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Search Results: 1 - 10 of 1543 matches for " Vishwa Nath Maurya "
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Investigation of Probability Generating Function in an Interdependent M/M/1:(∞; GD) Queueing Model with Controllable Arrival Rates Using Rouche’s Theorem  [PDF]
Vishwa Nath Maurya
Open Journal of Optimization (OJOp) , 2012, DOI: 10.4236/ojop.2012.12006
Abstract: Present paper deals a M/M/1:(∞; GD) queueing model with interdependent controllable arrival and service rates where- in customers arrive in the system according to poisson distribution with two different arrivals rates-slower and faster as per controllable arrival policy. Keeping in view the general trend of interdependent arrival and service processes, it is presumed that random variables of arrival and service processes follow a bivariate poisson distribution and the server provides his services under general discipline of service rule in an infinitely large waiting space. In this paper, our central attention is to explore the probability generating functions using Rouche’s theorem in both cases of slower and faster arrival rates of the queueing model taken into consideration; which may be helpful for mathematicians and researchers for establishing significant performance measures of the model. Moreover, for the purpose of high-lighting the application aspect of our investigated result, very recently Maurya [1] has derived successfully the expected busy periods of the server in both cases of slower and faster arrival rates, which have also been presented by the end of this paper.


Current Seasonal Variations in Physicochemical and Heavy Metals Parameters of Sewage Treatment Plant Effluent and Suitability for Irrigation  [PDF]
Chandan Maurya, Janendra Nath Srivastava
Journal of Water Resource and Protection (JWARP) , 2019, DOI: 10.4236/jwarp.2019.117052
Abstract: The study aims to investigate the current extent of physicochemical parameters and heavy metal contamination in the effluent of the Jaganpur sewage treatment plant (STP), Dayalbagh, Agra India. Majority of the nearby farmers have access to use of STP effluent in irrigation purposes for growing major edible crops. The problems of using STP effluent for irrigation purpose, continuous water quality analysis required. To check the quality of irrigation water, substantial physicochemical parameters accordance to Indian Standards (IS-Reaffirmed 2002/2003) analysed to calculate Sodium absorption ratio (SAR) and Residual sodium carbonate (RSC). To estimate the heavy metal pollution index (HPI) and metal quality index (MQI), toxic Heavy metals such as As, Cr, Mn, Fe, Ni, Cu, Zn, Pd, Cd, Co, and B also determined in the STP effluent with an AAS and results verified with ICP-OES against certified standards. The high value of SAR (range 13 to 20) and RSC (range -10 to 11) in STP effluent exceeded the permissible limit for irrigation purpose. On the other hand, HPI and MQI values (1692.4 and 58.1, respectively) show that high metal contamination mainly due to industrial and domestic wastewater does not treat appropriately in the sewage treatment plant. Thus it is suggested that further studies are carried out on the STP effluents to improve the water quality through proper treatment. Treated wastewater used for irrigation purposes needs to analyse the contamination like heavy metals and pinpoint the pollution sources.
2D-QSAR model development and analysis on variant groups of anti -tuberculosis drugs
Neeraja Dwivedi*,Bhartendu Nath Mishra,Vishwa Mohan Katoch
Bioinformation , 2011,
Abstract: A quantitative structure activity relationship study was performed on different groups of anti-tuberculosis drug compound for establishing quantitative relationship between biological activity and their physicochemical /structural properties. In recent years, a large number of herbal drugs are promoted in treatment of tuberculosis especially due to the emergence of MDR (multi drug resistance) and XDR (extensive drug resistance) tuberculosis. Multidrug-resistant TB (MDR-TB) is resistant to front-line drugs (isoniazid and rifampicin, the most powerful anti-TB drugs) and extensively drug-resistant TB (XDR-TB) is resistant to front-line and second-line drugs. The possibility of drug resistance TB increases when patient does not take prescribed drugs for defined time period. Natural products (secondary metabolites) isolated from the variety of sources including terrestrial and marine plants and animals, and microorganisms, have been recognized as having antituberculosis action and have recently been tested preclinically for their growth inhibitory activity towards Mycobacterium tuberculosis or related organisms. A quantitative structure activity relationship (QSAR) studies were performed to explore the antituberculosis compound from the derivatives of natural products . Theoretical results are in accord with the in vitro experimental data with reported growth inhibitory activity towards Mycobacterium tuberculosis or related organisms. Antitubercular activity was predicted through QSAR model, developed by forward feed multiple linear regression method with leave-one-out approach. Relationship correlating measure of QSAR model was 74% (R2 = 0.74) and predictive accuracy was 72% (RCV2 = 0.72). QSAR studies indicate that dipole energy and heat of formation correlate well with anti-tubercular activity. These results could offer useful references for understanding mechanisms and directing the molecular design of new lead compounds with improved anti-tubercular activity. The generated QSAR model revealed the importance of structural, thermodynamic and electro topological parameters. The quantitative structure activity relationship provides important structural insight in designing of potent antitubercular agent.
URLD (Ultrarapid Limited Dissection Abdominoplasty) Is Safe with Caesarean Section  [PDF]
Vishwa Prakash, Neeta Garg
Open Journal of Obstetrics and Gynecology (OJOG) , 2014, DOI: 10.4236/ojog.2014.49079

Abdominoplasty is not advisable with caesarean section because of high incidence of complications. We have changed the technique of proper abdominoplasty to ultrarapid limited dissection abdominoplasty in 25 women undergoing caesarean section successfully and found that there was no incidence of any complication usually described with proper abdominoplasty, and we recommend that limited dissection abdominoplasty can be combined with caesarean section.

