Epitaxial growth of highly transparent and conducting Sc-doped ZnO films on c-plane sapphire by sol.gel process without buffer

Ruchika Sharma,Kiran Sehrawat,R.M. Mehra 한국물리학회 2010 Current Applied Physics Vol.10 No.1

Highly transparent and conductive scandium doped zinc oxide (ZnO:Sc) films were deposited on c-plane sapphire substrates by sol.gel technique using zinc acetate dihydrate [Zn(CH3COO)2·2H2O] as precursor,2-methoxyethanol as solvent and monoethanolamine as a stabilizer. The doping with scandium is achieved by adding 0.5 wt% of scandium nitrate hexahydrate [(ScNO3·6H2O)] in the solution. The influence of annealing temperature (300-550 ℃) on the structural, optical and electrical properties was investigated. X-ray Diffraction study revealed that highly c-axis oriented films with full-width half maximum of 0.16˚ are obtained at an annealing temperature of 400 ℃. The surface morphology of the films was judged by SEM and AFM images which indicated formation of grains. The average transmittance was found to be above 92% in the visible region. ZnO:Sc film, annealed at 400 ℃ exhibited minimum resistivity of 1.91 × 10-4 Ω cm. Room-temperature photoluminescence measurements of the ZnO:Sc films annealed at 400 ℃ showed ultraviolet peak at ~3.31eV with a FWHM of 11.2 meV, which are comparable to those found in high-quality ZnO films. Reflection high-energy electron diffraction pattern confirmed the epitaxial nature of the films even without introducing any buffer layer.

Analyzing Machine Learning Techniques for Fault Prediction Using Web Applications

Ruchika Malhotra,Anjali Sharma 한국정보처리학회 2018 Journal of information processing systems Vol.14 No.3

Web applications are indispensable in the software industry and continuously evolve either meeting a newercriteria and/or including new functionalities. However, despite assuring quality via testing, what hinders astraightforward development is the presence of defects. Several factors contribute to defects and are oftenminimized at high expense in terms of man-hours. Thus, detection of fault proneness in early phases ofsoftware development is important. Therefore, a fault prediction model for identifying fault-prone classes in aweb application is highly desired. In this work, we compare 14 machine learning techniques to analyse therelationship between object oriented metrics and fault prediction in web applications. The study is carried outusing various releases of Apache Click and Apache Rave datasets. En-route to the predictive analysis, theinput basis set for each release is first optimized using filter based correlation feature selection (CFS) method. It is found that the LCOM3, WMC, NPM and DAM metrics are the most significant predictors. The statisticalanalysis of these metrics also finds good conformity with the CFS evaluation and affirms the role of thesemetrics in the defect prediction of web applications. The overall predictive ability of different fault predictionmodels is first ranked using Friedman technique and then statistically compared using Nemenyi post-hocanalysis. The results not only upholds the predictive capability of machine learning models for faulty classesusing web applications, but also finds that ensemble algorithms are most appropriate for defect prediction inApache datasets. Further, we also derive a consensus between the metrics selected by the CFS technique andthe statistical analysis of the datasets.

Analyzing Machine Learning Techniques for Fault Prediction Using Web Applications

Malhotra, Ruchika,Sharma, Anjali Korea Information Processing Society 2018 Journal of information processing systems Vol.14 No.3

Web applications are indispensable in the software industry and continuously evolve either meeting a newer criteria and/or including new functionalities. However, despite assuring quality via testing, what hinders a straightforward development is the presence of defects. Several factors contribute to defects and are often minimized at high expense in terms of man-hours. Thus, detection of fault proneness in early phases of software development is important. Therefore, a fault prediction model for identifying fault-prone classes in a web application is highly desired. In this work, we compare 14 machine learning techniques to analyse the relationship between object oriented metrics and fault prediction in web applications. The study is carried out using various releases of Apache Click and Apache Rave datasets. En-route to the predictive analysis, the input basis set for each release is first optimized using filter based correlation feature selection (CFS) method. It is found that the LCOM3, WMC, NPM and DAM metrics are the most significant predictors. The statistical analysis of these metrics also finds good conformity with the CFS evaluation and affirms the role of these metrics in the defect prediction of web applications. The overall predictive ability of different fault prediction models is first ranked using Friedman technique and then statistically compared using Nemenyi post-hoc analysis. The results not only upholds the predictive capability of machine learning models for faulty classes using web applications, but also finds that ensemble algorithms are most appropriate for defect prediction in Apache datasets. Further, we also derive a consensus between the metrics selected by the CFS technique and the statistical analysis of the datasets.

