Comprehensive Evaluation on the Level of Agricultural Economic Development in Hubei Province Based on Principal Component Analysis

[Objectives]Hubei Province has a superior geographical location,and is located in the middle and lower reaches of the Yangtze River,with pleasant climate and abundant natural resources.It is an important province of population,agriculture and resources in China.[Methods]Based on the data of Statistical Yearbook of Hubei 2018,the agricultural economic indicators of the cities and prefectures in Hubei Province were analyzed with principal component analysis method by using SPSS19.0.[Results]The comprehensive scores and rankings of the agricultural economic development level of the 17 cities and prefectures in Hubei Province were obtained.They were divided into four agricultural development levels.[Conclusions]According to the analysis results,corresponding policy recommendations were put forward to promote the development of agricultural economy in Hubei Province.

Predicting the alloying element yield in a ladle furnace using principal component analysis and deep neural network

The composition control of molten steel is one of the main functions in the ladle furnace(LF)refining process.In this study,a feasible model was established to predict the alloying element yield using principal component analysis(PCA)and deep neural network(DNN).The PCA was used to eliminate collinearity and reduce the dimension of the input variables,and then the data processed by PCA were used to establish the DNN model.The prediction hit ratios for the Si element yield in the error ranges of±1%,±3%,and±5%are 54.0%,93.8%,and98.8%,respectively,whereas those of the Mn element yield in the error ranges of±1%,±2%,and±3%are 77.0%,96.3%,and 99.5%,respectively,in the PCA-DNN model.The results demonstrate that the PCA-DNN model performs better than the known models,such as the reference heat method,multiple linear regression,modified backpropagation,and DNN model.Meanwhile,the accurate prediction of the alloying element yield can greatly contribute to realizing a“narrow window”control of composition in molten steel.The construction of the prediction model for the element yield can also provide a reference for the development of an alloying control model in LF intelligent refining in the modern iron and steel industry.

Application of Principal Component Analysis(PCA)to the Evaluation and Screening of Multiactivity Fungi

Continued innovation in screening methodologies remains important for the discovery of high-quality multiactive fungi,which have been of great significance to the development of new drugs.Mangrove-derived fungi,which are well recognized as prolific sources of natural products,are worth sustained attention and further study.In this study,118 fungi,which mainly included Aspergillus spp.(34.62%)and Penicillium spp.(15.38%),were isolated from the mangrove ecosystem of the Maowei Sea,and 83.1%of the cultured fungi showed at least one bioactivity in four antibacterial and three antioxidant assays.To accurately evaluate the fungal bioactivities,the fungi with multiple bioactivities were successfully evaluated and screened by principal component analysis(PCA),and this analysis provided a dataset for comparing and selecting multibioactive fungi.Among the 118 mangrove-derived fungi tested in this study,Aspergillus spp.showed the best comprehensive activity.Fungi such as A.clavatonanicus,A.flavipes and A.citrinoterreus,which exhibited high comprehensive bioactivity as determined by the PCA,have great potential in the exploitation of natural products and the development of new drugs.This study demonstrated the first use of PCA as a time-saving,scientific method with a strong ability to evaluate and screen multiactive fungi,which indicated that this method can affect the discovery and development of new drugs.

Ecological Safety Evaluation of Land Use in Ji'an City Based on the Principal Component Analysis

According to the ecological safety evaluation index data of land-use change in Ji'an City from 1999 to 2008,positive treatment on selected reverse indices is conducted by Reciprocal Method.Meanwhile,Index Method is used to standardize the selected indices,and Principal Component Analysis is applied by using year as a unit.FB is obtained,which is related with the ecological safety of land-use change from 1999 to 2008.According to the scientific,integrative,hierarchical,practical and dynamic principles,ecological safety evaluation index system of land-use change in Ji'an City is established.Principal Component Analysis and evaluation model are used to calculate four parameters,including the natural resources safety index of land use,the socio-economic safety indicators of land use,the eco-environmental safety index of land use,and the ecological safety degree of land use in Ji'an City.Result indicates that the ecological safety degree of land use in Ji'an City shows a slow upward trend as a whole.At the same time,ecological safety degree of land-use change is relatively low in Ji'an City with the safety value of 0.645,which is at a weak safety zone and needs further monitoring and maintenance.

Evaluation and Selection of Sealants and Fillers Using Principal Component Analysis for Cracks in Asphalt Concrete Pavements

The objective of this paper was to develop a comprehensive evaluation method and index to evaluate the performance of sealants and fillers for cracks in asphalt concrete pavements using the method of principal component analysis. The performance experiments including cone penetration, softening point, flow, resilience and tension at low temperature respectively were conducted by reference of ASTM D5329 for eight sealants and fillers often used in China. There by a principal component model was developed and weight of every index was calculated. The experimental results show that there are significantly different performances for sealants and fillers often used in China. Principal component analysis is an objective method that evaluates and selects the performance of sealants and fillers for cracks in asphalt concrete pavements.

