Genetic analysis for brix weight per stool and its component traits in sugarcane (Saccharum officinarum)

Brix weight per stool (BW) of sugarcane is a complex trait, which is the final product of a combination of many components. Diallel cross experiments were conducted during a period of two years for BW and its five component traits, in- cluding stalk diameter (SD), stalk length (SL), stalk number (SN), stalk weight (SW), and brix scale (BS) of sugarcane. Phenotypic data of all the six traits were analyzed by mixed linear model and their phenotype variances were portioned into additive (A), dominance (D), additive×environment interaction (AE) and dominance×environment interaction (DE) effects, and the correlations of A, D, AE and DE effects between BW and its components were estimated. Conditional analysis was employed to investigate the contribution of the components traits to the variances of A, D, AE and DE effects of BW. It was observed that the heritabilities of BW were significantly attributed to A, D and DE by 23.9%, 30.9% and 28.5%, respectively. The variance of A effect for BW was significantly affected by SL, SN and BS by 25.3%, 93.7% and 17.4%, respectively. The variances of D and DE effects for BW were also significantly influenced by all the five components by 5.1%～85.5%. These determinants might be helpful in sugarcane breeding and provide valuable information for multiple-trait improvement of BW.

Relationship of public preferences and behavior in residential outdoor spaces using analytic hierarchy process and principal component analysis—a case study of Hangzhou City, China

This study examined public attitudes concerning the value of outdoor spaces which people use daily. Two successive analyses were performed based on data from common residents and college students in the city of Hangzhou, China. First, citizens registered various items constituting desirable values of residential outdoor spaces through a preliminary questionnaire. The result proposed three general attributes (functional, aesthetic and ecological) and ten specific qualities of residential outdoor spaces. An analytic hierarchy process (AHP) was applied to an interview survey in order to clarify the weights among these attributes and qualities. Second, principal factors were extracted from the ten specific qualities with principal component analysis (PCA) for both the common case and the campus case. In addition, the variations of respondents’ groups were classified with cluster analysis (CA) using the results of the PCA. The results of the AHP application found that the public prefers the functional attribute, rather than the aesthetic attribute. The latter is always viewed as the core value of open spaces in the eyes of architects and designers. Fur-thermore, comparisons of ten specific qualities showed that the public prefers the open spaces that can be utilized conveniently and easily for group activities, because such spaces keep an active lifestyle of neighborhood communication, which is also seen to protect human-regarding residential environments. Moreover, different groups of respondents diverge largely in terms of gender, age, behavior and preference.

Nonlinear Statistical Process Monitoring Based on Control Charts with Memory Effect and Kernel Independent Component Analysis

A novel nonlinear combination process monitoring method was proposed based on techniques with memo- ry effect （multivariate exponentially weighted moving average （MEWMA）） and kernel independent component analysis （KICA）. The method was developed for dealing with nonlinear issues and detecting small or moderate drifts in one or more process variables with autocorrelation. MEWMA charts use additional information from the past history of the process for keeping the memory effect of the process behavior trend. KICA is a recently devel- oped statistical technique for revealing hidden, nonlinear statistically independent factors that underlie sets of mea- surements and it is a two-phase algorithm., whitened kernel principal component analysis （KPCA） plus indepen- dent component analysis （ICA）. The application to the fluid catalytic cracking unit （FCCU） simulated process in- dicates that the proposed combined method based on MEWMA and KICA can effectively capture the nonlinear rela- tionship and detect small drifts in process variables. Its performance significantly outperforms monitoring method based on ICA, MEWMA-ICA and KICA, especially for lonu-term performance deterioration.

