Evaluation of Comprehensive Performance of Agronomic Traits of Anmai 1241 by Grey Correlation Analysis and Cluster Analysis Methods

The agronomic traits of the new wheat variety Anmai 1241 were comprehensively evaluated,in order to provide comprehensive and objective theoretical basis for further improvement and production utilization of this variety.The winter water production test results of Anmai 1241 in 14 pilot sites of Henan Seed Management Station from 2016 to 2017 were summarized.The comprehensive performance of 11 agronomic traits of Anmai 1241 in different tests sites in Henan Province was evaluated by the grey correlation analysis and clustering analysis methods.The results showed that among the observed values of 11 traits,the variation coefficient,correlation degree and weight of black embryo rate were 181.64%,0.6679 and 0.1051,respectively.The clustering analysis showed that the 11 traits could be divided into 3 groups.The first type of traits(yield,number of grains per ear and 1000-grain weight)and the third group of traits(percentage of earbearing tillers,number of productive tillers and volume weight)belonged to the yield factor traits,and the sum of their weights was 0.5242.Yield and its related factors played an important role in the variety evaluation of Anmai 1241,and the effect of black embryo on yield should be eliminated in variety improvement.

Screening of Oilseed and Vegetable Dual-purpose Rapeseed Combinations Based on Grey Relation Analysis and Cluster Analysis

To screen out the rapeseed(Brassica napus) combinations that are suitable for the production of both oilseed and vegetable, we carried out a field experiment for 11 new combinations(hybrids) of rapeseed and then performed grey relation analysis and cluster analysis on 12 traits including the yield and quality of young stem,seed yield, and several agronomic traits after harvesting of young stem. The results showed that A11, A7, and A4 had higher main stalk yield than other combinations.The young stem/leaf ratios of A11, A5, A7, A4, A3, and A1 were in line with the quality requirements for young stem commodity. The soluble sugar content of A2,A8, and A10 was higher than that of CK(Fengyou 737), and the seed yields of A4,A3, A2, A1, A5, and A6 were higher than that of CK. The 11 rapeseed combinations were classified into 3 grades by grey relation analysis and cluster analysis. Two combinations, A4(Y20A×95C4R) and A11(3194A×09-5R), showed the weighted relation degrees higher than 0.95, which were clustered into grade I by cluster analysis. They had good agronomic traits and good performance as both oilseed and vegetable. A8, A5, A3, A7, A2, A10, A6, and A1 were clustered into grade Ⅱ and A9 into grade Ⅲ. In this study, the oilseed and vegetable dual-purpose rapeseed combinations were screened out based on grey relation analysis and cluster analysis,which can provide reference for the breeding of oilseed and vegetable dual-purpose rapeseed combinations.

A coin-tap method of composite materials non-destructive testing based on improved grey clustering

Aiming at the problems of low reliability and complex operation of traditional coin-tap test of composite material,this paper introduces the grey system theory and achieves better performance.The response signals of coin-tap are classified through the grey clustering based on relation analysis,and corresponding improvements are made to the calculation method of the relation degree of nearness.First,the time history of acceleration is taken as the system behavior sequence.The improved correlation calculation method is used to solve the relation degree of nearness between the sequences,and the matrix of degree of grey relation is constructed based on this.Then,the sequence groups are summarized through the matrix,and the response signals of coin-tap are qualitatively classified according to the location of the reference sequence.Finally,the defect detection of composite materials is completed without pre-testing.The test results show that the accuracy of the coin-tap test based on improved grey clustering reaches 100%,which simplifies the operation steps while ensuring the reliability of the coin-tap test results.

Grey incidence clustering method based on multidimensional dynamic time warping distance

The traditional grey incidence degree is mainly based on the distance analysis methods, which is measured by the displacement difference between corresponding points between sequences. When some data of sequences are missing (inconsistency in the length of the sequences), the only way is to delete the longer sequences or to fill the shorter sequences. Therefore, some uncertainty is introduced. To solve this problem, by introducing three-dimensional grey incidence degree (3D-GID), a novel GID based on the multidimensional dynamic time warping distance (MDDTW distance-GID) is proposed. On the basis of it, the corresponding grey incidence clustering (MDDTW distance-GIC) method is constructed. It not only has the simpler computation process, but also can be applied to the incidence comparison between uncertain multidimensional sequences directly. The experiment shows that MDDTW distance-GIC is more accurate when dealing with the uncertain sequences. Compared with the traditional GIC method, the precision of the MDDTW distance-GIC method has increased nearly 30%.

