Factors Affecting Fertility Control in Developing Countries

( Chaurasia ),( Alok Ranjan ) 한국보건사회연구원 1985 保健社會硏究 Vol.5 No.2

Simultaneous Operational Calculus Involving a Product of Two General Class of Polynomials, Fox's H-function and the H-function of Several Complex Variables

V.B.L. Chaurasia ...et al KYUNGPOOK UNIVERSITY 1999 Kyungpook mathematical journal Vol.39 No.1

The aim of this paper is to obtain a new operational relations between the original and the image for two dimensional Laplace transforms pertaining to Fox's H-function and the H-function of several complex variables with two general class of polynomials. A unification of the bivariate Laplace transforms for the H-function given by Chaurasia [2, 3] is provided by the result established here.

The key genomic regions harboring QTLs associated with salinity tolerance in bread wheat (Triticum aestivum L.): a comprehensive review

Chaurasia Shiksha,Kumar Arvind 한국작물학회 2024 Journal of crop science and biotechnology Vol.27 No.1

Wheat (Triticum aestivum L.) is one of the major cereal grain crops and losses grain yield exceeds over 60% due to salinity stress. Now, it is imperative to develop a comprehensive understanding of salt tolerance contrivances and the assortment of reliable tolerance indices is crucial for breeding salt-tolerant wheat cultivars. The mapping of reliable quantitative trait loci (QTLs) and better understanding of the physiological and molecular basis of salt tolerance have revealed new horizons for the development of salt-tolerant wheat cultivars. From the studies carried out in bread wheat, we have summarized the various information such as screening methods, evaluation parameters, key genomic regions (harboring QTLs/QTNs) and salt responsive candidate genes demarcated through bi-parental and genome wide association mapping strategies. The highly consistent QTLs were for plant height, grain yield, thousand grain weight, Na+ and K+ that should be validated using molecular techniques. We hope this review will serve as an important reference guide for scientific community as well as wheat breeders to select the QTLs for MAS to improve the wheat genotypes for salinity tolerance.

Fredholm Type Integral Equations and Certain Polynomials

Chaurasia, V.B.L.,Shekhawat, Ashok Singh Department of Mathematics 2005 Kyungpook mathematical journal Vol.45 No.4

This paper deals with some useful methods of solving the one-dimensional integral equation of Fredholm type. Application of the reduction techniques with a view to inverting a class of integral equation with Lauricella function in the kernel, Riemann-Liouville fractional integral operators as well as Weyl operators have been made to reduce to this class to generalized Stieltjes transform and inversion of which yields solution of the integral equation. Use of Mellin transform technique has also been made to solve the Fredholm integral equation pertaining to certain polynomials and H-functions.

SOME INTEGRALS ASSOCIATED WITH THE H-FUNCTION OF SEVERAL COMPLEX VARIABLES

Chaurasia, V.B.L. Department of Mathematics 1980 Kyungpook mathematical journal Vol.20 No.1

Fractional Derivative Associated with the Multivariable Polynomials

Chaurasia, Vinod Bihari Lal,Shekhawat, Ashok Singh Department of Mathematics 2007 Kyungpook mathematical journal Vol.47 No.4

The aim of this paper is to derive a fractional derivative of the multivariable H-function of Srivastava and Panda [7], associated with a general class of multivariable polynomials of Srivastava [4] and the generalized Lauricella functions of Srivastava and Daoust [9]. Certain special cases have also been discussed. The results derived here are of a very general nature and hence encompass several cases of interest hitherto scattered in the literature.

Immunohistochemical Analysis of ATRX, IDH1 and p53 in Glioblastoma and Their Correlations with Patient Survival

Ajay Chaurasia,박성혜,서정욱,박철기 대한의학회 2016 Journal of Korean medical science Vol.31 No.8

Glioblastoma (GBM) can be classified into molecular subgroups, on the basis of biomarker expression. Here, we classified our cohort of 163 adult GBMs into molecular subgroups according to the expression of proteins encoded by genes of alpha thalassemia/mental retardation syndrome X-linked (ATRX), isocitrate dehydrogenase (IDH) and TP53. We focused on the survival rate of molecular subgroups, depending on each and various combination of these biomarkers. ATRX, IDH1 and p53 protein expression were evaluated immunohistochemically and Kaplan-Meier analysis were carried out in each group. A total of 15.3% of enrolled GBMs demonstrated loss of ATRX expression (ATRX-), 10.4% expressed an aberrant IDH1 R132H protein (IDH1+), and 48.4% exhibited p53 overexpression (p53+). Survival differences were statistically significant when single protein expression or different combinations of expression of these proteins were analyzed. In conclusion, in the case of single protein expression, the patients with each IDH1+, or ATRX-, or p53- GBMs showed better survival than patients with counterparts protein expressed GBMs. In the case of double protein pairs, the patients with ATRX-/p53-, ATRX-/ IDH1+, and IDH1+/p53- GBMs revealed better survival than the patients with GBMs with the remained pairs. In the case of triple protein combinations, the patients with ATRX-/ p53-/IDH+ showed statistically significant survival gain than the patients with remained combination of proteins-expression status. Therefore, these three biomarkers, individually and as a combination, can stratify GBMs into prognostically relevant subgroups and have strong prognostic values in adult GBMs.

