Global NEST Journal

Green building materials are an important material basis for promoting the development of green buildings, and also the foundation of building energy conservation. In view of this, an insulation effect evaluation model of green building materials is constructed. The outer wall temperature index, the equivalent thermal resistance index, the inner surface maximum temperature and the thermal inertia index are used to characterize the insulation effect of the green building materials. According to the insulation effect evaluation principle of green building materials, the outer surface and inner surface temperature deviation curves and its’ fitting curves are made. The integral area between the fitting curve and the standard line is the temperature factor. The temperature factor can be used as an evaluation parameter for the insulation effect of green building materials in a fixed temperature range. It can characterize the average insulation effect of green building materials under a certain temperature range and comprehensive evaluation of different characterization indicators. The greater the temperature factor value is, the better the insulation effect is. The experimental results show that the model can accurately reflect the overall insulation effect of green building materials under the influence of various indicators in a certain temperature range, and the evaluation accuracy is high.

Objective: adaptation of tea disease spot recognition method based on image feature points extraction and matching, can achieve fast and accurate recognition of tea disease spot, and provide reference for remote sensing disease monitoring with plant protection UAV at low altitude. Methods: hyperspectral imaging technology and image processing technology are used to collect hyperspectral images of 4 kinds of samples, such as anthracnose, brown leaf spot disease, tea white star and healthy leaf, and the extracted spectral reflectance of sensitive waveband of interest region is regarded as spectral feature. Twice principal component analysis is performed to obtain the second principal component image after the second principal component analysis as the feature image. Computer processing technology and pattern recognition technology are utilized to analyze characteristic parameters of tea disease spot, like color, shape and texture. On the basis of the feature that tone of tea disease spot in HSI (hue-saturation-intensity) color space is different. H component image is employed to extract tea disease spot, so as to get the tea disease spot image. n total, 12 colors parameters, 11 shape parameters and 8 texture parameters of each disease spot region are extracted. 20 feature parameters of strong classification ability are selected using variance and principal component analysis method to form classification eigenvectors. 35 eigenvectors of tea non-disease spot and 35 eigenvectors of tea disease spot tea are randomly selected to compose the training set. Fischer discriminant function is utilized for accurate recognition of tea disease spot based on the training set. Results: information of disease spot of the leaves with different types of disease has obvious features in the PC2 image. The features of PC2 image is regarded as the feature image for disease spot extraction later. The segmentation effect of anthracnose and tea white star is better. Images of disease spot region of different types before and after segmentation are obtained. The correct recognition rate of tea disease test set is 90%, and the correct recognition rate of non-disease test set is 95%. Conclusion: the proposed method can accurately identify tea disease spot, and provide technical support for monitoring the disease of tea at low altitude.

Time series analysis is a very effective method to analyze the dynamic characteristics of soil moisture at long-term scale. In this study, we have used the time series to analyze the relationship between precipitation and soil moisture on fixed dune at different soil depths (from 0 to 120 cm) during the growing season (from May to September) of 2006-2010 in Korqin Sandy Land, northern China. The results indicate that: (1) The precipitation is a relatively independent time series and has no obvious autocorrelation. Precipitation in an earlier stage has no obvious effect on the later stage in the growing season. (2) Soil moisture in different soil layers has higher autocorrelation; and the autocorrelation of soil moisture in each soil layer is significantly weakened with the increase in time lag interval. (3) The correlation coefficient between soil moisture and precipitation in each soil layer is higher at the time lag interval of k = 0; with the increase in soil depth, the correlation is gradually weakened. (4) The maximum correlation coefficients of soil moisture series and precipitation series in different soil depths were obtained at the time lag interval of k = 0.

The resource environment loss has been evaluated using energy value and monetary value adopting complex ecological system analysis framework, Odum’s eco-econo mic system energy theory, and energy value analysis. The total amount of green ecological economy of Shenyang city from 2007 to 2016 was calculated in the paper. The results were shown as following: (1) Economic growth in Shenyang city was relied on resources consumption (including natural resources and the input resources) heavily and it was about 40%, which had brought about serious environment pollution from 2007 to 2013; (2) The mode of economic growth had been changed in Shenyang city between 2014- 2016 and the ecological environment had been improved, , ,however, it was not obvious. The present economic development model in Shenyang city is unsustainable. The development mode depending on resources and environment cost should be brought to develop ecological industry and circular economy, to avoid the economy into a vicious cycle.