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The objective of this paper is to predict the failure load of the composite laminates during tensile loading using an online Acoustic Emission (AE) monitoring and Artificial Neural Network. Bidirectional glass/epoxy laminates were subjected to tensile loading. The laminates were made for 12 layers of bi-directional glass mat in an epoxy matrix. The AE data recorded during the tensile testing was used to predict the failure load. The parameters such as amplitude, count, duration, energy, peak to count and rise-time were used for the analysis. Feed forward back propagation neural network model was generated from acoustic emission cumulative counts data taken during loading of bi-directional glass/epoxy tensile specimens. Cumulative counts recorded up to 50% and 75% of the failure load were used as the input data for simulation. The results show that the developed non-destructive method is capable of predicting the failure of composites subjected to tensile loading with an error of 3.5% and 7.6% for cumulative counts of 50% and 75% of loads respectively.

Determination of the proximate and anti-nutritional composition of two common edible insects: yam beetle (Heteroligus meles) and palm weevil (Rhynchophorus phoenicis) was carried out and the results showed that they both contained an appreciable levels of protein (38.10 and 50.01% respectvely), with moisture, ash, fibre, lipid, carbohydrate and gross energy levels being:(1.01, 5.78, 3.00, 32.01, 20.10% and 521.41Kcal/kg) and (1.16, 4.92, 2.56, 21.12, 20.23% and 480.02Kcal/kg) respectively. The results of the anti-nutritional analysis revealed that oxalate (total and soluble) were (29.00 and 19.32mg/100g) and (21.72 and 14.01mg/100g) for yam beetles and palm weevils respectively. These results were fairly high compared to other anti-nutritional components of the studied insects but generally fall within nutritionally accepted values. The lower values of phytic acid, HCN and tannins (0.311, 2.651 and 0.42mg/100g) and (0.276, 2.531 and 0.481 mg/100g) respectively for yam beetle and palm weevils. These results were generally far below toxic levels in men. Statistically, there exists positively high significant difference between the compositions of the two insects at r0.05 and n-2 degree of freedom while the index of forecasting efficiency (IFE) was very high at 99.5% which indicates that the error of prediction was very minimal. The moisture, ash and crude fibre of the insects were very low compared to fish meat and beef. These insects therefore could serve as additional promising sources of protein and fat for the teaming population and animal feeds formulations.

The problem of generating bivariate normal distributions is drawing the attention of the reliability analyst. Amongst those approaches, the characterization approach and the modelling approach are very appealing. In fact characterization approach is of interest to both theoreticians and applied workers. Here we have used a bivariate normal distribution for application from Mutivariate normal distribution through characterization approach. In our application we have considered days of Menstrual Cycle with Melatonin hormone as variable combined with LH, Estradiol and Progesterone hormones as variables as women stress effects. In many applications involving functions of random variables one may be interested in only the mean & variance of these functions. Furthermore, one may have available data from which the mean, variance and perhaps correlation coefficients among the variables can be calculated. Here we express the statistics of a function of a several random variables as a function of statistics on its component. In the discussion which follows the joint density is assumed to be a multivariate normal. The mean and variance of four variales have been utilized in the application part. From the clinical point of view menstrual changes due to secretion of hormones Melatonin, LH, Progesterone, Estradiol which are first sign of ovarian function. The curve for moment generating function of the above four variables are obtained here and explained by using a figure in the mathematical results. Keywords: Melatonin, LH, Progesterone, Estradiol

Educational Institution is a home away from home and it is a place of knowledge acquisition. It aims in developing the adolescents to fulfill the requirements of the needs of the society. It strives hard in implementing new tactics to enhance the academics, but it still fails in achieving it due to many reasons one among is, addiction to social networks. As we are living in techno world, we are bound to be a member of social networks (SN) and the adolescents are not an exception to it. In this present scenario we are highly dominated by our inventions and the best example is social networks. Though it is featured with many beneficial attributes its impact on adolescent’s academics is worse which indeed troubles the parents and bring them to a conclusion that social networks has connection with adolescent’s academic performance which is considered significant by the educational institution. To find the attribute which cause a strong impact on adolescent’s academic performance, induced linked fuzzy relational mapping using hexagonal fuzzy number approach is used which is a novel method.

