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Search Results: 1 - 10 of 33716 matches for " Jie Peng "
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Thermal Conductivity of Carbon/Carbon Composites with the Fiber/Matrix Interface Modified by Silicon Carbide Nanofibers  [PDF]
Jie Chen, Xiang Xiong, Peng Xiao
Advances in Chemical Engineering and Science (ACES) , 2016, DOI: 10.4236/aces.2016.64045
Abstract: Silicon carbide nanofibers grew on the surface of carbon fibers of a unidirectional carbon preform by CCVD and then chemical vapor infiltration was used to densify the preform to get the SiCNF-C/C composite. The effects of silicon carbide nanofibers on the microstructure of the pyrolytic carbon and the thermal conductivity of the SiCNF-C/C composite were investigated. Results show that silicon carbide nanofibers on the surface of carbon fibers induce the deposition of high texture pyrolytic carbon around them. The interface bonding between carbon fibers and pyrolytic carbon is well adjusted. So the efficiency of heat transfer in the interface of the composite is well enhanced. The thermal conductivity of the SiCNF-C/C composite is greater than that of the C/C composite, especially the thermal conductivity perpendicular to the fiber axis.
High-risk HPV E5-induced cell fusion: a critical initiating event in the early stage of HPV-associated cervical cancer
Peng Gao, Jie Zheng
Virology Journal , 2010, DOI: 10.1186/1743-422x-7-238
Abstract: We propose that high-risk HPV E5-induced cell fusion is a critical initiating event in the early stage of HPV-associated cervical cancer.Our hypothesis can be tested by comparing the likelihood for colony formation or tumorigenic ability in nude mice between normal HaCaT cells expressing all three oncogenic proteins and E5-induced bi-nucleated HaCaT cells expressing E6 and E7. Moreover, investigating premature chromosome condensation (PCC) in HPV-positive and negative precancerous cervical cells is another way to assess this hypothesis.This viewpoint would change our understanding of the mechanisms by which HPV induces cervical cancer. According to this hypothesis, blocking E5-induced cell fusion is a promising way to prevent the progression of cervical cancer. Additionally, establishment of a role of cell fusion in cervical carcinogenesis is of reference value for understanding the pathogenesis of other virus-associated cancers.Cervical cancer progression is strongly associated with infection of high-risk human papillomavirus (HPV) (e.g., HPV-16 and -18), which are detected in nearly all cervical cancers [1]. HPV is a small, nonenveloped DNA virus expressing three key oncoproteins: E5, E6 and E7, which possess the ability of transforming certain human cells in vitro and are considered to be associated with cervical carcinogenesis in vivo [2-5]. E6 and E7 are well known for their ability to inhibit the function of tumor suppressors p53 and pRb, respectively [6]. E5 has weak oncogenic properties which occur through increasing epidermal growth factor receptor (EGFR) and inhibiting the expression of major histocompatibility complex (MHC)-I and MHC-II on the plasma membrane [7]. Coexpression of E5 with either E6 or E7, however, promotes transformation by either oncoprotein alone [8].Recently, the view that oncogenic virus-induced cell fusion may contribute to oncogenesis is appealing as all well-known human oncogenic viruses, including HPV, Hepatitis B virus, Hepatitis
Effect of short-range interaction for collision of ultracold dipoles
Peng Zhang,Jianwen Jie
Physics , 2014, DOI: 10.1103/PhysRevA.90.062714
Abstract: We consider the low-energy scattering of two ultracold polarized dipoles with both a short-range interaction (SRI) and a weak dipole-dipole interaction (DDI) which is far away from shape-resonances. In previous analytical studies, the scattering amplitude in this system was often calculated via the first-order Born approximation (FBA). Our results show that significant derivations from this approximation can arise in some cases. In these cases, the SRI can significantly modify the inter-dipole scattering amplitudes even if the scattering amplitudes for the SRI alone are much smaller than the dipolar length of the DDI. We further obtain approximate analytical expressions for these inter-dipole scattering amplitudes.
