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Search Results: 1 - 10 of 13060 matches for " Xiaowei Hao "
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5,8-Dibromo-14,17-difluoro-2,11-dithia[3.3]paracyclophane
Xiaowei Hao,Di Wu
Acta Crystallographica Section E , 2010, DOI: 10.1107/s1600536810028904
Abstract: The title compound, C16H12Br2F2S2 [systematic name: 12,15-dibromo-52,55-difluoro-2,7-dithia-1,5(1,4)-dibenzenaoctaphane], has two approximately parallel benzene rings with a dihedral angle of 1.53 (15)° between them and with a centroid–centroid distance of 3.3066 (18) . In the crystal structure, molecules are stacked along the a axis through an intermolecular π–π interaction with a centroid–centroid distance of 3.7803 (18) . Molecules are also connected by a C—H...S interaction, forming a chain along the b axis.
Preparation mechanism and luminescence of Sr2SiO4: Eu phosphor from (Sr, Eu)CO3@SiO2 core-shell precursor  [PDF]
Yunsheng Hu, Weidong Zhuang, Jianhua Hao, Xiaowei Huang, Huaqiang He
Open Journal of Inorganic Chemistry (OJIC) , 2012, DOI: 10.4236/ojic.2012.21002
Abstract: Sr2SiO4: Eu phosphor for white light emitting diodes (LEDs) was synthesized by employing an as-prepared (Sr, Eu)CO3@SiO2 core-shell precursor as starting materials, and the effect of the core-shell precursor was also discussed on the crystal structure, particle morphology and luminescent properties of the resultant phosphor. The results showed that the hybrid β- and α′-Sr2SiO4: Eu phosphor with fine particle size and narrow distribution could be obtained at a lower firing temperature than that in conventional solidstate reaction method, and its formation mechanism was deduced to be (Sr, Eu)CO3 diffusion controlled reaction process. Responded to its hybrid crystal structure, this phosphor exhibited the combined luminescence of β- and α′-Sr2SiO4: Eu.
The Risk Premium of Treasury Bonds in China  [PDF]
Xiaowei Wu
Journal of Mathematical Finance (JMF) , 2016, DOI: 10.4236/jmf.2016.61015
Abstract:

This paper studied the macroeconomic and the term structure of treasury bonds in the Shanghai Stock Exchange Market. Different from previous studies, we used a group of 122 observed macroeconomic data to construct our model’s macro factor. Therefore the macro factor contained more information than previous studies in predicting the excess return of Treasury bond. Based on the Kalman-Filter estimation, the results show that the macro factor’s risk was compensated through the level factor and slope factor, especially the level factor. Further, based on the decomposition of the yield curve into expected future short rate part and risk premium part, we find that there is some correlation between the variability of the risk premium and monetary policy to some extent.

Ultrasonic-Assisted Extraction of Procyanidins Using Ionic Liquid Solution from Larix gmelinii Bark
Xiaowei Sun,Zhimin Jin,Lei Yang,Jingwei Hao
Journal of Chemistry , 2013, DOI: 10.1155/2013/541037
Abstract:
Experiments with Two New Boosting Algorithms  [PDF]
Xiaowei Sun, Hongbo Zhou
Intelligent Information Management (IIM) , 2010, DOI: 10.4236/iim.2010.26047
Abstract: Boosting is an effective classifier combination method, which can improve classification performance of an unstable learning algorithm. But it dose not make much more improvement of a stable learning algorithm. In this paper, multiple TAN classifiers are combined by a combination method called Boosting-MultiTAN that is compared with the Boosting-BAN classifier which is boosting based on BAN combination. We describe experiments that carried out to assess how well the two algorithms perform on real learning problems. Fi- nally, experimental results show that the Boosting-BAN has higher classification accuracy on most data sets, but Boosting-MultiTAN has good effect on others. These results argue that boosting algorithm deserve more attention in machine learning and data mining communities.
Nanofiltration Technology for Toxic or Harmful Ions Removal from Groundwater: Characteristics and Economic Analysis  [PDF]
Xinzhu Yang, Xiaowei Wang
Journal of Environmental Protection (JEP) , 2012, DOI: 10.4236/jep.2012.33031
Abstract: Nanofiltration (NF) membrane can efficiently remove the ions from groundwater, especially for high valence ions. Results show that the removal rate of fluoride was approximately 67% by the NF system, while for arsenic the removal rate was more than 93%. NF presented the well selective removal for fluoride. The quality of product water meets the national drinking water standards. Therefore, the application of nanofiltration technology can significantly improve the drinking water environment of rural areas, avoiding the secondary pollution caused by other chemical treatment processes. The water product cost of NF technology is about RMB 0.026 yuan per liter, application of the process of 2:1 NF membranes arrangement for toxic or harmful ions removal from groundwater, including investment cost and operating cost. Therefore, NF technology for harmful ions removal is more economical than the price of the market bottled water and suitable for application in rural areas of China.
A Case Study on the Relationship between Personality Traits and Parameters of Social Networks  [PDF]
Xiaowei Yan, Junhui Gao
Open Journal of Social Sciences (JSS) , 2016, DOI: 10.4236/jss.2016.47016
Abstract:

