撂荒农地再利用的生态经济效益及其影响因素——基于粤赣100家农业经营主体的调查

研究目的:通过对粤赣100家农业经营主体调查,研究撂荒农地再利用的生态经济效益及其影响因素,并提出相应的建议。研究方法:首先应用DEA模型中的CCR方法测度了撂荒农地再利用的生态经济效益,并进一步应用半对数回归模型和主成分分析法,实证分析了撂荒农地再利用的生态经济效益的影响因素。研究结果:总体上,撂荒农地再利用的生态经济效益最高的是种植业,最低的是养殖业;发达地区撂荒农地再利用的生态经济效益总体上低于不发达地区;绿色、生态农产品的出售比例,绿色、生态农产品与非绿色、非生态农产品的产值比,政府补贴额度,银行信贷额度,社会服务机构数量,主要农业经营者的文化程度、见识广度等因素对撂荒农地再利用的生态经济效益均产生正向的影响,而年龄则对其产生负向影响。研究结论:政府需要在绿色、生态农产品市场,财政补贴,信用贷款,社会服务机构和吸引优秀农民返乡进行绿色、生态创业上提供政策支持。

Improving autoencoder-based unsupervised damage detection in uncontrolled structural health monitoring under noisy conditions

Structural health monitoring is widely utilized in outdoor environments,especially under harsh conditions,which can introduce noise into the monitoring system.Therefore,designing an effective denoising strategy to enhance the performance of guided wave damage detection in noisy environments is crucial.This paper introduces a local temporal principal component analysis(PCA)reconstruction approach for denoising guided waves prior to implementing unsupervised damage detection,achieved through novel autoencoder-based reconstruction.Experimental results demonstrate that the proposed denoising method significantly enhances damage detection performance when guided waves are contaminated by noise,with SNR values ranging from 10 to-5 dB.Following the implementation of the proposed denoising approach,the AUC score can elevate from 0.65 to 0.96 when dealing with guided waves corrputed by noise at a level of-5 dB.Additionally,the paper provides guidance on selecting the appropriate number of components used in the denoising PCA reconstruction,aiding in the optimization of the damage detection in noisy conditions.

Identification of Heterogeneity of Social and Economic Environment of Land Uses in China

The robust principal component analysis （RPCA） is a technique of multivariate statistics to assess the social and economic environment quality. This paper aims to explore a RPCA algorithm to analyze the spatial heterogeneity of social and economic environment of land uses （SEELU）. RPCA supplies one of the most efficient methods to derive the most important components or factors affecting the regional difference of the social and economic environment. According to the spatial distributions of the levels of SEELU,the total land resources of China were divided into eight zones numbered by Ⅰ to Ⅷ which spatially referred to the eight levels of SEELU.

Construction of Anti-breaking Models of the Main Veins of Flue-cured Tobacco Leaves and Principal Component Analysis

[Objective] This study aimed to explore the related mechanisms of the breaking of flue-cured tobacco leaves. [Method] Anti-breaking models of the main veins of flue-cured tobacco leaves were constructed for principal component analysis on the anti-breaking index, leaf traits and cellulose contents. [Result] The results showed that the growth traits had certain relevance with the cellulose contents while the leaf weight assumed a significant negative correlation with the anti-breaking index, indicating that the heavier the leaf weight was, the weaker the anti-breaking capacity of flue-cured tobacco would be; the cross-sectional area of main veins and the cellulose contents had shown a positive correlation with the anti-breaking index, indicating that the thicker the main vein of flue-cured tobacco was, the higher the cellulose contents would be, and the stronger the anti-breaking capacity of flue-cured tobacco leaves would be. [Conclusion] This study provided theoretical basis and reference to improve tobacco production and enhance the quality of flue-cured tobacco.