Charged excitons and biexcitons in laterally coupled InGaAs quantum dots
Jie Peng,Gabriel Bester
Physics , 2010, DOI: 10.1103/PhysRevB.82.235314
Abstract: We present results of atomistic empirical pseudopotential calculations and configuration interaction for excitons, positive and negative trions (X\pm), positive and negative quartons (X2\pm) and biexcitons. The structure investigated are laterally aligned InGaAs quantum dot molecules embedded in GaAs under a lateral electric field. The rather simple energetic of excitons becomes more complex in the case of charged quasiparticles but remains tractable. The negative trion spectrum shows four anticrossings in the presently available range of fields while the positive trion shows two. The magnitude of the anticrossings reveals many-body effects in the carrier tunneling process that should be experimentally accessible.
Consistency of restricted maximum likelihood estimators of principal components
Debashis Paul,Jie Peng
Mathematics , 2008,
Abstract: In this paper we consider two closely related problems : estimation of eigenvalues and eigenfunctions of the covariance kernel of functional data based on (possibly) irregular measurements, and the problem of estimating the eigenvalues and eigenvectors of the covariance matrix for high-dimensional Gaussian vectors. In Peng and Paul (2007), a restricted maximum likelihood (REML) approach has been developed to deal with the first problem. In this paper, we establish consistency and derive rate of convergence of the REML estimator for the functional data case, under appropriate smoothness conditions. Moreover, we prove that when the number of measurements per sample curve is bounded, under squared-error loss, the rate of convergence of the REML estimators of eigenfunctions is near-optimal. In the case of Gaussian vectors, asymptotic consistency and an efficient score representation of the estimators are obtained under the assumption that the effective dimension grows at a rate slower than the sample size. These results are derived through an explicit utilization of the intrinsic geometry of the parameter space, which is non-Euclidean. Moreover, the results derived in this paper suggest an asymptotic equivalence between the inference on functional data with dense measurements and that of the high dimensional Gaussian vectors.
Principal components analysis for sparsely observed correlated functional data using a kernel smoothing approach
Debashis Paul,Jie Peng
Mathematics , 2008,
Abstract: In this paper, we consider the problem of estimating the covariance kernel and its eigenvalues and eigenfunctions from sparse, irregularly observed, noise corrupted and (possibly) correlated functional data. We present a method based on pre-smoothing of individual sample curves through an appropriate kernel. We show that the naive empirical covariance of the pre-smoothed sample curves gives highly biased estimator of the covariance kernel along its diagonal. We attend to this problem by estimating the diagonal and off-diagonal parts of the covariance kernel separately. We then present a practical and efficient method for choosing the bandwidth for the kernel by using an approximation to the leave-one-curve-out cross validation score. We prove that under standard regularity conditions on the covariance kernel and assuming i.i.d. samples, the risk of our estimator, under $L^2$ loss, achieves the optimal nonparametric rate when the number of measurements per curve is bounded. We also show that even when the sample curves are correlated in such a way that the noiseless data has a separable covariance structure, the proposed method is still consistent and we quantify the role of this correlation in the risk of the estimator.
A geometric approach to maximum likelihood estimation of the functional principal components from sparse longitudinal data
Jie Peng,Debashis Paul
Statistics , 2007,
Abstract: In this paper, we consider the problem of estimating the eigenvalues and eigenfunctions of the covariance kernel (i.e., the functional principal components) from sparse and irregularly observed longitudinal data. We approach this problem through a maximum likelihood method assuming that the covariance kernel is smooth and finite dimensional. We exploit the smoothness of the eigenfunctions to reduce dimensionality by restricting them to a lower dimensional space of smooth functions. The estimation scheme is developed based on a Newton-Raphson procedure using the fact that the basis coefficients representing the eigenfunctions lie on a Stiefel manifold. We also address the selection of the right number of basis functions, as well as that of the dimension of the covariance kernel by a second order approximation to the leave-one-curve-out cross-validation score that is computationally very efficient. The effectiveness of our procedure is demonstrated by simulation studies and an application to a CD4 counts data set. In the simulation studies, our method performs well on both estimation and model selection. It also outperforms two existing approaches: one based on a local polynomial smoothing of the empirical covariances, and another using an EM algorithm.