Estimation of crop water requirement based on principal component analysis and geographically weighted regression

In this study the principal component analysis (PCA) and geographically weighted regression (GWR) are combined to estimate the spatial distribution of water requirement of the winter wheat in North China while the effect of the macroand micro-topographic as well as the meteorological factors on the crop water requirement is taking into account. The spatial distribution characteristic of the water requirement of the winter wheat in North China and its formation are analyzed based on the spatial variation of the main affecting factors and the regression coefficients. The findings reveal that the collinearity can be effectively removed when PCA is applied to process all of the affecting factors. The regression coefficients of GWR displayed a strong variability in space, which can better explain the spatial differences of the effect of the affecting factors on the crop water requirement. The evaluation index of the proposed method in this study is more efficient than the widely used Kriging method. Besides, it could clearly show the effect of those affecting factors in different spatial locations on the crop water requirement and provide more detailed information on the region where those factors suddenly change. To sum up, it is of great reference significance for the estimation of the regional crop water requirement.

Multivariate Cluster and Principle Component Analyses of Selected Yield Traits in Uzbek Bread Wheat Cultivars

Investigation of genetic diversity of geographically distant wheat genotypes is a useful approach in wheat breeding providing efficient crop varieties. This article presents multivariate cluster and principal component analyses (PCA) of some yield traits of wheat, such as thousand-kernel weight (TKW), grain number, grain yield and plant height. Based on the results, an evaluation of economically valuable attributes by eigenvalues made it possible to determine the components that significantly contribute to the yield of common wheat genotypes. Twenty-five genotypes were grouped into four clusters on the basis of average linkage. The PCA showed four principal components (PC) with eigenvalues > 1, explaining approximately 90.8% of the total variability. According to PC analysis, the variance in the eigenvalues was the greatest (4.33) for PC-1, PC-2 (1.86) and PC-3 (1.01). The cluster analysis revealed the classification of 25 accessions into four diverse groups. Averages, standard deviations and variances for clusters based on morpho-physiological traits showed that the maximum average values for grain yield (742.2), biomass (1756.7), grains square meter (18,373.7), and grains per spike (45.3) were higher in cluster C compared to other clusters. Cluster D exhibited the maximum thousand-kernel weight (TKW) (46.6).

Unsupervised Functional Data Clustering Based on Adaptive Weights

In recent years, functional data has been widely used in finance, medicine, biology and other fields. The current clustering analysis can solve the problems in finite-dimensional space, but it is difficult to be directly used for the clustering of functional data. In this paper, we propose a new unsupervised clustering algorithm based on adaptive weights. In the absence of initialization parameter, we use entropy-type penalty terms and fuzzy partition matrix to find the optimal number of clusters. At the same time, we introduce a measure based on adaptive weights to reflect the difference in information content between different clustering metrics. Simulation experiments show that the proposed algorithm has higher purity than some algorithms.

Principal Component-Discrimination Model and Its Application

Having researched for many years, seismologists in China presented about 80 earthquake prediction factors which reflected omen information of earthquake. How to concentrate the information that the 80 earthquake prediction factors have and how to choose the main factors to predict earthquakes precisely have become one of the topics in seismology. The model of principal component-discrimination consists of principal component analysis, correlation analysis, weighted method of principal factor coefficients and Mahalanobis distance discrimination analysis. This model combines the method of maximization earthquake prediction factor information with the weighted method of principal factor coefficients and correlation analysis to choose earthquake prediction variables, applying Mahalanobis distance discrimination to establishing earthquake prediction discrimination model. This model was applied to analyzing the earthquake data of Northern China area and obtained good prediction results.

Modified Independent Component Regression Method Without Prewhitening

Independent component analysis(ICA) can reveal the essential underlying structure of data, and independent component regression(ICR) methods usually obtain better performance than other regression methods such as principal component regression. However, when existing ICR methods separate or extract independent components using prewhitened data, the backward propagation of inevitable prewhitened errors deteriorates the final linear prediction accuracy. To overcome this weakness, first, we proposed using weighted orthogonal constraint condition to replace the prewhitening of the data in ICA. Next, the statistical independence of ICs and the close relationship between ICs and quality variables are considered at the same time. Then, by combining the merits of improved ICR and ensemble ICR algorithm which solved the problem of selecting an appropriate nonquadratic function in ICA iteration procedure, a modified independent component regression(MICR) method that directly used the measured process data was proposed. Finally, three experimental results were used to validate excellent performance of modified algorithm.

