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Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Tourism Tribune / Lvyou Xuekan is the property of Tourism Institute of Beijing Union University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Oil Drilling & Production Technology / Shiyou Zuancai Gongyi is the property of Shiyou Zuancai Gongyi Bianjibu and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The bid-ask spread is an important indicator to evaluate the financial market liquidity and efficiency, which has been extensively studied by recent literature. While, the volatility of bid-ask spread as one important determinant of market risk did not arouse enough attention from researchers to our knowledge. In this paper, we study the dynamics of bid-ask spread volatility of 40 constituent stocks of Shenzhen Stock Exchange Index ( SZEI) in Chinese stock market, using GARCH( generalized autoregressive conditional heteroscedasticity) family models. Perfected GARCH model is identified according to AIC(Akaike information criterion) and BIC(Bayesian information criterion) criteria, then a hybrid GARCH-neural network ( GARCH-NN) model based on it is proposed to forecast the volatility of SZEI bid-ask spread. Forecast performances obtained by the perfected GARCH model are compared with that by GARCH-NN based on our SZEI bid-ask spread dataset. The results show that the proposed hybrid model in this paper performs better. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Image & Graphics is the property of Editorial Office of Journal of Image & Graphics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Cotton (Gossypium hirsutum) is one of the most important cash crops in China, The timely and effective removal of weeds in cotton seedling stage is an important measure to ensure high and stable yield of cotton. Nowadays, weed recognition based on machine vision is widely used. The fast and effective recognition of crop and weed in the field under natural illumination is one of the key technologies for the development of intelligent mechanization weeding pattern. In the one hand, cotton and weeds have similar color feature in the field. Feature presentation of the natural property of target is difficult to be obtained by the hand-engineered feature extractor. The spatial consistency of the obtained features is not good, and the real-time performance of recognition system is reduced for the complex feature extraction algorithm. On the other hand, the effect of image preprocessing has important influence on recognition results. In order to solve the main problems in the current research, we explored the way to improve the recognition accuracy, stability and real-time performance, and a recognition method of crop and weed based on Faster R-CNN．In this paper, cotton seedling at 2-5 leaves stages and weeding during the same stage were used as research objects under natural illumination. Weed identification from digital images taken under natural illumination at field level is still challenging in agricultural image processing applications, though a lot of research has been conducted related to this topic. To address this problem, images including cottons and weeds were taken vertically from top to bottom. A method based on Faster R-CNN convolutional neural network was proposed to identify weeds from cotton plants more accurately and quickly. The residual network was used to extract image features, with ReLU as the activation function and Max-pooling as the down-sampling method. In the region of proposal network, feature pyramid network was introduced to generate target candidate frame, and Softmax regression classifier was utilized to optimize the CNN network. The proposed methodology was implemented on 200 digital images taken under natural illumination. The experimental results demonstrated that, the average accuracy of weed identification reached 95.5%, and the average time for individual weed plant identification was 1.51 s, which was reduced to 0.09 s by using GPU. To test the efficiency of the proposed methodology, YOLOv3 method was also carried out on the same training and test datasets. The weed identification results were assessed by mean average precision and average precision. The experimental results showed that better performance was achieved by using our proposed methodology, and better identification accuracy was reached as well. This indicated that the proposed method had a good effect on weed detection under natural illumination, and it will greatly promote the development of precise weed control. [ABSTRACT FROM AUTHOR]

Political risk is taken as the starting point for assessing overseas investment projects, and it is also a crucial factor which influences the success of foreign investment. Therefore, to analyze the factors influencing the political risk and to construct a sensitive early warning system of political risk is of great practical and theoretical significance. Although there are some professional institutions worldwide that are specialized in measuring political risk, their assessments are simply conducted based on the host countries' own political, economic and social environment, ignoring the specific political risk of foreign direct investments regarding to a particular home country and a particular host country. Moreover, most of the political risk researches focused on risk assessment and prevention, but few of them paid attention to the early warning system. As a supplement of the previous researches, this paper aims to provide macro-level politicaI risk information for Chinese overseas investments by constructing an early warning system of political risk in 26 countries where Chinese investment flow abroad. On the basis of the summary of the relevant researches on political risk, this paper has explained the formation mechanism of the political risk and analyze the main factors influencing the political risk, which can be considered in the political, economic and social dimensions. We have proposed an indicators system to assess and predict the macro-political risk faced by Chinese OFDI. The indicators system suggested in this study consists of indicators such as political stability, the distance of the democracy degrees between China and the host country, the distance of the corruption levels between China and the host country, the host country's law culture, the host country's dependence on China, per capita income of the host country, GDP growth, inflation rate. With the annual data on the 8 indicators of the indicators system from 2002 to 2009, we have given scores to 26 host countries according to principal component analysis in terms of their macro political risk. Then this paper further classifies the political risk of these years into four levels. danger, alerting, basically safe and safe. Additionally, this paper has constructed an early warning system in political risk for foreign investment of Chinese companies based on BP Artificial Neural Network, through which we also predict the political risk of Chinese foreign investment in 2010. Based on the empirical study mentioned above, we argue that Chinese investors should guard against risk arising from political factors in politically and economically less-developed countries, against risk arising from economic factors in politically and economically moderately developed countries which also maintain good diplomatic relations and have less cultural differences with China,and against risk arising from political, cultural and social factors in politically and economically highly developed countries. [ABSTRACT FROM AUTHOR]

Sensitivity analysis, which can quantitatively estimate the contribution of input variable to the output, is an effective way to reveal the inherent laws of the model. In this paper, sensitivity analysis was applied to the algal blooms forecast model based on BP neural network in Chaohu Lake. The result of the analysis indicates that algal blooms in Chaohu are affected by many factors. There was a positive correlation among the change of water temperature, dissolved oxygen, turbidity, atmospheric temperature, illumination and the change of mass concentration of algal. Among these factors, atmospheric temperature is the most important, with a relative contribution up to 17. 01%; on the contrary, the rise of atmospheric pressure does harm to the algal; and the influence of high pH on the algal concentration is uncertain. [ABSTRACT FROM AUTHOR]

Copyright of China Sciencepaper is the property of China Sciencepaper and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Natural Resources / Ziran Ziyuan Xuebao is the property of Journal of Natural Resources and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)