PC-based artif icial neural network inversion for airborne time-domain electromagnetic data

Traditionally, airborne time-domain electromagnetic （ATEM） data are inverted to derive the earth model by iteration. However, the data are often highly correlated among channels and consequently cause ill-posed and over-determined problems in the inversion. The correlation complicates the mapping relation between the ATEM data and the earth parameters and thus increases the inversion complexity. To obviate this, we adopt principal component analysis to transform ATEM data into orthogonal principal components （PCs） to reduce the correlations and the data dimensionality and simultaneously suppress the unrelated noise. In this paper, we use an artificial neural network （ANN） to approach the PCs mapping relation with the earth model parameters, avoiding the calculation of Jacobian derivatives. The PC-based ANN algorithm is applied to synthetic data for layered models compared with data-based ANN for airborne time-domain electromagnetic inversion. The results demonstrate the PC-based ANN advantages of simpler network structure, less training steps, and better inversion results over data-based ANN, especially for contaminated data. Furthermore, the PC-based ANN algorithm effectiveness is examined by the inversion of the pseudo 2D model and comparison with data-based ANN and Zhody＇s methods. The results indicate that PC-based ANN inversion can achieve a better agreement with the true model and also proved that PC-based ANN is feasible to invert large ATEM datasets.

Locally linear embedding-based seismic attribute extraction and applications

How to extract optimal composite attributes from a variety of conventional seismic attributes to detect reservoir features is a reservoir predication key,which is usually solved by reducing dimensionality.Principle component analysis（PCA） is the most widely-used linear dimensionality reduction method at present.However,the relationships between seismic attributes and reservoir features are non-linear,so seismic attribute dimensionality reduction based on linear transforms can＇t solve non-linear problems well,reducing reservoir prediction precision.As a new non-linear learning method,manifold learning supplies a new method for seismic attribute analysis.It can discover the intrinsic features and rules hidden in the data by computing low-dimensional,neighborhood-preserving embeddings of high-dimensional inputs.In this paper,we try to extract seismic attributes using locally linear embedding（LLE）,realizing inter-horizon attributes dimensionality reduction of 3D seismic data first and discuss the optimization of its key parameters.Combining model analysis and case studies,we compare the dimensionality reduction and clustering effects of LLE and PCA,both of which indicate that LLE can retain the intrinsic structure of the inputs.The composite attributes and clustering results based on LLE better characterize the distribution of sedimentary facies,reservoir,and even reservoir fluids.

The Principal Component Analysis on Yielding and Agronomic Traits of Hybrid Rice of Liangyou 2111

In order to define the relationship between yield and important agronomic traits of two lines hybrid Uangyou 2111, the principal component analysis method was used to analyze the expadmental data of six test points in Yunnan Province. The results showed that the main factors influencing the production of Liangyou 2111 were grain number, grains seed number, panicle length, growth padod and panicle rate; then were 1 O00-grain weight, seed setting rate, effective panicle and highest stem tillers number; again was plant height. Therefore, when hybrid rice of Uangyou 2111 will be planted widely in yunnan province, we should focus on en- sudng the panicle traits, especially increase grain number and grain seed number, and coordinately develop other traits to achieve high yield.

Correlation and Principal Component Analysis on Main Agronomic Traits of New Waxy Corn Varieties

[Objective] This study was conducted to provide certain theoretical reference for the comprehensive evaluation and breeding of new fresh waxy corn vari- eties. [Method] With 5 good fresh waxy corn varieties as experimental materials, correlation analysis and principal component anatysis were performed on 13 agronomic traits, i.e., plant height, ear position, ear weight, ear diameter, axis diameter, ear length, bald tip length, ear row number, number of grains per row, 100-kernel weight, fresh ear yield, tassel length, and tassel branch number. [Result] The principal component analysis performed to the 13 agronomic traits showed that the first three principal components, i.e., the fresh ear yield factors, the tassel factors and the bald top factors, had an accumulative contribution rate over 87.2767%, and could basically represent the genetic information represented by the 13 traits. The first principal component is the main index for the selection and evaluation of good corn varieties which should have large ear, large ear diameter but small axis diameter, i.e., longer grains, larger number of grains per ear, higher, 100-grain weight and higher plant height. As to the second principal component, the plants of fresh corn varieties are best to have longer tassel and not too many branches, and under the premise of ensuring enough pollen for the female spike, the varieties with fewer tassel branches shoud be selected as far as possible. From the point of the third principal component, bald tip length affects the marketing quality of fresh corn, and during fariety evaluation and breeding, the bald top length should be control at the Iowest standard. [Conclusion] The fresh ear yield of corn is in close positive correlation with ear weight, 100-grain weight, ear diameter, number of grains per row and ear length, and plant height also affects fresh ear yield.

