Group cohesion differentials among low and high achievers of men volleyball players

The purpose of the study was to find out the group cohesion differential among low and high achievers of men volley ball players. The purpose of the study was to compare the group cohesion among senior state level Men Volley ball players. To achieve this purpose of the study forty-eight volleyball players selected as low achievers from income tax, Tamil Nadu Electricity Board, Tuticorin and Central Excise and forty eight volleyball players, selected as high achievers from Indian Overseas Bank, Indian Bank, State Bank and Integral Coach Factory. The subjects were aged between twenty to thirty five years. The investigator distributed group environment questionnaire to measure the group cohesion among the players. Conclusion: There was a significant difference in team involvement between low and high achievers of senior state level men volleyball players. There was no significant difference in personal involvement between low and high achievers of senior state level men volleyball players. There was a significant difference in personal involvement and term involvement among low achievers. There was a significant difference in personal involvement and team involvement among high achievers. There was a significant difference in group cohesion between low and high achievers among senior state level volleyball players, with high achievers indicating better cohesion

Salem Meiyanur Mariamman Temple Worship and People's Faith

All living beings in the world live depending on nature, likewise earlier man also led a life in harmony with nature. Nature appeared as an enigma to man. Natural objects such as thunder, lightning, rain, flood, volcano, darkness and sun are mysterious and it instilled fear among man. Man, who fears about nature and its disasters starts worshiping them as gods. The worship that appeared in this way is based on faith and is common to all human beings. Even today people follow some rituals to preserve the cultural heritage of a country or race. Today, worship is a necessity of the political background and reflects as a part of the society. Rituals are used to symbolize a culture. Since these rituals are mostly followed in villages. People go to temple to worship God and to seek His blessings. There are many methods to be followed in organizing a temple but most of the minor deity temples are worshiped without building structure considering trees and black stones as their deities. The purpose of this article is to investigate the location of Meiyanur Mariamman Temple which is located in Salem district, the structure of the temple, about the deities located in the temple, the ceremonies held for the deities and the beliefs people have on the deities, and the debts which they pay to deities if their faith is fulfilled

Worship of Pavai in Sangam Literature

Paavai worship is one of the invisible ancient forms of worship recorded in Sangam literary works. The word paavai appears in 53 places in Sangam literature.  paavai deity. Playing object, Painting, Sculpture, Plant, Eye, Woman, Koothu are handled in many objects. Here the word paavai is taken for study only in the sense of deity. This paavai worship can be classified into two as Kollimalai paavai worship and water god puppet worship. The paavai that freezes in the Kolli Hills is very devastating. Received divinity. Natural disasters like wind, rain, thunder, lightning, do not spoil the beauty of this mountain. That is why the Kollimalai paavai is called the Maya natural paavai. The deity is frozen in caves, ditches and rocks. Kollimalai paavai is the guardian deity who threatens and guards Condor in the Kolli Hills. Sangam poets have compared it to a Kollimalai paavai as a testament to the beauty of women. The next paavai worship is the water worship. Worship of the water god paavai took place in the early morning of the month of Tai, in the hope that a good husband would be obtained by worshiping the water god paavai, good rain, and the country would prosper

The Flexural Design of Pretensioned Bent Caps

The use of pretensioned bent caps has brought the opportunity to utilize the advantages of accelerated construction and increased worker safety. At the same time they offer the benefits of enhanced performance. To facilitate a widespread implementation of pretensioned bent caps, this research seeks to develop flexural design procedures and recommendations on design and detailing that can benefit design engineers with readily available guidelines. A design procedure for pretensioned bent caps is proposed in this work. In this procedure, the bent caps will be primarily designed to achieve zero tension under dead load, to provide adequate strength under design load combinations and to satisfy the stress limits specified in the American Association of State Highway and Transportation Officials (AASHTO) LRFD Bridge Design specifications. To evaluate the design procedure, a bridge inventory comprising standard Texas Department of Transportation (TxDOT) bridges with I-girders, box beams and X-beams, as well as non-standard bridges have been considered. Design results indicate no cracking expected under service loads and limited cracking expected under ultimate loads. End region detailing of the pretensioned bent caps, for resistance against tensile stresses during prestress transfer, have been reviewed from previous investigations. The pocket connections, used between pretensioned bent caps and columns, offer benefits in the use of concrete instead of grout and in the availability of large construction tolerance. A medium pocket size formed by corrugated pipe is preferable for accommodating accidental misalignment of column. The connection provides resistance to vehicle collision loads. Optimization of bridges with pretensioned bent caps has been assessed with modifications to the prestressing layout and the reconfiguration of the arrangement of columns. Change in strand design and geometry contributed in reduction of flexural cracking and increasing performance. Elimination of the column is expected to result in economic benefits

Image Quantification Learning Technique through Content based Image Retrieval

This paper proposes a Radial basis functionality incorporation in learning the quantification of images using Content based Image Retrieval (CBIR). The approach is trying to find out the effectiveness of Multi-Layer Perceptron (MLP) namely Radial Basis Function (RBF) through Content Based Image Retrieval. Extract the features of an image, the numeric values of each pixel is framed in to a definite input data set of image to that the neural networks MLP gives the accuracy of the prediction of that particular Image data set. This paper put forward us with new idea of neural networks structure efficiency in the accuracy of output data set which got increased by the adjustment of the weighted neurons through a Perceptron called Radial Basis Function promoting by applying k means clustering to form clusters which are parameterized with Gaussian function application. Finally compare the actual output with observed output promoting the weighted neurons adjustment for getting the actual accurate output. A new dimension, in work enhancement of neural networks technology with that of image processing