A Study of the Comprehensive Urban Competitiveness Based On Principal Component Analysis

This paper studies the comprehensive urban competitiveness and performs the principal component analysis. The results show that the comprehensive evaluation of urban competitiveness is not entirely dependent on the city's economic strength or GDP,and it is necessary to consider from resource allocation capacity,openness and public service capacity. By selecting various data concerning 11 prefecture-level cities in Jiangxi Province in 2006,2009 and 2012,this paper gets the ranking results and analyzes trends,to provide a basis for making future economic policy.

Effect of Two Kinds of Similarity Factors on Principal Component Analysis Fault Detection in Air Conditioning Systems

Screening similar historical fault-free candidate data would greatly affect the effectiveness of fault detection results based on principal component analysis(PCA).In order to find out the candidate data,this study compares unweighted and weighted similarity factors(SFs),which measure the similarity of the principal component subspace corresponding to the first k main components of two datasets.The fault detection employs the principal component subspace corresponding to the current measured data and the historical fault-free data.From the historical fault-free database,the load parameters are employed to locate the candidate data similar to the current operating data.Fault detection method for air conditioning systems is based on principal component.The results show that the weighted principal component SF can improve the effects of the fault-free detection and the fault detection.Compared with the unweighted SF,the average fault-free detection rate of the weighted SF is 17.33%higher than that of the unweighted,and the average fault detection rate is 7.51%higher than unweighted.

Real-time lane departure warning system based on principal component analysis of grayscale distribution and risk evaluation model

A technology for unintended lane departure warning was proposed. As crucial information, lane boundaries were detected based on principal component analysis of grayscale distribution in search bars of given number and then each search bar was tracked using Kalman filter between frames. The lane detection performance was evaluated and demonstrated in ways of receiver operating characteristic, dice similarity coefficient and real-time performance. For lane departure detection, a lane departure risk evaluation model based on lasting time and frequency was effectively executed on the ARM-based platform. Experimental results indicate that the algorithm generates satisfactory lane detection results under different traffic and lighting conditions, and the proposed warning mechanism sends effective warning signals, avoiding most false warning.

Comprehensive Evaluation of Processing Quality of Tibetan Native Hulless Barley Variety by Factor Analysis

The purpose of this study was to explore the quality of the Tibetan native hulless barley variety in depth and to evaluate the characteristics of its processing quality using ratio analysis.For this study,10 native barley varieties were chosen with the detection of 24 quality indexes in order to build a system of comprehensive evaluation.The results of the factor analysis indicated that seven common factors with an eigenvalue greater than 1 were extracted,cumulatively accounting for 96.21%of the total variance.The first common factor,including ASP,GLU,SER,GLY,ARG,TYR and CYS contents,accounted for 33.82% of the variance.The second common factor,including ash,the total starch,soluble fiber,VB_(3),Cu,Mn,Na and beta-glucan contents,accounted for 19.46%of the variance.The third common factor,including the total dietary fiber,α-VE,K,Zn and glutelin.The fourth common factor,including B,Ba and prolamin,explained the barley starch character of the rheological property.The fifth common factor included crude fiber.The sixth and the seventh common factors did not account for a substantial amount of variance.According to the comprehensive evaluation model,the score consequence was as the following:Zangqing25>Pengnaigabu>Lhasa changhei>2004Qing21>Lhasa duanbai>Liangamu>Zhikonggaxia>lianmubai>Jiangreejiu>Longzihei.

Comprehensive Evaluation on the Regional Economy of Commodity Grain Base in Heilongjiang Province Based on Factor Analysis Method

Taking a total of 13 areas in Heilongjiang commodity grain base as the research objects,9 indices are selected,which are regional GDP(X1),per capita GDP(X2),total value of tertiary industry(X3),financial revenue(X4),urban fixed assets investment(X5),average salary(X6),gross industrial output value(X7),total output value of farming,forestry,husbandry and fishing(X8),and retail sales of social consumer goods(X9).Based on this,evaluation index system of regional economy is established.According to the 2006-2008 Heilongjiang Statistical Yearbook,average values within 3 years are used as analytical data.Factor Analysis Method is adopted to establish regression model and to carry out comprehensive analysis.Result shows that Heilongjiang commodity grain base has extremely uneven regional economic development in different areas.According to the score order and actual situation,the 13 areas are divided into 4 types.The first and second types are Harbin and Daqing,respectively.The third type is Qiqihaer,Suihua,Mudanjiang and Jiamusi.And the forth type is Jixi,Shuangyashan,Heihe,Yichun,Qitaihe,Hegang and Daxinganling.Suggestions for the development of these areas are put forward.

