Nest-cavity characteristics of the Great Spotted Woodpecker Dendrocopos major in shelter plantations of west Inner Mongolia

The Great Spotted Woodpecker Dendrocopos major （L.）, one of the natural predators of Anoplophora glabripennis （Motsch.） （Coleoptera： Cerambycidae）, is resident to Wulate Qianqi County of the Inner Mongolia and widely found in shelter plantations. In August 2005 and 2006, 174 and 153 nest-cavities of Great Spotted Woodpeckers were found respectively in Wulate Qianqi County and 22 breeding nest-cavities were investigated in 2007. The results showed that mostly willow species were selected for nesting by the Great Spotted Woodpecker, but mature poplar trees also could be chosen. Nest cavities were often found with a protuberance above the cavity entrance or with a downward sloping gradient, or both. The selection of the height of the nest-cavity height was not significant. The vertical diameter of the nest-cavity entrance （VDE） and the horizontal diameter of the nest-cavity entrance （HDE） ranged from 5.0 to 5.8 cm. The results also indicated that the compass orientation of more than 60% of nest-cavities were towards the north, northeast and east. This study suggests a convergence of some nest-cavity characteristics of the Great Spotted Woodpecker in shelter plantations and will help us to make artificial nest for conserving the woodpecker and, as well, use the bird for controlling pests.

Stock Selection Based on a Hybrid Quantitative Method

Quantitative stock selection has become a research hotspot in the field of investment decision. As the data mining technology becomes mature, quantitative stock selection has made great progress. From the perspective of value investment, this paper selects top 200 stocks of A share in terms of market value. With the random forest (RF), financial characteristic variables with significant impact on SVR are screened out. At the same time with quantum genetic algorithm (QGA) superior to the traditional genetic algorithm (GA), SVR parameters are deeply and dynamically sought for, so as to build the RF-QGA-SVR model for year-to-year stock ranking. The quantitative stock selection model is built, and the empirical analysis of its stock selection performance is conducted. The conclusion is as follows: 1) Optimizing SVR with QGA has higher precision than the traditional genetic algorithm, and is more excellent than the traditional GA optimization;2) SVR after RF optimization of characteristic variables more significantly improves the accuracy of stock ranking and prediction;3) In the stock ranking obtained from the RF-QGA-SVR model, the yields of top stock portfolios are much higher than the market benchmark yield. At the same time, the yields of the top 10 stock portfolios are the highest, and the top 30 stock portfolios are the most stable. This study has positive reference significance on quantitative stock selection in the field of quantitative investment.

Point selection characteristics and rules of acupuncture in treatment of depression based on data mining

Objective To discuss the point selection characteristics and rules of prescription for acupuncture in treatment of depression. Method Clinical literature on acupuncture in treatment of depression in recent five years were retrieved by retrospective document research; the number of point and use frequency were calculated, and their characteristics and rules were analyzed by means of bibliometrics. Result One hundred and seventy-seven papers about acupuncture in treatment of depression were retrieved with 158 acupoints used for 1 630 times in total, including B ihuì（百会 GV 20）, Tàichōng（太冲 LR 3）, Sānyīnjiāo（三阴交 SP 6）, and Shénmén（神门 HT 7）, which were used most frequently. In view of meridians, governor vessel, the foot-taiyang bladder meridian and the foot-jueyin liver meridian were used most frequently, fiveshu points were used frequently in specific points, and the most frequently used point combination was distal and local point combination. Conclusion The primary prescription for acupuncture in treatment of depression is GV 20 in combination with distal LR 3, SP 6, HT 7, and other points.

