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Search Results: 1 - 10 of 95504 matches for " Fang Liu "
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Existence of Viscosity Solutions to a Parabolic Inhomogeneous Equation Associated with Infinity Laplacian  [PDF]
Fang Liu
Journal of Applied Mathematics and Physics (JAMP) , 2015, DOI: 10.4236/jamp.2015.35061
Abstract: In this paper, we obtain the existence result of viscosity solutions to the initial and boundary value problem for a nonlinear degenerate parabolic inhomogeneous equation of the form \"\", where \"\" denotes infinity Laplacian given by \"\".
A Social Network Theory of Stakeholders in China’s Project Governance  [PDF]
Ronggui Ding, Fang Liu
iBusiness (IB) , 2011, DOI: 10.4236/ib.2011.32017
Abstract: A project, especially for one in China, usually involves many organizations and each organization may have multiple projects. There is a clear social network relationship between these stakeholders in a project and the stakeholders’ behaviors are also subject to the structure of the social network. Based on an analysis of the social network, as a methodology that can be used to explain the relationships among project organizations, as well as an example of large-scale information system project in China, we construct in this article a way to establish the social network of governance structure, study network configurations and analyze some representative governance strategies adopted by stakeholders in China. By examining the network of project governance, we can predict the trend of the project governance structure and study the risk based on the structure.
A New Newton-Type Method with Third-Order for Solving Systems of Nonlinear Equations  [PDF]
Zhongli Liu, Quanyou Fang
Journal of Applied Mathematics and Physics (JAMP) , 2015, DOI: 10.4236/jamp.2015.310154
Abstract: In this paper, a new two-step Newton-type method with third-order convergence for solving systems of nonlinear equations is proposed. We construct the new method based on the integral interpolation of Newton’s method. Its cubic convergence and error equation are proved theoretically, and demonstrated numerically. Its application to systems of nonlinear equations and boundary-value problems of nonlinear ODEs are shown as well in the numerical examples.
Estimation of Nonparametric Multiple Regression Measurement Error Models with Validation Data  [PDF]
Zanhua Yin, Fang Liu
Open Journal of Statistics (OJS) , 2015, DOI: 10.4236/ojs.2015.57080

In this article, we develop estimation approaches for nonparametric multiple regression measurement error models when both independent validation data on covariables and primary data on the response variable and surrogate covariables are available. An estimator which integrates Fourier series estimation and truncated series approximation methods is derived without any error model structure assumption between the true covariables and surrogate variables. Most importantly, our proposed methodology can be readily extended to the case that only some of covariates are measured with errors with the assistance of validation data. Under mild conditions, we derive the convergence rates of the proposed estimators. The finite-sample properties of the estimators are investigated through simulation studies.