Learning directed acyclic graphs via bootstrap aggregating
Ru Wang,Jie Peng
Statistics , 2014,
Abstract: Probabilistic graphical models are graphical representations of probability distributions. Graphical models have applications in many fields including biology, social sciences, linguistic, neuroscience. In this paper, we propose directed acyclic graphs (DAGs) learning via bootstrap aggregating. The proposed procedure is named as DAGBag. Specifically, an ensemble of DAGs is first learned based on bootstrap resamples of the data and then an aggregated DAG is derived by minimizing the overall distance to the entire ensemble. A family of metrics based on the structural hamming distance is defined for the space of DAGs (of a given node set) and is used for aggregation. Under the high-dimensional-low-sample size setting, the graph learned on one data set often has excessive number of false positive edges due to over-fitting of the noise. Aggregation overcomes over-fitting through variance reduction and thus greatly reduces false positives. We also develop an efficient implementation of the hill climbing search algorithm of DAG learning which makes the proposed method computationally competitive for the high-dimensional regime. The DAGBag procedure is implemented in the R package dagbag.
Evaluating Model and BeiDou Based Management System for Scale Operation of Cotton-Pickers  [PDF]
Caicong Wu, Peng Qiao, Jing Zhao, Jie Wang, Yaping Cai
Positioning (POS) , 2016, DOI: 10.4236/pos.2016.71002
Abstract: For scale cotton-picker operation, combination of production resources including field, machine, and drivers, should be organized reasonably both in temporal and spatial dimensions. Xinjian Agri. is such a scale cotton picking service company, which owns more than 400 cotton-pickers, hires nearly 1000 personnel, and works for more than ten big farms each season. The total operation area is about 90,000 ha. In this paper, a Cotton-picker Operation Scheduling & Monitoring System (CPOSMS) was developed for Xinjian Agri. CPOSMS is a WebGIS and BeiDou based management software, which includes four main function modules. Overall scheduling module aims to help the company to create machine fleets for the farms based on operation demands and operation capacity. A real-time evaluation model was studied to adjust the rationality. Local scheduling module is to dispatch machines and personnel to form machine unit. Central navigating module is to guide staff to specific field. Operation monitoring module is to monitor and analyze operation process. Experiments in 2015 showed that the CPOSMS is the necessary tool for the company, and the evaluation model and BeiDou based system can improve management efficiency.
Addressing Uncertainty in Temporal and Spatial Scheduling for Farm Machinery Operation  [PDF]
Caicong Wu, Lin Zhou, Peng Qiao, Jie Wang
Positioning (POS) , 2016, DOI: 10.4236/pos.2016.71003
Abstract: Uncertainty is an important characteristic of scheduling model for scale farm machinery operation organizing. Practice shows that scheduling model without considering uncertainties is nearly useless. Uncertain influence factors arisen from natural environment, society and economy, market and supply, and customer and behavior, exist widely, emerge frequently, and affect production deeply. Uncertainties interfere with the allocation of productive factors on temporal and spatial dimensions for farm machinery operation scheduling and management. Questionnaire for farm machinery organizations was designed and finished in 2014. Both occurrence frequency and influence degree for each factor were quantified. Four influence factors including operation location change, weather mutation, parts supply delay, and operation skill defects appear in both list of high occurrence and deep influence. Then results of questionnaire and results of specific investigation were used to study temporal and spatial scheduling model and system for farm machinery management. Three case studies are introduced. The first case is about the uncertainty and countermeasure of forage harvesters scheduling and monitoring for a professional forage plantation company. The second case is about the uncertainty and counter measure of cotton-picker scheduling and monitoring for a professional cotton picking company. And the third case is about the uncertainty and countermeasure of social service management for a professional cooperative. The cases show that the research has strong pertinence to deal with uncertainties and can improve management efficiency of farm machinery operation.
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