reactions,including nausea,vomiting,and diarrhea,which make it challenging for them to adhere to treatment.As an effective traditional Chinese medicine,the Jianpi Bushen formula has been widely used to alleviate the side effects of chemotherapy.Objective:To evaluate the efficacy and safety of Jianpi Bushen formulae for patients who undergo chemotherapy.This statistical analysis plan(SAP)is intended to enhance the transparency and research quality of our randomized controlled trial.Methods:Our study is a multicenter,double-blind,randomized controlled clinical trial.This trial aimed to compare the completion rate of chemotherapy in colon cancer patients who are using and not using Jianpi Bushen formula.To attenuate possible selection bias in the final report,we declared the overall trial design,outcome measures,subgroup analyses,and safety measures.Also,we described the data management and statistical analysis methods in detail.Conclusion:The SAP provides more detailed information than the trial protocol for data management and statistical analysis methods.Further post-hoc analyses can be performed by referring to the SAP,and possible selection bias can be attenuated.

Improved statistical fluctuation analysis for two decoy-states phase-matching quantum key distribution

Phase-matching quantum key distribution is a promising scheme for remote quantum key distribution,breaking through the traditional linear key-rate bound.In practical applications,finite data size can cause significant system performance to deteriorate when data size is below 1010.In this work,an improved statistical fluctuation analysis method is applied for the first time to two decoy-states phase-matching quantum key distribution,offering a new insight and potential solutions for improving the key generation rate and the maximum transmission distance while maintaining security.Moreover,we also compare the influence of the proposed improved statistical fluctuation analysis method on system performance with those of the Gaussian approximation and Chernoff-Hoeffding boundary methods on system performance.The simulation results show that the proposed scheme significantly improves the key generation rate and maximum transmission distance in comparison with the Chernoff-Hoeffding approach,and approach the results obtained when the Gaussian approximation is employed.At the same time,the proposed scheme retains the same security level as the Chernoff-Hoeffding method,and is even more secure than the Gaussian approximation.

Statistical Analysis and Countermeasures for Specimen Rejection in the Emergency Department

The rejected specimens from the Emergency Department of the Center of Clinical Laboratory from January 1,2022 to January 1,2023 were analyzed to reduce the specimen rejection rates and to improve the quality of inspection.The results showed that there were 1488 samples of rejected specimens and the non-conforming rate was 0.58%.The departments involved were mainly the Emergency Department,the Hematology Department,the Cardiology Department,the Intensive Care Department,and the Brain Surgery Department.Among the reasons for rejection,blood hemolysis accounted for 43.15%,blood coagulation accounted for 26.61%,and the rate of insufficient specimens was 17.14%.Among them,the sample rejection rate for arterial blood gas analysis was the highest,which accounted for 1.74%;followed by specimens for coagulation test,which was 1.18%.These results indicate the main reason for producing rejected specimens is mainly due to not following the standard operating procedure.Specimen rejection can largely be avoided if the standards for specimen collection are strictly followed.

Load Profile Analysis for Mitigating Load-Shedding in Central Africa: Case of Kinshasa

Load shedding is a major problem in Central Africa, with negative consequences for both society and the economy. However, load profile analysis can help to alleviate this problem by providing valuable information about consumer demand. This information can be used by power utilities to forecast and reduce power cuts effectively. In this study, the direct method was used to create load profiles for residential feeders in Kinshasa. The results showed that load shedding on weekends results in significant financial losses and changes in people’s behavior. In November 2022 alone, load shedding was responsible for $ 23,4 08,984 and $ 2 80,9 07,808 for all year in losses. The study also found that the SAIDI index for the southern direction of the Kinshasa distribution network was 122.49 hours per feeder, on average. This means that each feeder experienced an average of 5 days of load shedding in November 2022. The SAIFI index was 20 interruptions per feeder, on average, and the CAIDI index was 6 hours, on average, before power was restored. This study also proposes ten strategies for the reduction of load shedding in the Kinshasa and central Africa power distribution network and for the improvement of its reliability, namely: Improved load forecasting, Improvement of the grid infrastructure, Scheduling of load shedding, Demand management programs, Energy efficiency initiatives, Distributed Generation, Automation and Monitoring of the Grid, Education and engagement of the consumer, Policy and regulatory assistance, and Updated load profile analysis.

