OALib Journal期刊

ISSN: 2333-9721




2019 ( 3 )

2018 ( 48 )

2017 ( 30 )

2016 ( 56 )


匹配条件: “Meng Ji” ,找到相关结果约22982条。
AAPP: An Anycast Based AODV Routing Protocol for Peer to Peer Services in MANET
Meng Qi,Ji Hong
International Journal of Distributed Sensor Networks , 2009, DOI: 10.1080/15501320802540140
Abstract: The study of Peer-to-Peer (P2P) network and Mobile Ad hoc Network (MANET) are currently two hotspots in distributed domain. We propose a novel anycast based AODV routing protocol which is a simple and efficient routing protocol designed especially for MANET for P2P applications called AAPP (an Anycast based AODV routing protocol for Peer to Peer services in MANET).
Challenges of Introducing Participant Observation to Community Health Research
Meng Zhao,Yingchun Ji
ISRN Nursing , 2014, DOI: 10.1155/2014/802490
Abstract: Participant observation elicits unique observation data from both an insider’s and an outsider’s perspectives. Despite the growing tendency to adopt participant observation strategies in health care research regarding health-related beliefs and types of behavior, the use of participant observation in current research is mostly limited to structured clinical settings rather than community settings. In this paper, we describe how we use participant observation in a community health research study with Chinese-born immigrant women. We document discrepancies between these women’s beliefs and types of behavior regarding health and health promotion. We further discuss the ethnical, time, and setting challenges in community health research using participant observation. Possible solutions are also discussed. 1. Introduction Derived from cultural anthropology, participant observation (PO) is a qualitative research methodology that is widely used by sociologists and anthropologists [1]. The objective of PO is to offer researchers a method to investigate the perspectives of a group in a given community [2]. What makes the PO method distinctive is that PO emphasizes the role of the researcher as a participant in a community [2]. Researchers do not merely observe their study informants distantly and objectively but actively participate in the informants’ daily activities to understand the informants’ daily dynamics from both an insider’s and an outsider’s perspectives [2]. The research setting for PO is the study informants’ own daily environment rather than a setting assigned by researchers [2]. Therefore, as an exploratory qualitative approach, PO is particularly appropriate for any community health research [2, 3]. Data elicited from PO strategies are unique, offering a different perspective from the self-reported data retrieved from interviews, focused groups, or quantitative research methods [2]. Therefore PO complements other approaches for data collection [2]. The PO strategies can help researchers to gain an understanding about the sociocultural context where the study informants’ daily activities occur [2]. It provides researchers with unique opportunities to explore the study informants’ unanticipated types of behavior or activities [2]. It further allows researchers to investigate these types of behavior or activities and reframe the research questions with a deeper understanding of the research problem [2]. Despite the growing tendency to adopt PO strategies in health care research regarding health-related beliefs and behavior [4], the use of PO in
Modern Computational Techniques for the HMMER Sequence Analysis
Xiandong Meng,Yanqing Ji
ISRN Bioinformatics , 2013, DOI: 10.1155/2013/252183
Abstract: This paper focuses on the latest research and critical reviews on modern computing architectures, software and hardware accelerated algorithms for bioinformatics data analysis with an emphasis on one of the most important sequence analysis applications—hidden Markov models (HMM). We show the detailed performance comparison of sequence analysis tools on various computing platforms recently developed in the bioinformatics society. The characteristics of the sequence analysis, such as data and compute-intensive natures, make it very attractive to optimize and parallelize by using both traditional software approach and innovated hardware acceleration technologies. 