Comparison of Principal Components Analysis,Independent Components Analysis and Common Components Analysis

The aim of this work is to describe and compare three exploratory chemometrical tools,principal components analysis,independent components analysis and common components analysis,the last one being a modification of the multi-block statistical method known as common components and specific weights analysis.The three methods were applied to a set of data to show the differences and similarities of the results obtained,highlighting their complementarity.

Water Quality Evaluation Model Based on Principal Component Analysis and Information Entropy:Application in Jinshui River

水质评价对决策者决定水的使用功效尤为重要。水质综合评价系统中涉及到大量因子与指标,因子之间相互作用,致使水质的评价工作相对困难。主成分分析法可以消除因子间的相关性,因而被广泛应用于水质评价,但其忽略了数据离散程度的问题。熵值法则考虑了数据的离散特点。为更好地进行水质的综合评价,本文提出把主成分分析法和熵值法结合起来确定指标权重的方法,建立了水质评价模型,并采用该模型对郑州市金水河再生水2009年的水质情况进行评价,将评价结果与单独采用主成分分析或熵值法的结果进行了比较。结果表明了该方法的可行性与实用性,能够为非常规水资源利用提供理论依据和决策参考。

Component-Based Software Trustworthiness Fuzzy Analytic Hierarchy Process Model

For a more accurate and comprehensive assessment of the trustworthiness of component-based soft- ware system, the fuzzy analytic hierarchy process is introduced to establish the analysis model. Combine qualitative and quantitative analyses, the impacts to overall trustworthiness by the different types of components are distinguished. Considering the coupling relationship between components, dividing the system into several layers from target layer to scheme layer, evaluating the scheme advantages disadvantages by group decision-making, the trustworthiness of a typical J2EE structured component-based software is assessed. The trustworthiness asses model of the software components provides an effective methods of operation.

Intelligent diagnosis of the solder bumps defects using fuzzy C-means algorithm with the weighted coefficients

Solder bump technology has been widely used in electronic packaging. With the development of solder bumps towards higher density and finer pitch, it is more difficult to inspect the defects of solder bumps as they are hidden in the package. A nondestructive method using the transient active thermography has been proposed to inspect the defects of a solder bump, and we aim at developing an intelligent diagnosis system to eliminate the influence of emissivity unevenness and non-uniform heating on defects recognition in active infrared testing. An improved fuzzy c-means(FCM) algorithm based on the entropy weights is investigated in this paper. The captured thermograms are preprocessed to enhance the thermal contrast between the defective and good bumps. Hot spots corresponding to 16 solder bumps are segmented from the thermal images. The statistical features are calculated and selected appropriately to characterize the status of solder bumps in FCM clustering. The missing bump is identified in the FCM result, which is also validated by the principle component analysis. The intelligent diagnosis system using FCM algorithm with the entropy weights is effective for defects recognition in electronic packages.

Water Quality Evaluation of Chapurson Valley in Hunza Nagar, Gilgit Baltistan, Pakistan, Based on Statistical Analysis and Water Quality Index