Comparison of Several Statistical Analysis Models for Genotypic Stability of Saccharum officinarum

[Objective] The study aimed to compare several statistical analysis models for estimating the sugarcane （Saccharum spp.） genotypic stability. [Method] The data of sugarcane regional trials in Guangdong, in 2009 was analyzed by three models respectively： Finlay and Wilkinson model： the additive main effects and multiplicative interaction （AMMI） model and linear regression-principal components analysis （LR- PCA） model, so as to compare the models. [Result] The Finlay and Wilkinson model was easier, but the analysis of the other two models was more comprehensive, and there was a bit difference between the additive main effects and multiplicative inter- action （AMMI） model and linear regression-principal components analysis （LR-PCA） model. [Conclusion] In practice, while the proper statistical method was usually con- sidered according to the different data, it should be also considered that the same data should be analyzed with different statistical methods in order to get a more reasonable result by comparison.

Statistical analysis of fracture properties based on particle swarm optimization and Pearson correlation coefficient method

Prediction of reservoir fracture is the key to explore fracture-type reservoir. When a shear-wave propagates in anisotropic media containing fracture,it splits into two polarized shear waves: fast shear wave and slow shear wave. The polarization and time delay of the fast and slow shear wave can be used to predict the azimuth and density of fracture. The current identification method of fracture azimuth and fracture density is cross-correlation method. It is assumed that fast and slow shear waves were symmetrical wavelets after completely separating,and use the most similar characteristics of wavelets to identify fracture azimuth and density,but in the experiment the identification is poor in accuracy. Pearson correlation coefficient method is one of the methods for separating the fast wave and slow wave. This method is faster in calculating speed and better in noise immunity and resolution compared with the traditional cross-correlation method. Pearson correlation coefficient method is a non-linear problem,particle swarm optimization( PSO) is a good nonlinear global optimization method which converges fast and is easy to implement. In this study,PSO is combined with the Pearson correlation coefficient method to achieve identifying fracture property and improve the computational efficiency.

Statistical and causes analysis of storm surges along Tianjin coast during the past 20 years

Based on tidal data statistical analysis for 20 years of Tanggu Marine Environmental Monitoring Station from 1991 to 2010, we concluded that an average of nearly 10 days of 100 cm above water increase took place at Tianjin coast every year. The maximum high tide and average tide of Tianjin coast occurred in summer and autumn, and the maximum water increase also occurred in summer and autumn. Days with water increase more than 100 cm mostly occurred in spring, autumn and winter. Then we summarized the causes of coastal storm surge disaster in Tianjin based on astronomical tide factors, meteorological factors, sea level rise, land subsidence, and geographic factors, et al. Finally, we proposed storm surge disaster prevention measures.

Application of Integration of Spatial Statistical Analysis with GIS to Regional Economic Analysis

This paper summarizes a few spatial statistical analysis methods for to measuring spatial autocorrelation and spatial association, discusses the criteria for the identification of spatial association by the use of global Moran Coefficient, Local Moran and Local Geary. Furthermore, a user-friendly statistical module, combining spatial statistical analysis methods with GIS visual techniques, is developed in Arcview using Avenue. An example is also given to show the usefulness of this module in identifying and quantifying the underlying spatial association patterns between economic units.

Statistical Analysis on Field Efficacy Trial of Pesticide Using Visual Basic

[Objective] To introduce a convenient and easy way for the statistical anal- ysis on field efficacy trials of pesticide by using Visual Basic. [Method] The calcula- tion procedure of using Visual Basic to conduct statistical analysis on the field efficacy of pesticides was introduced, and an example was used to illustrate the usage and skill of the program. [Result] The procedure could quickly and accurately con- duct statistical analysis on the field efficacy of pesticide by only inputting initial data of the test, and it could compare the significance of differences between various fac- tors. Its calculated results were consistent with the results of the specialized statisti- cal software DPS. [Conclusion] It is a quick and simple method with high accuracy and reliability, which can greatly improve the efficiency of pesticide formulation opti- mization.