In this paper, the relationship between personality traits and the parameters of certain social networks is investigated directly through data collection by precise questionnaire surveys. First, social network analysis is employed to calculate multiple parameters of the networks. Then, a correlation analysis is conducted on the relationship between the personality traits and the parameters. The results show that some network parameters are in consistent with the students’ personality traits. Results also reveal that the parameters of the respondents who mark themselves as “extroverted” are much closer to those of respondents who mark themselves as “neutral” than “introverted”.

IONIZATION AND OPTICAL EMISSION SPECTRA OF CLUSTER IN ULTRA-INTENSE LASER
强激光场中原子团簇的电离和光辐射

Hao Xiping,Song Xiaowei,
郝希平
,宋霄薇

光子学报 , 2000,
Abstract: 通过数值求解一维含时薛定谔方程,研究了原子与原子团簇的电离、电子时间演化和光辐射现象。结果表明,原子团簇比原子更易于电离,原子团簇中电子的时间演和原子中电子的时间演化不同,光辐射频率也要高很多。
Environmental Load of Anhui Province and Its Reduction:An Approach of Material Flow Analysis
基于物质流分析方法的安徽省环境载荷及其减量化研究

WU Kaiy,LIU Xiaowei,ZHANG Hao,
吴开亚
,刘晓薇,张浩

资源科学 , 2011,
Abstract: The goal of this paper was to quantify the trends and the reduction status of environmental load of Anhui province during the period 1990-2007. Based on key indices recommend by Eurostat, seven indices, including total material requirement (TMR), direct material input (DMI), hiding flow (HF), input (I), indirect flow (IF), domestic processing output (DPO), and total domestic output (TDO), were adopted. Subsequently, the material flow analysis (MFA) method was employed to construct the indictor system of env...
DuSK: A Dual Structure-preserving Kernel for Supervised Tensor Learning with Applications to Neuroimages
Lifang He,Xiangnan Kong,Philip S. Yu,Ann B. Ragin,Zhifeng Hao,Xiaowei Yang
Computer Science , 2014,
Abstract: With advances in data collection technologies, tensor data is assuming increasing prominence in many applications and the problem of supervised tensor learning has emerged as a topic of critical significance in the data mining and machine learning community. Conventional methods for supervised tensor learning mainly focus on learning kernels by flattening the tensor into vectors or matrices, however structural information within the tensors will be lost. In this paper, we introduce a new scheme to design structure-preserving kernels for supervised tensor learning. Specifically, we demonstrate how to leverage the naturally available structure within the tensorial representation to encode prior knowledge in the kernel. We proposed a tensor kernel that can preserve tensor structures based upon dual-tensorial mapping. The dual-tensorial mapping function can map each tensor instance in the input space to another tensor in the feature space while preserving the tensorial structure. Theoretically, our approach is an extension of the conventional kernels in the vector space to tensor space. We applied our novel kernel in conjunction with SVM to real-world tensor classification problems including brain fMRI classification for three different diseases (i.e., Alzheimer's disease, ADHD and brain damage by HIV). Extensive empirical studies demonstrate that our proposed approach can effectively boost tensor classification performances, particularly with small sample sizes.
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