Comparative potential of Simvastatin, Rosuvastatin and Fluvastatin against bacterial infection: an in silico and in vitro study

Meenakshi Gupta,Ruchika Sharma,Anoop Kumar 경희대학교 융합한의과학연구소 2019 Oriental Pharmacy and Experimental Medicine Vol.19 No.3

In the current investigation, we have compared the potential of statin drugs (Simvastatin, Rosuvastatin and Fluvastatin) as potential anti-bacterial agent by conducting in silico, in vitro and ex vivo studies. In silico study was conducted to check the interaction of statin drugs towards various targets of bacteria. The percentage growth retardation, bacterial growth kinetics, MIC determination, post antibiotic efect and bioflm formation assay were conducted to check the anti-bacterial efect of statin drugs under in vitro conditions. Finally, MTT assay was used to check the percentage of immune cell viability after Simvastatin treatment. Docking studies have revealed good interaction of Simvastatin, Rosuvastatin and Fluvastatin towards various targets of bacterial strains as that of the internal ligand. Simvastatin has shown good antibacterial activity against S. aureus, B. pumilus, P. aeruginosa and S. enterica as compared to Rosuvastatin and Fluvastatin. In vitro results have shown concentration and time dependent inhibition of bacterial growth by Simvastatin in concentration range of 64–256 μg/ml. Finally, MTT assay have shown non-cytotoxic efect of Simvastatin against adaptive immune system. In conclusion, Simvastatin could be a potential candidate as an anti-bacterial agent against a wide range of bacterial infections. However, further studies are required to check its complete role before starting phase I clinical trial.

Comparative potential of hydrocortisone, deoxycorticosterone and dexamethasone in the prevention of cataract: an in silico and in vitro study

Divya Rana,Ruchika Sharma,Anoop Kumar 경희대학교 융합한의과학연구소 2018 Oriental Pharmacy and Experimental Medicine Vol.18 No.4

Cataract is visual impairment which arises due to disturbance of lens transparency due to aggregation of the protein. Currently, surgery is the only choice for the treatment of cataract. Thus, there is a need for new drugs in the prevention of cataract. In the current investigation, we have checked the potential of hydrocortisone, deoxycorticosterone and dexamethasone against cataract by using in silico and in vitro studies. The structure of a desired target (aldose reductase) has been selected and extracted from Protein Data Bank (http://www.rcsb.org/pdb). The structures of various ligands (hydrocortisone, deoxycorticosterone and dexamethasone) have been drawn by using Chem Draw Ultra 12.0 software. The docking was performed in a Mole Gro Virtual Docker version 6.0. The cataract was induced by using glucose under in vitro conditions and anticataract potential of selected drugs have been analysed by assessing various biochemical parameters. In silico studies have revealed that hydrocortisone, deoxycorticosterone and dexamethasone have good binding interaction with aldose reductase. Further, in vitro studies have been shown the anti-cataract potential of steroidal drugs. The oxidative stress induced by glucose was decreased more significantly in the lens treated with dexamethasone as compared to deoxycorticosterone and hydrocortisone. Further, the protein level was significantly increased after treatment with dexamethasone which indicates its more anti-cataract potential as compared to deoxycorticosterone and hydrocortisone. In conclusion, dexamethasone has potential to prevent the cataract as compared to hydrocortisone and deoxycorticosterone. However, further studies are needed to confirm its complete role as anti-cataract drug.