Construction of Inorganic Elemental Fingerprint and Multivariate Statistical Analysis of Marine Traditional Chinese Medicine Meretricis concha from Rushan Bay

Meretricis concha is a kind of marine traditional Chinese medicine(TCM), and has been commonly used for the treatment of asthma and scald burns. In order to investigate the relationship between the inorganic elemental fingerprint and the geographical origin identification of Meretricis concha, the elemental contents of M. concha from five sampling points in Rushan Bay have been determined by means of inductively coupled plasma optical emission spectrometry(ICP-OES). Based on the contents of 14 inorganic elements(Al, As, Cd, Co, Cr, Cu, Fe, Hg, Mn, Mo, Ni, Pb, Se, and Zn), the inorganic elemental fingerprint which well reflects the elemental characteristics was constructed. All the data from the five sampling points were discriminated with accuracy through hierarchical cluster analysis(HCA) and principle component analysis(PCA), indicating that a four-factor model which could explain approximately 80% of the detection data was established, and the elements Al, As, Cd, Cu, Ni and Pb could be viewed as the characteristic elements. This investigation suggests that the inorganic elemental fingerprint combined with multivariate statistical analysis is a promising method for verifying the geographical origin of M. concha, and this strategy should be valuable for the authenticity discrimination of some marine TCM.

STATISTICAL ANALYSIS AND NUMERICAL SIMULATION OF POYANG LAKE'S INFLUENCE ON TROPICAL CYCLONES

Based on the Tropical Cyclone(TC briefly thereafter)Yearbook 1980-2009,this paper first analyzes the number and intensity change of the TCs which passed directly over or by the side of Poyang Lake(the distance of TC center is less than 1°longitude or 1°latitude from the Lake)among all the landfalling TCs in China during the past 30 years.Two cases are examined in detail in this paper.One is severe typhoon Rananim with a speed of 3.26 m/s and a change of 1 hPa in intensity when it was passing the Lake.The other is super typhoon Saomai with a faster moving speed of 6.50 m/s and a larger change in intensity of 6 hPa.Through numerical simulation experiments,this paper analyzes how the change of underlying surface from water to land contributes to the differences in intensity,speed and mesoscale convection of the two TCs when they passed the Lake.Results show that the moisture and dynamic condition above the Lake were favorable for the maintenance of the intensity when Rananim was passing through Poyang Lake,despite the moisture supply from the ocean was cut off.As a result,there was strong convection around the lake which led to a rainfall spinning counter-clockwise as it was affected by the TC movement.However,little impact was seen in the Saomai case.These results indicate that for the TCs coming ashore on Poyang Lake with a slow speed,the large water body is conducive to the sustaining of the intensity and strengthening of the convection around the TC center and the subsequent heavy rainfall.On the contrary,a fast-moving TC is less likely to be influenced by the underlying surface in terms of intensity and speed.

Simulation on a Car Interior Aerodynamic Noise Control Based on Statistical Energy Analysis

How to simulate interior aerodynamic noise accurately is an important question of a car interior noise reduction. The unsteady aerodynamic pressure on body surfaces is proved to be the key effect factor of car interior aerodynamic noise control in high frequency on high speed. In this paper, a detail statistical energy analysis （SEA） model is built. And the vibra-acoustic power inputs are loaded on the model for the valid result of car interior noise analysis. The model is the solid foundation for further optimization on car interior noise control. After the most sensitive subsystems for the power contribution to car interior noise are pointed by SEA comprehensive analysis, the sound pressure level of car interior aerodynamic noise can be reduced by improving their sound and damping characteristics. The further vehicle testing results show that it is available to improve the interior acoustic performance by using detailed SEA model, which comprised by more than 80 subsystems, with the unsteady aerodynamic pressure calculation on body surfaces and the materials improvement of sound/damping properties. It is able to acquire more than 2 dB reduction on the central frequency in the spectrum over 800 Hz. The proposed optimization method can be looked as a reference of car interior aerodynamic noise control by the detail SEA model integrated unsteady computational fluid dynamics （CFD） and sensitivity analysis of acoustic contribution.