Feature Evolution using Factorization Methods in Multimodal Authentication � Smart Security Devices

Most of the industries and business organizations use various kinds of biometric patterns such as face, finger, palm, vein, skin surface, footprint and some structural parts of the human body and also to consider some video patterns of the stakeholder/customeras secure data for the customers /stakeholders in terms of authentication / verification / authorization/ recognition of traits and also use secure agents for the personal data as well as social/ business/ web/ banking transactions. Also, the government sector use the biometric patterns of stakeholders to claim the government policies, facilities and issue the authorized cards like aadhaar, driving license, passport, pancard etc.., for their needs. The stake holders would provide different patterns of his/ her poster as face, fingerprint, palm, vein, footprint, and/ or other structural patterns of the human body in terms of 2D and 3D patterns. In order to compute such patterns the extraction of features/ recognition of patterns is complex and NP-hard due to the representation of captured patterns with noise or acquisition of sensed data or noisy of sensing devices. To overcome such complexities the vector logic (cognitive logic) models for minimizing the noise and computations of best features for recognition of such patterns

Machine Learning Promising Prediction in Feature Extraction

The world is crunching with high volumes data and high end technologies, instead our still news edition giving place to �cancer affected died for the cause of no recognition but not merely due to cancer�. This paper provides the identification, feature extraction of cancer on the board of machine learning. The achievement of prediction accuracy rate improvement through the defined algorithms namely, KNN, Fast KNN,GLM,SVM is done. Initial way of performing the cross fold validation and checking the fitted turn out of feature extraction is the major contribution with 569 object data set and 32 attribute value of breast cancer data. Secondly GLM-Net with Feature Extraction using KNN and comparisons with SVM classifier for feature extraction with KNN, and subsequently SVM Basic model with Radial function without feature extraction is achieved, Finally SVM Classifier with Radial Basis function on Breast Cancer Dataset and Regularized Linear Support Vector Machines with Class Weights through RFE done and concluded as Fast KNN and SVM are the most promising classifiers of Machine learning and futuristic data science classification evaluators

Neural Networks as Radial-Interval Systems through Learning Function

This paper presents a novel dimension of neural networks through the approach of interval systems for great more forecasting activity. The artificial neural network (ANN) based models are the most popular ones for load forecasting and other applications. This approach is only a thought line that can enhance the fundamental requirement of all networks giving and incorporating the analytical and expertise knowledge in forecasting from the existential approaches. [1] Interval systems as an approach to approximate interval models by neural networks is proposed

Advancements in Multi-Layer Perceptron Training to Improve Classification Accuracy

Neural Networks are the popular classification tools used in Medical diagnosis for early disease detection. The performance of Neural Networks is highly depended on the training process. In the training process, the individual weights between each of the neuron are adjusted for better classification results. Many Gradient-based and Meta-heuristic training algorithms are proposed and used by the researchers to improve the training performance of Neural Network. However, there are some limitations in both Gradient-based and Meta-heuristic algorithms when there are used individually. To overcome these limitations and to improve the Multi-Layer Perceptron Network performance Hybrid algorithms are useful. In this study, a review on advancements in Multi-Layer Perceptron Network training process for the improvement of classification performance is presented

ZINC AND CALCIUM SUPPLEMENTATION TO COMBAT CADMIUM INDUCED BIOACCUMULATION IN FRESH WATER TELEOST OREOCHROMIS MOSSAMBICUS (TILAPIA)

Objective: The present work was carried out to know the therapeutic role of zinc (Zn) and calcium (Ca) supplementation to combat cadmium (Cd) induced toxicity in selected tissues of freshwater teleost, Oreochromis mossambicus (Tilapia) exposed to Cd.Methods: The freshwater fish, Oreochromis mossambicus (Tilapia) was brought from the local ponds and were allowed for acclimatization to the laboratory conditions. After acclimatization, fishes were exposed to 1/10th of LC50/48 hr of Cd i.e.5 ppm for 7, 15 and 30days (d) long sojourn. After 15d Cd exposure, fish were divided into three groups. The 1st group was supplemented with Zn at a dose of 1 ppm; Group–II was supplemented with Ca at the dose of 1 ppm and Group–III received a combination of both Zn and Ca at the above said doses for 7, 15 and 30d. After specific time intervals, fish were sacrificed and liver, kidney, brain, gill and muscle tissues were isolated in ice cold conditions. Then the tissues were used for bioaccumulation studies.Results: A significant (P<0.05) elevation was observed in bioaccumulation levels during Cd exposure. The high amount of Cd accumulation was found in 30d Cd-exposed kidney (22.353±0.410 µg/g) followed by other tissues. After supplementation with Zn and Ca, Cd accumulation was progressively decreased in all the test tissues. The maximum percentage of Cd depletion was found in 30d Ca-supplemented muscle tissue.Conclusion: Our findings clearly envisage that the Zn and/or Ca supplementation is very effective in reducing the Cd toxicity in the teleostean fish, Oreochromis mossambicus