Equally Knocking on the Door of Medical Breakthroughs: Safety and Efficacy of a New Anti-Cancer Drug Pamica (PICNH2Ca) for Late Stage Cancer Patients  [PDF]
Haixiang Lin, Fang Liu
Journal of Biosciences and Medicines (JBM) , 2017, DOI: 10.4236/jbm.2017.52005
Abstract: The composition of Pamica (PICNH2Ca2) is dsRNA polyI:C, non-biotics amino compounds and CaCl2. It is the second generation noncytotoxic product of PICKCa (PIKA) adjuvant which was found effective in phase II clinical trial for PICKCa rabies vaccine and phase I trial for PICKCa hepatitis B vaccine in Singapore. With their knowledge and consent, cancer patients, regardless of their tumor types or performance status (ECOG above 5 or ECOG > 20), who were unresponsive to standard treatment, were treated free of charge with Pamica by injection or nasal spray. The primary results indicated that Pamica had no side effect except for a few cases of local pain in the site of injection and appeared to markedly improve cancer patients’ appetite, physical strength, quality of life and extended patients’ life for months and counting. These preliminary findings underlined promise of this potentially life-saving drug. In the hope of cooperating with the proper authorities, companies, and hospitals to benefit patients at the earliest opportunity, we share our findings in this paper.
Estimation of Nonparametric Regression Models with Measurement Error Using Validation Data  [PDF]
Fang Liu, Zanhua Yin
Applied Mathematics (AM) , 2017, DOI: 10.4236/am.2017.810106
We consider the problem of estimating a function g in nonparametric regression model when only some of covariates are measured with errors with the assistance of validation data. Without specifying any error model structure between the surrogate and true covariables, we propose an estimator which integrates orthogonal series estimation and truncated series approximation method. Under general regularity conditions, we get the convergence rate of this estimator. Simulations demonstrate the finite-sample properties of the new estimator.
Weighted Time-Variant Slide Fuzzy Time-Series Models for Short-Term Load Forecasting  [PDF]
Xiaojuan Liu, Enjian Bai, Jian’an Fang
Journal of Intelligent Learning Systems and Applications (JILSA) , 2012, DOI: 10.4236/jilsa.2012.44030
Abstract: Short-term load forecast plays an important role in the day-to-day operation and scheduling of generating units. Season and temperature are the most important factors that affect the load change, but random factors such as big sport events or popular TV shows can change demand consumption in particular hours, which will lead to sudden load changes. A weighted time-variant slide fuzzy time-series model (WTVS) for short-term load forecasting is proposed to improve forecasting accuracy. The WTVS model is divided into three parts, including the data preprocessing, the trend training and the load forecasting. In the data preprocessing phase, the impact of random factors will be weakened by smoothing the historical data. In the trend training and load forecasting phase, the seasonal factor and the weighted historical data are introduced into the Time-variant Slide Fuzzy Time-series Models (TVS) for short-term load forecasting. The WTVS model is tested on the load of the National Electric Power Company in Jordan. Results show that the proposed WTVS model achieves a significant improvement in load forecasting accuracy as compared to TVS models.
Melatonin in Pain Modulation: Analgesic or Proalgesic?  [PDF]
Yongliang Liu, Hongwen He, Fang Huang
Pain Studies and Treatment (PST) , 2014, DOI: 10.4236/pst.2014.22009

Melatonin, as an endogenous hormone produced mainly in the pineal gland in a clear circadian fashion, has diverse physiological functions, such as the control of circadian rhythms, sleep regulation, antioxidant, etc. Recently, accumulating researches about melatonin and pain have been reported. In this review, comparison and summary about analgesic/proalgesic effect of melatonin in inflammatory and neuropathic pain will be made. Some possible pathways of melatonin involved in dual pain modulation might help to explain all these conflicting results in various situations.

Painlevé Property and Exact Solutions to a (2 + 1) Dimensional KdV-mKdV Equation  [PDF]
Yuqing Liu, Fang Duan, Chao Hu
Journal of Applied Mathematics and Physics (JAMP) , 2015, DOI: 10.4236/jamp.2015.36083
Abstract: A (2 + 1) dimensional KdV-mKdV equation is proposed and integrability in the sense of Painlevé and some exact solutions are discussed. The B?cklund transformation and bilinear equations are obtained through Painlevé analysis. Some exact solutions are deduced by Hirota method and generalized Wronskian method.
Nonparametric Regression Estimation with Mixed Measurement Errors  [PDF]
Zanhua Yin, Fang Liu, Yuanfu Xie
Applied Mathematics (AM) , 2016, DOI: 10.4236/am.2016.717179
Abstract: We consider the estimation of nonparametric regression models with predictors being measured with a mixture of Berkson and classical errors. In practice, the Berkson error arises when the variable X of interest is unobservable and only a proxy of X can be measured while the inaccuracy related to the observation of the proxy causes an error of classical type. In this paper, we propose two nonparametric estimators of the regression function in the presence of either or both types of errors. We prove the asymptotic normality of our estimators and derive their rates of convergence. The finite-sample properties of the estimators are investigated through simulation studies.
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