Application of PCA and HCA to the Structure-Activity Relationship Study of Fluoroquinolones

Density functional theory （DFT） was used to calculate molecular descriptors （properties） for 12 fluoro-quinolone with anti-S.pneumoniae activity. Principal component analysis （PCA） and hierarchical cluster analysis （HCA） were employed to reduce dimensionality and investigate in which variables should be more effective for classifying fluoroquinolones according to their degree of an-S.pneumoniae activity. The PCA results showed that the variables ELUMO, Q3, Q5, QA, logP, MR, VOL and △EHL of these compounds were responsible for the anti-S.pneumoniae activity. The HCA results were similar to those obtained with PCA.The methodologies of PCA and HCA provide a reliable rule for classifying new fluoroquinolones with antiS.pneumoniae activity. By using the chemometric results, 6 synthetic compounds were analyzed through the PCA and HCA and two of them are proposed as active molecules with anti-S.pneumoniae, which is consistent with the results of clinic experiments.

Study on Phenological Character in Seedling Period of Introduced Populus deltoids Clones

In order to provide directionally genetically improved breeding materials of poplar by exploring the phenological traits genetic variation level and its develop- ment potential of Populus deltoides and the resource evaluation was carried out; 8 phenological phases in seedling period were observed and analyzed of 60 Populus deltoids clones introduced from America. The results showed that： （1） there was obvious difference in phonological character among clones, especially in leaf-spread- ing peak stage and the end term of leaf-falling stage, with the largest variation co- efficient of 14.97% and the minimum of 3.83% respectively. （2） Leaf-spreading peak stage scattered but the end term of leaf-falling stage concentrated the most. The phonological character in early stage of seedling growth was the main factor influ- encing the length of growing season. （3） By principal component analysis, pheno- logical phases were classified into 3 typical periods, including germination stage, leaf-spreading peak stage and leaf-falling stage. （4） Totaling 60 clones were classi- fied into 4 types by using clustering analysis in phenological time variables of clones.

Real-Time Face Tracking and Recognition in Video Sequence

A framework of real time face tracking and recognition is presented, which integrates skin color based tracking and PCA/BPNN (principle component analysis/back propagation neural network) hybrid recognition techniques. The algorithm is able to track the human face against a complex background and also works well when temporary occlusion occurs. We also obtain a very high recognition rate by averaging a number of samples over a long image sequence. The proposed approach has been successfully tested by many experiments, and can operate at 20 frames/s on an 800 MHz PC.

Study of pile shaft resistance in clayey soils

Based on principal component analysis, the rules of clayey soil's behaviors affected by varied indices were studied. It was discovered that the common method of the single liquidity index IL used to determine the consistency of silt-clay or silt-loam was not rational. It was more rational that the liquidity index IL combined with the void ratio e characterized the behavior of silt-clay. Similarly the index of e depicted the nature of sandy loam more rationally than IL. The method of predicting the pile shafted resistance by the two indices of e and IL, which was more accurate, was obtained by the methodology of back propagation (BP) artificial neural networks combined with principal component analysis. It was also observed that the pile shaft resistance increased with the increase of depth within a critical affect-depth ranging from 20 to 30 m, and the harder the clayey soil consistency was, the shallower the critical depth was.

PCA-CMAC based machine performance degradation assessment

A principal component analysis-cerebellar model articulation controller （PCA-CMAC） model is proposed for machine performance degradation assessment.PCA is used to feature selection,which eliminates the redundant information among the features from the sensor signals and reduces the dimension of the input to CMAC.CMAC is used to assess degradation states quantitatively based on its local generalization ability.The implementation of the model is presented and the model is applied in a drilling machine to assess the states of the cutting tool. The results show that the model can assess the wear states quantitatively based on the normal state of the cutting tool.The influence of the quantization parameter g and the generalization parameter r in the CMAC model on the assessment results is analyzed.If g is larger,the generalization ability is better,but the difference of degradation states is not obvious.If r is smaller,the different states are distinct,but memory requirements for storing the weights are larger.The principle for selecting two parameters is that the memory storing the weights should be small while the degradation states should be easily distinguished.

