Raj Kumar Arya

Concentration profiles in two binary polymeric coatings - poly (styrene)- p-xylene and poly (methyl methacrylate) - ethylbenzene, have been measured using confocal laser Raman spectroscopy. Measured profiles are very different from as shown earlier for rubbery coatings. Sigmoidal profiles are observed in these polymeric coatings during the course of drying because they went through the glass transition temperature. Fick's law of diffusion is inadequate to explain such type of diffusion.

R.Senthamizh Selvi

Polyesters containing chalcone moiety were synthesised from 1, 3-bis (4-hydroxy-3-methoxyphenyl) propenone (BHMPP) and 1- (3, 5-dihydroxyphenyl)-3-(4-methoxyphenyl) propenone (DHPMPP) with suberoyl and sebacoyl chlorides by an interfacial polycondensation technique. Fundamental studies on the two phase polycondensation using phase transfer catalyst (tetra n-butylammonium bromide) was done. The synthesised polymers were characterised by solubility measurement, intrinsic viscosity, IR, 1H and 13C NMR studies. Keywords:Copolyesters; spectral studies; phase-transfer catalyst; polycondensation

Patrick A O Adegbuyi

An investigation was made on the effect of seawater, brine and palm oil on stressed and unstressed mild steel. The objective was to determine the changes in physical and mechanical properties of the mild steel specimens when immersed in the different media Three stressed and three unstressed mild steel specimens were used, the specimens were immersed in the three different media for six weeks after which their weights, tensile strength and hardness value were determined The results show that the weights and hardness value of the unstressed specimens in the three media after six weeks of immersion did not change. The weights of the stressed specimens did not change but there were slight differences in their hardness value after immersion for six weeks.

Uwaifo, Victor Oziengbe

It is usually believed by most employers of labor that university graduates in practical based programme in Nigeria lack enough practical potentials and capabilities required to contend with the demand of the labor force, this according to them is because less emphasis is placed on practices compared to their knowledge in theory during their training programmes. This study attempt to investigate the relationship between the student's theory and practical performance in Technology based subjects of students in the Ambrose Alli University, Ekpoma, Nigeria. The sample consisted of 75 students. Using the Pearson Product Moment Correlation Coefficient, the Coefficient of Correlation obtained are 0.61, 0.52, 0.44, for Technical Drawing, Metal-Work Technology and Wood-Work Technology subjects respectively, as a result of which the null hypotheses were rejected. The result showed that there is a statistically significant relationship between students' theory and practical performance. Thus the theory knowledge acquired by the students has influenced their performances in the practical exercises. In the light of this, it is suggested that more of this type of study should be done to constantly bridge the gap between theory and practice at all levels of our educational endeavor.

Smita Pathak

Optimal Power Flow (OPF) problem in electrical power system is consider as a static, non-linear, multi-objective or a single objective optimization problem. As the power industrial companies have been moving in to a more competitive environment, OPF has been used as a tool to define the level of the inter utility power exchange. Basically, this research work provide a new approach to solve the single objective OPF problem considering critical objective function of reactive loss minimization for utility/ industrial companies, while satisfying a set of system operating constraints, including constraints dedicated by the electrical network. Particle Swarm Optimization (PSO) has been used for this purpose. Particle Swarm Optimization (PSO) is a population based stochastic optimization technique. The system is initialized with a population of random feasible solutions and searches for optima by updating generations. The IEEE- 30 bus system is considered throughout this research work to test the proposed algorithm.

Archana Sharma

The Cloud has become a new vehicle for delivering resources such as computing and storage to customers on demand. Rather than being a new technology in itself, the cloud is a new business model wrapped around new technologies such as server virtualization that take advantage of economies of scale and multi-tenancy to reduce the cost of using information technology resources. From one perspective, cloud computing is nothing new because it uses approaches, concepts, and best practices that have already been established. From another perspective, everything is new because cloud computing changes how we invent, develop, deploy, scale, update, maintain, and pay for applications and the infrastructure on which they run. Nonetheless, there exist an increasing number of large companies that are offering cloud computing infrastructure products and services that do not entirely resemble the visions of these individual component topics. The challenge of building consistent, available, and scalable data management systems capable of serving petabytes of data for millions of users has confronted the data management research community as well as large internet enterprises. Financial institutions are not strangers to cloud computing adoption. One of the earlier cloud uses in banks and financial institutions were for SaaS deployments, which allowed for more social media banking. However, now FI's face the issue of security due to the increased number of data leaks. As a result, cloud within IT strategies and architecture for FIs will increase the risk of a security breach among servers and networks unless there is an adoption of a multiyear cloud strategy to keep data protected. This paper highlights the data management in cloud applications and deployments of various services of cloud computing in Financial Institutions with the study of risk factors in deployment of transaction data of Financial Institutions on clouds.

Y Vamsidhar, P Samba Siva Raju, T Ravi Kumar

Software reliability is one of a number of aspects of computer software which can be taken into consideration when determining the quality of the software. Building good reliability models is one of the key problems in the field of software reliability. A good software reliability model should give good predictions of future failure behavior, compute useful quantities and be widely applicable. Software Reliability Growth Models (SRGMs) are very important for estimating and predicting software reliability. An ideal SRGM should provide consistently accurate reliability estimation and prediction across different projects. However, that there is no single such model which can obtain accurate results for different cases. The reason is that the performance of SRGMs highly depends on the assumptions on the failure behavior and the application data-sets. In other words, many models may be shown to perform well with one failure data-set, but bad with the other data-set. Thus, combining some individual SRGMs than single model is helpful to obtain more accurate estimation and prediction. SRGM parameters are estimated using the least square estimation (LSE) or Maximum Likelihood Estimation (MLE). Several combinational methods of SRGMs have been proposed to improve the reliability estimation and prediction accuracy. The AdaBoosting algorithm is one of the most popular machine learning algorithms. An AdaBoosting based Combinational Model (ACM) is used to combine the several models. The key idea of this approach is that we select several SRGMs as the weak predictors and use AdaBoosting algorithm to determine the weights of these models for obtaining the final linear combinational model. In this paper, the Fitness and Prediction of various Software Reliability Growth Models (SRGMs) can be compared with AdaBoosting based Combinational Model (ACM) with the help of Maximum likelihood estimation to estimate the model parameters.