1. Introduction At the beginning of the 21st century, an explosion of information was discovered from the living organisms, especially in areas of molecular biology and genetics. The focus of bioinformatics deals with this flood of information, which comes from academy, industry, and government labs, and turning it into useful knowledge. Bioinformatics is important to a virtually unlimited number of fields. As the genetic information being organized into computerized databases and their sizes steadily grow, molecular biologists need effective and efficient computational tools to store and retrieve the cognate information such as biological information from the databases, to analyze the sequence patterns they contain, and to extract the biological knowledge the sequences contain. The field of bioinformatics computing is advancing at an unprecedented rate. For people working with genomics and high-throughput sequencing data analysis, it is a serious challenge to analyze the vast amounts of data coming from the next generation sequencing (NGS) instruments. For example, there were approximately 126,?551,?501, and 141 bases in 135,?440, and 924 sequence records in the traditional GenBank divisions as of April 2011 [1]. The tendency is likely only to be reinforced by new generation sequencers, for example, Illumina HiSeq 2500 generating up to 120?Gb of data in 17 hours per run [2]. Data in itself is almost useless until it is analyzed and correctly interpreted. The draft of the human genome has given us a genetic list of what is necessary for building a human: approximately 35,000 genes. For a genome as large as the human genome, it may take many days of CPU time on large-memory, multiprocessor computers to analyze. To handle this much data, computational strategies are important to tackle this vital bottleneck, which can aid scientists in the extraction of useful and important biological data.
FastMMD: Ensemble of Circular Discrepancy for Efficient Two-Sample Test
Ji Zhao,Deyu Meng
Computer Science , 2014, DOI: 10.1162/NECO_a_00732
Abstract: The maximum mean discrepancy (MMD) is a recently proposed test statistic for two-sample test. Its quadratic time complexity, however, greatly hampers its availability to large-scale applications. To accelerate the MMD calculation, in this study we propose an efficient method called FastMMD. The core idea of FastMMD is to equivalently transform the MMD with shift-invariant kernels into the amplitude expectation of a linear combination of sinusoid components based on Bochner's theorem and Fourier transform (Rahimi & Recht, 2007). Taking advantage of sampling of Fourier transform, FastMMD decreases the time complexity for MMD calculation from $O(N^2 d)$ to $O(L N d)$, where $N$ and $d$ are the size and dimension of the sample set, respectively. Here $L$ is the number of basis functions for approximating kernels which determines the approximation accuracy. For kernels that are spherically invariant, the computation can be further accelerated to $O(L N \log d)$ by using the Fastfood technique (Le et al., 2013). The uniform convergence of our method has also been theoretically proved in both unbiased and biased estimates. We have further provided a geometric explanation for our method, namely ensemble of circular discrepancy, which facilitates us to understand the insight of MMD, and is hopeful to help arouse more extensive metrics for assessing two-sample test. Experimental results substantiate that FastMMD is with similar accuracy as exact MMD, while with faster computation speed and lower variance than the existing MMD approximation methods.
The Research of Magneto-Rheological Fluid Yield Stress Model  [PDF]
Meng Ji, Yiping Luo
Open Access Library Journal (OALib Journal) , 2018, DOI: 10.4236/oalib.1104643
Magnetorheological Fluid (MRF), as an advanced and smart material which was controlled by magnetic field, was a kind of stable suspension in which magnetic particle dissolved in base fluid. The yield stress, one of main performance parameters of MRF, was the demarcation point between liquid and solid. At present, the yield stress calculation model did not have a uniform standard. The research on yield stress model was significant to the research on MRF. First, the research was based on the MRF characteristic and the research status of MRF sheer yield stress; second the classic dipole model, local field dipole model, polarized pellet model, continuous models on the average had been calculated and compared. The classic dipole model and local field dipole model had a well ability to describe the yield stress of MRF.
Multispectral Imaging for Authenticity Identification and Quality Evaluation of Flos carthami  [PDF]
Cuiying Hu, Qingxia Meng, Ji Ma, Qichang Pang, Jing Zhao
Optics and Photonics Journal (OPJ) , 2013, DOI: 10.4236/opj.2013.33037