Analysis and Evaluation Indicator Selection of Chilling Tolerance of Different Cotton Genotypes

[Objectivc] This study aimed to investigate the chilling tolerance of seedlings of different cotton genotypes and screen appropriate indicators for assess- ing chilling tolerance, to establish reliable mathematical evaluation model for chilling tolerance of cotton, thus providing theoretical basis for breeding and promoting new chilling-tolerant cotton germplasms and large-scale evaluation of chilling tolerance of cotton varieties. [Method] Fifteen cotton varieties （lines） were used as experimental materials. The photosynthetic gas exchange parameters, chlorophyll fluorescence ki- netic parameters, chlorophyll content, relative soluble sugar content, malonaldehyde content, relative proiine content, relative conductivity and other 12 physiological indi- cators of seedling leaves under low temperature treatment （5 ℃, 12 h） and recovery treatment （25 ℃. 24 h） were determined; based on the chilling tolerance coefficient （CTC） of various individual indicators, the comprehensive evaluation of chilling toler- ance was conducled by using principal component analysis, hierarchical cluster anal- ysis and stepwise regression analysis. [Result] The results showed that the 12 indi- vidual physiological indicators could be classified into 7 independent comprehensive components by principal component analysis; 15 cotton varieties （lines） were clus- tered into three categories by using membership function method and hierarchical cluster analysis; the mathematical model for evaluating chilling tolerance of cotton seedlings was established： D =0.275 -0.244Fo1 ＋0.206Fv/Fm1＋0.326g,%-0.056SS ＋ 0.225MDA＋O.O38REC （FF=0.995）, and the evaluation accuracy of the equation was higher than 94.25%,0. Six identification indicators closely related to chilling tolerance were screened, including Fo,, Fv/Fm1, Seedling leaves of cotton varieties （lines） gs2, SS, MDA, and REC. [Conclusion] with high chilling tolerance are less dam- aged under low temperature stress, and are able to maintain relatively high photo- synthetic electron transport capacity and high stomatal conductance after recovery treatment, which is contributed to gas exchange and recovery of photosynthetic ca- pacity. Determination of the six indicators under the same stress condition can be adopted for rapid identification and prediction of the chilling tolerance of other cotton varieties, which provides basis for the breeding, promotion, identification and screen- ing of chilling tolerant germplasms.

Morphological Diversity Analysis of Red-seed Watermelon (Citrullus lanatus ssp. vulgaris var. megalaspermus Lin et Chao) Germplasm Resources

[Objective] This study aimed to analyze the morphological diversity of red- seed watermelon （Citrullus lanatus ssp. vulgaris var. megalaspermus Lin et Chao） germplasm resources. [Method] Multiple cluster analysis and principal components analysis on the morphological traits of 51 red-seed watermelon germplasm resources were carried out. [Result] The coefficient of variations （CVs） of 39 morphological traits in 51 red-seed watermelon idioplasm resources ranged from 5.37% to 66.95%, with an average of 22.87%. The average of Shannon diversity information indices was 1.55. Among them, the Shannon diversity information index of seed length was the highest （2.16） and that of seed shell figure pattern was the lowest （0.32）. In ad- dition, the morphological diversity information indices of quantity characters were higher than that of quality characters. The principal components analysis revealed that the variance contribution rates of the first, second and third principal compo- nents were 19.49%, 15.32% and 9.55%, respectively. Cluster analysis divided the 51 materials into three broad branches based on the morphological traits. There was only one material in the fist branch and two in the second branch, and all the three materials were wild. The other 48 materials were divided into the third branch and all of them were cultivars. [Conclusion] This study provided a theoretical basis for the protection and utilization of red-seed watermelon resources.

Inorganic Elements in Kernel of Amygdalus communis L. Measured Using ICP-OES Method

[Objective] The aim was to study on distribution of inorganic elements in kernel of Amygdalus communis L., providing reference for quality evaluation of A. communis L. species. [Method] Totally 26 species of inorganic elements in kernel, including Al, B, Be, Ca, Co, Cu, Fe, Mg, Mn, Mo, Na, Ni, P, Pb, Si, Sn, Sr, Ti, Zn, Cd, As, Se, V, Hg, Cr and K were measured with inductively coupled plasma emission spectrum （ICP-OES） and principal components analysis （PCA）. [Result] A. communis L. of different species and in different factories showed a similar curve in content of inorganic elements; absolute contents of the elements differed significantly. In addition, the accumulated variance contribution of five principle factors achieved as high as 84.371% and the variance contribution made by the first three factors accounted for 67.546%, proving that Fe, Ti, Pb, Na, Se, Cu, Mo, K, Zn, Ni, Ca and Sr were characteristic elements. [Conclusion] The method, which is brief, rapid and accurate, can be used for determination of inorganic elements in kernel of A. communis L., providing theoretical references for further development and utilization of A. communis L.