Optimization Study of Outburst Prevention Measures for Tuzhu Coal Mine Based on Fixed Weight Clustering Analysis

Affected by many involved factors, different dimensions, data with large difference, incomplete information and so on, the most optimal selection of regional outburst prevention measures for outburst mine has become a complicated system project. The traditional way of outburst prevention measure selection belongs to qualitative method, which may cause high-cost of gas control, huge quantities of drilling work, long construction time and even secondary disaster. To solve the above-mentioned problems, in light of occurrence status of coal seam gas in No. 21 mining area of Jinzhushan Tuzhu Mine, through grey fixed weight clustering theory and a combination method of qualitative and quantitative analysis, the judging model with multi-objective classification for optimization of outburst prevention measures was established. The three weight coefficients of outburst prevention technology scheme are sorted, in order to determine the advantages and disadvantages of each outburst prevention technology scheme under the comprehensive evaluation of multi-target. Finally, the problem of quantitative selection for regional outburst prevention technology scheme is solved under the situation of multi-factor mode and incomplete information, which provides reasonable and effective technical measures for prevention of coal and gas outburst disaster.

The Establishment of Mathematical Models for the Composition Analysis and Identification of Ancient Glass Products

Glass is the precious material evidence of the trade of the early Silk Road. The ancient glass was easily affected by the environmental impact and weathering, and the change of composition ratios affected the correct judgment of its category. In this paper, mathematical models and methods such as Chi-square test, weighted average method, principal component analysis, cluster analysis, binary classification model and grey correlation analysis were used comprehensively to analyze the data of sample glass products combined with their categories. The results showed that the weathered high-potassium glass could be divided into 12, 9, 10 and 27, 7, 22 and so on.

Prediction method of highway pavement rutting based on the grey theory

In order to make a scientific pavement maintenance decision, a grey-theory-based prediction methodological framework is proposed to predict pavement performance. Based on the field pavement rutting data,analysis of variance （ANOVA）was first used to study the influence of different factors on pavement rutting. Cluster analysis was then employed to investigate the rutting development trend.Based on the clustering results,the grey theory was applied to build pavement rutting models for each cluster, which can effectively reduce the complexity of the predictive model.The results show that axial load and asphalt binder type play important roles in rutting development.The prediction model is capable of capturing the uncertainty in the pavement performance prediction process and can meet the requirements of highway pavement maintenance,and,therefore,has a wide application prospects.

Employment Quality EvaluationModel Based on Hybrid Intelligent Algorithm

In order to solve the defect of large error in current employment quality evaluation,an employment quality evaluation model based on grey correlation degree method and fuzzy C-means(FCM)is proposed.Firstly,it analyzes the related research work of employment quality evaluation,establishes the employment quality evaluation index system,collects the index data,and normalizes the index data;Then,the weight value of employment quality evaluation index is determined by Grey relational analysis method,and some unimportant indexes are removed;Finally,the employment quality evaluation model is established by using fuzzy cluster analysis algorithm,and compared with other employment quality evaluation models.The test results show that the employment quality evaluation accuracy of the design model exceeds 93%,the employment quality evaluation error can meet the requirements of practical application,and the employment quality evaluation effect is much better than the comparison model.The comparison test verifies the superiority of the model.

“中苜一号”紫花苜蓿耐盐遗传特性初步研究

以“中苜一号”紫花苜蓿耐盐新品种为材料，应用NCⅡ杂交试验方，研究其耐盐遗传参数，旨在为今后苜蓿耐盐育种提供理论依据。结果表明，在盐胁迫下，苜蓿的干重、株高和分校数诸性状的加性效应大于显性效应，狭义遗传力较大，分别为0.425、0．387和0．484，具有通过选择进一步提高耐盐性的遗传潜力。通过盐碱地表型选择的研究，从中得到102个单株的一股配合力，应用聚类分析方法将其划分为三类：其中，高、中、低一般配合力植株分别为47株，33株和22株。并已从中已得到高度和中度一般配合力植株相互杂交后所产生的种子。

机床主轴热误差建模算法

针对机床主轴热误差补偿过程中现有建模方法的不足,提出一种新的热误差建模算法。首先应用FCM(模糊C-均值聚类)算法将众多温度测点予以分类,减少测点数量,提高测量精度;其次应用GCA(灰色关联分析)算法对同类测点的热敏感度进行排序,选出该类中的关键测点;最后以优选出的测点为输入变量,以热位移为输出变量,利用ANFIS进行热误差模型设计,并与BP算法建立的模型进行了比较。实验数据表明,该方法降低了机床热误差,具有预测精度高的优点,能较好地实现机床主轴热误差的补偿。

