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Search Results: 1 - 10 of 58945 matches for " Hengshan Yang "
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Effects of N rates on N uptake and yield in erect panicle rice  [PDF]
Guiyun Song, Zhengjin Xu, Hengshan Yang
Agricultural Sciences (AS) , 2013, DOI: 10.4236/as.2013.49067

The field experiment was conducted in 2005 and 2006 at Northern Japonica Rice Cultivation and Breeding Research Center, Shenyang Agricultural University,Shenyang, northeast China. Shennong 265 (typical erect panicle rice cultivar), and Liaojing 294 (traditional semi-erect panicle rice cultivar) were grown under different N rates to assess N uptake and N use efficiency. Nitrgen (N) uptake of two rice cultivars increased in their response to N improvement. Grain N of Liaojing 294 predominantly came from root absorption on low N treatments, while grain N of Shennong 265 mainly came from root absorption and had less N re-transferring from vegetative organs under high N rates. Shennong 265 produced less N uptake before heading and more N uptake after heading than Liaojing 294. GY was highly related with N fertilizer rate (r2 = 0.870** for Shennong 265, r2 = 0.613* for Liaojing 294). Shennong 265 was a N-unefficient genotype, since it produced low yield at low N levels and responded well to N application. Liaojing 294 was a N-efficient genotype producing high yield at both low and high N rates. NNG and NFUE exhibited positive correlation with N application rates, but NUEPG showed negative correlation with N application rates; GY as well as BIO and N uses efficiency parameters (TN, NNG, NFUE) which were all positively correlate, while the correlation between GY as well as BIO and the other N efficiency indicators expressed negative correlation. The relationship between GY and TN as well as BIO and TN was observed with significant difference (r2 = 0.824**, r2 = -0.858

Analyzing and Predicting the Economic Growth, Energy Consumption and CO2 Emissions in Shanghai
Guangyong Yang,Hengshan Wang,Jiping Zhou,Xinhui Liu
Energy and Environment Research , 2012, DOI: 10.5539/eer.v2n2p83
Abstract: Based on the data from 1978-2010, this paper analyzes the causal relationships between carbon emissions, energy consumption, and economic growth in Shanghai, adopting the co-integration and vector error correction methods. The Grey prediction model is applied to forecast three variables for the period between 2011 and 2020. As the empirical results showed, in the long-run equilibrium, there is a positive relationship of a long-term equilibrium between carbon emission and energy consumption in Shanghai. However, between carbon emission and real GDP, there is a negative correlation. Besides, in the short-run equilibrium, energy consumption is the important impact on carbon emission. The causality results show that there is a bidirectional causality relationship between carbon emission, real GDP and energy consumption. For the purposes of reducing carbon emissions and not adversely affecting economic growth, Shanghai should optimize the structure of energy consumption and develop new energy. In addition, the optimal forecasting models of real GDP, energy consumption and carbon emissions have good prediction precision with MAPEs of less than 3%.
Financial Volatility Forecasting by Least Square Support Vector Machine Based on GARCH, EGARCH and GJR Models: Evidence from ASEAN Stock Markets
Phichhang Ou,Hengshan Wang
International Journal of Economics and Finance , 2010, DOI: 10.5539/ijef.v2n1p51
Abstract: In this paper, we aim at comparing semi-parametric method, LSSVM (Least square support vector machine), with the classical GARCH(1,1), EGARCH(1,1) and GJR(1,1) models to forecast financial volatilities of three major ASEAN stock markets. More precisely, the experimental results suggest that using hybrid models, GARCH-LSSVM, EGARCH-LSSVM and GJR-LSSVM provides improved performances in forecasting the leverage effect volatilities, especially during the recently global financial market crashes in 2008.
Modeling and Forecasting Stock Market Volatility by Gaussian Processes based on GARCH, EGARCH and GJR Models
PhichHang Ou,Hengshan Wang
Lecture Notes in Engineering and Computer Science , 2011,
Prediction of Stock Market Index Movement by Ten Data Mining Techniques
Phichhang Ou,Hengshan Wang
Modern Applied Science , 2009, DOI: 10.5539/mas.v3n12p28
Abstract: Ability to predict direction of stock/index price accurately is crucial for market dealers or investors to maximize their profits. Data mining techniques have been successfully shown to generate high forecasting accuracy of stock price movement. Nowadays, in stead of a single method, traders need to use various forecasting techniques to gain multiple signals and more information about the future of the markets. In this paper, ten different techniques of data mining are discussed and applied to predict price movement of Hang Seng index of Hong Kong stock market. The approaches include Linear discriminant analysis (LDA), Quadratic discriminant analysis (QDA), K-nearest neighbor classification, Na ve Bayes based on kernel estimation, Logit model, Tree based classification, neural network, Bayesian classification with Gaussian process, Support vector machine (SVM) and Least squares support vector machine (LS-SVM). Experimental results show that the SVM and LS-SVM generate superior predictive performances among the other models. Specifically, SVM is better than LS-SVM for in-sample prediction but LS-SVM is, in turn, better than the SVM for the out-of-sample forecasts in term of hit rate and error rate criteria.
Empirical Research on Associations among Information Technology, Supply Chain Robustness and Supply Chain Performance
Xinrui Zhang,Hengshan Wang
International Journal of Business and Management , 2011, DOI: 10.5539/ijbm.v6n2p231
Abstract: Supply chain reliability and ability to do with risks are important research fields in SCM. Although it is intuitive that supply chain’s ability to resist risks is likely to have a positive impact on supply chain performance, there is little systematic analysis and documentation of the magnitude of these impacts in the literature. This paper empirically documents the associations among information technology, supply chain robustness, and supply chain performance. Based on a sample of 186 questionnaires, the results show the positive impact of information technology on supply chain robustness and supply chain performance, and also the positive impact of supply chain robustness and supply chain performance.
Review on SCM Empirical Research Published in Chinese Journals: Trends and Future Research Directions
Xinrui Zhang,Hengshan Wang
Asian Social Science , 2010, DOI: 10.5539/ass.v6n8p104
Abstract: Supply chain management (SCM) is an important research field and has yield many valuable theories and methods. To use empirical research methods to build and testify the theories in SCM field is not only necessary but fruitful. This paper reviews and evaluates SCM empirical research in 63 papers published in Chinese journals from 2001 to 2009, to assess these papers’ research purposes, research industries, research contents, data collection approaches, and data analysis technologies. To compare the SCM empirical research in China with the research in USA, we find some interesting differences and make a conclusion of the problems in Chinese research. We also discuss the trends and directions for further empirical research in SCM.
Predict GARCH Based Volatility of Shanghai Composite Index by Recurrent Relevant Vector Machines and Recurrent Least Square Support Vector Machines
Phichhang Ou,Hengshan Wang
Journal of Mathematics Research , 2010, DOI: 10.5539/jmr.v2n2p11
Abstract: A new machine learning method so called Relevant Vector Machine (RVM) is an efficiently learning technique for classification and regression problems, including financial time series forecasting. One of the main advantages is that the model is treated by Bayesian approach and its functional form is identical to a powerful prediction tool Support Vector Machine. In this paper, we propose a new recurrent algorithm of the relevant vector machine to predict GARCH (1,1) based volatility of Shanghai composite index. The recurrent support vector machine, recurrent least square support vector machine and normal GARCH (1,1) models are also employed to make a comparison with the proposed model. Our empirical results show that the proposed approach generates superior forecasting performance.
Simulation of Acousto-Electric Well Logging Based on Simplified Pride Equations