Ramu Maurya
International Journal of Economics and Research , 2011,
Abstract: Microfinance is seen to be a remedy of poverty eradication and globally it is perceived that microfinance can remove the problem of poverty. Basically microfinance works on joint liability model. Traditional theories of credit lending say that rural credit markets are imperfectly competitive and acquiring information about borrowers type that is who is risky and who is safe is not costless. This market imperfection leads to high interest rate and drives out safe borrower from the credit market. In economic literature this problem is considered as adverse selection problem. Joint liability model try to solve the problem of adverse selection through group lending.This paper explores the idea of joint liability model and tries to solve the problem of adverse selection through the positive assortative matching. Paper concludes that in positive assortative matching, the payoffs of borrowers would be more than the payoffs of negative assortative matching. Paper, also try to show that self financing can bring down the interest rate and size of penalty and improve the borrower’s expected payoffs
Spatial Semantic Scan: Detecting Subtle, Spatially Localized Events in Text Streams
Abhinav Maurya
Computer Science , 2015,
Abstract: Many methods have been proposed for detecting emerging events in text streams using topic modeling. However, these methods have shortcomings that make them unsuitable for rapid detection of locally emerging events on massive text streams. We describe Spatially Compact Semantic Scan (SCSS) that has been developed specifically to overcome the shortcomings of current methods in detecting new spatially compact events in text streams. SCSS employs alternating optimization between using semantic scan to estimate contrastive foreground topics in documents, and discovering spatial neighborhoods with high occurrence of documents containing the foreground topics. We evaluate our method on Emergency Department chief complaints dataset (ED dataset) to verify the effectiveness of our method in detecting real-world disease outbreaks from free-text ED chief complaint data.
A Well Conditioned and Sparse Estimate of Covariance and Inverse Covariance Matrix Using a Joint Penalty
Ashwini Maurya
Statistics , 2014,
Abstract: We develop a method for estimating a well conditioned and sparse covariance matrix from a sample of vectors drawn from a sub-gaussian distribution in high dimensional setting. The proposed estimator is obtained by minimizing the squared loss function and joint penalty of $\ell_1$ norm and sum of squared deviation of the eigenvalues from a positive constant. The joint penalty plays two important roles: i) $\ell_1$ penalty on each entry of covariance matrix reduces the effective number of parameters and consequently the estimate is sparse and ii) the sum of squared deviations penalty on the eigenvalues controls the over-dispersion in the eigenvalues of sample covariance matrix. In contrast to some of the existing methods of covariance matrix estimation, where often the interest is to estimate a sparse matrix, the proposed method is flexible in estimating both a sparse and well-conditioned covariance matrix simultaneously. We also extend the method to inverse covariance matrix estimation and establish the consistency of the proposed estimators in both Frobenius and Operator norm. The proposed algorithm of covariance and inverse covariance matrix estimation is very fast, efficient and easily scalable to large scale data analysis problems. The simulation studies for varying sample size and number of variables shows that the proposed estimator performs better than graphical lasso, PDSCE estimates for various choices of structured covariance and inverse covariance matrices. We also use our proposed estimator for tumor tissues classification using gene expression data and compare its performance with some other classification methods.
Nanotechnology Method Comparison for Early Detection of Cancer
Wamakshi Bhati,Alka Vishwa
International Journal of Intelligent Systems and Applications , 2013,
Abstract: Since 1999, cancer has been the leading cause of death under the age of 85 years and the eradication of this disease has been the long sought-after goal of scientists and physicians. Cancer is a disease in which abnormal cells divide uncontrollably. These abnormal cells have the ability to invade and destroy normal body cells, which is life threatening. One of the most important factors in effective cancer treatment is the detection of cancerous tumour cells in an early stage. Nanotechnology brings new hope to the arena of cancer detection research, owing to nanoparticles’ unique physical and chemical properties, giving them the potential to be used in the detection and monitoring of cancer. One such approach is quantum dots based detection which is rapid, easy and economical enabling quick point-of-care screening of cancer markers. QDs have got unique properties which make them ideal for detecting tumours. On the other hand, Gold nanoparticles have been in the bio-imaging spotlight due to their special optical properties. Au-NPs with strong surface-plasmon-enhanced absorption and scattering have allowed them to emerge as powerful imaging labels and contrast agents. This paper includes the comparative study of both the methods. Compared with quantum dots, the gold-nanoparticles are more than 200 times brighter on a particle-to-particle basis, although they are about 60 times larger by volume. Thus, Gold nanoparticles in suspension, offers advantages compared with quantum dots in that the gold appears to be non-toxic and the particles produce a brighter, sharper signal.
Modified Method for Denoising the Ultrasound Images by Wavelet Thresholding
Alka Vishwa,Shilpa Sharma
International Journal of Intelligent Systems and Applications , 2012,
Abstract: Medical practitioners are increasingly using digital images during disease diagnosis. Several state-of-the-art medical equipment are producing images of different organs, which are used during various stages of analysis. Examples of such equipment include MRI, CT, ultrasound and X-Ray. In medical image processing, image denoising has become a very essential exercise all through the diagnosis as Ultrasound images are normally affected by speckle noise. The noise in the image has two negative outcomes, the first being the degradation of the image quality and the second and more important, obscures important information required for accurate diagnosis.Arbitration between the perpetuation of useful diagnostic information and noise suppression must be treasured in medical images. In general we rely on the intervention of a proficient to control the quality of processed images. In certain cases, for instance in Ultrasound images, the noise can suppress the information which is valuable for the general practitioner. Consequently medical images can be very inconsistent, and it is crucial to operate case to case. This paper presents a wavelet-based thresholding scheme for noise suppression in Ultrasound images and provides the knowledge about adaptive and anisotropic diffusion techniques for speckle noise removal from different types of images, like Ultrasound.
Efficient method of inoculation by Fusarium udum, the incitant of pigeonpea wilt
Indian Phytopathology , 2012,
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