碳达峰视阈下煤炭工业高质量发展内涵、评价及发展路径

碳达峰目标下煤炭工业高质量发展对实现国家战略目标起到至关重要的作用。在碳达峰背景约束下通过界定煤炭工业高质量发展的内涵,构建煤炭工业高质量发展基础能力评价指标体系,采用改进层次分析法和灰色聚类法分析其关键制约因素和发展短板,结合煤炭工业实际,提出其发展路径。研究结果表明现阶段煤炭工业高质量发展基础能力介于中等和较强之间,且呈现逐年增强态势。问题主要有研发投入不足、体制模式落后、人才机制不完善、双碳激励缺乏等,为实现高质量发展,煤炭工业应加强顶层设计、优化创新体系,明确研发方向、推进产业协同化与行业多元化发展、充分依靠市场构建高质量发展环境、做好人才储备和生态修复等。

Multi-scale analysis of schizophrenia risk genes, brain structure, and clinical symptoms reveals integrative dues for subtyping schizophrenia patients

Analysis Unking directly genomics, neuroimaging phenotypes and clinical measurements is crucial for understanding psychiatric disorders, but remains rare.Here, we describe a multi-scale analysis using genome-wide SNPs, gene expression, grey matter volume (GMV), and the positive and negative syndrome scale scores (PANSS) to explore the etiology of schizophrenia. With 72 drug-naive schizophrenic first episode patients (FEPs) and 73 matched heathy controls, we identified 108 genes, from schizophrenia risk genes, that correlated significantly with GMV, which are highly co-expressed in the brain during development. Among these 108 candidates, 19 distinct genes were found associated with 16 brain regions referred to as hot clusters (HCs), primarily in the frontal cortex, sensory-motor regions and temporal and parietal regions.The patients were subtyped into three groups with distinguishable PANSS scores by the GMV of the identified HCs. Furthermore, we found that HCs with common GMV among patient groups are related to genes that mostly mapped to pathways relevant to neural signaling, which are associated with the risk for schizophrenia.Our results provide an integrated view of how genetic variants may affect brain structures that lead to distinct disease phenotypes.The method of multi-scale analysis that was described in this research, may help to advance the understanding of the etiology of schizophrenia.

Association discovery and outlier detection of air pollution emissions from industrial enterprises driven by big data

Air pollution is a major issue related to national economy and people's livelihood.At present,the researches on air pollution mostly focus on the pollutant emissions in a specific industry or region as a whole,and is a lack of attention to enterprise pollutant emissions from the micro level.Limited by the amount and time granularity of data from enterprises,enterprise pollutant emissions are stll understudied.Driven by big data of air pollution emissions of industrial enterprises monitored in Beijing-Tianjin-Hebei,the data mining of enterprises pollution emissions is carried out in the paper,including the association analysis between different features based on grey association,the association mining between different data based on association rule and the outlier detection based on clustering.The results show that:(1)The industries affecting NOx and SO2 mainly are electric power,heat production and supply industry,metal smelting and processing industries in Beijing-Tianjin-Hebei;(2)These districts nearby Hengshui and Shijiazhuang city in Hebei province form strong association rules;(3)The industrial enterprises in Beijing-Tianjin-Hebei are divided into six clusters,of which three categories belong to outliers with excessive emissions of total vOCs,PM and NH3 respectively.

Comprehensive evaluation of multiple cropping systems on upland red soil

According to the principles and methods of ecology and system engineering,we set up an evaluation indicator system for multi-component and multiple crop-ping systems,evaluated the comprehensive benefits of multi-component and multiple cropping systems using grey relation clustering analysis and screened out the optimized model based on research done in the upland red soil in Jiangxi Agricultural University from 1984 to 2004.The results show that the grey relation degree of"cabbage/potato/maize-sesame"was the highest among 23 multi-component and multiple cropping systems and was clustered into the optimized system.This indicates that"cabbage/potato/maize-sesame"can bring the best social,economic and ecological benefits,increase product yield and farmers’income and promote sustainable development of agricultural production.Therefore,it is suitable for promotion on upland red soil.The grey relation degree of"canola/Chinese milk vetch/maize/mung bean/maize"was second,which is suitable for imple-mentation at the city outskirts.In conclusion,these two planting patterns are expected to play important roles in the reconstruction of the planting structure and optimization of the planting patterns on upland red soil.