地球物理学报 , 2003,
Abstract: A numerical full-waveform simulation method is proposed to study the acousto-electric well logging response. The acoustic field, taken as uninfluenced by the converted electric field, is solved separately. The electric field is taken as quasi-steady. A point pressure source is assumed to be on the borehole axis. The full Biot theory is adopted to obtain expressions of the acoustic field around the borehole. These acoustic expressions are then used to formulate the converted electric fields in and out of the borehole. In the calculated full waveforms of the converted electric field, there are electric waves that accompany the compressional, the shear and the Stoneley waves. And there is a critically refracted electromagnetic wave, which travels along the borehole wall. The waveforms are practically the same as calculated with the full Pride theory under frequency of 25kHz.
Reduction of Decoy Receptor 3 Enhances TRAIL-Mediated Apoptosis in Pancreatic Cancer
Wei Wang, Mei Zhang, Weimin Sun, Shanmin Yang, Ying Su, Hengshan Zhang, Chaomei Liu, Xinfeng Li, Ling Lin, Sunghee Kim, Paul Okunieff, Zhenhuan Zhang, Lurong Zhang
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0074272
Abstract: Most human pancreatic cancer cells are resistant to tumor necrosis factor (TNF)-related apoptosis-inducing ligand (TRAIL)-mediated apoptosis. However, the mechanisms by which pancreatic cancer cells utilize their extracellular molecules to counteract the proapoptotic signaling mediated by the TNF family are largely unknown. In this study, we demonstrate for the first time that DcR3, a secreted decoy receptor that malignant pancreatic cancer cells express at a high level, acts as an extracellular antiapoptotic molecule by binding to TRAIL and counteracting its death-promoting function. The reduction of DcR3 with siRNA unmasked TRAIL and greatly enhanced TRAIL-induced apoptosis. Gemcitabine, a first-line drug for pancreatic cancer, also reduced the level of DcR3. The addition of DcR3 siRNA further enhanced gemcitabine-induced apoptosis. Notably, our in vivo study demonstrated that the therapeutic effect of gemcitabine could be enhanced via further reduction of DcR3, suggesting that downregulation of DcR3 in tumor cells could tip the balance of pancreatic cells towards apoptosis and potentially serve as a new strategy for pancreatic cancer therapy.
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