Estimation of the Number of Collapsed Houses Damaged by Typhoon Based on Principal Components Analysis and Support Vector Machine

The evaluation model was established to estimate the number of houses collapsed during typhoon disaster for Zhejiang Province.The factor leading to disaster,the environment fostering disaster and the exposure of buildings were processed by Principal Component Analysis.The key factor was extracted to support input of vector machine model and to build an evaluation model;the historical fitting result kept in line with the fact.In the real evaluation of two typhoons landed in Zhejiang Province in 2008 and 2009,the coincidence of evaluating result and actual value proved the feasibility of this model.

Analysis of the Employment Situation of Non Private Enterprises in Various Regions of China

In the past 30 years, Chinese enterprises have been a hot topic of discussion and concern among the general public in terms of economic and social status, ownership structure, business mechanism, and management level. Solving the problem of employment for the people is an important prerequisite for their peaceful living and work, as well as a prerequisite and foundation for building a harmonious society. The employment situation of private enterprises has always been of great concern to the outside world, and these two major jobs have always occupied an important position in the employment field of China that cannot be ignored. With the establishment of the market economy system, individual and private enterprises have become important components of the socialist economy, making significant contributions to economic development and social progress. The rapid development of China’s economy, on the one hand, is the embodiment of the superiority of China’s socialist market economic system, and on the other hand, it is the role of the tertiary industry and private enterprises in promoting the national economy. Since the 1990s, China’s private enterprises have become a new economic growth point for local and even national countries, and are one of the important ways to arrange employment and achieve social stability. This paper studies the employment of private enterprises and individuals from the perspective of statistics, extracts relevant data from China statistical Yearbook, uses the relevant knowledge of statistics to process the data, obtains the conclusion and puts forward relevant constructive suggestions.

WEB SERVICE SELECTION ALGORITHM BASED ON PRINCIPAL COMPONENT ANALYSIS

Existing Web service selection approaches usually assume that preferences of users have been provided in a quantitative form by users. However, due to the subjectivity and vagueness of preferences, it may be impractical for users to specify quantitative and exact preferences. Moreover, due to that Quality of Service (QoS) attributes are often interrelated, existing Web service selection approaches which employ weighted summation of QoS attribute values to compute the overall QoS of Web services may produce inaccurate results, since they do not take correlations among QoS attributes into account. To resolve these problems, a Web service selection framework considering user's preference priority is proposed, which incorporates a searching mechanism with QoS range setting to identify services satisfying the user's QoS constraints. With the identified service candidates, based on the idea of Principal Component Analysis (PCA), an algorithm of Web service selection named PCA-WSS (Web Service Selection based on PCA) is proposed, which can eliminate the correlations among QoS attributes and compute the overall QoS of Web services accurately. After computing the overall QoS for each service, the algorithm ranks the Web service candidates based on their overall QoS and recommends services with top QoS values to users. Finally, the effectiveness and feasibility of our approach are validated by experiments, i.e. the selected Web service by our approach is given high average evaluation than other ones by users and the time cost of PCA-WSS algorithm is not affected acutely by the number of service candidates.

Assessment of Environmental Carrying Capacity Using Principal Component Analysis

Regional environmental carrying capacity (ECC) is nonlinear and spatially specific. A hierarchy index system including resources, environmental and socio-economic elements was established using an analytic hierarchy process. Principal component analysis (PCA) was used to estimate the regional size and differences of environmental carrying capacities. Main information of four principal components, i.e., carrying capacity of resources supply, carrying capacity of environmental quality, carrying capacity of social economy and carrying capacity of infrastructure construction, was extracted. The ECC evaluation value was divided into five levels of lowest carrying capacity, low carrying capacity, medium carrying capacity, high carrying capacity and highest carrying capacity, respectively. The results showed that on the whole ECC was at the medium carrying capacity level. ECC was generally highest in Guanzhong plain, followed by Loess Plateau, and was lowest in Qiba mountain. The carrying capacity of water resources and environmental quality was relatively low, and the infrastructure carrying capacity was highest among the four components. The temporal spatial variation of ECC was closely related to vulnerability of the natural resources and environment in the regions. Verification was proven that PCA was a useful tool when applied to evaluate ECC and reflect the spatial distribution of large-quantity ECC indices on a large regional scale. This study provides a basis for comprehensive understanding of resources, environment and management for regional balanced development.