The potential use of custard apple peel powder for the removal of Cu (II) from waste water has been investigated in batch mode experiments. Influences of parameters like initial Cu (II) concentration (20-60 mg/l), pH (5-7), and biomass dosage (25-35 g/l) on Cu (II) adsorption were examined using response surface methodology. The Box-Behnken experimental design in response surface methodology was used for designing the experiments as well as for full response surface estimation and 15 trials as per the model were run. The optimum conditions for maximum removal of Cu (II) from an aqueous solution of 20 mg/l were as follows: adsorbent dosage (28.139 g/l), pH (6.40213) and initial Cu (II) concentration (17.6572 mg/l). The high correlation coefficient (R² =0.991) between the model and the experimental data showed that the model was able to predict the removal of Cu (II) from waste water using Custard apple peel powder efficiently.

As India changes and reinvents itself at a remarkably accelerated pace, the retail patterns of its population have been transformed. What is new about these changes in retail patterns is not only the change in consumer behaviour of 1.15 billion individuals but also changes in the outlook of retailers in India. Historically, change has been a gradual and largely predictable process, allowing industry experts to reasonably forecast consumption patterns and consumer behaviour in the near future based on the current and immediate past. Those days are history. The fundamental shifts in consumer spending patterns have far-reaching implications not only for manufacturers, marketers and retailers of consumer products and services, but for all of India and Indian society as a whole.This paper highlights and analyses these shifts in the retail sector and the implications for manufacturers, marketers and brand owners, and retailers. The key lies in understanding the nature of these changes in the retail landscape and thereby the changes in the wallet-share of Indian consumers. Today’s reality consists of many new, unique and disparate factors that have come into play simultaneously

Education through educational institutions is at the center of the evolution of societies because it contributes to the development process of a country. It has been the subject of many international consultations, including the World Conference on Education for All (EFA) in Thailand in 1990 and the World Education Forum in Dakar in 2000, etc. This effectively explains why education is one of the indicators of human development.

In the first part of this work we will present the modelling of a high school information system with the use of WebProtege. System ontologies and class properties will be presented. In the second part we will present an introduction for SPARQL and examples of queries that were made, with the results returned to us.

The suggested algorithm for shape classification described in this paper is based on several steps. The algorithm analyzes the contour of pairs of shapes. Their contours are recovered and represented by a pair of N points. Given two points pi and qj from the two shapes the cost of their matching is evaluated by using the shape context and by using dynamic programming, the best matching between the point sets is obtained. Dynamic programming not only recovers the best matching, but also identifies occlusions, i.e. points in the two shapes which cannot be properly matched. From dynamic programming we obtain the minimum cost for matching pairs of shapes. After computing pair wise minimum cost between input and all reference shapes in the given database, we sort based on the minimum cost in ascending order and select first two shapes to check if it belongs to the input class. If it belongs to the input class, then we say that the shape is classified as a perfect match, else it is a mismatch. The algorithm has been tested on a set of shape databases like Kimia-25, Kimia-99, Kimia-216 and MPEG-7 providing good performances for shape classification.

In this paper, we introduced Samarandache-2-algebraic structure of Soft Neutrosophic Near-ring namely Smarandache –Soft Neutrosophic Near-ring. A Samarandache-2-algebraic structure on a set N means a weak algebraic structure S1 on N such that there exist a proper subset M of N, Which is embedded with a stronger algebraic structure S2 ,stronger algebraic structure means satisfying more axioms, that is S1 << S2 ,by proper subset one can understand a subset different from the empty set, from the unit element if any , from the Whole set. We define Smarandache - Soft Neutrosophic Near-ring and obtain the some of its characterization through Generalized soft neutrosophic near-field and Generalized soft neutrosophic (r,m) bi-ideals.For basic concept of near-ring we refer to G.Pilz ,for generalized near-field we refer T.Tamilchelvam and for soft neutrosophic algebraic structure we refer to Muhammed Shabir ,Mumtaz Ali,Munazza Naz, and Florentin Smarandache.