A Hybrid Approach of Clustering and Time-Aware Based Novel Test Case Prioritization Technique

Geetanjali Chaurasia,Sonali Agarwal 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.4

Regression testing is an activity during the maintenance phase to validate the changes made to the software and to ensure that these changes would not affect the previously verified code or functionality. Often, regression testing is performed with limited computing resources and time budget. So, fully comprehensive testing is not possible at this stage. Test-case prioritization techniques are applied to ensure the execution of test cases in some prioritized order and to achieve some specific goals in minimum possible time like, increasing the rate of fault detection, detecting the most critical faults as early as possible etc. The main objective of this paper is to achieve higher value of average percentage of faults detected, execute the higher priority test cases before lower priority test cases and also we target to decrease the execution time for achieving the maximum value of average percentage of faults detected. We proposed a new prioritization technique that uses a clustering approach and also considers various factors like, execution time of every test case, code coverage metric, fault detection ratio, test case failure rate and code complexity metric to reorder the execution of test cases. The results of this research work will show the importance of clustering technique and various factors taken into consideration, for achieving effective prioritization of test cases. The results of implementation will subsequently show that the proposed approach is more effective than the existing coverage and clustering based prioritization techniques. From the experimental results, we found that our proposed approach achieved higher value of average percentage of faults detected than other clustering based and coverage based techniques. Also, this approach reduces the execution time taken by the prioritized test cases.

Ensemble Rumor Text Classification Model Applied to Different Tweet Features

Sandeep Chaurasia 한국지능시스템학회 2022 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.22 No.3

Today, social media has evolved into user-friendly and useful platforms to spread messagesand receive information about different activities. Thus, social media users’ daily approachesto billions of messages, and checking the credibility of such information is a challenging task. False information or rumors are spread to misguide people. Previous approaches utilized thehelp of users or third parties to flag suspect information, but this was highly inefficient andredundant. Moreover, previous studies have focused on rumor classification using state-ofthe-art and deep learning methods with different tweet features. This analysis focused oncombining three different feature models of tweets and suggested an ensemble model forclassifying tweets as rumor and non-rumor. In this study, the experimental results of fourdifferent rumor and non-rumor classification models, which were based on a neural networkmodel, were compared. The strength of this research is that the experiments were performedon different tweet features, such as word vectors, user metadata features, reaction features,and ensemble model probabilistic features, and the results were classified using layered neuralnetwork architectures. The correlations of the different features determine the importanceand selection of useful features for experimental purposes. These findings suggest that theensemble model performed well, and provided better validation accuracy and better results forunseen data.

Deep-learning performance in identifying and classifying dental implant systems from dental imaging: a systematic review and meta-analysis

Akhilanand Chaurasia,Arunkumar Namachivayam,Revan Birke Koca-Ünsal,이재홍 대한치주과학회 2024 Journal of Periodontal & Implant Science Vol.54 No.1

Deep learning (DL) offers promising performance in computer vision tasks and is highly suitable for dental image recognition and analysis. We evaluated the accuracy of DL algorithms in identifying and classifying dental implant systems (DISs) using dental imaging. In this systematic review and meta-analysis, we explored the MEDLINE/ PubMed, Scopus, Embase, and Google Scholar databases and identified studies published between January 2011 and March 2022. Studies conducted on DL approaches for DIS identification or classification were included, and the accuracy of the DL models was evaluated using panoramic and periapical radiographic images. The quality of the selected studies was assessed using QUADAS-2. This review was registered with PROSPERO (CRDCRD42022309624). From 1,293 identified records, 9 studies were included in this systematic review and meta-analysis. The DL-based implant classification accuracy was no less than 70.75% (95% confidence interval [CI], 65.6%–75.9%) and no higher than 98.19 (95% CI, 97.8%–98.5%). The weighted accuracy was calculated, and the pooled sample size was 46,645, with an overall accuracy of 92.16% (95% CI, 90.8%–93.5%). The risk of bias and applicability concerns were judged as high for most studies, mainly regarding data selection and reference standards. DL models showed high accuracy in identifying and classifying DISs using panoramic and periapical radiographic images. Therefore, DL models are promising prospects for use as decision aids and decision-making tools; however, there are limitations with respect to their application in actual clinical practice.