Application of Principal Component Analysis with SPSS in Water Quality Assessment of Shenzhen Eastern Beach

Survey and analysis were conducted on water quality of offshore seas in eastern region of Shenzhen by principal component analysis with SPSS. Then, 8 pollutants indices were then reduced to 5. Based on weighted analysis of principal component weights, comprehensive scores of different monitored stations were com- puted and sequenced in order to make evaluation on sea quality of eastern region of Shenzhen.

Spatial Statistical Analysis and Comprehensive Evaluation of High-Tech Industry Development

After 30 years of economic development, the high-tech industry has played an important role in China’s national economy. The development of high-level technological industry plays a leading role in guiding the transformation of China’s economy from “investment-driven” to “technology-driven”. The high-tech industry represents the future industrial development direction and plays a positive role in promoting the transformation of traditional industries. The rapid development of high-tech industry is the key to social progress. In this paper, the traditional analytical model of statistics is combined with principal component analysis and spatial analysis, and R language is used to express the analytical results intuitively on the map. Finally, a comprehensive evaluation is established.

Parallel Active Subspace Decomposition for Tensor Robust Principal Component Analysis

Tensor robust principal component analysis has received a substantial amount of attention in various fields.Most existing methods,normally relying on tensor nuclear norm minimization,need to pay an expensive computational cost due to multiple singular value decompositions at each iteration.To overcome the drawback,we propose a scalable and efficient method,named parallel active subspace decomposition,which divides the unfolding along each mode of the tensor into a columnwise orthonormal matrix(active subspace)and another small-size matrix in parallel.Such a transformation leads to a nonconvex optimization problem in which the scale of nuclear norm minimization is generally much smaller than that in the original problem.We solve the optimization problem by an alternating direction method of multipliers and show that the iterates can be convergent within the given stopping criterion and the convergent solution is close to the global optimum solution within the prescribed bound.Experimental results are given to demonstrate that the performance of the proposed model is better than the state-of-the-art methods.

Analysis and Evaluation of Nutritional Quality in Chinese Radish (Raphanus sativus L.)

Radish （Raphanus sativus L.） is an important vegetable crop worldwide. High nutritional quality was critical in its genetic improvement and production. The nutritional quality of 42 Chinese radish cultivars was analyzed in this study. The contents of six nutritional facts, dry matter （DM）, crude fiber （CF）, total soluble sugar （TSS）, vitamin C （Vc）, protein, and nitrate, ranged from 29.7 to 88.2, 4.507 to 18.546, 2.233 to 15.457, 0.1416 to 0.3341, 0.34 to 1.15, and 1.81 to 5.89 g·kg^-1 fresh weight （FW）, respectively. Significant differences among the 42 radish cultivars were detected in the contents of nutritional facts. The data were subjected to cross-correlation analysis and principal component analysis （PCA）. It was found that DM content was positively correlated with the content of TSS （r=0.7104）, Vc （r=0.4011） and protein （r=0.4120）. Vitamin C （Vc） content of radish showed a positive correlation （r = 0.3300） with the protein content. According to the principal component analysis, out of the 42 radish cultivars, Nau-17, Nau-28, Nau-6, Nau-11, Nau-10, Nau-27, and Nau-31 were detected with very high scores in comprehensive evaluation. It could be concluded that abundant diversity of nutritional fact content occurred in different radish genotypes, and PCA analysis was effective for selecting radish germplasm with high quality. The results could contribute useful knowledge of nutritional quality, and provide important germplasms for the elite cultivar development and the inheritance study of nutritional facts in radish.

Ecophysiology and multivariate analysis for production of Tachigali vulgaris in Brazil:Influence of rainfall seasonality and fertilization

Studies on fertilization management of species native to the Amazon for energy plantations contribute to the diversity of species use and reduce biological risk due to the excessive use of clones or hybrids of Eucalyptus.This study evaluates the effect of precipitation seasonality and phosphorus and potassium fertilization on gas exchange in a Tachigali vulgaris plantation.Three levels of P(zero,65.2,130.4 kg ha^(-1))and three of K(zero,100.0,200.0 kg ha^(-1))were applied in a 3×3 factorial randomized block design.Gas exchange measurements were conducted in April and November 2018.In low rainfall,high irradiance period,photo synthetic rates were up to four times higher than in the high rainfall period,reaching 20.3μmol m^(-2)s^(-1)in the treatment with 130.4 g kg^(-1)of P and 100.0 g kg^(-1)of K.Factor analysis and principal component analysis reduced the initial eight gas exchange variables to two and three principal components in periods of high and low rainfall,respectively.The multivariate method used in this study readily identified variations in the variables as a function of rainfall,with high reliability in explaining the data set.