Water borne ailments are of serious public health concern in Gilgit Baltistan’s (GB) region of Pakistan. The pollution load on the glacio-fluvial streams and surface water resources of the Chapurson Valley in the Hunza Nagar area of the GB is increasing as a result of anthropogenic activities and tourism. The present study focuses on the public health quality of drinking water of Chapurson valley. The study addressed the fundamental drinking water quality criteria in order to understand the state of the public health in the valley. To ascertain the current status of physico-chemical, metals, and bacteriological parameters, 25 water samples were collected through deterministic sampling strategy and examined accordingly. The physico-chemical parameters of the water samples collected from the valley were found to meet the World Health Organization (WHO) guidelines of drinking water. The water samples showed a pattern of mean metal concentrations in order of Arsenic (As) > Lead (Pb) > Iron (Fe) > Zinc (Zn) > Copper (Cu) > Magnesium (Mg) > Calcium (Ca). As, Cu, Zn, Ca and Mg concentration were under the WHO guidelines range. However, results showed that Pb and Fe are present at much higher concentrations than recommended WHO guidelines. Similarly, the results of the bacteriological analysis indicate that the water samples are heavily contaminated with the organisms of public health importance (including total coliforms (TCC), total faecal coliforms (TFC) and total fecal streptococci (TFS) are more than 3 MPN/100mL). Three principal components, accounting for 48.44% of the total variance, were revealed using principal component analysis (PCA). Bacteriological parameters were shown to be the main determinants of the water quality as depicted by the PCA analysis. The dendrogram of Cluster analysis using the Ward’s method validated the same traits of the sampling locations that were found to be contaminated during geospatial analysis using the Inverse Distance Weight (IDW) method. Based on these findings, it is most likely that those anthropogenic activities and essentially the tourism results in pollution load from upstream channels. Metals may be released into surface and groundwater from a few underlying sources as a result of weathering and erosion. This study suggests that the valley water resources are more susceptible to bacteriological contamination and as such no water treatment facilities or protective measure have been taken to encounter the pollution load. People are drinking the contaminated water without questioning about the quality. It is recommended that the water resources of the valley should be monitored using standard protocol so as to protect not only the public health but to safe guard sustainable tourism in the valley.

Analytic Hierarchy Process for Evaluating Flipped Classroom Learning

In reality,the flipped classroom has gained popularity as a modern way of structuring teaching,where lectures move from in-class procedures to digitally-based assignments,freeing up the debate,and practice exercises class time.Therefore,it is essential to implement and analyze a way of teaching that will improve student performance.The paper aims to develop a model of the method of teaching science in Iraqi schools,and to assess whether teaching flipped classroom affects the achievement,motivation,and creative thinking of students by using the methodology of Multi-Criteria Decision Making(MCDM)in the Analytic Hierarchy Process(AHP).The AHP approach includes several steps,including setting assessment criteria and their weights,and by assessing the methodology of the flipped classroom as compared to the conventional cognitive learning process.An experiment was carried out in Iraqi secondary schools to examine the attitude of the students towards the subject of Chemistry.The findings have indicated that the students and teachers favored flipped classroom learning more than conventional cognitive learning.The study took the following parameters compared to the traditional approach:teaching techniques,learning flexibility,teaching aids effectiveness,student participation and working environment.This paper indicates that the teachers in Iraqi schools will be able to improve and do more preparation to shift towards flipped learning in the classroom.

Spatial analysis and evaluation of a coal deposit by coupling AHP & GIS techniques

Nowadays,the evaluation of coal deposits becomes crucial,due to many uncontrollable factors,which affect the energy sector.A comparative evaluation of coal deposits is essential for their hierarchical classification regarding their sustainable exploitation,when compared to other coal deposits or competitive fuels,which may be used as alternative solutions for electricity generation.In this paper,a method for spatial analysis and evaluation of a lignite deposit is proposed,by creating four spatial key indicators via GIS analysis,which are then aggregated by applying a weighted linear combination.The analytical hierarchy process is applied to estimate the relative weights of the indicators,in order to perform a weighted cartographic overlay.Through the synthesis of the indicators,an overall,total spatial quality indicator is calculated.The weighted analysis was shown to be more effective compared to the unweighted one,because it can provide more reliable results regarding the exploitation of the examined lignite deposit.The implementation of GIS-based analytical hierarchy process in spatial analysis and evaluation of lignite deposits,in terms of sustainable exploitation,demonstrates that this method can be extensively applied for evaluating the economic potential of mineral deposits.