Comparison of algorithms for monitoring wheat powdery mildew using multi-angular remote sensing data

Powdery mildew is a disease that threatens wheat production and causes severe economic losses worldwide. Its timely diagnosis is imperative for preventing and controlling its spread. In this study, the multiangle canopy spectra and disease severity of wheat were investigated at several developmental stages and degrees of disease severity. Four wavelength variable-selected algorithms: successive projection(SPA), competitive adaptive reweighted sampling(CARS), feature selection learning(Relief-F), and genetic algorithm(GA), were used to identify bands sensitive to powdery mildew. The wavelength variables selected were used as input variables for partial least squares(PLS), extreme learning machine(ELM), random forest(RF), and support vector machine(SVM) algorithms, to construct a suitable prediction model for powdery mildew. Spectral reflectance and conventional vegetation indices(VIs) displayed angle effects under several disease severity indices(DIs). The CARS method selected relatively few wavelength variables and showed a relatively homogeneous distribution across the 13 viewing zenith angles.Overall accuracies of the four modeling algorithms were ranked as follows: ELM(0.70–0.82) > PLS(0.63–0.79) > SVM(0.49–0.69) > RF(0.43–0.69). Combinations of features and algorithms generated varied accuracies, with coefficients of determination(R^(2)) single-peaked at different observation angles. The constructed CARS-ELM model extracted a predictable bivariate relationship between the multi-angle canopy spectrum and disease severity, yielding an R^(2)> 0.8 at each measured angle. Especially for larger angles,monitoring accuracies were increased relative to the optimal VI model(40% at-60°, 33% at +60°), indicating that the CARS-ELM model is suitable for extreme angles of-60° and +60°. The results are proposed to provide a technical basis for rapid and large-scale monitoring of wheat powdery mildew.

Application of swarm intelligence algorithms to the characteristic wavelength selection of soil moisture content

Swarm intelligence algorithms own superior performance in solving high-dimensional and multi-objective optimization problems.The application of the swarm intelligence algorithms to visible and near-infrared(VIS-NIR)spectral analysis of soil moisture can contribute to the optimization of the soil moisture prediction model and the development of the real-time soil moisture sensor.In this study,a high-resolution spectrometer was used to obtain spectral data of different levels of soil moisture which were manually configured.Isolation Forest algorithm(iForest)was used to eliminate outliers from the data.Based on the root mean square error of prediction RMSEP of Back Propagation Neural Network(BPNN)model results,a series of new swarm intelligence algorithms,including Manta Ray Foraging Optimization(MRFO),Slime Mould Algorithm(SMA),etc.,were used to select the characteristic wavelengths of soil moisture.The analysis results showed that MRFO owned the best performance if only from the predictive capability perspective and SMA had a better performance when considering the proportion of the selecting wavelengths and the results of the model prediction.By comparing and analyzing the modeling results of traditional intelligence algorithms Genetic Algorithm(GA)and Particle Swarm Optimization(PSO),it was found that the new swarm intelligence had a better performance in selecting the characteristic wavelengths of soil moisture.Integrating the results of all intelligence algorithms used,soil moisture sensitive wavelengths were selected as 490 nm,513 nm,543 nm,900 nm and 926 nm,which provide the basis for the design of real-time soil moisture sensor based on VIS-NIR.

Rules of acupoint and meridian selection in acupuncture-moxibustion for stable angina pectoris

To summarize and analyze the acupoint selection rules in acupuncture-moxibustion for stable angina pectoris. Clinical studies and literature on acupuncture-moxibustion for stable angina pectoris with definite acupoint selection were included through retrieving China National Knowledge Infrastructure（CNKI）,Wanfang Database and VIP in order to analyze the characteristics and rules of acupoint selection in acupuncture-moxibustion treatment for stable angina pectoris. It has been found that the acupoint selection of acupuncture-moxibustion prescriptions for stable angina pectoris focused on specific acupoints.The top 5 acupoints with the highest using frequency included Neiguan（内关PC 6）,Danzhdng（膻中CV17）,Xinshu（心俞BL 15）,Ziusanli（足三里ST 36） and Sanyinjiao（三阴交SP 6）. Acupuncture-moxibustion treatment of stable angina pectoris involved 12 meridians, mainly including the hand-jueyin pericardium meridian, the foot-taiyang bladder meridian, conception vessel, the foot-yangming stomach meridian, the foot-taiyin spleen meridian, and the hand-shaoyin heart meridian, etc., embodying the characteristics and rules of acupoint selection such as ＂highlighting the special treatment effect of acupoints＂ ＂selecting acupoints along the pericardium meridian, bladder meridian and conception vessel, combining the anterior and the posterior acupoints＂, ＂selecting the distal acupoints of spleen and stomach meridians, and focusing on specific acupoints＂, etc.