FTIR Spectroscopic Study of Broad Bean Diseased Leaves

[Objective] The aim was to indentify diseased leaves of broad bean by vibra- tional spectroscopy. [Method] In this paper, broad bean rust, fusarium rhizome rot, broad bean zonate spot, yellow leaf curl virus and normal leaves were studied using Fourier transform infrared spectroscopy combined with chemometrics. [Result] The spectra of the samples were similar, only with minor differences in absorption inten- sity of several peaks. Second derivative analyses show that the significant difference of all samples was in the range of 1 200-700 cm2. The data in the range of 1 200- 700 cm＇ were selected to evaluate correlation coefficients, hierarchical cluster analy- sis （HCA） and principal component analysis （PCA）. Results showed that the correla- tion coefficients are larger than 0.928 not only between the healthy leaves, but also between the same diseased leaves. The values between healthy and diseased leaves, and among diseased leaves, are all declined. HCA and PCA yielded about 73.3% and 82.2% accuracy, respectively. [Conclusion] This study demonstrated that FTIR techniques might be used to detect crop diseases.

Low-dimensional multi-spectral space for color reproduction based on nonnegative constrained principal component analysis

In order to overcome the shortcomings that the reconstructed spectral reflectance may be negative when using the classic principal component analysis （PCA）to reduce the dimensions of the multi-spectral data, a nonnegative constrained principal component analysis method is proposed to construct a low-dimensional multi-spectral space and accomplish the conversion between the new constructed space and the multispectral space. First, the reason behind the negative data is analyzed and a nonnegative constraint is imposed on the classic PCA. Then a set of nonnegative linear independence weight vectors of principal components is obtained, by which a lowdimensional space is constructed. Finally, a nonlinear optimization technique is used to determine the projection vectors of the high-dimensional multi-spectral data in the constructed space. Experimental results show that the proposed method can keep the reconstructed spectral data in [ 0, 1 ]. The precision of the space created by the proposed method is equivalent to or even higher than that by the PCA.

Study on the Extraction of Vegetation in Mineralized Alteration Zone based on the ETM+Image

Vegetation is an important element and has such a complexity and uncertainty in remote sensing image that the extraction of vegetation by using remote sensing is a very difficult work. Vegetation is extracted using rich ETM＋data in mineralized alteration zone of west Tianshan, Xinjiang to subsequently extract the minerals alteration. In order to ensure better minerals alteration, the feature-oriented principal component analysis ＋ optimal density segmentation was proposed based on the analysis of spectral feature of vegetation and minerals alteration. The results showed that using ETM＋ band 3 and band 4 combinations can better extract vegetation and this method is of great value in use.

Correlation, Principal Component and Grey Relation Analysis of Sweetpotato Root Biological Traits

[Objective] This study was conducted to explore the internal relationship among root biological traits of sweetpotato, as well as the regularity in their formation and differentiation. [Method] The root traits of 10 sweetpotato cultivars were measured through hydroponic culture in a greenhouse and field survey, and then their correlations were analyzed by statistical methods. [Result] The root morphological traits of sweetpotato at seedling stage such as projected area, surface area, average diameter and volume processed the highest contribution rate （80.56%） 10 d after transplanting, and the contribution rate of root average diameter reached 27.79% 20 d after transplanting. Storage root fresh weight per plant shared extremely significant positive correlations with storage root fresh weight of penultimate node and storage root fresh weight of antepenultimate node, and a significant positive corre- lation with commercial storage root number, and a significant negative correlation with storage root number of penultimate node. Among them, the correlation coeffi- cient of storage root fresh weight per plant with storage root fresh weight of antepenultimate node was the highest （0.659 5）. Fifteen days after transplanting, storage root fresh weight per plant had significant negative correlations with root projected area, surface area and volume. There was a significant positive correlation between root dry weight and storage root fresh weight per plant 25 d after transplanting. Root dry weight, volume, length, average diameter of sweetpotato seedlings had higher relational degrees with storage root fresh weight per plant. Ten and twenty days after transplanting were important time for the growth and differentiation of sweetpotato roots. In addition, node length and planting depth had certain influence on sweetpotato yield, and direct relationship existed between the seedling root biological traits and storage root yield of sweetpotato. [Conclusion] The results provide theoretical support for standard cultivation and new variety breeding of sweetpotato.