The identification and quality evaluation of Flos carthami were studied using tunable liquid spectral imaging instrument, to discuss the application range and advantages of spectral imaging technology in Chinese medicine identification and quality control field. The Flos carthami was indentified by extracting the normalized characteristic spectral curves of Flos carthami, Crocus sativus and Dendranthema morifolium, which were standard samples supplied by National Institute for Drug Control. The qualities of Flos carthamies collecting from different pharmacies were evaluated by extracting their normalized characteristic spectral curves. The imaging spectrum testing system was designed independently. The spectral resolution was 5 nm, and the spectral range was from 400 nm to 680 nm. The results showed that the normalized characteristic spectral curve of Flos carthami was significantly different from those of Crocus sativus and Dendranthema

Application of Multispectral Imaging Method in Rapid Identification and Analysis of Chinese Herbal Medicine Powders  [PDF]
Cuiying Hu, Qingxia Meng, Qichang Pang, Ji Ma, Jing Zhao
Optics and Photonics Journal (OPJ) , 2013, DOI: 10.4236/opj.2013.32024

Five kinds of traditional flower Chinese medicine powders (TFCMD) were identified using tuneable liquid spectral imaging instrument, to discuss the application range and advantages of spectral imaging technology in Chinese medicine identification and analysis field. The testing system is the liquid crystal multispectral imaging system designed by ourselves. All the tests are standard samples supplied by National Institute for Drug Control. The spectral cubes of Campsis grandiflora, Carthamus tinctorius, Albizzia julibrissin, Dendranthema morifolium, and Dendranthema indicum were captured, and then the normalized characteristic spectral curves of them were picked up. The spectral resolution is 5 nm, and the spectral range is 400 nm - 650 nm. The result shows that different TFCMD has different normalized characteristic fluorescence spectral curve. Spectral imaging technology can be used to identify TFCMD, and the testing course is convenient, quick, noninvasive and without pre-treatment.

Statistical Distortion: Consequences of Data Cleaning
Tamraparni Dasu,Ji Meng Loh
Computer Science , 2012,
Abstract: We introduce the notion of statistical distortion as an essential metric for measuring the effectiveness of data cleaning strategies. We use this metric to propose a widely applicable yet scalable experimental framework for evaluating data cleaning strategies along three dimensions: glitch improvement, statistical distortion and cost-related criteria. Existing metrics focus on glitch improvement and cost, but not on the statistical impact of data cleaning strategies. We illustrate our framework on real world data, with a comprehensive suite of experiments and analyses.
Density estimation for grouped data with application to line transect sampling
Woncheol Jang,Ji Meng Loh
Statistics , 2010, DOI: 10.1214/09-AOAS307
Abstract: Line transect sampling is a method used to estimate wildlife populations, with the resulting data often grouped in intervals. Estimating the density from grouped data can be challenging. In this paper we propose a kernel density estimator of wildlife population density for such grouped data. Our method uses a combined cross-validation and smoothed bootstrap approach to select the optimal bandwidth for grouped data. Our simulation study shows that with the smoothing parameter selected with this method, the estimated density from grouped data matches the true density more closely than with other approaches. Using smoothed bootstrap, we also construct bias-adjusted confidence intervals for the value of the density at the boundary. We apply the proposed method to two grouped data sets, one from a wooden stake study where the true density is known, and the other from a survey of kangaroos in Australia.
Accounting for spatial correlation in the scan statistic
Ji Meng Loh,Zhengyuan Zhu
Statistics , 2007, DOI: 10.1214/07-AOAS129
Abstract: The spatial scan statistic is widely used in epidemiology and medical studies as a tool to identify hotspots of diseases. The classical spatial scan statistic assumes the number of disease cases in different locations have independent Poisson distributions, while in practice the data may exhibit overdispersion and spatial correlation. In this work, we examine the behavior of the spatial scan statistic when overdispersion and spatial correlation are present, and propose a modified spatial scan statistic to account for that. Some theoretical results are provided to demonstrate that ignoring the overdispersion and spatial correlation leads to an increased rate of false positives, which is verified through a simulation study. Simulation studies also show that our modified procedure can substantially reduce the rate of false alarms. Two data examples involving brain cancer cases in New Mexico and chickenpox incidence data in France are used to illustrate the practical relevance of the modified procedure.

Copyright © 2008-2017 Open Access Library. All rights reserved.