Statistical analysis of nondestructive testing results of cast steel joints in civil engineering structures

To study the distribution law and random characteristics of casting defects in steel castings,24 civil engineering structural cast steel joints were divided into two groups:simple cast steel joints and complex cast steel joints.Three kinds of nondestructive testing(NDT)methods,namely,visual,magnetic particle,and ultrasonic inspections,were used to detect the macroscopic defects in joints.The NDT results were then statistically analyzed.The results show that the unfused core support is a common defect of complex cast steel joints,and the defect can be seen visually,so excavation and repair welding should be carried out before castings leave factories.Casting cracks are extremely likely to occur in the intersection area of tubes,which is called the ultrasonic inspection blind zone.The occurrence probability of gas pores on simple cast steel joints is the largest,and the occurrence probability of core support incomplete fusions on complex cast steel joints is the largest.However,when cast steel joints are counted as a whole sample,the occurrence probability of gas pores is larger than that of core support incomplete fusions.Therefore,it is the most common defect in cast steel joints.

Statistical Analysis and Prediction of the Concentration of Harmful Algae in Bohai Bay

Based on the data of phytoplankton concentration and environmental factors in Bohai Bay from May to September in 2003. the relationship between environmental factors and phytoplankton biomass was analyzed. By analysis of variance, the weather condition was found to have no direct relation with phytoplankton biomass. Correlation coefficients showed that temperature, pH value,the concentrations of silicate and nitrate exhibited linear relationship with phytoplankton biomass.With principal component analysis, pollution types which affected the abundance of phytoplankton included point sources such as municipal and industrial effluents, agricultural runoff and earth＇s surface water. Using multivariate stepwise regression method and taking the correlation analysis results into consideration, a multi-step regression equation was developed to predict the concentration of phytoplankton in September 2003. Combined results show that temperature, pH value, the concentrations of silicate and nitrate are the critical ecological factors affecting the phytoplankton biomass in Bohai Bay.

A Statistical Parameter Analysis and SVM Based Fault Diagnosis Strategy for Dynamically Tuned Gyroscopes

Gyro's fault diagnosis plays a critical role in inertia navigation systems for higher reliability and precision. A new fault diagnosis strategy based on the statistical parameter analysis (SPA) and support vector machine (SVM) classification model was proposed for dynamically tuned gyroscopes (DTG). The SPA, a kind of time domain analysis approach, was introduced to compute a set of statistical parameters of vibration signal as the state features of DTG, with which the SVM model, a novel learning machine based on statistical learning theory (SLT), was applied and constructed to train and identify the working state of DTG. The experimental results verify that the proposed diagnostic strategy can simply and effectively extract the state features of DTG, and it outperforms the radial-basis function (RBF) neural network based diagnostic method and can more reliably and accurately diagnose the working state of DTG.

Statistical Characteristics and Mechanistic Analysis of Suddenly Reversed Tropical Cyclones over the Western North Pacific Ocean

Based on best track data of tropical cyclones（TCs） from the Japan Meteorological Agency, the characteristics of suddenly reversed TCs（SRTCs）, which have turning angles usually approaching 180°, are statistically analyzed from 1949 to 2011 over the western North Pacific Ocean. The typical large-scale circulation patterns of SRTCs are investigated using reanalysis data and dynamical composite analysis. Results show that turnings mainly occur in low latitudes between 10°N and 20°N,and mainly west of 135°E. The majority of SRTCs reach their peak intensity at, or slightly before, the turning time and subsequently decrease at some variable rate. Specifically, SRTCs are divided into four types, each containing two groups（i.e.eight groups in total） in terms of the moving-direction changes. The moving speed of all SRTC types except the south–north type decreases to its lowest during the 24 h, corresponding to a significant reduction in the primary steering components.According to the analysis of the 13 typical flow patterns found in this study, we suggest that sudden track changes are caused by the reversal steering flow. The original balance of the background flow patterns are broken up by new systems, e.g. binary TCs or dispersion-induced anticyclones. Additionally, sudden track changes are often due to double ridge variations of the subtropical high or weakened/strengthened high pressure in the east and west, respectively.