Weighting indicators of building energy efficiency assessment taking account of experts' priority

Analytic hierarchy process(Group AHP) is combined with two different methods of assigning experts' priority to weight indicators in building energy efficiency assessment.One is to assign the experts' priority averagely,and the other is to use cluster analysis to assign experts' priority.The results show that,1) Different expert's priority assigns result in great different weights of indicators in building energy efficiency assessment,therefore,the method of assigning experts' priority should be taken into account carefully while weighting indicators of building energy efficiency assessment using Group AHP;2) Three indicators are found to be overwhelmingly important in residential building energy efficiency assessment in the hot summer and cold winter zone in China.They are 'Outdoor & indoor shadow','Heating & air-conditioning facilities' and 'Insulation of envelope';3) The method combining cluster analysis with Group AHP to weight indicator of building energy efficiency assessment has the advantage of finding overwhelming important indicator,whereas,some less important indicators have a tendency to be ignored.A useful reference is provided for building energy conservation including policy revision and energy efficient residential building design.

Mathematical Modeling in Social Network Analysis： Using TOPSIS to Find Node Influences in a Social Network

In a social network analysis the output provided includes many measures and metrics. For each of these measures and metric, the output provides the ability to obtain a rank ordering of the nodes in terms of these measures. We might use this information in decision making concerning disrupting or deceiving a given network. All is fine when all the measures indicate the same node as the key or influential node. What happens when the measures indicate different key nodes？ Our goal in this paper is to explore two methodologies to identify the key players or nodes in a given network. We apply TOPSIS to analyze these outputs to find the most influential nodes as a function of the decision makers＇ inputs as a process to consider both subjective and objectives inputs through pairwise comparison matrices. We illustrate our results using two common networks from the literature： the Kite network and the Information flow network from Knoke and Wood. We discuss some basic sensitivity analysis can may be applied to the methods. We find the use of TOPSIS as a flexible method to weight the criterion based upon the decision makers＇ inputs or the topology of the network.

不同软枣猕猴桃资源果实抗氧化活性比较分析

为评价不同软枣猕猴桃资源果实抗氧化活性,该研究以32份软枣猕猴桃资源果实为材料,测定其总酚、总黄酮、维生素C、花青素含量、DPPH自由基、羟基自由基、超氧阴离子自由基清除率、总抗氧化能力和总还原力,并对其进行差异性分析、加权综合评价、相关性分析与主成分分析。结果表明:各资源果实的总酚、总黄酮、维生素C和花青素含量存在差异。综合评价抗氧化能力,排名在前5位的资源为B080401、A180902、A100703、B020802、A100101。总酚和维生素C含量对软枣猕猴桃抗氧化活性贡献较大。DPPH自由基清除率、超氧阴离子自由基清除率、总抗氧化能力和总还原力4种测定方法适用于软枣猕猴桃资源果实抗氧化能力测定。总酚含量、总黄酮含量、维生素C含量、DPPH自由基清除率、羟基自由基清除率、超氧阴离子自由基清除率、总抗氧化能力和总还原力为软枣猕猴桃抗氧化能力的主要测定项目。

几种综合评价方法比较研究——以转型发展本科高校教师职业能力评价为例

教师的职业能力在转型发展的本科高校中对教育质量具有决定性影响。建立转型发展本科高校教师职业能力评价指标体系,构造主观判断矩阵,分别用层次分析法(AHP)与模糊层次分析法(FAHP)求得各指标权重。开展相关问卷调研,搜集高校教师职业能力相关数据,运用主成分分析法(PCA)对所获数据进行分析,得到各指标系数。将三种方法得到的各指标权重进行比较,分析三种评价方法的优缺点及适用范围。结果表明,在定量数据较少、定性数据较多时,可应用层次分析法与模糊层次分析法,在数据来源于客观实际时,更适合采用主成分分析法。